esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion
### Numerical Results and Experimental Data Comparison  
As aforementioned, material characterization in the FE model does not consider the effects of both adhesive promoters and paints, but it refers only to material properties reported in Tables 1 and 2. Result postprocessing is performed to identify the level of accuracy of the developed FE model in simulating the structural behavior of all specimen series. For this purpose, a representative load vs. displacement curve for each specimen series was selected and is reported in Figure 7, where the curve provided by FE simulation is also reported. According to Figure 7, it can be noticed that all series where adhesive promoters are used are quite overlapped. CTRL, A, B, and EPX specimen curves, instead, appear as dissociated from the previous ones, being characterized by a reduced stiffness.  
The predicted curve (solid line) is quite overlapped with the curves' group linked to the specimens where adhesive promoters are used. It must be highlighted that the FE model, in this case, permitted the simulation of an ideal specimen, characterized by a uniform layer of glue, by the lack of adherends misalignment and all those imperfections usually associated with specimen manual manufacturing. Due to the good and acceptable level of accuracy provided by the FE simulation with respect to all series including adhesive promoters, it can be concluded that the usage of such additives permits mitigating imperfections in specimens. In terms of the prevalent failure mode, FE simulation appears to be coherent with those experimentally detected. The main reason for collapse of the joint, in fact, lies in the glass break.  
Figure 8a shows the von Mises stress distribution over the whole joint with focus on the glass adherends. According to Figure 8a, it can be observed that the maximum stress amplitude is reached. In Figure 8b, showing the deformation distribution at the glass adherends, it can be observed that the axial (with respect to the joint) strain component reaches the typical value of elongation at break, equal to 5%. Following the glass break, due to the load redistribution, the adhesive layers start to fail, as depicted in the contour plot shown in Figure 8c extracted some increments later. According to Figure 8c, blue zones represent the undamaged areas of the adhesive. Fully collapsed finite elements are deleted during the simulation.  
As demonstrated by Figure 9, the adhesive failure is mainly induced by mixed opening modes with a prevalence of the first shear mode (mode II), followed by mode I. All stress components are measured before and after the finite element deletion along the path illustrated in Figure 9.  
Figure 7: Numerical—experimental comparison of all treated double-lap joints.  
Figure 8: Von [PERSON] distribution (units in MPa) (**a**), strain distribution (**b**), and adhesive layers (**c**).  
Figure 9: Adhesive opening modes: (**a**) mode I; (**b**) mode II; (**c**) mode III; (**d**) selected path.
# Real-Time Spatial-Division Multiplexing Transmission with Commercial 400 Gb/s Transponders
## 1 Introduction
However, for each pair of degenerate modes, their modal field will rotate randomly along the propagation and strong coupling will occur [18; 19]. Therefore, 4 \(\times\) 4 MIMO DSP is still required in WC-FMF transmission to handle the mode degeneracy and polarization. Despite some recent works having been moving towards real-time 4 \(\times\) 4 MIMO processing, it is obvious that the unavoidable mixing between the degenerate modes cannot be compensated by current single-mode coherent transponders that are only equipped with 2 \(\times\) 2 real-time MIMO DSP hardware [20; 21]. In this paper, we experimentally demonstrate both single-mode WC-MCF and WC-FMF based SDM-WDM transmission systems employing commercial 400 G WDM transponders. In the WC-MCF transmission, link loss and end-to-end crosstalk of two optical lines constituted of single-roll 60 km or 20-roll 3 km 7-core fibers with fan-in/fan-out (FIFO) devices are characterized. Optical signal-to-noise ratio (OSNR) and bit error rate (BER) performance of the real-time system are also evaluated. In the transmission over WC-FMF, we show two non-degenerate LP-mode MDM-WDM transmission with 400 G WDM systems using 6-wavelength channels over the C band. In order to test the capability of the proposed WC-FMF and mode multiplexers in different applications, the transmission performances of 60 km, 20 \(\times\) 3 km and 150 km FMF are all demonstrated. The impact of splice points on SDM systems are also investigated. To our knowledge, this is the first splice experiment of MCF and FMF link based on actual fiber cable deployment conditions. The experimental results proves that the near-term deployment of SDM systems could be accelerated by utilizing the proposed SDM transmission approaches.
## 5 Data Analysis and Hypothesis Testing
### Perceived Information, Perceived Risk, and Purchase Intention  
Based on the SOR model, consumers' perceived information affects their perceived risk, thereby affecting their purchase intention. [PERSON] proposed the mediating effect test procedure [41], and a Bootstrap mediation variable test was carried out. The selected sample quantity was 5000. At a 95% confidence interval, the data showed a confidence interval for the Bootstrap test of [-0.0118, 0.0583], which includes 0, indicating that the perceived risk does not mediate the perceived information and purchase intention.
# High Q-Factor Resonance in a Symmetric Array of All-Dielectric Bars
Xiangjun Li  
Centre for THz Research, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (J.L.) Xiangjun Li  
Centre for THz Research, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (J.L.) Xiangjun Li  
Centre for THz Research, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (J.L.) Xiangjun Li  
Centre for THz Research, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (J.L.) Xiangjun Li  
Centre for THz Research, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS] (C.S. ); [EMAIL_ADDRESS] (X.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (J.L.) Xiangjun Li  
Centre for THz Research, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS] (C.S. ); [PERSON]DDRESS] (X.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (J.L.) Xiangjun Li  
Centre for THz Research, China Jiliang University, Hangzhou 310018, China; [EMAIL_ADDRESS] (C.S. ); [PERSON]DRESS] (X.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (T.L. ); [EMAIL_ADDRESS] (X.J. ); [EMAIL_ADDRESS] (J.L. )metasurfaces consisting of asymmetric rods and rings [5; 6; 35; 36; 37], and experimentally reachable of \(\sim\)600 in the near-IR wavelength range [6; 35]. And strong field enhancement either inside the dielectric resonators or in SRR gaps accompany with high Q, which is useful for nonlinear interaction [7; 38] or sensing applications [6; 36; 37]. High Q resonance can also be expected in a simple rod arrays with high permittivity (\(\varepsilon\geq 60\)) dielectrics at microwave frequencies [4], however, as the permittivity of dielectrics such as commonly used silicon decreases to around 13 in the optical range, high Q over \(\sim\)100 for electrical resonance (ER) or magnetic response (MR) in a simple silicon metasurface without asymmetric structure has not been reported yet, to the best of our knowledge. In this paper, we have numerically demonstrated that strong ER with high Q value over thousands can be achieved in a simple all-dielectric silicon rod arrays without asymmetric structure. The electric field coupling between adjacent rods is greatly enhanced by increasing the length of the rods, thus the radiative loss of the ER is significantly depressed, and high Q value over several thousands is achieved. At same time, the electric field enhancement both inside and surrounding the rod is greatly improved.
## 3 Numerical Examples
### 2D-Riemann Problem
#### 3.6.2 Configuration 6  
The initial conditions are  
\[(\rho,u,v,p)=\begin{cases}(1.0,0.75,-0.5,1.0),&1.0\leq x\leq 2.0,\ 1.0\leq y\leq 2.0, \\ (2.0,0.75,0.5,1.0),&0.0\leq x<1.0,\ 1.0\leq y\leq 2.0,\\ (1.0,-0.75,0.5,1.0),&0.0\leq x<1.0,\ 0.0\leq y<1.0,\\ (3.0,-0.75,-0.5,1.0),&1.0\leq x\leq 2.0,\ 0.0\leq y<1.0.\end{cases} \tag{53}\]  
The computational domain spans from [0, 2] in both the \(x\) and \(y\) directions, divided into four regions by \(x=1.0\) and \(y=1.0\), using a regular grid with a mesh of \(400\times 400\). The ultimate simulation time is set to \(t=1.6\). Other conditions are the same as configuration 3.  
The simulation results of LF, ROE, and URLBFS schemes are given in Figure 15. We can see that the URLBFS exhibits stable and sharp shock wave capture capabilities in both the WENO5 and WENO7 schemes.  
Figure 15: Two-dimensional Riemann problem (configuration 6): Density contours with 35 equally spaced isolines ranging from 0.15 to 2.4 were obtained using the LF, ROE, and URLBFS schemes. The color gradient represents the corresponding density values, with blue indicating the minimum density value. The simulation was conducted at t = 1.6 with a resolution of 400\(\times\) 400. Panels (**a–c**) show results using the WENO5 reconstruction method with LF, ROE, and URLBFS schemes, respectively. Panels (**d–f**) display corresponding results with the WENO7 reconstruction method.
### Contributions and Key Features  
1. We present a comprehensive application of device mapping research workflow that can be commonly adapted for optimizing RNN models onto a resource-constrained edge (see Section 3). This will help with reducing the research time required for method workflows in this context.
2. We focused on the HAR-based EI-RNN evaluation and optimization of five different RNN units sandboxed apart from the classic LSTM structures (see Sections 2.1-2.6).
3. We used eight different HAR datasets (see Tables 3 and 4, Section 4.2.9 for important details) and two evaluation methods, namely, the hold-out method and cross-validation method.
4. We conducted an in-depth performance analysis of both training and compression techniques applied to RNNs (see Sections 5.1 and 5.2). To the best of our knowledge, the exact details of both these critical aspects have rarely been presented with clarity from an implementation point of view.
5. The key takeaways based on this empirical evaluation study will be important for practitioners and researchers in this problem domain.  
We describe the background and related work with respect to RNNs, their training challenges, and compression schemes for edge mapping studies in Section 2. We then present our research workflow that captured the essential steps to be carried out for mapping any application to a device via RNN modeling and evaluation in Section 3. Furthermore, we present the experimental details covering the dataset description, evaluation methods and metrics used in Section 4. We then discuss EI-RNN modeling and provide an analysis in Section 5, which includes training, compression and inference. Next, we discuss  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Computation**} & \multirow{2}{*}{**MACs**} & **Muls** & **Adds** \\  & & **(Elem. Wise)** & **(Adder Tree)** \\ \hline \(f_{t}\) & 680960 & 1024 & 3072 \\ \(i_{t}\) & 680960 & 1024 & 3072 \\ \(g_{t}\) & 680960 & 0 & 2048 \\ \(c_{t}\) & 0 & 2048 & 1024 \\ \(o_{t}\) & 680960 & 1024 & 3072 \\ \(h_{t}\) & 0 & 1024 & 0 \\ \(r_{t}\) & 524288 & 0 & 0 \\ \hline \(1\) LSTM cell computation & 3248128 & 6144 & 12,288 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Computations involved in one Long Short-Term Memory cell for speech recognition.  
the results obtained and present the key takeaways in Section 6. Finally, we present the conclusions drawn in Section 7 including the future scope of the research problem.
## 4 Numerical Results and Discussion
### Algorithm Efficiency  
Table 2 shows the number of LMIs solved in a series of tests while varying the vehicle and the target. The baseline used was the _classical_\(A^{*}\) with Euclidean heuristics, while the tests with \(K_{f}=1\) used the hybrid \(A^{*}\) heuristics with the SVM classifier. The improvement ranged from roughly 12% (vehicle B/target D) to 28% (vehicle D/target C).
# Unlocking the Potential of RFA and Stabilizers in High Moisture Geotechnical Applications
## 3 Results
### UCS Stress-Strain Curve  
The test results show a correlation between UCS results and water immersion curing age, recycled aggregate content, and stabilizer content. Similar studies have indicated that  
\begin{table}
\begin{tabular}{c c c c} \hline
**Moisture** & **Recycled Aggregate** & **Dosage of Soft Soil** & **Age of** \\
**Content** & **Admixture (R)** & **Curing Agent (C)** & **Conservation (M)** \\ \hline \multirow{4}{*}{20\%} & & 10\% & \\  & 20\% & 15\% & Standard curing \\  & & 20\% & 28 d + immersion \\ \cline{1-1}  & 40\% & 20\% & curing 1 d, 5 d, 20 d, \\ \cline{1-1}  & 60\% & 20\% & 40 d \\ \hline \end{tabular}
\end{table}
Table 2: Design solutions for unconfined compressive tests and triaxial tests.  
these factors influence the peak strength \(q_{\text{u}}\)[35; 36; 37; 38]. Figures 4 and 5 illustrate the effects of water immersion curing age on UCS.  
The stress-strain curves for all the specimens exhibited strain-softening behavior. At water immersion curing times of 1 day, 5 days, 20 days, and 40 days, the R20C20 specimen consistently achieved the highest peak stress, while the R20C10 specimen consistently achieved the lowest.  
The peak UCS of the specimens increased with the extension of curing age (1 day, 5 days, 20 days, 40 days). Furthermore, when the recycled aggregate content was 20%, increasing the stabilizer content (R20C10, R20C15, R20C20) raised the peak UCS. Conversely, when the stabilizer content was 20%, increasing the recycled aggregate content (R20C20, R40C20, R60C20) reduced the peak UCS. This phenomenon was attributed to the hydration reaction producing cementitious materials randomly distributed within the specimen, forming distinct structural features with red clay. These random distributions influenced both peak stress and strain characteristics, leading to irregular strain behaviors.  
Figure 4: (a) The stress–strain curve at an age of 1 d of submerged conservation; (b) the stress–strain curve at an age of 5 d of submerged conservation.  
Figure 5: (a) The stress–strain curve at an age of 20 d of submerged conservation; (b) the stress–strain curve at an age of 40 d of submerged conservation.
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 3 Deep Learning-Based Decoder
### Soft Decision Decoding Based on DNN-Based Binary Classification  
[PERSON] and [PERSON] [13] developed the binary classification framework for decoding as shown in Figure 4. The main concept of this framework is to use the syndrome and the reliability of the received word as the inputs to train a DNN. The output of the DNN is the possible error pattern. One advantage of this framework is that it takes into account both the syndrome value \(\mathbf{s}\) and the reliability of the received word \(\left.\left|y\right.\right|,\) so as to improve the decoding performance. The output is the \(n\)-bit error pattern \(e^{*}=\left[e_{0}^{*},e_{1}^{*},\ldots,e_{n-1}^{*}\right]\), where \(e_{i}^{*}\in\{0,1\},0\leq i\leq n-1\). Each bit \(e_{i}^{*}\) is determined by the binary classification of the DNN. As e\({}^{*}\) is obtained, the decoded codeword is \(dc=r\bigoplus e^{*}\), where \(\bigoplus\) is exclusive OR (XOR) operation.  
The DNN architecture in [13] is illustrated in Figure 5 It consists of ten fully connected layers and the number of nodes in each layer is determined by the codeword length \(n\). The activation function used in each layer is the rectified linear unit (ReLU) function, except for the output layer, where the sigmoid function is used.
# Optimal Modeling for Dynamic Response of Energy Storage Systems
## 1 Introduction  
In recent years, the rapid development of industry, massive exploitation, and consumption of fossil fuels has resulted in the exhaustion of natural resources and a worse climatic environment, so various countries are searching for new alternative energy sources. Photovoltaic and wind power are the focus of the new age, and many countries have legislated the future development goal of emerging energy [1]. However, even if they have the advantages of cleanliness and almost zero pollution, they are excessively restricted by the weather, leading to intermittence, so they still are not reliable enough to fully substitute fossil fuels. Many countries have proposed standards to normalize the influence of grid-connected renewable energy on systems (e.g., the \"Rule 21\" of California, \"Std. 1547\" of IEEE, \"Renewable Energy Power Generation System Parallel Technology Guidelines\" of Taipower Company) [2].  
The energy storage system provides a solution to the intermittence of renewable energy. The electricity is stored when there is surplus electricity generation, and the ratio of renewable energy put in the power grid is reduced to enhance stability. The energy is supplied to the power grid when the electricity generation is insufficient to make up for the lack of renewable energy. Renewable energy and energy storage systems supplement each other, and their coordination can make the supply smoother and enhance the stability and ductility of the system.  
The renewable energy equipment or energy storage systems must be evaluated by the power supplier of each country before they are connected to the grid, and the impact on the system is analyzed by power system simulation software. An appropriate model can reflectthe equipment performance and prevent misjudgment of the feasibility of connection to the grid. Most internal plants in the simulation software use the model defined by the WECC. The model simulates the real system response by adjusting the internal parameters.  
Every construction for a power grid should also be evaluated in Taiwan. The tool that Taipower company uses is PSS/E, which uses models defined by the WECC to represent the default of the power facilities. Before being connected to the power grid, the behavior of the energy storage system should also be evaluated, so we chose the WECC model in PSS/E as the generic model to conduct this research.
## 2 Materials and Methods
### Hardware Structure of the Communication System  
The four-wheel drive agricultural mobile robot, which was developed in our previous study [29] and can be steered both autonomously and manually, was used in this study. Advantech ARK-1382 industrial all-in-one touchscreen industrial computer (Advantech Co., Ltd., Taipei, Taiwan) was used to manage and communicate with each other all of the electronic-based equipment placed on the mobile robot. The ARK-1382 fanless and ultra-compact embedded Box PC is an embedded system engaged in edge computing and highly suitable for remote monitoring, control, and mobile applications. It was designed with Intel Core Duo 1.06 GHz processor. It is equipped with an 802.11b/g WLAN (Wireless Local Area Network) card and supports a wide range of input voltages from 9 VDC to 35 VDC. It supports 4\(\times\) USB 2.0, 1\(\times\) Giga LAN, and 2\(\times\) COM ports.  
A Topcon AGRI-4 RTK GPS (Topcon Positioning Systems, Inc., Livermore, CA, USA) receiver was used to acquire the location and speed data of the mobile robot. The receiver has up to 10 Hz data output rate. It is easily upgradeable to 2 cm accuracy with RTK radio options, and NTRIP capabilities allow it to connect to Corse-TR (Continuously Operating Reference Stations-Turkey) via a phone data card (SIM Card) to receive correction signals.  
Honeywell HMR3000 digital compass was used to collect the heading, pitch, and roll data of the mobile robot. It is used for different applications such as compassing and navigation, dead reckoning backup to GPS systems, marine navigation, antenna positioning, and land surveying. This electronically gimbaled compass provides a fast refresh frequency  
Figure 1: Whole architecture of the wireless R2R communication system.  
of up to 20 Hz and a high direction accuracy of 0.5 degrees with 0.1-degree resolution. The tilt range is +/ -40 degrees. The hardware components used in the communication system are shown in Figure 2.
# Data Exchange in Cluster Structure for Longevity of IoT
## 2 Related Work
### Internet of Things and Edge Computing  
The IoT is based on the existing WSNs. Recently, various IT high technologies have been applied to smarten things and to configure advanced networks among things. Particularly, the IoT employs networking technologies to support intelligent, cooperative interaction among smart things. As a result, many applications services operating on WSNs are moving to the IoT domain and moreover are attempting to provide more intelligent functions.  
As the IoT network grows, the number of things in IoT increases significantly. Consequently, the amount of information produced by things increases highly. In IoT, each thing should co-share the desired information with other things or collect information actively. These intentions are represented as Brillo [14] to support various devices, HomeKit [15] for smart homes, and MinT [16] to support the integration of heterogeneous devices and provide the tools to develop IoT service applications. Furthermore, for effective cooperation between things, research on the middleware in IoT and interconnection of things and the information management of things has been undertaken [17; 18].  
The implementation of IoT is a challenging task, and all the implementation scenarios require the use of different technologies and the emergence of new ones, such as Edge Computing (EC). Over the last few years, there has been a great increase solution that decentralize communications, data collection and processing, moving all those tasks to the edge. Edge computing directs computational data, applications, and services away from cloud servers to the edge of a network. The content providers and application developers can use the edge computing systems by offering the users services closer to them. Edge Computing allows for improving the performance of computer systems by lowering latency, reducing the cost of resources and increasing responsiveness, scalability, reliability, security or privacy [19; 20].  
The management of IoT networks is challenging because of the heterogeneity of its resources, creating difficulties in communication protocols, real-time processes, data management, big data storage, security or privacy. In this regard, Edge Computing architectures offer a solution to IoT infrastructures because they can manage the heterogeneous data generated by IoT devices [21].
## 2 Material and Methods
### Proposed Analytical Model
The dedicated short-range communication (DSRC), a wireless standard, is considered the most promising protocol to be used for V2X communications. Specifically, for automotive use, the one-way or two-way short-range wireless communication channels can be involved, which is designed by DSRC technology [32]. For short-range communications, the 5.9 GHz band has been allocated by The US Federal Communications Commission (FCC) to support wireless communication in the vehicular environment, including V2X [33]. The IEEE 802.11p standard has been employed by the MAC and physical layer of DSRC layered for vehicular communication. Furthermore, based on the orthogonal frequency division multiplexing (OFDM) method, eight different data rates can be supported by the IEEE 802.11p standard [34].