Architectural Synthesis and Deep-Dive Analysis of the Vulkan and Valkan Software Ecosystems

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1. Introduction and Semantic Disambiguation of the Ecosystem

The modern software engineering and cybersecurity landscape is increasingly defined by systems that require high-performance hardware interfacing, concurrent network operations, and robust defenses against adversarial manipulation. An exhaustive analysis of current repositories, frameworks, and application ecosystems reveals a complex convergence of nomenclature around the terms "Vulkan," "Valkan," and various regional derivatives such as "Balkan." While linguistically similar—and frequently conflated in search indexing and community discourse—these terms refer to entirely distinct technological, cultural, and adversarial domains. To provide a comprehensive architectural deep dive, it is imperative to first disambiguate the entities operating within this namespace. The data indicates several primary vectors of development and activity operating under these designations, ranging from industry-standard graphics APIs to niche cybersecurity tools, and extending into illicit trade networks and cultural collectives.

This report provides an exhaustive, multi-disciplinary deep dive into the coding paradigms, structural functions, and underlying mechanics of these distinct ecosystems. By deconstructing the application of C99/C++ in graphics rendering, the utilization of Golang in concurrent network scanning, the low-level memory manipulation techniques in adversarial engineering, and the organizational structures of regional hubs, this analysis identifies broader implications for system design, security posture management, and network resilience.

2. The Vulkan Graphics Processing Paradigm: Architecture and Explicit Control

The transition from implicit, driver-managed graphics APIs (such as OpenGL and Direct3D 11\) to explicit, application-managed APIs represents one of the most significant paradigm shifts in modern rendering architecture. Vulkan is designed to provide high-efficiency, cross-vendor access to modern graphics processing units (GPUs) across varying platforms, ranging from embedded mobile devices to high-performance computing (HPC) environments1.

2.1 Explicit Resource Management and the Vulkan C99 Core

At its architectural core, the Vulkan API is written in C99, requiring application developers to explicitly manage hardware state, memory allocation, and thread synchronization3. Unlike legacy APIs where the graphics driver heuristically manages pipeline state, infers application intent, and performs extensive runtime error checking, Vulkan delegates these profound responsibilities entirely to the application layer. This design philosophy radically reduces central processing unit (CPU) overhead and enables highly predictable, multi-threaded command buffer generation. The API relies heavily on structured, verbose initialization. Object creation is achieved by passing deeply nested C-structures, typically prefixed with Vk, utilizing enumerated values prefixed with VK\_4. A critical aspect of this design is the capability query system. For example, the initialization of physical device features requires the population of a VkPhysicalDeviceFeatures structure, where individual boolean members (VkBool32) define the availability of specific hardware capabilities, such as shaderDrawParameters or samplerMirrorClampToEdge5. If a device does not support a specifically requested feature, the initialization explicitly fails at creation time, returning VK\_ERROR\_FEATURE\_NOT\_PRESENT5. This explicit nature demands rigorous coding conventions. Advanced functionality is continually introduced via extensions, which are dynamically linked using a pNext pointer chain attached to standard initialization structures (e.g., VkPhysicalDeviceFeatures2)5. This highly extensible architecture allows hardware vendors (such as NVIDIA, AMD, and Intel) to rapidly prototype and integrate bleeding-edge features—such as hardware-accelerated ray tracing or Deep Learning Super Sampling (DLSS)—without waiting for formal revisions to the core API2.

2.2 Abstraction Layers: C++ Bindings (Vulkan-Hpp) and Python Integration

To mitigate the extreme verbosity and cognitive load of the native C99 API, the software engineering community has developed sophisticated abstraction layers. These wrappers map Vulkan's explicit control mechanisms to higher-level programming paradigms while attempting to preserve the zero-overhead philosophy. The Khronos Group officially maintains Vulkan-Hpp, a header-only C++ binding library6. The primary function of this library is to inject compile-time type safety and object-oriented semantics into the API. Vulkan-Hpp heavily utilizes Resource Acquisition Is Initialization (RAII) idioms via its vk::raii classes, which automatically manage the lifecycle of Vulkan handles, ensuring that GPU resources are destroyed deterministically when the C++ object falls out of scope6. Furthermore, it systematically replaces unsafe C-style arrays with Standard Template Library (STL) containers (such as std::array and std::vector) and maps Vulkan's VkResult return codes directly into C++ exceptions6. The evolution of this library reflects modern C++ standards; recent updates (e.g., version 1.4.351) introduced breaking changes that mandate the use of std::array for functions taking C-arrays (such as setFragmentShadingRateKHR) to enhance memory safety, and the library requires compilers supporting at least C++206. For rapid prototyping, data science integrations, and academic research, Python extensions such as the realitix/vulkan repository map the API to Python 2 and 3 environments4. This wrapper utilizes the C Foreign Function Interface (CFFI) to interact with the underlying shared operating system libraries (libvulkan.so on Linux or vulkan-1.dll on Windows). The implementation details of this wrapper reveal a sophisticated automated generation process. A dedicated script (generator/generate.py) parses the official Khronos vk.xml specification using xmltodict to build a semantic data model, which is then fed into a Jinja2 template engine to output the final Python script (\_vulkan.py)4. This automated translation allows developers to instantiate Vulkan structures using standard keyword arguments, entirely abstracting away the strict sequential parameter ordering required in the C/C++ equivalents4. Similar to Vulkan-Hpp, the Python wrapper intercepts VkResult codes; any outcome other than VK\_SUCCESS raises a localized, "Pythonic" exception (e.g., VkErrorExtensionNotPresent), streamlining error management4.

2.3 Compilation and Pipeline Execution: SPIR-V and the AMDVLK Driver

Vulkan fundamentally alters how shader code is consumed and executed by the GPU. Instead of relying on the graphics driver to compile high-level shading languages (such as GLSL, Slang, or HLSL) at runtime—a process that historically led to stuttering and unpredictable performance—Vulkan ingests Standard Portable Intermediate Representation (SPIR-V) bytecode1. SPIR-V serves as a standardized, hardware-agnostic intermediate form that eliminates the need for complex, bug-prone frontend compilers within the device driver itself1. On the driver implementation side, open-source projects like the AMD Open Source Driver for Vulkan (AMDVLK) illustrate the deep complexity of translating SPIR-V into machine-specific hardware instructions. Designed to support modern Radeon graphics adapters (from the RX 5500 series up to the RX 7900 series) on Linux distributions like Ubuntu and RedHat, AMDVLK utilizes a highly modular compilation pipeline9. The driver relies on the LLVM-Based Pipeline Compiler (LLPC), which builds upon the LLVM project's existing compiler infrastructure to translate SPIR-V code objects into a format compatible with AMD's Platform Abstraction Library (PAL)9. PAL acts as a shared, encapsulating component that standardizes hardware and operating system-specific programming details across AMD's entire suite of 3D and compute drivers9. This architectural separation allows the driver to support advanced features such as mid-command buffer preemption, Single Root I/O Virtualization (SR-IOV), and hardware performance counter collection through debuggers like RenderDoc1.

2.4 Edge Implementations: WSL, Android, and Diagnostics

The flexibility of the Vulkan ecosystem is further demonstrated by its deployment in non-traditional and highly constrained environments. In the realm of operating system virtualization, the Windows Subsystem for Linux (WSL) presents unique challenges for GPU acceleration. Repositories such as Tanusoni/wsl-vulkan-mesa adapt the Mesa 3D graphics library to provide Vulkan support within WSL environments10. Because native GPU pass-through in earlier WSL iterations was complex, this implementation utilizes lavapipe, a Vulkan frontend in Mesa that acts as a software renderer via llvmpipe10. By compiling the driver with specific Meson flags (-Dgallium-drivers="swrast" \-Dvulkan-drivers=swrast) and rendering the output through an X server like VcXsrv, developers can validate Vulkan code on a Windows host within a Linux container, albeit strictly for testing rather than conformant high-performance rendering10. In the mobile ecosystem, customized kernel and boot-image modifications (such as Magisk modules like tryigit/EnableVulkan) are utilized to force Android 10+ operating systems to utilize Vulkan for System UI and hardware rendering11. Operating on processors like the Snapdragon 888, these modifications aim to reduce battery consumption and improve UI animation latency by bypassing older OpenGL ES implementations, highlighting the efficiency gains of the API even outside of gaming11. Furthermore, the deterministic nature of Vulkan makes it an exceptional tool for hardware diagnostics. The memtest\_vulkan utility utilizes Vulkan compute shaders to stress-test Video RAM (VRAM) for stability during overclocking or hardware repair12. By writing massive datasets to memory and calculating read-back throughput (often exceeding 750 GB/s on high-end GPUs like the RTX 3090), the tool can isolate specific bit-flip errors and memory degradation at the hardware level, outputting hexadecimal error address ranges when physical defects are detected12.

3. VulkanHub: The Evolution of API Documentation Architecture

The sheer volume of structures, enumerations, extensions, and valid usage rules within the Vulkan ecosystem necessitates an advanced documentation architecture. Historically, Vulkan specifications were distributed by the Khronos Group as massive, single-file HTML documents or highly fragmented chunked HTML files13. These legacy formats suffered from severe browser performance degradation, crashing frequently due to Document Object Model (DOM) bloat, and offered poor searchability13. The introduction of VulkanHub (vkdoc.net) represents a structural overhaul in how technical specifications are processed and served to developers.

3.1 Web-First Engineering and Algolia Integration

VulkanHub is a community-driven initiative that rebuilds the documentation experience by parsing the official Khronos AsciiDoc source code and converting it into a web-first format13. The platform is engineered using Nuxt.js, a modern Vue.js framework that allows for rapid static site generation and dynamic client-side interactivity13. A critical failure of previous documentation iterations was the inability to quickly locate specific structs or functions buried within thousands of pages of text. VulkanHub resolves this by integrating Algolia, a highly optimized, full-text search engine13. This allows developers to utilize partial term matching (e.g., typing "vkgpci" to instantly locate VkGraphicsPipelineCreateInfo), instantly routing the user to dynamically generated reference pages (refpages) where API items are collapsed by default to reduce visual clutter13.

3.2 The Valid Usage (VUID) Mapping Mechanism

Because Vulkan deliberately eschews runtime state validation to maximize performance, developers rely entirely on optional "Validation Layers" (such as the LunarG SDK layers) during the debugging phase15. When an application violates the API's constraints—for example, attempting to bind a memory object that lacks the correct alignment—the Validation Layer outputs an error containing a specific Valid Usage ID (VUID), such as VUID-vkCmdDraw-viewType-0775213. VulkanHub optimizes the debugging workflow by explicitly mapping these VUIDs directly to the specification text. Developers can query the specific VUID generated by their local terminal directly into the VulkanHub search bar. The platform instantly cross-references the ID and highlights the exact architectural constraint that was violated within the documentation, dramatically accelerating the mean time to resolution (MTTR) for rendering bugs13.

4. The Valkan Network Scanning and Vulnerability Exploitation Framework

Parallel to the graphics domain, the identifier "Valkan" designates a specific, modern open-source cybersecurity project. Found under the GitHub repository Pnkcaht/Valkan (and architecturally associated with the developer Vyzer9), this tool is a network scanning and vulnerability exploitation framework explicitly designed for authorized security assessments in controlled environments16.

4.1 Golang Concurrency in Network Discovery

The Valkan framework is engineered entirely using the Go programming language (Golang, specifically requiring version 1.24.4), a highly strategic architectural choice that heavily influences its operational capacity16. Network scanning is inherently an I/O-bound operation; scanners are typically constrained by the latency of network socket responses and packet drops rather than CPU processing limits. Golang's concurrency model, which utilizes lightweight goroutines and an internal M:N scheduler (multiplexing thousands of goroutines onto a small pool of OS threads), is uniquely suited for this workload18. Traditional network scanners utilizing standard POSIX threading models often encounter severe performance bottlenecks due to the context-switching overhead imposed by the operating system. In contrast, a Go-based scanner can effortlessly spawn tens of thousands of concurrent goroutines to probe disparate IP addresses simultaneously, maintaining a massive volume of in-flight connections with minimal memory overhead.

4.2 Dependency Architecture: Nmap Wrapper and CIDR Processing

An analysis of the go.mod file within the Valkan repository reveals its core dependencies, which provide insight into its operational mechanics and design philosophy.

Rather than implementing raw TCP/UDP packet crafting from scratch—a process fraught with edge cases involving firewall statefulness and OS-level packet filtering—Valkan leverages the Ullaakut/nmap library to interact with the industry-standard Nmap security scanner17. This dependency acts as a programmatic bridge, allowing Valkan to spawn Nmap processes, execute complex scans (such as stealth SYN scans or UDP payload probes), and natively parse the resulting XML output into Go structs. This delegates the highly complex tasks of OS fingerprinting and service version detection to a hardened engine while maintaining centralized control flow within the Go application17. Simultaneously, the integration of go-cidr enables the scanner to programmatically calculate network boundaries17. By efficiently parsing subnet masks, the tool can iterate through millions of usable IP addresses, dynamically partitioning workloads across multiple concurrent worker pools to ensure rapid discovery across enterprise-scale environments.

4.3 Integration into the Security Ecosystem

The development of Valkan aligns with broader trends in continuous exposure management and DevSecOps. The primary author of the repository, identified as Sam Richard (@Pnkcaht), is also deeply involved in the Jenkins CI/CD ecosystem, having established the @JenkinsSecurity community hub to aggregate security-focused plugins and tools20. The theoretical deployment of a tool like Valkan within a CI/CD pipeline suggests a shift from periodic compliance auditing to continuous, automated vulnerability scanning. By integrating concurrent scanning frameworks directly into the deployment pipeline, organizations can automatically detect misconfigurations, map open ports, and correlate service banners against known CVE databases immediately following infrastructure changes, thereby dramatically reducing the window of opportunity for adversarial exploitation.

5. Adversarial "Hubs": Memory Manipulation and Anti-Tamper Evasion

The third domain associated with the "Vulkan/Valkan Hub" nomenclature relates to client-side game exploitation, specifically within execution environments like Roblox. Adversaries develop tools—frequently branded as "Hubs" (e.g., Valkan Hub, Volcano Hub, Speed Hub X)—to inject Lua scripts that automate gameplay logic or grant unfair advantages22. Furthermore, advanced utilities like the "Vulkan PE Dumper" are designed to dissect the underlying memory protections of the client software itself25.

5.1 User-Mode Anti-Tamper Solutions and Memory Encryption

Modern digital rights management (DRM) and anti-cheat systems, such as Hyperion or Theia, employ highly sophisticated dynamic code encryption methodologies to prevent static reverse engineering and unauthorized memory hooking25. At an architectural level, these user-mode anti-tamper solutions encrypt the .text sections (the executable code) of a Portable Executable (PE) file on disk. When the executable is loaded into the host memory, the pages are marked with highly restrictive access rights, frequently utilizing the Windows API flag PAGE\_NOACCESS25. When the CPU's instruction pointer attempts to execute code within a NOACCESS page, the operating system predictably throws an access violation exception. The anti-tamper software utilizes a registered exception handler to intercept this violation. It decrypts the specific memory page just-in-time (JIT), modifies the page permissions to allow execution (e.g., PAGE\_EXECUTE\_READ), allows the CPU to run the requisite instructions, and immediately re-encrypts the page. This continuous encryption cycle ensures that the entire binary is never present in a decrypted state simultaneously, fundamentally breaking traditional memory dumping tools.

5.2 The "Vulkan" PE Dumper: Dynamic Decryption and IAT Reconstruction

The utility identified as the "Vulkan" PE Dumper (authored by Atrexus) represents a targeted, theoretical countermeasure against these dynamic encryption schemes25. Written predominantly in C++, this command-line tool attempts to restore PE images from memory by systematically circumventing the obfuscation layers25. Decryption Polling and Thresholds: Because the anti-tamper solution dynamically decrypts pages only when executed, a static memory dump will yield encrypted, completely unanalyzable data. The Vulkan dumper operates by continuously polling the memory pages of the target module, waiting for the application's natural execution flow to trigger the decryption of the NOACCESS pages25. Recognizing that some pages containing obscure, edge-case logic may never be naturally executed (and thus never decrypted), the tool implements a \--decryption-factor threshold (e.g., [Figure omitted from source export] or [Figure omitted from source export])25. Once the proportion of decrypted pages crosses this statistical threshold, the tool captures the memory state, operating on the premise that a partially decrypted image is sufficient for subsequent static analysis in tools like Ghidra or IDA Pro. Import Address Table (IAT) Reconstruction: A fundamental technique of modern anti-tamper solutions is the destruction or obfuscation of the standard Windows Import Address Table (IAT). The IAT dictates where external API functions (like VirtualAlloc or CreateThread) are located in memory. By routing API calls through custom, heavily obfuscated pointers, the anti-tamper software makes it incredibly difficult for an analyst to deduce the binary's behavior. The Vulkan dumper features an \--resolve-imports flag designed to counteract this obfuscation25. The tool heuristically scans the dumped memory space to locate the custom IAT generated by the anti-tamper software. Once located, it traces the pointers back to their original dynamic-link libraries (DLLs) and reconstructs a standard, valid import directory within a newly appended PE section25. This theoretical reconstruction effectively repairs the broken binary structure, allowing analysts to view the external dependencies of the protected application.

5.3 Lua-Based Script Hubs and Engine Configuration Manipulation

In multiplayer platforms like Roblox, the client-server architecture generally trusts the server for physics and state validation but heavily relies on the client for rendering and local input calculations. Adversarial "Script Hubs" exploit this necessary trust by utilizing executors (e.g., Xeno, Bytebreaker, Volcano) to inject third-party execution environments into the client memory space26. These hubs execute arbitrary Lua bytecode to perform actions such as "Auto Farm" (automating quest progression) or "Fruit Sniper" (monitoring network traffic for the instantiation of rare items and simulating the network remote events necessary to acquire them before legitimate players can)24. Interestingly, the intersection of game manipulation and the Vulkan Graphics API is explicitly visible in the use of engine configuration flags. Roblox utilizes a configuration system known as Fast Flags (FFlags) to toggle internal engine features. Bootstrapper utilities like Bloxstrap allow users to manipulate these JSON-based configurations directly30. Players and adversaries frequently manipulate these flags to force the game client to use specific graphics rendering APIs. For instance, injecting the configuration:

JSON { "FFlagDebugGraphicsDisableDirect3D11": "True", "FFlagDebugGraphicsPreferVulkan": "True" }

forces the engine to utilize the Vulkan API instead of Direct3D 1130. While legitimately used by players on Linux (via Wine/DXVK) or those attempting to bypass software incompatibilities (such as OBS game capture failures caused by anti-cheat overlays), manipulating the rendering API alters how the client interacts with memory and the GPU30. Disabling certain UI rendering flags or forcing alternative APIs can reduce client overhead, allowing automated Lua scripts to run more efficiently in multi-instance virtual machine setups without crashing the host architecture34.

6. Semantic Outliers: Cultural Collectives and Illicit Trade Hubs

The final dimension of the Valkan/Balkan Hub namespace extends beyond software engineering into the physical and geopolitical realms. The Trans Europe Halles (TEH) Balkan Hub: In the cultural sector, the "Balkan Hub" refers to a collective of independent, grassroots cultural and creative organizations spanning Eastern Europe35. Coordinated by the Trans Europe Halles (TEH) network and initiated in 2019 by figures like Irena Boljuncic Gracin, this hub focuses on securing funding (such as from the Swedish Institute and Erasmus+) to repurpose abandoned infrastructure into multi-purpose arts and research spaces35. The organizational structure relies on regular offline workshops (held in cities like Peja, Skopje, and Sofia) to foster regional unity and advocate for democratic arts policies35. The Eastern Balkan Hub (Illicit Tobacco Trade): Conversely, in the realm of criminology, the "Eastern Balkan Hub" designates a specific geopolitical vulnerability region centered around Bulgaria, Greece, and Turkey, characterized by the illicit trade in tobacco products (ITTP)36. Research indicates that lack of stringent legislative measures and disadvantaged socio-economic contexts allow transnational criminal groups to exploit regional supply chains36. Small-scale actors (who account for 81% of the participants but only 3% of the volume) utilize motor vehicles to smuggle goods, while large-scale actors (7.2% of participants) control nearly 90% of the illicit market, moving unbranded "illicit whites" into Western Europe36. Note: The term "Valkan" also occasionally surfaces in fictional contexts, such as the "Gra-Valkas Empire" in Japanese light novel literature or the "Valkan Marrin" enemy archetype in the Xenoblade Chronicles video game series, further emphasizing the namespace collision across digital text corpora37.

7. Conclusions and Strategic Implications

The exhaustive analysis of the Vulkan and Valkan ecosystems yields several critical observations regarding modern software architecture, security posture, and the implications of explicit system control.

1. The Double-Edged Nature of Explicit APIs: The architectural shift embodied by the Vulkan Graphics API empowers developers to maximize hardware utilization by eliminating driver overhead. However, this demands a heightened level of defensive programming. When software relies entirely on the application layer for memory synchronization and state validation, the surface area for fatal faults increases dramatically. The development of centralized, dynamic documentation architectures like VulkanHub is not merely a convenience; it is a structural necessity for enforcing the strict Valid Usage rules required to maintain system stability.

2. Asymmetric Advantage in Automated Discovery: Tools like the Valkan network scanner demonstrate how highly concurrent programming languages (Golang) and reliable dependencies (Nmap) have commoditized vulnerability discovery. Defenders must assume that adversarial entities are mapping network topologies with equal or greater efficiency. Consequently, organizations must transition from reactive patching to proactive, continuous attack surface management (ASM).

3. The Escalation of Anti-Tamper vs. Evasion Mechanics: The JIT-decryption mechanisms utilized by modern anti-cheat systems and the probabilistic memory dumping techniques employed by the Vulkan PE dumper illustrate a highly sophisticated, escalating arms race. Standard heuristic detection is largely ineffective against JIT-decrypted memory. To mitigate these evasion techniques, software engineers must employ multi-layered defensive strategies, including continuous server-side validation and the integration of hardware-backed execution environments, recognizing that client-side trust models are inherently fragile.

Moving forward, the principles of explicit resource management seen in graphics processing must be mirrored in security engineering. Systems can no longer rely on implicit protections or simple obfuscation. The persistent development of concurrent network scanners and automated memory dumpers dictates that resilience must be built deterministically into the architecture itself.

Works cited

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7. One stop solution for all Vulkan samples \- GitHub, https://github.com/KhronosGroup/Vulkan-Samples

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9. GPUOpen-Drivers/AMDVLK: AMD Open Source Driver For Vulkan \- GitHub, https://github.com/GPUOpen-Drivers/AMDVLK

10. GitHub \- Tanusoni/wsl-vulkan-mesa: Mesa 3D graphics library (read-only mirror), https://github.com/Tanusoni/wsl-vulkan-mesa

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13. Introducing VulkanHub, Vulkan specs made better\! \- Reddit, https://www.reddit.com/r/vulkan/comments/1dhutaf/introducing\_vulkanhub\_vulkan\_specs\_made\_better/

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22. NEW ROBLOX EXPLOIT "VOLCANO" \- FREE EXECUTOR 2025 \[Educational\] \- YouTube, https://www.youtube.com/watch?v=rDXyJ0XSz6M\&vl=en

23. Blox Fruits Script • NO KEY • AUTO FARM, AUTO RAID, MASTERY FARM, SEA EVENT, VOLCANO, RACE V4 (OP) \- YouTube, https://www.youtube.com/watch?v=Th4MKRzjTOU

24. \[ NEW\] Blox Fruits Script Hack | Auto Farm \+ Instant Mastery \- YouTube, https://www.youtube.com/watch?v=\_28aU9ff4\_4

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26. Roblox Exploit No Key & Free (Working 2026\) \- YouTube, https://www.youtube.com/watch?v=iZcvNKhIuX8

27. This FREE Roblox EXPLOIT can execute ANY SCRIPT\! \- NO KEY, DECOMPILER 2026, https://www.youtube.com/watch?v=cHPDbuZ0QoI

28. Blox Fruits Script \NO KEY\ — Auto Farm, Mastery Farm, Auto Raid, Sea Events, Fruit ESP and More\! \- YouTube, https://www.youtube.com/watch?v=hi2H9ccgSDU

29. Blox Fruits Script \[ NO KEY \] Gui \- Auto Farm, Auto Quest, Fast Attack, ESP, Auto Dungeon & MORE\! \- YouTube, https://www.youtube.com/watch?v=T9mWSh34O10

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31. Our repository is packed with insider knowledge about Fast Flags. Enhance your experience with roblox Fast Flags \- GitHub, https://github.com/Dantezz025/Roblox-Fast-Flags

32. How to Enable OBS Game Capture with Bloxstrap\! (Vulkan Renderer) | Roblox \- YouTube, https://www.youtube.com/watch?v=yh8nobmj7YQ

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34. Roblox Volt Exploit Review \- The Best For Multiple Instances\! \- YouTube, https://www.youtube.com/watch?v=rnZK1g-mff0

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37. Gra Valkas Empire's Mu Invasion plan (Japan at Baltica) : r/nihonkoku\_shoukan \- Reddit, https://www.reddit.com/r/nihonkoku\_shoukan/comments/1juc8g8/gra\_valkas\_empires\_mu\_invasion\_plan\_japan\_at/

38. Valkan Marrin \- Xeno Series Wiki, https://www.xenoserieswiki.org/wiki/Valkan\_Marrin

39. Gra-Valkan battleship vs This Mother f\\\*er : r/nihonkoku\_shoukan \- Reddit, https://www.reddit.com/r/nihonkoku\_shoukan/comments/1efwair/gravalkan\_battleship\_vs\_this\_mother\_fer/