esa-sceva/satcom-chunk-collection huggingface dataset:
# Model Validation and Scenario Selection for Virtual-Based Homologation of Automated Vehicles
## 4 Results and Discussion
### Data-Driven Application  
This subsection begins with a pre-analysis of the minimum required event length \(\Delta t_{min}\) to parameterize the event finder. The longer the minimum length is, the more meaningful the events are, but the fewer are found. Therefore, we investigate the number of extracted events, their duration and their coverage of the scenario space for varying values of the minimum length hyperparameter and select 4.5 s as a reasonable trade-off. After the data-driven extraction of application scenarios, the shortest event has a duration of 4.55 s, the longest event of 40.38 s and the average event of 10.01 s. The distribution of the data-driven application scenarios is shown in Figure 4. As desired, it contains randomness to generate unforeseen test scenarios for type approval that do not follow a predefined pattern. The rule-based algorithm extracts 62 application scenarios from the road data set. The latter is reused from the coverage-based scenario design and consists of selected curves from the German roads A7 and B19 and connecting straight sections in between. This exploits the efficiency advantage of the virtual environment. Since the length of the road data set is 153.86 km, this corresponds to a frequency of 0.88 events per kilometer. Due to the randomness in the scenario distribution, the granularity of the points varies across the scenario space and includes small holes. Nevertheless, the distribution and amount of scenarios is suitable for a first proof of concept.
## 2 Overview of Reinforcement Learning  
### Reinforcement Learning  
Reinforcement learning (RL) is a category of machine learning. The agent in this case learns how to reach a goal through trial and error. The feedback may either be a reward or a punishment. It designates how good or bad the action was. The agent learns by adjusting its behavior in order to maximize the total reward received. RL is applied to solve a vast range of control and optimization problems involving sequential decision making. The sequential decision-making framework is illustrated as follows in Figure 1:As a goal, the agent should maximize the total collected reward--this drives policy (value function) adjustments. Policy function produces an action to execute given a specific state \(\pi(s)\to a\).  
RL algorithms can be divided into two types: value-based or policy-based. Value-based RL algorithms learn optimal action-value function \(Q^{*}(s,a)\). The policy may be derived from \(Q^{*}(s,a)\). This is what we call Q-learning. Contrarily, policy-based RL algorithms search directly for the optimal policy \(\pi^{*}\).  
In order to observe the optimal policy \(\pi^{*}(s)\) for each state in the value-based RL algorithms, we have to choose the action that yields the highest Q-value \(\pi^{*}(s)=argmax_{a}Q^{*}(s,a)\). Q-values are the optimal \(Q^{*}(s,a)\) for each (\(s,a\)) pair.  
To calculate the Q-values, we rely on the Bellman update equation:  
\[Q(s,a)\gets Q(s,a)+\alpha[\,r+\gamma max_{a}^{\prime}Q(s^{\prime},a^{ \prime})-Q(s,a)] \tag{1}\]  
The first \(Q(s,\,a)\) is the next estimate. The second \(Q(s,\,a)\) is the current estimate. \(\alpha\) is the learning rate. \(r+\gamma max_{a}^{\prime}Q(s^{\prime},a^{\prime})\) is the target value- Bellman equation. \(r+\gamma max_{a}^{\prime}Q(s^{\prime},a^{\prime})-Q(s,a)\) is the \(Error=\delta(s,a,r,s^{\prime})\).  
Therefore,  
\[Q(s,a)\gets Q(s,a)+\alpha\Delta(s,a,r,s^{\prime}) \tag{2}\]
# Stabilization of Pickering Emulsions by Hairy Nanoparticles Bearing Polyanions
## 3 Results and Discussion  
### Synthesis of PS@PSS Hairy Nanoparticles  
Dynamic light scattering (DLS) and transmission electron microscopy (TEM) were used for size analysis of polystyrene (PS) core emulsion and subsequent hairy nanoparticles. First, DLS that could measure the hydrodynamic diameter (Rh) was applied to demonstrate the size increase from PS core to PS@PSS in an aqueous solution. Table 1 shows that the size of PS core used for preparing PS@PSS was 74.2 nm. After photoemulsion polymerization, the size of the PS@PSS was 252.8 nm, and the PDI was 0.018. The PS@PSS nanoparticles had narrow diameter distribution with solid content of 1.02 wt.%.  
TEM was used to study the size and morphology of these nanoparticles (Figure 2). The PS core size was around 70 nm (Figure 2a) and the PS@PSS size was around 190 nm (Figure 2b). Thus, the thickness of the PSS chain was about 60 nm (Figure 2b), indicating that the PSS chains were successfully grafted  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Size and Size Distribution** & **PS Core** & **PS@PSS** & **Thickness (nm)** \\ \hline Diameter (nm) \({}^{1}\) & 74.2 \(\pm\) 0.3 & 252.8 \(\pm\) 1.2 & 89.3 \(\pm\) 0.7 \\ PDI \({}^{1}\) & 0.001 \(\pm\) 0.001 & 0.018 \(\pm\) 0.009 & - \\ \hline \hline \end{tabular} \({}^{1}\) Measured by DLS and took the average as the result.  
\end{table}
Table 1: Size of hairy nanoparticles by Dynamic Light Scattering (DLS).  
onto the PS core. In general, the PS core size agreed with the DLS data, but the PS@PSS size was much smaller than 252.8 nm (DLS data). This was because the PSS chains would shrink and the size would decrease during the drying process of TEM sample preparation; the edge of the PSS chains was also too sparse to be observed easily in Figure 2b. In addition, DLS gives the hydrodynamic diameter (Rh), which considers the sphere of hydration. This made the apparent size (252.8 nm) larger than that from TEM (\(\sim\)190 nm).
## 2 Materials and Methods
### Field Test Methodology  
Movement on the court was specifically monitored for attack and defense positions using the same equipment as in the 10 m speed test. Each athlete completed the course three times, with the best repetition used for statistical analysis. Athletes were coached to achieve their best time during each attempt. The total distance covered was 10.2 m, which included three recording points: the starting position, intermediate point 1 (3.30 m), intermediate point 2 (3.30 m), and the final point (3.80 m). This route followed an adapted Y-line [29] designed to test movement on the court, as illustrated in Figure 1.  
The maximum velocity was measured at each intermediate point, from which we calculated the parameters average field velocity (afv, m/s), average field force (aff, N), average field power (afp, W), relative field force (rmf, N/kg), and relative field power (rfp, W/kg), according to the equations outlined in Equation (1). The test began at the start line, where the athlete sprinted to point 1. From point 1, the athlete reversed direction and ran backward to point 2. Finally, the athlete sprinted forward to the finish line at point 3, covering a total distance of 10.3 m.  
Assessing Exercise Capacity: Lactate Threshold 1 (LT\({}_{1}\)) and Lactate Threshold 2 (LT\({}_{2}\)) Measurement  
Each athlete underwent a test to evaluate exercise capacity [30] and identify lactate thresholds 1 (LT\({}_{1}\), mmol/l) and 2 (LT\({}_{2}\), mmol/l). The test was conducted individually on a treadmill in an indoor environment at a stable temperature (22\({}^{\circ}\) Celsius). Athletes followed an adapted Bruce protocol, running in 3 min stages at speeds ranging from 3.5 to 16 km/h, with a 0% incline, until they reached and validated the LT\({}_{2}\) value (LT\({}_{2}\) = 4.0 mmol/l). The thresholds were determined by measuring lactate levels from capillary blood samples (LactatePro 2, Arkray, Kyoto, Japan), with LT\({}_{1}\) set at 2.0 mmol/l and LT2 at 4.0 mmol/l. Using  
Figure 1: Illustration of the field test and the measuring point (1–3) during the field test.  
this protocol, we established exercise zones for each participant: aerobic (0-2 mmol/l), high aerobic (2-3.9 mmol/l), and anaerobic (>4.0 mmol/l).
## 4 Results and Discussion
### Case Study for DMSA in Static Scheduling
#### 4.1.3 Typical Algorithms for Comparison  
This paper selects the centralized genetic algorithm (CGA) [40], the centralized particle swarm algorithm (CPSOA) [41], and the centralized tabu search algorithm (CTSA) [16] and compares their performances in different cases with the DMSA proposed in this paper.
# Interaction in eXtended Reality Applications for Cultural Heritage
## 4 Interaction in Digital Storytelling Applications
### Interaction in VR Applications
#### 4.3.2 Interaction with User Interface  
The user interface (UI) is an important part of each Virtual Reality application, and interaction should be intuitive and easy to understand. In most of our Virtual Reality applications, we used the triggers of the controllers to interact with the UI. For the user to see which part of the UI they were selecting, a laser pointer was used, as can be seen in Figure 3. The laser pointer creates a line that extends from the controller, and if the user presses the trigger when the line of the laser pointer touches one of the UI buttons, the UI button will be activated. As a way of preventing a wrong-button click, we also tried highlighting the button which was being pointed at so that the user could see what he was trying to click on.  
We also experimented with the use of controller movement for button clicks. In the Sarajevo War Tunnel VR, we tried to implement the user interface clicks in a way that the user needed to put the controller through the button he wanted to click. Even though this kind of interaction was easy to understand and use, it produced some problems when the place where the Virtual Reality was set up was small, so the buttons were sometimes unreachable.
# Classification between Failed Nodes and Left Nodes in Mobile Asset Tracking Systems +
## 4 Node Classification
### Estimating Neighbor Counts  
The number of neighbors of a mobile node at a location within a monitoring area or on the boundary of the area is estimated in this section. The estimated neighbor count is used to determine \(T_{nc}\) and \(T_{r}\) and to calculate the missing neighbor rate, defined as \((NC_{E}-NC_{O})/NC_{E}\), where \(NC_{O}\leq NC_{E}\), and \(NC_{O}\) and \(NC_{E}\) indicate the observed neighbor count and the estimated neighbor count, respectively.  
Figure 4: Concepts on the trends of the neighbor counts and the ratios of the boundary nodes and two thresholds.
# Mechanical Properties and Freeze-thaw Durability of Basalt Fiber Reactive Powder Concrete
## 3 Results and Discussion
### Mechanical Properties
#### 3.1.2 Flexural Strength  
The experiment result of the flexural strength test is shown in Figure 5. Figure 5 shows that the flexural strength of RPC increases as the basalt fiber content increases, which is the same as the improvement of basalt fiber on the compressive strength. The increased trend of flexural strength is a slightly different from that of compressive strength. Compared with compressive strength, when the basalt fiber is from 8 to 12 kg/m\({}^{3}\), a significant increase in flexural strength of BFRPC occurs and the flexural strength of BFRPC reaches 16.23 MPa, which is 18.5% higher than that of PRPC. Compared with compressive strength, the improvement of basalt fiber on the flexural strength of RPC is greater, aggregating with the conclusion conducted by [PERSON] et al. [28]. The improvement of the flexural strength of BFRPC is related to the uniform distribution of basalt fiber inside the RPC. The uniform distributed basalt fiber could inhibit the generation and propagation of cracks and bear part of the stress, reducing the stress concentration near the crack and redistributing the stress [16], thus improving the flexural strength of RPC. Besides, the basalt fiber inside the RPC submits a three-dimensional random distribution; this distribution system can limit the deformation of RPC under loading conditions, which leads to an increase in the flexural strength of RPC. The three-dimensional distribution system of basalt fiber is gradually improved as the basalt fiber content increase, which is reflected in the significant increase in flexural strength of BFRPC with a basalt fiber content of 12 kg/m\({}^{3}\). Moreover, comparing the flexural strength of SFRPC with that of BFRPC (12 kg/m\({}^{3}\) content), it can be known that the flexural strength of BFRPC and SFRPC is similar, which indicates that when the basalt fiber content is 12 kg/m\({}^{3}\), the reinforcement effect of basalt fiber on the flexural strength of RPC is about same as steel fiber; [PERSON] et al. [25] reported a similar conclusion.  
Figure 4: Effect of basalt fiber content and fiber type on the compressive strength of RPC.
# On-Orbit Experimental Result of a Non-Deployable 430-MHz-Band Antenna Using a 1U CubeSat Structure
## 2 Loop Hentenna  
The \"Hentenna\" is an antenna developed by Japanese radio amateurs (such as JE1 DEU [11]). The \"Loop Hentenna\" is a looped version of the \"Hentenna\". Figure 1 shows the structure of a Hentenna and a Loop Hentenna [12]. The black lines indicate conductors. Both antennas operate by feeding from the feed point indicated by the red circle in the figure.  
A Hentenna has a planar structure. The impedance varies depending on the distance from the right end to the vertical bar where the feed point is located. A Loop Hentenna has a cubic shape with each side having 1/6 wavelength. The impedance is around 50 \(\Omega\). Both antennas have a figure of eight radiation pattern like a dipole antenna and the maximum gain is about 3 dBi. The polarization is linear polarization [11; 12]. By setting one side of the Loop Hentenna to 10 cm, the frequency is 500 MHz. This shows that it is possible to build a 430-MHz antenna by applying the main structure of the 1U CubeSat.  
_Comparison with Other Exsisting Designs_  
Table 1 shows the comparison with existing non-deployable UHF antennas (300 MHz \(\sim\)1 GHz) for 1U or 1.5U CubeSat. This table shows that the antenna proposed in this paper occupies smaller area than the other antennas because the rails and structural frame that are required for CubeSat anyway are converted to antennas. Our antenna requires the least amount of additional resource in terms of volume and surface area.  
\begin{table}
\begin{tabular}{c c c c c} \hline \multirow{2}{*}{**Reference**} & **Type** & **Frequency** & **Gain** & **Occupied Area** \\  & **(Target)** & **(MHz)** & **(dBi)** & & \\ \hline \multirow{3}{*}{[7]} & Slot & & & 2568 mm\({}^{2}\) \\  & (1.5U/3U) & 485/500 & 4 (simulated) & (3 mm slot \\  & & & & Total length: 856 mm) *1 \\  & Patch & & & 2720 mm\({}^{2}\) \\  & (>1U) & 437.375 & 1.01 (measured) & (80 \(\times\) 34 \(\times\) 2 mm) \\  & Patch & 437.525 MHz & & \(\sim\)2400 mm\({}^{2}\) \\  & (>1U) & 145 & \(-\)23 (measured) *2 & (80 \(\times\) 30 mm) \\ \multirow{2}{*}{Proposed} & Loop type & & & 1008 mm\({}^{2}\) \\  & (1U) & 437.375 & \(-\)5.7 (measured) & (84 mm \(\times\) 12 mm) *3 \\ \hline \end{tabular} *1 Ignore the phasing line. *2 A 435 MHz band. *3 Ignore rail and structure surface area.  
\end{table}
Table 1: Comparison of non-deployable UHF antennas (300 MHz\(\sim\)1 GHz) for 1U/1.5U CubeSat.  
Figure 1: Structure of a Hentenna and Loop Hentenna.
# Convolutional Graph Network-Based Feature Extraction to Detect Phishing Attacks
## 3 Materials and Methods
### Computational Complexity  
Our method involves three stages: feature extraction, graph construction, and classification. The most computationally demanding part is the construction of the hyperlink graph and the subsequent GNN-based feature selection and learning. Let n be the number of nodes (HTML elements/URLs) and d the feature dimension. The feature extraction process is O(n\(\cdot\)d), and the graph construction using pairwise distance-based edge creation has a complexity of O(n\({}^{2}\)). The node random walk (NRW) procedure, which involves transition matrix calculations and repeated matrix multiplications, has an approximate computational complexity of O(n\({}^{3}\)). The GCN training is linear in the number of edges and epochs; i.e., O(e\(\cdot\)h\(\cdot\)T), where e is the number of edges, h is the number of hidden units per layer, and T is the number of epochs. In our case, n is moderate (\(\sim\)100-300), so the method is computationally feasible even without GPU support. Finally, the SVM classifier is trained on the reduced feature set, with a complexity of roughly O(m\({}^{2}\cdot\)k), where m is the number of training samples, and k is the number of selected features. Due to dimensionality reduction, this step is efficient and fast.  
```
Input:
* Dataset X, Y (features and labels)
* Number of features k
* SVM and GNN hyperparameters (learning rate, epochs, etc.) Output:
* Selected features and classification metrics (accuracy, precision, etc.)
1. Preprocessing:
* Load and split the datasets X, Y into training and testing sets.
2. Graph Construction:
* Build graph G from pairwise distances between features.
* Use Manhattan distance to calculate pairwise distances between feature vectors.
* Add self-loops to nodes.
3. Initialize GNN:
* Define the GraphSAGE model with initialized node embeddings.
4. GNN Training:
* For each epoch: \(\odot\) Update node embeddings by aggregating neighbor messages. \(\odot\) Compute softmax output and Hinge loss. \(\odot\) Backpropagate to update model weights.
5. Neighborhood Random Walk (NRW):
* Calculate the NRW distance between nodes in the graph G using the random walk with restart process.
* Construct an adjacency matrix based on NRW to capture relationships between nodes.
6. Feature Selection:
* Rank features by gradient importance and select top k.
7. Train SVM:
* Train an SVM using the selected features.
* Tune hyperparameters via cross-validation.
8. Evaluate:
* Predict on test set and compute performance metrics.
```  
**Algorithm 1**Phishing Detection with GNN and SVM
# An Algorithm for Producing Fuzzy Negations via Conical Sections
## 3 Main Results
### Production of Fuzzy Negations via Conical Sections
Once more, due to the symmetry of the conical section to the straight line \(y=x\), if Equation (11) is solved for variable \(x\), it will generate the same formula of function, namely  
\[\alpha x^{2}+by^{2}+2 cxy+(-a-f)x+(-b-f)y+f=0,\ x,y\in[0,1],\ f\
eq 0, \tag{2}\]7  
According to Equations (14), \(\kappa\alpha\) (16), and Remark 1, the equation  
\(N(x)=\sqrt{(m^{2}-1)x^{2}+1}+mx,\ x\in[0,1],\ \ m\leq 0\), which is Equation (14), and is a strong fuzzy negation. In an overview of the results stated above, the following proposition is ensured as a reference to Equation (6). **Proposition 3**. : _Equation (6) namely_  
\[\alpha x^{2}+by^{2}+2 cxy+(-a-f)x+(-b-f)y+f=0,\ x,y\in[0,1],\ f\
eq 0, \tag{2}\]8  
_expresses conical sections, where \(k=0\) produces strong fuzzy negations with the formula \(N(x)=\frac{1-x}{1+mx},\ m>-1\) which are known as the Sugeno negations, while for \(k=-1\) it expresses conical sections, which produce strong fuzzy negations with the formula \(N(x)=\sqrt{(m^{2}-1)x^{2}+1}+mx,\ x\in[0,1],\ m\leq 0\), which is Equation (14)._  
**Remark 2**. : _As far as the \(N(x)\) of the aforementioned proposition is concerned, since it forms a part and segment of the conical section, with the beginning of the axes serving as the center of the symmetry, its symmetrical segment for the center of symmetry belongs to the same conical section. The symmetrical segment with the appropriate movement (a single unit upwards and a single unit towards the right) equally constitutes a strong negation and is particularly symmetrical to \(N(x),\ \)with the straight line \(y=1-x\) as an axis of symmetry. Therefore, it has the following form: \(N^{d}(x)=1-N(1-x)\) and by Definition 2, it is the dual negation. For instance, if \(N(x)=\sqrt{(m^{2}-1)x^{2}+1}+mx,\ x\in[0,1],\ m\leq 0\). Then by Equation (15), we conclude that the function \(f(x)=-\sqrt{(m^{2}-1)x^{2}+1}+mx,\ x\in[0,1],\ m\leq 0\), equally constitutes a segment of the conical section, like \(N(x)\), then the \(1+f(x-1)\) is nothing more than the dual negation, namely \(1+f(x-1)=N^{d}(x)=1-N(1-x)\)._  
Certain examples are given below for the explanation and the verification of the aforementioned theoretical results.
# Material Flow Optimization as a Tool for Improving Logistics Processes in the Company
## 2 Literature Review
The study in [40] analyzes the use of sensor networks in logistics and demonstrates how the Internetof Things (IoT) contributes to the real-time monitoring of material flows, leading to a reduction in operational costs. The authors of [41] present the application of blockchain in combination with IoT for transparent and efficient real-time monitoring of logistics operations. The study in [42] focuses on the application of machine learning for optimizing logistics route planning and predicting transportation obstacles in supply chains. The study in [43] explores the use of digital twin simulations to test different supply chain scenarios before their implementation in real-world operations. The authors of the study in [44] provide an overview of digital twin implementation across various manufacturing and logistics sectors. The authors of [45] analyze how Industry 4.0 digital technologies improve supply chain resilience in response to market disruptions or global crises. Material flow cost accounting (MFCA) provides a framework for evaluating material losses and improving resource efficiency. The authors of the study in [46] introduced the MFCA method, which assesses inefficiencies in monetary terms, while the authors of the study in [47] applied MFCA to a ceramic tile manufacturing company, highlighting its impact on process optimization and data utilization for sustainable production. Numerous case studies demonstrate practical applications of material flow optimization. In [48], the authors analyze material flow in an automotive company, proposing internal transport programs and workspace reorganization. The authors of [49] develop an evolutionary algorithm to synchronize supply chains, achieving optimal solutions for various logistical scenarios. These studies highlight the transformative impact of modern digital technologies on material flow optimization, not only in terms of efficiency and cost reduction but also in sustainability. Their application in logistics and supply chain management enables better planning, more accurate predictions, and increased resilience to unforeseen market fluctuations.