esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Application of Generic Patterns for an SOA IDS
### Detection of Anomaly Patterns
In the case that an attacker interferes the communication behavior by compromising an SOA node, a modified communication pattern occurs [R9]. For example, a modified function can lead to a different calculation time, which results in a changed response time. **Publish/Subscribe Causality:** For publish/subscribe patterns, it is determinable which change within the payload of a publish/subscribe service results in a change of the payload of another service [R5] or vice versa. This causal dependency can be determined with a machine learning algorithm which is trained with correlating data [R8, R9]. **Stream Causality:** For streams, expected behavior is harder to determine. In this case, it is more easy to check the causality according to an exclusion procedure [R5]. In case a stream-service \(A\) is missing, then a stream-service \(B\) might also be missing because it is based upon the stream-service \(A\). **Context Plausibility:** The state of different devices together with the payload of different services can be fused to create a context of a device ensemble (e.g., the OR). Thus, it is possible to make a plausibility check if a device state is possible within a given system context that is similar, as proposed by [PERSON] et al. [61]. In case the states and payloads are not plausible within a given overall context, an anomaly is detected [R3, R4, R8].
## 6 Experimental Results
\begin{table}
\begin{tabular}{l c c c c c c} \hline
**Benchmark** & **Auto** & **One-Hot** & **JEDI** & \(\boldsymbol{PTY}\) & **Our Approach** & **Complexity** \\ \hline bbara & 21 & 21 & 14 & 13 & 12 & C1 \\ bbsse & 40 & 44 & 31 & 26 & 22 & C1 \\ bbtas & 7 & 7 & 7 & 10 & 10 & C0 \\ beecount & 22 & 22 & 17 & 13 & 11 & C1 \\ cse & 47 & 73 & 43 & 35 & 31 & C1 \\ dk14 & 19 & 30 & 13 & 13 & 11 & C1 \\ dk15 & 18 & 19 & 15 & 11 & 10 & C1 \\ dk16 & 17 & 36 & 14 & 12 & 12 & C1 \\ dk17 & 7 & 14 & 7 & 10 & 10 & C0 \\ dk27 & 4 & 6 & 5 & 9 & 9 & C0 \\ dk512 & 11 & 11 & 10 & 12 & 12 & C0 \\ donfile & 33 & 33 & 26 & 21 & 17 & C1 \\ ex1 & 79 & 83 & 62 & 47 & 44 & C2 \\ ex2 & 11 & 11 & 10 & 10 & 9 & C1 \\ ex3 & 11 & 11 & 11 & 12 & 12 & C0 \\ ex4 & 21 & 19 & 18 & 14 & 12 & C1 \\ ex5 & 11 & 11 & 11 & 12 & 12 & C0 \\ ex6 & 29 & 41 & 27 & 24 & 20 & C1 \\ ex7 & 6 & 7 & 6 & 6 & 6 & C1 \\ keyb & 50 & 68 & 47 & 41 & 38 & C1 \\ kirkman & 54 & 70 & 51 & 40 & 35 & C2 \\ lion & 4 & 7 & 4 & 7 & 7 & C0 \\ lion9 & 8 & 13 & 7 & 9 & 9 & C0 \\ mark1 & 28 & 28 & 25 & 21 & 18 & C1 \\ mc & 7 & 10 & 7 & 9 & 9 & C0 \\ modulo12 & 8 & 8 & 8 & 10 & 10 & C0 \\ opus & 33 & 33 & 27 & 24 & 20 & C1 \\ planet & 138 & 138 & 95 & 80 & 76 & C2 \\ planet1 & 138 & 138 & 95 & 80 & 76 & C2 \\ pma & 102 & 102 & 94 & 81 & 73 & C2 \\ s1 & 73 & 107 & 69 & 60 & 54 & C2 \\ s1488 & 132 & 139 & 116 & 93 & 89 & C2 \\ s1494 & 134 & 140 & 118 & 101 & 92 & C2 \\ s1a & 57 & 89 & 51 & 43 & 39 & C2 \\ s208 & 23 & 42 & 21 & 19 & 16 & C2 \\ s27 & 10 & 22 & 10 & 9 & 9 & C1 \\ s386 & 33 & 46 & 29 & 23 & 19 & C1 \\ s420 & 29 & 50 & 28 & 24 & 20 & C4 \\ s510 & 67 & 67 & 51 & 44 & 38 & C4 \\ s820 & 13 & 13 & 13 & 11 & 10 & C1 \\ s832 & 106 & 100 & 86 & 72 & 67 & C4 \\ s840 & 98 & 97 & 80 & 70 & 62 & C4 \\ sand & 143 & 143 & 125 & 107 & 101 & C3 \\ shiftree & 3 & 7 & 3 & 8 & 8 & C0 \\ sse & 40 & 44 & 37 & 31 & 27 & C1 \\ styr & 102 & 129 & 90 & 78 & 73 & C2 \\ tma & 52 & 46 & 46 & 37 & 32 & C2 \\ \hline
**Sum** & 2099 & 2395 & 1780 & 1542 & 1409 & \\ \hline
**Percentage, \%** & 148.97 & 169.98 & 126.33 & 109.44 & 100.00 & \\ \hline \end{tabular}
\end{table} 
Table 7: Experimental results (LUT counts).
# Rolling Bearing Fault Diagnosis Based on Wavelet Packet Transform and Convolutional Neural Network
## 3 Proposed CNN-Based Fault Diagnosis Method  
This section demonstrates the proposed two-step fault diagnosis method. In the first step, a data-to-image method is designed to obtain the 2-D gray image from conditional signals as the input of a CNN model. In the second step, a high-effective CNN model is built to implement the feature extraction and fault diagnosis.
# Multi-Source Satellite Imagery and Machine Learning
## 4 Materials and Methods
### The General Architecture of Our Methodology  
We conducted our geological formation detection study as follows:  
* **Step 1: Data Acquisition** We collect data from different sources, including geographic parameters (Data 1), ratio index parameters (Data 2), PCA parameters (Data 3), OLI spectrometric parameters (Data 4), and OR spectrometric parameters (Data 5).
* **Step 2: Data Aggregation** We store individual datasets in a central repository and create combinations of datasets, such as Data 1 + Data 2, Data 1 + Data 4, and Data 3 + Data 4, to explore the interactions between different parameters for optimal results.
* **Step 3: Data Splitting** We split the aggregated datasets into training and validation sets. The training sets are used to train machine learning models, while the validation sets are reserved for model evaluation.
* **Step 4: Model Training and Evaluation** We apply 10-fold cross-validation on the training and validation sets and train models using three machine learning algorithms: Random Forest, XGBoost, and Gradient Boosting.  
In the Feature Selection and Ensemble Learning step, we evaluate each algorithm's performance, extract feature weights to identify significant predictors, and save the best-performing models for future use.  
* **Step 5: Geological Formation Detection**  
We use the optimized model to predict geological formations, leveraging the integration of diverse datasets and advanced machine-learning techniques for reliable and accurate detection. This workflow ensures thorough testing, robust model training, and a systematic approach to geological formation detection, making it dependable and practical for real-world applications.  
Figure 5 presents the overall structure of our approach.
## 4 Experiments and Results
### Experiment Setup
#### 4.1.2 Baselines  
1. Models based on single time scale features * Model based on \(F_{1}\): As shown in Figure 3a, the model directly treats the daily price series as a relatively short-term feature sequence that is subsequently learned by an LSTM and fully connected layers. * Model based on \(F_{2}\): As shown in Figure 3b, the model uses the convolutional neural network with one layer to extract the relatively medium-term features, and then predicts the price trend through an LSTM and fully connected layers. * Model based on \(F_{3}\): As shown in Figure 3c, the relatively long-term features are extracted by a CNN with two layers. Then it uses an LSTM and fully connected layers to forecast the trend of the closing price.  
Existing models
* Simplistic Model: This is a simplistic model that directly takes the trend of the last \(n\) days of historical price series as the future trend.
* SVM: SVM is a machine learning method commonly used in financial forecasting, such as [3,4]. We adjust parameters \(c\) (error penalty), \(k\) (kernel function), and \(\gamma\) (kernel coefficient) to make the model reach the best state. In order to make the model perform better, parameters are selected from the sets \(c\in\left\{10^{-5},10^{-4},10^{-3},10^{-2},0.1,1,10,10^{2},10^{3},10^{4},10^{5}\right\}\), \(k\in\left\{rbf,sigmoid\right\}\), \(\gamma\in\left\{10^{-5},10^{-4},10^{-3},10^{-2},0.1,1,10,10^{2},10^{3},10^{4},10^{5}\right\}\), respectively.
* LSTM: Because of the time dependencies in financial time series, LSTM is often used in financial forecasting, such as [11,12,15]. We mainly adjusted the parameters \(L\) (number of network layers), and \(N\) (number of hidden units). We select appropriate parameters in the sets \(L\in\left\{1,2,3\right\}\) and \(N\in\left\{10,20,30\right\}\).
* CNN: Similar to LSTM, CNN is also a common model in this field, such as [16, 17, 18]. We mainly adjusted parameters \(L\) (number of network layers), \(S\) (convolution kernel size) and \(N\) (number of convolution kernels). Here we select appropriate parameters in the sets \(L\in\left\{1,2,3\right\}\), \(S\in\left\{3,5,7\right\}\) and \(N\in\left\{10,20,30,40\right\}\).
* Multiple Pipeline Model: In [21], the authors proposed a new deep learning model that combines multiple pipelines. Each pipeline contains a CNN for feature extraction, a Bi-directional LSTM for temporal data analysis, and a Dense layer to give the output for each individual pipeline. Then they use a Dense layer to combine the different outputs to make predictions.
* MFNN: In [22], the authors proposed a novel end-to-end model named multi-filters neural network (MFNN) specifically for prediction on financial time series. Both convolutional and recurrent neurons are integrated to build the multi-filters structure, so that the information from different feature spaces and market views can be obtained.
# Automatic Detection of Ballast Unevenness Using Deep Neural Network
## 2 Algorithm for Automatic Detection of Unevenness in Ballast
### Detection of Sleepers
#### 2.1.2 YOLACT Network  
The YOLACT network is a one-stage algorithm used for instance segmentation. The algorithm can be divided into two parallel subtasks, namely a prototype network block and a target detection block. Figure 6 shows a block diagram for the YOLACT algorithm. Both blocks use a convolutional neural network along with a Feature Pyramid Network (FPN) [33]. The FPN adds lateral connections between the P5 and P3 layers of the pyramid. The idea is to take top-down strong features from the C5 layer of the CNN and propagate them to the high-resolution feature maps in the C3 layer of the CNN, thus creating strong features across all levels. The prototype network block generates a set of image-sized \"prototype masks\". These masks are independent of the instance. It predicts a set of k prototype masks for the entire image using the FCN. The P3 layer from the Feature Pyramid Network (FPN) is fed to an FCN.  
Figure 5: Structure of the Mask R-CNN algorithm.  
The target detection block consists of three parallel branches. The first branch predicts \(c\) class confidences and the second one predicts the four coordinates of the bounding box. The third branch predicts \(k\) mask coefficients, one corresponding to each prototype. After generating \(k\) mask coefficients and passing them through the Non-Maximum Suppression algorithm (NMS), they are combined with prototype masks using the following formula [34]:  
\[M=\sigma\Big{(}PC^{T}\Big{)} \tag{2}\]  
where \(P\) is an \(h\times w\times k\) matrix of prototype masks and \(C\) is an \(n\times k\) matrix of mask coefficients for n instances surviving NMS. The final mask \(M\) is cropped with the predicted bounding box generated by the target detection block.  
YOLACT uses the following loss function while training [34]:  
\[L=L_{CLS}+L_{box}+L_{mask} \tag{3}\]  
The classification loss \(L_{CLS}\) and the box regression loss \(L_{box}\) are defined in the same way as in [35] and the mask loss \(L_{mask}\) is the pixel-wise binary cross entropy between the assembled masks and the ground truth masks.
## 5 Discussions
### Intensity, Ultrasonic Field, and Beam Aperture  
One of the objectives of this work was to study the behavior of USCT when the signals were of low amplitudes, generating low-intensity waves and low pressures. The main idea was to limit the ultrasonic intensities so that USCT could be operated on children's fragile tissues. In B-mode ultrasound imaging, the intensity was between 30 and 200 mW/cm\({}^{2}\), and the time of exposure of the organ was a few seconds. In USCT, the acquisition time was longer (today USCT is not a real-time modality like B-mode ultrasound imaging) and the intensity needed to be as low as possible. In this study, the electro-acoustic devices were set to limit the spatial-peak temporal-average intensity (\(I_{spta}\)) to values below 30 mW/cm\({}^{2}\). At 500 kHz, the measured \(I_{spta}\) was 27.6 mW/cm\({}^{2}\) (Table 5) for an MI = 0.93. These values were higher than those obtained with the transducers at 1 MHz and 2.25 MHz, respectively (\(I_{spta}\) = 18.8 mW/cm\({}^{2}\) (MI = 0.56) and \(I_{spta}\) = 19.6 mW/cm\({}^{2}\) (MI = 0.38)). For all three frequencies, the MI values were well within expectations for pediatric use. The \(I_{spta}\) were well below 30 mW/cm\({}^{2}\). The objective was achieved because, despite these low intensities, the resolution of USCT is satisfactory. The higher intensity at 500 kHz compared with other frequencies explained the good results obtained with USCT at this low frequency, regardless of the pulse- or WCE-mode, because more energy was transported. The resolution decreased but the penetration increased, and combined with a large acoustic aperture of the beam, USCT had a correct CNR, strongly enhanced in WCE-mode. The lateral opening of the beam was about 40 mm, and was 30 mm for the axial opening. This represented an insonification surface of about 12 cm\({}^{2}\). For the newborn arm phantom, this surface was of the same order of magnitude as the diameter of the soft body. All areas of the object, bones and vessels, were sufficiently illuminated to return relevant information, even at low intensity and low acoustic impedance contrasts. The shape of the beam with a cylindrical aperture, for a small slice thickness and a large lateral aperture, favored complete organ insonification. This was particularly interesting in the case of the chicken drumstick, since this part was larger than the beam width. The disadvantage was that the object was required to be as central as possible. This was difficult to achieve, even with a precision scanner. This explained why part of the skin of the chicken drumstick (on the right of point 6) was less visible.
# A Near-Ultraviolet Photodetector Based on the TaC: Cu/4 H Silicon Carbide Heterostructure
###### Abstract
Refractory transitional metal carbides such as tantalum carbide (TaC) have gained much interest as an ultra-high temperature ceramic for applications in harsh environments. It is composed of tantalum (Ta) and carbon (C) atoms produced through a reaction between tantalum metal and carbon at elevated temperatures. This material is known for its high melting point (3786 \({}^{\circ}\)C) [33], outstanding hardness (13.5-20 GPa) [34], remarkable resistance to chemical attacks [35], high density (14.6 g/cm\({}^{3}\)) [36], specific heat (5.44 \(\times\) 10\({}^{5}\) J/kg) [37], elastic modulus (\(\sim\)477 GPa) [38], bending strength of 340-400 MPa [39], and small thermal expansion coefficient of 6 \(\times\) 10\({}^{6}\) (1/K) [40]. These remarkable characteristics and chemical properties are ascribed to the mixed covalent metallic bond [41; 42], making it an appealing material for different applications, such as cutting tools, aerospace components, and rocket propulsion [43]. In addition to its excellent mechanical properties, tantalum carbide has a low work function of \(\sim\)3.14 eV [44] and low resistivity (42.1 \(\upmu\Omega\) cm at 25 \({}^{\circ}\)C) [45]. This property makes it an attractive material for applications that demand high working temperatures, such as metal contact for optoelectronic devices [46; 47] and heating elements [48]. Moreover, tantalum carbide exhibits an inherent wavelength selectivity, making it an attractive material for applications requiring high temperatures, such as solar absorbers [49]. On the other hand, copper metal with a purity of 99.95% possesses outstanding properties such as a relatively high melting point of (1085 \({}^{\circ}\)C), high mechanical strength, excellent electrical conductivity, remarkable thermal conductivity, and high oxidation resistivity [50, 51, 52]. It also has sufficient hardness when functioning at temperatures below 100 \({}^{\circ}\)C [53, 54]. Therefore, owing to all these properties together with its easy fabrication process [55], copper is a suitable material to form a composite with other materials and is applicable in different photodetectors [56, 57]. Inspired by the properties of SiC, TaC, and copper materials, the integration of these materials in photodetector design can enhance the performance of 4H-SiC. Consequently, a near-ultraviolet photodetector based on a TaC (80%)/Cu (20%)/4H-SiC heterostructure was proposed and fabricated using the co-sputtering method, achieving a notable responsivity of 1.66 A/W and 2.69 \(\times\) 10\({}^{8}\) Jones at a wavelength of 405 nm. The device also extends its photoresponse to the visible region, demonstrating its potential for applications in astronomical observations, DNA analysis, and fluorescence microscopy.
# A Low-Cost Sensorized Vehicle for In-Field Crop Phenotyping
## 2 Materials and Methods
### Electronic Components
#### 2.2.1 Electric Motorization  
Electric motorization consists of two motorized cycle-type wheels suitable for off-road use with brushless motors integrated in the hub (Figure 5).  
The characteristics of the motor integrated in the wheel are power 500 watts; voltage 48 V; torque moment 50 Nm; mounting width 100 mm; water protection IP 65; brushless motor; total weight of motor and wheel 4.4 kg.
## 2 The Geometry of the Ejector Used
### Ejector for the CFD Validation (Ejector G1)  
[PERSON] [9] used a coordinate measuring machine (CMM) to measure the internal contour of the ejector accurately. The measurement results were presented in terms of piece-wise equations and were used in the current research to build the CFD computational domain. The equations for the geometry were divided into three sets, i.e., the primary nozzle section set, the mixing section set, and the diffuser section as shown in Equations (1)to (3). The radius, \(R\), was presented as a function of the axial distance, \(x\), measured from the primary flow inlet-plane. Both variables are in millimeter. This geometry, also called ejector G1, was used for the CFD validation. The contour can be seen in Figure 3 below.  
For the primary nozzle section:  
\[\bullet\ \text{nozzle converging section}\ :\] \[R =-0.1782864x+10.6096; 0\leq x\leq 6.2\] \[R =9.504; 6.2\leq x\leq 16.6\] \[R =-1.37414x+32.315; 16.6\leq x\leq 18.0\] \[R =-0.16645x+10.576; 18.0\leq x\leq 39.7 \tag{1}\] \[\bullet\ \text{nozzle throat}\ :\] \[R =3.968; 39.7\leq x\leq 43.2\] \[\bullet\ \text{nozzle diverging section}\ :\] \[R =0.093565x-0.074; 43.2\leq x\leq 72.9\]  
For the mixing section:  
\[\bullet\ \text{converging mixing section}\ :\] \[R =-0.423313x+49.0172; 80.7\leq x\leq 97.0\] \[R =6.98+0.97e^{(29.311-0.302x)}; 97.0\leq x\leq 110.0 \tag{2}\] \[\bullet\ \text{constant area mixing section}\ (\text{ ejector throat})\ :\] \[R =6.999; 110.0\leq x\leq 174.0\]  
For the diffuser section:  
\[R =179.3466-2.583078x+0.01261313x^{2}-0.0000198874x^{3};\ \ 174.0\leq x\leq 229.0 \tag{3}\]
## 4 Results and Analysis
### Analysis of Meteorological Factors on Historical Forest Fire Occurrences
#### 4.1.2 Analysis of Temperature's Impact on Forest Fires  
In examining the temporal dynamics of forest fires from 1991 to 2020, Figure 5a,b reveal a positive correlation between the number of fires and the total affected area with annual temperature. Notably, peak occurrences and areas affected by forest fires were particularly pronounced in 1991, 1999, 2001, and 2003, corresponding to periods of rising temperatures. This suggests that increasing temperatures may amplify both the likelihood and extent of forest fires. The upward trend in annual temperatures highlights the potential impact of climate on forest fire behavior, underscoring the importance of incorporating temperature into fire risk assessment models [48; 49].
# Two-Dimensional Human Pose Estimation with Deep Learning: A Review
###### Abstract  
Two-dimensional human pose estimation (2D HPE) has become a fundamental task in computer vision, driven by growing demands in intelligent surveillance, sports analytics, and healthcare. The rapid advancement of deep learning has led to the development of numerous methods. However, the resulting diversity in research directions and model architectures has made systematic assessment and comparison difficult. This review presents a comprehensive overview of recent advances in 2D HPE, focusing on method classification, technical evolution, and performance evaluation. We classify mainstream approaches by task type (single-person vs. multi-person), output strategy (regression vs. heatmap), and architectural design (top-down vs. bottom-up) and analyze their respective strengths, limitations, and application scenarios. Additionally, we summarize commonly used evaluation metrics and benchmark datasets, such as MPII, COCO, LSP, OCHuman, and CrowdPose. A major contribution of this review is the detailed comparison of the top six models on each benchmark, highlighting their network architectures, input resolutions, evaluation results, and key innovations. In light of current challenges, we also outline future research directions, including model compression, occlusion handling, and cross-domain generalization. This review serves as a valuable reference for researchers seeking both foundational insights and practical guidance in 2D human pose estimation.  
2D human pose estimation; deep learning; top-down methods; bottom-up methods; benchmark datasets; evaluation metrics; keypoint detection +
Footnote †: journal: Applied sciences
## 5 Data Analyses and Results
### DIC Analyses
#### 5.2.1 Analyses of the Background Noise  
Because the surrounding sectors of the Montescalioso area were unaffected by the general movement of the slope failure (i.e., they were not affected by any type of displacement), they were subjected to a DIC analysis to assess the background noise.  
To that aim, a methodological analysis based on two different region of interest (ROI) approaches was conducted.  
Figure 5: Pre- and post-failure CSK SAR amplitude datasets (in ascending geometry). The difference between the absolute amplitude (**A,B**) and the temporal average amplitude imagery (**C,D**) is clearly visible.  
In approach #1, equivalent ROIs were designed in the surroundings of the Montescaglioso landslide using the DTM's footprint (i.e., the smallest one) as a general reference for the entirety of the datasets (Figure 6), thereby reducing any ROI-related uncertainty. Through this approach, the background noise for each dataset was estimated and consistent measurements were therefore obtained.  
In approach #2, different ROIs were selected in the surroundings of the Montescaglioso landslide using the overall footprint of each dataset as a reference, thereby accounting for the different geometric resolutions of the data. In this way, the analyses were performed on different areas for each dataset to achieve different background noise estimations, and the results indicated different sources of error (Figure 7, Table 2).  
Figure 6: Regions of interest (ROIs) (orange polygons) used in approach #1 and located in the immediate surroundings of the landslide (red polygon).  
Figure 7: Regions of interest (ROIs) used in approach #2. Here, the ROIs have been intentionally designed over a wider sector by arbitrarily choosing those areas not affected by the general movement of the slope failure. In (**A**,**B**), two different series of ROIs have been highlighted in green tones (**A**) and red tones (**B**).  
In both approaches, the measured background noise values were estimated. A comparison of the results (Table 2) showed similar findings within a small error fraction, which may be related to the unconstrained choice of the ROI selections in the same areas and differences in the geometrical and temporal resolutions.