esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The quality and freshness of fruits and vegetables are critical factors in consumer acceptance and are significantly affected during transport and storage. This study aimed to evaluate the quality of greenhouse-grown tomatoes stored for 24 days by combining non-destructive image analysis, spectrophotometric assays (including total phenolic content and antioxidant and antiradical activity assessments), and attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. Additionally, water activity, moisture content, total soluble solids, texture, and color were evaluated. Most physicochemical changes occurred between days 14 and 17, without major impact on overall fruit quality. A progressive transition in peel hue from orange to dark orange, and increased surface irregularity of their textural image were noted. Moreover, the combined use of instrumental and image analyses results via multivariate analysis allowed the clear discrimination of tomatoes according to storage days. In this sense, tomato samples were effectively classified by ATR-FTIR spectral bands, linked to carotenoids, phenolics, and polysaccharides. Machine learning (ML) models, including Random Forest and Gradient Boosting, were trained on image-derived features and accurately predicted shelf life and quality traits, achieving R\({}^{2}\) values exceeding 0.9. The findings demonstrate the effectiveness of combining imaging, spectroscopy, and ML for non-invasive tomato quality monitoring and support the development of predictive tools to improve postharvest handling and reduce food waste.  
today  
tomato; image analysis; physicochemical parameters; spectrophotometric assays; attenuated total reflection-Fourier transform infrared spectroscopy; machine learning
###### Abstract  
Table olive pitting machines (DRRs) are essential in the agri-food industry but face significant limitations that constrain their performance and compromise process reliability. The main defect, known as the \"boat error\", results from improper olive orientation during pitting, leading to bone fragmentation, pulp damage, and potential risks to consumer safety. Traditional quality control methods, such as the use of flotation tanks and expert sensory evaluation, rely on destructive sampling, are time-consuming, and reduce overall productivity. To address these challenges, this study presents a novel computer vision (CV) system integrated into a commercial DRR machine. The system captures high-speed images of Gordal olives (_Olea europaea regalis_) just before pitting; these are later analyzed offline using a custom MATLAB application that applies HSV-based segmentation and morphological analysis to quantify the olive size and orientation. The method accurately identifies boat error cases based on angular thresholds, without interrupting the production flow or damaging the product. The results show that 97% of olives were correctly aligned, with only 1.1% presenting critical misorientation. Additionally, for the first time, the system allowed a detailed evaluation of the olive size distribution at the machine inlet, revealing an unexpected proportion of off-caliber olives. This contamination in sizing suggests a possible link between calibration deviations and the occurrence of boat errors, introducing a new hypothesis for future investigation. While the current implementation is limited to offline analysis, it represents a non-destructive, low-cost, and highly precise diagnostic tool. This work lays the foundation for a deeper understanding of DRR machine behavior and provides a framework for future developments aimed at optimizing their performance through targeted correction strategies.  
DRR machines; computer vision; image segmentation; morphological analysis; boat error; Gordal; MATLAB; table olive; olive destoning; quality control Article
# Model of Emotion Judgment Based on Features of Multiple Physiological Signals
## 4 Extraction of Optimal Feature and Model of Emotion Judgment
### Dimensionality Reduction of Original Signal Feature  
The direct fusion of original signal features will make the dimension of feature matrix too high, resulting in too much computation. So, the dimensionality reduction of original signal feature is performed using the method of principal component analysis (PCA), making the classifier more efficient and accurate in emotion recognition.  
PCA is mainly composed of three key steps: (1) calculation of matrix of correlation coefficient and load matrix of principal component; (2) weight analysis and calculation of the original feature data; (3) getting feature subset.  
In our experiment, the emotion is divided into four types according to the V-A model, and the segments of physiological signal corresponding game events are labeled according to the emotion trend as shown in Table 2. The sample numbers of physiological signal for four emotion trends HVHA, HVLA, LVHA, and LVLA are 276 \(\times\) 3, 300 \(\times\) 3, 241 \(\times\) 3, and 223 \(\times\) 3, respectively. Each sample contains 206 features of skin electrical signal, ECG and pulse wave signal. In order to unify the number of samples, we randomly extract 660 samples equally from the four-emotion trend based on the least number of samples, forming a 660 \(\times\) 218 feature matrix \(X\):  
\[X=\left[\begin{array}{cccc}x_{1,1}&x_{1,2}&\cdots&x_{1,149}\\ x_{2,1}&x_{2,2}&\cdots&x_{2,149}\\ \vdots&\vdots&&\vdots\\ x_{220,1}&x_{220,2}&\cdots&x_{220,149}\\ \end{array}\right] \tag{11}\]  
\begin{table}
\begin{tabular}{c c c c} \hline
**Game Event** & **k-NN (k = 5)\%** & **SVM\%** & **LR\%** \\ \hline P/N & 76.8 & 84.4 & 90.2 \\ \hline P1/P2 & 68.4 & 73.6 & 69.4 \\ \hline P1/P3 & 67.8 & 75.0 & 71.3 \\ \hline P2/P3 & 41.2 & 31.5 & 44.3 \\ \hline N1/N2 & 43.7 & 47.7 & 37.0 \\ \hline N1/N3 & 73.9 & 75.1 & 68.0 \\ \hline N2/N3 & 77.2 & 80.8 & 78.8 \\ \end{tabular}
\end{table}
Table 3: The recognition accuracy of methods.  
The main purpose of PCA is to reduce the dimension of feature space, therefore, the weight of the original feature in the principal component should be calculated, and the original features with large weight are selected to form a new feature subset, so as to reach the aim of feature dimensionality reduction and speed up the operation speed.
###### Abstract  
In response to the situation in the Huanghuai region, where corn fertilization and pesticide application primarily rely on manual methods such as hand broadcasting fertilizer and using manual backpack sprayers, resulting in low levels of mechanization, this study designed an electric integrated work vehicle in line with the trend of developing new energy. The vehicle is powered by six 12 V, 100 Ah lead-acid batteries and integrates the functions of fertilization and pesticide spraying. It can achieve precise hole-fertilization, applying fertilizer to a depth of 100 to 150 mm near the roots of corn, and can also perform multi-row pesticide spraying. The vehicle's electronic control system is divided into two functional areas: 220 V and 24 V. The walking system uses a 220 V, 2 kW AC servo motor, which is driven by converting the voltage of the 72 V battery group into a 220 V sine wave AC through an inverter, and the motor speed can be adjusted. The working width is adjusted by two fixed electric cylinders at the top of the rear wheel frame. The user can preset the width through the control panel, and during operation, the electric cylinders can be automatically controlled to the optimal working width via a whisker-type limit switch. Analysis using ADAMS software shows that when the vehicle speed is 2, 3, and 5 km per hour, the opening angles of the duckbill controller are 66\({}^{\circ}\), 58\({}^{\circ}\), and 48\({}^{\circ}\), respectively, indicating that the higher the speed, the smaller the opening angle. This shortens the fertilization interval time and makes the fertilization spacing more stable. The maximum opening angle of the adjacent duckbill is 25\({}^{\circ}\), indicating that the fertilization amount remains stable. When the vehicle is moving in reverse, the duckbill always remains closed, and at different speeds, the opening angle change curve of the duckbill controller is smooth and regular. This vehicle significantly improves the efficiency and precision of corn planting. However, improvements are still needed in battery technology, control system optimization, and the high cost of electric agricultural machinery to promote the widespread application of agricultural mechanization.  
com; fertilization; pesticide spraying corn; mechanization; electric vehicle; agricultural technology; ADAMS software analysis +
Footnote †: journal: applied sciences
# Experimental and Numerical Study on UHPC-RC Decks within Hogging Moment Region
## 5 Comparison and Analysis of Results
### UHPC Layer Ratio of 0  
#### 5.1.1 Crack Development  
For the FE analyses, when the load increases to 70 kN, cracks start to occur at the bottom of the concrete layer in the loading position. As the load continues to increase, multiple cracks occur near the midspan, while the depth of the existing cracks is deepened. Eventually, two penetration cracks form near the loading position and the model fails, as shown in Figure 8. Note that PE denotes the plastic strain in concrete, and the value is larger than 0.0001, while RC cracks and those cracks (approximately 0.05 mm wide) are created in the concrete. The crack development in the finite element model analyses is similar to that in specimen A.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Dilation Angle** & \multirow{2}{*}{**Eccentricity**} & \multirow{2}{*}{\(f_{b0}\)\({}^{1}\)/\(f_{c0}\)\({}^{2}\)**} & \multirow{2}{*}{\(K\)\({}^{3}\)} & **Viscosity Parameter** \\ \hline UHPC & 54 & 0.1 & 1.07 & 0.667 & 0.01 \\ \hline RC & 30 & 0.1 & 1.16 & 0.667 & 1 \(\times\) 10\({}^{-5}\) \\ \hline \hline \end{tabular} \({}^{1}\)_f\({}_{b0}\): Equibiaxial compressive stress strength. \({}^{2}\)_f\({}_{c0}\): yield compressive stress.\({}^{3}\)_K_: a constant that governs the spacing between compressive and tensile meridians and the shape of failure surface on deviatory plane.  
\end{table}
Table 3: Parameters of constitutive models used for UHPC and RC.
# Research on a Measurement Method for the Ocean Wave Field Based on Stereo Vision
## 2 Materials and Methods
### Wave Parameter Inversion  
1. Wave Height and Period  
The center point of the matching block was selected as the wave inversion point. In order to ensure the accuracy of the calculation, the center point of the four matching blocks was selected in order to calculate the wave's height and the changes in the wave data throughout 10 min was recorded. Next, the spectrum was obtained by the use of the Fourier transform and the main frequency range was selected. Following this, we selected the frequency of 0-0.5 Hz, filtered out the high-frequency part, and performed an inverse Fourier transform in order to obtain the time-domain curve corresponding to the main frequency. Then, we used the zero-crossing method to calculate the wave height and period.
2. Wave Direction  
The wave direction calculation method that was employed in this paper is as follows: Essentially, the Hough transform is a feature extraction method. In this work, the Hough transform was performed on the left image and the specific steps are as follows:  
1. Use a two-dimensional Gaussian function to the detect the extreme points. Obtain the coordinates of those extreme points \((\text{u}_{\text{i}},\text{v}_{\text{i}})\).
2. Convert the planar coordinates \((\text{u}_{\text{i}},\text{v}_{\text{i}})\) to polar coordinates \((\rho,\theta)\) by using: \[\rho=\text{ucos}\theta+\text{vsin}\theta\] (24)
3. Calculate the maximum value with a two-dimensional accumulator \(\text{A}(\text{a},\text{b})\) as: \[\text{A}(\text{a},\text{b})=\text{A}(\text{a},\text{b})+1\] (25)The camera resolution is M\({}_{1}\times\) M\({}_{2}\) and, therefore, the parameter coordinate range is:  
\[\rho\in\left[-2\sqrt{\text{M}_{1}}/2,2\sqrt{\text{M}_{2}}/2\right] \tag{26}\]  
\[\theta\in\left[-\pi/2,\pi/2\right] \tag{27}\]  
The maximum value corresponding to the accumulator in the statistical parameter coordinate system is \(\left[\rho,\theta\right]\). Record all of the pixel coordinates on the line.  
Next, calculate the corresponding \(\left(\text{u}_{\text{ih}},\text{v}_{\text{ih}}\right)\) and \(\left(\text{x}_{\text{ihc}},\text{y}_{\text{ihc}},\text{z}_{\text{ihc}}\right)\). If \(\text{z}_{\text{ihc}}\geq 0.95\times\text{z}_{\text{max}}\), calculate the normal vector of the straight line \(\rho=\text{x}\cos\theta+\text{y}\sin\theta\). On the other hand, if \(\text{z}_{\text{ihc}}\leq 0.95\times\text{z}_{\text{max}}\), select the corresponding coordinates to carry out the least squares fitting, so as to calculate the normal vector of the main peak line;  
The normal vector of each straight line corresponds to two angles; thus it is necessary to determine the direction of the wave motion. In this paper, true north and counterclockwise rotation are considered as the positive direction when judging the change in the intercept of the wave crest line that was calculated in the frame images afterwards, so as to determine the wave movement direction.
# Robust Symbol and Frequency Synchronization Method for Burst OFDM Systems in UAV Communication
## 3 Improved Burst OFDM Synchronization Method
### Frequency Synchronization Algorithm Based on AGC Gain Compensation
#### 3.3.2. Frequency Synchronization Scheme Based on AGC Compensation  
To mitigate the frequency synchronization performance loss due to gain adjustment in burst OFDM systems, we introduce a robust frequency synchronization based on AGC compensation. The diagram of the proposed synchronization method is illustrated in Figure 8.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Traditional** & **1-bit** & **2-bit Uniform** & **Proposed** \\  & **Method** & **Quantization** & **Quantization** & **Method** \\ \hline Multiplication & \(4L\) & \(0\) & \(0\) & \(0\) \\ Addition & \(4L-1\) & \(4L-1\) & \(8L-1\) & \(8L-1\) \\ XOR & \(0\) & \(4L\) & \(4L\) & \(4L\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computational complexity comparison of different calculation methods of the correlation value.  
An amplitude compensation module is introduced between the symbol timing synchronization and frequency offset estimation modules. The AGC gain value \(G(n)\) is not only sent to the VGA but also to the amplitude compensation module. The symbol timing synchronization is complete when the cross-correlation value \(V(n)\) exceeds the synchronization threshold \(V_{th}\). Therefore, the accurate position of the preamble and the gain \(G(n)_{dB}\) for each sample is known before frequency synchronization. We can multiply the output signal of the AGC module by a coefficient so that it maintains a constant power over the entire duration.  
The amplitude compensation method can be expressed as  
\[\hat{\gamma}(n) =10\frac{G_{ref}-G(n)}{20}\gamma(n)\ \, \tag{21}\] \[=\frac{S_{ref}}{S(n)}\gamma(n)\ \,\]  
where \(G_{ref}\) denotes the AGC gain that adjusts the received signal power to approach the given reference power. The time domain waveform of the compensated signal \(\hat{\gamma}(n)\) is  
Figure 6: Time domain waveform of preamble after AGC adjustment.  
Figure 7: Time domain waveform of preamble after AGC compensation.  
illustrated in Figure 7, showing that the distorted preamble is transformed into a distortion-free preamble when the channel noise is absent.  
With the compensated training sequences within the preamble, we can rewrite Equation (4) as  
\[\begin{split}\hat{R}&=\sum_{n=0}^{L-1}\hat{\gamma}( \hat{n}+n)\hat{\gamma}^{*}(\hat{n}+n+L)\\ &=e^{-(j2\pi f_{\mu}\text{L}T_{s})}\sum_{n=0}^{L-1}g_{ref}^{2}|x( n)|^{2}\end{split}\, \tag{22}\]  
where \(\hat{n}\) denotes the accurate start position of the synchronized preamble. Using Equation (6), we can determine the estimated frequency offset \(\hat{f}_{w}\).
# Utility Analysis about Log Data Anomaly Detection Based on Federated Learning
## 4 Performance Evaluation
### Environmental Setup and Evaluation  
The experiment was conducted using Windows 10 on an Intel (R) Core (TM) i7-8700 CPU, with an NVIDIA GeForce RTX 2080 Ti 11 GB GPU in a 16 GB RAM environment. Feature extraction and deep learning were conducted using Python 3.8.13 and the Tensorflow-gpu 2.9.1 module.  
Federated learning is designed to use distributed data that cannot be collected directly from a central server, but in the research and development phase, it is convenient to design the model for download and manipulation on a local server, so we experimented in this way. The experiment was conducted on the premise that there was no network error between each client server. All three models applied to learning are classified as normal or abnormal, as in previous studies [14; 15; 16; 18]; thus, the loss function was _categorical cross-entropy_, and _Adam_ was employed as the optimizer. The learning rate was set to 0.01, and the batch size was set to 128.  
The weighted F1-score [23] was used as an evaluation metric, a weighted average according to the number of data by class after calculating the F1-score through precision and recall, commonly used for classification models. These indicators derive values between 0 and 1, where values closer to 1 indicate higher performance (calculated via (4), (5), and (6));  
\[Precision=\frac{TP}{TP+FP} \tag{5}\]  
\[Recall=\frac{TP}{TP+FN} \tag{6}\]  
\[F1\text{-}score=\frac{2\times Recall\times Precision}{Recall+Precision} \tag{7}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**HDFS (Local)** & **Train** & **Test** & **Total** \\ \hline Client 1 & 1,788,100 & 447,026 & 2,235,126 \\ Client 2 & 1,788,100 & 447,026 & 2,235,126 \\ Client 3 & 1,788,100 & 447,026 & 2,235,126 \\ Client 4 & 1,788,100 & 447,026 & 2,235,126 \\ Client 5 & 1,788,100 & 447,025 & 2,235,125 \\ \hline \hline \end{tabular}
\end{table}
Table 4: HDFS datasets for each local server learning.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Train** & **Test** & **Total** \\ \hline HDFS & 8,940,503 & 2,235,126 & 11,175,629 \\ BGL & 3,798,370 & 949,593 & 4,747,963 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Log datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**BGL (Local)** & **Train** & **Test** & **Total** \\ \hline Client 1 & 759,674 & 189,919 & 949,593 \\ Client 2 & 759,674 & 189,919 & 949,593 \\ Client 3 & 759,674 & 189,919 & 949,593 \\ Client 4 & 759,674 & 189,918 & 949,592 \\ Client 5 & 759,674 & 189,918 & 949,592 \\ \hline \hline \end{tabular}
\end{table}
Table 5:True positive (\(TP\)) represents the number of correctly predicted abnormal samples. False positive (\(FP\)) indicates the number of normally predicted abnormal samples, and false negative (\(FN\)) represents the number of normally predicted abnormal samples.
# SatGuard: Satellite Networks Penetration Testing and Vulnerability Risk Assessment Methods
## 1 Introduction  
### Background  
Satellite networks have become indispensable to global connectivity [1], enabling critical services such as broadband communication in remote regions and precision navigation through global navigation satellite systems (GNSSs). These systems are particularly vital where terrestrial infrastructure is impractical, such as in oceanic, desert, or disaster-stricken areas. The advent of low Earth orbit (LEO) constellations--exemplified by SpaceX's Starlink [2] and Amazon's Project Kuiper [3]--has further revolutionized performance by minimizing latency through proximity to Earth.  
However, the rapid deployment and operational complexity have introduced unprecedented cybersecurity risks. For instance, the 2022 Viasat cyberattack exploited ground infrastructure vulnerabilities, disrupting civilian internet access and military operations across Europe [4, 5]. Such incidents underscore the unique challenges of securing satellite networks, which span multi-segment architectures (space, ground, and user), rely on legacy protocols, and face extended patch deployment cycles.  
Traditional security methods, such as the Common Vulnerability Scoring System (CVSS), widely used in information technology (IT) environments, face limitations when applied to satellite networks. Satellite networks have unique system complexity due to their vast interconnected components, including the user, ground, and space segments. Thus, there is an urgent need for tailored security solutions to address the specific challenges of satellite networks.
# Detecting AI-Generated Images Using a Hybrid ResNet-SE Attention Model
## 4 Results and Discussion
### Performance of Proposed Hybrid Model (SE-ResNet-50)
#### 4.4.2 Comparative Analysis and Discussion  
To visualize comparative performance, ROC curves and confusion matrices for the baseline models and the proposed SE-ResNet50 are analyzed.  
Figure 3 illustrates the comparative ROC curves of the hybrid and baseline models. The SE-ResNet50 model achieves an area under the curve (AUC) of 0.98, demonstrating superior classification performance compared with VGGNet and DenseNet. Its ROC curve remains consistently above those of the baselines, particularly in the low false-positive rate region, reaffirming its heightened sensitivity and specificity in detecting AI-generated images.  
To provide a comparative diagnostic analysis of classification behavior, Table 7 presents a side-by-side confusion matrix comparison between ResNet-50 and the proposed SE-ResNet50 model. The SE-ResNet50 model exhibits a notable reduction in both false positives and false negatives, further evidencing its enhanced discriminatory capability and robustness in practical deployment scenarios.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Predicted Real** & **Predicted AI-Generated** \\ \hline \multirow{2}{*}{ResNet-50} & 9541 (Real) & 459 (Real) \\  & 328 (AI) & 9672 (AI) \\ \hline \multirow{2}{*}{SE-ResNet50} & 8894 (Real) & 1106 (Real) \\  & 271 (AI) & 9729 (AI) \\ \hline \hline \end{tabular}
\end{table}
Table 7: Confusion matrix comparison: ResNet-50 vs. SE-ResNet50.  
Furthermore, Figure 4 presents the graphical representation of confusion matrices for both ResNet-50 and SE-ResNet50. The hybrid model demonstrates significantly fewer misclassifications, especially in detecting synthetic (AI-generated) images. This improvement is particularly vital for high-stakes applications in digital forensics, media integrity verification, and identity protection.  
The remarkable improvement observed in SE-ResNet50 can be attributed to the integration of Squeeze-and-Excitation (SE) blocks. These attention mechanisms dynamically recalibrate feature maps across channels, enabling the network to better capture subtle, class-discriminative features commonly embedded in AI-generated imagery.  
Figure 3: ROC curve comparison—(**a**) VGGNet, (**b**) DenseNet, (**c**) SE-ResNet-50.