esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
In summary, a self-powered and autonomous vibrational wake-up system based on the TENGs and MEMS switch was proposed. The E-TENG had been experimentally proven to have outstanding characteristics, which can provide sustainable and stable energy to the system in a continuous vibration environment. The S-TENG had also been demonstrated to have good linearity in the acceleration range of 1-4.5 m/s2 at 30 Hz with a sensitivity of about 14.6 V/(m/s2). The SAVWS realized the energy harvesting and vibration monitoring by integrating the E-TENG and the S-TENG. Through the MEMS switch, the SAVWS could monitor an acceleration threshold in only 2.82 s, which could be adjusted by changing the mass. With a low-powered design, the wireless transmitter could transmit an alarm signal by the energy from the E-TENG with only 0.64 mJ. As a result, the system could realize the efficient utilization of the energy generated by the E-TENG and fast response to abnormal acceleration. Using the TENG simultaneously as an energy source and a sensor, the SAVWS provided a self-powered vibration monitoring solution for unattended environments, and showed extensive applications and great promise in smart factories, autonomous driving, and the Internet of Things.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s22103752/s1](https://www.mdpi.com/article/10.3390/s22103752/s1)]([https://www.mdpi.com/article/10.3390/s22103752/s1](https://www.mdpi.com/article/10.3390/s22103752/s1)), Figure S1: The structure schematic of the integrated vibration TENG; Figure S2: Characteristics of the S-TENG and E-TENG; Figure S3: The open-circuit voltage of S-TENG and E-TENG in different frequencies, Figure S4: The durability test results of the S-TENG and E-TENG, Figure S5: Comparison of direct charge circuit with only one rectifier bridge and through the PMS charging for a 330 uF capacitance, Figure S6: Structure and microstructural diagram of MEMS switch, Figure S7: The circuit schematic diagram of the load, Figure S8: Leakage current of MEMS switch, Figure S9: The optical picture of the specific internal structure of the integrated TENG, Video S1: The working demo of SAVWS.  
Conceptualization, Y.L. ([PERSON]) and C.Z.; methodology, Y.L. ([PERSON]) and Y.Q.; investigation, J.W., Z.W., Y.L. ([PERSON]), N.T., M.W. and Z.Z.; writing--original draft preparation, Y.L. ([PERSON]); writing--review and editing, Y.Q., G.L., J.Z., Z.Z., Y.C. and C.Z.; supervision, Y.C. and C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China (Nos. 51922023, 61874011), Fundamental Research Funds for the Central Universities (E1 EG6804), Guangxi Hundred Talent Program (T3010097923).  
Not applicable.  
The authors declare no conflict of interest.
## 2 Background and Method  
### Background of the Study Area  
The Guojiahe coal mine is located in Shanxi Province, China. Many folds and faults occur in the mining area and the groundwater resources are rich. In August 2020, during the mining of the 1309 working face in the first mining area, a serious water intrusion accident occurred, with the maximum water intrusion reaching 1200 m\({}^{3}\)/h. The maximum water depth of approximately 1 m and the cumulative water inflow was approximately 12.6 \(\times\) 10\({}^{4}\) m\({}^{3}\). The collapse of the hydraulic support for coal seam protection in mining reduced the transportation efficiency of the belt conveyor and caused an economic loss of nearly 10 million US dollars [35; 36]. The second mining area to be mined in the future is located west of the Guojiahe coal mine. The thickness of the aquifer in part of the second mining area is similar to that of the 1309 working face. Therefore, to ensure coal mining safety, large-scale geophysical exploration was planned to be carried out in the second mining area, covering nearly 11 km\({}^{2}\). Then, 80 boreholes were designed in the second mining area to extract water from the aquifer. The plan would cost a lot of time and money and cause irreparable damage to groundwater (Figure 1).  
The main mining coal seam is the Jurassic 3\({}^{\#}\) coal seam, and the main roof aquifers are the Cretaceous Luo'he Formation and Jurassic Zhi'luo and Yan'an formations. The average thickness of the Luo'he Formation is 191 m, and its main lithology is brown-red sandstone with weak cementation. The average thickness of the Zhi'luo Formation is 42.5 m, and its main lithology is greyish green sandstone with medium sorting, secondary calcareous cementation and high hardness. The average thickness of the Yan'an Formation is 27.6 m and its main lithology is greyish black sandstone, which is poorly sorted and contains fine quartz gravel. In general, sandstone aquifers have large pores and locally developed fractures in water-rich heterogeneous layers (Figure 2).  
In this experiment, sandstone samples were obtained from the Luo'he, Zhi'luo and Yan'an formations to conduct porosity and water absorption measurement experiments to predict the water-bearing capacity of aquifers with various lithologies.
# A Cluster-Based Channel Assignment Technique in IEEE 802.11 Networks
## 4 Experimental Evaluation
### Performance Evaluation
Comparing AIIM with CB-AIIM, we can conclude that considering the interferences produced by STAs lets us obtain much better results, not only in terms of average utility, but also in terms of variability of results, so the effect of STAs in channel assignment, although it has been barely studied in the literature, lets us achieve much better channel assignment distributions.
# High-Resolution Multi-Scale Feature Fusion Network for Running Posture Estimation
## 4 Experiments
### Results on the MPII Dataset  
The experimental results on the MPII validation set are presented in Table 3. For all methods, the image size was set to 256 \(\times\) 256. Our RHPNet achieves a score of 92.0 [EMAIL_ADDRESS], which is significantly better than most methods, falling only 0.5 [EMAIL_ADDRESS] behind the state-of-the-art method, PCT [7].  
RHPNet outperforms previous state-of-the-art methods (except for PCT)in estimating poses for most individual joint groups, showcasing the effectiveness and robustness of our framework. Particularly noteworthy is the superior performance of RHPNet compared to the cutting-edge PCT method for the detection of difficult-to-detect joints, such as wrist joints. Additionally, RHPNet achieves the best results compared to methods tfor which the _[EMAIL_ADDRESS] metric has previously been reported. Figure 8 showcases the detection results of RHPNet on a subset of images from the MPII validation set. From the figure, it can be observed that RHPNet effectively handles the detection of occluded joint points.  
\begin{table}
\begin{tabular}{l l l l l l l l l l} \hline \hline
**Method** & **Backbone** & **Input Size** & **Params** & **GFLOPs** & **AP** & **AP\({}^{\text{50}}\)** & **AP\({}^{\text{75}}\)** & **AP\({}^{\text{PM}}\)** & **AP\({}^{\text{L}}\)** & **AR** \\ \hline HRFormer-B [32] & HRFormer-B & 384 \(\times\) 288 & 43 M & 29.1 & 77.2 & 91.0 & 83.6 & - & - & 82.0 \\ ViTPose-B [6] & ViT-Base & 256 \(\times\) 192 & 86 M & 17.9 & 75.8 & 90.7 & 83.2 & - & - & 81.1 \\ ViTPose-L [6] & ViT-Large & 256 \(\times\) 192 & 307 M & 59.8 & **78.3** & 91.4 & 85.2 & - & - & 83.5 \\ PCT [7] & Swin-Base & 256 \(\times\) 256 & - & 15.2 & 77.7 & 91.2 & 84.7 & - & - & - \\ PCT [7] & Swin-Large & 256 \(\times\) 256 & - & 34.1 & **78.3** & 91.4 & **85.3** & - & - & - \\ \hline RHPNet & HRNet-W32 & 256 \(\times\) 192 & 30.5 M & 7.51 & **78.3** & **93.5** & 84.7 & 75.6 & 83.0 & 81.0 \\ \hline \hline \end{tabular} Bold represents the optimal result.  
\end{table}
Table 2: _Cont._  
Figure 7: Visualization results for some example images from the COCO Val dataset with keypoints, occlusions, and multiperson examples.
## 2 Methodology
### Construction Method of Special Wireless Sensor Network for Railway Status Monitoring
As shown in Figure 2, in sub-monitoring area, sensor nodes are responsible for sensing service status data of infrastructure and its operating environment on the one hand, and sending sensing data to sink nodes on the other hand. Because the energy storage of sensor nodes is limited, the energy efficiency of sensor nodes is mainly considered in routing protocol selection and optimization. The communication energy consumption of wireless sensor nodes is mainly determined by the amount of data transmitted and the transmission distance. As can be seen from Figure 2, the distances between sensor nodes and sink nodes are different. When each sensor node transmits the same amount of data to sink nodes, the energy consumption of each sensor node will be unbalanced, and the nodes far away will fail prematurely due to energy exhaustion. In order to improve the energy efficiency of sensor nodes in the sub-network, cluster communication routing protocol [13] is adopted in this system. In a sub-network, one node is selected as the cluster head according to the needs in the cluster, and the other nodes are the members of the cluster. Cluster members are responsible for the data perception of key components of the railway system and send the perception data to cluster head nodes. After receiving the data, the cluster head node  
Figure 2: Structure of sub-monitoring area. carries out simple processing and analysis, and forwards the data to the sink node. By using clustering communication routing protocol, the choice of cluster head and the result of clustering have a great influence on the total energy consumption of all nodes and the balance of energy consumption among nodes. In addition, the communication energy consumption of wireless sensor nodes is also related to the amount of data transmitted. Cluster heads collect data from cluster members and then process data before sending. This can reduce the amount of data transmitted and corresponding communication energy consumption, but at the same time it will increase the corresponding data processing energy consumption. The adjustment and balance between them can effectively reduce the total energy consumption of cluster head nodes and improve the overall energy utilization efficiency of the sub-network. In order to improve the energy efficiency of nodes in the sub-network and the lifecycle of the sub-network, the optimization methods of cluster communication routing protocol and data fusion efficiency of cluster head nodes will be studied.
# An Experimental Tethered UAV-Based Communication System with Continuous Power Supply
## 4 Results
### Data Obtained on the Different Flights  
From the data obtained during the flights, the necessary information was compiled to generate Table 14 to compare the energetic behavior of the UAV in different conditions. This table shows that as altitude increases, both voltage and current increase, with current being the parameter that varies most significantly. For example, when the UAV is at an altitude of 0.3 m, the current is 9.12 A, while at 4 m, the current increases to 15.04 A. This behavior is due to increased load and the additional effort required to maintain UAV stability at higher altitudes.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline  & \multicolumn{2}{c}{**Closed Environment**} & \multicolumn{2}{c}{**Open Environment**} \\ \hline
**Parameter** & **Test 1** & **Test 2** & **Test 3** & **Test 4** & **Test 5** \\ \hline
**Voltage [V]** & 15.64 & 14.97 & 16.56 & 16.64 & 16.54 \\ \hline
**Current [A]** & 9.12 & 11.57 & 10.01 & 15.04 & 10.50 \\ \hline
**Time [min]** & 1 & 2–3 & 2–3 & 7.4 & 3.2 \\ \hline
**Height [m]** & 0.3 & 0.4 & 0.6 & 4 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 14: Data obtained from tests conducted at different heights, both in open and closed environments.  
Figure 35: UAV operating in a real environment at an altitude of 0.8 m.  
Figure 36: Variation of voltage and current consumption in an open environment during the interval from 30 to 190 s.
## 2 Data and Methods
### Evaluation Metrics  
The trained models were evaluated based on five metrics, namely Precision, Recall, F1-Score, Intersection over Union (IoU), and [PERSON]'s kappa score (\(\kappa\)). The recall is how much of the road that should be predicted is actually a road. F1-Score is the equilibrium level between Precision and Recall. IoU indicates the ratio of the overlapping area to the size of the total area of prediction and the ground truth. All metrics range from 0 to 1 and they can be calculated as  
Figure 4: Proposed model structure.  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}}\] \[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}}\] \[\text{F1-Score}=\frac{2\text{TP}}{2\text{TP}+\text{FP}+\text{FN}} \tag{16}\] \[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}}\] \[\text{Kappa}=\frac{2(\text{TP}\times\text{TN}-\text{FN}\times \text{FP})}{(\text{TP}+\text{FP})(\text{FP}+\text{TN})+(\text{TP}+\text{FN})( \text{FN}+\text{TN})}\]  
where TP, TN, FP, and FN are the true positive, true negative, false positive, and false negative, respectively.
# Combining Optimization and Simulation for Designing a Robust Short-Sea Feeder Network
## 4 Optimization and Simulation Model
### Optimization Model
The deterministic optimization problem can be formulated as follows:  
\[\min\sum_{m\in\mathcal{R}^{M}}C_{m}^{M}x_{m}+\sum_{d\in\mathcal{R}^{D}}C_{d}^{ D}z_{d}+\sum_{s\in\mathcal{S}}C_{s}^{\mathrm{S}}y_{s} \tag{1}\]  
subject to  
\[\sum_{m\in\mathcal{R}^{M}}x_{m}=1, \tag{2}\] \[\sum_{d\in\mathcal{R}_{p}^{D}}z_{d}-M^{OH}\sum_{m\in\mathcal{R}_{p }^{M}}x_{m}\leq 0, p\in\mathcal{P}^{OH},\] (3) \[\sum_{d\in\mathcal{R}_{pn}^{D}}z_{d}-\sum_{d\in\mathcal{R}_{p}^{D }}z_{d}\geq M^{OH}(x_{m}-1), p\in\mathcal{P}^{OH},m\in\mathcal{R}_{p}^{M},\] (4) \[\sum_{d\in\mathcal{R}_{pn}^{D}}z_{d}-x_{m}\geq 0, p\in\mathcal{P}^{OH},m\in\mathcal{R}_{p}^{M},\] (5) \[\sum_{m\in\mathcal{R}_{p}^{M}}x_{m}+\sum_{d\in\mathcal{R}_{p}^{D }}z_{d}=1, p\in\mathcal{P}^{CM},\] (6) \[\sum_{d\in\mathcal{R}_{p}^{D}}z_{d}=1, p\in\mathcal{P}^{CD},\] (7) \[\sum_{m\in\mathcal{R}^{M}}S_{ms}^{M}x_{m}+\sum_{d\in\mathcal{R}^ {D}}S_{ds}^{D}z_{d}-S_{s}+\varepsilon\leq M^{\mathrm{S}}y_{s}, s\in\mathcal{S},\] (8) \[x_{m}\in\{0,1\}, m\in\mathcal{R}^{M},\] (9) \[y_{s}\in\{0,1\}, s\in\mathcal{S},\] (10) \[\sum_{m\in\mathcal{R}^{M}}x_{m}=1, \tag{2}\]0  
The objective function (1) minimizes the total weekly cost of the shipping system. The first and second terms of (1) are related to the costs of mother and daughter vessels deployed in the system. The last term represents the penalty cost of a solution. Equation (2) forces the optimal solution to select only one main route. Constraints (3) to (5) are the synchronization constraints, ensuring that selected daughter routes can synchronize with the main route at an ocean hub visited by both routes. The reader is referred to [PERSON] et al. (2016) for a more detailed description of how synchronization is taken care of through these constraints. Further, Equation (6) ensure that a main port is visited by either a mother or a daughter vessel. Equations (7) ensure that each small Norwegian port is served by a daughter route. Constraints (8) set the indicator variable \(y_{s}\). If the master problem selects a route combination that constitutes a previously simulated solution, \(y_{s}=1\) and the penalty cost is added to the objective. Lastly, Constraints (9)-(11) restrict the variables to take binary values.
# Characterisation of Holographic Recording in Environmentally Stable Photopolymerisable Glass
###### Abstract  
Photopolymerisable glasses are holographic recording materials which provide good recording capability, improved dimensional stability, and negligible shrinkage. Recently, a novel photopolymerisable hybrid sol-gel (PHSG) for holographic recording of volume gratings has been reported. The PHSG has significantly improved gelation time and high water resistance, both of which make it an attractive material for mass production of holographic optical elements (HOEs) with no sensitivity to ambient humidity. In order to achieve full control over the performance of the material and further improve its properties, a study of grating formation under holographic patterning is essential. This paper reports characterisation of the grating recording in PHSG. The approach is based on the analysis of grating parameters during exposure and post-recording dark processes. The obtained results suggest that photopolymerisation of the methacrylate groups is the main contributor to the creation of refractive index modulation during exposure. During the dark process, the enhancement of the refractive index modulation is observed, probably due to further polycondensation. The observations made facilitate controlled and predictable diffraction efficiency of gratings recorded on the PHSG, thereby furthering the prospect of the development of HOEs with customisable specification.  
photopolymerisable glass; volume holography; holographic recording material; water-resistant material; sol-gel chemistry; holographic optical element +
Footnote †: journal: Applied Sciences
## 1 Introduction
Another important issue is the mechanical stability of microdevices subjected to continuous vibrations, especially when they are deposited on flexible substrates. Oxide materials are brittle, and bending over more than 1000-2000 cycles can causes microcracks in the films, complete physical degradation, and failure of the device. In order to reduce the difference in the Young modulus of the flexible substrate and the oxide piezoelectric films, supplementary polymer nanoparticles are added as intermediate nanocoatings formed hybrid organic/inorganic interfaces. Another approach is to use blends for making nanocomposite ZnO/polymer [23; 24]. Their function in the device is to pick up plastic deformations and decrease stress. In addition, the substrate/sublayer materials and their structures play an important role in the determination of the ZnO nanostructures [25]. It is preferable to use conductive polymers such as poly(3,_A_-ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS) and polypyrrole (PPY) to avoid electrical isolation of the electrodes, or to serve as electrodes by themselves. It was demonstrated that PEDOT:PSS is the most frequently used polymer in combination with the most spread flexible substrate of polyethylene terephthalate (PET), due to the favorable wetting conditions of the PET surface from the PEDOT:PSS solution, and good adhesive strength of the polymer coating to PET [26]. In this study, ZnO film was sputtered on a PEDOT:PSS-coated PET substrate at modified deposition mode with oxygen deficit in order to obtain a 3D nanobranched structure. The structure is intended to serve as a piezoelectric microgenerator. The function of PEDOT:PSS is to enhance the mechanical stability of the entire microdevice, and to serve as an amorphous sublayer for non-ordered ZnO growth. It is believed that in this way, the obtained laterally aligned nanophranches on a bendable substrate will be subjected to maximal deformation without incurring a fast degradation process. The aim of using nanobranched ZnO is to facilitate the dipoles' orientation and their movement to the electrical poles, which is expected to decrease dielectric losses. In order to extract information about the dielectric behavior of the microdevice, dielectric losses are measured at different frequencies from 1 Hz to 1 MHz, and for temperatures ranging from \(-10\)\({}^{\circ}\)C to 40 \({}^{\circ}\)C. Relative permittivity \(\varepsilon_{r}\) is calculated for the same values. Structures' workability is demonstrated by presenting the generated voltage oscillograms. The processes and the curve trends are discussed with regard to the ZnO microstructure and its morphology. The significance of the work consists of the finding of new knowledge about the novel nanophranched piezoelectric structures with polymeric electrodes, and defining their frequency and temperature working range, thus determining their potential application.
# Numerical Voids Detection in Bonded Metal/Composite Assemblies Using Acousto-Ultrasonic Method
## 4 Results and Discussion
### Influence of the Voids on AE Parameters
The data calculated with the perfect sample confirm a significant decrease in the frequency content above 100 kHz for sensor 2, with a magnitude depending on the defect size. Indeed, in the undamaged sample, 78% of the spectrum energy was in the 125-200 kHz frequency range, whereas it was only 53% for \(\beta\)1 and 17% for \(\beta\)2. The modification of this frequency content can be correlated with the void size. At the sensor 1 location, no significant differences were observed. Nevertheless, the R15 sensor amplified the frequency content around 150 kHz due to its resonance and eliminated this reduction in frequency content (Figure 10a,b) even for sensor 2. The energy contained in the 125-200 kHz frequency band was 91% and 87%, respectively, for the undamaged sample and the sample containing a \(\beta\)1-type defect. Separation from the undamaged material was therefore difficult, and the probability of detection decreased with the size of the void.
## 3 Signal Processing
### Signal Model
\right]R_{az}\left(\theta\right) \tag{31}\]  
where \(r_{m}\left(t_{r}\right)\) is defined as a function of fast time as follows:  
\[r_{m}\left(t_{r}\right) \tag{32}\] \[=a_{nm}\text{rect}\left(\frac{t_{r}-\tau_{nm}}{T_{d}}\right)\cdot \text{rect}\left(\frac{t_{r}-\tau_{ref}}{T_{d,ref}}\right)\] \[\cdot\exp\left[j2\pi m\Delta f_{b}\left(t_{r}-\tau_{0}\right)\right]\] \[\cdot\exp\left[-j2\pi k_{r}\left(t_{r}-\tau_{0}\right)\left(\tau _{0}-\tau_{ref}\right)-j\pi k_{r}\left(\tau_{0}-\tau_{ref}\right)^{2}\right]\] \[\cdot\exp\left[-j\frac{4\pi}{\lambda}r_{0}\right]\cdot\exp\left(j 2\pi f_{d}t_{r}\right)\cdot\exp\left(j2\pi f_{c}\tau_{ref}\right)\]  
and \(R_{az}\left(\theta\right)\) is calculated in the azimuth angle domain from \(R_{az}\left(f_{a}\right)\) using Equation (27). Note that the \(N\times M\) MIMO channel matrix is defined as follows [3]:  
\[\mathbf{A}\left(\theta\right)=\mathbf{b}\left(\theta\right)\mathbf{a}\left( \theta\right)^{T} \tag{33}\]  
In the case of orthogonal waveforms, the MIMO steering vector for angle \(\theta\) is \(\mathbf{g}\left(\theta\right)\equiv\text{Vec}\left\{\mathbf{A}\left(\theta \right)\right\}\), where \(\text{Vec}\left\{\cdot\right\}\) denotes the vectorization operator.
## 1 Introduction
By analyzing the characteristics of the optimization sub-problems, the proposed experience replay mechanism was applied to a multi-agent DDPG (MADDPG), which was named the prioritized MADDPG (P-MADDPG), which trained valuable experiences to improve the throughput in the training process, thereby surpassing the issue of infinite power action space. * The proposed JPRL method is shown in Figure 1. It consists of a multi-agent DDQN (MADDQN) algorithm and the P-MADDPG algorithm, where MADDQN was developed to solve the bandwidth assignment sub-problem, and the P-MADDPG was employed to solve the transmit power allocation. Besides, both the MADDQN and P-MADDPG used a centralized training framework with a joint action-value function. The remainder of this paper is organized as follows. Section 2 introduces the system model and optimization problem. The proposed JPRL method is described in Section 3. Section 4 demonstrates the simulation results, and the conclusions are presented in Section 5.