esa-sceva/satcom-chunk-collection huggingface dataset:
# Satellite Image Categorization Using Scalable Deep Learning
## 4 Results
### Experimental Results
The comparison shown in Figure 7 is evident of the fact that EfficientNets provide better feature embeddings than other methods, forming obvious clusters for different classes. EfficientNet has better feature embeddings due to its ability to cluster data points of similar classes together while keeping them distinct from other classes. Compared to other embeddings, the clusters of different classes are well-separated, showing that the model has learned to differentiate between classes effectively. Other models, however, have more overlapping clusters, indicating that they have difficulty distinguishing between different classes. Loss-accuracy curves for training and validation are presented in Figure 8. Variation in a number of epochs results in an early stopping feature. Analyzing the loss-accuracy curves of various architectures can offer valuable insights into their training dynamics, convergence rates, and generalization performance. This understanding is crucial for identifying their strengths and weaknesses. The loss-accuracy curves of Inception Net and MobileNet indicate that the model achieves a high accuracy early on in the training process, but the loss continues to fluctuate. However, the loss-accuracy curve of ResNet \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline  & **Accuracy** & **Precision** & **Recall** & **F1 Score** & **Cohen’s Kappa** & **ROC AUC** \\ \hline Inception V3 & 0.828 & 0.868 & 0.816 & 0.822 & 0.768 & 0.986 \\ \hline ResNet & 0.977 & 0.978 & 0.976 & 0.977 & 0.969 & 0.999 \\ \hline MobileNet & 0.949 & 0.954 & 0.951 & 0.951 & 0.931 & 0.997 \\ \hline EfficientNet B4 & 0.966 & 0.969 & 0.968 & 0.967 & 0.955 & 0.999 \\ \hline EfficientNet B5 & 0.979 & 0.979 & 0.980 & 0.979 & 0.972 & 0.999 \\ \hline EfficientNet B6 & 0.993 & 0.993 & 0.993 & 0.993 & 0.991 & 0.999 \\ \hline EfficientNet B7 & 0.996 & 0.997 & 0.997 & 0.997 & 0.995 & 0.999 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Interpretation results for different architectures. Figure 6: Confusion matrix: (**s**) reference labeled matrix; (**a**) Inception Net; (**b**) MobileNet; (**c**) ResNet; (**d**–**g**) EfficientNet B4–B7. shows a rapid initial decrease in loss with corresponding accuracy improvements. This suggests that ResNet benefits from its deep residual connections and converges quickly during training. The loss-accuracy curves of EfficientNet B4-B7 exhibit similar trends to those of ResNet, but with potentially even faster convergence and a higher accuracy due to their efficient architecture and advanced scaling methods. The stated results characterize the top accuracy assessments attained with each network configuration. Figure 7: Feature embeddings: (**a**) Inception Net; (**b**) MobileNet; (**c**) ResNet; (**d**–**g**) EfficientNet B4–B7.
## 2 Materials and Methods
### Experimental Design and Optimization by Response Surface Methodology  
The experimental design was created using Design Expert 7.1.6 (StatEase, Minneapolis, MN, USA) software. Three independent variables (temperature, time, and liquid/solid ratio) affecting extraction conditions and the effects of their interactions on pectin yield were examined by a three-factor-three-level Box-Behnken design of RSM. The limits of independent variables were 100-150 \({}^{\circ}\)C, 5-15 min, and 10-20 mL/g for temperature, time, and liquid/solid ratio, respectively (Table 1). After the experiments were performed at conditions given in the design, data analysis and optimization for the MW-HPOO\({}_{2}\) extraction were also performed using the software.  
Experimental data were fitted to a second-order polynomial model Equation (2) [25] and regression coefficients were obtained. Coefficient of determination R\({}^{2}\) was used to evaluate the fitness of the model.  
\[\mathrm{Y}=\mathrm{A}_{0}+\sum_{i=1}\mathrm{A}_{ii}\mathrm{X}_{i}^{2}+\sum_{i= 1}^{n}\sum_{j=i+1}^{n}\mathrm{A}_{ii}\mathrm{X}_{i}\mathrm{X}_{j} \tag{2}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Run**} & \multicolumn{3}{c}{**Factors**} & \multicolumn{2}{c}{**Response**} \\ \cline{2-5}  & **A: Temperature** & & & \\  & **(\({}^{\circ}\)C)** & **B: Time (min)** & & **C: Liquid/Solid** & **Pectin Yield (\%)** \\ \hline
1 & 150 & 2 & 15 & 27.00 \\
2 & 100 & 2 & 15 & 2.70 \\
3 & 125 & 5 & 15 & 20.90 \\
4 & 125 & 8 & 10 & 22.00 \\
5  
where Y is the response variable (pectin yield); A\({}_{0}\), A\({}_{i}\), A\({}_{ii}\), and A\({}_{ij}\) are the regression coefficients for intercept, linear, quadratic, and interaction terms, respectively; X\({}_{i}\) and X\({}_{j}\) represent the independent variables (i \(\
eq\) j).  
For optimization, independent variables were kept within the studied range and extraction conditions were optimized for maximum pectin yield (%).
# Telehealth Secure Solution to Provide Childhood Obesity Monitoring
## 6 Security Analysis
### Encryption Time and Throughput  
With the use of MATLAB programming, and using the hardware described in Section 4 (Experimental results), the functions \"tic\" and \"toc\" are used, considering five users with 384 bits per user and the algorithm [22]. The cryptogram has 19,200 data points, with 64-bit double floating-point format, i.e., 1,228,800 bits. The average encryption time is 0.246374 s, and the average decryption time is 0.211884 s. The throughput can be calculated as the total transmitted bits over the elapsed encryption time, thus the throughput is 4.99 Mb/sec. In [22], the throughput in a three-patient scenario with four biosignals is 0.66 Mbps.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**SNR (dB)** & **0.001** & **0.01** & **0.1** & **1** & **3** & **0** \\ \hline User 1 & 45.05 & 53.12 & 47.91 & 48.95 & 48.95 & 48.69 \\ BER (\%) & 0.89 & 0.59 & 0.59 & 0.59 & 0 & 0 \\ User 3 & 0.89 & 0.29 & 0.19 & 0.29 & 0.29 & 0 \\ BER (\%) & 50.52 & 48.17 & 50.00 & 51.56 & 49.47 & 50.78 \\ User 5 & 0.59 & 0.52 & 0.59 & 0.27 & 0.26 & 0 \\ BER (\%) & \multicolumn{1}{c}{} & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 6: BER between plain text and decrypted text for noise robustness.
# Cross Domain Adaptation of Crowd Counting with Model-Agnostic Meta-Learning
## 3 Methods  
In this paper, we break down the task of crowd counting into several parts: feature extraction, density estimation, and the counting map. We also study the different aspects of model generalization approaches. Many algorithms achieve excellent results when their training and testing data are in the same domain. However, in cross-domain scenarios [58], there are domain-shift issues, which is a result of training and testing on different domains. Generally, the basic steps of crowd-counting algorithms' flow [59] consists of three parts: a feature extractor, a density estimator, and a crowd-counting mapper, as shown in Figure 1. In most CNN-based algorithms, such as [6; 60; 61], the three parts are trained from end to end. Taking CSRNet [60] as an example, whose first ten network layers are utilized as feature extractors, and whose remaining parts are used as density estimators. Following the idea of unsupervised domain adaptation [28], we propose an adaptive domain-invariant feature extracting module to align the two domains along a feature level. Moreover, to make the density estimator more universal, we propose dynamic-\(\beta\) MAML, based on the idea of Alpha-MAML [62; 63]. Finally, we study the counting map refiner, which transforms a density map from coarse to refined, regresses the crowd number based on a synthetic dataset, and applies the refiner to new domains.
## 5 Investigation of the MIMO-Antenna Diversity Performance
### Envelope Correlation Coefficient (ECC)  
The envelope correlation coefficient (ECC) is an important parameter used to measure the MIMO-antenna performance. It indicates the correlation or isolation between the branch signals received by the different MIMO-antenna elements. For a perfect inter-element isolation, the ideal value of the ECC is 0; however, in practice, a value under 0.5 is generally  
Figure 11: Radiation patterns of the proposed UWB-MIMO antenna at **(a)** 3.1 GHz, **(b)** 7.5 GHz, and (c) 10.6 GHz.  
Figure 12: Simulated and measured peak gains and radiation efficiencies of the proposed MIMO antenna.  
considered to be good enough to achieve a good diversity performance [30; 33; 36; 37]. In the literature, three methods are proposed to evaluate the ECC. It can be calculated from the S parameters of the different antenna elements, from their radiation patterns, or from the combination of the radiation efficiency and S parameters. Calculating the ECC using the far field is more reliable but time-consuming and expensive because it is tedious in terms of the measurements and calculation [38; 39; 40]. Its calculation using the combination of the S parameters and radiation efficiency is complex but more reliable for higher efficiencies and gives the correct estimation for lossy antennas; however, this technique presents some limitations: it gives higher ECC values in the case of antennas presenting low radiation efficiencies and it is not suitable for tilted-beam radiation patterns [36; 39]. In [40], an exact ECC expression using the S parameters is given. This expression is derived from the far-field ECC formula for the case of a two-element antenna, and the technique is easy, fast, and provides a clear understanding of the effects of mutual coupling and impedance matching on the MIMO diversity performance, but it is less reliable in the case of lossy antennas for ultra-wideband computation [36; 38; 39]. Equations (1)-(3) give the mathematical expressions used to calculate the ECC using the far field, the S parameters without consideration of the losses, and the combination of the S parameters and radiation efficiency, respectively, in the general case of an N-element MIMO antenna [36; 38; 39]:  
\[\rho_{ij}(\epsilon)=\frac{\left|\int_{4\pi}\overset{\rightarrow*}{F}_{i}^{*} (\theta,\phi).\overset{\rightarrow*}{F}_{j}^{*}(\theta,\phi)d\Omega\right|^{2 }}{\int_{4\pi}\left|\overset{\rightarrow*}{F}_{i}^{*}(
# Privacy-Preserved Visual Simultaneous Localization and Mapping Based on a Dual-Component Approach
## 2 Related Work  
### Visual SLAM  
Visual SLAM can be broadly categorized into direct-based [15, 16] and feature-based approaches [17, 18]. Direct methods estimate camera motion by tracking pixel intensity variations between consecutive frames. Since they do not require keypoint detection or descriptor computation, they are computationally efficient. However, they are highly sensitive to image noise and uncertainty, limiting their effectiveness in complex environments. In contrast, feature-based methods rely on multi-view geometry principles. They first detect feature points in images, then match them with 2D or 3D points to compute camera poses and reconstruct scenes. These methods provide more distinctive and stable features, enhancing adaptability to diverse lighting conditions and environments, including indoor and outdoor settings. A notable example in this category is oriented fast and rotated brief (ORB)-SLAM [17]. This paper employs ORB-SLAM3 [18] as the operational framework for implementing the proposed privacy-preserving approach.
## 2 Related Work
### Node Activity Scheduling
#### 2.2.1 Solutions Independent of the Medium Access  
In this type of solution, nodes are assumed to be redundant to achieve the functionalities required by the application. The goal of these protocols is then to build active sets, sequentially activated. Only nodes belonging to the active set are active, all the other nodes are sleeping. When these sets are disjoint, network lifetime is equal to the sum of the active sets lifetimes. In [22], the authors show that maximizing the number of disjoint active sets is NP-complete. The authors of [22] and [23] propose two centralized solutions assuming synchronized nodes and the same energy consumption for all nodes in the same active set. They show in [23] that considering non disjoint sets improve the network lifetime. Distributed solutions also exist as in [24], where each node only needs to know information about its one-hop and two-hop neighborhood. A node decides to stay active or to sleep depending on its residual energy and the decision of its neighbors. More precisely, a node whose function is ensured by active nodes can sleep if and only if (1) the active nodes are connected, (2) all its neighbors have at least one active neighbor and (3) all active nodes have a residual energy higher than its own.
###### Abstract  
Azorean _Cryptomeria japonica_ is widely used for local wood production, generating large amounts of aerial-part biomass residues that can be a sustainable source of value-added bioproducts. This comparative study aimed (i) to determine the yield and chemical profile of the essential oils (EOs) extracted by hydrodistillation from Azorean _C. japonica_ foliage, leaves, male cones (MCs), and female cones (FCs), and (ii) to investigate the antimicrobial and brine shrimp lethality (BSL) effects of the obtained EOs and some major components. The EOs yield revealed a wide range (ca. 1-3%, \(w\)/d.w.) and their chemical composition, analysed by GC-MS and GC-FID, showed the presence of seventy-one components. Monoterpene hydrocarbons (38-71%) dominated in all the studied EOs, mainly \(\alpha\)-pinene (17-45% of total EOs), decreasing as follows: cone EOs > leaf EOs > foliage EOs. Oxygen-containing monoterpenes (mainly terpinen-4-ol) also dominated in cone EOs. Contrariwise, oxygen-containing sesquiterpenes (mainly elemol) and diterpene hydrocarbons (mainly phyllocladene) dominated in foliage/leaf EOs. The studied EOs exhibited activity against Gram-positive bacteria but no activity against Gram-negative bacteria. A similar trend was displayed by \(\alpha\)-pinene. Only the cone EOs showed antifungal activity against _Penicillium chrysogenum_, but this was significantly lower than those of \(\alpha\)-pinene and terpinen-4-ol. Moreover, FC EO should also be considered for further investigation due to its high toxicity on BSL bioassay. In conclusion, this study demonstrated the variability of yield, chemical profile, and bioactivities in the EOs from different parts of the Azorean _C. japonica_, expanding the knowledge of the potentialities of _C. japonica_ aerial-part EOs, particularly the ones from the cone samples.  
Azores archipelago; Japanese cedar; biomass waste biovalorisation; strobili (female and male cones); essential oil; \(\alpha\)-pinene; terpinen-4-ol +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Health Risks  
Figures 6-8 show the health risks of potentially toxic elements in the Aconcagua River basin. For soil samples from the Aconcagua River basin without mining activity, the non-carcinogenic risk for adults and children (Figure 5a,b, respectively) indicates that all elements are within the safe limits, except for arsenic, in which 6% of the samples present a health risk for children. Regarding carcinogenic risk for adults and children (Figure 5c,d, respectively), they fall within the acceptable limits.  
Figure 5: Environmental indexes for soil in the Aconcagua River basin without mining activity (**a**), in random sectors including mining activity (**b**), and the Casablanca River basin (**c**). The red lines are the classification for each environmental index. * Difference is significant at the 0.05 level. ** Difference is significant at the 0.01 level. *** Difference is significant at the 0.001 level.  
Figure 6: Healthy risk in the Aconcagua River basin without mining activity: (**a**) non-carcinogenic risk in adults, (**b**) non-carcinogenic risk to children, (**c**) carcinogenic risk in adults, and (**d**) carcinogenic risk in children. The red dotted lines show the risk values.  
Figure 7: (**a**) Non-cancer risk in adults in the Aconcagua River basin, including mining activity. (**b**) Carcinogenic risk in children in the Aconcagua River basin, including mining activity. (**c**) Carcinogenic risk in adults in the Aconcagua River basin, including mining activity. (**d**) Carcinogenic risk in children in the Aconcagua River basin, including mining activity. The red dotted lines show the risk values. * Difference is significant at the 0.05 level.  
Soil samples that include mining activity in the Aconcagua River basin contain elements within the safe limits for adults (Figure 6a, b). However, for children, 13% of the arsenic samples, 3% of the copper samples, and 1% of the lead samples present non-carcinogenic risks. They are within the acceptable limits regarding carcinogenic risk for adults and children (Figure 6c,d).
# Jumping Locomotion Strategies: From Animals to Bioinspired Robots
## 2 Jumping Animals and Biomimetic Miniature Jumping Robots
### Vertebrates
#### 2.2.3 Kangaroo-Inspired Jumping Robots  
Hopping technique is quite widespread among jumping vertebrates. In Australia, hopping has evolved both in the macropods (kangaroos, wallabies, and bettongs) and in hopping mice [162]. Outside of Australia, the biggest mammals which do hopping are springhares in Africa and lemurs. [PERSON] et al. [163] compared five groups of mammals that evolved bipedal hopping independently from each other, and proposed that bipedal hopping evolution is associated with predator avoidance by relatively small species in forest environments.  
As regards hopping of kangaroos, in the low velocity range the oxygen consumption increases as velocity increases, while it shows a slightly decreasing trend in the high velocity range. This means that high speed hopping is an efficient locomotion mode for kangaroos [164]. This is probably due to the exceptional elastic energy storage and recovery via long compliant tendons in the kangaroos' legs [8; 165]. The highest locomotion velocity in kangaroos are remarkably correlated to the body mass [166].  
The German robotics company Festo developed \"BionicKangaroo\" [167; 41]. It exploits an elastic spring that mimics the biological leg tendons to efficiently recover energy from one jump and to release it for powering the next jump. It can jump 0.4 m vertically and 0.8 m horizontally, with 7 kg body weight and 1 m body height. Nonlinear controllers are designed for the flight and stance phases [41]. The tail is used to compensate the torque created at the hip joint.  
[PERSON] et al. [168; 169] developed another kangaroo-inspired jumping robot. The locomotion of the Center of Mass (CoM) of the robot is based on the reduced-order Rolling Spring Loaded Inverted Pendulum (R-SLIP) model and an active tail is equipped for aerial righting. Compared to stationary tail and active tail with open-loop control, the experimental results show that active tail with closed-loop control can achieve much better aerial righting performance during flight.  
Another remarkable kangaroo-inspired jumping robot was developed by [PERSON] et al. [170]. It uses a four-bar mechanism and an eccentric cam to achieve intermittent jumping. The tail is used both in aerial righting and landing buffering.
## 2 PV/PDC-CTR-BSF Approach
### Reflected Solar Rays and Splitter Filter  
After their reflection from the PDC, solar rays intercept on the BSF surface, whose design is defined as the following:  
\[z=\frac{x^{2}+y^{2}}{4f_{s}}+z_{fs} \tag{2}\]  
where \(z_{fs}\) is the height of the BSF above the parabolic reflector and \(f_{s}\) is the BSF focal length. As was already established, the wavelength values determine when the reflected sunlight is discharged. The spectral width's bandpass area is expressed as the following [23]:  
\[\begin{cases}\rho(\lambda)=1&\quad if\ 380\ \text{nm}<\lambda<1100\ \text{nm}\\ \rho(\lambda)=0&\quad if\ 1100\ \text{nm}<\lambda<2500\ \text{nm}\end{cases} \tag{3}\]  
Equation (3) demonstrate that sun rays, having wavelengths values above 1100 nm, will be transmitted by the BSF and absorbed by the conical cavity receiver (CCR), whereas other sun rays are re-reflected and illuminate the solar cells panel.  
Figure 1: Two-dimensional and three-dimensional schematics representations of a parabolic dish solar system.
## 3 The Proposed Method
### Proposed Fault Diagnosis Method Based on MDFT-GAN  
Figure 3 illustrates the overall framework of the fault diagnosis method based on MDFT-GAN. The method consists of three components: multi-domain data fusion, the data augmentation model, and the fault classification model. These components work together to achieve multi-domain feature representation, high-quality sample generation, and accurate fault classification.  
The multi-domain feature representation process converts a one-dimensional vibration signal into a two-dimensional RGB image by extracting time-domain signals, frequency-domain features using the FFT, and time-frequency domain features via the STFT. This  
Figure 2: The structure of the Transformer.  
fusion retains the signal's local details while capturing its global distribution characteristics, providing high-quality input for downstream tasks.  
The data augmentation model employs the MDFT-GAN structure, comprising a generator (G) and a discriminator (D). The generator produces high-quality samples by modeling multi-domain features, while the discriminator enhances the quality of sample generation through adversarial training. The model utilizes multi-level MDFT modules to extract both global and local features, accurately capturing the data distribution and alleviating the impact of sample imbalance on classification performance.  
The fault classification model is structured as a hybrid feature extraction framework, incorporating a front-end convolutional module, a Transformer encoder, and an improved classification layer. Local features are extracted through the front-end convolutional module, while the Transformer encoder models global dependencies using a self-attention mechanism. Finally, the enhanced classification head ensures high-precision predictions across multiple fault categories.  
In summary, this method enables efficient modeling and diagnosis of vibration signals by leveraging multi-domain information fusion, data augmentation, and classification framework optimization.
## 4 Shear Strength Prediction Using Neural Hybrids of SFLA-ANN and WDO-ANN
### Defining Modeling Parameters  
As explained, the SFLA and WDO algorithms function as optimization techniques that aim to find the best solution for a given problem. When these algorithms are synthesized with ANN, the problem is appropriately finding the best values of the weights (\(W\)) and biases (\(b\)) of each neuron through minimizing the difference between the actual and predicted shear strength. An objective function (\(OF\)) is defined here to measure the error of performance. The optimization process is implemented by a vast number of iterations. In general, the OF is expected to follow a downward trend during these iterations, which indicates an increase in accuracy. Notably, the \(OF\) in this study was considered to be the root-mean-square error (RMSE) which is defined in Section 4.5. Considering \(X\) as the input of the output neuron, the following mathematical relationships exist:  
\[OF=RMSE\ (Output,Target), \tag{12}\]  
\[Output=Tansig\ \big{(}\sum WX+b\big{)}. \tag{13}\]