esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 D-Leach
### Set-Up Phase  
The BS relays the start of the round to all sensor nodes. Each sensor node that receives the start message selects a CH using the CH probability in Equation (7).  
\[T(n)=\begin{cases}\frac{Wi}{1-Wi\times\left(r\mod\frac{1}{Wi}\right)}&\text{ if }n\in G\dot{i}\\ &En>Initial\ Energy\times 0.1\\ 0&\text{otherwise}\end{cases} \tag{7}\]  
The sensor node's layer number is \(i\), and \(G_{i}\) is the set of sensor nodes in the \(i\) layer not yet selected as CH. Further, \(W_{i}\) is the CH selection probability value of the layer \(i\), as in Equation (6), and \(r\) is the current round. The sensor node n is included in \(G_{i}\) for the calculation \(T(n)\) using \(W_{i}\) of its layer and \(r\). \(E_{n}\) is the remaining energy of sensor node n. If \(E_{n}\) of sensor node n included in \(G_{i}\) is less than 10%, it is excluded from the CH election candidate. Each sensor node generates an arbitrary number. Then, this number is compared with \(T(n)\). If \(T(n)\) is larger than the arbitrary number generated by the sensor node n, the sensor node n becomes the CH.  
The CH transmits an ADV including relative distance information between itself and the BS to the surrounding sensor nodes. Figure 9 shows the ADV of the CH. The ADV message contains the ADV header, ID of the sensor node, ID of the CH, and information regarding the distance to the BS.  
Each sensor node that receives the ADV compares the relative distance and selects a CH to which Join-REQ is transmitted. Figure 10 shows the CH selection process for the sensor node.  
Figure 10: Cluster configuration of sensor nodes.  
Figure 9: Advertisement message (ADV) of cluster header (CH).  
1. The sensor node calculates the distance from the CH using the reception strength of the ADV.
2. The sensor node compares the BS distance information of the ADV with its direct transmission distance information and selects CHs located close to the BS.
3. The sensor node selects CHs that have a shorter distance than the direct transmission distance among the CHs located close to the BS.
4. The sensor node transmits Join-REQ to the CH with the shortest transmission distance among the CHs of (c).  
If there is no CH satisfying this condition, the sensor node does not transmit Join-REQ. The CH assigns a time slot to the sensor node that transmits Join-REQ and transmits the TDMA schedule. The sensor node receiving the TDMA schedule becomes a member node of the CH, and the cluster configuration is completed.
# Epileptic Seizure Detection in Neonatal EEG Using a Multi-Band Graph Neural Network Model
## 3 Methods
### Graph Generation  
#### 3.2.1 Decomposition  
In the EEG classification task, it is a routine practice to slice the continuous recordings into segments. In order to match the features for all segments, the consistency of the frequency range should be ensured when decomposing the segments into sub-band signals. WPD is a powerful decomposition method derived from wavelet decomposition (WD). In WPD, a complete binary tree is created by decomposing both detail coefficients and approximation coefficients of the previous level without omission or redundancy. As a result, WPD can provide a multi-scale signal set with higher frequency resolution and more detailed information.  
Given that \(h\) and \(g\) are quadrature mirror filters associated with scaling function and mother wavelet function, the wavelet packet coefficients at the \(n\)-th node on the \(j\)-th level can be obtained by the following:  
\[\begin{array}{l}d_{l}^{j,2n}=\sum\limits_{k}h_{k-2l}d_{k}^{j-1,n}\\ d_{l}^{j,2n+1}=\sum\limits_{k}g_{k-2l}d_{k}^{j-1,n}\end{array} \tag{1}\]  
where \(k\) is the localization parameter and \(l\) is the time index. Accordingly, the reconstruction formula is given by the following:  
\[d_{l}^{j,n}=\sum\limits_{k}h_{l-2l}d_{k}^{j+1,2n}+\sum\limits_{k}g_{l-2l}d_{k }^{j+1,2n+1} \tag{2}\]  
In this paper, an 8-level WPD is implemented on the raw EEG signals with Daubechies 4 (db4) wavelet, which is widely used for analyzing EEG data, especially epileptic EEG [22; 23; 24]. According to the intrinsic frequency bands commonly used in human brain researches, the wavelet packet coefficients are combined to reconstruct the signals in six sub-bands that are sub-band 1 (0.5-3 Hz), sub-band 2 (3-8 Hz), sub-band 3 (8-14 Hz), sub-band 4 (14-20 Hz), sub-band 5 (20-26 Hz), and sub-band 6 (26-32 Hz).
# A Polarity Capturing Sphere for Word to Vector Representation
## 5 Results and Discussion
### Antonym/Synonym Discrimination  
In this section, we show using statistical measures how our approach perform in the task of discriminating between antonyms and synonyms. Moreover, our approach's performance is compared to the other published word vectors' models: word2 vec [17], GloVe [19], and fastText [20]. The used dataset is introduced by [35]. This dataset considers word pairs in three categories: adjectives, nouns and verbs. The pairs are marked as antonyms or synonyms. Therefore, we have conducted this experiment as a binary classification task with two classes namely, antonyms and synonyms. The classification is done based on the similarity score between the vectors of the word pair. If this similarity score is equal to or greater than a certain threshold (more specifically 0.5), then the pair is classified as synonyms otherwise the pair is classified as antonyms. The used dataset is refined such that pairs that have any non-existent word in any of the models are removed. After this refinement the dataset has the following structure:  
* 470 adjectives: (238 antonyms and 232 synonyms);
* 547 nouns: (276 antonyms and 271 synonyms);  
\begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multicolumn{3}{c}{**Development Set**} & \multicolumn{3}{c}{**Test Set 950**} & \multicolumn{3}{c}{**Test Set 790**} \\  & **Precision** & **Recall** & **F-Score** & **Precision** & **Recall** & **F-Score** & **Precision** & **Recall** & **F-Score** \\ \hline Sphere (our approach) & **0.98** & **0.98** & **0.98** & 0.83 & 0.83 & 0.83 & 0.8 & 0.8 & 0.8 \\ [PERSON] et al., 2008 [27] & 0.76 & 0.66 & 0.70 & 0.76 & 0.64 & 0.70 & - & - & - \\ [PERSON] et al., 2012 [29] & 0.88 & 0.87 & 0.87 & 0.81 & 0.80 & 0.81 & - & - & - \\ [PERSON] et al., 2013 [30] & 0.79 & 0.66 & 0.72 & - & - & - & 0.77 & 0.63 & 0.69 \\ [PERSON] et al., 2013 [31] & 0.88 & 0.85 & 0.87 & 0.81 & 0.77 & 0.79 & - & - & - \\ [PERSON] et al., 2014 [32] & 0.88 & 0.88 & 0.88 & 0.82 & 0.82 & 0.82 & - & - & - \\ [PERSON] et al., 2015 [34] & 0.92 & 0.91 & 0.91 & **0.90** & **0.88** & **0.89** & **0.89** & **0.87** & **0.88** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Results of closest-opposite GRE.  
* 632 verbs: (311 antonyms and 321 synonyms).  
In Tables 5-7 the performance measures are recorded for the four models and for the three categories respectively. The results show that our model outperforms all the other three models in all the categories.
## 2 Fundamental Concepts
### Wiretap Channel Models
#### 2.3.4 Interference Wiretap Channels  
An interference channel is one of the channel models that are used in PLS, in which multiple legitimate parties (i.e., [PERSON] and [PERSON]) communicate with each other at the same time and using the same channel [52], as illustrated in Figure 5. At the same time, the communication between [PERSON] and [PERSON] is observed and decoded by an eavesdropper. Since all of the legitimate users are in the broadcast domain of service, there is a high probability of information leakage within this communication system. In order to avoid leakages to intended users and to enhance the security performance of the system, several multi-antenna techniques have been studied in the existing literature, such as interference cancellation and mitigation techniques [53; 54]. It is assumed that the channel model with \(K\) transmitter and receiver pairs communicates with unintended users, where Alice \(k\) is equipped with \(\mathbf{N}_{Ak}\) source antennae and the destination receiver and unintended user each have receiving antennae of \(\mathbf{N}_{Bk}\) and \(\mathbf{N}_{E}\), respectively. Hence, the signals that are received by the intended user and [PERSON], respectively, are given by [27]:  
\[y_{Bk}=\mathbf{H}_{k}x_{sk}+\sum_{i\
eq k}^{k}\mathbf{H}_{ki}x_{ski}+\mathbf{n}_{Bk} \tag{7}\]  
\[y_{E}=\sum_{i=1}^{k}\mathbf{G}_{i}x_{si}+\mathbf{n}_{E} \tag{8}\]  
where \(x_{si}\) is the \(\mathbf{N}_{Ak}\times 1\) encoded signal of Alice with an average power constraint or a covariance matrix constraint. \(\mathbf{H}_{ki}\in\mathbb{C}^{N_{Bk}\times N_{Ak}}\) and \(\mathbf{G}_{i}\in\mathbb{C}^{N_{E}\times N_{Ak}}\) are the channel matrices from Alice to the intended receiver and unintended user, respectively. \(\mathbf{n}_{E}\) and \(\mathbf{n}_{Bk}\) are the AWGN vectors at the eavesdropper and intended user, respectively. Furthermore, a typical configuration for interference channels with an external eavesdropper and an interference alignment was studied in [55]. It was shown that when an interference channel uses an interference alignment with separate confidential messages to reduce interference, the secrecy of the system can be improved. On the other hand, when an interference channel uses an interference alignment with an external eavesdropper, it lacks knowledge about the CSI of the eavesdropper and the secrecy of the system can be compromised. Therefore, the authors concluded that an interference alignment with separately secure information can provide more secure degrees of freedom than that with an unintended user.
# An Experimental Study of Industrial Site and Shaft Pillar Mining at Jinggezhuang Coal Mine
## 3 Mining Experiment of Panel 0091 and Subsidence Monitoring  
### Introduction  
Panel 0091 was mined as an experiment to mine the shaft pillar and maintain the safety of the buildings in the industrial area. In addition, the site deformation was monitored to obtain the surface movement law of the investigated area. Panel 0091 is located on the southern boundary of the industrial and shaft pillars. Around the panel are the goafs of the 1970s and 1980s. The average floor elevation and surface elevation of the panel are \(-\)295 m and +35.5 m, respectively. The average mine thickness is 6.9 m. The coal seam angle is 3\({}^{\circ}\)\(\sim\)12\({}^{\circ}\), with an average of 7\({}^{\circ}\). The strike length and tendency length were 1050 m and 116 m, respectively. The panel was mined from 1 August 2008 to 30 April 2010. Three site survey lines were set in June 2008. The survey started in September 2008 and ended in May 2010 and it was carried out 29 times. The schematic of the panel and survey lines is shown in Figure 4. Line A consisted of 19 points (A1-A18 and XM). All site points were set along a road. The interval between consecutive points was 30 m.  
Line B consisted of 11 points (B1-B11). It was located in the western part of the industrial area. The distance between consecutive points was 30 m.  
Line C consisted of 9 points (C1-C9). It was located close to the hoist house of the auxiliary shaft and used to obtain the deformation of the hoist house. The interval between consecutive points was 10 m.  
Figure 4: Schematic of survey lines and panel 0091.  
\(S_{3}\) leveling was used in the survey with fourth-order leveling (mean square error per kilometer \(<\)5 mm). All the results were consistent with mine surveying regulations [23].
## 5 Conclusions  
Reducing the model order in hybrid simulations is crucial because FEM is a very computationally demanding method, and performing it in real-time with small time steps can be difficult. For hybrid simulations with dynamic loading, the smaller the time step is, the more accurate results can be obtained. Therefore, by reducing the model order, the accuracy of hybrid simulation may increase.  
Provided examples proved that the real-time computations in the hybrid simulation could be significantly reduced while maintaining high accuracy by substituting the FE model with an ANN. The presented algorithm for searching the ANN allowed finding the smallest possible network if the parameters of the algorithm were appropriately chosen.  
The whole point of performing hybrid simulation was that the nonlinearities of the physical subsystem could not be accurately simulated. As proven, by performing a set of linear FE simulations to generate the reference data for training, the ANN was able to generalize it and give accurate results in hybrid simulations with high nonlinearities in the physical subsystem.  
The presented algorithm was highly nondeterministic and could not guarantee a positive result each time, or it could give a positive result after large calculation costs. However, often, it is worth conducting several dozen hours of calculations offline in order to reduce the time step by 1 ms in real-time. After performing a hybrid simulation with ANN and obtaining all the data from it, the exact behavior of the whole mechanical system could be easily recreated using FEM.  
Currently, to perform fully dynamic real-time hybrid simulations of systems, several dozen of DOFs distributed computing is introduced, involving powerful hardware resources like computer clusters or supercomputers with communication using P2P. The method presented in the paper allowed performing the same hybrid simulations using much smaller hardware resources or perform hybrid simulations with much smaller time steps on the same hardware resources.  
The research was partially funded from financial resources from the statutory subsidy of the Faculty of Mechanical Engineering, Silesian University of Technology, in 2019.  
The author declares no conflict of interest.
# Generative Artificial Intelligence and Risk Appetite in Medical Decisions in Rheumatoid Arthritis
## 3 Results
### Differences Between Evaluators  
The Kruskal-Wallis test was used to compare the distribution of total scores among the five GenAI evaluators, separately for each scenario (A, B, and P), the results are presented in Table 3. In both scenario A (Good Prognosis) and scenario B (Poor Prognosis), no statistically significant differences were found between evaluators (see Figure 1).  
\begin{table}
\begin{tabular}{c c c} \hline
**Evaluator** & **Scenario 1** & **CV** \\ \hline CHAT & P & 12.49\% \\ CHAT & A & 13.86\% \\ CHAT & B & 16.83\% \\ GEMINI & P & 5.31\% \\ GEMINI & A & 39.15\% \\ GEMINI & B & 11.18\% \\ QWEN & P & 9.97\% \\ QWEN & A & 11.76\% \\ QWEN & B & 13.86\% \\ DEEPSEEK & P & 11.60\% \\ DEEPSEEK & A & 25.90\% \\ DEEPSEEK & B & 19.64\% \\ PERPLEXITY & P & 3.51\% \\ PERPLEXITY & A & 16.24\% \\ PERPLEXITY & B & 18.44\% \\ \hline \end{tabular} 1  
\end{table}
Table 2: The consistency of each GenAI’s responses to repeated questions in the second phase.  
Figure 1: Average values for each GRiPS statement provided by the tested GenAI systems in Scenario P—non-medical (**top**). A—medical case with the best prognosis (**middle**), and B—medical case with the worst prognosis (**bottom**).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario 1** & **Kruskal–Wallis H** & _p_**-Value** & **Interpretation** \\ \hline P & 14.687 & 0.005 & 12.49\% \\ A & 4.339 & 0.362 & 13.86\% \\ B & 5.089 & 0.278 & 18.44\% \\ \hline \hline \end{tabular} \({}^{\dagger}\)P—general nonmedical, A—Good Prognosis, B—Poor Prognosis.  
\end{table}
Table 3: The distribution of total scores among the five GenAI evaluators, separately for each scenario (A, B, and P), in the second phase.  
This suggests that the GenAIs had a relatively similar approach to risk when evaluating these cases. In scenario P (general nonmedical), statistically significant differences were found between evaluators. This result indicates that, in the absence of a specific clinical context (Good Prognosis or Poor Prognosis), the GenAIs exhibit significant differences in their general risk propensity, as measured by the adapted GRiPS questionnaire.  
To identify which evaluators differed significantly in Scenario P, post hoc Mann-Whitney U tests were performed. The results show that ChatGPT had significantly lower scores (potentially indicating lower risk aversion in this context) compared to each of the other four evaluators (\(p\) = 0.020 vs. Perplexity; \(p\) = 0.041 vs. Gemini; \(p\) = 0.017 vs. Owen; \(p\) = 0.017 vs. DeepSeek), while Gemini had significantly lower scores than Owen (\(p\) = 0.017) and DeepSeek (\(p\) = 0.017). There were no significant differences between Owen, DeepSeek, and Perplexity relative to each other, but they tended to have higher scores (higher risk aversion) compared to ChatGPT and Gemini. These results suggest a potential ranking of evaluators based on risk aversion in the general scenario: ChatGPT < Gemini < (Perplexity, Owen, and DeepSeek).
## 2 Materials and Methods
### EPS' Extraction and Analysis  
To evaluate the quantity of biofilm residue, EPS were extracted from the membranes. The membranes immersed in PBSs were vortexed for 5 min and sonicated for 1 h. After sonication, the membranes were removed from the solution. The solution was centrifuged at 12,000\(\times\)\(g\), 4 \({}^{\circ}\)C for 15 min, and 30 mL of supernatant was filtered through a 0.45 \(\upmu\)m filter. Filtered supernatant was defined as soluble EPS (SEPS) [32]. An additional 30 mL of PBS was added to compensate for the removed supernatant. Residual solution was stirred with a cation exchange resin (CER 5 g/60-80 cm\({}^{2}\)) at 900 RPM, 4 \({}^{\circ}\)C, for 90 min [33; 34]. After stirring, the resin was removed from the solution. The solution was centrifuged, and the supernatant was filtered by the same procedure. The filtered supernatant was defined as bound EPS (BEPS) [28; 33]. The following analysis is based on total EPS, which are the sum of SEPS and BEPS. Extracted EPS samples were characterized by polysaccharide, protein, DNA, and three-dimensional fluorescence excitation emission matrix (EEM). Polysaccharide was analyzed by the phenol-sulfuric acid method using glucose as standard [35; 36]. Protein was analyzed by the Bradford assay using bovine serum albumin as standard [28; 36; 37]. DNA was analyzed by the Burton assay using low-molecular-weight DNA from salmon sperm as standard [38; 39]. EEM spectra were obtained by a spectro-fluoro-photometer (RF-5301, Shimadzu, Kyoto, Japan). Excitation was scanned from 220 to 400 nm with a 10 mm increment, and emission was scanned for each excitation wavelength from 280 nm to 600 nm with a 1 nm increment. To avoid the inner filter effect, each sample  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Substance** & **Concentration(g/L)** \\ \hline Glucose & 0.4000 \\ Yeast extract & 0.0140 \\ BactoTMpptone & 0.1150 \\ (NH\({}_{4}\))\({}_{2}\)SO\({}_{4}\) & 0.1048 \\ KH\({}_{2}\)PO\({}_{4}\) & 0.0218 \\ MgSO\({}_{4}\)\(\cdot\)7H\({}_{2}\)O & 0.0320 \\ FeCl\({}_{3}\)\(\cdot\)6H\({}_{2}\)O & 0.0001 \\ CaCl\({}_{2}\)\(\cdot\)2H\({}_{2}\)O & 0.0033 \\ MnSO\({}_{4}\)\(\cdot\)5H\({}_{2}\)O & 0.0029 \\ NaHCO\({}_{3}\) & 0.2555 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Composition of synthetic wastewater.  
was diluted to DOC below 1 mg/L before scanning. The scanned intensity was normalized by dividing the DOC concentration and multiplying the dilution factor.
# Incorporating Grey Total Influence into Tolerance Rough Sets for Classification Problems
## 5 Computer Simulations
### Evaluating Classification Performance
It is clear that the classification performance of GTI-TRSC was comparable to that of FTRSC and GTRSC. This means that different measures can impose a certain impact on classification results. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{6}{c}{**Classification Methods**} \\ \cline{2-7}  & **HLM** & **RSES-H** & **RSES-O** & **RIONA** & **TRSC-SU** & **TRSC-CO** \\ \hline Australian approval & 92.0 & 87.0 & 86.4 & 85.7 & 85.9 & 87.1 \\ Glass & 71.3 & 63.4 & 61.2 & 66.1 & 65.7 & 68.1 \\ Hepatitis & 78.7 & 81.9 & 82.6 & 82.0 & 83.9 & 83.5 \\ Iris & 94.1 & 95.5 & 94.9 & 94.4 & 95.7 & 95.2 \\ Diabetes & 72.6 & 73.8 & 73.8 & 75.4 & 74.1 & 73.6 \\ Sonar & 73.7 & 75.3 & 74.3 & 86.1 & 74.3 & 75.0 \\ Stallog Heart & 79.0 & 84.0 & 83.8 & 82.3 & 82.9 & 83.3 \\ TTT & 95.0 & 99.1 & 99.0 & 93.6 & 82.3 & 82.3 \\ Voting & 95.4 & 96.5 & 96.4 & 95.3 & 93.4 & 94.0 \\ Wine & 92.6 & 91.2 & 90.7 & 95.4 & 93.0 & 95.3 \\ Average rank & 9.20 & 7.90 & 8.80 & 8.80 & 9.55 & 9.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification accuracy of different classification methods. HLM: A hierarchical version of the lattice machine; RSES-O: a rule-based method with shortening optimization; RSES-H: A hierarchical form of RSES-O; RIONA: Rule Induction with Optimal Neighborhood Algorithm; TRSC: Tolerance-rough-set-based classifiers; SU: Subset approximations; CO: Concept approximations.
## 6 System Performance and Test Case Analysis
### Testing System Performance for Large-Scale Loads
#### 6.1.2 Regular Load Performance Test  
The purpose of the regular load performance test is to obtain the network throughput faced by the master and the average BSM pass-through interface latency under load, which is closely related to the effectiveness of the test system solution.  
Here are the relevant parameter settings for the regular load performance test:  
* Testing hardware environment: MacBook Air (M1) 8 G RAM (Apple, Cupertino, CA, USA);
* Number of nodes accessed: 10, 20, 30, 40, 50;
* BSM frequency: 10 Hz, 20 Hz, 30 Hz.  
Table 3 shows the results of the regular load performance test of the master controller. When deploying a scenario with 50 nodes broadcasting Basic Safety Messages (BSMs) at a frequency of 30 Hz, it can simulate 150 vehicles, effectively mimicking extreme congestion in a real-world physical scenario. As indicated in the table, when the number of nodes is 50, the master controller experiences a peak data throughput of 127 Mbps. However, it still provides an average response delay of 0.58 ms for BSM transparent transmission. Across all testing conditions, the response delay fluctuates between 0.38 ms and 0.6 ms, remaining unaffected by the task load on the master controller.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Number of Nodes** & **10** & **20** & **30** & **40** & **50** \\ \hline \multirow{2}{*}{10 Hz} & 0.53 ms & 0.51 ms & 0.37 ms & 0.46 ms & 0.46 ms \\  & @1.68 m/s & @6.49 m/s & @14.8 m/s & @25.5 m/s & @39.3 m/s \\ \hline \multirow{2}{*}{20 Hz} & 0.38 ms & 0.53 ms & 0.57 ms & 0.51 ms & 0.57 ms \\  & @3.22 m/s & @12.6 m/s & @27.8 m/s & @49.2 m/s & @78.3 m/s \\ \hline \multirow{2}{*}{30 Hz} & 0.57 ms & 0.52 ms & 0.51 ms & 0.36 ms & 0.58 ms \\  & 4.68 m/s & 18.4 m/s & 41.4m/s & @73.7 m/s & @127 m/s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Regular load performance test results.  
Figure 9: High-load performance test results.
# Unmanned Aerial Traffic Management System Architecture for U-Space In-Flight Services
###### Abstract  
This paper presents a software architecture for _Unmanned aerial system Traffic Management_ (UTM). The work is framed within the U-space ecosystem, which is the European initiative for UTM in the civil airspace. We propose a system that focuses on providing the required services for automated decision-making during real-time threat management and conflict resolution, which is the main gap in current UTM solutions. Nonetheless, our software architecture follows an open-source design that is modular and flexible enough to accommodate additional U-space services in future developments. In its current implementation, our UTM solution is capable of tracking the aerial operations and monitoring the airspace in real time, in order to perform in-flight emergency management and tactical deconfiliation. We show experimental results in order to demonstrate the UTM system working in a realistic simulation setup. For that, we performed our tests with the UTM system and the operators of the aerial aircraft located at remote locations with the consequent communication issues, and we showcased that the system was capable of managing in real time the conflicting events in two different use cases.  
UTM; system architecture; U-space; UAS +
Footnote †: journal: Applied