esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid Multi-Agent GraphRAG for E-Government: Towards a Trustworthy AI Assistant
### Research Questions  
Given the need for explainable, trustworthy, and scalable AI solutions in policy-oriented e-government contexts, this research is motivated by the challenge of operationalizing advanced RAG frameworks for structured public-sector datasets. Building on the aims to develop a modular GraphRAG architecture that integrates knowledge graph reasoning, dense vector retrieval, and web search, within a unified multi-agent pipeline, the study addresses critical gaps in transparency, auditability, and adaptability for digital governance.  
The Research Questions (RQs) therefore seek to evaluate the effectiveness of this hybrid system in supporting factual, relevant, and interpretable Q&A workflows, to explore the added value of combining graph-based and embedding-based retrieval over diverse data modalities, and to assess how such a framework advances the goals of explainable and accountable AI in real-world e-government applications.  
This study is faced with three core research questions, each addressing a critical challenge in the development of trustworthy AI assistants for public sector data applications.  
**RQ1:**_How can GraphRAG's modular architecture be operationalized within e-government applications to support structured data and explainable AI-based assistant framework?_  
E-government data is highly structured, requiring models that can reason over entities, timelines, and policies. This question explores how GraphRAG's modular components, such as graph indexing, subgraph retrieval, and traceable generation, can be tailored for explainable, transparent, and policy-aligned applications.  
**RQ2:**_To what extent does integrating GraphRAG with standard RAG (embedding + web search) optimize knowledge retrieval and question answering for complex, structured public e-government data?_  
Hybrid systems combining graph, dense, and web-based retrieval may outperform any single approach. This question investigates how such integration improves factuality, coverage, and adaptability, particularly for diverse query types in dynamic, open-domain public datasets.  
**RQ3:**_How can an agent-based RAG framework, supporting various types of RAG architectures, enhance response quality, while aiming for comprehensive and factually accurate, with no hallucinations question answering?_  
Multi-agent orchestration allows specialized agents (e.g., for graph traversal, web lookup, or verification) to collaborate, improving coherence and factual grounding. This question evaluates how agent-based design supports reliable AI assistants for public-sector use.
# Adversarial Hierarchical-Aware Edge Attention Learning Method for Network Intrusion Detection
## 4 Methodology  
AH-EAT integrates a graph attention mechanism and adversarial training to solve the problems encountered by existing graph-based NIDSs, such as the lack of a fusion mechanism of node embedding edge features and topology, single detection granularity, and adversarial vulnerability. Therefore, we aim to leverage edge-level feature information and learning edge embedding by GAT, hierarchical detection, and adversarial learning.  
Figure 1 presents an overview of the anomaly detection process performed using AH-EAT, which comprises three organized modules designed to address multi-grained network intrusion detection. The pipeline initiates with a sophisticated graph construction phase (left panel), where raw network flows transform into network flow graph representations. We use the IP address of each network flow as a node and other features as edge features to construct the graph. At the same time, we construct training and testing graphs while preserving both coarse-grained (e.g., attack categories) and fine-grained labels (e.g., specific attack variants), thus maintaining hierarchical threat intelligence crucial for multi-level detection. Finally, we divide the training graph and test graph according to the defined ratio. The core innovation resides in the adversarial graph generation module (central panel), where we implement a graph adversarial training that injects perturbation patterns constrained by attack semantics. Unlike conventional adversarial methods, our model employs edge feature perturbation and feature masking to simulate sophisticated evasion tactics while preserving the integrity of genuine attack flow signatures. This adversarially augmented graph is subsequently fed into a graph neural network equipped with edge-based attention mechanisms. Afterwards, the embedding vectors obtained from the origin graph and the adversarial graph are concatenated and used as the embedding representation of the edge at the same time, so that the representation features obtained by each network flow training have both the original features and the adversarial perturbation feature information. The final detection phase (right panel) implements a hierarchical classification architecture that synergistically combines coarse-grained anomaly detection with fine-grained label prediction. The system first performs coarse-grained detection to identify fundamental attack categories (benign and attack flow), then executes fine-grained classification using multi-scale feature fusion that correlates with global graph properties. This dual-detection approach enables simultaneous macro-pattern (coarse-grained) recognition and micro-anomaly (fine-grained) localization, effectively addressing the challenge of detecting both malicious intent and broad attack types. The entire process is optimized through adversarial training, ensuring robustness against adversarial attack patterns. This results in a highly robust model that considers the possibility of being applied to actual NIDSs in the future to protect network security.
# FLGQM: Robust Federated Learning Based on Geometric and Qualitative Metrics
## 2 Materials and Methods
### Flgom  
#### 2.2.1 Overview  
In this paper, we design a new federated learning method: robust federated learning based on geometric and qualitative metrics. The adversary can control the malicious client'smodel in an arbitrary manner. When we calculate the global model, we consider two aspects, the intuitive model quality and the geometric direction and size of the uploaded models, and use them to assign a metric score (MS) to the local model. A high metric score represents better local models.  
Specifically, we first consider a scenario where the adversary has arbitrary control over the geometric direction and size of the local model and therefore performs geometric metrics on all uploaded local models from this perspective. Formally, we use similarity based on cosine and the Euclidean distance. To guide the global model's direction, we ensure that each global iteration aligns with the trajectory of the previous round's global model. First, the server retains a collection of models uploaded by the clients who participated in the previous round of training. Considering that the uploaded model in the current round of benign clients cannot deviate from the uploaded model in the previous round, we quantify the similarity between the uploaded model in the current round and the model uploaded in the previous round using cosine similarity. This process allows us to identify and eliminate some of the malicious clients. Next, we utilize cosine similarity to quantify the alignment in direction between the local model and the global model from the previous round. We then identify and exclude malicious clients whose behavior deviates significantly from the global model using the ReLU function. Subsequently, we quantify the similarity in size between the local model and the global model from the previous round using Euclidean distance.  
The similarity based on Euclidean distance can identify the difference in malicious models and then assign geometric metric scores (GMSs) to different clients. Also, considering that certain advanced poisoning attacks can geometrically make malicious updates similar to benign ones, we perform quality metrics on all uploaded local models from this perspective. Specifically, we randomly select some of the clients that do not participate in this round of federated learning as union clients. They verify the models uploaded by all local clients separately using their own local data, evaluate the models for different clients based on the trimmed mean, and then evaluate the model quality for all uploaded models as a way to assign quality metric scores (QMSs) to different clients. Finally, the global model is updated by taking a weighted average of the combined quality and geometric metric scores.
# Virtual Reality Collision Detection Based on Improved Ant Colony Algorithm
## 1 Introduction
[PERSON] proposed a random collision detection algorithm based on the particle swarm optimization algorithm. This method transforms the problem of detecting objects colliding in three-dimensional space into an optimization problem in a discrete two-dimensional space and then uses the particle swarm optimization algorithm to solve it. The algorithm can deal with detecting collisions between arbitrary polyhedron models while ensuring efficiency and has good versatility. However, it is not guaranteed to find all feature pairs of the collision, and there is room for improvement in terms of speed and accuracy [8; 9; 10]. By summarizing the characteristics of the existing collision detection algorithms at this stage, we find that although the research results regarding collision detection algorithms have been relatively rich, they are mainly used to deal with collision detection between the two models. When dealing with the problem of detecting collision in large-scale complex scenes, the current algorithms still have problems and cannot deal with the problem of detecting collisions between objects quickly and accurately. In order to solve the above problems, this paper introduced the ant colony algorithm into the random collision detection algorithm, and combined the bounding volume hierarchy technology to study and design the Sphere-OBB-MACO algorithm. The algorithm is divided into two steps: in the initial detection stage, the OBB bounding box and the spherical bounding box are combined to form a hybrid hierarchical bounding box for detecting collisions, and the bounding boxes that may collide are selected. In the accurate detection stage, the selected bounding boxes are sampled, and the sampled feature pairs are used to form the search space. The improved ant colony algorithm was introduced to solve the optimization problem of the search space. Compared with other collision detection algorithms, this paper uses the OBB bounding box and the spherical bounding box to construct the bounding volume hierarchy instead of a single bounding box for detection in the preliminary detection stage. Although the time consumed in the preliminary detection stage becomes longer, it improves the accuracy of collision detection and reduces the time required for accurate detection. Because of the large number of feature pairs in complex scenes, the ant colony algorithm with higher search efficiency was selected in the accurate detection stage to replace the particle swarm algorithm used by other researchers, and the defects of the ant colony algorithm are improved.
# Post-Quantum Authentication in the MQTT Protocol
## 4 Materials and Methods
### Implementation of MQTT Authenticated with KEM  
We created skeleton implementations of MQTT with signature-based and KEM-based authentication using Python. Python implementations of postquantum algorithms were imported from the pqcrypto library ([[https://pypi.org/project/pqcrypto/](https://pypi.org/project/pqcrypto/)]([https://pypi.org/project/pqcrypto/](https://pypi.org/project/pqcrypto/)) (accessed on 2 February 2023)). The skeleton implementations include separate packages for both the client and the broker. The client can act as both a publisher and subscriber. Only the part of the MQTT protocol required to demonstrate the postquantum authentication process was implemented, namely the handshake process. The implemented parts of the MQTT protocol were programmed exactly as per the documentation of MQTT, except the additional options, such as higher quality of service. The choice to use a minimal implementation of MQTT was made to focus on the objective of determining the impact of implementing postquantum authentication in MQTT. We used Kyber-512 (security level 1) and dilithium2 (security level 2) for our experiments. We used two virtual machines for the client and broker. These machines both had 1 GB of RAM and 8 GB of storage. The code used can be found in this GitHub repository ([[https://github.com/raven-townsend-nz/quantum-safe-mqtt](https://github.com/raven-townsend-nz/quantum-safe-mqtt)]([https://github.com/raven-townsend-nz/quantum-safe-mqtt](https://github.com/raven-townsend-nz/quantum-safe-mqtt)) (accessed on 9 May 2023)). As this implementation is similar to a proof of concept and not a full implementation, security considerations such as side-channel attacks, which are implementation specific, have not been considered.
## Materials and Methods  
### Preparation of Feedstocks  
A relatively coarse spherical Ti powder Grade 2 (Arcam, Molndal, Sweden) with a mean particle size of about 75 \(\upmu\)m (as shown in Figure 1) was used in preparing a feedstock with a 58 vol. % solid content by mixing it with a three-component polymeric binder. The first part of the binder (the low viscosity part) was Polyethylene Glycol (PEG) 1500, and it constituted 70 vol. % of the total binder. PEG was chosen due to its availability, as well its good solubility in the water. The second part of the binder (the high viscosity part) was Poly Methyl Methacrylate (PMMA), and it comprised 25 vol. % of the binder. PMMA was chosen due to its high strength in comparison with other common polymers used in MIM, such as low and high density polyethylene, so that green samples can be easily handled after removal of PEG without any sample collapse [12]. Stearic Acid (SA) was the third part, which constituted 5 vol. % of the total binder (added as a lubricant). The mixing process of the powders was carried out in a high speed centrifugal mixer in a similar manner to our previous experiments using Speedmixer 800 FZ (Hauschild; Synergy Devices Ltd., High Wycombe, UK) [13]. The feedstock prepared was extruded twice using a vertical plunger-type injection moulder at a temperature of 150 \({}^{\circ}\)C and pressure of 45 MPa and subsequently pelleted into small pellets to be ready for injection. The commercial Ti feedstock used in this study was PolyMIM Grade 2 supplied by PolyMIM\({}^{\circledR}\) Germany.
## 2 Materials and Methods
The GES controller shown in Figure 1 has the following operational relationships [41; 42; 43]:  
\[y=f(v_{1},v_{2}),\ y_{N}=k_{Ny}\cdot y \tag{12a}\] \[\dot{y}_{f}=-\omega_{h}\cdot y_{f}+\omega_{h}\cdot y_{N},\ y_{HPF}=y_{N}-y_{f}, \ \dot{y}_{BPF}=-\omega_{l}\cdot y_{BPF}+\omega_{l}\cdot y_{HPF}\] (12b) \[\omega_{h}=b_{h}\omega,\ \omega_{l}=b_{l}\omega,\ s_{d}=\sin(\omega t),\ \omega=2\pi f_{d}\] (12c) \[y_{DM}=y_{BPF}\cdot s_{d},\ \dot{y}_{Gradient}=y_{DM},p_{1}=k_{1} \cdot y_{Gradient}\] (12d) \[y_{M}=\bigg{|}\frac{1}{T_{d}}\cdot\int y_{BPF}dt\bigg{|},\ \ \ p_{2}=k_{2}\cdot y_{M}\cdot s_{d}\] (12e) \[I_{ref(GES)}=k_{Np}\cdot(p_{1}+p_{2}) \tag{12f}\]  
where the first harmonic of the FC power (\(y_{BPF}\)) is approximated using a band-pass filter with the cut-off frequencies \(\omega_{l}=b_{l}\omega\) and \(\omega_{h}=b_{h}\omega\), where \(\beta_{l}=1.5\) and \(\beta_{h}=0.1\). This is demodulated with a sinusoidal dither, \(s_{d}=\sin(\omega t)\), and integrated to obtain the search gradient (\(y_{Gradient}\)), where \(\omega=2\pi f_{d}\) and \(f_{d}=100\) Hz. The search and location signals (\(p_{1}\) and \(p_{2}\)) are tuned using \(k_{1}\) and \(k_{2}\) to speed up tracking of the optimum. In this study, \(k_{1}=1\) and \(k_{2}=2\), and the input and output are normalized using \(k_{Ny}=1/1000\) and \(k_{Np}=20\). It is worth mentioning that after the transitory regime, the stationary values are almost zero [40], resulting in a negligible ripple of FC power and a 99.9% tracking accuracy [43]. OER (\(\lambda_{O_{2}}\)) is used as an indicator of safe operation of FC/renewable HPS [51]:  
\[\lambda_{O_{2}}=\frac{c_{3}\cdot I_{FC}^{3}+c_{2}\cdot I_{FC}^{2}+c_{1}\cdot I _{FC}+c_{0}}{d_{1}\cdot I_{FC}+d_{0}} \tag{13}\]  
where \(c_{0}=402.6\), \(c_{1}=8.476\cdot 10^{-5}\ [1/A]\), \(c_{2}=-0.81252\ [1/A^{2}]\), \(d_{3}=0.02673\ [1/A^{3}]\), \(d_{0}=0.997\), and \(d_{1}=61.38\). The fuel consumption measured in liters [I] is the performance indicator. This is estimated during 20 s from one minute using (14):  
\[Fuel_{T}=\int FuelFr(t)dt \tag{14}\]  
Because \(FuelFr\) is measured in liters per minute (Lpm), a gain of 1/3 (= 20 s/60 s) is requested to compute the fuel consumption (Gain Fuel_T in Figure 3).
# Short-Term Traffic Flow Prediction Based on CNN-BILSTM with Multicomponent Information
## 4 Experiment
### Performance Index  
To determine the prediction performance of the proposed model, we evaluated the prediction results based on three evaluation indicators, that is, mean absolute error (_MAE_), mean absolute percentage error (_MAPE_), and root mean square error (_RMSE_), which are expressed as below:  
\[MAE=\frac{1}{n}{\sum_{i=1}^{n}}{\left|{{\hat{y}}_{i}-{y}_{i}}\right|} \tag{14}\]  
\[MAPE=\frac{1}{n}{\sum_{i=1}^{n}}{\left|{\frac{{{y}_{i}-{\hat{y}}_{i}}}{{{y}_{i }}}}\right|} \tag{15}\]  
\[RMSE=\sqrt{\frac{1}{n}{\sum_{i=1}^{n}}{\left({{\hat{y}}_{i}-{y}_{i}}\right)^{ 2}}} \tag{16}\]  
where \(n\) represents the number of samples, \(y_{i}\) represents the true value of the data, and \({\hat{y}}_{i}\) represents the predicted value of the data sample.
# A Robust Timing Synchronization Algorithm Based on PSSS for LTE-V2X
## 5 Results and Discussion
### Software Simulation
Specifically, it achieves a performance improvement of up to 14 dB compared to the M = 4 and M = 2 BCCAs. In comparison to the SSA, the improvement reaches 10 dB. The improved algorithm attains a CDR of 0.9 at \(-\)14 dB. 2. The CCA loses its ability to detect the PSS when the normalized frequency offset reaches 1. The CDR of the CCA remains consistently at 0.
## 3 Path-Planning Design and Dynamic-Obstacle Avoidance Strategy
### Global Path-Planning Design Based on 3D JPS Algorithm
#### 3.1.2 3D Extension of JPS Algorithm
(**b**) Pruning rule under the diagonal extension direction of the same plane. The expansion direction of the 3D JPS algorithm is restricted to the original expansion direction in the unique plane as well as above and to the right of the current grid. (**c**) Pruning rules under the diagonal expansion direction of the body. The extension direction of the 3D JPS algorithm will involve two planes. Where the green arrow indicates the expansion direction of the parent node from the lower layer to the current node, the red arrow indicates the expansion direction of the middle layer, and the blue arrow indicates the expansion direction from the current node to the upper layer. In the process of 3D JPS algorithm path planning, openlist and closelist are used to record the search process, where the openlist list stores the candidate jump points in the current search. After computing the heuristic function for each candidate jump point, the point with the smallest heuristic function value is taken from the openlist and added to the closelist for this round. During the next round of search, this jump point is used as the parent of the current node to continue the search until the target is reached. The heuristic function of the 3D JPS algorithm is shown below. \[f(x)=g(x)+h(x) \tag{4}\]  
where \(x\) denotes the current node and \(g(x)\) denotes the actual cost from the initial node to the current node \(x\). \(h(x)\) denotes the heuristic evaluation cost of reaching the target point \(goal\) from the current node \(x\). In general, the three-dimensional spatial Euclidean distance formula is used for evaluation, which is shown below. \[dist(x,goal)=\sqrt{\left(x_{x}-goal_{x}\right)^{2}+\left(x_{y}-goal_{y} \right)^{2}+\left(x_{z}-goal_{z}\right)^{2}} \tag{5}\]  
In this paper, the path-planning steps of the 3D JPS algorithm are shown below. 1. Initialize the static spatial environment and discretize the drone flight area into a grid model. 2. Set the initial position of the drone to \(x_{start}\) and the target point \(x_{end}\) and add \(x_{start}\) to the openlist. 3. The neighbor-pruning rule is selected according to the position information of the current node and its parent node, and the expansion direction of the current node is calculated. 4. Find jump points by forcing the location of neighbors and add them to the openlist. 5. Each node in the openlist is computed by a heuristic function. The node with the smallest computed value is selected as the jump point and the parent node for the next search process. 6. Add the nodes that play the role of parent nodes in this search process to the closelist. If there is \(x_{end}\), the search will be terminated, and the search path will be formed by the list of closelist. If \(x_{end}\) is not present, repeat steps 3 through 6.