esa-sceva/satcom-chunk-collection huggingface dataset:
# Parallelization of the Honeybee Search Algorithm for Object Tracking
## 2 Materials And Methods
### Workflow of Parallel Experiments
#### 2.5.4 Honeybee Search Algorithm Parallelization  
Each honeybee is logically composed of \(q\) GPU work-items. The maximum number of individuals that can work simultaneously is \(p/q\), where \(p\) is the maximum work-group. To take advantage of these \(p/q\) individuals, the size of populations \(\mu_{e}\), \(\lambda_{e}\), \(\mu_{h}\) and \(\lambda_{h}\) are all set to the same size of \(p/q\).  
When evaluating the fitness function, all work-items that belong to a certain individual have work to do. However, there are activities in HSA that require a lower level of parallelization and only use one processor per individual. The list of activities that fall in this category are:  
* generation of the initial random \(\mu_{e}\) population for the exploration phase
* generation of \(\lambda\) populations using mutation, crossover and random exploration
* merge \(\mu\) and \(\lambda\) populations  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Description** & **BEE** & **NO BEE** \\ \hline \(\mu_{e}\) & Size of all populations & 64 & – \\ Generations & – & 2 & – \\ \(\eta_{m}\) & Parameter for mutation & 25 & – \\ \(\eta_{c}\) & Parameter for crossover & 2 & – \\ \(\alpha_{e}/\lambda_{e}\) & Mutation sons, exploration & 60\% & – \\ \(\beta_{e}/\lambda_{e}\) & Crossover sons, exploration & 10\% & – \\ \(\gamma_{e}/\lambda_{e}\) & Random sons, exploration & 30\% & – \\ \(\alpha_{e}/\lambda_{e}\) & Mutation sons, harvest & 60\% & – \\ \(\beta_{e}/\lambda_{e}\) & Crossover sons, harvest & 30\% & – \\ \(\gamma_{e}/\lambda_{e}\) & Random sons, harvest & 10\% & – \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of the configuration parameters used in the tests.  
selection of the \(\mu\) best from \(\mu+\lambda\) populations  
Before the selection of the \(\mu\) best from the \(\mu+\lambda\) population, said population has to be sorted by fitness value. The sorting algorithm used in the described implementation is a parallelized merge-sort, that uses one work-item per honeybee [40].  
The only phase not parallelized at all is the recruitment phase. One processor keeps working while the rest remain idle. This processor assigns how many foragers will be recruited by each explorer bee and defines the new search space.
# Exploring Pre-Trained Models for Skin Cancer Classification
## 3 Materials and Methods
### Implementation and Evaluation  
Four pre-trained models, namely ResNet50, VGG19, ViT-b16, and ViT-b32, were utilized for classifying skin cancer. The utilized models were customized for specific tasks, specifically binary classification. Various hyperparameters were fine-tuned, and the model's performance was closely monitored to ensure effective discrimination between benign and malignant skin cancers. The training process used Adam optimizer, with a learning rate of 0.001 and a batch size of 32. The models were trained over 30 epochs, providing sufficient iterations for weight adjustment and improving classification accuracy.  
Performance evaluation was performed using metrics such as accuracy, precision, recall, and F1 score, as following equations [36]:  
\[accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{1}\]  
\[precision=\frac{TP}{TP+FP} \tag{2}\]  
\[recall=\frac{TP}{FN+TP} \tag{3}\]  
\[F1\ score=2\times\frac{precision\times recall}{precision+recall} \tag{4}\]  
where \(TN\) true negatives, \(TP\) true positives, \(FP\) false positives, and \(FN\) false negatives.
# Dual-Band RF Wireless Power Transfer System with a Shared-Aperture Dual-Band Tx Array Antenna
## 2 Implementation of a Dual-Band RF Wireless Power Transfer (WPT) System
### Design of a Shared-Aperture Dual-Band Tx Array Antenna
As shown in Figure 7b, the 3 dB axial ratio (AR) bandwidth of the 2.4 GHz Tx array antenna in the \(+z\)-direction is 2.474-2.490 GHz and the AR values at 2.482 GHz is 1.16 dB. For the 5.8 GHz Tx array antenna, the 3 dB AR bandwidth in the \(+z\)-direction is 5.691-5.769 GHz and the AR value is 0.758 dB at 5.73 GHz. Figure 4: Simulated surface current distributions on the corrugated patch at the operating frequency. Figure 5: Optimized shared-aperture dual-band array: (**a**) configuration; (**b**) photograph. Figure 3: Radiating elements: (**a**) 2.4 GHz RHCP (right-handed circular polarization) patch; (**b**) 5.8 GHz RHCP patch; (**c**) sideview of the patches. Furthermore, adopting the shared-aperture array employing 2.4 and 5.8 GHz radiating elements simultaneously in a single aperture has the effect of reducing the total area of the overall array antenna. Figure 8a shows a 2.4 GHz array with 2.4 GHz 32 elements and a gain of 20 dBic at 2.482 GHz, having performance equivalent to the shared-aperture array. Figure 8b is a 5.8 GHz array with 5.8 GHz 32 elements and gain of 20.8 dBic at 5.73 GHz, also showing 5.8 GHz performance corresponding to the shared-aperture array. Table 1 presents the results of a comparison of these two single-band arrays, shown in Figure 8a,b, and the proposed shared-aperture array. If implementing a dual-band WPT system by stacking the two single-band arrays shown in Figures 8a,b, a total area of 57.3 \(\times\) 39 cm\({}^{2}\) is required. In contrast, the area of the proposed shared-aperture array as depicted in Figure 5 is 43.7 \(\times\) 43.7 cm\({}^{2}\), representing a size reduction of about 15% compared to when the two individual array antennas are stacked. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **2.4 GHz Array** & **5.8 GHz Array** & **Proposed Dual-Band Array** \\  & **(Figure 8a)** & **(Figure 8b)** & **(Figure 5)** \\ \hline
**Operating frequency** & 2.482 GHz & 5.73 GHz & 2.482 GHz & 5.73 GHz \\
**Radiating elements** & 32 & 32 & 32 & 32 \\
**Realized gain** & 20 dBic & 20.8 dBic & 20 dBic & 20.8 dBic \\
**Aperture area** & 39 \(\times\) 39 cm\({}^{2}\) & 18.3 \(\times\) 18.3 cm\({}^{2}\) & 43.7 \(\times\) 43.7 cm\({}^{2}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison according to the implementation method of the dual-band array system. Figure 6: Reflection coefficients of the radiating elements in the proposed aperture-shared dual-band thinned array: (**a**) 2.4 GHz band; (**b**) 5.8 GHz band. Figure 7: Simulated results of the proposed aperture-shared dual-band thinned array: (**a**) realized gain; (**b**) axial ratio.
## 4 Simulation Results
### Model Validation: Comparison of Simulated Uniform (SMP) and Actual Market Prices  
To validate the reliability of the simulation model, we compared the simulated market prices from our Uniform (SMP) scheme with the actual market prices for 2023. The analysis shows strong alignment between the two price series, with simulated prices ranging from 83.68 to 270.33 KRW/kWh and actual prices from 82.91 to 265.65 KRW/kWh. The annual load-weighted average price derived from our model was 164.75 KRW/kWh, closely aligning with the actual market price of 167.00 KRW/kWh [44], thus indicating a deviation of only 1.35%. The Pearson correlation coefficient of 0.96 demonstrates a strong positive correlation between simulated and actual prices. Although perfect alignment is challenging owing to limitations in accessing precise maintenance schedules, heat demand for combined heat and power (CHP) sources, and other factors, this level of accuracy substantiates the reliability of the simulation model.  
Figure 4 illustrates a daily comparison between the simulated and actual market prices. As evident, our model effectively captured both overall trends and short-term fluctuations throughout the year. This comprehensive validation, encompassing both annual averages and daily dynamics, provides a robust foundation for subsequent analyses of the zonal and nodal pricing schemes.
## 6 Experiment Evaluation
### Continuous Authentication Evaluation  
The results of Scenario A (a single mouse movement action), Scenario B (a single point-and-click action, and Scenario C (a set of mouse movement and point-and-click actions) were evaluated using FAR, FRR, EER, and the ROC curve. The corresponding FAR, FRR, and EER were achieved. The following were the lowest FAR results: Scenario A (KNN: 0.009%), Scenario B (RF: 0.012%), and Scenario C (DT: 0.002%). The following were the lowest FRR results: Scenario A (KNN: 0.182%), Scenario B (RF: 0.027%), and Scenario C (DT: 0.007%). The following were the lowest EER results: Scenario A (CNN: 0.021%), Scenario B (CNN: 0.107%), and Scenario C (DT: 0.005%). The detailed results in terms of FAR, FRR, and EER are reported in Tables 9-11. In addition, ROC curves are plotted in Figures 12-14. We found that for CA, the DT achieved the lowest EER of 0.005% for MM and PC actions.
# Experimental Study on the Dynamic Characteristics of Fractured Coal Under Cumulative Impact
## 2 Experimental Design  
### Specimen Preparation  
The coal block samples are obtained from a coal mine in Xunyi County, Shaanxi Province. The depth of this coal mine is 377.5 m, and the coal seam thickness is 7 m, which is underlain by mudstone strata. Homogeneous coal specimens from the same mining batch are prepared, which use a 50 mm diameter core drilling system for initial sampling. Diamond wire cutting technology is utilized for cross-sectional processing to eliminate hydro-cutting-induced alterations in physical-mechanical properties. The stringent geometric tolerances of this technique require that the flatness error of the two ends of the coal sample is not larger than 0.05 mm and the diameter deviation of the two ends is not greater than 0.02 mm.  
Prefabricated central fractures are created through wire cutting, with specimens preliminarily processed into 50 mm diameter \(\times\) 17.5 mm height cylindrical units for operational feasibility. Controlled fracture parameters include 10 mm length discontinuities with dip angles of 30\({}^{\circ}\), 60\({}^{\circ}\) and 90\({}^{\circ}\) relative to horizontal plane. Final specimens are assembled by bonding two 17.5 mm thick subunits using epoxy resin adhesive (Type AB). The flatness of the surfaces is crucial. When bonding with Type AB glue, a brush was used to lightly apply the adhesive to the surface, resulting in a negligible glue layer thickness, as illustrated in Figure 1. To ensure stress uniformity under stress wave propagation in SHPB tests, specimens are designed with a length-to-diameter ratio typically below 1.0 [42]. Specimens are specifically set to 50 mm \(\times\) 35 mm in this study, ensuring compliance with the critical length-to-diameter ratio requirement for dynamic impact testing.
## 3 Results
### WMSD Localisation  
Most WMSDs (Figure 1) were in the lower extremity (40.8% total, 35.8% male, and 42.0% female) followed by the spine/torso (35.2% total, 39.2% male, and 34.2% female). The lumbar spine was most frequently affected with 14.9% (17.5% male, 14.2% female).  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **n** & **Mean** & **SD** & **Range** & **Z-Score** & \(p\)**-Value** & **Effect Size r** \\ \hline Age (yrs) & & & & & & & \\ Total & 229 & 43.1 & 11.0 & 22.0–77.0 & & & \\ Females & 181 & 42.0 & 10.5 & 22.0–68.0 & \(-\)2.278 & 0.023 * & 0.151 \\ Males & 48 & 47.1 & 12.2 & 22.0–77.0 & & & \\ Height (cm) & & & & & & \\ Total & 229 & 170.0 & 7.7 & 145.0–195.0 & & & \\ Females & 181 & 167.8 & 6.2 & 145.0–183.0 & \(-\)7.763 & \(<\)0.000 * & 0.513 \\ Males & 48 & 178.0 & 7.5 & 156.0–195.0 & & & \\ Weight (kg) & & & & & & \\ Total & 229 & 61.6 & 10.7 & 40.0–100.0 & & & \\ Females & 181 & 58.3 & 8.2 & 40.0–89.0 & \(-\)8.384 & \(<\)0.000 * & 0.554 \\ Males & 48 & 74.2 & 9.8 & 54.0–100.0 & & & \\ BMI (kg *m\({}^{-2}\)) & & & & & & \\ Total & 229 & 21.2 & 2.7 & 17.0–33.0 & & & \\ Females & 181 & 20.7 & 2.4 & 17.0–33.0 & & & \\ Males & 48 & 23.4 & 2.6 & 19.0–31.0 & & & \\ Teaching experience (yrs) & & & & & & \\ Total & 227 & 14.0 & 10.0 & 1.0–41.0 & & & \\ Females & 180 & 14.0 & 10.3 & 1.0–41.0 & \(-\)0.077 & 0.938 & 0.005 \\ Males & 47 & 13.9 & 9.2 & 1.0–40.0 & & & \\ Weekly teaching time (hrs) & & & & & & \\ Total & 203 & 18.4 & 10.1 & 2.0–50.0 & & & \\ Females & 166 & 18.0 & 9.5 & 2.0–44.0 & \(-\)0.444 & 0.657 & 0.031 \\ Males & 37 & 20.0 & 12.4 & 3.0–50.0 & & & \\ \hline \end{tabular} Note: Testing for sex differences was based on the Mann–Whitney U test. Significant differences (*\(p\) \(<\) 0.05) are marked with an asterisk; BMI = body mass index.  
\end{table}
Table 1: General sample characteristics for total group, female, and male participants.  
For all body regions, no sex-specific differences between men and women could be found. There was only a statistical tendency (\(p\) = 0.064) for a more frequent occurrence of WMSDs in the area of the hands in men. Among the dance styles taught, there were no significant differences in WMSD localisation between DTs (Figure 2).
## 3 Research Method
### Study of Maturity  
Initially, we have established a historical timeline of the versions of each CO framework by storing the date when each version of a CO framework has been released. We have extracted this information from official release notes (see Figure 2).  
Then, for each Co framework, we have determined a historical timeline for each common feature. The historical timeline of a common feature starts with a feature addition event, then has zero or more feature update events and optionally ends with a feature removal/deprecation event. We annotate these events with the version of the CO framework during which the events have occurred. The pseudo-algorithm for defining the timelines is detailed in Section 3.3 of the technical report [31].  
The obtained timelines of different CO frameworks are then merged per common feature in order to understand which CO framework pioneered in which feature and which functional sub-aspects. Detailed timelines to answer RQ7 for each common feature are available in Tables 18-26 of the technical report [31], Section 6, RQ7.  
Finally, an overall assessment of the maturity of the sub-aspects has been conducted (see Section 7, RQ8). We define a sub-aspect as mature and well-understood if it meets the following three criteria: (i) the sub-aspect has been consolidated by the pioneering framework at least two traditional release cycles of 18 months [57] ago, (ii) the corresponding feature implementation strategies of the pioneering framework have at least reached beta-stage in the meantime and (iii) there are no deprecation or removal events of important features in the latest traditional release cycle.
# Federated Learning for Cybersecurity: A Privacy-Preserving Approach
## 5 Explainability in Federated Intrusion Detection
### Proposed Architecture for Explainable FL-Based IDS  
We propose a modular architecture in which explainability mechanisms are embedded in the local client model lifecycle. Each client is responsible for both generating predictions and computing interpretable explanations using SHAP or LIME for locally flagged anomalies. These explanations are compressed into sparse feature attribution vectors and securely transmitted (with noise or encryption) to the aggregator.  
At the aggregator level, an explanation fusion layer synthesizes global interpretability maps, revealing the most influential features (e.g., packet rate, connection duration, unusual port activity) associated with malicious predictions across the federation. Figure 10 illustrates the explainability flow built into the FL pipeline [41].  
This architecture demonstrates a privacy-preserving, interpretable learning pipeline that integrates federated explainable AI (XAI) mechanisms at the client and aggregation levels. Local SHAP or LIME explanations are generated without exposing raw data, and the global heatmap preserves privacy during cross-client interpretation. This design provides insight into model decisions without compromising sensitive information. This modular architecture also supports future enhancements, such as dashboard visualizations and integration with audit logging systems [43].
## 2 Input and Output DATA (Independent and Dependent Variables)  
Six basic input features (variables) that influence the fatigue life span of plain concrete under a uniaxial fatigue test in compression were chosen, as shown in Table 1. One output variable is the logarithm value of the maximum number of cycles at failure, representing the fatigue life of the test. The number of the first group of the key input variables, which are related to the material and dimensional properties of the test specimens, included the compressive strength of concrete (\(f^{c}\)), height to width ratio (_h/w_), and shape of the test specimens. The other three variables that reflect the loading conditions of the fatigue test specimens include the maximum stress level (\(S_{\text{max}}\)), minimum stress to maximum stress ratio (\(R\)), and loading frequency (\(f\)).  
This study covers low-strength hydraulic concrete (10-30 MPa), ordinary concrete (30-60 MPa), and high-strength concrete (60-120 MPa). The _h/w_ of the test specimens ranged from 1.0 to 3.0, and the specimen's shape includes the cube, prism, and cylinder. The loading conditions were also greatly diverse, with the \(S_{\text{max}}\) ranging from 0.457 to 0.95, the \(R\) covering 0 to about 0.67, and a loading frequency ranging from 0.0625 to 150 Hz. The dataset used in this study is summarized below.  
\(f_{c}\): the compressive strength of concrete by MPa;  
_h/w_: height to width ratio of the tested specimens;  
Shape: shape of the test specimens;  
\(S_{\text{max}}\): maximum stress level;  
\(R\): minimum stress to maximum stress ratio;  
\(f\) (Hz): loading frequency by Hz;  
Log\(N\): logarithm number of cycles to failure of the specimen.
# Positioning by Multicell Fingerprinting in Urban NB-IoT Networks +
## 3 Proposed Fingerprinting System
### The WkNN Algorithm  
Let \(N\) be the total number of RPs in the service area \(\mathcal{A}\) of the positioning system, with \(\mathbf{s}_{i}\) (\(i=1,2,\ldots,N\)) being the fingerprint collected at a generic \(i\)-th RP and with \(\mathbf{s}_{j}\) being the fingerprint provided by a target device \(j\). The position estimation of \(j\) via WkNN relies on the computation of distances \(d(i,j)=d(\mathbf{s}_{i},\mathbf{s}_{j})\) for all RPs, which is followed by the sorting of RPs in terms of increasing distance [26]; the distances proposed in this work are introduced in Section 3.2. Once the \(k\leq N\) RPs closer to \(\mathbf{s}_{j}\) according to the adopted distance are selected, \(W\)kNN provides an estimate \(\hat{\mathbf{p}}_{j}=(\hat{x}_{j},\hat{y}_{j},\hat{z}_{j})\) of the real position of \(j\), denoted as \(\mathbf{p}_{j}=(x_{j},y_{j},z_{j})\) and as follows:  
\[\hat{\mathbf{p}}_{j}=\frac{\sum_{n=1}^{k}(w(n,j))\mathbf{p}_{n}}{\sum_{n=1}^{k}w(n,j)}, \tag{1}\]  
where \(\mathbf{p}_{n}=(x_{n},y_{n},z_{n})\) is the position of the \(n\)-th selected RP (\(n=1,2,\ldots,k\)) in a 3D coordinate system, and \(w(n,j)\) is the weight attributed to the \(n\)-th RP in estimating the position of \(j\). In general, the weight \(w(n,j)\) can be defined independently from the distance metric \(d(n,j)\)[27]; the most common approach, also adopted in this work, is however to choose \(w(n,j)\equiv 1/d(n,j)\).  
A critical aspect in fine-tuning the \(W\)kNN algorithm is selecting \(k\), both of which are static \(k\) strategies, where \(k\) is set once and for all during system setup [28], and dynamic \(k\) strategies, where \(k\) is selected at each positioning attempt as a function of \(\mathbf{s}_{j}\)[29; 30], have been proposed in the literature. In this work, a static \(k\) strategy is adopted, and the impact of \(k\) on positioning accuracy is investigated.
## 2 Technical Background
### Lstm  
LSTM networks, developed by [PERSON] and [PERSON], represent an advancement over traditional recurrent neural networks (RNNs). They use several gate structures designed to manage and retain both short- and long-term information simultaneously. The architecture of LSTM networks revolves around three key gates:  
1. Input Gate: Controls the flow of information into the cell state, regulating how much new information is added.
2. Forget Gate: Manages the removal of information from the cell state, determining what past information is irrelevant.
3. Output Gate: Controls the flow of information from the cell state to the output, regulating what information the network outputs based on the current cell state.  
These gates collectively enable LSTM networks to effectively capture and utilize long-range dependencies in sequential data, making them particularly suitable for tasks requiring memory over time. Figure 2 depicts the basic architecture of LSTM. In Figure 2, \(f_{t}\), \(i_{t}\), and \(o_{t}\) represent the forget, input, and output gates, respectively. Each gate uses a sigmoid activation function, allowing it to regulate the flow of information by either passing or blocking it based on learned weights.  
Figure 1: Schematics of a basic 1D CNN architecture.
## 2 Results and Discussion
### Immobilization of Laccase  
Next, two important immobilized laccase parameters, the initial laccase concentration and the cross-linking time, were investigated. The number of amino-functionalized PIL particles determines the number of laccases. As shown in Figure 3a, with a higher initial concentration, more laccase was able to be immobilized. In addition, the immobilized efficiencies of laccase corresponded to the nature of PILs, such as their specific surface area and pore size. A higher specific surface area and larger pore size can facilitate the entry of enzymes into the support to achieve a higher enzyme immobilization [40]. When the initial concentration of laccase was below 1 mg/mL, both the immobilized laccase and activity recovery increased. It should be noted that when the concentration was 1 mg/mL, the maximum laccase activity recovery was obtained (82%, 0.34 u/mg; the specific activity of free laccase was 0.41 u/mg under the same test conditions). However, the activity recovery did not increase when the concentration was over 1 mg/mL, which might have been due to the fact that an aggregation of the excessive laccase in the pores occurred or that a multilayer structure formed on the surface of the PILs.  
Cross-linking time is another parameter that influences the immobilization efficiency of enzymes. As shown in Figure 3b, the activity recovery of the laccase activity increased from 1 to 5 h due to sufficient cross-linking. However, the immobilization rate gradually slowed as the time extended, as shown by the slope (comparing the data in the figure, it was found that the maximum immobilized rate generally occurred within 2 h) in Figure 3b. For example, when the cross-linking time was 2 h, the immobilized laccase of PIL (1)-NH\({}_{2}\)-GA-Lac and PIL (2)-NH\({}_{2}\)-GA-Lac increased by 17 mg/g and 26 mg/g, respectively. In addition, the activity recovery of PIL (2)-NH\({}_{2}\)-GA-Lac increased by 14% during this period. This was the most efficient period of enzyme immobilization. From the viewpoint of dynamics, a large concentration gradient in the early cross-linking time (0.5-2 h) promoted  
Figure 3: Effect of laccase concentration (**a**) and cross-linking time (**b**) on the activity recovery and the immobilized laccase.  
the immobilization of increase. A maximum activity recovery was achieved when the cross-linking time was around 5 h (i.e., 82%, 0.31 u/mg). It is worth noting that PIL (2)-NH\({}_{2}\)-GA-Lac, having a larger specific surface area, had a higher capture ability for increase than PIL (1)-NH\({}_{2}\) under the same enzyme concentration. However, the excessive immobilization of enzymes makes the aperture of the support relatively thin, resulting in the reduced accessibility of the substrate to the active sites [41; 42].