esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Information Processing Module (IPM) Design
### Model Training and Evaluation  
In order to strengthen the conclusions from the previous section, the model was trained and tested on the full dataset (Figure 12) through 100 independent evaluations. In each evaluation, 20% of the original samples were randomly selected for training, while the remaining 80%, unseen by the model, were used for testing. The training and testing sets were unique in each run. The performance outcomes of the 100 trained and tested models are illustrated in Figure 15.  
The 100 models demonstrated consistently high performance, with the mean accuracy, precision, recall, and F1 scores all exceeding 99.8%.  
Considering that the FDT prioritizes simplified implementation on microcontrollers, two ML algorithms are particularly well suited for this binary classification task: logistic regression and decision trees. The LR model only requires the evaluation of whether the decision boundary is greater than zero, which eliminates the need to calculate the sigmoid and makes the model computationally efficient. Decision trees offer variable complexity, which depends on the depth of the tree: shallow trees require fewer sequential comparisons and corresponding \"_if-else_\" statements, while deeper trees demand more.  
A decision tree-based FDT was implemented to compare its performance against the LR FDT solution. The decision tree (DT) model was implemented with specific constraints to facilitate its implementation on a microcontroller and improve its generalization. The DT model was limited to a maximum depth of 6 and a minimum of 10 samples required to split an internal node, as both constraints improve the generalization of the model and maintain the simplicity of its implementation. The DT model demonstrated lower performance compared to LR. Following the same evaluation protocol applied to LR, the DT model was trained and tested on the full dataset (Figure 12) through 100 independent runs (Figure 16).  
Analysis of these 100 DT models revealed an average performance below 95.1% across all metrics.  
Therefore,  
Figure 17 shows the response of the proposed model (17) for the entire dataset (Figure 12).  
As expected, the previous figure shows that the proposed model has more difficulty distinguishing the capacitor state at severity levels S\({}_{3}\) and S\({}_{4}\), as these levels are closest to the transition between the two classes. Despite the previous incorrect predictions, the model's overall performance is excellent, with all metrics exceeding 99.8%.
## 5 Results and Discussion
### Numerical Studies
#### 5.1.3 Simultaneous Reconstruction of Absorption and Scattering Coefficient of Top Layer  
We then implemented the MC-LUT based optimization to solve Problem-III mentioned in Section 4.4.2. Synthetic SRR data were generated using MC simulation for two-layered numerical phantoms. The absorption and scattering coefficients of the bottom layer were fixed at 4.3 cm\({}^{-1}\) and 250 cm\({}^{-1}\). The true and recovered values of the optical parameters of the top layer, obtained by solving Equation (6) are tabulated in Table 3.  
It is well established that the optical parameters of the bottom layer can be recovered with little error [4; 15; 25; 26; 27; 31; 47; 48]. Therefore, we carried out the recovery of the top layer optical parameters, when the optical parameters of the bottom layer are known with \(\sim\)10% error and the results thus obtained are tabulated in Table 4. The first two columns of Table 4 consist of the true values of the optical parameters of the numerical phantoms. However, the MC-LUT used for extraction of top layer optical parameters correspond to the bottom layer optical parameters \(\mu_{a}=~{}4.3\) cm\({}^{-1}\) and \(\mu_{s}=~{}250\) cm\({}^{-1}\). We observed that the inaccuracy in the optical properties of the bottom layer affects the accuracy of the recovered top layer absorption coefficient (\(\sim\)20% error). However, the top layer scattering coefficient is still recovered accurately (\(<\)6% error).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Phantom ID**} & **True Value** & **Extracted Value** & **Std. Deviation** \\  & \(\mu_{s}\) **(cm\({}^{-1}\))** & \(\mu_{s}\) **(cm\({}^{-1}\))** & **(cm\({}^{-1}\))** \\ \hline
1 & 30 & 31 & 3 \\
2 & 50 & 51 & 2 \\
3 & 70 & 69 & 2 \\
4 & 90 & 91 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Reconstruction of scattering coefficient of top layer from simulated data with prior knowledge of absorption coefficient of top layer and both optical properties of bottom layer (Problem-II).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Phantom**} & **True Value** & **Extracted Value** & **Std. Deviation** \\  & **(cm\({}^{-1}\))** & & **(cm\({}^{-1}\))** & & **(cm\({}^{-1}\))** \\ \cline{2-6}  & \(\mu_{a}\) & \(\mu_{s}\) & \(\mu_{a}\) & \(\mu_{s}\) & \(\delta\mu_{a}\) & \(\delta\mu_{s}\) \\ \hline
1 & 5.2 & 72 & 5.4 & 74 & 0.1 & 2 \\
2 & 1.7 & 72 & 1.6 & 69 & 0.2 & 3 \\
3 & 5.2 & 32 & 5.4 & 36 & 0.1 & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Simultaneous reconstruction of absorption and scattering coefficients of the top layer from simulated data with prior knowledge of both the optical properties of bottom layer.
# Water Quality Monitoring: A Water Quality Dataset from an On-Site Study in Macao
## 5 Data Validation
### Data Validation Through Anomaly Detection Algorithms
#### 5.2.3 Logistic Regression  
Logistic Regression [30] is a simple interpretable model used for classification tasks, making it a good baseline for evaluating the usability of a dataset. In water quality monitoring, Logistic Regression is applied to predict water quality labels (normal, borderline, and abnormal) based on parameters such as pH and TDSs. While it may not capture complex non-linear interactions between features as well as more sophisticated models like Random Forest, it provides a valuable benchmark for determining whether the relationships between the data and the labels are sufficiently simple to be modeled using linear decision boundaries. Logistic Regression is also easy to interpret, which makes it useful for understanding how changes in water quality parameters influence the predicted labels. Figure 21 presents the Random Forest AUC.
## 4 Pairwise Clock Synchronization under Exponential Delays
### Joint Estimation of Clock Offset and Skew under Exponential Delays  
In general, the clock synchronization in WSNs involves two steps. The first step assumes synchronizing the nodes by adjusting the clock phase offset (close offset), while the second step resumes correcting the clock frequency offset (clock skew). The clock skew correction plays a critical role in clock synchronization, especially for long-term synchronization, since it reduces the number of exchanged messages and, thus, brings down the energy consumption. Therefore, we discuss the joint estimation of clock offset and skew in this section.  
In the literature, based on a two-way message exchange mechanism, the studies for joint estimation of clock offset and skew mainly concentrated on the development of MLEs, and there is no closed-form solution for the joint estimation of clock offset and skew under exponential delays. Roughly speaking, the algorithms for developing MLEs can be categorized into two types: in the first category, a suboptimal model is built by removing some nuisance parameters, and the corresponding MLEs are derived, while in the second category, the joint estimation is conducted directly.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Clock Offset**} & \multicolumn{4}{c}{**Symmetric Delays**} & \multicolumn{4}{c}{**Asymmetric Delays**} \\ \cline{2-9}  & **Formula** & **Bias** & **Variance** & **MSE** & **Formula** & **Bias** & **Variance** & **MSE** \\ \hline MLE [28, 29] & \(\frac{U_{(1)}-V_{(1)}}{2}\) & 0 & \(\frac{\lambda^{2}}{2N^{2}}\) & \(\frac{\lambda^{2}}{2N^{2}}\) & \(\frac{U_{(1)}-V_{(1)}}{2}\) & \(\frac{\lambda_{1}-\lambda_{2}}{2N}\) & \(\frac{\lambda_{1}^{2}+\lambda_{2}^{2}}{4N^{2}}\) & \(\frac{\lambda_{1}^{2}+\lambda_{2}^{2}-\lambda_{1}\lambda_{2}}{2N^{2}}\) \\ \hline BLUE [30, 31] & \(\frac{U_{(1)}-V_{(1)}}{2}\) & 0 & \(\frac{\lambda^{2}}{2N^{2}}\) & \(\frac{\lambda^{2}}{2N^{2}}\) & \(\frac{N(U_{(1)}-V_{(1)})-(\overline{U}-\overline{V})}{2(N-1)}\) & 0 & \(\frac{\lambda_{1}^{2}+\lambda_{2}^{2}}{4N(N-1)}\) & \(\frac{\lambda_{1}^{2}+\lambda_{2}^{2}}{4N(N-1)}\) \\ \hline MVUE [31] & \(\frac{U_{(1)}-V_{(1)}}{2}\) & 0 & \(\frac{\lambda^{2}}{2N^{2}}\) & \(\frac{\lambda^{2}}{2N^{2}}\) & \(\frac{N(U_{(1)}-V_{(1)})-(\overline{U}-\overline{V})}{2(N-1)}\) & 0 & \(\frac{\lambda_{1}^{2}+\lambda_{2}^{2}}{4N(N-1)}\) & \(\frac{\lambda_{1}^{2}+\lambda_{2}^{2}}{4N(N-1)}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of estimators.
## 6 Experimental Results
### Experiment D
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Dataset C: Imbalanced +** & **Accuracy** & **Precision (+)** & **Recall (+)** & **Precision (—)** & **Recall (—)** \\ \hline Decision Trees & 96.21\% & 2.50\% & 16.97\% & 98.02\% & 98.22\% \\ SVM & 61.5\% & 5.2\% & 74.1\% & 98.3\% & 63.8\% \\ Naive Bayes & 77.75\% & 9.1\% & 65.7\% & 98.8\% & 74.3\% \\ Deep Learning & 75.15\% & 8.50\% & 69.20\% & 99.5\% & 76.53\% \\
**GBM** & **88.20\%** & **21.5\%** & **75.1\%** & **99.6\%** & **97.9\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Performance of GBM against other benchmarking methods for Dataset C positive imbalanced without any measures for the class imbalance problem. classifier and even considering advanced feature selection techniques cannot guarantee the creation of a robust prediction model. As with the previous dataset, Figure 5 represents the outcome of the evaluation, this time by considering the sub-sampling measures for class imbalance. The improvement in the negative class (both precision and recall) is apparent when compared to the previous table. Notice that precision was initially varying from 6.5% to 19.9%, and now, it has reached almost 58.5%. Finally, we should mention that GBM was constantly found to be the most robust classification method, in all forms of datasets, with and without measures for the class imbalance problem. The parameters for the third experiment, either for the positive or the negative imbalanced sets, are tabulated below, on Table 10. \begin{table}
\begin{tabular}{c c} \hline \hline
**Algorithm** & **Parameters** \\ \hline Decision Trees & Criterion: Gain Ratio; Maximal Depth: 30; Confidence: 0.2; Minimal Leaf Size: 4 \\ SVM & Kernel Type: radial; kernel gamma: 1.8; C.0.5; Epsilon: 4 \(\times\) 10\({}^{-4}\) \\ Naive Bayes & _No parameters to optimize_ \\ Deep Learning & Activation: Rectifier; Hidden Layers: 2; Loss Function: Huber; Epochs: 50 \\ GBM & Loss Function: Ada-boost; Number of Trees: 60; Max-depth: 4; Learning rate: 0.25 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Parameter values, upon completion of the parameter tuning process for Experiment D.  
Figure 5: Evaluation scores of all classifiers against the negative imbalanced set upon taking measures for the class imbalance problem.
## 4 Numerical Simulation Analysis of Loose Layer Fissure Evolution
### Mechanical Experiment of the Loose Layer  
A cylindrical specimen, measuring 50 mm in diameter and 100 mm in height, was prepared following the Geotechnical Test Methods Standard (GB/T50123-2019) [26], ensuring a height-to-diameter ratio (h/D) between 2.0 and 2.5. The \"TAS-LF automatic triaxial system\" was utilized to conduct a \"consolidated-undrained shear test\" on the soil samples. The experiment utilized a shear rate of 0.5 mm/min under varying confining pressures of 100 kPa, 200 kPa, 300 kPa, and 400 kPa to simulate different stress conditions. The average results from the four tests were taken as the final mechanical parameters for the corresponding soil samples (Figure 8). The test parameters comprised shear strength, elastic modulus, Poisson's ratio, internal friction angle, cohesion, and friction coefficient. The test results categorized the loose layers into five distinct types, as outlined in Table 1.  
Figure 8: Shear failure of the specimen at varying pressures: (**a**) 100 kPa; (**b**) 200 kPa; (**c**) 300 kPa; and (**d**) 400 kPa.
# Virtual Pseudonym-Changing and Dynamic Grouping Policy for Privacy Preservation in VANETs
## 4 Proposed Solution
### Pseudonym-Changing Protocol
#### 4.2.1 The Normal Pseudonym Update Process  
The pseudonym update process takes road context information such as vehicle velocity range, direction, and transmission range neighbors. The GH will monitor the road environment for the change of the pseudonym process. The number of neighboring vehicles is calculated with the help of Algorithm 5. Once the \(NeighThreshold\) is met, the GH announces the group formation protocol for the collective pseudonyms change process as discussed in Section 4.1. Initially, each vehicle will set a flag value to 1, which means it is ready for the pseudonym updating process. A timer is used to trigger every member of a group to change pseudonyms simultaneously. After changing pseudonyms, the member vehicle notifies the GH and sets its flag value to 0, which means pseudonyms are successfully updated. The flow diagram of the pseudonyms update process at certain road context information is shown in Figure 7.
## 4 Discussion
### Effects of LAI and Climate Change on WUE Variation
Vegetation must change its structure and physiological function to adapt to the arid environment, and its water-use mode is different from that in other regions. In these regions, soil evaporation is intensified by excessive precipitation since precipitation may be asynchronous with water needs for vegetation growth. [PERSON] et al. [66] and [PERSON] et al. [67] found that increasing precipitation might facilitate the growth of ET under extremely arid conditions, but it slightly influenced GPP, consequently decreasing the WUE. It is interesting that increasing precipitation can result in a relatively higher contribution to the WUE trend of forest and swamp in northeast IM. This might be because the WUE and precipitation formed a positive correlation under the background of low temperature, which limits ET. Meanwhile, increasing precipitation may increase soil volumetric water content and thereby facilitate the photosynthesis-induced carbon assimilation of vegetation, finally increasing WUE [68]. Soil erosion caused by wind is one of the major environmental issues in IM. Increasing wind speed may facilitate the increase in ET and extreme wind erosion may blow away organics and nutrients in soils, thereby deteriorating soil fertility and influencing vegetation growth [21]. Our study found that wind speed had the largest negative effects on the WUE. Increasing wind speed had a negative contribution to the WUE in 52.19% of the study area (Table 2). In arid regions, increasing relative humidity might increase ET [69], and we found that a decrease in relative humidity had a positive contribution to the increase in WUE. In summary, the influences of the changes in different factors on the WUE variation can reflect different responses of an ecosystem to climate change to some extent. In addition to LAI and climate factors, other factors such as elevated atmospheric CO\({}_{2}\) concentration can have an impact on the WUE variation. Elevated CO\({}_{2}\) can influence the changes in GPP and ET, thereby influencing the WUE [70; 71; 72]. Previous studies found that increased CO\({}_{2}\) dominated the increased global WUE over the last three decades [73; 74]. In the southern region of China, CO\({}_{2}\) is the driving factor with the highest contribution rate over four seasons in a year [67]. However, the carbon balance of ecosystems in semi-arid regions is strongly influenced by climate change [75]. [PERSON] et al. [27] found that the increase in GPP and ET in the Mongolia-Xinjiang sub-region is attributed to the increase in precipitation. [PERSON] et al. [19] found the contribution of increased atmospheric CO\({}_{2}\) on the WUE was limited in northwest China. [PERSON] et al. [76] showed that the WUE variations in most of the tropical forests were dominated by the increased CO\({}_{2}\), while those in the semi-arid regions and boreal forests were dominated by the LAI or climate change. Thus, the increased CO\({}_{2}\) may only have a weak impact on the WUE variation, relative to that of vegetation or climate factors in our study region.
# Matching of Energy Provisions in Multihop Wireless Infra-Structures
## 3 System Model  
### Wireless Multihop Infrastructures (WMI) Network Model and Application Scenarios  
Figure 1 shows the basic WMI network model and its application scenarios. A WMI network consists of a gateway (GW), Base Stations (BSs), and user stations (STAs), supporting multihop wireless access from STAs to an external network. Each BS has integrated wireless access point (AP) function and wireless mesh point (MP) function. The GW has a MP interface and a bridge to an external network. In general, STAs can communicate locally with BSs, or communicate with each other in a WMI network via BSs, or communicate with external networks via BSs and the GW.  
This paper concentrates on the discussion of homogeneous WMI networks. The homogeneous type of base stations with regard to the communication protocol stacks and energy support methods facilitate the quick deployment of networks, easy maintenance, and low cost of networks.  
There are various radio types of base stations with regards to diversity of capacity, interfaces, and frequency utilizations. On the other hand, the most basic radio type of a BS is with single interface and shared channel for easy configuration and quick applications [34; 35; 36]. From the application aspect, this paper focuses on the applications of quick network deployment, low cost BSs for emergency situations, event networks, and temporary networks, _etc._ Therefore, we basically adopt single interface BS stations with shared communication channel in this paper.  
Figure 1: WMI network scenarios. **(a)** Night-time operation of a smart sensor network; **(b)** Emergency operation of a network for emergency information and support services.
# Design, Simulation, and Comparison of Advanced Control Strategies for a 3-Degree-of-Freedom Robot
## 6 Discussion
#### Practical Applications  
Currently, many robotic systems are used in industry, agriculture, and healthcare. For example, the authors in [29] present the simulation of two control schemes that allow decoupling of the joint control scheme, plus a dynamic compensation loop--a feature that gave it the name PD+ and Computed Torque for manipulator robot trajectory using the dynamic model of the CoBot UR5. Their results indicate that both controllers performed adequately in trajectory tracking. In precision agriculture, robotic arms present significant technical advantages, such as improved operational precision and efficiency [32]. Today, harvesting heavy crops is considered a laborious task, specifically in Japan, which has faced a severe labor shortage in agricultural fields [33]. The authors present a procedure for the development and evaluation of a 4-DoF articulated robotic arm, designed as a drive unit for an outdoor agricultural environment tractor robot. In the medical field, the authors in [34] analyze the control of a 7-DoF robotic arm for medical delivery, generating a point cloud map of the workspace of the redundant robotic arm within the allowable joint range to obtain the foundations for motion control and trajectory planning of medical delivery robots. In [35], the authors indicate that robotics and artificial intelligence are gaining increasing importance in the pharmaceutical industry. Future applications of artificial intelligence and robots in healthcare include the development and manipulation of medications, identification of fatal diseases, remote prescription monitoring, and geriatric care, among others. In [36], the authors report that the pharmaceutical industry is undergoing rapid progress due to advancements in digitalization and automation, from drug invention to administration. Robotics plays a vital role in complex processes such as research, development, production, and packaging. Worker safety, precision, drug delivery speed, and product quality have also improved due to the development of robots. As a result, today, a large volume of production robots is seeking more roles in small to medium-sized menus. Robotics in the pharmaceutical field accelerates the manufacturing process, allowing multiple medications to be prepared in a short time. In [37], the authors indicate that bolt support is the main form of support for underground roadways in coal mines in China. Currently, there are some problems in domestic coal mines, such as low efficiency and poor safety. The authors designed a structural scheme for an omnidirectional, multi-angle automatic roof bolter. First, based on the DH
## 3 Results  
### Application of 4 HR Increases GPx Activity and TAC in Dental Pulp Cells  
TAC in the H2O2 treated control was 0.223 \(\pm\) 0.003 mM (Figure 1a). TAC in the resveratrol group was 0.231 \(\pm\) 0.002 mM, 0.236 \(\pm\) 0.004 mM and 0.245 \(\pm\) 0.005 mM in the 1 \(\upmu\)M, 10 \(\upmu\)M and 100 \(\upmu\)M treated groups, respectively. Treatment with 10 \(\upmu\)M and 100 \(\upmu\)M resveratrol significantly increased TAC compared to that of the controls (\(p\) = 0.002 and 0.001, respectively); while TAC in the 4 HR group was shown to increase in a dose-dependent manner, with 0.228 \(\pm\) 0.003 mM, 0.235 \(\pm\) 0.007 mM and 0.247 \(\pm\) 0.002 mM in the 1 \(\upmu\)M, 10 \(\upmu\)M and 100 \(\upmu\)M groups, respectively. Treatment with 10 \(\upmu\)M and 100 \(\upmu\)M 4 HR significantly increased TAC compared to the controls (\(p\) = 0.013 and \(<\)0.001, respectively), while there were no significant differences between the 4 HR and resveratrol groups (\(p\) > 0.05). GPx activity in the H2O2 treated control was 1.089 \(\pm\) 0.022 mu/mL (Figure 1b), while for the resveratrol treatment, it was 1.244 \(\pm\) 0.001 mu/mL, 1.246 \(\pm\) 0.002 mu/mL and 1.559 \(\pm\) 0.008 mu/mL in the 1 \(\upmu\)M, 10 \(\upmu\)M and 100 \(\upmu\)M groups, respectively. Treatment with 10 \(\upmu\)M and 100 \(\upmu\)M resveratrol significantly increased GPx activity compared to the controls (\(p\) = 0.009 and \(<\)0.001, respectively) and GPx activity in the 4 HR group was 1.247 \(\pm\) 0.021 mu/mL, 1.309 \(\pm\) 0.015 mu/mL and 1.411 \(\pm\) 0.017 mu/mL in the 1 \(\upmu\)M, 10 \(\upmu\)M and 100 \(\upmu\)M groups, respectively. Treatment with 10 \(\upmu\)M and 100 \(\upmu\)M 4 HR significantly increased GPx activity compared to the controls (\(p\) = 0.005 and 0.001, respectively). However, no significant differences were observed between the 4 HR and resveratrol groups (\(p\) > 0.05).  
4 HR Decreases the TNF-\(\upalpha\) and IL1\(\beta\) Expression Induced by Hydrogen Peroxidase  
Hydrogen peroxide treatment increased TNF-\(\upalpha\) and IL1\(\beta\) expression (Figure 2) and this effect was reversed upon the addition of 4 HR in the pre-treatment step.  
Figure 1: Results of the antioxidant activity assays. (**a**) Total antioxidant capacity (TAC). 4-Hexylrescorinol (4 HR) (black) induced a similar level of TAC production as that of resveratrol (grey). (**b**) Glutathione peroxidase (GPx) activity. 4 HR (black) and resveratrol (grey) significantly increased GPx activity compared to H2O2 only treated control (* \(p<0.05\) compared to the hydrogen peroxide group, mean \(\pm\) standard deviation).