esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Numerical Simulation
### Finite Element Model  
The numerical model of the soil, piles, and heat exchange pipes is shown in Figure 4. All the conditions were the same with the model tests. For the heat transfer process in the non-isothermal pipeline flow, the pipeline flow was simplified to a one-dimensional flow, ignoring the radial heat exchange inside the pipeline. This method can avoid the problem of pipeline grid division caused by the relatively large length and radius. Based on the results of finite element analysis with or without reinforcing bars in concrete, the influence of reinforcing bars was ignored to improve computational efficiency. The heat transfer phenomenon in the tube domain played a leading role in the whole numerical analysis. To ensure accuracy and save calculation time, the double U-shaped heat exchange tube adopted a refined boundary grid, in which the inlet and outlet grid were super-refined. The concrete structure and soil were modeled using free tetrahedral mesh elements. To optimize computational costs, meshes were chosen with a minimum length of 0.045 m and an average size of 0.6 m, and the non-uniform mesh from dense to sparse was used to reduce the computational cost, resulting in the generation of 52,026 elements, which was sufficient to meet the accuracy requirements.  
Boundaries and initial conditions were set based on the model test, assuming that the fluid was an incompressible Newtonian. The pipe parameters were defined using the [PERSON] friction model, with a surface roughness of profiled steel (0.046 mm). The inlet temperature (T\({}_{\rm in}\)) was set at 45 \({}^{\circ}\)C, and the same volume flow rate was used in the model test and numerical simulation. The outer surfaces of the model tank were insulated by wrapping the foam board to avoid heat exchange with the external environment. All soil surfaces in the finite element model were designated as thermally insulating boundary conditions.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Items** & **Solid** & **Liquid** \\ \hline Density (kg/m\({}^{3}\)) & 833.8 & 786.7 \\ Thermal conductivity (W/(m\(\cdot\)K)) & 0.3 & 0.167 \\ Specific heat capacity (J/(g\(\cdot\)K)) & 2.16 & 2.02 \\ Phase change temperature (\({}^{\circ}\)C) & & \(23\pm 1\) \\ Latent heat (J/g) & & 188.04 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Thermophysical parameters of paraffin.  
In the following, the model's validity was verified by comparing the experimental data with the simulated data, and the thermal-mechanical response of the two different piles was further explored.
# Evaluation of Adhesion and Hysteresis Friction of Rubber-Pavement System
## 4 Test Materials and Laboratory Experiments  
In this study, six different rubber materials were used to investigate the frictional properties between laboratory-prepared rubber sliders and HMA slabs prepared from limestone and gabbro aggregates. The rubber materials used in this study included pure gum, Styrene Butadiene Rubber (SBR), Nitrile, Ethylene Propylene Diene Monomer (EPDM), Neoprene, and Butyl. These materials were selected in testing as they cover a wide range of rubber properties in terms of dynamic modulus, elongation, and durability. Table 2 summarizes the rubber properties as provided by the manufacturer.  
Two sources of aggregates were evaluated in this study (gabbro and limestone). The gabbro and limestone aggregates were used to prepare HMA slabs. The gabbro is an igneous rock while the limestone is a sedimentary rock. Gabbro has rough surface compared to limestone [19]. These aggregates are used in road construction in the State of Qatar. Table 3 presents the aggregate gradation used to prepare both gabbro and limestone substrates. It should be noted that the same aggregate gradation and binder type (Pen 60-70) were used in preparing the asphalt mixture substrates. The optimum binder content was found to be 4.3% for gabbro and 4.7% for limestone. The mixing and compaction temperatures were 143 \({}^{\circ}\)C and 135 \({}^{\circ}\)C, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Rubber Type** & **Tensile Strength (PSI)** & **Elongation (\%)** & **Composition** & **Durometer** \\ \hline
**Pure Gum** & 3000 & 600 & Organic Gum & 40 A Nominal \\
**SBR** & 800 & 250 & Styrene Butadiene & 65 Shore A \\
**EPDM** & 800 & 400 & Synthetic & 60 A Nominal \\
**Nitrile** & 950 & 250 & Synthetic & 50 A Nominal \\
**Neoprene** & 1000 & 220 & Synthetic & 60 A Nominal \\
**Butyl** & 1000 & 350 & Isobutylene Isoprene & 55 Shore A \\ \hline \hline \end{tabular}
\end{table}
Table 2: Rubber properties as provided by the manufacturer.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Sieve Size** & **\% Passing** \\ \hline
**1.5\({}^{\prime\prime}\)** & 100.0 \\
**1\({}^{\prime\prime}\)** & 98.6 \\
**3/4\({}^{\prime\prime}\)** & 88.2 \\
**12\({}^{\prime\prime}\)** & 76.9 \\
**3/8\({}^{\prime\prime}\)** & 68.9 \\
**N4** & 47.1 \\
**N8** & 26.5 \\
**N16** & 15.8 \\
**N30** & 10.5 \\
**N50** & 7.9 \\
**N100** & 6.1 \\
**N200** & 4.2 \\
**Pan** & 0.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Aggregate gradation used for asphalt mixture slabs.  
The asphalt mixtures were prepared following the AASHTO T-312, and the test slabs were compacted using a small vibratory compact as shown in Figure 3.
# Highly Adaptive Reconfigurable Receiver Front-End for 5G and Satellite Applications
## 4 Active Device Technology S-Parameter Extraction
### Transconductance Characteristics  
Figures 5 and 6 display the transconductance characteristics of two different sizes of InGaAs pHEMTs, 2 \(\times\) 50 \(\upmu\)m and 4 \(\times\) 50 \(\upmu\)m, respectively. The transconductance \(g_{m}\) is defined as:  
\[g_{m}=\frac{\partial I_{ds}}{\partial V_{gs}} \tag{29}\]  
Figure 4: I-V characteristics for a 4 \(\times\) 50 \(\upmu\)m InGaAs pHEMT ([15,18]).  
Figure 3: I-V characteristics for a 2 \(\times\) 50 \(\upmu\)m InGaAs pHEMT ([15,18]).  
From Figure 5, the transconductance value obtained is 81 mS at \(V_{ds}=2\) V, \(V_{gs}=-0.3\) V, and \(I_{ds}=0.021\) A.
## 5 Discussion
### Multi-Class Classification
Its precision in distinguishing between various attack types further emphasizes its potential for real-world deployment in intrusion detection systems, where accurate and reliable classification is crucial. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Label** & **Accuracy** & **Precision** & **Recall** & **F-Score** \\ \hline Benign & 99.63\% & 100\% & 99.63\% & 99.81\% \\ \hline Exploits & 96.90\% & 99.31\% & 96.90\% & 98.09\% \\ \hline Fuzzers & 99.16\% & 97.93\% & 99.16\% & 98.54\% \\ \hline Reconnaissance & 99.42\% & 98.29\% & 99.42\% & 98.85\% \\ \hline Generic & 93.62\% & 100\% & 93.62\% & 96.70\% \\ \hline DoS & 97.44\% & 92.68\% & 97.44\% & 95\% \\ \hline Shellcode & 100\% & 100\% & 100\% & 100\% \\ \hline Backdoor & 87.50\% & 45.16\% & 87.50\% & 59.57\% \\ \hline Analysis & 80\% & 34.78\% & 80\% & 48.48\% \\ \hline Worms & 100\% & 83.33\% & 100\% & 90.91\% \\ \hline \hline \end{tabular}
\end{table} 
Table 36: Performance metrics for Transformer-CNN across several classes in multi-class classification on NF-UNSW-NB15-v2 dataset. Figure 8: Proposed Transformer-CNN versus multi-class classifiers on CICIDS2017 dataset. The Transformer-CNN model exhibited exceptional effectiveness in multi-class classification, as evidenced by its performance metrics across various attack classes on the CICIDS2017 dataset. The model achieved outstanding results, particularly for certain attack types. For instance, the DoS Slowhttpttest class recorded perfect scores, with 100% accuracy, precision, recall, and F1-score, demonstrating the model's capability to accurately classify this attack type without any errors. Similarly, the DoS GoldenEye class achieved 100% accuracy and recall, along with precision and an F1-score exceeding 98%. The model also excels in distinguishing between other classes. For example, the PortScan class recorded an accuracy of 99.89%, precision of 96.78%, recall of 99.89%, and an F1-score of 98.31%. The \"DDoS\" class similarly performed exceptionally, with accuracy and recall of 99.90% and an F1-score of 99.69%. Despite the inherent complexity of multi-class classification, the Transformer-CNN model maintained high metrics for a majority of the attack types, such as FTP-Patator and SSH-Patator, which achieved F1-scores of 97.70% and 97.39%, respectively. However, for more challenging attack classes like Infiltration and Web Attack-SQL Injection, the model's performance was relatively lower, recording F1-scores of 46.15% and 57.14%, respectively. These results highlight potential areas for improvement in handling low-frequency or highly complex attack types. Overall, the comprehensive metrics detailed in Table 37 underscore the Transformer-CNN model's ability to manage the complexities of multi-class classification effectively. Its precision in distinguishing between various attack types emphasizes its robustness and potential for real-world deployment in intrusion detection systems, where accurate and reliable classification across a wide range of threats is essential.
## 4 Discussion
### Other Effects
The water in every chamber was, however, mixed twice a day, so we expected homogeneous conditions to be provided in the water column. At the same time, it has to be noted that the lake water properties were most likely influenced by the water-air gas exchange and processing of the organic material, so their dynamics might not have clearly reflected the possible transformations of the limestone present on laminates and modules (i.e., its dissolution), especially due to the fact that the calcium carbonate doses in the two treatments were different. What is more, the decay of organic matter and calcium carbonate behavior can be coupled. This is because the decomposition of detritus produces carbonate alkalinity by releasing inorganic carbon species. At the same time, it is supposed to promote calcium carbonate dissolution by the production of H\({}^{+}\) ions [70]. Meanwhile, dissolved organic compounds, adsorbing on the surface of calcite, show inhibiting effects on its dissolution [74; 75] similarly to biofilm developing on the mineral surface [71]. Therefore, it should be expected that the pH, concentration of calcium, and alkalinity dynamics were modified by complex processes and interactions taking place in the lake water. In large-scale applications in the field, the effect of the treatment on the lake water properties discussed here will be affected by a number of factors, similarly to the performance in OP uptake (see Section 4.1). The possible dissolution and/or precipitation of CaCO\({}_{3}\) (and resulting change in lake water properties) will depend on the initial and subsequent physio-chemical conditions in the lake (pH; temperature) as they affect calcite solubility, with generally higher dissolution rates if applied in deep layers (hypolimnion) [62] due to low temperature, typically lower pH, and undersaturation with respect to calcite (see the review provided in Barkowska-Sobczak [62]). Specific substances (e.g., iron [76] or magnesium [77]) can also influence the CaCO\({}_{3}\) transformations, thus the variability observed in field applications can be also determined by the geochemical features of lakes. To summarize, the effects of both solutions tested, laminates and modules, on the lake water physico-chemistry were similar and mainly seen in the lake water pH, which was slightly lower (by up to 0.2 point) under both treatments compared to the control chamber. Laminates tended to decrease the lake water pH a bit more compared to modules. However, in both cases, the pH was only slightly modified and remained in a range typical for natural waters. This was in agreement with our previous studies on laminates [41], conducted under a range of environmental conditions, although in a short-term experiment only.
## 1 Contextualization  
Volatile organic compounds (VOCs) are known air pollutants emitted from a variety of indoor and outdoor sources. Considering that the lifestyles associated with modern society require that significant periods of time be spent in indoor environments, in turn leading to extended periods of exposure, it is important to be aware and actively work toward a more careful monitoring of these compounds. Wood-based panels (WBPs) have been a fast-growing branch of the wood-transforming industry and have found a place in homes worldwide, in furniture, cabinets, doors, flooring, and other construction materials. They have been actively linked to the emission of several potentially toxic VOCs, with the most relevant being formaldehyde. While formaldehyde, due to its classification as a carcinogenic compound (Group 1) by the International Agency for Research on Cancer (IARC), is a predominant topic in WBPs and general indoor-air quality publications [1; 2; 3; 4; 5], the same focus is not given to the broader spectrum of VOCs that can be released from these products. This work offers a multidisciplinary view of WBPs, from production to legislation, describing their role as a source of VOCs and the different emitting classes associated with WBPs, as well as the health hazards resulting from VOC exposure and the analytical methods currently available to be utilized for their control.  
This review will be structured into five major sections:  
1. Contextualization: Presents the objectives and structure of the work;
2. Wood-based panels: Contains an overview of the wood and WBP industry, the different WBPs and their manufacturing processes, the adhesives employed, and the legal restrictions the production of WBPs must abide by;
3. Volatile Organic Compounds Emitted from WBPs: Focuses on VOCs, specifically, the identification of possible sources within WBPs, followed by an in-depth discussion of the most prominent groups of compounds emitted;
4. Methodologies Used for the Analysis of VOCs in WBPs: Presents both the standard methods used for the determination of VOCs in WBPs and alternative analytical methods;
5. Comparison of Emission Results Found in the Literature: Provides a summary and discussion of the determined values for terpenes, carbonyl compounds, and others for different types of wood and WBPs.
## 5 Sensor Faults and Outlier Detection Models for IoTs
### Comparison of Fault and Outlier Detection Techniques for IoTs
#### 5.6.1 Statistical Techniques  
**Pros:**  
* Can efficiently identify any sensor faults and outliers in an IoT once a proper probability distribution model is obtained.
* The sensor faults and outliers can be detected by using temporal correlation. Any unforeseen change in the data distribution immediately decreases the temporal correlations thereby detecting outliers.
**Cons:**  
* As the IoTs are often used in real-life settings, where there is often no previous sensor data distribution knowledge, the parametric statistical approach is not beneficial.
* The non-parametric statistical models are not suitable for data-intense IoTs working in a real-time setting.
* Often there is a high computational cost of managing multivariate data produced.
# DeepST-Net: Leveraging Spatiotemporal Data Refinement for Enhanced Travel Time Prediction
## 6 Results and Discussions
### Model Training Dynamics  
This subsection examines the evolution of training and validation loss across the four models. These loss curves provide essential insights into each model's convergence during training and its ability to generalize to unseen data. Figure 9 visually illustrates the performance trajectories of the models throughout both training and validation phases.  
The performance of the model trained on raw data reveals several limitations indicative of inadequate learning capacity. The training loss plateaus around the 30 th epoch, suggesting premature convergence, while the validation loss remains consistently higher than the training loss throughout, accompanied by noticeable fluctuations. This divergence between training and validation losses is a clear indicator of overfitting, where the model struggles to generalize beyond the training data. Furthermore, the early stopping mechanism halted training at the 80 th epoch to prevent further degradation of performance, underscoring the inadequacy of using raw, unprocessed data for predictive modeling. These findings highlight the critical need for robust preprocessing techniques to improve data quality, address inherent inconsistencies, and facilitate better model generalization.  
In contrast, the model trained on clean data demonstrates significant improvements, as evidenced by the steady decrease in both training and validation losses, coupled with minimal divergence between the two. These results underscore the effectiveness of preprocessing steps, such as noise reduction and outlier removal, in mitigating overfitting and enhancing the model's ability to generalize to unseen data. By addressing inconsistencies and irregularities in the raw dataset, the preprocessing pipeline provides a cleaner foundation, enabling the model to capture meaningful patterns more effectively. This highlights the importance of a systematic data refinement approach in optimizing model training dynamics and performance.  
Building on these improvements, the temporal model further enhances performance by incorporating temporal clustering, which captures periodic variations such as daily and weekly patterns. The consistent convergence of training and validation losses reflects the model's adaptability to dynamic travel behaviors. These results validate the importance of temporal features in augmenting the model's ability to generalize across diverse contexts.  
Finally, DeepST-Net integrates the strengths of both data refinement and feature engineering to achieve the most robust and consistent training dynamics. The model's training and validation losses converge steadily, with minimal divergence, demonstrating its ability to avoid both overfitting and underfitting. By leveraging temporal and pattern-based features, combined with a comprehensive preprocessing pipeline, DeepST-Net achieves superior performance, establishing itself as a scalable and reliable solution for travel time prediction in complex urban environments.
# Wireless Sensor Networks for Smart Homes:
## 3 Fuzzy Controller
### Description of the FLC  
The FLC introduced in this paper considers three MFs (_Low_, _Medium_, _High_) for the input variables. These functions fuzzify the crisp inputs, while the ranges of which are:  
* \(Th/Wl\): \([0,100]\) (percentage);
* \(battery\_level\): \([0,1024]\), where 0 is the lowest level of battery while 1024 is the highest one and it is the maximum value at the output of a 10-bit AD converter (with an appropriate electronic signal conditioning circuit.  
Figure 3: Trapezoidal membership functions.  
Furthermore, three MFs (_Low, Medium, High_) are applied for the _sleeping_time_. In this case, the range of the crisp values of this output variable is:  
* _sleeping_time_: \([0,10]*sampling\_time\) (seconds).  
where the \(sampling\_time\) value is a constant value defined at design time for each field device. Trapezoidal MFs of the \(Th/Wl\), the _battery_level_ and the _sleeping_time_ are depicted in Figure 4, where the degree of membership is represented by normalized values \([0,1]\). Moreover, considering the Equation (3), the different values of the variables are depicted in Table 1.  
As presented in Table 2, the output value is discovered through 9 fuzzy rules. They are based on the _IF-THEN_ statement of standard programming languages. For instance, regarding rule 5, if the \(Th/Wl\) is _Medium_ and _battery_level_ is _High_, then the _sleeping_time_ will be _Low_. The outputs of the inference mechanism are fuzzy output variables. The FLC needs to turn its internal fuzzy output variables toward crisp values, through the defuzzification method, so that the system can practice these variables. The defuzzification can be performed in several ways. In this paper, the Centroid of Area (COA) method [58] has been chosen.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Universe of Discorse** & **Set** & **a** & **b** & **c** & **d** \\ \hline \multirow{3}{*}{\(Th/Wl\)} & _Low_ & \(-40\) & \(-10\) & \(10\) & \(40\) \\ \cline{2-6}  & _Medium_ & \(10\) & \(40\) & \(60\) & \(90\) \\ \cline{2-6}  & _High_ & \(60\) & \(90\) & \(110\) & \(140\) \\ \hline \multirow{3}{*}{\(battery\_level\)} & _Low_ & \(-400\) & \(-100\) & \(100\) & \(400\) \\ \cline{2-6}  & _Medium_ & \(100\) & \(400\) & \(600\) & \(900\) \\ \cline{2-6}  & _High_ & \(600\) & \(900\) & \(1100\) & \(1400\) \\ \hline \multirow{3}{*}{_sleeping\_time_} & _Low_ & \(-4\) & \(-1\) & \(1\) & \(4\) \\ \cline{2-6}  & _Medium_ & \(1\) & \(4\) & \(6\) & \(9\) \\ \cline{2-6}  & _High_ & \(6\) & \(9\) & \(11\) & \(14\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Values of variables used in definition of trapezoidal membership functions.  
Figure 4: Trapezoidal membership functions of input and output parameters for the FLC: (**a**) \(Th/Wl\); (**b**) _battery_level_; (**c**) _sleeping_time_.
## 3 The Cooperative Beam Association and Power Allocation Scheme  
The optimization problem \(\mathbf{P1}\) is a mixed-integer nonlinear programming (MINLP) problem with binary beam association variables \(\{v_{k,m}\},\forall k,\forall m\) and power allocation variables \(\{P_{k,m}\},\forall k,\forall m\). Due to the non-convex constraints as (7b) and (7c), problem \(\mathbf{P1}\) is non-convex and difficult to solve. In addition, the beam association variables and power allocation variables are coupled, and it is difficult to obtain a global optimal solution of \(\mathbf{P1}\).  
In this paper, the above MINLP and non-convex problem is decomposed into two sub-problems. We first propose a many-to-many matching game-based algorithm to solve the multiple LEO satellites and LBCs association sub-problem. Then, an SCA-based iterative algorithm is proposed to solve the power allocation sub-problem for multiple LEO satellite beams.