esa-sceva/satcom-chunk-collection huggingface dataset:
# A Risk-Sensitive Intelligent Control Algorithm for Servo Motor Based on Value Distribution
## 4 The Proposed Risk-Sensitive Intelligent Control Method
### The Structure of the Control Method
#### 4.1.2 Structure and Loss Function of Strategy Network  
As shown in Figure 8, the inputs of the strategy network are motor state \(s_{t}=[o_{t},a_{t}]\) and target \(g_{t}\), and its output is the duty ratio after normalization, which is denoted as \(\pi(s_{t},g_{t})\).  
In order to obtain a control strategy sensitive to the overcurrent problem, the risk-sensitive motor control algorithm based on the value distribution combines the expected value of the cumulative discount return with the conditional value at risk. When training a strategy network, it is necessary to assign a confidence level \(\varphi\) in advance to the algorithm, so that the  
\(\tau_{tt}\), respectively; (3) calculating its value of expectation, and then, obtaining the conditional value at risk at confidence level \(\varphi\), which can be denoted as follows:  
\[CVaR=\sum_{n=1}^{N}\hat{\tau}_{n}Z(s_{t},g_{t},a_{t},\tau_{n}). \tag{9}\]  
where \(\hat{\tau}_{n}=\frac{\tau_{n-1}+\tau_{n}}{2}\) denotes the mean of adjacent quantiles.  
In addition to calculating the conditional value at risk, the algorithm also needs to calculate the expectation of the whole probability distribution as a second-strategy evaluation index. Similar to the training of the value network, the positions of the quantiles are first uniformly sampled in the range \([0,1]\), then the probability distribution of the cumulative discount returns is obtained and its expected value is calculated. Therefore, the value network's actual evaluation for duty cycle output by the agent can be expressed as Equation (10).  
\[I_{a}=(1-\rho)E(Z(s_{t},g_{t},a_{t}))+\rho CVaR(Z(s_{t},g_{t},a_{t},\varphi)) \tag{10}\]  
Therefore, the objective function of the strategy network can be expressed as Equation (11).  
\[J(\varphi)=E_{s_{t}\sim\mathcal{D}}[\log\pi(a_{t}|s_{t},g_{t})-I_{a}] \tag{11}\]  
Equation (11) represents the updated objective function of the policy network, which adjusts the objective of the proposed control algorithm based on the structure and input-output of the value network and policy network, while the training method remains basically the same.  
In order to use the gradient descent method to train the policy network, it is necessary to make the random policy output of the network derivable. Therefore, the algorithm uses the reparameterization technique to calculate the output of the strategy network. The strategy network itself outputs two real values, \(\mu\) and \(\sigma\), corresponding to the mean and standard deviation of the normal distribution, respectively. The value of the output action \(a_{t}\) can be calculated via Equation (12).  
\[a_{t}=\varepsilon_{t}\times\sigma(s_{t},g_{t})+\mu(s_{t},g_{t}) \tag{12}\]  
where \(\varepsilon_{t}\) is noise sampled from a standard normal distribution, as shown in Figure 8.  
Figure 8: The structure of the strategy network.
## 2 Study Area and Dataset
### Sentinel 1A  
Sentinel 1 is a satellite constellation from the European Space Agency (ESA). Sentinel 1 includes two satellites--Sentinel 1 A/B--that carry C-band SAR imagery with a 6-day revisit with the availability of both satellites only in Europe and in some other limited areas while in the rest of the world, the images are provided every 12 days. Sentinel 1 provides dual-polarized Interferometric Wide (IW) swath data with Vertical Transmit-Vertical Receive (VV) and Vertical Transmit-Horizontal Receive (VH) polarizations data. IW swath mode features 5 \(\times\) 20 m spatial resolution and a 250 km swath. S1A images used in the study are shown in Table 2. In particular, all S1A Level-1 Ground Range Detected (GRD) images in 2018 were used for permanent water mapping. Ten S1A-GRD images between 1 February 2018 and 31 May 2018, were used to produce the rice map for the winter-spring season 2018. Nine S1A-GRD images from 1 July 2018 to 30 October 2018 were used to generate flood maps and the flood frequency map. Four S1A scenes were requested and mosaicked to fully cover the whole RRD region, in which two scenes with Relative Orbit Number (RoN) of 55 cover Quang Ninh and two other scenes with RoN of 91 cover the rest.
## 4 Experiments
### Evaluation Protocols  
To evaluate CNN-BiLSTM hybrid model classification performance in this study, we experimented with DS1 and adopted Accuracy, Precision, Recall, and F1-score as metrics. Furthermore, to evaluate the prediction performance of the proposed recommendation framework, we experimented with DS2 and adopted Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) metrics. We set 80% of each dataset as a training dataset and measure the performance with the remaining dataset [12; 58; 59].  
First, to evaluate the classification performance of the CNN-BiLSTM hybrid model, we adopted Accuracy, Precision, Recall, and F1-score as metrics using the confusion matrix shown in Table 4. Accuracy is the most used evaluation metric when measuring classification performance and represents the number of accurate classifications ratio of helpful and unhelpful reviews in the total classification results. Precision represents the contained ratio of actual helpful reviews to the classified helpful review by the model. The recall represents the contained ratio of the classified helpful review by the model to actual helpful reviews. The F1 score represents a balance weight average between precision and recall. A higher F1 score means a higher classification ability of the recommender system. The Accuracy, Precision, Recall, and F1-Score are defined in Equations (13)-(16).  
\[Accuracy=\frac{TP+TN}{TP+FN+TN+FP} \tag{13}\]  
\[Precision=\frac{TP}{TP+FP} \tag{14}\]  
\[Recall=\frac{TP}{TP+FN} \tag{15}\]  
Figure 4: Examples of helpful reviews and unhelpful reviews.  
Figure 3: Distributions of helpfulness scores.  
\[F1-Score=\frac{2\times Precision\times Recall}{Precision+Recall} \tag{16}\]  
The MAE and RMSE are statistical accuracy metric that evaluate prediction performance by comparing the difference between predicted and actual ratings, as defined in Equations (17) and (18) [7; 10]. The MAE gives the same weight regardless of the magnitude of the error between the actual and predicted ratings. However, RMSE gives a relatively high value weight with a large error between the actual and predicted ratings. When the value is low, the corresponding recommendation prediction is more accurate.  
\[MAE=\frac{1}{N}\sum_{i=1}^{N}\lvert y_{ui}-\hat{y}_{ui}\rvert \tag{17}\]  
\[RMSE=\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(y_{ui}-\hat{y}_{ui}\right)^{2}} \tag{18}\]  
where \(N\) is the total test dataset, \(\hat{y}_{ui}\) is the predicted rating, and \(y_{ui}\) is the actual rating by the user \(u\) for the item \(i\).
## 5 IoRT Systems Based on Space-Borne NTN Networks  
This category of satellite-based IoRT systems is the most prevalent, as it represents the most fundamental approach to establishing remote connectivity. This can be achieved through either the direct connection of remote devices to the satellite or through the establishment of a local network that utilizes a central communication gateway to interface with the satellite. Therefore, in this category, we identified 54 related research works that are classified according to their main research objective into the following subsections.
## 4 Result Analysis
### Theoretical and Review Studies
Specifically, besides the use of application-related data, they commented on the use of feedback and interaction as a means of multimodal learning analytics that takes students' motoric and physiological behavior into consideration to capture the social, situational, and environmental contexts in which they are engaged. The study also showcased related tools, sensors, and techniques to gather and present such data. [PERSON] and [PERSON] [70] focused on examining important issues related to the data that is generated from virtual reality experiences. Specifically, their study focused on the use of learning analytics and investigated the concept of algorithmic bias. They highlighted the need to regulate and standardize the application of learning analytics to virtual reality generated data. Based on the aforementioned studies, it is observed that efforts have been made to propose and present relevant frameworks regarding learning analytics and their use in augmented reality and virtual reality applications. However, little is known regarding the adoption of educational data mining within these environments. To define effective frameworks, it is important to consider the unique traits of extended reality technologies, the characteristics of learning analytics and educational data mining, design options, and ethical considerations. Additionally, studies have mentioned the use of appropriate APIs to aid in the identification, tracking, and monitoring of the data to offer personalized learning and customized learning paths. Efforts have also been made to present system architectures to develop applications that integrate these technologies and approaches and are capable of capturing, analyzing, and visualization learning data in real time and, in turn, aid both students and teachers. The studies also highlighted the need to carefully examine the influence of design elements on students' interaction and learning performance and to establish relevant learning and instruction theories and approaches. Artificial intelligence also arose as a key technology that can facilitate and improve the combination of extended reality technologies with learning analytics and educational data mining. Finally, the studies indicated that through the use of appropriate sensors, multimodal data generated from extended reality environments can effectively be identified, processed, and analyzed to provide students with interactive, immersive, adaptive, and personalized learning.
# Differential Evolution in Waveform Design for Wireless Power Transfer
## 2 Wireless Power Transfer System Model  
We consider a Single Input Single Output (SISO) WPT system model. This model corresponds to the simplest utilized WPT system, consisting of a transmitter, a multipath propagation channel, and a receiver rectenna (Figure 1). The wireless transfer of the power is obtained by generating and transmitting an RF signal which, after propagating across the medium, is received and rectified by the receiver rectenna (rectifier + antenna) to supply an electrical load. Hence, the objective is to design a waveform \(s(t)\), such that the DC power at the output of the rectenna (\(P_{out}\)) is maximized.
## 3 Results and Discussion  
### Evolution of the Mass and Appearance  
As the chemical ion erosion time increases, the appearance of CGB specimens at different periods is shown in Figure 2. At the early stage of erosion, the surface morphology changes are not noticeable; the shape is intact, and only some tiny pits appear. With the extension of the erosion time, the surface started to float slightly; when the erosion time increased to 120 days, CGB surface white crystals precipitated. At the same time, in the CGB surface layer dissolution phenomenon, the aggregate particles appear to be partially exposed, and the filling body from the outside to the inside of the whole became \"fluffy.\" After 150 days of erosion, the surface of the filling body turned yellow, and the surface layer was partially detached, with corrosion products attached, and numerous tiny holes and microcracks appeared.  
Figure 1: Experimental procedure and equipment.  
The recorded mass change in CGB under different erosion times, and the relevant data were shown in Figure 3. It can be found that with the increase in the erosion time, the mass of the CGB specimens immersed in the acidic aqueous solution did not decrease immediately at the beginning of erosion, but showed a trend of increasing first, and then decreasing. When the time of erosion reached 60 days, the mass of the specimen increased by 1.6% compared with that of the initial one, and then it began to decrease gradually. When the time of erosion reached 150 days, the rate of change in specimen mass was \(-0.5\%\), indicating that the specimen mass was lower than the initial specimen mass. Combined with the specimen's appearance in Figure 2, the ions in the erosion solution entered the specimen through the pores on the surface of the specimen to undergo a chemical reaction. The generated insoluble substances adhered to the inside of the pores, leading to a decrease in the overall porosity of the specimen, making the total mass increase. As erosion time was prolonged, excess insoluble material accumulated, leading to the expansion of microcracks within the specimen and the formation of small cracks. More erosion ions from the solution entered the interior of the CGB, and eventually, some of the products dissolve, leading to a decrease in the total mass.
# Capability Analysis of Earth Observation Data for Integrated Emergency Management
## 4 R2-D2 Case Study Results
### Synthesis
an extremely cost-effective solution for hosting applications of this nature, with running costs averaging approximately USD 0.20 per day during peak development and testing of the current prototype dashboard. The process for updating the dashboard focuses on pre-processing any new data to be incorporated and providing key results in a common format for display on the user interface. Testing and preliminary development were performed locally, with only the visual outputs published online. However, the development chain was built in such a way that the process could be automated. In particular, as illustrated in Figure 12, when new data become available from providers, they are processed by a series of Python tools to first convert them to a common data format before performing geospatial analytics. Such data can then be made available to the front-end via an API, providing access to static and dynamic data in near-real time, rendering end-user access to the most up-to-date information possible. Using cloud features such as S3 storage buckets and distributed APIs, the dashboard can provide access to a vast data collection, including historical archives, with minimal load on the client to ensure fast access. Building the dashboard from smaller components and pre-processing steps allows quick and easy expansion of the system to other case studies, as required. Supplementary data, such as satellite imagery, are already integrated into the dashboard and show selected tiles on the prototype dashboard available online. It is possible to expand this to show full satellite image archives, including historical imagery, to highlight changes in environmental data over time. Moving forward, a fully deployed operational version of the dashboard would incorporate additional functionality for the live reporting of critical issues, with a WebSocket system trialled to alert dashboard users of new or updated data as they become available and highlight any major issues that may require further analysis. Where severe issues are identified, cloud services such as AWS's Simple Notification Service could be used to directly alert key parties of actions that need to be taken via specific email or SMS updates. Figure 12: Architecture of the R2-D2 dashboard platform, including data storage, processing steps and distribution to the dashboard. This can be directly integrated into the rest of the dashboard to act on data immediately as they are processed, ensuring these critical issues are addressed with minimal delay.
# Exploring Cutout and Mixup for Robust Human Activity Recognition on Sensor and Skeleton Data +
## 2 Background Literature
In particular, approaches such as the aforementioned ST-GCNs and Dynamic Graph Convolutional Networks (DGCNs) [56] have been particularly successful in skeleton-based HAR, where the model is designed to simultaneously learn spatial features from the skeleton's structure and temporal features from the sequential nature of the data. Data augmentation is a technique used to improve the generalization and robustness of Machine Learning models by artificially increasing the diversity of the training dataset. The effectiveness of data augmentation has been demonstrated across various domains, including vision and natural language, which have led to improvements in model performance and stability [57]. Mixup is a specific data augmentation technique that generates new training samples by linearly interpolating between pairs of existing samples and their corresponding labels [44]. Initially introduced for image classification tasks, mixup has been shown to improve model generalization and robustness by encouraging the model to behave linearly between training examples. The mathematical formulation of mixup involves creating a new sample as a weighted combination of two samples and their labels, effectively smoothing the decision boundary of the classifier, according to the authors [PERSON] et al. in their research [44]. Previous studies have extended mixup data augmentation to other domains, including NLP, where it has also demonstrated significant benefits [58]. Another powerful data augmentation technique is cutout, which enhances model robustness by randomly masking out sections of the input data during training. Initially proposed for image classification by [PERSON] et al. in 2017 [45], cutout works by occluding parts of the input samples, forcing the model to rely on surrounding information to make accurate predictions. This technique has been shown to improve generalization by making the model less reliant on specific features and more capable of capturing broader patterns in the data. In the context of Human Activity Recognition (HAR), cutout data augmentation can be adapted to mask out key-point sections in skeleton data, simulating real-world conditions where some sensor data might be noisy or missing. This research shows that introducing such variability during training cutout helps models become more robust to partial occlusions and missing data, ultimately leading to improved performance in HAR tasks. By demonstrating the effectiveness of our approach through our experiments, we provide a potential solution to some of the critical challenges in HAR. The promising results from our experiments clearly show the value of this approach, paving the way for further exploration and application of data augmentation techniques in different domains of Human Activity Recognition.
## 4 The Performance of the Optimized SVR Model on Driving Decision-Making
### Influence Analysis of Road Conditions on the Reasoning Accuracy of DDM  
In order to verify the effects of road conditions on the accuracy of DDM, the reasoning results of three DDMs (include SVR-DDM with weighted hybrid kernel function, SVR-DDM with RBF kernel function and BPNN-DDM) with the following reference index combinations are compared:  
1. vehicle states + Road conditions are used as inputs; and,
2. only vehicle states are used as inputs.  
Three DDMs with the first reference index combination has already been trained and validated in the previous Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
For the second reference index combination, road conditions information is eliminated from the above 75% training samples and the remaining 25% testing samples. Then, three DDMs without considering road conditions are established using the same training method and tested with the testing samples. The reasoning results of three DDMs without considering road conditions are shown in the following Table 5.  
As illustrated in Tables 4 and 5, after eliminating the information of road conditions from the reference index set, the accuracy of SVR-DDM with weighted hybrid kernel function for free driving, car following and lane changing is reduced from 93.1% to 82.3%, 94.7% to 85.9% and 89.1% to 78.2%, respectively, SVR-DDM with RBF kernel function is reduced from 89.3% to 78.5%, 92.7% to 82.2% and 86.8% to 76.8% respectively, and the BPNN-DDM is reduced from 89.9% to 78.1%, 91.4% to 80.4% and 87.1% to 75.1% respectively. The results support the effectiveness of making driving decision with road conditions. In addition, although the average reasoning time of DDMs with added road conditions is higher than that of DDMs without added road conditions, the reasoning stability of DDMs with added road conditions is much better than that of DDMs without added road conditions. In general, DDM has better performance on reasoning driving decisions with added road conditions, which is further explained that the road condition cannot be ignored in driving decision-making.
# Sustainable Building Tool by Energy Baseline: Case Study
## 4 Analysis and Results
### Univariate and Multivariate Linear Regression Models for EnBL
The univariate linear regression model carried out (Figure 9) is described by Equation (5):  
\[\text{EnBL Consumption = 0.5082 \cdot Occupancy + 8015 + \varepsilon i} \tag{5}\]  
Figure 8: Total final energy consumption correlation with independent variables—RStudio software. In this study, a multivariate linear regression model was also conducted for one year, with all the available variables (temperature, humidity, radiation, and total occupancy). The statistical analysis of the variables for one year shows that the variables temperature and radiation are not significant in the multivariate linear regression model, since their \(p\) values are greater than 0.05. In this case, the significant variables in the model are the outdoor humidity and the occupancy variable. The optimal multivariate linear regression model built in RStudio statistical software is (Equation (6)):  
\[ \begin{split} Electricity\ Consumption_{EnBL}=-401.64\cdot Humidity+2.31 \cdot Occupancy+38,239.93+\varepsilon_{i}\end{split}  \tag{6}\]  
The model has an adjusted \(R2\) = 0.9082, indicating that the model explains 90.82% of the variability in the observations (data). The adjusted \(R2\) value measures the percentage of the variance in the model's response; however, it adjusts for the number of predictor variables in the model. The model has a \(p\) value of \(8.7\times 10^{-6}\) for the F-statistic, indicating that the linear correlation is statistically significant (\(8.7\times 10^{-6}\) < 0.05). To ensure that the model is valid, the overall test of statistical assumptions and the multicollinearity test were performed. Global test results (gvlma) and VIF test results for multicollinearity confirm that the regression model performed complies with the statistical assumptions. This result means it is valid for quantifying energy consumption as a function of the relevant variables \"humidity\" and \"total occupancy\". Table 7 shows a comparison of the estimated energy consumption of EnBL with the monitored energy consumption of the building and the MAPE forecast error.