esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Modeling for Each Scenario  
Based on the 10-fold cross-validation method, we validated the soil pH prediction models for all scenarios (Table 4, Figure 4). The predictive results of scenarios 1 and 2, using radar or optical satellite composites alone, were quite similar, while scenario 3, using both sensors, evidently enhanced the predictability of soil pH, with an RMSE of 0.69 and an R\({}^{2}\) of 0.29. As shown by the results of scenarios 4 and 5, combining Sentinel data with the natural variables (soil, climate, and terrain) or cropland data did not bring further improvement but instead reduced the prediction accuracy. However, combining Sentinel data with the maps of crop rotation systems significantly improved the prediction accuracy, with a decrease in the RMSE of 4% and an increase in the R\({}^{2}\) of 24% compared to scenario 3, which combined Sentinel-1 and Sentinel-2 composites. As for scenario 7, which incorporated all the environmental variables in soil pH prediction, a similar accuracy was observed to that of scenario 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Scenarios** & **Number of Selected Variables** & **ME** & **RMSE** & **R\({}^{2}\)** \\ \hline
1 & 9 & 0.00 & 0.72 & 0.25 \\
2 & 7 & 0.01 & 0.72 & 0.24 \\
3 & 11 & 0.01 & 0.69 & 0.29 \\
4 & 3 & \(-\)0.01 & 0.72 & 0.23 \\
5 & 3 & \(-\)0.01 & 0.72 & 0.23 \\
6 & 9 & 0.02 & 0.66 & 0.36 \\
7 & 8 & 0.02 & 0.70 & 0.29 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Soil pH mapping accuracies for different scenarios.  
Figure 4: Scatter plots of predicted versus observed soil pH from the 10-fold cross-validation for scenarios 1 (**a**), 2 (**b**), 3 (**c**), 4 and 5 (**d**), 6 (**e**), and 7 (**f**). The dashed line represents the 1:1 line, and the solid line represents the fitted line.
# A Tunable Metamaterial Joint for Mechanical Shock Applications Inspired by Carbon Nanotubes
## 4 Results  
### Fundamental Equations and Numerical Process  
It is convenient to develop the finite element model according to a global Cartesian coordinate system \((x,y,z)\). Based on the aforementioned explained geometry of the joint, shown in Figure 1, a variety of FE analyses may be performed [34]. The aim is the development of an accurate and simultaneously computationally efficient FE model to simulate the mechanical behavior of the joint at room temperature. An elastoplastic constitutive model is generally required for representing the quasi-static mechanical behavior of the metamaterial, while an implicit time integration scheme should be adopted to numerically predict its dynamic response. Generally, the equation of motion for the joint may be expressed in a matricial form in the global coordinate system by applying the conventional displacement formulation. After discretizing the joint with finite elements, assigning the material attributes, and applying the boundary conditions, the dynamic elemental equation becomes:  
\[\mathbf{M}\mathbf{\ddot{x}}+\mathbf{C}\mathbf{\dot{x}}+\mathbf{K}\mathbf{x}= \mathbf{F} \tag{6}\]  
where \(\mathbf{M}\), \(\mathbf{C}\), and \(\mathbf{K}\) is the assembled mass, damping, and stiffness matrix of the meshed problem domain, \(\mathbf{x}\) is a vector containing all nodal translations and rotations, while \(\mathbf{F}\) is the force vector representing the applied time-dependent load. The dot denotes integration over time \(t\).  
The above system of equations is solved in an incremental-iterative manner to treat the inherent material nonlinearity of the polymeric material [34]. On the other hand, when the elastic stability analysis of the joint is to be carried out, a different numerical treatment is required. The problem of linear structural stability may be simulated by applying a reference level of loading \(\mathbf{F}_{\text{ref}}\) to the system by ignoring the plasticity of the 3D-printed material. Then, a linear elastic static analysis must be performed, using Equation (6) with \(\mathbf{\ddot{x}}=\mathbf{\ddot{x}}=0\), to estimate the load required to form the geometric stiffness matrix \(\mathbf{K}_{\text{G}}\)[34]. The stability load is finally calculated by solving the eigenvalue equation:  
\[[\mathbf{K}-\lambda\mathbf{K}_{\text{G}}]\mathbf{x}_{\text{e}}=0 \tag{7}\]  
where \(\lambda\) is the multiplier to the reference load, while the vector \(\mathbf{x}_{\text{e}}\) denotes the eigenvector corresponding to the eigenvalue.  
The eigenvalue problem may be solved by adopting a standard solution algorithm such as the Lanczos method [33]. The critical load \(\mathbf{F}_{\text{cr}}\), which leads to the principal stability  
Figure 4: Experimental stress–strain curves for the 3D-printed material.  
loss of the joint, is the most important and it is associated with the lowest eigenvalue \(\lambda_{\text{cr}}\) according to the equation:  
\[\mathbf{F}_{\text{cr}}=\lambda_{\text{cr}}\mathbf{F}_{\text{ref}} \tag{8}\]
# Hybrid Electrostatic-Atomic Accelerometer for Future Space Gravity Missions
## 2 Materials and Methods
### Cold Atom Gravimeter  
The cold atom gravimeter used for the hybridization tests is described in detail in [68]. In brief, the cold atoms are produced in a vacuum chamber based on a glass cell located inside a magnetic shield consisting of four layers of mu-metal. The sensor head containing the vacuum chamber, the magnetic shield, the magnetic coils, and the optics for shaping the laser beams and collecting the fluorescence has a height of 40 cm and a diameter of 33 cm. The maximum allowed interaction time \(T\) is 46 ms, and the cycling time is \(T_{c}=250\) ms. The Raman pulse duration is typically 3.5 \(\upmu\)s for a \(\pi/2\) transition. The achieved contrast is typically \(C=0.39\). In standard conditions, the achieved sensitivity is \(8\times 10^{-7}\) m\(\cdot\)s\({}^{-2}/\)Hz\({}^{1/2}\) and is limited by the residual vibration noise on the vibration isolation platform.
# A Review of Tunable Orbital Angular Momentum Modes in Fiber: Principle and Generation
## 2 The Classical Orbital Angular Momentum (OAM) Mode
\begin{array}{l} \overrightarrow{\text{x}}\cos(l\theta)-\overrightarrow{\text{y}}\sin(l\theta )\\ \overrightarrow{\text{x}}\sin(l\theta)+\overrightarrow{\text{y}}\cos(l\theta)\\ \overrightarrow{\text{x}}\cos(l\theta)+\overrightarrow{\text{y}}\sin(l\theta) \\ \overrightarrow{\text{x}}\sin(l\theta)-\overrightarrow{\text{y}}\cos(l\theta) \\ \end{array} \right\}\\ \left\{ \begin{array}{l}\text{TM}_{0,m}\\ \text{TE}_{0,m}\\ \end{array} \right\}=\text{F}_{l,m}(\text{r})\left\{ \begin{array}{l} \overrightarrow{\text{x}}\cos(l\theta)+\overrightarrow{\text{y}}\sin(l\theta )\\ \overrightarrow{\text{x}}\sin(\theta)-\overrightarrow{\text{y}}\cos(\theta) \\ \end{array} \right\}\\ \end{array}\right.\quad(l=1) \tag{2}\]  
When two LP modes owning the same polarization directions are combined with a \(\pm\pi\)/2 phase shift, the LP-OAM mode with same polarization as the LP modes is generated [26], as shown in Equation (3); When the even and odd modes of same vector mode (HE or EH) are combined with \(\pm\pi\)/2 phase shift, the superimposed mode is CP-OAM mode, as shown in Equation (4). The OAM handedness and SAM handedness of modes based on the HE bases are the same, while those of modes based on the EH bases are opposite [25]. \[\left\{ \begin{array}{l}\text{LP}_{lmax}\pm i\text{LP}_{lmbx}\\ \text{LP}_{lmay}\pm i\text{LP}_{lmby}\\ \end{array} \right\}=\text{F}_{lm}(\text{r})\left\{ \begin{array}{l} \overrightarrow{\text{x}}\text{OAM}_{\pm l,m}\\ \overrightarrow{\text{y}}\text{OAM}_{\pm l,m}\\ \end{array} \right\} \tag{3}\]  
\[\left\{ \begin{array}{l}\text{HE}_{l+1,m}^{\text{e}}\pm i\text{HE}_{l+1,m}^{ \text{o}}\\ \text{HE}_{l-1,m}^{\text{e}}\pm i\text{EH}_{l-1,m}^{\text{o}}\\ \end{array} \right.\quad(l>1)\\ \end{array}\right\}\text{or}\left\{ \begin{array}{l}\text{HE}_{l+1,m}^{\text{e} }\pm i\text{HE}_{l+1,m}^{\text{o}}&(l=1)\\ \text{EH}_{l-1,m}^{\text{e}}\pm i\text{EH}_{l-1,m}^{\text{o}}&(l=1)\\ \end{array} \right\}=\text{F}_{l,m}(\text{r})\left\{ \begin{array}{l} \sigma^{\text{e}}\text{OAM}_{\pm l,m}&(l\geq 1)\\ \sigma^{\text{e}}\text{OAM}_{\pm l,m}&(l\geq 1)\\ \end{array} \right\} \tag{4}\]  
Figure 1: Helical phase fronts for (**a**) \(l=0\), (**b**) \(l=1\), (**c**) \(l=-1\), and (**d**) \(l=2\). Reprinted with permission from [19], Copyright 2011 The Optical Society of America.
# Secure Image Transmission Using Multilevel Chaotic Encryption and Video Steganography
## 4 The Proposed System
### Encryption, Embedding, and Extraction Procedure  
The proposed scheme can protect multimedia communication by fusing adaptive steganographic embedding with image encryption in video frames. Firstly, the input image is transformed into a byte stream and is encrypted according to the AES (CTR mode), improved by a chaotic key generated dynamically by a multilevel chaotic map structure. As shown in Figure 5, this key scheduling makes the encryption process more unpredictable and statistically strong.  
Subsequently, the video is extracted frame by frame using the OpenCV library. Unlike other selective algorithms, the proposed approach puts uniformly encrypted data into stego data to achieve synchronization and reduce detectability.  
Adaptive LSB with higher texture variance based on frames realizes the embedded steganography. This perceptually motivated embedding of secret data trades off reduced visual distortion for higher steganalysis resistance. The system obtains the cipher data at the receiving end, verifies its correctness, and decrypts it with the related chaos key.  
Figure 5: Workflow diagram of the proposed hybrid chaotic–AES encryption and steganographic embedding system.  
This unified scheme provides high imperceptibility, randomness, and resistance to cryptanalysis. It also integrates the computation efficacy of AES with the complexity of multistage chaotic key generation.  
Figure 5 illustrates the proposed system's overall encryption and embedding workflow. It integrates multilevel chaotic key generation with AES encryption in CTR mode, followed by adaptive steganographic embedding into video frames. Each stage enhances the system's overall security, imperceptibility, and robustness.  
Block diagram of the proposed hybrid encryption-steganography framework. The system generates a chaotic encryption key using a multilevel map structure. Based on the threshold value (\(\geq\)0.5 or \(<\)0.5), the system dynamically selects between Subsystem 1 and Subsystem 2. The selected key is then used in the AES encryption of the image byte stream. The encrypted data is embedded into video frames using an adaptive LSB technique. The process concludes with extracting and decrypting the hidden image on the receiver side.
# Study of Self-Heating and High-Power Microwave Effects for Enhancement-Mode p-Gate GaN HEMT
## 3 Results and Discussion
### Thermal Characteristics  
Joule heat is generated by the interaction of electric current and resistance. Semiconductor devices will generate heat during normal operation, especially HEMTs composed of third-generation wide-gap semiconductor material with the high output power density and high-frequency characteristics. The increase in temperature will cause performance degradation and even unrecoverable damage to the device [8; 19; 36], which contributes to an obvious gap between actual performance and theoretical performance of HEMTs. Therefore, in order to improve the working efficiency of HEMTs, the temperature rise phenomenon needs to be studied emphatically.
# A ResNet1D-AttLSTM-Based Approach for Ship Trajectory Classification
## 3 Experiment
### Model Evaluation and Analysis  
#### 3.3.1 Evaluation Metrics  
To analyze the classification performance of the model, this study employs four key metrics: accuracy, precision, recall, and F1-score. The specific calculation methods for these metrics are provided in Equations (13)-(16).  
\[\text{Accuracy}=\frac{N_{\text{Correct}}}{N} \tag{13}\]  
\[\text{Precision}=\frac{N_{\text{TP}}}{N_{\text{TP}}+N_{\text{FP}}} \tag{14}\]  
\[\text{Recall}=\frac{N_{\text{TP}}}{N_{\text{TP}}+N_{\text{FN}}} \tag{15}\]  
\[F_{1}=\frac{2\times\text{Precision}\times\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{16}\]  
where the following applies:  
* \(N_{\text{Correct}}\): The number of correctly classified ship trajectory sequences.
* \(N\): The total number of ship trajectory sequences.
* \(N_{\text{TP}}\): The number of true positives, i.e., the number of ship trajectory sequences correctly classified as belonging to a particular class.
* \(N_{\text{FN}}\): The number of false negatives, i.e., the number of ship trajectory sequences that belong to a particular class but were incorrectly classified as belonging to another class.
* \(N_{\text{FP}}\): The number of false positives, i.e., the number of ship trajectory sequences that do not belong to a particular class but were incorrectly classified as belonging to that class.  
These metrics provide a comprehensive evaluation of the model's performance by considering both the correctness of classifications and the balance between precision and recall.
# ITFDS: Channel-Aware Integrated Time and Frequency-Based Downlink LTE Scheduling in MANET
## 3 Experimental Results
### Complexity Analysis  
The complexity analysis of the proposed method is computed as follows. Let \(n\) be the total amount of users, every resource block is used to transmit the packet through LTE. Every time the user can select the improved rate to utilize the resources for transmitting data packet. If the proposed technique identifies \(p\) users from the total amount of users, the complexity for the time period is \(O(\log p)\). The mean rate is used for selecting per user as \(O(1)\). The resource block (RB) is responsible for identifying the capable user to transmit the packets. The average arrival time is computed and analyzed the final transmitted packet in the active queue. The estimated time is also computed within the specified time period. The probability through the data transmission is calculated for analyzing the time complexity in the queue. The scheduling process is also analyzed for transmitting the data packet, and the overall time complexity is computed as \(O(\log p)\) in the resource block.
## 4 Performance Evaluation for S Band Signals  
### PSD of S Band Signals  
The cos-phase BOCc(4,4), sin-phase BOCs(4,4), MSK-BOC(4,4) [25] and BPSK(8) have been presented as candidates for the future Compass system in the S band [26]. The normalized (unit power over infinite transmission bandwidth) PSD of the above signals as well as B_M_2 RC(8) are shown in Figure 3. Compared with other candidates, the proposed B_M_2 RC(8) reveals a stronger spectrum roll-off in the side lobes, and performs even 35 dB lower than MSK-BOC(4,4) at \(\pm 30\) MHz while still maintaining the characteristic of spectrum splitting.  
Figure 3: PSD of candidate signals in S band.
## 6 Analysis of Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Volume (No. of Vehicles)**} \\ \cline{2-9}
**Prediction Horizons** & \multicolumn{2}{c}{**15 min**} & \multicolumn{2}{c}{**30 min**} & \multicolumn{2}{c}{**45 min**} & \multicolumn{2}{c}{**60 min**} \\ \cline{2-9}  & **MAPE (\%)** & **Accuracy** & **MAPE (\%)** & **Accuracy** & **MAPE (\%)** & **Accuracy** & **MAPE (\%)** & **Accuracy** \\ \hline BiLSTM\_No weather & 1.16 & 98.84 & 2.46 & 97.54 & 2.38 & 97.62 & 3.14 & 96.86 \\ \hline BiLSTM\_Rain & 0.87 & 99.13 & 2.47 & 97.53 & 2.62 & 97.38 & 2.87 & 97.13 \\ \hline BiLSTM\_Rain and Wind & 2.34 & 97.66 & 2.60 & 97.40 & 2.75 & 97.25 & 2.87 & 97.13 \\ \hline BiLSTM\_Weather & 2.13 & 97.87 & 2.40 & 97.60 & 3.42 & 96.58 & 2.94 & 97.06 \\ \hline LSTM\_No weather & 4.37 & 95.63 & 4.60 & 95.40 & 4.89 & 95.11 & 5.97 & 94.03 \\ \hline LSTM\_Rain & 5.02 & 94.98 & 5.03 & 94.97 & 5.39 & 94.61 & 6.00 & 94.00 \\ \hline LSTM\_Rain and Wind & 4.73 & 95.27 & 5.52 & 94.48 & 5.37 & 94.63 & 6.19 & 93.81 \\ \hline LSTM\_All Weather & 4.93 & 95.07 & 5.50 & 94.50 & 5.86 & 94.14 & 6.61 & 93.39 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Volume prediction accuracies under multiple weather conditions results. temporal dependencies. The unidirectional LSTM model processes data in a forward direction, limiting its ability to capture dependencies that rely on future and past information. This can impact prediction accuracy in cases where forward-looking context is crucial, such as predicting sudden traffic changes influenced by upcoming intersections or congestion. On the other hand, the BiLSTM model processes sequences in both forward and backward directions, enabling it to be influenced by information from past and future states. This bidirectional processing allows BiLSTM to capture complex, fluctuating traffic conditions. As a result, the BiLSTM model demonstrated higher accuracy, especially in scenarios where traffic flow is influenced by prior and forthcoming events, leading to improved predictions over the unidirectional LSTM model.
## 5 Results and Discussion
### Elemental Composition and Inter-Correlation
Contrariwise, the results show that there is a statistically significant difference and failure to reject the null hypothesis concerning the determined elements (\(p\)-value \(\leq\) 0.001) and the interaction element and mines (\(p\)-value \(\leq\) 0.001). Specifically, [PERSON]'s test shows that there are no statistically significant differences in the mean values of the elemental composition of the studied mines as the probabilities were calculated to be 0.028, 0.96, and 0.018 for Sukari-Hamash, Um Hagalig-Hamash, and Um Hagalig-Sukari, respectively. These findings prove the fact of the similarity of granitic rock samples collected from Hamash and Um Hagalig in terms of the geochemical features. The correlation coefficients were extracted based on the Pearson method. The highest correlation coefficients were noticed for some pairs, specifically, Yb:Lu, Au:Th, and Tb:Yb (r > 0.99) for the Sukari mine, whereas for the Hamash mine, the highest correlated pairs were noticed for Co:W, Yb:Lu, Au:U, Au:Sm, and Zn:Rb (r > 0.90). Finally, for Um Hagalig, the most linked pairs are calculated to be Yb:Lu, Sc:Eu, Rb:Cs, and Zn:Th (r > 0.99). It seems that the highest correlation coefficients were calculated between the rare earth elements (REE). The results obtained from REE are largely consistent with the corresponding values obtained from [PERSON] et al. [37] from Phosphate mines. In addition, there is a good correlation between Au and U at the Hamash mine. This finding proves a significant association between the two elements in the mine and again suggests that attention should be paid to this feature.
# Inf-OSRGAN: Optimized Blind Super-Resolution GAN for Infrared Images
## 4 Experiments
### Experiment Setup
#### 4.1.4 Evaluation Metrics  
We evaluated different models using mean peak signal to noise ratio (PSNR), structural similarity (SSIM), and Frechet inception distance (FID).  
PSNR evaluates the quality of reconstructed images by quantifying the difference between the original and the reconstructed signal by calculating the ratio of the maximum possible power of a signal to the power of the noise, determining the ratio of the maximum possible power of a signal to the noise power. A higher PSNR value signifies that the reconstructed image closely resembles the original, indicating better quality. PSNR is usually measured in decibels (dB), calculated as in Equation (10):  
\[\text{PSNR}=10\cdot\text{log}_{10}\bigg{(}\frac{R^{2}}{\text{MSE}}\bigg{)} \tag{10}\]  
where \(R\) is the maximum possible pixel value of the image and MSE is the mean squared error.  
SSIM, examining the similarity of two images, takes into account their structural information, luminescence, and contrast. The SSIM index, which varies from \(-1\) to \(1\), has a value of 1 when images perfectly match. The SSIM index can be broken down into three components: luminance (L), contrast (C), and structure (S), with the overall formula given by Equation (11):  
\[\text{SSIM}(I,K)=\frac{(2\mu_{I}\mu_{K}+C_{1})(2\sigma_{IK}+C_{2})}{(\mu_{I}^{2}+ \mu_{K}^{2}+C_{1})(\sigma_{I}^{2}+\sigma_{K}^{2}+C_{2})} \tag{11}\]  
where \(\mu_{I}\) and \(\mu_{K}\) are the mean luminance of images \(I\) and \(K\); \(\sigma_{I}^{2}\) and \(\sigma_{K}^{2}\) are the variances of the respective images; and \(\sigma_{IK}\) is the covariance between the two images. \(C_{1}\) and \(C_{2}\) are constants used for stability in the calculations.  
FID gauges the resemblance between two image sets by computing the distance between their feature representations, with lower FID values suggesting a closer resemblance between the actual and generated images. The calculation of FID involves the following steps. We extracted features from real and generated images, then calculated the mean and covariance of the features for both sets, including the mean and covariance of the real image features \((\mu_{r},\Sigma_{r})\), and the mean and covariance of the generated image features \((\mu_{g},\Sigma_{g})\). The FID is calculated as in Equation (12):  
\[\text{FID}=\|\mu_{r}-\mu_{g}\|^{2}+\text{Tr}(\Sigma_{r}+\Sigma_{g}-2(\Sigma_{ r}\Sigma_{g})^{1/2}) \tag{12}\]  
where Tr denotes the trace operator.
## 4 Conclusions  
A quantitative spectrophotometric AChE assay for the studied CM and OP pesticides monitoring in lettuce and strawberry extracts was presented. Importantly, the AChE assay was validated in the food matrices and its performance was confirmed by an accredited LC-MS/MS, a practice commonly missing or mispresented in the available literature. The short analysis time (20 min for sample preparation/6 samples and 15 min for AChE assay) combined to the affordable cost (0.16 euro/96-microwell plate) showed that the AChE assay can be applied by laboratories with limited resources as a pre-screening tool. In contrast to chromatographic methods, which are serial meaning that only one sample is analyzed per run, up to 96 samples could be analyzed using the developed method achieving high-throughput. All in all, our contribution can be extremely useful for monitoring carbofuran in countries where this pesticide is still used or testing imports originating from those countries and a rapid answer is needed. Future work is under way to expand target analytes-food matrices portfolio and further improve LODs.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/2/565/s1](http://www.mdpi.com/2076-3417/10/2/565/s1)]([http://www.mdpi.com/2076-3417/10/2/565/s1](http://www.mdpi.com/2076-3417/10/2/565/s1)), Figure S1: AChE tolerance towards various organic solvents after 30 min of incubation with ethanol, methanol, acetonitrile and dimethyl sulfoxide (\(n=3\)). The 100% AChE activity was set for 0% organic solvent in PBS, Table S1: Calibration ranges for each pesticide investigated for potential cross-reaction with carbofuran. (\(n=7\) calibration points and one blank sample), Table S2: Carbofuran-3-hydroxy and dichlorvos validation results in strawberry and lettuce for AChE assay, Table S3: Carbofuran-3-hydroxy and dichlorvos validation results in strawberry and lettuce for LC-MS/MS method.  
Conceptualization, A.S.T., J.P., J.H.; methodology, A.S.T. and J.H.; software, A.S.T. and L.U.; validation, A.S.T., L.U., J.H.; resources, J.P. and J.H.; writing--original draft preparation, A.S.T.; writing--review and editing, A.S.T., L.U., J.P. and J.H.; supervision, J.H.; project administration, J.P. and J.H.; funding acquisition, J.P. and J.H. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 720325, FoodSmartphone and the \"National Programme of Sustainability I\"--NPU I (LO1601--No.: MSMT-43760/2015).  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.