esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Framework and Model Training
### Datasets Description  
#### 4.1.1. CWRU Bearing Fault Dataset  
The bearing fault dataset of Case Western Reserve University (CWRU) Bearing Data Center [25] is one of the most widely used datasets in the field of fault diagnosis. This dataset collects data from a ball bearing on a test rig, which is driven by a 2 hp Reliance electric motor, and accelerometers are used to record vibration data of the bearing at the motor drive end. Using electro-discharge machining (EDM) technology, artificially created defects ranging from 0.007 inches (0.1778 mm) to 0.028 inches (0.7112 mm) in diameter located in the inner race, outer race, and rolling elements of the bearing. And vibration data is recorded for motor loads of 0 to 3 horsepower (speed 1797 rpm to 1720 rpm). The torque sensor and encoder are used to record the load and motor speed, respectively.  
This article uses bearing vibration data at the drive end of the motor. As shown in Table 2, a total of 10 types of working conditions are used as classification categories, including one normal and 11 fault conditions. We use the fault data at a 12 kHz sampling rate, and the 48 kHz normal data is resampled to 12 kHz to ensure the consistency of data. First, by splitting the original data into segments of 1024 data points in the time domain, we obtained a dataset of 1000 samples for each category, for a total of 10,000 samples. Then, Fast Fourier transform (FFT) is used as a data preprocessing method to obtain frequency domain signals, and the magnitude of each sample is normalized into [0,1]. Additionally, to avoid the effect of spectral leakage, a Hamming window function is applied for each segment of the signal before FFT. Finally, due to the symmetry of the FFT results, we usually use only the first half of the results, so the length of each sample is 512 eventually.
# Fast Localization and High Accuracy Recognition of Tire Surface Embossed Characters Based on CNN
## 6 Conclusions  
1. A combination of traditional machine vision and a convolutional neural network is used for tire character detection, and an improved SSR algorithm is proposed to improve the contrast between the character and the tire, which facilitates character extraction.
2. The NCC template matching method based on Gaussian pyramid acceleration is proposed to locate the location of the tire \"DOT\" character, and self-built image samples are used to verify the feasibility of template matching.  
Figure 24: Improved accuracy and loss of LeNet-5.  
Figure 25: Comparison of accuracy (**a**) Training Collection (**b**) Verification Collection.  
3. The classical LeNet-5 network is improved by changing the traditional convolutional layer to asymmetric convolution to improve the network's ability to change non-linearly, adding a BN layer to reduce parameters, replacing SoftMax with an SVM classifier to accelerate convergence, and replacing the Flatten layer with a global pooling layer to reduce overfitting. The experimental results show that the improved LeNet-5 network converges faster and has 6.9% higher accuracy, and the training time is reduced by 1 min 34 s.  
In summary, the algorithm proposed in this paper has improved performance and only needs to run on a CPU, which greatly reduces the equipment cost. We still need to further optimize the network to make it lighter, reduce the configuration requirements of the network, and increase the number of images for further testing. The final implementation does not require pre-processing of the image, and the characters are recognized directly by the neural network.  
Z.G.: Conception of the study, proposition of the theory and method, supervision; J.Y.: literature search, figures, manuscript preparation, and writing X.Q.: programming, testing of exiting code components; Y.L.: data collection. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Liaoning Provincial Education Department Project (Grant No. LJKZ0114).  
Not applicable.  
The authors declare no conflict of interest.
# A Comprehensive Review and Assessment of Cybersecurity Vulnerability Detection Methodologies
## 5 Challenges and Potential Solutions for Automating Vulnerability Detection
### False Positives and Negatives Challenges  
Automated systems are prone to generating false positives, which can overwhelm cybersecurity teams with unnecessary alerts, and false negatives, where real threats go undetected. Both scenarios are problematic: false positives can lead to alert overload, causing critical warnings to be missed, while false negatives can leave systems vulnerable to exploitation. Cybersecurity professionals need to fine-tune these systems to balance sensitivity and specificity.  
Potential Solutions. To address this challenge, many ways, among others, are proposed based on advanced and real-world applications of ML [153], heuristic-based detection and behavioral analysis [154], unsupervised learning techniques leveraging deviation from normal behavior [155], using multiple machine learning models in tandem, advanced data representation by including graph-based representations (control flow, syntax, and semantic graphs) [156], and contextual embeddings [157], which allow the model to understand more complex relationships.  
Thus, multiple challenges are raised facing the advanced technologies and the complexity of existing systems. Next, we will delve into the discussion section to underscore certain points.
## 4 Results
### Ablation Study Results  
Ablation experiments validated hierarchical module contributions (Table 4):  
* Bidirectional Cross-Attention (BCA): Removal caused a 2.54% drop, highlighting its core role in disaster-specific semantic binding (e.g., \"landslide\" text-image alignment).
* Adaptive Gating: Disabling led to a 1.12% decline, confirming its necessity for balancing modality reliability.
* Data Augmentation: Elimination resulted in a 0.83% drop, underscoring its supplementary role in scene diversity.
## 4 Materials and Methods
### Transformer Training for Wildfire Prediction  
Transformer models have revolutionized sequential data processing through self-attention mechanisms [44]. This architecture can effectively analyze temporal relationships in meteorological and vegetation data sequences for wildfire prediction.  
The specific training procedure for the Transformer model is available in the Supplementary Material (Algorithm S3), where the self-attention mechanism enabled the model to capture long-term dependencies in wildfire-related features. Some advantages of Transformers include their ability to capture long-range dependencies in wildfire data and their flexibility in handling multiple input types. However, Transformers require high computational resources and large datasets, making them challenging to implement in real-time wildfire prediction systems.
# Unsupervised Adaptation of Deep Speech Activity Detection Models to Unseen Domains
###### Abstract  
Speech Activity Detection (SAD) aims to accurately classify audio fragments containing human speech. Current state-of-the-art systems for the SAD task are mainly based on deep learning solutions. These applications usually show a significant drop in performance when test data are different from training data due to the domain shift observed. Furthermore, machine learning algorithms require large amounts of labelled data, which may be hard to obtain in real applications. Considering both ideas, in this paper we evaluate three unsupervised domain adaptation techniques applied to the SAD task. A baseline system is trained on a combination of data from different domains and then adapted to a new unseen domain, namely, data from Apollo space missions coming from the Fearless Steps Challenge. Experimental results demonstrate that domain adaptation techniques seeking to minimise the statistical distribution shift provide the most promising results. In particular, Deep CORAL method reports a 13% relative improvement in the original evaluation metric when compared to the unadapted baseline model. Further experiments show that the cascaded application of Deep CORAL and pseudo-labelling techniques can improve even more the results, yielding a significant 24% relative improvement in the evaluation metric when compared to the baseline system.  
Speech Activity Detection; unsupervised domain adaptation; Fearless Steps Challenge 2022
# Analysis of a Landscape Intensely Modified by Agriculture in the Tiete-Jacare Watershed, Brazil
## 2 Materials and Methods
### Methodology  
The methodological approaches involved the use of the region's environmental database [36], land use and land cover from Sentinel 2 satellite images, and the application of the change detection analysis with random forest techniques. First, the Tiete-Jacare Watershed data were obtained from the Brazilian Institute of Geography and Statistics (IBGE) digital grid, version 2015 [36]. Then, the information was analysed in the geographic information systems ArcGIS(r) 10.5 and Datum SIRGAS2000 (for the geographic projection latitude/longitude). Finally, the Sentinel-2 remote sensing dataset was processed in SNAP v 6.0 (Sentinel Application Platform) [37] in four stages (Figure 2).  
Figure 2: Flowchart of the methodology.  
We digitized the land use and land cover using the on-screen visual technique, and the classification was based on the multi-level classification system proposed by the Technical Manual of Land Use from the Brazilian Institute of Geography and Statistics [38], which considers four classes indicating the main land use category, followed by the type of land uses that were included and the explanation of the land uses themselves (Table 1).
## 4 SSF Evaluation  
In Figures 9 and 10, reciprocal values of the SSF and the lowest SSF (or highest reciprocal of SSF) point at each test case are shown when the dam model is subjected to Artificial earthquakes I and II. In this study, the reciprocal of the SSF is used in the plot rather than SSF values in order to avoid unimportant high peak values of the SSF generated by the nearly zero external shear force. The red dashed line denotes where the SSF is unity, which indicates the threshold for sliding of a dam structure.  
Figure 8: Tensile principal stresses contours when the largest sliding displacements are observed: (**a**) Case 1 subjected to Artificial earthquake I (t = 3.76 s); (**b**) Case 3 subjected to Artificial earthquake I (t = 3.76 s); (**c**) Case 1 subjected to Artificial earthquake II (t = 1.86 s); (**d**) Case 3 subjected to Artificial earthquake II (t = 1.86 s).  
Figure 9: _Cont._  
coefficient of the internal friction changes to the coulomb friction coefficient, \(\tan\phi_{f}\), and consequently, the friction force and SSF decrease. Depending on the ratio of the fractured area, the resistant force against the external force highly changes.  
According to the results in Table 3, for both artificial earthquakes, the minimums of the SSF in Case 2 are about 75 percent of those in Case 1. Furthermore, the minimums of the SSF in Case 3 show much smaller values, about 20 percent of those in Case 1. Considering that partial fractures at a dam base can exist due to the hydraulic pressure acting at the upstream heel and/or past earthquakes, the SSF in Equation (1), which is widely used as a sliding criterion, significantly overestimates the sliding stability of a dam. It is noted that Cases 1 and 2 show safety satisfaction if unity is used for the safety factor threshold, while only Case 1 passes the sliding safety condition if the safety factor becomes two. The SSF formulation, Equation (3), used in Case 3 produces no satisfaction for the unity safety threshold, which means the other two SSFs possibly result in incorrect judgements on sliding safety.
# Dual-Branch Multimodal Fusion Network for Driver Facial Emotion Recognition
## 4 Experiment
### Experimental Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Encoder** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline \multirow{4}{*}{Dual-CDERNet + FPN} & **Layer** & **FPN = 1** & \multicolumn{2}{c}{**FPN = 2**} & \multicolumn{2}{c}{**FPN = 3**} & \multicolumn{2}{c}{**FPN = 4**} \\ \cline{2-9}  & Resnet 18 & 0.4924 & 0.4877 & 0.5 & 0.4877 & 0.5404 & 0.5387 & 0.5379 & 0.5077 \\  & Resnet 34 & 0.5025 & 0.4921 & 0.5076 & 0.4976 & 0.5455 & 0.5302 & 0.5404 & 0.5345 \\  & Resnet 50 & 0.5101 & 0.4892 & 0.5152 & 0.4898 & 0.5657 & 0.5533 & 0.5303 & 0.5278 \\  & Resnet 101 & 0.5136 & 0.5019 & 0.521 & 0.5122 & 0.5259 & 0.5128 & 0.5531 & 0.5437 \\ \hline \multirow{4}{*}{DMFNet} & Multiscale & \multicolumn{2}{c}{Scale = 1} & \multicolumn{2}{c}{Scale = 2} & \multicolumn{2}{c}{Scale = 3} & \multicolumn{2}{c}{Scale = 4} \\ \cline{2-9}  & Resnet 18 & 0.5852 & 0.5765 & 0.6741 & 0.6543 & 0.5253 & 0.5137 & 0.6938 & 0.6739 \\ \cline{1-1}  & Resnet 34 & 0.6272 & 0.6032 & 0.6222 & 0.5969 & 0.6198 & 0.6022 & 0.6074 & 0.597 \\ \cline{1-1}  & Resnet 50 & 0.6222 & 0.6123 & 0.6099 & 0.6543 & 0.6444 & 0.6431 & 0.6395 & 0.6328 \\ \cline{1-1}  & Resnet 101 & 0.6519 & 0.6234 & 0.6815 & 0.6778 & 0.7062 & 0.6896 & 0.637 & 0.6191 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental Results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Method** & **Encoder** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** & **Accuracy** & **F1-Score** \\ \hline \multirow{4}{*}{CDERNet + FPN [62]} & Resnet 18 & 0.5136 & 0.4897 & 0.5383 & 0.5221 & 0.5284 & 0.5111 & 0.5112 & 0.502 \\  & Resnet 34 & 0.4988 & 0.4677 & 0.5111 & 0.4897 & 0.5259 & 0.5027 & 0.5309 & 0.5232 \\  & Resnet 50 & 0.5309 & 0.5107 & 0.5432 & 0.5321 & 0.5701 & 0.5545 & 0.5556 & 0.5401 \\  & Resnet 101 & 0.5259 & 0.4962 & 0.5556 & 0.5432 & 0.5481 & 0.5336 & 0.5432 & 0.5278 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Experimental Results. Based on the experimental results in Tables 1-4 we found that Resnet and Transformer have good experimental performance when the backbone is used. Therefore, in the dual-branch experiment of multimodal fusion, Resnet, Vit, and SVit were used for the dual-branch experiment. Afterward, Vit and SVit were used for modal backbone cross-testing with Resnet, respectively. The proposed DMFNet model with Resnetas the backbone significantly improves the performance of emotion recognition, compared to the original driver emotion recognition model (CDERNet [53]), the result is improved by 18.77%. In the above experiments, we used URNet with a multiscale of 1 layer. The experimental results are shown in Table 5, which demonstrates the effectiveness of Resnet as the backbone.
## 3 Results and Discussion
### Model Application
The correlation between the model and experimental data is depicted in Figure 4; the coefficient of determination (R\({}^{2}\)) was 0.919. For in-situ electro-chlorination, the initial chlorine concentration in the control volume facing the electrode (\(C_{s}\)) was defined in this study and determined as follows:  
\[C_{s}=r_{e}/Q \tag{9}\]  
where \(r_{e}\) is the chlorine-forming rate of an electrode under a given condition (mg min\({}^{-1}\)) and \(Q\) is the flow rate (L min\({}^{-1}\)). The chlorine-forming rate must be obtained experimentally because it changes according to the material of the electrode, current density, electrode gap, and the presence of electrolytes. Figure 3: Free chlorine decay curves in seawater with different TOC and initial chlorine concentration. Each line represents the predicted concentration of chlorine under a given condition. (**a**) Artificial seawater solution with 1 mg L\({}^{-1}\) of TOC; (**b**) artificial seawater solution with 2 mg L\({}^{-1}\) of TOC; (**c**) artificial seawater solution with 3 mg L\({}^{-1}\) of TOC; (**d**) West Sea water (1.3 mg L\({}^{-1}\) of TOC). Many studies determined the rate constants using kinetic data from experiments conducted over the course of several hours, sometimes over 100 h [17; 18; 30]. It is possible to obtain a well-fitted curve by reducing the number of samples taken during the first few minutes before the chlorine concentration stabilizes and focusing on samples drawn after the initial oxidant demand period. However, the retention time of seawater in a cooling system is generally less than 30 min [12]. Thus, the model should be fitted to data collected within an hour to better predict the performance of the cooling system.
# Application of Machine Learning Techniques to
## 3 Methodology
### Validation of Selected GCMs
#### 3.3.2 Kolmogorov-Smirnov Test  
In this study, the KS test stats were derived as the highest vertical difference between the two cumulative distribution functions (CDFs) of a time series data. For CDF of APHRODITE data \(o_{n}(x)\) and the CDF of the selected GCM or MME mean at a grid station \(o\left(x\right)\), the KS test statistics were obtained as given in Equations (7) and (8).  
\[S_{n}=sup_{x}\big{|}o_{n}(x)-o(x)\big{|}, \tag{7}\]  
where  
\[o_{n}(x)=\frac{1}{n}\sum_{i=1}^{n}I_{x_{i}\leq x}. \tag{8}\]  
Here, \(sup_{x}\) is the upper bound of the set of distances. The indicator function is represented by \(I_{x_{i}\leq x}\) (If \(x_{i}\ \leq\ x\), \(I_{x_{i}\leq x}\) equal to 1 or if \(x_{i}\ >\ x\ I_{x_{i}\leq x}\) equal to 0). The null hypothesis of a similar distribution is rejected if \(S_{n}\geq 0.05\) significance level. The KS test was applied at every grid station using scikit-learn.
# Properties of Different Varieties of Durian
## 3 Results and Discussion
### Determination of Bioactive Compounds  
Figure 2 presents the bioactive substances in three durian cultivars. Total polyphenol compounds (TPC, mg GAE/g DW) were in the range from 4.99 \(\pm\) 0.45 to 3.17 \(\pm\) 0.20 with the average values for Chanee (CN) and the lowest for Puangmaee (PM). Total flavonoid compounds (TFC, mg CE/g DW) were estimated as the highest for Monthong (MT) of 2.10 \(\pm\) 0.12 and 1.33 \(\pm\) 0.07 for PM. The two phenolic substances were the highest in the MT cultivar.  
The obtained results of bioactive substances are in agreement with other recent reports and as well with our previous data. The TPC (mg GAE/g FW) and TFC (mg CE/g FW) in Chanee (CN) were in the range of 0.21-3.21 and 0.02-0.82; for Puangmaee (PM) estimated of 3.11 and 0.03-0.18 and for Monthong (MT) showed 0.56-3.74 and 0.04-0.94, respectively [3; 4; 6; 10; 14; 42]. Unknown variety of durian [5] showed 0.99 mg GAE/g FW, which was similar to the present data of MT. Similar total polyphenol data were obtained from an unknown variety in another report [43] as 0.79 mg GAE/g FW. It was  
Figure 2: Total polyphenol compounds (TPC, mg GAE/g DW) and total flavonoid compounds (TFC, mg CE/g DW) in local cultivars of Thai durians: Monthong (MT), Chanee (CN) and Puangmaee (PM). Values are means \(\pm\) SD per g dry weight (DW); n = 5 samples per cultivar, each subsampled and analyzed 5 times. Values with different superscript letters are significantly different (\(p<0.05\)). Abbreviations: GAE, gallic acid equivalent; CE, catechhin equivalent.  
interesting the comparison of Monthong, Kradum, and Kobtakam varieties which had higher phenolic content of 2.69-2.89 mg GAE/g DW [4] than that of the Chance variety (0.67 GAE/g DW), which was lower than the level reported from Malaysia (2.54 mg GAE/g DW) [5]. The comparison of total polyphenol content (6.96-9.30 mg GAE/g DW) of five varieties of durian (Monthong, Chanee, Kradum, Kanyao, and Puangmanee) showed that the Monthong variety had the highest TP content, whereas the Kradum variety had the lowest [13]. In the report of four locally available varieties of durian fruit [44], where the total phenolics and flavonoids were extracted using dichloromethane: pentane (1:1 _v/v_) the results differed from the presented and total phenolics were found in the range of 690.62-998.29 mg/L, showing the significant inter-varietals variations. The total flavonoids were in the range of 211.36-220.34 mg/L. Caffeic acid and quercetin were the dominant antioxidant substances found in durian, therefore for fluorescence studies, these phenolics were used as standards. The bioactivity of ripe durian was high, and the total polyphenols were the main contributors to the overall antioxidant capacity [13; 42]. The differences in the results of bioactive metabolites depend on durian investigated varieties, such as Monthong (MT), Chanee (CN), and Puangmanee (PM), extraction time and solvent used, and analytical methods. This affects the physicochemical and biological properties of the plant as a food ingredient.
# Caching-Aware Intelligent Handover Strategy for LEO Satellite Networks
## 2 System Model  
### System Architecture and Handover Factors  
LEO satellites are typically deployed at low altitude, such as 500 km to 1500 km, and the LEO satellites move with a high speed relative to the users on the ground. Thus, frequent handover may occur during the service time of users. This paper considers a constellation of LEO satellites in the sun-synchronous orbit. There are 12 orbits within the constellation, and 9 satellites are located at each orbit. The satellites are located at an altitude of 1000 km and have an orbital inclination of 99.4843 deg. A typical handover scenario in the LEO satellite system is presented in Figure 1. Each terrestrial user terminal establishes a communication link with an LEO satellite for transmitting data. Due to the high mobility of the LEO satellite, the terrestrial users will move out of the coverage of the serving satellite after a period of connecting, and handover is required to ensure continuous communication. In addition to this case, the access of new coming users can also lead to handover within the system. And the adjacent satellites will continuously exchange the remaining resource information through the inter-satellite link. When the source satellite detects that the handover is needed for the connected user links, it will analyze the resource information and select the best satellite from the candidate satellite list. Before the handover, the data that has not been sent will be cached, and then the cached data should be sent to the optimal candidate satellite after the new link is established.  
In the \(P\) time slot, the serving satellite refers to the satellite currently connected to the subscriber, which provides communication services to the subscriber during the time slot. Candidate satellites refer to satellites that are available for connection in the \(P\) time slot other than the serving satellite. The candidate satellites can be selected when the handover is activated for the user's communication link. There are 2 to 4 visual satellites for a user in this system. In addition to the serving satellites, there are 1 to 3 candidate satellites. Once the handover is activated, the optimal candidate satellite is selected from the candidate satellites according to the intelligent policy.