esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 WBANs: The Key M2M Area Network Technology for mHealth Applications  
As seen in the previous section, many of the challenges present in M2M mHealth systems are related to the area network domain, thus stressing the important role of WBAN technologies. Hence, in this section, we aim to provide an overview of the research work on some key aspects of WBAN design. First, we provide a concise description of IEEE 802.15.6, which is the latest IEEE standardization activity targeted specifically to WBANS. Then, we describe the main channel modeling methods and propagation characteristics for body area communications. In continuation, we focus on different PHY layer technologies that can be employed in WBANs. Finally, this section ends with an up-to-date survey of the most representative MAC mechanisms designed in the context of mHealth applications in WBANs.
## 3 Results
### Comparison between Different Orbits  
It is found that precipitation estimated from SMAP_D delivers more favorable consistency with rain gauge observations than SMAP_A with higher \(R\) (0.72), lower _RMSE_ (8.35 mm/5 d), higher _POD_ (0.73), and higher _TS_ (0.71). As demonstrated in the above section, given the relatively good performance of SM2 RAIN based on SMAP_D, OBS_D delivers negligible improvement in precipitation estimation.  
To compare the differences in precipitation estimates between various SMAP orbits, Figure 7a was reorganized as Figure 14. Obviously, among all three SM2 RAIN estimates with SMAP, SMAP_D and SMAP_AD produce more accurate estimation of precipitation, followed by SMAP_A, as seen from most of the evaluation metrics except for _BIAS_ and _FAR_.  
Specifically, SMAP_D outperforms SMAP_AD in terms of RMSE, BIAS, and FAR, while SMAP_AD outperforms SMAP_D in terms of R, POD, and TS. This indicates that better agreement with rain gauge observations can be achieved by considering both ascending and descending orbits. Furthermore, SMAP_D outperforms SMAP_A, probably due to two reasons: (1) relatively more accurate soil moisture retrievals in the morning owing to increased thermal equilibrium conditions among the near-surface air, vegetation canopy, and surface soil, as demonstrated by [PERSON] et al. [66]; (2) more retrievals and smaller sampling intervals for SMAP_D than for SMAP_A, as seen from Figure 5. Hence, the SMAP retrievals from descending and combined orbits are recommended to derive SM2 RAIN precipitation products for the central TP.
# Limitations and Performance Analysis of Spherical Sector Harmonics for Sound Field Processing
## 5 Near Edge Distortion Problem
Due to this distortion issue, to enhance the performance of SSH in decomposition and reconstruction, the measurement region \(\mathbb{M}\) should be larger than \(\mathbb{T}\). Figure 11: Reconstruction error in the elevation direction \(\epsilon_{e}(\theta,k)\) with different \(\theta\) values. Figure 10: Illustration of the mapping process for both elevation angle and azimuth angle perspective: (**a**) map the target spherical sector region \(\mathbb{T}\) (\(\theta\in[\theta_{1},\theta_{2}],\phi\in[0,2\pi)\)) to a whole sphere \(\mathbb{S}^{2}\) (\(\theta\in[0,\pi],\phi\in[0,2\pi)\)); (**b**) map the target spherical sector region \(\mathbb{T}\) (\(\theta\in[0,\pi],\phi\in[\phi_{1},\phi_{2})\)) to a whole sphere \(\mathbb{S}^{2}\) (\(\theta\in[0,\pi],\phi\in[0,2\pi)\)).
## 3 Results
### Three-Class Problems  
#### 3.2.1 Multi-Class MMFT for Three-Class Cross-Session Classification  
In this section, we investigate the impact of a three-class cross-session classification problem on transfer learning performance. Multi-class MMFT framework is employed and compared with MEKT, and two ML algorithms are implemented through a traditional BCI pipeline. A three-class (Left, Right, and Up) EEG dataset consisting of nine sessions acquired from a single subject on different days is used for performance evaluation. Moreover, for this experiment, multi-class MMFT only performs transfer learning without domain selection. As such, when multi-class MMFT is employed, eight sessions are considered source domains, and a single session is a target domain; all nine sessions take a turn as a target domain.  
Figure 5 shows the comparison results between the proposed method, MEKT, and ML algorithms (LDA and RegTree) [34]. From the comparison, one finds that both transfer learning algorithms achieved the best performance across individual target domains. Multi-class MMFT, in this instance, achieved the highest CA of 88.7% when Se6 is a TD and (Se1-Se5 and Se7-Se9) are SDs, while MEKT recorded a highest CA of 80% when Se3 is a TD and (Se1-Se2 and Se4-Se9) are SDs. Moreover, utilizing samples from sessions acquired on different days to train and test the classifiers demonstrated to be an obstacle for ML algorithms, mainly due to the challenge of overfitting, which in turn deteriorated the classification performance. Hence, an inferior CA of 48%, which is the best performance by LDA, is observed when Se7 is used to test and (Se1-Se6 and Se8-Se9) are used to train the classifier, while a highest CA of 42% was recorded when (Se1-Se8) are used to train, and Se9 is used to test RegTree [17; 34; 35].  
Multi-class MMFT recorded superior classification performance, however, NT proved to pose a severe implication on classification performance across individual TDs. Hence, a significant decline in accuracy was observed when Se8 is the TD and (Se1-Se7 and Se9) are SDs. In this instance, a lowest CA of 22.7% was achieved when multi-class MMFT is employed. A drastic decrease in CA is also recorded when Se2 is the TD and (Se1 and Se3-Se9) are SDs. Subsequently, a lowest CA of 20% was achieved when MEKT is employed. The challenge of overfitting demonstrated to be an obstacle when ML algorithms are employed and contributed to significantly poor classification performance. Hence,lowest accuracies of 34% and 26% were recorded for both LDA and RegTree when Se2 is used to test and (Se1 and Se3-Se9) are used to train both classifiers, respectively [17; 36].  
Multi-class MMFT in this experiment illustrated that individual sessions could yield a significant high prediction rate for a three-class cross-session classification. However, the existence of variations across sessions leads to the transfer of knowledge from non-related domains, which in turn affects the prediction rate across individual sessions, as illustrated in Se 4, 7-9 in Figure 5, where the multi-class MMFT performed poorly.
## 3 Results and Discussion
The differences between the BCN node entropies are less important for this RF classifier. The most important feature was the BCN node entropy containing information about the nodes placed at a minimum distance of 2--Sh\({}_{2}\)(B). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Fold** & **DT** & **RF2 1** & **RF5 2** & **RF10 3** & **RF50 4** & **RF100 5** \\ \hline
1 & 0.9926 & 0.9948 & 0.9976 & 0.9983 & 0.9990 & 0.9992 \\
2 & 0.9924 & 0.9944 & 0.9971 & 0.9982 & 0.9990 & 0.9992 \\
3 & 0.9920 & 0.9943 & 0.9971 & 0.9983 & 0.9992 & 0.9993 \\
4 & 0.9927 & 0.9945 & 0.9974 & 0.9981 & 0.9992 & 0.9993 \\
5 & 0.9924 & 0.9942 & 0.9972 & 0.9983 & 0.9991 & 0.9993 \\
6 & 0.9925 & 0.9945 & 0.9974 & 0.9982 & 0.9991 & 0.9992 \\
7 & 0.9925 & 0.9944 & 0.9974 & 0.9983 & 0.9992 & 0.9992 \\
8 & 0.9927 & 0.9943 & 0.9973 & 0.9981 & 0.9990 & 0.9992 \\
9 & 0.9922 & 0.9942 & 0.9972 & 0.9982 & 0.9991 & 0.9992 \\
10 & 0.9933 & 0.9948 & 0.9975 & 0.9984 & 0.9991 & 0.9992 \\ \hline
**Mean** & **0.9925** & **0.9944** & **0.9973** & **0.9983** & **0.9991** & **0.9992** \\
**SD** & **0.0003** & **0.0002** & **0.0002** & **0.0001** & **0.0001** & **0.0000** \\ \hline \hline \end{tabular} \({}^{1}\) RF with two trees; 2 RF with five trees; 3 RF with 10 trees; 4 RF with 50 trees; 5 RF with 100 trees.  
\end{table} 
Table 3: AUROC for Net-Net AutoML classification models. Figure 3: Box plot for the AUROC values of the NET-NET AutoML classification models.
# Research on Scene Classification Method of High-Resolution Remote Sensing Images Based on RFPNet
## 4 Results
### Evaluation Indicators  
Accuracy is a common indicator for classification evaluation, but the proposed method cannot be comprehensively evaluated based on accuracy alone. Therefore, the paper uses accuracy, precision, recall, and F1-score as the evaluation indicators. For a sample classification problem with a number of \(M\), which contains \(P\) positive examples, and \(N\) negative examples, the sample can be divided into four cases according to the combination of real category and the predicted category: true positive (\(TP\)), false positive (\(FP\)), true negative (\(TN\)), and false negative (\(FN\)).  
Among them, \(TP\) is the positive example predicted to be positive, and the \(FN\) is the positive example predicted to be negative, so the number of the positive example \(P\) is  
\[P=TP+FN, \tag{23}\]  
Similarly, \(TN\) is the negative example judged to be negative, and \(FP\) is the negative cases predicted to be positive, so the number of the negative example \(N\) is  
\[N=TN+FP, \tag{24}\]  
The accuracy is the proportion of correct cases, and the calculation equation is  
\[accuracy=\frac{TP}{P+N}, \tag{25}\]  
Precision is the proportion of truly positive examples in all cases predicted to be positive examples. The calculation equation is  
\[precision=\frac{TP}{TP+FP}, \tag{26}\]  
Recall is the proportion of all positive examples predicted to be positive examples, and the calculation equation is  
\[recall=\frac{TP}{TP+FN}, \tag{27}\]F1-score is the comprehensive evaluation indicator of precision and recall, and the calculation equation is  
\[F1=\frac{2\times\textit{precision}\times\textit{recall}}{\textit{precision}+ \textit{recall}}, \tag{28}\]
## 3 Results and Discussion
### Input Values Definition for the Implemented Scenarios
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**Power Levels**} & \multicolumn{2}{c}{**Charging Power**} \\ \cline{2-3}  & **2020–2024** & **2025–2030** \\ \hline Slow—\(\left(P_{s}\right)_{y}\) & 7 kW & 22 kW \\ Medium—\(\left(P_{m}\right)_{y}\) & 22 kW & 50 kW \\ Fast—\(\left(P_{f}\right)_{y}\) & 50 kW & 100 kW \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Charging power levels assumed in the model for this study. \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **2020** & **2025** & **2030** \\ \hline Private & 900 \(\upxi\) & 810 \(\upxi\) & 729 \(\upxi\) \\ Residential—2 kW \(\left(C_{s}\right)_{y}\) & 1783 \(\upxi\) & 1630 \(\upxi\) & 1630 \(\upxi\) \\ Slow—2 kW \(\left(C_{f}\right)_{y}\) & 4800 \(\upxi\) & 4264 \(\upxi\) & 4264 \(\upxi\) \\ Fast—50/100 kW \(\left(C_{r}\right)_{y}\) & 44,000 \(\upxi\) & 38,798 \(\upxi\) & 32,564 \(\upxi\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Charging station (CS) costs forecast for the various power levels. The shares of PHEV and BEV, which are considered as using the publicly accessible charging infrastructure, were calculated using data coming from [(31; 60)]; the results are reported in Table 12:  
Finally, the charging events probability distribution \(p\) over the estimated range of the vehicle, was defined for three different scenarios (see Figure 3):  
* Linear: assumes a linear correlation between the number of days since the last charging events and the charging event probability. * Regressive: assumes a correlation between the number of days since the last charging events and the charging event probability of the kind \(y=\sqrt{x}\). * Progressive: assumes a correlation between the number of days since the last charging events and the charging event probability of the kind \(y=x^{5/3}\).
# Task Scheduling Mechanism Based on Reinforcement Learning in Cloud Computing
## 4 QMTSF Algorithm
### Uqrl Algorithm
#### 4.2.3 Reward
In line 12 of the algorithm, the \(b_{k}\) term affects the \(Q\) value based on the number of times the action has been executed. For each task in the SQ, the UQRL algorithm is executed to assign it to a VM until all tasks in the SQ are assigned. Considering the current state \(s_{t}\), the action with the maximum reward is selected and then the state is changed to \(s_{t+1}\). There are m VMs in the server, and the current state is represented as \(s_{t}=(|VMQ_{1}|,|VMQ_{2}|,\ldots,|VMQ_{k}|,\ldots,|VMQ_{m}|)\). Assuming that the selected action is \(a_{t}=k\), according to step 8 of the algorithm, the next state is \(s_{t+1}=(|VMQ_{1}|,|VMQ_{2}|,\ldots,|VMQ_{k}|+1,\ldots,|VMQ_{m}|)\). Each VMQ has a queue capacity \(c\), indicating that a maximum of tasks \(c\) can be assigned to the VM. The value can be set based on the actual state of the cloud environment and the total number of tasks. The size of the \(c\) value affects the dimension of the state space. Different queue capacities can be set for each VM, or they can be set to the same capacity. Before executing each action, it is first necessary to check whether the current queue capacity \(c\) of the VM is full. If it is full, the task is assigned to other VMs. If all VMQs in the server are full, the scheduler waits until there is a VMQ with available capacity before performing task assignment. Here, the number of states in Q-Learning is denoted as S and the number of available actions in each state is denoted as a. The time complexity of the Q-Learning algorithm is O(S*a*N), where N represents the number of iterations required for convergence. When comparing the UCB strategy with the greedy strategy, the UCB strategy requires fewer iterations to converge, resulting in a lower time complexity. The space complexity of the Q-Learning algorithm is O(S*a), primarily due to the memory space occupied by the Q-table. Neither the Q-Learning algorithms using the UCB strategy nor the greedy strategy alters the size of the Q-table; therefore, they have the same space complexity.
# Deep Point Cloud Facet Segmentation and Applications in Downsampling and Crop Organ Extraction
## 3 Results
### Quantitative Comparison  
To validate FFN's effectiveness, we conducted facet segmentation experiments on a representative subset of ShapeNet. Comparative algorithms: traditional unsupervised SLIC [6] and facets [25]. All three methods were set to output 50 facets for a fair comparison. Results are in Table 1.  
Figure 2: Isoperimetric entropy calculation method for a single-sided piece.  
FFN outperforms SLIC and Facets in ASA and UE: ASA is 0.46 pp higher, and UE is 0.12 pp lower, indicating that FFN better captures semantic boundaries and yields purer facets. Although FFN's CPT (19.44%) is slightly lower than that of SLIC, it still produces geometrically regular facets while preserving semantics. SLIC's higher CPT often comes at the cost of cutting across semantic regions. Thus, the FFN achieves a more practical'semantic-geometry' balance, which is ideal for downstream tasks requiring semantic integrity. Additionally, FFN's enforced fixed facet count avoids empty facets caused by uneven densities in the SLIC, improving controllability and stability.
# Trusted Energy-Aware Hierarchical Routing (TEAHR) for Wireless Sensor Networks
###### Abstract  
These days, wireless sensor networks (WSNs) are expanding fast and are used in many fields such as healthcare, battlefields, etc. Depending upon the type of sensor, they are transmitting a considerable amount of data in a short duration, so security is a significant issue while transferring the data. So, it is essential to solve security concerns while transferring data by secure routing in wireless sensor networks. We address this challenge by proposing Trusted Energy-Aware Hierarchical Routing (TEAHR), a new framework for a multi-level trust assessment that raises the security level in WSNs. TEAHR introduces a variety of trust metrics ranging from energy trust to forwarding trust to consistency trust to behavioral trust to anomaly detection, unlike existing models, enabling it to effectively address the challenges of dynamic network topologies and evolving cyber threats. Trust-based routing mechanisms are usually associated with high computation and storage complexity and susceptibility to collusive attacks such as spoofing. The mechanism in TEAHR overcomes these challenges by placing an adaptive trust assessment mechanism that adapts to the background network conditions and real-time activities of the nodes. We show through empirical analysis in this paper that TEAHR not only uses computational and storage resources efficiently but also enhances network performance and security. Our experimental setup presents the simulation approach to prove our proposed protocol of TEAHR in comparison with typical trust models under different scenarios of node mobility, variable node density, and sophisticated security attacks such as Sybil, wormhole, and replay attacks. TEAHR keeps the network connected, even when the nodes are isolated due to trust misbehavior, and demonstrates that widely it reduces the chances of misjudgment in trust evaluation. Moreover, we explore the scalability of TEAHR across large networks as well as its performance in computationally constrained contexts. We have verified through our detailed investigation that the energy metrics used uniquely in TEAHR extend the life of the network while increasing data routing trust and trustworthiness. The comparisons of TEAHR with conventional techniques show that the proposed algorithm reduces total latency by 15%, enhances energy efficiency by around 20%, and maintains a stable packet forwarding rate, which is highly desirable for accurate operation in adversarial environments, as demonstrated through comparative analysis. Through in-depth theoretical and practical analysis, TEAHR is confirmed as a high-performance framework that outperforms currently existing studies for WSN security, making TEAHR a strong candidate for use in industrial IoT applications and urban sensor networks.  
wireless sensor network; trust management; energy; anomaly; routing +
Footnote †: journal: Sensors  
0000-0002-0001-5000]Vikas  
0000-0002-0002-0000]C. [PERSON]  
0000-0002-0002-0002-0000]B. [PERSON]  
000-0002-000
## 3 Application
### Result Discussion  
From Day 1, the CV indicating the health status was not equal to 1 when the spread parameter \(\sigma\) was tuned by the GA. Because the GRNN uses probabilistic-based network regression, certain standard deviations were likely to be within the prediction range. Such statistical effects resulted in the manipulation of the CV score by the input layer, resulting in lower weightings despite the initial set (estimate) being labeled 1; this was relayed to a lower CV, for example, 0.73, as shown in Figure 11. However, a high weighting factor from the other nodes rendered from the rest of the pattern layer nodes contributed to the summation layer and resulted in a low predicted output CV. That is, in this study, the predicted GRNN score (CV, \(y_{i}\)) was influenced by the machine degradation, and the CV score was recalculated based on the entire WHRA's operation history until the call for maintenance. An eight-point moving average of the output CV is plotted as a red curve in Figure 11. Double-flat curves with an inflection (decrease) in their health status due to machine degradation were similar to those obtained by the LR method, as presented in Figure 12. Four high-score features (RMS error, mean, maximum, and minimum of signals) of the call for maintenance are input into LR.  
From Figure 12, according to the observations, the machine performance degraded from Days 20-50 and reverted from Days 230-260. Conventional LR models to assess machine run-to-failure performance, for example, adopt the first seven CV = 1 and last seven CV = 0 cells. The other CVs used to assess the machine health status of the RUL were unfeasible and inefficient; they contributed little to the RUL model when only 14 days' data were used for LR. Nevertheless, the proposed GA-GRNN model for the RUL maintained the diagnosis trend when, as can be seen by comparing Figures 11 and 12. For Days 20-50 and reverted for Days 230-260: back to normal or abnormal status during the history of the RUL, as determined using the GA-GRNN; additionally, the LR looks pretty similar.  
Figure 11: GRNN score (CV) for the RUL of the WHRA obtained by the GA–GRNN model.  
It is also interesting that when we reverse the plot of Figure 6, as in Figure 13, the trend of the CV for the WHRA is the same as that obtained by the GA-GRNN, as presented in Figure 11. Therefore, the feature selection-based GA-GRNN demonstrates powerful, probabilistic-based network regression for practically determining the trend of RUL. This method heuristically mimics the relaying of original data into the RUL model. Meanwhile, the conventional LR assumes the health and end failure statuses in advance to determine whether a work asset is close to deterministic failure mode. In fact, during over 1 year of experimentation (369 days), one machine part was replaced, but no maintenance was performed on the WHRA studied in this research.
## 3 Results and Discussion
### Effect of Temperature  
Calibration studies were performed with the same biosensors as mentioned earlier, over the temperature range 25 \({}^{\circ}\)C to 40 \({}^{\circ}\)C, with fatty acid concentrations between 2 to 20 uM (Table 2); these were achieved with a buffer solution pH 8 and with 15 units of LOX.  
The highest sensitivity for linoleic acid was achieved using a temperature of 37 \({}^{\circ}\)C whereas \(\alpha\)-linolenic acid showed slightly greater sensitivity at 40 \({}^{\circ}\)C; however, the linear range was better at 37 \({}^{\circ}\)C. At this temperature the linear range for both compounds was 2 to 10 uM.  
As we wished to ascertain the ability of the devices to measure the trace concentrations likely to be present in food safety situations, we next carried out calibration studies using lower concentrations (0.2 to 2 uM) of the two fatty acids.  
Figure 6 shows the amperometric responses over the lower concentration range studied. Clearly, well defined responses were achieved down to 0.2 uM and the detection limit obtained at a signal to noise ratio of 3:1 was 24 nM.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fatty Acid** & **pH** & **Slope (μ/μM)** & **Linear Range (μM)** \\ \hline \multirow{4}{*}{\(\alpha\)-Linolenic acid} & 6 & 0.005 & 2 to 6 \\  & 7 & 0.006 & 2 to 10 \\  & 8 & 0.009 & 2 to 10 \\  & 9 & 0.009 & 2 to 10 \\ \hline \multirow{4}{*}{Linoleic acid} & 6 & 0.003 & 2 to 6 \\  & 7 & 0.009 & 2 to 10 \\ \cline{1-1}  & 8 & 0.014 & 2 to 10 \\ \cline{1-1}  & 9 & 0.012 & 2 to 10 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Slopes and linear ranges of \(\alpha\)-linolenic and linoleic acid at 25 \({}^{\circ}\)C and pH 6.0, 7, 8.0 and 9.0.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Fatty Acid** & **Temperature (\({}^{\circ}\)C)** & **Slope (μ/μM)** & **Linear Range (μM)** \\ \hline \multirow{4}{*}{\(\alpha\)-Linolenic} & 25 & 0.008 & 2 to 10 \\  & 30 & 0.013 & 2 to 8 \\ \cline{1-1}  & 37 & 0.017 & 2 to 10 \\ \cline{1-1}  & 40 & 0.021 & 2 to 8 \\ \hline \multirow{4}{*}{Linoleic acid} & 25 & 0.010 & 2 to 10 \\ \cline{1-1}  & 30 & 0.012 & 2 to 10 \\ \cline{1-1}  & 37 & 0.021 & 2 to 10 \\ \cline{1-1}  & 40 & 0.017 & 2 to 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Slopes and linear ranges of \(\alpha\)-linolenic and linoleic acid at pH 8.0 and 25, 30, 37 and 40 \({}^{\circ}\)C.