esa-sceva/satcom-chunk-collection huggingface dataset:
# Unsupervised SAR Imagery Feature Learning with Median Filter-Based Loss Value
## 6 Results
### Features and Weights of the Kernels  
The neural networks were supplied with displays of the kernel weights of the convolutional neural networks and features displayed for each layer. An example is shown in Figure 11. The kernels were extracted from the trained simple convolutional network's first layer (index 0) trained with filtering. These are not all the kernels in these layers, but they do represent a very distinct few tendencies of the CNN. The lower layers of the network create features that are angle-invariant. For example, kernels in the first column, second row, and fourth column 3 rd row represent quasi-symmetrical functions similar to the \(sinc\) function. Other variants of the same/very similar patterns occur in four directions (edge and gradient detectors can be seen easily in this figure).  
For comparison, a set of weights of a neural network trained without filtered labels is shown in Figure 11. The comparison of the networks is difficult kernel-wise, but an analysis of the kernel weights during different experiments leads to the conclusion that networks with filtered labels create more chaotic weights for a human observer. This can be seen in the attached starting convolutional layers kernels.  
Due to the problem with kernel analysis, a set of features for a given image was calculated for each network. For comparison, in Figure 12 the features of the starting and middle layers of \"Compressing CNN\" are shown as examples. This setup clearly shows the advantage of the filtered labels as more features are extracted in the deeper layers. This leads to the creation of a better set of features and better image replication.  
It is worth noting that in the appliance of the network as a data compressor, the first network would be better suited as a high percentage of the kernels could be pruned, especially if a metric such as L1 or L2 was used during training.
## 7 Conclusions  
1. A novel anisotropic damage model is proposed to improve the conventional isotropic damage model. It avoids the interpenetration of crack surfaces and unifies three forms of damage, which include pure tension, tension-shear, and compression-shear, as one smooth criterion. In addition, the shear damage evolution equation considers the equivalent shear strain on the failure face as the independent variable, which has a clearer physical meaning.  
Figure 17: Load–displacement curve of Brazilian splitting test.  
Figure 18: Fracture mode of disc specimen, (**a**) simulated result of elastic modulus; (**b**) experimental result, reprinted with permission from [52], 2021 Elsevier.  
The proposed anisotropic damage model is able to simulate the failure behavior of quasi-brittle materials more effectively. It can effectively simulate not only the mesoscale pure tension, tension shear, and compression shear damages but also the macroscale fracture mode and its evolution. It can also simulate the strength characteristics, including the peak and residual strengths, approximately.
* The simplified two-dimensional geometries were used to verify the proposed anisotropic damage model by comparison with a conventional damage model. However, the proposed anisotropic damage model and finite element implementation strategy proposed in this study also apply to three-dimensional, multi-scale, and multi-field coupling problems.
* Numerical tests of the failure behavior of quasi-brittle materials have the advantages of strong universality, convenience, flexibility, and repeatability compared with laboratory tests. In addition, they can provide mesoscale mechanical information that is difficult to observe in laboratory tests. However, owing to the limitations of the nonlinear solution method, numerical tests will take longer if there are more loading steps. The combination of numerical simulation and machine learning algorithms will help improve the efficiency of numerical tests better to serve the scientific research and teaching of quasi-brittle materials, which is also a follow-up research goal of the authors.  
Software, X.D.; formal analysis, P.J. and X.Z.; writing--original draft preparation, H.W.; writing--review and editing, B.Z.; supervision, S.X.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by [National Natural Science Foundation of China] grant number [51804323] and [Independent Innovation Research Program of China University of Petroleum (East China)] grant number [27 RA2215005].  
The authors thank the supports from the National Natural Science Foundation of China (grant no. 51804323) and the Independent Innovation Research Program of China University of Petroleum (East China) (grant no. 27 RA2215005).  
The authors declare no conflict of interest.
# Computational Load Analysis of a Galileo OSNMA-Read Receiver for ARM-Based Embedded Platforms +
## 5 Computational Burden Analysis of the Complete Receiver
### Test Setup  
The tests of the complete receiver signal processing chain have been carried out by feeding the navigation message employed in the OSNMA functions profiling to a NAVX-NCS professional GNSS signal generator [37]. Table 8 summarizes the main parameters of the test setup. In particular, for the Galileo signals' generation, the navigation message with the set of values S4 in Table 3 was used, thereby requiring the heaviest load for the digital signature, as shown in the previous section. The generated RF signal is then given as input to a RF FE, whose configuration is reported in Table 8.  
Two test campaigns have been carried out: in the former the receiver was launched in post-processing mode, thereby reading the file of raw samples previously grabbed, and in the latter it was launched in real-time, thereby elaborating on the fly the RF signal. In particular, the former configuration was needed to perform the software profiling analysis of the main receiving functions, whereas the latter one was adopted to measure the real-time processor load required by the receiver over time. The next two subsections report the results achieved during the two test campaigns.
## 3 Methodology
### NPP Estimation
#### 3.1.1 Model Description
\[\mathrm{R_{m,i}}=\mathrm{M_{i}r_{m,i}Q_{10}^{(T-T_{b})/10}} \tag{19}\]  
where \(\mathrm{M_{i}}\) is the biomass of different plant tissues, \(\mathrm{r_{m,i}}\) is the maintenance respiration coefficient for the tissue \(\mathrm{i}\), \(\mathrm{Q_{10}}\) is the temperature sensitivity factor, \(\mathrm{T}\) is the air temperature, and \(\mathrm{T_{b}}\) is the base temperature. These allometric relationships were derived from an extensive literature review and incorporated the same parameters as those used in the BIOME-BGC ecosystem process models ([PERSON] 1993; [PERSON] et al., 2000). The \(\mathrm{R_{g}}\) was linearly correlated with the NPP [71]. \[\mathrm{R_{g}}=0.25\times\mathrm{NPP} \tag{20}\]  
Then, Equation (13) can be calculated as follows:  
\[\mathrm{NPP}=\mathrm{GPP-R_{m}-R_{g}}=\mathrm{GPP-R_{m}-0.25\times\mathrm{NPP}} \tag{21}\]  
Further,  
\[\mathrm{NPP}=0.8\times\left(\mathrm{GPP-R_{m}}\right)\ \ \mathrm{When\ GPP-R_{m}}\geq\ 0 \tag{22}\]  
\[\mathrm{NPP}=0\ \ \mathrm{When\ GPP-R_{m}}<0 \tag{23}\]
# Cosmo ArduSiPM: An All-in-One Scintillation-Based Particle Detector for Earth and Space Application
## 5 Preliminary Test of the System as Spectrometer  
Once the performance of Cosmo ArduSiPM electronics is detailed, we can try to observe the device being assembled into a scintillation detector. Specifically, we provide an overview of its potential as a gamma spectrometer.  
The readout system we just characterized is composed of a SiPM Hamamatsu 13360-6025 CS (Hamamatsu, Hamamatsu City, Japan) which, thanks to its 6 \(\times\) 6 mm\({}^{2}\) surface, allows for a broader light collection. The SiPM is then coupled to a BrillanCe(r)880 scintillator (Saint-Gobain Ceramics & Plastics, Inc., St. Pierre less Nemours, France) [25] (Cerium-doped Lanthanum Bromide) and exposed to a \({}^{60}\)Co source, as shown in Figure 18.  
\({}^{60}\)Co is an excellent calibration source because it undergoes beta decay with a maximum energy of 317.9 keV and emits gamma rays with well-defined energies of 1173.2 keV and 1332.5 keV. These distinct and well-characterized energy peaks provide precise reference points for calibration.  
LaBr\({}_{3}\)(Ce) has a high radiation yield and response times suitable for the chosen SiPM without any special adjustments.  
The acquisition results denote the detector's ability to function as a gamma spectrometer [26].  
Referring to Figure 19, we distinguish, from left to right, the 209.8 keV backscattering peak, the 963.4 keV Compton edge, the 1173.2 keV first gamma peak, the 1332.5 keV second gamma peak, and the 2614 keV peak, which is the sum of the previous two. By fitting the two gamma peaks with a double Gaussian, we can obtain the mean and deviation of the measurement, performing an energy calibration for this SiPM-scintillator coupling. With these data, we calculate two \"ADC step to keV\" scale conversion factors and choose to use the average of the two as the reliable value. For the configuration 6025 CS SiPM-LaBr\({}_{3}\)(Ce) scintillator, the \({}^{60}\)Co calibration gives us 1 keV = 1.82 \(\pm\) 0.03 ADC steps.  
Figure 18: Block diagram of the Cosmo ArduSiPM test bench for spectrometric measurements.
## 3 Methodology
### Overview  
This study employs a mixed-method approach, combining quantitative and qualitative analyses to investigate the impact of incorporating regional variations in language on synthetic datasets for emotion detection. The methodology encompasses four key stages:  
1. **Dataset Generation**: Synthetic datasets are generated using the GPT-3.5 language model, incorporating English variations from the United States, United Kingdom,and India, alongside a general baseline dataset. Two levels of prompt specificity are employed to assess the influence of regional linguistic nuances on the generated data.
2. **Statistical Analysis**: A range of statistical analyses are conducted to evaluate the linguistic diversity and regional differences in the generated datasets. These analyses include frequency distribution, TF-IDF, type-token ratio, hapax legomena, PMI scores, and key-phrase extraction.
3. **Classification Experiments**: BERT and BART models are employed to conduct classification experiments, starting with zero-shot classification on the CARE dataset, followed by fine-tuning with both baseline and region-specific datasets. This stage evaluates the impact of region-specific synthetic datasets on emotion detection performance.
4. **Evaluation**: The performance of the models is evaluated using standard metrics, and comparisons are made between the baseline and region-specific datasets to assess the impact of regional language variations on model performance.  
This comprehensive methodology allows for a thorough investigation of the research questions and hypotheses, providing insights into the role of regional linguistic variations in synthetic dataset generation and their impact on emotion detection performance. The complete methodological framework is depicted in Figure 1.
## 3 Results
### Analysis of Classification Results  
#### 3.2.1 Evaluation Index  
In the field of machine learning, the confusion matrix is also called the possibility table or error matrix. It is a visualization tool used by a specific matrix to show the performance of the algorithm [27]. In this study, a confusion matrix was used to evaluate the performance of the classifier. As shown in Figure 5, each row of the matrix represents the actual category for the sample, and each column represents the predicted category for the sample. The numbers on the diagonal of the matrix represent the number of samples correctly identified for each category. It can be seen from Figure 5 that 55, 58, and 52 samples were correctly identified from classes 1, 2, and 3. Among them, 5 samples from class 1 were predicted to be class 3. For class 3, 7 samples were predicted to be class 1 and 1 sample was predicted to be class 2. For class 2, 1 sample was predicted to be class 1 and 1 sample was predicted to be class 3. This shows that some spores of the three types of airborne disease spores of greenhouse crops may be similar in color, texture, and shape characteristics. In particular, the three types of airborne disease spores have similar shape characteristics. The results were consistent with the size parameters for the three types of airborne disease spores measured in this study.  
In addition, common classification performance metrics are used to evaluate classification models in this study. These classification performance metrics include accuracy, precision, recall, and a comprehensive evaluation index F value (F1-score). The calculation equations are shown in Equations (17)-(20):  
\[A=\frac{\mathit{TP}+\mathit{TN}}{\mathit{TP}+\mathit{TN}+\mathit{FP}+\mathit{ FN}} \tag{17}\]  
\[P=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FP}} \tag{18}\]  
\[R=\frac{\mathit{TP}}{\mathit{TP}+\mathit{FN}} \tag{19}\]  
Figure 5: Confusion matrix for the SVM model. Note: Axis number representations: (1) B. cinerea spores; (2) P. xanthi spores; (3) P. cubensis spores.  
\[F=\frac{2 PR}{P+R} \tag{20}\]  
In the equation, TP (True positive) is used to represent the actual number of samples predicted to be positive. TN (True negative) is used to indicate the number of samples that are actually in the negative class and predicted to be negative. FP (Fasle positive) is used to indicate the number of samples that are actually negative and predicted to be positive. FN (Fasle negative) is used to indicate the number of samples that are actually in the positive class and predicted to be negative. When the classifier performs the classification task, the actual number of disease spores to be predicted is regarded as the number of positive samples, while the sum of the other two types of disease spores is the number of negative samples. The classification results for the support vector machine are shown in Table 2.
## 3 Materials and Methods
### Models Overview  
#### 3.7.1 Decision Tree  
The Decision Tree (DT) model, a widely used non-parametric supervised learning algorithm [33], was employed in this study to classify human activities using data from multiple sensors. The core strength of DT lies in its ability to recursively partition the feature space based on decision rules, ensuring interpretable classification. However, recognizing diverse human activities from multi-sensor data presents challenges, requiring careful parameter tuning to balance model complexity and prevent overfitting.  
In this study, we optimized the DT using the Gini impurity criterion to guide node splits, aiming to maximize the purity of the resulting partitions. The formula for Gini impurity is as follows:  
\[Gini=1-\sum_{i=1}^{n}p_{i}^{2}, \tag{12}\]  
where \(p_{i}\) is the probability of a data point belonging to class \(i\).  
Hyperparameters such as the criterion for splitting, maximum depth of the tree, minimum samples per leaf, and minimum samples required to split an internal node were optimized using the nested cross-validation and Keras Tuners as described in Section 3.6. The optimal hyperparameters for each sensor configuration are presented in Table 7.  
In the non-overlapping configurations, the _entropy_ criterion was preferred, with a maximum depth of 5 to prevent overfitting and ensure robust pattern recognition. For 50% overlapping data, the minimum samples per split were increased to better handle the additional data points introduced by overlapping windows, addressing potential overfitting.  
The switch from _entropy_ to _Gini impurity_ for the four-sensor 50%-overlapping setup reflects the need to handle more complex feature interactions. This adjustment allowed for better computational efficiency while maintaining high classification accuracy [26].  
In summary, tuning the DT hyperparameters ensured the model could generalize effectively across sensor setups and data segmentation strategies, offering accurate classification without unnecessary complexity. The DT model proved to be a reliable classifier for human activity recognition, especially when optimized for different sensor configurations [33].
# Analysis of Ground Subsidence Vulnerability in Urban Areas Using Spatial Regression Analysis
## 1 Introduction
Additionally, a prediction map of ground subsidence in the target area was created based on the regression equation of underground utilities and ground subsidence history information derived through the spatial regression analysis. It is expected that ground subsidence accidents can be prevented by using ground penetrating radar (GPR) and other types of equipment to explore the ground holes in the high-risk areas presented in the ground subsidence risk prediction map created in this study.
# Energy-Efficient Connected-Coverage Scheme in Wireless Sensor Networks
## 5 Algorithm for the Energy Saved Maximization Problem
### Improved DEEC Protocol
#### 5.2.2 Uneven Clustering  
To more reasonably divide the network, the uneven cluster structure is constructed. For the CH around the sink (which is defined as the CHA), except for processing the data from the cluster, it is also responsible to transmit the sending data from other CHs far away from the sink. Therefore, this kind of the CH has much higher energy consumption rate than others. To avoid the early death of the CHAs, their energy consumption load should be released, which means that the cluster size of the CHAs should be smaller. While, for the CH away from the sink, since the energy consumption rate of them is small, the cluster size of the CHs can be increased. As described above, the calculation of the cluster radius is defined as  
\[R_{c}=\left(1+\alpha\frac{E_{i}(r)}{E(r)}-\beta\frac{d_{\text{max}}-d(i,\sin k )}{d_{\text{max}}-d_{\text{min}}}\right)*R_{r}. \tag{17}\]  
where \(R_{r}\) is assumed as a constant, e.g, 40 m.
## 2 Materials and Methods
### Validation and Assessment of Model Approach  
Model validation was performed by comparing the independent validation dataset with the prediction results using confusion matrices and calculating the performance statistics, namely the Overall Accuracy and the Kappa Coefficient, used to access the overall performance and the quality of the models, the Producer's Accuracy (omission errors), and the User's Accuracy (commission errors), calculated for each habitat class. The Overall Accuracy is the percentage of correctly classified pixels when considering all pixels. The Kappa Coefficient describes how much better the classification produced is when compared to a classification produced purely by chance, with values ranging between 0 (no better than a random classification) and 100 (100% better). The Producer's Accuracy is the number of true positives divided by the total number of predictions within a class,expressed as the percentage of pixels of a certain habitat class that were correctly predicted. It describes for each habitat class the probability (expressed as a percentage) of a random pixel in the field being correctly classified on the map. The User's Accuracy for each habitat class describes the probability (expressed between 0 and 100) of a random pixel on the map corresponding with the same habitat class on the ground [98; 99; 100; 101; 102]. See Table S2 for details on the equations of all the performance statistics used.  
To assess and quantify the advantages of associating Sentinel-2 with Sentinel-1 and DEM to map the intertidal areas, we compared the final model with three other starting models: a model using only Sentinel-2 bands and indexes, a model including Sentinel-2 and Sentinel-1 imagery, and a model including Sentinel-2 and DEM information layers. The three models were subjected to the same workflow, and the comparisons with the best model were made by comparing their Overall Accuracies and Kappa Coefficients.
# Production of Alternative Fat from Adipose-Derived Stem Cell from Bovine in 3D Culture
## 1 Introduction
Moreover, cultured meat should be produced in a 3D culture using supporting materials such as biomaterials, since tissues are in 3D structures composed of diverse cell types. Lately, scientists have unveiled methodologies for cultured meat production through diverse scaffold types, constructed by 3D printed bioink or textured soy proteins, to replicate the structural composition of meat. This study aimed to generate fat, a key component of cultivated meat, by cultivating bADSCs and combining with TVP as a scaffold during differentiation period. To obtain bADSCs in vitro, stromal vascular fraction (SVF) was isolated from bovine subcutaneous fat tissue and subsequently cultured. Cellular characterization was performed using immunocytochemistry, Western blot, and flow cytometry. Adipogenic differentiation was carried out in both 2D and 3D culture systems. Subsequently, the cellular components of adipocytes were analyzed by gas chromatography to determine whether the ADSCs-derived adipocytes have a fatty acid composition similar to animal-derived fatty acids.
## 3 Results and Discussion
### Total Phenolic Content  
The sum of phenolic compounds depended on the type of tea and the additive used in the infusion (Table 1). The black tea infusions contained slightly lower levels of phenolic compounds (256.2 mg/100 mL) than the white tea infusions (270.6 mg/100 mL), which confirms previous results reported by [PERSON] et al. [12]. In turn, [PERSON] et al. [37] observed more pronounced discrepancies in the content of these compounds between varieties. The differences between the presented results may be associated with, e.g., the origin of the analysed teas, different climatic conditions, and the method of harvesting, the preparation of the product, and its storage [10; 38]. The additives used had a different effect on the total phenolic compound content depending on the type of tea. In the black tea infusions, each of the additives used had a positive effect on the TPC concentration (Table 1). The highest concentration of total phenolic compounds was observed in the black tea infusion supplemented with strawberry fruit and sweet pepper, where a 20% increase in the content of these compounds was noted, compared with the black tea infusion without the additives (Table 1). Similarly, [PERSON] and [PERSON] [39] showed that the content of active compounds in lemon juice-supplemented infusions was higher than that in non-supplemented ones. The amount of polyphenolic compounds contained in tea infusions largely depends on the temperature and time of brewing. [PERSON] et al. [8] reported the highest content of active compounds in infusions brewed at a temperature of 98 \({}^{\circ}\)C for 15 min. These conditions were also used during the preparation of the infusions described in the present study. The additives used were selected considering their high content of vitamin C [40; 41; 42]. Lemon, quince, and strawberry are widely known and used tea additives. Sweet pepper was used in the present research because it has a number of health-promoting properties in addition to the high content of vitamin C [22; 43]. Moreover, it can be an attractive element of novelty for the rapidly growing tea market, which is looking for new experimental solutions. In contrast to the black tea infusions, a negative effect of the fruit additives on the content of phenolic compounds was observed in the white tea (Table 1). The strongest negative effect was exerted by the quince addition, where the infusion contained almost 21% fewer phenolic compounds than the infusion of pure white tea