esa-sceva/satcom-chunk-collection huggingface dataset:
# Mapping irrigated Areas Using Sentinel-1 Time Series in Catalonia, Spain
## 4 Results
### Classification Using Convolutional Neural Network
#### 4.3.3 CNN Using Combined SAR and Optical Data  
The application of the two branches of the CNN model (SAR and optical branches) discussed in Section 3.7 shows that the classification model using both datasets did not considerably increase the accuracy of the classification. The overall accuracy of the combined model reaches 94.5% \(\pm\) 0.05. The kappa coefficient and the F-score increased by 1% only. The accuracy of the irrigated class has increased with 1% while that of the non-irrigated class remained stable (Table 9).
## 4 Discussion
### Limitations and Future Research  
Although this study focused on the impact of soil moisture at different depths on ecosystem stability (measured by EVI and GPP), it primarily examined soil moisture as the main variable. Other factors influencing ecosystem stability, such as atmospheric CO\({}_{2}\) concentration, temperature, precipitation, and radiation, were not considered. It is worth noting that potential uncertainties stemming from data sources and modeling approaches, such as MODIS signal saturation effects, inherent assumptions in GPP model formulations, and potential biases in reanalysis datasets, were not explicitly discussed in this study. These aspects warrant careful consideration and evaluation in future research endeavors. Previous studies have shown that changes in atmospheric CO\({}_{2}\) concentration significantly affect GPP, with its contribution varying spatially across regions. While some of these variables also contribute importantly to ecosystem stability, they were excluded from our study. Moreover, changes in ecosystems are not solely driven by natural factors; human activities, such as land use changes and irrigation, may interfere with and even amplify or weaken the impact of soil moisture on ecosystem stability. Our study did not quantitatively analyze the effects of such human activities. Therefore, further research is needed to comprehensively and quantitatively evaluate the impact of various environmental factors on ecosystem stability. This study used EVI and GPP data to assess the influence of soil moisture at different depths on ecosystem stability across different climatic zones in China, thereby enhancing our understanding of potential changes in ecosystem stability under future climate change.
## 4 Discussion
### Feature Variables Selection, Model Construction, and Variable Contribution to the Model
#### 4.3.4 Analysis of Performance Variations Across Training, Validation, and Test Sets  
RF's performance varied across the training, validation, and test sets, raising questions about the causes of this inconsistency, particularly in feature importance.  
(1) Random forest: as an ensemble learning method, RF typically fits the training set well by constructing multiple decision trees and combining their predictions. This often leads to better performance on the training set than on the validation or test sets. For instance, the R\({}^{2}\) for the training set on January 15 is 0.81, higher than 0.58 for the validation set and 0.55 for the test set. Similarly, on August 26, the R\({}^{2}\) for the training set is 0.82, while the validation and test sets have R\({}^{2}\) values of 0.37 and 0.36, respectively. This shows random forest's tendency to fit the training data strongly. The similar performance of the validation and test sets suggests the model is reliable.  
(2) Real-World Complexity: It is nearly inevitable that the training set will perform better than the validation and test sets. Salinity prediction is inherently challenging, and the model cannot capture all real-world data during training. The unpredictability of real-world scenarios means that new, unknown factors may arise during actual predictions, which is why training set performance often surpasses that of the validation and test sets (unless, as discussed later, independent test sets are not used, leading to overfitting or data leakage).  
(3) Data Leakage Risk in Other Studies: Many studies rely on the performance of training and validation sets to draw conclusions, which can be unreliable due to potential data leakage, particularly in multi-round training. High R\({}^{2}\) values in these sets may indicate overfitting rather than genuine model performance. This study mitigated this issue by introducing an independent test set, tuning hyperparameters based on the validation set performance, and verifying conclusions with the training set. The consistency between training and validation set performance further validated the reliability of the research findings.
## 3 Potential Strength Assessment
### Pucinotti Method  
In this section, the analytic expression introduced in Section 2 is updated with the addition of new coefficients in order to consider the influence of excess voidage and addition of water to the cement mixer:  
\[f_{cm,p,p}=\left(\sum_{i=1}^{n}\alpha_{2}^{i}\cdot C_{dia}^{i}\cdot C_{d}^{i} \cdot C_{1/d}^{i}\cdot C_{stelf}^{i}\cdot C_{mc}^{i}\cdot C_{t}^{i}\cdot C_{H_{ 2}O}^{i}\cdot C_{com}^{i}\cdot f_{carota,i}\right)/n \tag{8}\]  
The following additional parameters are considered:  
* \(C_{H_{2}O}\) takes into account the addition of water to the cement mixer. According to [PERSON] et al. [10], the following value is suggested: \(C_{H_{2}O}^{i}=\frac{1}{1-0.007\cdot\left(\frac{Acq}{881}\right)}\), where \(A_{cq}\) is the quantity of water (expressed in N) added to the mix for each m\({}^{3}\) of concrete.
* \(C_{com}^{i}\) takes into account the influence of excess voidage. It is therefore evident how compaction plays an important role in reducing the strength of in situ concrete when compared to the potential strength.  
With regards to the influence of excess voidage, a new estimate procedure is introduced in the following section.  
Influence of Excess Voidage  
As already mentioned, when the potential density (unit weight) of cube (or cylindric) specimens is not available, report 11 [1] suggests comparing the number and size of voids exposed on the drilled surface of the same core with those displayed in some figures contained in report 11. The method consists of a visual evaluation of the compactness of the concrete estimating the percentage of excess voids in the test specimen with respect to those in the reference specimens, i.e., comparing the core surface with photos of cores of different masked voidage content in BS 1881-120 (Figure 1a-e) [1,3] by measuring excess voidage, which is the amount by which actual voidage exceeds the voidage of a well-made cube of the same concrete.  
British standards suggest that _where the description needs to be amplified, this shall be done by reference to the following terms:_  
1. _Small void. A void measuring not less than 0.5 mm and not more than 3 mm across in any direction._
2. _Medium void. A void having a dimension greater than 3 mm but not greater than 6 mm._
3. _Large void. A void having a dimension greater than 6 mm._
4. _Honeycombing. Interconnected voids arising from, for example, inadequate compaction or a lack of mortar._
## 4 Design of the Algorithm  
### K-Means Algorithm  
The consumer locations are distributed in a wide area. The goods delivery efficiency can be improved if each rider only serves users in a certain area. The consumer nodes should be divided into different clusters based on their location. Hierarchical, graph-based, and K-means clustering methods can be considered for the node-dividing process. Considering that the hierarchical clustering method has a high computational complexity and the graph-based method has poor scalability, the K-means clustering method, which is computationally efficient and adaptable to dynamic delivery conditions, is employed to distribute multiple consumer locations into several groups based on their distance from each other. The purpose of the K-means algorithm is to divide n data objects into k clusters to minimize the sum of the squared Euclidean distances from each data point to its cluster center [30; 31; 32]. The value of k can be determined upon the actual application.  
The evaluation standard of the K-means algorithm is shown in Equation (7).  
\[J(c_{j})=\sum_{x_{i}\in c_{j}}\big{\|}x_{i}-\mu_{j}\big{\|}^{2},\quad j=1,\ldots,k. \tag{7}\]  
where \(k\) indicates how many clusters the data needs to be divided into, \(x_{i}\) is the dataset to be classified, \(c_{j}\) is the \(j\)-th cluster, and \(\mu_{j}\) is the center of the \(j\)-th cluster.  
The clustering goal is to minimize the total sum of the squared distances, which can be realized according to the flow chart, as shown in Figure 1.  
Step 1: Import the data that needs to be clustered, and set the value of cluster number \(k\) according to the actual application.  
Step 2: Select the initial cluster centers randomly.  
Step 3: Calculate the distance between each data and each cluster, and allocate each data to its closest cluster.  
Step 4: Update the cluster and recalculate each cluster center to be the average of its data.  
Step 5: Go back to step 3 until the result satisfies the iteration termination condition.
# Landslide Susceptibility Assessment Using the Geographical-Optimal-Similarity Model
## 3 Research Methodology
### Geographical-Optimal-Similarity Model Prediction
#### 3.4.3 Estimation of the Optimal Similarity Threshold  
The sample set is divided into training and testing sets in a 1:1 ratio for similarity evaluation. The training and testing process is repeated 20 times to ensure result accuracy and stability. Values from the interval (0, 1] are sequentially selected based on a set interval, with corresponding sample data selected from the training and testing sets. The average RMSE value from multiple experiments serves as a reference, with the value corresponding to the minimum RMSE chosen as the optimal similarity threshold for the best training model.
## Appendix C Deep Reinforcement Learning Fundamentals  
In reinforcement learning, Markov Decision Processes (MDPs) are utilized to represent the controlled environment with a set of states \(\mathbf{s}\in\mathcal{S}\) and a set of actions \(\mathbf{a}\in\mathcal{A}\) (cf. [27; 28]). The state transition probability \(p:\mathcal{S}\times\mathcal{S}\times\mathcal{A}\rightarrow[0,\,\infty)\) determines the likelihood of observing the state \(\mathbf{s}_{k+1}\) in the next time step \(k+1\) after applying the action \(\mathbf{a}_{k}\) in the state \(\mathbf{s}_{k}\) at time step \(k\). After every state transition, a reward \(r_{k+1}=r(\mathbf{s}_{k},\mathbf{a}_{k})\) is observed. This setup describes the so-called agent-environment interaction and is shown in Figure 14. During this interaction, the agent learns to find an optimal stochastic control policy \(\pi^{*}(\mathbf{a}_{k}|\mathbf{s}_{k})\) which maximizes the expected discounted sum of rewards, also called the return \(G\), represented by  
\[G=\sum_{k}\mathbb{E}_{(\mathbf{s}_{k},\mathbf{a}_{k})\sim\rho_{\pi}}[r(\mathbf{s}_{k},\mathbf{a} _{k})], \tag{15}\]with \(\mathbb{E}[\cdot]\) denoting the expected value and \(\rho_{\pi}\) representing the state-action marginal of the trajectory distribution caused by the stochastic policy \(\pi(\mathbf{a}_{k}|s_{k})\)[27]. During training, the agent should prefer (exploit) actions that have generated high rewards in the past but also try (explore) new actions that might potentially generate higher rewards. Once the training procedure is completed, a deterministic policy is retrieved by applying the expected value of the stochastic policy in every state \(s_{k}\).  
Recently, several methods have been proposed that solve reinforcement learning tasks by applying artificial neural networks. In this work, we utilize the Soft-Actor-Critic (SAC) [27] algorithm which addresses the maximum entropy learning objective [29] and aims at finding an optimal policy \(\pi^{*}\) by solving  
\[\pi^{*}=\arg\max_{\pi}\sum_{k=0}^{T}\mathbb{E}_{\pi}[r(\mathbf{s}_{k},\mathbf{a}_{k})+ \alpha\mathcal{H}(\pi(\cdot|\mathbf{s}_{k}))] \tag{10}\]  
with \(\alpha\) denoting the temperature parameter and \(\mathcal{H}(\cdot)\) the entropy of the policy. This objective in Equation (10), compared with the standard reinforcement learning objective introduced in Equation (11), initiates the maximization of the entropy in each state, where the entropy is viewed as a measure of randomness. Inherently, the policy is encouraged to apply an increased amount of exploration during the training process. It should be noted that the standard reinforcement learning objective can be restored by setting the temperature parameter \(\alpha\) to zero.
## 4 Performance Analysis of Proposed Algorithm  
To estimate the time complexity and convergence speed of the given Algorithm 1 to simulate the proposed models in a CRN, we need to analyze each step of the pseudocode and derive the complexity in terms of the number of users \(N\) and the buffer capacity \(K\).
# EMD-Based Noninvasive Blood Glucose Estimation from PPG Signals Using Machine Learning Algorithms
## 2 Materials and Methods
### Segmentation  
The PPG signal data are systematically segmented for feature analysis. The process begins with extracting data from CSV files, each containing PPG signal information including date, subject details, meal status, and blood glucose levels. The data are loaded into pandas DataFrames, and unnecessary columns are removed to focus only on the red, green, and blue (RGB) wavelength values. The segmentation process divides the continuous PPG signal into 30-s intervals. This is accomplished by splitting each complete DataFrame (the entire PPG signal record) into several smaller sub-DataFrames, each representing one of these 30-s windows. After removing certain columns, the data are converted to numpy arrays. These arrays are then layered to create a three-dimensional numpy array for each subject. This array captures the segmented RGB signal data over time. In the final step, we combine the data from all subjects into a standardized order. To maintain consistency, each subject's data are truncated to the size of the smallest subject data length. The end result is a uniform three-dimensional array, containing segmented PPG signal data from all participants, ready for further analysis. With a sampling rate of 24 Hz, each of these smaller sub-DataFrames contains 720 samples, corresponding to a time span of 30 s.
## 2 Related Work
### Deep Learning Methods  
The deep learning methods were introduced to overcome the drawbacks of traditional methods. Various deep learning models have been introduced to date. In the paper [20], the author introduced an end-to-end defect detection network (EDDN) on the metallic surface based on the single shot multibox detector (SSD). The detection model base comprises two modules; one is the visual geometry group (VGG)16 for feature extraction and non-maximum suppression, as the model focuses on different scales of defects. Due to class imbalance, the author introduces a hard negative mining technique to resolve the issue. The mAP evaluated on the NEU dataset is around 72.4%. In the paper [21], [PERSON] et al. introduced an automated and more sophisticated approach for defect detection on the steel surface. The architecture uses Xception as the CNN model for defect detection. The model performs a two-step classification of four types of defects, first the binary classification for identifying the presence of a defect or not with resulting accuracy of 94% and then the multi-classification to categorize the different types of defects with 85% accuracy. This technique focuses on the classification method and not localization. In the paper [22], the author proposes a defect classification model that endorses GoogleLeNet as its base model for feature extraction. This model uses inception modules for extraction and a softmax classifier for classification. The model achieves 98.5% classification accuracy. In the paper [23], the author uses the VGG model [24] for defect classification with an accuracy of 90%. In the paper [25], the author proposed a defect detection system DDN consisting of residual neural network (ResNet) 50 [26] as a base model for classification. They introduce the multi-level feature network for fusing all the features into one, which further helps the region proposal network to evaluate the regions of interest for better detection. These regions of interest are severed into two fully connected layers for classification and detection, resulting in 82.3% detection accuracy. In the paper [27], the author proposes an improvised faster region-based convolutional neural network (Faster-RCNN) [28], along with the support of multi-scale feature fusion for defect detection, resulting in 98.26% accuracy. The model performed data augmentation to reach this result. Various other convolutional neural networks (CNN), such as the OverFeat network [29], are the pre-trained models on the ImageNet and COCO dataset and are used as a feature extractor for defect detection. Various state-of-the-art approaches, such as Faster-RCNN [28], SSD [30], You Only Look Once V2 (YOLOV2) [31; 32], YOLO-V3 [33; 34], including the above two, are introduced for the defect detection on steel or metal surface. These models outperform the traditional methods on all classes of defects. In the coming section, the proposed model is compared with the traditional and state-of-the-art methods.
## 5 Simulation and Analysis
### Comparison of Methods
As the number of primary jammers increases, the jamming gain increases significantly. Due to the constant number of total jammers, when the primary jammers account for a large proportion, the number of available relative jammers decreases accordingly, which forces some of the primary jammers to have to choose the relative \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **Time Complexity** & **Interference Success Rate** \\ \hline MSGA & \(O\)(\(10^{4}\)) & 95.682\% \\ GA & \(O\)(\(10^{6}\)) & 91.492\% \\ DP & \(O\)(\(10^{8}\)) & 87.403\% \\ GD & \(O\)(\(10^{4}\)) & 60.733\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Method performance comparison. Figure 13: BER comparison under different optimisation methods. jammers with lower gains, which leads to a decrease in the overall utility value. From the utility function point of view, the utility function values of the algorithms in this paper are higher than the other three methods for different numbers of primary jammers. Compared to the GA algorithm, the utility function value of the proposed algorithm in this paper is improved by a maximum of 0.7187 times when the number of primary jammers is 8, and a minimum of 0.4993 times when the number of primary jammers is 6. Compared to the DP algorithm, the utility function value of the proposed algorithm in this paper is improved by a maximum of 1.2110 times when the number of primary jammers is 4, and a minimum of 1.0502 times when the number of primary jammers is 10. Compared to the GD algorithm, the proposed algorithm in this paper improves the utility function value by a maximum of 3.4386 times when the number of primary jammers is 3, and a minimum of 2.8927 times when the number of primary jammers is 10.
# Effect of Working Atmospheres on the Detection of Diacetyl by Resistive SnO2 Sensor
## 3 Results and Discussion
### Gas Sensor Measurements
#### 3.2.4 Sensor Response towards Diacetyl in Aqueous and Alcoholic Solutions  
Once the effect of the different carrier/regeneration atmospheres on the performance of SnO\({}_{2}\) towards diacetyl was evaluated, just oxygen-deficient atmospheres were considered. Indeed, alcoholic fermentation, producing ethanol and carbon dioxide, usually occurs in anaerobic environments, since it does not require oxygen. Hence, tests were further performed in oxygen-deficient atmospheres, N\({}_{2}\) and CO\({}_{2}\), in: (i) aqueous diacetyl solution (0.4 mg/L); (ii) alcoholic (5% ethanol) diacetyl solution (0.4 mg/L), and (iii) alcoholic (5% ethanol) solution, for comparison purposes. The response values along with the response and recovery time are summarized in Figure 4.  
The sensor in both N\({}_{2}\) and CO\({}_{2}\) environments shows a higher response to ethanol than diacetyl, reaching 1.80 and 1.22, respectively, with shorter response and recovery times. In alcoholic diacetyl solution, the response in N\({}_{2}\) ambient registers a value of 1.61, similar to the value found with the only-diacetyl solution, and response and recovery time results are similar, showing that under N\({}_{2}\) atmosphere it is very difficult to distinguish diacetyl effects in aqueous or alcoholic solutions. Instead, in a CO\({}_{2}\) atmosphere an increment in the response towards diacetyl alcoholic solution, with a final value of 1.27, is detected. In turns, the sensor, despite the lower response than in CO\({}_{2}\), succeeds in distinguishing diacetyl under fermentation conditions. Moreover, response time in CO\({}_{2}\) shows a marked decrement (46%) compared to only diacetyl. Similarly, recovery time is reduced too.
## 3 Results  
### Shear Behaviors of Loess and Paleosol Samples  
The outcomes of the direct shear tests for loess and paleosol samples are illustrated in Figures 2 and 3, respectively. Under this experimental condition, whether the shear stress-shear displacement curve belongs to strain softening or strain hardening depends not only on normal stress but also on the initial moisture content. As the initial moisture content and normal stress increase, their shear failure behavior gradually transitions from strain softening to strain hardening. For loess samples, when the initial moisture content is smaller than 17% and the normal stress is 50-100 kPa, the shear stress-shear displacement curves are of strain softening, and as the normal stress increases to 400 kPa, their curves under an identical initial moisture content gradually transition to strain hardening. Moreover, under a state of a high initial moisture content, they exhibit strain hardening characteristics even with a low normal stress. The shear behavior of paleosol samples is consistent with that of loess samples, and yet the strain softening phenomenon is more notable when compared with loess samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{6}{c}{**Components/\%**} \\ \cline{2-7}  & **Quartz** & **Feldspar** & **Calcite** & **Chlorite** & **Illite** & **Amphibole** \\ \hline Loess & 52.3 & 14.8 & 18.7 & 8.4 & 4.9 & 0.9 \\ Paleosol & 54.1 & 16.3 & 14.4 & 9.9 & 5.3 & / \\ \hline \end{tabular}
\end{table}
Table 2: Mineral compositions of loess and paleosol samples.  
Figure 2: Curves of shear stress versus shear displacement for loess samples.
# Research on Backstepping Linear Active Disturbance Rejection Control of Hypersonic Vehicle
## 2 Basic Theory
### Altitude Subsystem
#### 2.2.1 Track Angle Loop  
By modifying Equation (2), the track angle loop can be expressed as a first-order system, as shown in Equation (18).  
\[\left\{\begin{aligned} \dot{\gamma}&=f_{\gamma}+g_{ \gamma}\theta\\ y_{\gamma}&=\gamma\end{aligned}\right. \tag{18}\]  
where \(f_{\gamma}=\frac{T\sin\alpha-0.31015\
u V^{2}S\gamma}{mV}-\frac{(\mu-V^{2}r \cos\gamma)}{V^{2}r^{2}}\) and \(g_{\gamma}=\frac{0.31015\
u V^{2}S}{mV}\).