esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
The paper presents an FEA investigation of the influence of the second tunnel construction on adjacent first tunnel lining. A comprehensive monitoring program was conducted on the internal force of the tunnel lining. The FEA model was validated by the comparison with the monitoring results. The ground surface settlement, as well as the deformation of the first tunnel, was also analyzed to study the influence of the adjacent TBM-driven process. The follow conclusions can be drawn.  
1. A typical horizontal ovalization of the first tunnel was monitored, with the maximum and minimum bending moment located at the sidewall and top of the tunnel, respectively.
2. To take the TBM geometry into consideration, a non-uniform contraction mode with the maximum contraction \(\delta=6\) mm was employed in the FEA model. The model was validated by comparison with the monitored bending moment. The FEA results show that a double-bottom settlement type occurred during the tunneling process of the second tunnel. In addition, the surface settlement of the first tunnel induced by the second tunnel advance is more sensitive to the grouting pressure than face supporting pressure.
3. The grouting pressure during the second tunnel construction has significant influence on the convergence of the adjacent first tunnel lining, ascribing to the squeezing effect caused by the grouting pressure. However, the face supporting pressure does not have notably influence on the first tunnel convergence.
4. The relationship between the bending moment and the grouting pressure coefficient \(K_{g}\) can be fitted in a linear function. A \(K_{g}=1.0\) is the most suitable choice since the variation of the bending moment in the first tunnel is stable with the TBM advance of the second tunnel. The relationship between the face supporting pressure and the bending moment under different tunnel depths can be fitted by a lognormal function. A critical C/D ratio, under which the horizontal and vertical soil arching effect disappear, can be deduced to be within the range of 0.55-0.60.  
Conceptualization, C.L.; Funding acquisition, C.L.; Resources, H.J.; Supervision, H.L. and Y.Y.; Validation, W.C.; Writing--original draft, C.L. and Z.P.; Writing--review & editing, L.P. All authors have read and agreed to the published version of the manuscript.  
The research was conducted with funding provided by Guangdong Natural Science Foundation (Grant No. 2018A030313132), National Science Foundation of China (Grant No. 51808150), and National Science Foundation of China (Grant No. 51808230).  
The authors declare no conflict of interest.
## 3 Methodology  
### SBAS-InSAR Techniques  
The SBAS-InSAR method was first proposed by [PERSON] et al. (2019). The SBAS-InSAR method can extract distributed point targets that maintain coherence within a certain period and further increase the point density. It is very suitable for natural ground deformation monitoring and can achieve long-term, millimeter-level deformation monitoring at the regional scale.  
The principle of SBAS-InSAR technology is as follows: First, calculate the time and space baselines of multiscene SAR images covering a certain area in different time periods,  
Figure 2: (a) Geology map for the study area; (b) historical slope hazard records within the study area.  
select the appropriate temporal and spatial baseline threshold to generate the interferometric pairs; then perform differential interferometry processing on the selected master-slave image pairs and conduct the phase unwrapping; finally, phase errors caused by topography and orbit are removed, and the least square method or singular value decomposition (SVD) method is used on the interferogram subsets to estimate the deformation parameters. In actual processing, the method of temporal and spatial filtering is used to remove the atmospheric delayed image to separate the nonlinear deformation. The sum of the estimated low-frequency deformation and this nonlinear deformation is the deformation information of the entire study area.  
The SNAP (official open-access software for Sentinel data, developed by European Space Agency) and StaMPS/MTI (version 4.1b, Stanford Method for Persistent Scatterers/ Multi-Temporal InSAR developed by [PERSON] et al.) were adopted to process the SAR images. The processing flow of SBAS-InSAR method is shown in Figure 3[33, 34, 35]. The small baseline interferograms were first generated by SNAP, and the amplitude dispersion threshold of 0.6 was adopted to preprocess the candidate PS pixels [5,36]. Then, phase unwrapping, phase error corrections, and deformation estimation were carried out by StaMPS/MTI.  
Taking the image of 12 November 2020 as the super-master image, the time baseline threshold is set to 72d, and 90 interferometric pairs can be obtained. The small baseline dataset combinations are shown in Figure 4.  
Figure 4: Temporal and spatial baselines of SAR datasets.  
Figure 3: Procedures of SBAS-InSAR method based on SNAP-StaMPS workflow.
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Real Data Experiments
#### 4.2.2 Performance of Intrinsic Calibration  
In this section, we utilize the reprojection error to verify our intrinsic parameters calibration method of the camera.  
\[r=\frac{1}{3N}\sum_{i=1}^{3}\sum_{j=1}^{N}\sqrt{\left(u_{i}-u_{i^{\prime}} \right)^{2}+\left(v_{i}-v_{i^{\prime}}\right)^{2}}, \tag{24}\]  
where \(N\) is the number of corner points on one chessboard, \(\left[u_{i},v_{i}\right]^{\mathrm{T}}\) and \(\left[u_{i^{\prime}},v_{i^{\prime}}\right]^{T}\) are the theoretical coordinate and experimental coordinate of each corner point.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **LiDAR Value** \\ \hline \(f_{u}/\mathrm{(pixel)}\) & 300 \\ \(f_{v}/\mathrm{(pixel)}\) & 300 \\ \(u_{0}/\mathrm{(pixel)}\) & 159 \\ \(v_{0}/\mathrm{(pixel)}\) & 125 \\ Resolution/(pixel) & 320 \(\times\) 240 \\ Frame Rate/(fps) & 30 \\ Range/(m) & 0.1\(\sim\)25 \\ Field of View/(degree) & 50 \(\times\) 40 \\ Range Accuracy/cm & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Parameters of flash LiDAR.  
Figure 9: Average absolute error of translation for different number of frames.  
We take 20 images of the triangular pyramid with different positions and angles. Each image is divided to three parts by hand. Then, we calculate the intrinsic parameters of the camera and reprojection error of 20 images. Figure 10a shows the result of each image. The reprojection error of each image is below 0.5 pixel, and most are about 0.35 pixel.
# Robustness Analysis for Multi-Agent Consensus Systems with Application to DC Motor Synchronization
## 1 Introduction
A parametric uncertainty-based approach is proposed in [25]; here, the multi-agent system is transformed into a controllable form where the state matrix's coefficients are assumed to be uncertain. Then, the maximal input's time delay for which the multi-agent system stability is guaranteed is determined using the value set method. Here, an assumption is made in the multi-agent system to achieve the particular structure that allows for using the value set tool in contrast with our paper where that assumption is not made. In this paper, we present a multi-agent consensus-based control scheme to synchronize the speed of DC motors. Unlike the methods discussed above, it can control multiple motors. In addition, in the multi-agent scheme, the motors are synchronized first to reach the consensus and then follow the reference, unlike the parallel or master-slave scheme. In this work, the leader's control is based on the center of the state's mass and the motors' speed; thus, the response depends on all speeds, making the transient smoother. Previous to experimentation, an in-depth analysis was carried out in the system's robustness to guarantee the proper function of the multi-agent system; that is, the tolerance to phenomena not considered in the mathematical model. First, uncertainty limits that may be withstood by the closed-loop multi-agent system are calculated in the model parameters. Then, time delay is added to the system, and the maximum value it can withstand is computed; both calculations are performed without losing the stability property. The above provides the confidence to apply the control strategy for synchronizing the speed of the three DC motors, which is perfectly validated with the experimental results presented at the end of this work. This paper's organization is as follows: the modeling of the information flow among agents using algebraic graph theory is presented in Section 2. The DC motor dynamic model and a control law based on multi-agent consensus control developed for DC motor speed synchronization are presented in Section 3. In Section 4, the robustness analysis of the multi-agent system in the presence of uncertainty and time delays is presented. In Section 5, experimental results are discussed. Finally, conclusions and future work are presented in Section 6.
# Smart Collection of Real-Time Vehicular Mobility Traces
## 4 Targeted Goals
### Alerts  
The framework administrator receives warning notifications when a predefined event is triggered. These alerts may indicate that the vehicle is entering or leaving the geo-zone, speed changes, oil burning, battery consumption, low fuel level, and GPRS signal reception. All of these possible warnings are shown by default on the dashboard framework in real time. If the platform remains inactive during a predefined period, these alerts will be sent to the administrator by email or by text message (SMS) if the alert that occurred is specified as critical. Moreover, PM2 [19] allows for flexible alert monitoring, as shown in Figure 10.  
Figure 11 shows an example where \"vehicle speeding\" is noticed. When the vehicle exceeds a specific speed limit, the administrator is informed by setting a warning which indicates the recorded speed and the full timing of the event. This operation is performed in real time to warn the vehicle  
Figure 10: PM2 alert monitoring.  
Figure 9: Geo-zone restriction.  
driver about this penalty infraction on time. This alert contains a collection of useful information including the vehicle, driver, and event details, such as the conductor stamp, exact timing, latitude, longitude, and recorded speed.  
All of the recorded notifications are classified by type, as shown in Figure 12. It is worth noting that, during the whole deployment and testing time for seven months,  
* the absence of the GSM/GPRS signal represents 18% of all notifications,
* reports about battery exhaustion correspond to 9%, and
* tracker disconnection issues identify 5% of reports.  
These notifications are generated periodically by default or immediately after an incident happens. These alerts are instantaneously corrected to generate a complete report thanks to the continuous monitoring, recording and administration of the tracking framework. They are drawn on the \"statistics tab\" depending on the filtering parameters.  
Figure 11: Screenshot of the server console depicting the speeding alert reporting information details.  
Figure 12: Chart representing the percentage value of the appearance of the different types of notifications.The most frequent is 'zone exit’.
## 3 Problem Description and Formulation
### Formulation
#### 3.2.2 Establishment of the Model  
The proposed MFHVRP-PCEC is formulated as shown below:  
\[MinC_{1}+C_{2}+C_{3} \tag{5}\]  
\[x^{f}\big{(}\alpha^{-}(i)\big{)}-x^{f}\big{(}\alpha^{+}(i)\big{)}=0,\forall i \in C,f\in F \tag{6}\]  
\[\sum_{f\in F}x^{f}\big{(}\alpha^{-}(i)\big{)}=1,\forall i\in C \tag{7}\]  
\[x^{f}\big{(}\alpha^{+}(0)\big{)}\leq|V|,\forall f\in F \tag{8}\]  
\[y_{iv}^{f}\geq y_{iv}^{f}+d_{j}-Q_{v}\Big{(}1-x_{ijv}^{f}\Big{)},\forall f\in F,v\in V \tag{9}\]  
\[0\leq y_{iv}^{f}\leq Q_{v}-d_{i},\forall i\in C,f\in F,v\in V \tag{10}\]  
\[x_{ijv}^{f}\in\{0,1\},\forall i,j\in C,f\in F,v\in V \tag{11}\]  
where \(y_{iv}^{f}\) is the cargo quantity remaining after service at customer \(i\) by vehicle \(v\) at its _f-th_ trip. The objective is to minimize the total transportation cost of all vehicles in Constraint (5). Constraint (6) captures the vehicle flow conservation at each customer. Each customer is visited only once in Constraint (7). The number of vehicle departures from the depot is limit at most \(|V|\) by Constraint (8). Constraints (9) and (10) ensure that the required quantities are transported to the customers and each route does not exceed the vehicle capacity. The decision variables are captured by Constraint (11).
## 2 Related Works
### Attention Mechanism and Feature Fusion Methods  
To enhance grasp-related features, various approaches have been adopted, including attention mechanisms and feature fusion. By integrating these techniques, models can improve their modeling capabilities, adaptability, and generalization to input data, thus enabling more effective handling of complex input scenarios.  
In recent years, some methods have begun to utilize attention mechanisms to enhance grasping performance. While Transformer's self-attention mechanism [10] was effective in learning global features, it suffered from local feature loss. Squeeze-and-Excitation attention [11; 12] yielded effective channel characteristics but ignored spatial information. The latest AAGDN [14] employed Coordinate Attention [15], capturing position-related features but overlooking mutual features across spatial positions, while the 1 \(\times\) 1 convolution was not conducive to cross-channel interactions. To address these limitations, we developed a novel Residual Efficient Multi-Scale Attention (Res-EMA) module.  
The approach of attention mechanisms has also been widely applied in other fields. For example, one study [24] utilized attention mechanisms in standard convolutions to highlight important features for better detection of the dynamic behavior of melt pools in metal additive manufacturing (AM) processes. Another study [25] employed channel attention mechanisms in models to identify rock thin sections. Additionally, in [26], attention mechanisms were incorporated into the YOLOv5 model for improved detection of tomato viruses.  
Feature fusion, a technique in deep learning, involves merging feature information from different levels or branches. In encoder-decoder networks, low-level features provide spatial details, and high-level features offer semantic richness. However, down-sampling during the encoder process can result in spatial resolution loss. Feature fusion methods, such as those in [27; 28], address this by combining low-level and high-level features. Inspired by Deeplab [29], we developed a feature fusion module with a smaller dilation rate to mitigate information loss during encoder subsampling, thereby enhancing the network's ability to capture grasp-related features.
# Wireless Communication Technologies for Safe Cooperative Cyber Physical Systems
## 2 Technical Background
### SafeCOP Use Cases
USV application to bathymetry will result in a twofold gain: saving costs and reducing HSE risk for survey personnel [45]. This gain increases for bathymetry measurements in extreme conditions like the Arctic Ocean, where USV may replace a fully-crewed ship and shows better performance in adverse environments and implement weather. At the current stage, the USV has to be remotely controlled by a human operator who is located on another vessel. Such cooperation between the USV and the manned vessel can dramatically increase navigation safety while heavily relying on wireless communications between them, as illustrated in Figure 2. The USV and manned vessel have to periodically exchange critical information, such as vessel speed, course and position, to maintain a certain formation. The USV receives instructions and commands from the manned vessel to maneuver or stop. When safety-critical events occur (e.g., potential collisions), the manned vessel sends safety control commands to the USV, which has to respond within a certain time to avoid collisions. Therefore, packets carrying critical information and safety control commands are subject to _very low latency requirements_.
## 3 Results and Discussion
The saturation magnetization values were found to be roughly 60 emu/g (Figure 2D, top red line) for the bare MNPs and 12 emu/g for S-4 (Figure 2D(b) bottom black line). Based on these values, the estimated MNP content in the composite is approximately 20 wt.%, closely matching the TGA-based estimate and affirming the effective embedding of superparamagnetic particles within the polymer matrix. This high magnetic loading enhances the composite's potential utility in diverse fields, such as biomedical engineering, environmental technology, and pharmaceuticals.
## 1 Introduction  
### Air Blast Wave  
An explosion is a physical phenomenon in which there is a sudden, very rapid release of energy. The phenomenon lasts only some milliseconds, and it results in the production of gas with very high temperature and pressure. During detonation, the hot gases that are produced expand in order to occupy the available space, leading to wave-type propagation through space that is transmitted spherically through an unbounded surrounding medium. Along with the produced gases, the air around the blast (for air blasts) also expands, and its molecules pile up, resulting in what is known as a blast wave and shock front. The blast wave contains a large part of the energy that was released during detonation and moves faster than the speed of sound [1].  
This shock wave is characterized by an abrupt pressure rise followed by a relatively slow decrease to a value below atmospheric pressure and with a subsequent return to the positive value [1,2]. This phenomenon, which initially takes a few milliseconds, dependson the explosive mass and the distance to the initiation of the explosion. Subsequently, this waveform derived in a series of damped oscillations.  
The study of the air wave produced by the detonation of explosives in the open air inevitably requires analyzing different controlled detonations and measuring the different parameters that characterize the air wave. This experimental level is not at all easy in the civil sphere, since the detonation of explosive substances involves having the availability of both the explosive and the initiator and the appropriate place to carry out the different detonations without affecting the surrounding environment--people, buildings, and communication ways.  
The most characteristic effect of an explosion is the sudden increase in pressure that happens in the surrounding air, which propagates in the form of a spherical wave in all directions. The shape, characteristics, and magnitude of the wave depend on the type of explosion, the environment, and the distance from the origin where it was generated.  
If the explosion takes place at a point far from the ground, the blast wave expands spherically, and its characteristics (maximum overpressure, duration, impulse, arrival time, etc.) are known as open-air explosion parameters. If the explosion occurs in the vicinity of the ground or on it, the parameters are known as surface explosion. In the first, any point will be affected by two shock waves: first, the incident one from the explosion and then the one reflected from the ground. In the second, the reflection on the ground is linked to the incident wave from the point of explosion, forming a single practically hemispherical wave, whose amplitude, for the same mass of explosive, is considerably greater than in the first case, since the energy must be distributed only in one hemisphere.
## 4 Discussion
### Limitations and Future Studies  
This research has limitations and needs to be further studied. First, the street vending distribution and the areas where street vendors might operate are dynamic, for example, they could be examined in different seasons of the year (i.e., when there is ripe produce available). Some areas not noted by urban managers might temporarily have street vendors, which potentially has an impact on the findings. Secondly, the number of street vendors and pedestrians at particular streets at different times needs to be accounted for, requiring further examination of the nuanced associations of Centrality of the street network. Finally, this study has only focused on one city, and further subtle distinctions need to be studied in other places within different cities, which may provide further insight on different numbers, types and timing of street vending, related spatial properties, walking behaviour and volumes of pedestrian movement.
# To What Extent Are the Effects of UV Radiation on Grapes Conserved in the Resulting Wines?
## 3 Discussion
### Summarizing the Effects of UV Radiation on Wines  
The results described above were summarized through a PCA (Figure 3). PCA corroborated the strong influence of wine flavonoids on the ordination of samples, as expectedon the basis of previous studies [16]. Flavonols were loading factors for the positive part of axes I and II, which led to the clear grouping of PAB\(\uparrow\) samples in the first quadrant. Thus, the combination of UV-A and enhanced levels of UV-B was the UV treatment most associated with flavonoid concentrations, whereas the remaining treatments were less or not associated with these variables. On the other hand, the influence of wine esters on the clear separation of PA samples in the PCA plot was surprising, because these compounds are produced during vinicification and their responses to the UV radiation received by grapes were unclear in previous studies [16; 22]. Overall, the relationship of UV-A radiation with two groups of compounds determining wine quality (flavonols and esters) is curious because UV-A has frequently been ignored in grapevine experimentation, despite its effects on other plants [38]. Hence, it would be recommendable to consider UV-A in future studies on grapevines. Other diverse phenolic (anthocyanins, flavanols, phenolic acids, and stilbenes) and volatile (fatty acids, 2-methyl-1-butanol) compounds were significant loading factors in the PCA performed, but their effects on the ordination of samples were lower than those of flavonols and esters.  
Another relevant point regarding PCA ordination is that P samples were placed in the fourth quadrant, clearly separated from the remaining samples. This provides further evidence of the crucial influence of the UV radiation received by grapes on the composition of wines. Nevertheless, the specific influence of the different UV wavelengths and levels used in our study was complex, because one PAB replicate was intermixed with PAB\(\uparrow\) samples, whereas the remaining two PAB replicates and all the PB samples were located in an intermediate place between PAB\(\uparrow\) and PA samples. Diverse loading factors were responsible for this ordination, and more experimentation is needed to disentangle the specific role of both UV wavelengths and levels on wine composition and quality.
## 4 Results and Discussion
### Analysis of Questionnaire Respondents  
The respondents involved in the questionnaire survey were classified according to their working experience, organizational role, organization type, education, and position. Figure 2 explains the breakdown of 205 returned questionnaires by the (a) working experience, (b) organizational role, (c) organization type, (d) education, and (e) position.