esa-sceva/satcom-chunk-collection huggingface dataset:
# SCADA System Testbed for Cybersecurity Research Using Machine Learning Approach
###### Abstract  
This paper presents the development of a Supervisory Control and Data Acquisition (SCADA) system testbed used for cybersecurity research. The testbed consists of a water storage tank's control system, which is a stage in the process of water treatment and distribution. Sophisticated cyber-attacks were conducted against the testbed. During the attacks, the network traffic was captured, and features were extracted from the traffic to build a dataset for training and testing different machine learning algorithms. Five traditional machine learning algorithms were trained to detect the attacks: Random Forest, Decision Tree, Logistic Regression, Naive Bayes and KNN. Then, the trained machine learning models were built and deployed in the network, where new tests were made using online network traffic. The performance obtained during the training and testing of the machine learning models was compared to the performance obtained during the online deployment of these models in the network. The results show the efficiency of the machine learning models in detecting the attacks in real time. The testbed provides a good understanding of the effects and consequences of attacks on real SCADA environments.  
Keywords:cybersecurity; machine learning; SCADA system; network security +
Footnote †: journal: Computer Science and Engineering
# Optimizing Apache Spark MLlib: Predictive Performance of Large-Scale Models for Big Data Analytics
## 4 Experimental Results
### KMeans Operator  
The KMeans operator \(O_{\text{KMeans}}\) was used with a set of different datasets to investigate how the number of samples \(n\), features \(f\), and clusters \(c\) influenced the overall computation time \(T(O)\) and memory requirements \(M(O)\).
# Evaluating Novel Speech Transcription Architectures on the Spanish RTVE2020 Database +
## 4 Systems Description and Configuration
### Novel ASR Architectures  
#### 4.1.1 Multistream CNN Based System  
The Multistream CNN based ASR engine was built on the top of the Kaldi toolkit through the _nnet3_ DNN setup and following the egas/librispeech/s5/local/chain/run_multistream_cnn_1a.sh recipe from the ASAPP Research repository ([[https://github.com/asappresearch](https://github.com/asappresearch)]([https://github.com/asappresearch](https://github.com/asappresearch)), accessed on 28 December 2021).  
The Multistream CNN architecture is illustrated in Figure 1. The acoustic model is composed by an initial set of five 2D-CNN layers in charge of processing the given input speech frames augmented dynamically through the SpecAugment [28] technique. Each embedding vector outputted from the single-streamed set of CNN layers in each time step is then inserted as the input of each of the three stacks of TDNN-F layers, combined with a dilation rate configuration of 6-9-12. Each stack is composed of 17 TDNN-F layers, with an internal cell-dimension of 512, a bottleneck-dimension of 80 and a dropout schedule of '0,000.20,0.500.5,0'. The number of training epochs was set to six, with an initial and final learning rates of \(10^{-3}\) and \(10^{-5}\), respectively, and a mini-batch size of 64. The input vector corresponded to a concatenation of 40-dimensional high-resolution MFCC coefficients, augmented through speed (using factors of 0.9, 1.0, and 1.1) [29] and volume (with a random factor between 0.125 and 2) [30] perturbation techniques, and the appended 100 dimensional \(i\)-Vectors.  
This system included a 3-gram language model for decoding and a 4-gram pruned RNNLM model for lattice-rescoring following the work presented in [31]. The 3-gram LM was trained with texts coming from the _Transcriptions_, _RTVE2018_ and _A la Carta_ corpora presented in Table 2, and the 4-gram pruned RNNLM model was estimated adding the _Wikipedia_ text corpus as well.  
Figure 1: Architecture of the Multistream CNN acoustic model composed by an initial set of five 2D-CNN layers followed by three stacks of 17 TDNN-F layers. This figure is inspired by the one presented in [17].
## 4 Discussion
Furthermore, patients with disc displacement had numerically higher jaw-opening forces at baseline (geometric mean 23.7 N) than patients with myofascal pain (17 N) and both myofascal pain and disc displacement (17 N) [20]. A possible explanation is that most patients diagnosed with disc displacement were asymptomatic, presenting no pain during their baseline measurement [32; 33]. The above results indicate that the two groups, myofascal pain and myofascal pain and disc displacement, had similar jaw-opening forces both pre- and post-treatment. This could imply that, in these groups, the predominant problem was muscular with associated pain; this is in agreement with current treatment guidelines for these groups that recommend a non-surgical approach to the management of the condition [25; 30; 32]. Until now, the assessment of clinical improvement of TMD patients has been based on subjective measures, including questionnaires (e.g., graded chronic pain scale, dental anxiety scale, and McGill pain questionnaire), and physical examination, which assess the pain intensity (CPI), parafunctional activities, and psychological status [35; 36; 37]. However, questionnaires are invariably susceptible to recall bias, which limits the validity of such data [5; 38]. In this study, the investigator involved in measuring jaw-opening forces was not blinded to the participant's TMD status, but the measurement process was objective, and this seems unlikely to introduce bias. Despite the changes in jaw-opening forces reported here for TMD patients following treatment, this study has limitations. Limitations regarding the design of the jaw-opening device were described in our previous study [20]. Disc displacement with or without reduction was considered as one category, as we did not differentiate them into two subgroups. The association between jaw-opening forces and the specific form of conservative treatment adopted was not investigated here. Future studies should investigate the correlation between jaw-opening forces and the treatment provided to evaluate the degree of TMD improvement in regards to specific treatment modalities.
## 2 Material and Methods
### Climate Data  
We also calculated the correlation coefficients between meteorological data and tree-ring chronologies to further reveal the factors driving radial growth dynamics. Meteorological datasets were obtained from the meteorological stations in Yinchuan, Ningxia, from 1982 to 2018 from the China International Meteorological Center ([[http://cdc.nmic.cn/home.do](http://cdc.nmic.cn/home.do)]([http://cdc.nmic.cn/home.do](http://cdc.nmic.cn/home.do)), accessed on 12 July 2022), and the meteorological factors were selected as precipitation (Pre), monthly mean temperature (Mean-T), monthly maximum temperature (Max-T), monthly minimum temperature (Min-T), relative humidity (RH), and sunshine duration (SD). In addition, the standardized precipitation evapotranspiration index (SPEI) data were also used, which was calculated based on the meteorological data obtained from the China International Meteorological Center using the EToCalculator software on the official website of FAO to calculate ETo (Penman-Monteith formula) and then based on ETo and precipitation data using Climate Data Toolbox (CDT) in MATLAB to obtain the SPEI data [27].  
The climate conditions are shown in Figure 2. During the period 1982-2018, the multi-year monthly average precipitation was 193.2 mm, which was mainly focused on the period from July-August, accounting for 43.70% of the annual precipitation; the highest and lowest monthly average temperature occurred in July at 24.10 \({}^{\circ}\)C and January at \(-\)7.11 \({}^{\circ}\)C, respectively; the highest and lowest SPEI was 1.4735 in June and 0.2422 in December, respectively; the highest and lowest monthly average relative humidity was 64.86% in July and 38.36% in April, respectively; and the highest and lowest monthly average sunshine duration was 283.24 h in May and 181.30 h in December, respectively.
# Activation-Based Pruning of Neural Networks
## 5 Results and Discussion
### Local Activation-Based Pruning  
Table 3 demonstrates the results of local activation-based pruning. Compared to Table 2 for global pruning, local pruning demonstrated a superior pruning percentage, while maintaining the training, validation, and test accuracy at a comparable level. Table 3 also demonstrates that the validation and test accuracy for local pruning was better than the standard run. For top-1% accuracy, nearly all pruning runs had better results than the standard run, except for the validation accuracy for Neurons & Weights in the fourth row, where the percentage of pruning achieved was 79.88%. For top-5% accuracy, all results were better than the standard run, except L1. In summary, it was evident that L2, L3, and L4 yielded significantly improved results compared to the standard run.  
Table 1 demonstrates that local pruning exhibited superior space and time complexity compared to global pruning. A comparison of Tables 2 and 3 demonstrates that local pruning was much closer to achieving the target pruning percentage while maintaining comparable training, validation, and test accuracy. Local pruning delivered equivalent accuracy and pruning percentages to global activation-based pruning, while significantly enhancing processing time efficiency and reducing memory consumption.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **Pruning** & \multirow{2}{*}{**Pruning**} & \multirow{2}{*}{**Epochs**} & \multirow{2}{*}{**Target Prune \%**} & \multicolumn{2}{c}{**Pruning**} & \multicolumn{3}{c}{**Top 1\% Accuracy**} & \multicolumn{3}{c}{**Top 5\% Accuracy**} \\  & & & & & & & **Achieved** & & & **Training** & **Validation** & **Test** \\ \hline S1 & NA & Standard & 46 & NA & NA & 10.19 & 8.17 & 8.22 & 69.10 & 68.72 & 69.40 \\ G1 & 5 & Neurons & 62 & 80 & 70.25 & 10.24 & 9.75 & 10.16 & 70.45 & 66.47 & 65.69 \\ G2 & 5 & Neurons & 62 & 85 & 74.64 & 10.13 & 9.23 & 9.54 & 72.23 & 71.47 & 71.67 \\ G3 & 5 & Neurons \& Weights & 47 & 80 & 70.25 & 10.13 & 9.35 & 9.66 & 81.30 & 81.50 & 81.83 \\ G4 & 5 & Neurons \& Weights & 62 & 85 & 85.00 & 10.15 & 9.00 & 9.46 & 66.59 & 66.65 & 66.77 \\ G5 & 10 & Neurons & 67 & 80 & 74.59 & 10.25 & 9.32 & 9.57 & 80.99 & 80.57 & 80.24 \\ G6 & 10 & Neurons & 74 & 85 & 79.25 & 10.17 & 9.62 & 9.69 & 71.44 & 66.10 & 64.99 \\ G7 & 10 & Neurons \& Weights & 53 & 80 & 77.59 & 10.16 & 12.17 & 12.30 & 77.19 & 79.60 & 79.23 \\ G8 & 10 & Neurons \& Weights & 54 & 85 & 82.44 & 10.15 & 11.42 & 11.58 & 78.35 & 78.45 & 79.31 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global activation-based pruning.
# The Electric Vehicle Routing Problem with Time Windows, Partial Readers, and Parcel Lockers
## 4 Solution Method
### Evaluation of Penalties  
To extend the search space, we accept infeasible solutions in terms of time windows, battery consumption, and vehicle loading, along with adding penalty costs [53]. Let \(\sigma=\{V_{1},\dots,V_{n}\}\) denote a solution consisting of a set of \(n\) routes. The generalized cost function of a solution \(\sigma\) is denoted by \(f_{gen}(\sigma)\) as follows. Next, the total cost function (1) is generalized by taking into account three penalty terms related to time windows, battery, and vehicle load, as shown in the following expression.  
\[f_{gen}(\sigma)=\sum_{V\in\sigma}f_{gen}(V)=f(\sigma)+\sum_{V\in\sigma}\left( \rho_{tu}\lambda_{tu}(V)+\rho_{ba}\lambda_{ba}(V)+\rho_{lo}\lambda_{lo}(V)\right) \tag{21}\]  
Here, \(f(\sigma)\) is equivalent to the total cost (1). Values \(\lambda_{tu}(V)\), \(\lambda_{ba}(V)\), and \(\lambda_{ql}(V)\) denote the time window, battery, and load penalties, respectively. These penalties are included in the generalized total cost with associated weights \(\rho_{tu},\rho_{bu}\), and \(\rho_{lo}\), which are initialized with \(\rho_{tu}^{0},\rho_{ba}^{0}\), and \(\rho_{lo}^{0}\). These weights are adjusted during the ALNS procedure with the ranges \([\rho_{tu}^{min},\rho_{tu}^{max}]\), \([\rho_{bu}^{min},\rho_{ba}^{max}]\), and \([\rho_{lo}^{min},\rho_{lo}^{max}]\). Based on [53], lower bounds are set as 0.5, and upper bounds are set as the objective value of the initial solution.  
It is worth noting that dynamic penalty weights are used to reduce the occurrence of infeasible solutions during the execution of our ALNS procedure. More specifically, the penalty weights are increased to prevent infeasible solutions when the algorithm keeps failing to explore feasible solutions. This concept has been successfully implemented to solve EVRP variants such as [52; 53; 54].  
Load penalty \(\lambda_{lo}\) is easily determined in \(\mathcal{O}(1)\) time complexity [55]. Battery and time window penalties are more challenging, but [PERSON] and [PERSON] [52] propose an efficient approach by introducing a set of forward and backward variables. For the sake of calculations, all variables mentioned in this section are used in the same unit as travel time. We also introduce some supported parameters; i.e., \(h_{ij}=grd_{ij}\) denotes the recharge time with enough energy to traverse an arc \((i,j)\), and \(H=gB\) denotes the time for recharging a full battery capacity. All variables are applied to concatenation operators for evaluating these penalties in \(\mathcal{O}(1)\) time complexity. In more detail, forward and backward functions are explained as follows.
## 1 Introduction
In spite of these advancements, there are still substantial gaps in our comprehension of the specific characteristics of seismic records that regulate the responses of structures, particularly in the context of nonlinear effects. The majority of the studies conducted thus far have only utilized a restricted set of seismic parameters, such as PGA, without conducting a comprehensive analysis of the structural damage caused by energy-based and frequency-dependent parameters [15]. Additionally, there is a dearth of research on older buildings, particularly those that were constructed prior to the implementation of current seismic design codes, as these structures frequently exhibit distinct damage patterns in comparison to their more recent counterparts [16]. The majority of the current research is concentrated on newly constructed buildings, while older buildings continue to pose a substantial risk, particularly in areas with high-seismic activity. The objective of this research was to investigate the nonlinear response of existing structures in relation to seismic record attributes. Tise research concentrated on reinforced concrete structures that were constructed prior to the implementation of contemporary seismic regulations. The objective of this research was to determine the most critical seismic parameters that affect the damage to structures caused by earthquakes, quantified by the interstory drift ratio, a damage demand measure, and to evaluate their mutual correlation. By examining these relationships, this study evaluated the most effective IMsfor predicting structural damage in seismic events in order to evaluate correlated input data for the appropriate selection of earthquake records for reliable nonlinear dynamic analysis results.
# DGANet: Dynamic Gradient Adjustment Anchor-Free Object Detection in Optical Remote Sensing Images
## 4 Experiment and Discussion  
In the experimental section, we describe a series of experiments conducted to demonstrate the effectiveness of the proposed network in optical remote sensing images. First, we introduce datasets used and evaluation metrics for the performance. Second, we describe four groups of experiments conducted to evaluate the performance of the proposed DGANet. Finally, we compare the method with published state-of-the-art approaches to adequately prove the advantages of our DGANet framework.
## 2 Methodology
### GIS-Based Highway Route Selection  
The process of GIS-based Highway Route Selection (HRS) involves calculating the weighted risk intensity for each design factor to determine the threat score of each 20-m-long segment. Subsequently, the total threat score for each alternative is computed to choose the best route with the least threat. The following process is presented in Figure 3.  
**Step 1:**: An Excel-based computation is employed to initially tabulate northing and easting coordinates and geometric, hydrology, and geology design factors as outlined in Table 1 for each alternative. Figure 4 shows the Excel-based set-up.
**Step 2:**: Next, the geometric and environmental characteristics are standardized by computing a normalized risk intensity value (\(RIN\)) ranging from 1 (indicating the lowest risk intensity) to 10 (representing the highest risk intensity). An example of Excel output is shown in Figure 5. Based on the value of the design factor (\(DF_{i}\)), a corresponding risk intensity is assigned using the scale outlined in Table 1.
**Step 3:**: Subsequently, the tables are exported to GIS, where the threat score (\(TS\)) for each segment is determined by summing the products of the assigned risk intensities (\(RIN_{i}\)) and the weights associated with each design factor (\(W_{i}\)) as shown in Equation (10). These weights are established in the preceding DEMATEL step. An example of GIS output is shown in Figure 6.  
\[TS_{i}=\sum_{i=1}^{n}RIN_{i}\cdot W_{i} \tag{10}\]  
GIS was utilized in this study to facilitate the calculation process and map production. The GIS platform is employed to acquire and integrate various raster and shapefile layers relevant to different factors affecting the road route. This included data sources, such as  
Figure 5: Step 2: Computing Normalized Risk Intensity Value (_RIN_) to Standardize Geometric and Environmental Characteristics.  
digital elevation models, geology maps, and hydrology maps detailing flood water depth, velocity, and flood hazards. Then, a series of geospatial analyses were conducted to quantify hazard risks and scores for each factor affecting the road design. These analyses included hydrologic modeling and zonal statistics. Finally, thematic maps and visualizations were generated to effectively communicate the spatial distribution of flood hazards relative to the road route.
## 2 Material and Methods
### KOMPSAT-3 and KOMPSAT-3A Dataset Construction Process
This overlap ensures seamless transitions between patches during model application, helping the model generalize effectively across diverse conditions. As a result, the number of training images increased from 130 to 22,890, and the validation images increased from 16 to 2772, bringing the total to 25,662 images. However, of these, 12,504 images contained only clear sky without any clouds or shadows, leading to an imbalance in the dataset. To improve model performance, data augmentation techniques such as horizontal and vertical flipping, rotation, and brightness adjustment were applied during preprocessing to balance the number of samples across different classes (Figure 3). In this study, we evaluated various satellite channel combinations for digital numbers (DNs) and Top-of-Atmosphere (TOA) reflectance to assess the accuracy of each dataset. Figure 4 shows sampled images from KOMPSAT with different channel combinations, including RGB, NRG, and RGB with NIR. Additionally, we tested RGBN combined with the NDVI (Normalized Difference Vegetation Index). TOA-based channel combinations  
Figure 1: The training dataset of cloud masks from KOMPSAT-3 and 3A on 1 January 2020 and 1 February 2023 (www.aihub.or.kr and [[https://data.kisti.re.kr](https://data.kisti.re.kr)]([https://data.kisti.re.kr](https://data.kisti.re.kr)), accessed on 7 April 2025). From (**a**–**c**), there are RGB, NGR, and TOA-corrected RGBN, respectively, and (**d**) is the label of cloud mask. Figure 2: The patching schema of the cloud detection dataset for each of the KOMPSAT satellite images and the corresponding label dataset. were also evaluated, specifically TOA RGB and TOA RGBN with the NDVI, to determine their performance in deep learning models. For the TOA analysis, only the channel combinations that achieved the highest accuracy in the DN-based evaluations, namely RGBN and RGBN with the NDVI, were further assessed. The model performance for each channel combination was then compared to identify the most effective setup for accurate data interpretation.
# Recent Advances in Wearable Devices for Non-Invasive Sensing
## 2 Materials and Structural Design
### Structural Design
#### 2.2.3 Wavy and Serpentine Structure  
To achieve both flexibility and stretchability, wavy and serpentine geometries provide additional benefits through the elongation and contraction of the structure. A device attached to a uniaxial or a biaxial pre-strained elastomer enables the fabrication of a wavy structural device by releasing the strain [84]. [PERSON] et al. introduced a stretchable, periodic, wavy, structural electronic device with an inorganic semiconductor and silicone nanoribbons on a pre-strained polydimethylsiloxane (PDMS) substrate, as shown in Figure 2d [74]. When the PDMS and Si ribbons that were patterned with the activated surface were exposed to UV, -OH is formed on the surface, and a strong siloxane linkage was formed through the condensation reaction. These adhesion regions enable well-defined, wavy geometry to be obtained when the strain is released. The resulting device has high stretchability (~100%), compressibility (~25%), and bendability (curvature radius down to ~5 mm). [PERSON] et al. introduced a highly-sensitive, ultrathin, wrinkled Au film strain sensor [75]. Figure 3e shows an image of the strain sensor with Au/parylene film on a pre-strained dielectric layer. This capacitive-based strain sensor achieved a 3.05 gauge factor and high stretchability to 140% with mechanical stability. A Serpentine design is a certain curved shape with periodically repeating geometry. Such a geometry enables large scale stretchability by releasing strain energy when mechanically deformed and conformally attached to the skin [85]. [PERSON] et al. introduced stretchable electronics with fractal-shaped electrodes patterned on the elastomer, and the devices had a mechanical stretchability of 32% on the \(x\)-axis and 28% on the \(y\)-axis, as shown in Figure 3f [76]. This serpentine structure supports conformal attachment to the skin and minimizes the electromagnetic disturbances during magnetic resonance imaging. [PERSON] et al. developed 16 temperature sensors with serpentine geometry interconnect on the microperforated elastomer, as shown in Figure 3g [77]. This geometry minimizes the difference in the strains between the sensors and the actuators, and under an external strain of 10%, the temperature coefficient of resistance (TCR) of the device's strain of sensors/actuators was <0.02%.