esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Seismic Response of the Bridge  
#### 5.2.1 Soil-Structure Interaction Effects  
The seismic response of the bridge structure under the action of five selected near-fault ground motions was thoroughly investigated. The analysis encompassed both elastic and inelastic behavioral regimes, utilizing the advanced elasticity-damage constitutive model of Section 2.1 and was implemented in the NASTRAN finite element program specifically for concrete structures.  
Firstly, the time-history response of the horizontal displacement of the midspan point of the bridge's central span is shown in Figures 11 and 12. Thus, Figure 11 depicts the elastic response of the bridge, examining the following loading combination: dead loads + earthquake action, ignoring (rigid soil) or considering (SSI) the flexibility of soil. Figure 12 shows the inelastic response of the bridge.  
Figure 11: Time–history of the horizontal displacement of the midspan point of the bridge’s central span: elastic behavior.  
Figure 12: _Cont._  
The time-history analysis demonstrates the complex influence of soil-structure interaction (SSI) on bridge response. While most seismic events, considering SSI (red lines), result in reduced displacement amplitudes compared to the rigid soil assumption (black lines), the Northridge earthquake presents a notable exception. In this case, considering SSI leads to larger displacement demands, highlighting the importance of including these effects in seismic analysis for safe design. Moreover, the inelastic behavior analysis reveals that the consideration of SSI generally results in smaller residual displacements after the vibration has been damped out. This indicates that neglecting the effects of SSI may lead to unrealistic response estimates during the strong motion phase and could lead to an overestimation of permanent deformations. The findings highlight that a comprehensive seismic analysis should always incorporate soil-structure interaction effects, as their impact can be either beneficial or detrimental depending on the specific characteristics of the seismic event and structural system.  
In conclusion, the observed divergence in displacement responses highlights the intricate mechanisms of soil-structure interaction (SSI) under dynamic loading conditions. This variation stems from the complex nonlinear material behavior and progressive energy  
Figure 12: Time–history of the horizontal displacement of the midspan point of the bridge’s central span: inelastic behavior.  
dissipation mechanisms that emerge during seismic excitation. The time-dependent gap between different soil response models reveals the sophisticated interactions between structural components and foundation systems, demonstrating the challenges in precisely predicting structural response. By carefully analyzing these nuanced interactions, researchers can develop refined modeling approaches that capture the subtle yet critical dynamic characteristics of bridge structures under seismic loading.
# The Rapid Detection Technology of Lamb Wave for Microcracks in Thin-Walled Tubes
## 2 Theoretical Principle
### Calculation of Incident Angle  
The method of water immersion was adopted in the detection process, so it was necessary to calculate the incident angle of the \(A_{0}\) mode Lamb wave produced by the ultrasonic transducer when the Lamb waves propagate from water into the steel. The mathematical formula is as follows:  
\[\alpha=\arcsin\frac{c_{L}}{c_{p}}, \tag{6}\]  
where \(\alpha\) is the incident angle, \(c_{L}\) is the P-wave velocity in water, and \(c_{p}\) is the phase velocity of Lamb wave in steel. The relationship between the incident angle and frequency-times-thickness is shown in Figure 4.  
As shown in Figure 4, when the frequency-times-thickness was 4-6 MHz mm, the incident angle of the mode \(A_{0}\) corresponded to 30.5-29.99\({}^{\circ}\). In practical applications, 30\({}^{\circ}\) was chosen as the incident angle to obtain the \(A_{0}\) mode of the Lamb waves.  
Figure 3: Particle displacement-amplitude curves of \(A_{0}\) and \(S_{0}\) modes in steel plate ((a) \(A_{0}\) mode; (b) \(S_{0}\) mode).
## 3 System Design
### Target Devices and Operating Environment  
The system is a mobile application that is mainly intended to be used on mobile information terminals both indoors and outdoors. Additionally, the system is an Android application that requires Android (5.0 or higher) for the OS. Android Studio, which is an integrated development environment for Android applications [43], the Wikitude Software Development Kit (SDK), which is an SDK for AR development provided by the Wikitude GmbH (Salzburg, Austria) [44], and Mapbox, which is used in the development of Web-GIS and is provided by the Mapbox Japan G.K. (Tokyo, Japan) [45], were used for application development. According to the system requirements of Wikitude SDK, in addition to a camera and GPS, mobile information terminals must also be equipped with a compass, network positioning, accelerometer, high-resolution device, high-performance CPU, and OpenGL (2.0 or higher).
## 2 Literature Review
### Factors That Influence (the Perception of) the Added Value of Occupancy Sensing
#### 2.3.3 Quality of Sensors  
[PERSON] [50] conducted a study on the methodologies to monitor people count. They found discrepancies from 5% to 22% compared to manual counting. This is an old study, and the methods are probably better now, but this shows that sensing is not always that accurate. [PERSON] et al. [50] showed a model that gathered data with cameras and CO\({}_{2}\) sensors that reduced the variance of occupancy from 70% to 11%. It is [51] advise to use sensors based on ultrasonic waves for monitoring occupancy within a building. These sensors are easy to use and install, but the data handling is not that easy. [PERSON] et al. [39] (2015) reviewed the existing conventional systems that are utilised in buildings for occupancy sensing. They showed that obtaining information is often challenging because of human behaviour and the limitation of tools. The review showed that chair sensors are an excellent system to measure occupancy. A 2020 study on building occupancy measurement systems [52] compared different systems on the merits and limitations. They reviewed cameras, WiFi-based sensors, infrared sensors, CO\({}_{2}\) sensors and electricity metres. The study showed that the fusion of sensors tends to perform the best because different sensors can compensate for each other. The before mention authors predicted that current developments like 5G and artificial intelligence (deep learning technologies) will be a significant addition to occupancy sensing and the use of sensors.
# Energy-Saving Network Ventilation Technology of Extra-Long Tunnel in Climate Separation Zone
## 3 Results and Discussion
### Energy-Saving Analysis  
For most short road tunnels, only few fans are installed in the tunnels and the mechanical ventilation is not used as much as long tunnels. Thus, ventilation system occupies a small proportion (10-35%) of the total energy consumption compared to the lighting system [35]. However, for extra-long road tunnels, the effect of natural wind on tunnel ventilation is rarely sufficient for sustaining good air quality without jet fans of large power. For example, the Nibashan Tunnel in Daxiang Range is 10 km long. The installed power of its ventilation system is 6500 kW. The electricity costs will be 3.06 million  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Time**} & \multirow{2}{*}{**Ventilation Mode**} & \multicolumn{3}{c}{**Working Conditions**} \\ \cline{3-5}  & & **Section 1** & **Section 2** & **Section 3** \\ \hline Daytime & 18 & SZ-1-B & SZ-1-C & SZ-1-C \\ Night & 9 & SZ-1-A & SZ-1-C & SZ-1-C \\ \hline \hline \end{tabular} Note: S stands for jet fan; Z stands for left line; 1, 2 and 3 is the section number; A, B and C is the control strategy.  
\end{table}
Table 5: Control strategy of the Nibashan Tunnel (left line).  
Figure 9: Control strategies by hour: (**a**) control strategy in Section 1; (**b**) control strategy in Section 2; and (**c**) control strategy in Section 3.  
dollars annually if the system operates eight hours a day, which takes up 65% of its operation energy consumption (only 25% for illumination).  
During the tunnel operation, the ventilation costs account for a large proportion of the overall tunnel operation costs. A reasonable fan control strategy, which takes full advantages of the natural wind dynamics, will lower the operation costs greatly [36; 37]. The strategy of combining natural ventilation with mechanical ventilation was implemented in the Yuanliangshan Tunnel empirically, which has halved the number of jet fans in the tunnel but maintained the same ventilation effect [38].
## 4 Empirical Results
### Estimation of PSM Method  
In Table 4, m1-m6 respectively represent the five nearest neighbors caliper matching (caliper radius 0.01), the one nearest neighbor caliper matching (caliper radius 0.01), caliper radius matching (caliper radius 0.01), caliper radius matching (caliper radius 0.05), kernel matching (quadratic kernel), and kernel matching (Gaussian kernel). From the [bias] values of the six matching methods, we can see that the biases of the other control variables except population and energy consumption are significantly reduced after matching, which indicate that the PSM has indeed significantly improved the data error rate. The criterion for the success of matching is whether each variable passes the balance test after matching, that is, whether the p value of the t test after matching is significant or not. After all matching methods, the p \(>\) |t| value of each variable in the table below is not significant (more than 10%). That is, the original hypothesis is rejected, indicating that there is no systematic difference in the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Variable** & **Obs** & **Mean** & **Std.** & **Min** & **Max** \\ \hline Inc & 390 & 10.26 & 0.83 & 7.58 & 12.35 \\ Inp & 390 & 3.55 & 0.32 & 2.73 & 4.05 \\ us & 390 & 52.96 & 13.96 & 26.87 & 89.60 \\ a & 390 & 2.82 & 1.76 & 0.54 & 10.89 \\ is & 390 & 46.40 & 8.03 & 19.00 & 59.05 \\ ci & 390 & 3.87 & 2.47 & 1.47 & 18.26 \\ energs & 390 & 72.01 & 14.65 & 11.15 & 94.33 \\ environs & 390 & 1.36 & 0.67 & 0.30 & 4.24 \\ rs & 390 & 0.23 & 0.24 & 0.01 & 1.24 \\ ri & 390 & 1.42 & 1.05 & 0.18 & 6.01 \\ lngdp & 390 & 9.05 & 0.95 & 6.30 & 11.07 \\ lnk & 390 & 10.08 & 0.92 & 7.39 & 12.04 \\ lnl & 390 & 7.60 & 0.81 & 5.67 & 8.83 \\ lne & 390 & 10.26 & 0.83 & 7.58 & 12.35 \\ \hline \hline \end{tabular}  
* Due to the availability of data, Shenzhen is included in the statistics of Guangdong Province.  
\end{table}
Table 2: Descriptive statistics table.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable** & \multicolumn{2}{c}{**Before Pilot Policy (2005–2013)**} & \multicolumn{2}{c}{**After Pilot Policy (2014–2017)**} \\ \cline{2-5}  & **Un-Pilot Province** & **Pilot Province** & **Un-Pilot Province** & **Pilot Province** \\ \hline Inc & 9.98 & 10.03 & 10.88 & 10.73 \\ lnp & 3.56 & 3.48 & 3.57 & 3.53 \\ us & 45.91 & 69.50 & 54.20 & 74.26 \\ a & 2.17 & 4.35 & 3.04 & 5.79 \\ is & 48.80 & 44.51 & 44.06 & 38.50 \\ ci & 3.69 & 2.15 & 5.57 & 2.60 \\ energs & 77.20 & 66.40 & 52.33 & 37.11 \\ environs & 1.35 & 1.26 & 1.49 & 1.10 \\ rs & 0.13 & 0.44 & 0.20 & 0.60 \\ ri & 0.99 & 2.45 & 1.25 & 3.06 \\ lngdp & 8.78 & 9.29 & 9.31 & 9.90 \\ lnk & 9.73 & 10.09 & 10.67 & 10.88 \\ lnl & 7.60 & 7.43 & 7.70 & 7.59 \\ lne & 9.98 & 10.03 & 10.88 & 10.73 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of variables before and after implementation of the policy.  
data before and after matching; therefore, the carbon trading pilot policy can be considered to be a random allocation experiment.
# Smart Logistics Warehouse Moving-Object Tracking Based on YOLOv5 and DeepSORT
## 3 Logistics Warehouse Object Detection
### YOLO Algorithm Network Structure  
The YOLO algorithm, known as You Only Look Once, employs a single-stage detection approach inspired by the human visual system. The YOLO algorithm swiftly encompasses the whole-image data for prediction to avoid confusing the target with the background. In some cases, the YOLO detection model struggles with targets that occupy a small image size. Compared to offline detection algorithms, the YOLO algorithm might not exhibit the highest detection accuracy, but the detection accuracy and detection speed should be taken into account in practical applications [18].  
As shown in Figure 3[19], YOLOv5s consists of four main components: Input, Backbone, Neck, and Prediction, where the Backbone convolves and pools the input image to obtain the image feature vector; the Neck transfers the extracted image features to the Prediction part through sampling; and the Prediction part makes predictions based on the image features to obtain the target bounding boxes and prediction classification. There are some basic composite network structures in the network structure, as stated separately below [20].  
Figure 3: YOLOv5s network structure.  
1.1 Cbl (Convolutional Operation, Batch Normalization Algorithm, and Leaky ReLU Activation Function)  
The convolutional operation, the batch normalization algorithm, and the leaky ReLU activation function compose the CBL module, which mainly plays the role of data sampling. The structure is shown in Figure 4.
## 4 Discussion
In this study, RadWet-L was calibrated and evaluated using ALOS-2 PALSAR-2 ScanSAR data from 2019-late to mid-2023. Corroboration with GRACE-FO data demonstrates that RadWet-L can depict both intra and inter-annual variations in inundation, albeit over a relatively short timescale. The next step is to apply RadWet-L to the entire ALOS-2 PALSAR-2 ScanSAR archive, potentially generating data on inundation dynamics from 2014 to present for a range of wetlands across the tropics. Analysis of historical ALOS PALSAR ScanSAR data would also allow the dynamics of the 2007-2010 period to be included. In doing so, we may be able to unpick the relationship between longer-term inundation dynamics and longer-term climatic factors, including ENSO cycles, and understand how climate change might affect tropical wetlands. For instance, a longer time series of information can be used to establish long-term typical inundation extent, specifically the median wet/dry season extent. This can then be used to benchmark against the extent recorded on an annual basis, revealing the impact of El Nino/La Nina events. It can identify areas where significant 'excess' in inundation is occurring and the resulting flood hazard, or areas that are particularly affected by drought conditions. Such information can help inform policy to safeguard wetland ecosystems and livelihoods.
## 2 Related Work
### Fault Tolerant Control (FTC)  
Fault tolerant control (FTC) schemes are widely used in all kind of industries, including renewable power systems (nuclear, solar, and wind), aircraft, helicopters, and spacecraft. The structures include highway systems, automotive systems, engine and propulsion control, chemical plants, robots, building and air-conditions systems, industrial furnaces and heat exchanger, drug infusion, motors and drives, networks, paper machines, magnetic bearing systems, winding machines, and water-tank systems [9; 33; 34]. The FTC scheme not only deals with wind perturb conditions, but reduces the short-circuit overshoots disorder and applies the MPPT strategy to ensure the healthy operation of the DFIG-WT-based power system. In addition, the FTC scheme has been applied to ensure the smooth healthy operation of DFIG and grid connectivity under various wind speeds and short-circuit faults [20; 25; 35].
# Trajectory Tracking Algorithm Study of Coal Mine Water Detector Drilling Bar Installation
## 4 Stability Design of the Controller System
### Proof of the Stability of the Supplied Material Device's Control System  
To prove the stability of the reaching law, select the Lyapunov function validation:  
\[V=\frac{1}{2}s^{2} \tag{14}\]  
It can be obtained from Formulas (13) and (14) that  
\[\dot{s}\dot{s}=-s\cdot\varepsilon\cdot sat(s)-k\cdot s^{2}{<}0 \tag{15}\]  
From Formula (15), it is obtained, based on saturation functions, that the exponential approach law satisfies [PERSON]'s theory of stability [18].  
The system control rates obtained from Formulas (12) and (13) are as follows:  
\[u(t)=C(q,\dot{q})\dot{q}+G(q)+d+M[\dot{c}\dot{e}(t)+\ddot{q}_{d}+\varepsilon \cdot\text{sat(s)}+k\cdot s] \tag{16}\]
# Birds Eye View Look-Up Table Estimation with Semantic Segmentation
## 5 Experiments
### Quantitative Evaluation  
In this paper, the seg loss is a learning measure for segmentation features inside the network, and the LUT and pose losses are quantitative indicators for the LUT/3D spatial angle, respectively (Table 6).  
We gradually changed the layers to infer the change in performance from the structural evolution of our network (Table 7).  
The resolution representation of the encoder, decoder, and LUT generator was tested with 1 (single scale)/4 (multi-scale), and the parallel path of the decoder was tested with two pixel shuffles or pixel shuffle and convolution transposed. Finally, we tried to improve the performance through the combination with a pose regressor.  
All tests were inferenced in the NVIDIA GTX 1080 environment, and three losses were evaluated.  
If the parallel path composed of two pixel shuffles is changed to a layer consisting of pixel shuffle and convolution transposed, the speed is 1.11 times slower, but the segmentation loss is reduced 0.89 times and the LUT loss is significantly reduced as well (0.06 times).  
If the resolution representation considered by the encoder, decoder, and LUT generator is changed from single scale to multi-scale, the speed is 3.7 times slower, the seg-mentation loss is 0.38 times less, and the LUT loss is 0.44 times less, so we can see that the loss decreases. When changing to multi-scale, we can see that the segmentation loss is significantly reduced.  
When learning that by adding a pose regressor to the end of the encoder, the speed is only reduced 1.09 times, but the segmentation loss is reduced 0.64 times and the LUT loss by 0.25 times, we can see that the loss cost is significantly reduced, while the processing speed is slightly increased. Through this, we can understand that it is meaningful to connect the pose regressor to the end of the encoder to obtain the direction of the entire network.
## 4 MARL-Based Routing Solution
### Reward Modeling  
Rewards are the mechanism by which the environment informs the agent about the quality of its policy. A reward function is developed to guide the agent in learning how to establish optimal paths whenever possible and other paths in the rest of the cases. The expression for the reward function is presented in Equation (15):  
\[r_{n}=r_{n-1}-d_{ij}+\delta(50+100\phi), \tag{15}\]  
where:  
* \(r_{n}\) is the reward at step \(n\).
* \(r_{n-1}\) is the accumulated reward of the episode, initialized to 0 at the beginning.
* \(d_{ij}\) is the delay of the selected link at step \(n\).
* \(\delta\) is a binary value set to 1 when the path reaches the destination TSN switch _dst_; otherwise, it is 0.
* \(\phi\) is another binary value set to 1 if the shortest path is formed between the source and destination end devices; otherwise, it is 0.  
In summary, the delay of the selected link is applied as a penalty at each step by subtracting \(d_{i,j}\). Upon reaching \(dst\), the agent receives a positive reward of 50 points with an additional 100 points granted if the optimal path has been established. This structure encourages the agent to minimize delays while prioritizing the formation of optimal paths.
## 2 Related Work
On the basis of experimental work, this technology exhibited high reliability and accuracy, in addition to low power consumption. These results identify that this technology has a high potential for SHM with IoT capabilities [37]. Another result is a review on small smart sensors that are able to collect data with IoT technology. The aim of the study was to employ algorithms to infer hidden information [38]. This investigation effort was based on a reported work in progress about smart sensors to measure strain [39], and the developing proof of concept of a smart elastomer for sensing force and vibration [40]. The preliminary experiments that were carried out and the obtained results were extended to obtain broader results that allowed for stronger conclusions. Additionally, the present effort went further into the design and manufacture of a complete prototype, which had not been developed by these previous outcomes, and the energy-harvesting prototype was improved by including more tests, data analysis, and higher levels of generated voltage. The motivation of this work was to improve reported achievements on the health monitoring of structures through smart sensors. A preliminary result of MRE application on structural monitoring was recently reported [41]. Moreover, another study determined vibration levels and resonance frequencies for vehicles. A Volvo FM12 with air suspension, driven at 30, 50, 70, or 90 km/h on three road types, was used for transportation. Vehicle speed was measured with a global-positioning system (GPS), and vibrations were read with two accelerometers placed on the chassis and floor. As a result, four main resonance frequencies were identified for the vertical movement at 1.3, 5.1, 12.6, and 23 Hz. The vibration-exposure values (considering an 8 h transport period) were 0.61 \(\pm\) 0.12 m/s\({}^{2}\), and amplitude of 0.019 \(\pm\) 0.0037 m, respectively. According to the results, the energy-harvesting mechanism could be applied in the automotive industry [33]. To the best of the authors' knowledge, there are no reported outcomes regarding energy harvesting with low-cost vibrating cantilevers with embedded piezoelectric sensors that reported generated voltage-peak values greater than 1.0 V for frequencies of less than 10 Hz. Moreover, a review of the state-of-the-art research did not find reported work about SHM with MRE, IoT characteristics, and energy-harvesting capabilities.
# Multi-Granularity Mission Negotiation for a Decentralized Remote Sensing Satellite Cluster
## 3 Experiments and Analyses
### Experimental Data
#### 3.1.2 Imaging Satellite  
Satellite parameters are shown in Table 4. They are ten isomorphic satellites in the scene, with an interval phase of \(-5^{\circ}\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Mission Status ID** & **Mission Status** \\ \hline
0 & not discovered \\
1 & discovered \\
2 & identified \\
3 & confirmed \\
4 & tracking \\
5 & monitoring \\ \hline \hline \end{tabular}
\end{table}
Table 2: Definition of the mission status.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Target ID** & **Position** & **Course Angle** & **Velocity** & **Mission Status** \\ \hline
1 & 46.78\({}^{\circ}\) E, 0.16\({}^{\circ}\) N & 13\({}^{\circ}\) & 3.2 & 3 \\
2 & 135.27\({}^{\circ}\) E, 1.05\({}^{\circ}\) S & 78\({}^{\circ}\) & 16.3 & 1 \\
3 & 148.65\({}^{\circ}\) E, 1.62\({}^{\circ}\) N & \(-51^{\circ}\) & 8.6 & 0 \\ \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) & \(\cdots\) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Definition of the mission status.  
At the beginning of simulation, mission 1 was assigned to satellite 4. Therefore, satellite 4 executed a tracking mission stage which affects the negotiation.