esa-sceva/satcom-chunk-collection huggingface dataset:
# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 6 Conclusions  
Owing to the rapid developments of IoT relay and MEC, a novel framework of a relay-enabled task offloading system is proposed in this paper to handle the intensive computing tasks sent from an implanted sensor. We first proposed the network model and the task offloading decision model. Moreover, resource management of relays was determined based on the computation task, the residual energy of relays, and the computation resources, so as to be capable of balancing the energy consumption among all relays. Furthermore, we analyzed the channel capacity and the data rate under the predetermined acceptable BER condition. The results proved that the task offloading technique outperforms the non-offloading scheme in multiple aspects. Specifically, the higher computation capacity relay-based network achieved a lower PL and higher total number of locally executed packets. Moreover, the results showed that the non-offloading scheme achieves the highest network lifetime. The outcomes of this paper can be applied to future home/hospital-based healthcare monitoring services such as transplanted organ monitoring and intra-body high data rate transmission scenarios.  
In this future, we are interested in evaluating the proposed technique on a real experiment testbed. Moreover, the comprehensive tradeoff between wireless communication and computation resources for the wireless relay-enabled task offloading scheme could be selected as a future research topic. In addition, the power allocation of implanted and wearable devices is of great significance when designing healthcare systems. We are also interested in exploiting efficient power allocation techniques for WBANs by employing optimization solutions [37; 38].  
[PERSON] and [PERSON] conducted the research ideas of this manuscript; [PERSON] conceived the research subject and designed the simulation codes; [PERSON] analyzed the data; [PERSON] and [PERSON] wrote the paper. [PERSON] supervised the paperwork, provided a review, comments, assessment, and revised the paper.  
This work was supported by the National Natural Science Foundation of China (Grant No. 61801342 and Grant No. 61801341) and the Fundamental Research Funds for the Central Universities (Grant No. 2018 IVA098 and 2018 IVA099).  
The authors declare no conflict of interest.  
Figure 7: The overall delay under different scenarios.
# A New Centralized Clustering Algorithm for Wireless Sensor Networks
## 3 Proposed Method
### Computation of the Skip Value
However, for higher values of the \(skip\) (over 10), this behaviour does not hold and the \(FND\) dramatically deteriorates. This second type of behaviour was also predicted by the second hypothesis mentioned previously. The values obtained for \(HND\) can be seen in Figure 8. Here it can be observed that, when the \(skip\) value increases, \(HND\) values also increase, as predicted by the first hypothesis. This improvement is kept  
Figure 6: Energy consumed by CH and nodes. Figure 7: Round when the first node dies (\(FND\)) versus skip. nearly constant when skip gets to 100 rounds. This behaviour is coherent with the second hypothesis with which we have been working. The values obtained for \(LND\) are presented in Figure 9. \(LND\) increases almost linearly as the value of \(skip\) does, but when the skip gets to 100, \(LND\) is also kept almost unaltered. Consequently, the performance of the metrics confirms the hypothesis exposed in our introduction Section. As a result, from the analysis of Figures 7-9 it can be concluded that the effects of the increase of the skip value is inverse for \(FND\) than for \(HND\) and \(LND\). This occurs because the energy cost of being CH is much higher than being a contributing node as is depicted in Figure 6. Thus, when the skip value is high, the CHs selected in the initial stages deplete their battery faster than any other node, which eventually drives to a premature \(FND\) value. However, if the CHs are selected more often (low values of skip), the balance of energy is better, but better nodes will die sooner than in other scenarios with higher skip values. Hence, a trade off between a high value of \(FND\) (reliability) and both \(HND\) and \(LND\) (network lifetime) is necessary in order to achieve the best performance of the network and its application. Figure 8: Round where half of the nodes are dead (\(HND\)) versus skip. Figure 9: Round when at least 90% nodes are dead (\(LND\)) versus skip. Therefore, considering reliable applications to be deployed in the WSN, we take into account those three metrics when choosing the value of the skip parameter. Analysing the three metrics altogether, we focus on Figure 7, because \(FND\) is the most restrictive metric. From the simulations results, we set the skip parameter to 7 as the \(FND\) starts to deteriorate for higher values. The behaviour is similar for the three scenarios.
# Effect of the Umbrella Arch Technique Modelled as a Homogenized Area above a Cross Passage
## 1 Introduction
[8; 9] confirmed that the umbrella arch pipe was very effective at minimizing the soil surface settlement by more than 60%. Certain reinforcement systems are usually applied in combination with the umbrella arch method; in the example of face bolting, the conjunction of these two types of pre-supports has proven to reduce the surface settlements as well as convergence values in the tunnel face [10; 11; 12; 13; 14]. Due to the complex geometry of a tunnel intersection with a cross passage, the numerical simulation of individual umbrella pipes over a cross-passage section is very complicated. Therefore, a simplified approach based on the homogenization method is implemented in a 3D numerical model to evaluate the effect of an umbrella arch on ground settlements caused by the excavation of a cross passage. The homogenization method consists of replacing the reinforced medium with an equivalent homogenized medium. The properties of the latter are obtained from those of the individual components (i.e., the rock and the bolts) using the theoretical tools of homogenization for periodic media [15]. The effectiveness of  
Figure 1: Cross section and longitudinal profil of an umbrella arch technique. the homogenization method has already been evaluated and proven by several authors, including [15; 16; 17]. The tunnelling procedure using UAM, either independently or in combination with other pre-reinforcement techniques, is well explained in the literature by several authors [18; 19; 20; 21]. However, its implementation in a tunnel junction area is seldom discussed. With the exception of [22], which assessed the effect of both deep soil-mixing and umbrella vault methods to stabilize the excavation of a cross passage, and [23] who studied the effect of the umbrella arch technique on a cross passage using a 2D homogenized numerical model, few studies have examined the effect of the umbrella arch technique solely on the stability of a cross passage excavation in a 3D simulation. To bridge this gap, this paper presents a finite element modelling (FEM) approach of an umbrella arch method modelled as a homogenized area using the theoretical tools of homogenization for periodic media applied to a cross passage excavated with the conventional method (NATM). The approach relies on three-dimensional finite element simulations using the software CESAR-LCPC 3D V5. The novelty of this study lies in the fact that the sensitivities of a particular umbrella arch configuration are investigated in detail and with precision by means of a three-dimensional finite element analysis taking into account the staged excavation. Furthermore, with the description of the modelling and simulation process, the results provide a benchmark reference for similar numerical investigations that are rare in the scientific literature.
## 2 State-of-the-Art
### The Era beyond 5G
Focusing on TI, which is the main theme of this research work, it has been enabled by the deployment of 5G networks. However, with 6G improved E2E (exchange-to-exchange), latency can be achieved, rendering feasible a system reaction time of 1 millisecond or even less, which is a cornerstone requirement for reaching the human sense time. Moreover, high reliability, availability, security, and throughput all add up to achieving real-time monitoring and system tele-operation. As a result of the increased bandwidth support, greater network traffic can be supported. For example in applications where sensor data as well as control data must be transmitted/exchanged, the distributed control systems (enabled by 6G), can manage network traffic more efficiently.
## 2 Related Works
### Specific Technical Approaches
#### 2.2.2 Testbed-Based Studies  
However, while simulations allow for extensive testing, it is difficult to replicate real-world environmental conditions. Moreover, the required equipment is not always available or capable of supporting the proposed solutions, especially in industrial settings. Therefore, alongside analytical and simulation-based research, work is also being conducted on more advanced technological solutions using various testbeds. During our literature review, we found significantly fewer studies focusing on latency minimization and throughput maximization that follow this approach. One of them is [33], which investigated the practical challenges of minimizing latency in a controlled experimental environment. The authors analyzed the impact of multiple factors on latency performance, including core network configurations, radio access technologies (RATs), and transport protocols. They also highlighted the importance of parameters such as gain, attenuation, and sampling rate. The test setup in this study consisted of a 5G testbed platform in a single-UE (User Equipment) setup and a 5G core with RAN software-Amarisoft Callbox Classic version 2023-06-10 platforms hosted on a single PC.  
In the context of maximizing throughput, several recent testbed-based works are worth highlighting. In [34], the authors implemented a real-time NOMA transmitter and receiver on SDR hardware compatible with 5G, introducing variable symbol rates for different NOMA streams. Over-the-air experiments showed that this technique achieved significantly higher aggregate throughput compared to traditional orthogonal multiple access schemes. In another study, ref. [35] described the design and field validation of a full-scale outdoor massive MIMO testbed for 5G, where directional beamforming and multi-user scheduling yielded cell throughputs in the order of several Gbps, confirming the practicality of massive MIMO under real-world conditions. Furthermore, ref. [36] demonstrated how coordinated network slicing, using controlled constructive interference between base stations, can improve overall spectral efficiency. Tested on a live LTE testbed, their solution increased aggregate network throughput by up to 27 percent while simultaneously improving users' signal-to-noise ratio (SNR).
## 3 Results and Discussions
### Validation Results of Improved Non-Destructive Detection Method for SSC in Bunch-Harvested Grapes
#### 3.3.3 Efficiency Comparison between Existing Methods and Improved Methods  
In recent years, researchers have successively carried out research on the non-destructive testing of grape soluble solids content based on HSI. In 2017, [PERSON] et al. established a prediction model for SSC based on near-infrared hyperspectral images (500-1000 nm) for red grapes and achieved a good accuracy [14]. Such existing methods usually use ENVI software (version number from 4.7 to 5.3) to manually select Rols in order to obtain the average spectral reflectance of grapes.  
Therefore, the existing method presented in article [14] and the improved method proposed in this research were used to obtain the reflectance spectra of grapes, and the non-destructive detection of SSC was carried out on eight bunches of grapes. Then, we compared the speed and precision of the two methods on the same computer in a laboratory. The existing methods use ENVI 5.3 for manual RoI selection, as indicated by the colored rectangular blocks in Figure 15a. The improved method uses Mask R-CNN for recognition and segmentation and uses the threshold method to remove overbright areas, as shown in Figure 15b.  
Figure 14: Visualization of SSC non-destructive detection results for bunch-harvested grapes.  
The timing started from the moment when the spectral data were extracted based on the hyperspectral image, and the timing stopped when the prediction of the SSC of the eight bunches of grapes was completed. For a total of 157 grapes, the existing method took 35 min and 15 s, and the improved method took 2 min and 8 s. The performance comparison is shown in Table 4.  
It can be seen from Table 4 that although the model accuracy of the improved method proposed in this research was slightly lower than that of the model proposed in [14], the average prediction speed per grape of the improved method was 16.6 times that of the former, which proves that the improved method greatly improves the performance and non-destructive detection efficiency.
# TRC-Based High-Precision Spot Position Detection in Inter-Satellite Laser Communication
## 4 TRC Based Photocurrent Amplitude Detection  
### Time Reversal Convolution Algorithm  
The time reversal convolution operation process can be expressed as [24]:  
\[\begin{array}{ll}F_{x}(t)&=x(-t)*x(t)\\ &=[s(-t)+n(-t)]*[s(t)+n(t)]\\ &=s(-t)*s(t)+s(-t)*n(t)+n(-t)*s(t)+n(-t)*n(t)\\ &=F_{ss}(t)+F_{sn}(t)+F_{ns}(t)+F_{nm}(t)\end{array} \tag{10}\]  
If the signal \(s(t)\) and the noise \(n(t)\) are independent of each other, then \(F_{sn}\) and \(F_{ns}\) are approximately equal to 0, so Equation (10) is simplified as:  
\[F_{x}(t)\approx F_{ss}(t)+F_{nm}(t) \tag{11}\]  
Define a signal \(x(t)\) with noise as:  
\[x(t)\ =\ s(t)+n(t)\ =\ A\cos(2\pi\cdot f\cdot t+\varphi)+n(t) \tag{12}\]  
where \(A\) is the signal amplitude, \(f\) is the signal frequency, \(s(t)\) is the signal phase, and \(n(t)\) is Gaussian noise with a mean of 0 and a variance of \(o_{n}^{2}\). Then, \(F_{ss}(t)\) can be written as:  
\[F_{ss}(t)\ =\ \left\{\begin{array}{ll}\frac{A^{2}(T+t)}{2}\cos(2\pi\cdot f \cdot t)\ -T\leq t\leq 0\\ \frac{A^{2}(T-t)}{2}\cos(2\pi\cdot f\cdot t)\ 0<t\leq T\end{array}\right. \tag{13}\]  
If the power spectrum density of \(n(t)\) is \(P_{0}\), the TRC operation result of \(n(t)\) is:  
\[F_{nm}(t)\ =\ P_{0}\delta(t) \tag{14}\]  
So, the result of TRC operation of \(x(t)\) can be shown as the curve in Figure 2.  
Figure 2 shows that after TRC operation, the energy distribution of \(F_{ss}(t)\) and \(F_{nm}(t)\) are different. The energy of \(s(t)\) is still contained in the cosine signal of the same frequency, distributed throughout the whole convolution time. The energy of \(n(t)\) is concentrated at the convolution time point \(t=0\). If \(F_{x}(0)\) is set to 0, most of the noise energy can be removed at the cost of less valid signal energy. After using the TRC method to suppress the noise, the output signal is transformed by the fast Fourier transform (FFT) operation, which can help us to focus on detecting the amplitude at the  
Figure 2: Result of the time reversal convolution (TRC) operation.  
thereby avoiding the interference of harmonics. The processing flow of the proposed method is shown in Figure 3.  
In Figure 3, \(W(k)\) is the weighted denoising window function, which is written as:  
\[W(k)\ =\ \left\{\begin{array}{ll}1&\quad k\
eq 0\\ 0&\quad k\ =\ 0\end{array}\right. \tag{15}\]
###### Abstract  
We investigated the pollution levels of 6 heavy metals and 29 dioxins (polychlorinated dibenzo-\(p\)-dioxins (PCDDs), polychlorinated dibenzofurans (PCDFs), and dioxin-like polychlorinated biphenyls (DL-PCBs)) in intertidal and supratidal zones by using wharf roaches (_Ligia_ spp.) collected from 12 sampling sites on the coast of Northeast Japan from November 2011 to June 2012. The total concentrations of heavy metals ranged from 177 to 377 \(\mu\)g/g-dry weight (dw), and the predominant metals were copper, zinc, and aluminum. The order of the detected level of heavy metals was zinc \(>\) aluminum \(>\) copper \(>\) cadmium \(>\) lead \(>\) chromium, and this trend was similar to a previous report. The total toxic equivalent (TEQ) value of the PCDD/Fs ranged from less than the limit of detection (\(<\)LOD) to 2.33 pg-TEQ/g-dw, and the predominant congener was octachlorodibenzodoxin (\(<\)LOD to 110 pg/g-dw). Compared with PCDD/Fs, DL-PCBs were detected at a predominantly higher level (total TEQ value: 0.64-27.79 pg-TEQ/g-dw). Detected levels of dioxins, especially DL-PCBs in the wharf roach, were like those in the bivalves. These results indicate that the wharf roach could reflect heavy metals and dioxin pollution in the supratidal zones and is a suitable environmental indicator for these environmental pollutants. This is the first study to investigate heavy metals, PCDD/Fs, and DL-PCBs pollution in coastal isopods in Japan.  
supratidal zone; _Ligia_ spp.; heavy metals; polychlorinated dibenzo-\(p\)-dioxin/furan; dioxin-like polychlorinated biphenyls
## 4 Proposed Methodology
### Data Redundancy Process  
When multimedia sensors are placed close to each other, an overlapped field of view (FoV) is determined. The threshold value \(\tau\) mainly denotes the part of the isosceles triangle in FoV and M represents the congruent side of the triangle. If any item is present in between the FoV of two interlinked sensor nodes, then it results in a polygon. The polygon mainly implies that the data is redundant if two nodes send the image of the item. The shapes such as quadrilateral and triangle are not always large enough to fit inside the threshold or minimal threshold. Since the triangle and quadrilateral generated can be small or big, the data redundancy algorithm used in our paper is also formulated using the same concept. The data redundancy algorithm is specified in Algorithm 1 and the object that overlaps in the FOV regions is said to be redundant. The nodes with the high energy elected as the cluster head by the FCSEO optimization algorithm are used to transmit the data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sl.No.** & **Residual Energy \((\mathcal{G})\)** & **Centrality \((\partial_{j})\)** & **Probability of CH Selection \((\Delta\partial_{j})\)** \\ \hline Rule 1 & \(H\) & \(H\) & \(VH\) \\ \hline Rule 2 & \(H\) & \(M\) & \(H\) \\ \hline Rule 3 & \(H\) & \(L\) & \(M\) \\ \hline Rule 4 & \(M\) & \(H\) & \(M\) \\ \hline Rule 5 & \(M\) & \(M\) & \(L\) \\ \hline Rule 6 & \(M\) & \(L\) & \(L\) \\ \hline Rule 7 & \(L\) & \(H\) & \(L\) \\ \hline Rule 8 & \(L\) & \(M\) & \(VL\) \\ \hline Rule 9 & \(L\) & \(L\) & \(VL\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Fuzzy set for CH selection.
## 7 Comparative Analysis
### Transmission Success Rate  
For the transmission success rate, ASC-RL was implemented in different settings with the other three schemes, and it was found that the proposed scheme obtained a better transmission success rate with maximum latency thresholds. It performed better than the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Episode** & **CAS-IIoT-RL** & **LSTM-RL** & **AEM-RL** & **ASC-RL** & **Improvement (\%)** \\ \hline
100 & 0.355 & 0.336 & 0.315 & 0.296 & 10.10\% \\
200 & 0.305 & 0.292 & 0.275 & 0.252 & 13.54\% \\
300 & 0.255 & 0.248 & 0.235 & 0.208 & 16.96\% \\
400 & 0.205 & 0.200 & 0.195 & 0.160 & 19.61\% \\
500 & 0.155 & 0.152 & 0.145 & 0.112 & 24.06\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: False alarm probability of ASC-RL with CAS-IIoT-RL, LSTM-RL, and AEM-RL.  
Figure 10: False alarm probability comparison of ASC-RL with CAS-IIoT-RL, LSTM-RL, and AEM-RL.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**\(\mathbf{Pr_{St}}\)** & **CAS-IIoT-RL** & **LSTM-RL** & **AEM-RL** & **ASC-RL** & **\% Increase** \\ \hline
0.0 & 0.55 & 0.55 & 0.55 & 0.55 & 0.00\% \\
0.2 & 0.56 & 0.57 & 0.58 & 0.59 & 5.41\% \\
0.4 & 0.61 & 0.63 & 0.64 & 0.66 & 6.84\% \\
0.6 & 0.73 & 0.75 & 0.77 & 0.80 & 6.25\% \\
0.8 & 0.86 & 0.89 & 0.92 & 0.95 & 6.93\% \\
1.0 & 0.92 & 0.96 & 0.98 & 1.00 & 5.43\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Detection probability comparison of ASC-RL with CAS-IIoT-RL, LSTM-RL, and AEM-RL.  
other schemes, achieved about 5.73% on average, and finally reached 6.25%, as shown in Figure 11. This steady improvement suggests that ASC-RL is more suited for high-latency dynamic settings where performance is greatly affected by clever adaptive techniques, as shown in Table 7.
# Backscatter Communications: Inception of the Battery-Free Era--A Comprehensive Survey
## 2 Energy Constraints--A Persistent Problem
### Power Management in Disasters--A D2D Perspective  
Efficient reliable and agile communications during disaster can be achieved if we address the hindrance of limited battery life. Different techniques are proposed using D2D communications to optimize the battery life in critical communications. Survival in sharing mechanism uses D2D-based communications features to overcome battery power limitations during disasters [4]. A D2D discovery mechanism was considered by exploring the proximity area concept, for energy-efficient D2D communications [24]. A D2D-based messaging solution was also proposed to overcome battery power limitation issues in disaster situations [25]. Unfortunately, battery has finite energy, and the stored energy drains as all electrical charges flow out. Hence, state-of-the-art network architecture for critical communications is of no use if devices suffer from power constraints.
## 2 Sample Expansion Methodology Based on CVAE-Skegan
### Assessment of Sample Generation Capability
#### 1.1.1 Mmd  
MMD is used to evaluate whether the generated samples and the real samples conform the same probability distributions so as to reflect the sample generation ability. MMD mainly calculates the maximum average difference between the two distributions in the continuous function \(f\). The larger MMD value, the more significant the difference between two distributions. Meanwhile, a smaller MMD value indicates that the difference in distributions is smaller, i.e., the distribution of the generated data is closer to that of the real data.  
\[MMD[F,p,q]=\underset{f\in F}{\sup}\big{(}E_{x\sim p}[f(x)]-E_{y\sim q}[f(y)] \big{)} \tag{11}\]  
where \(F\) denotes a set of continuous functions in the sample space, \(p\) and \(q\) denote two distributions, and \(x\) and \(y\) denote sample sets that satisfy the distributions \(p\) and \(q\), respectively.  
The MMD values are shown in Table 1. It can be seen that the CVAE-SKEGAN model has a better sample-generation ability in model training.  
Figure 3: Optimization process of CVAE-SKEGAN generator.  
(2)  
The K-L divergence can measure the information loss between two distributions. It is used to assess the generation loss of the generated data distribution compared to the real data distribution, thereby indicating the quality of the generated samples. The K-L divergence is calculated as follows:  
\[KL(p\|q)=\sum_{i=1}^{n}p(x_{i})\cdot\log\frac{p(x_{i})}{q(x_{i})} \tag{12}\]  
where \(p\) is the original distribution, and \(q\) is another distribution to approximate \(p\).  
As shown in Table 2, the CVAE-SKEGAN model can generate the best-quality samples.  
MMD values and K-L divergence values of CVAE-SKEGAN are the smallest; the generated samples from this algorithm are the closest to the real sample distribution. The information loss of the generation process is minimal, and the generation ability is improved, indicating that the model improvement is effective. Therefore, CVAE-SKEGAN has an excellent performance in the sample-generation ability.