esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Results and Analysis
### Traffic Parameter Extraction Results
#### 4.1.2 Link Average Speed  
Speed represents a core indicator for evaluating traffic operational efficiency. The dual calculation method employed in this study provides more reliable speed estimates, with specific results shown in Figure 7.  
Figure 6: Box plot of link flows.  
Figure 7: Box plot of link average speeds.  
The spatial heterogeneity of speed distribution is pronounced. Certain links, such as those between intersections 4 and 6, exhibit extreme speed fluctuations, with box plots revealing numerous outliers. Further analysis suggests these links may experience severe stop-and-go phenomena: free-flow conditions with high speeds during some periods, contrasted with severe congestion and near-zero speeds during others. This unstable traffic state poses challenges for signal control, potentially necessitating adaptive control strategies.  
Links '21,22' and '16,22' also demonstrate substantial speed variations, albeit with slightly different patterns. The speed distributions for these links exhibit multimodal characteristics, potentially reflecting the coexistence of different traffic states: free-flow and congested conditions alternating across different time periods. This characteristic suggests these links may represent sensitive areas for traffic state transitions, requiring special attention in signal control design.  
The two speed calculation methods (instantaneous speed-based mspeed and time difference-based mspeed_dt) yield generally consistent results, though systematic differences exist on certain links. These differences likely stem from the inherent characteristics of each method: the instantaneous speed approach is more sensitive to localized speed variations, while the time difference method provides link-level average effects. By fusing results from both methods, the robustness of speed estimation is enhanced.
## 2 Materials and Methods
### Data Acquisition and Reference Point Cloud
#### 2.3.3 UAV-Based Laser Scanning Measurements and Processing
\begin{table}
\begin{tabular}{l c c} \hline \hline  & **GNSS** & **Baseline Length [km]** \\ \hline Own master station (pillar) & GPS/GLO/GAL/BDS & 1 \\ Virtual Reference Station 1 (VRS1) & GPS/GLO/GAL/BDS & 1 \\ Virtual Reference Station 2 (VRS2) & GPS/GLO/GAL/BDS & 2 \\ CORS station (SAPOS NRW) & GPS/GLO/GAL/BDS & 16 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: GNSS master stations used for trajectory estimation of single strip measurements for experiment (4). \begin{table}
\begin{tabular}{l c c} \hline \hline  & **Single Strip** & **Multiple Strip** \\ \hline flight repetitions & 4 & 4 \\ flight height [m] & 10 & 25 \\ flight speed [m/s] & 1 & 3 \\ laser scanner line speed [lps] & 55 & 58.92 \\ avg. point distance [m] & 0.022 & 0.055 \\ point density [pts/m\({}^{2}\)] & 2495 & 333 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: UAV flight parameters for single and multiple strip measurements. overlapping flight strips (Figure 1). This processing corrects the initial UAV trajectory by the alignment of multiple laser scans from the same area and a following reprocessing of the optimized point cloud. The corrections are performed with the point clouds from multiple strips, derived geometric point cloud features, and sensor information such as amplitude or reflectance used in various types of distance minimization to reduce modeling disparities. Within RiPRECISION all flight strips are selected for the optimization with automatic feature detection and a non-rigid adjustment mode with translation. The capability of this additional processing step with RiPRECISION is analyzed shortly with the noise parameter for parts of the point cloud. If the correction of overlapping scans from different flight directions would fail, scan strips are misaligned and the estimated residuals for a plane surface within the point cloud will increase systematically. Since the general accuracy and especially the georeferencing of the UAV system is investigated, no additional GCPs are used.
## 3 Proposed Methodology
### The Proposed DRL-Aided Algorithm  
DNNs consist of several layers of interconnected neurons and enable the learning of complex non-linear functions. RL teaches an agent how to execute actions upon entering a new state to maximize the cumulative future returns [23; 24; 25]. A DRL methodology refines its policy by directly calculating the policy gradient to maximize the cumulative rewards. The referred policy is a DNN that uses a continuous state as input and produces a probability distribution as output. A continuous action is then sampled. The core idea is to parameterize the policy using a DNN's weights parameter \(\theta\in\mathbb{R}^{w}\) where \(w=\sum\limits_{i=1}^{Q}w_{i}w_{i+1}\) assuming a DNN with \(Q\) layers, and each has a corresponding dimension \(w_{i}\)[23; 24; 25]. Then, we denote the parameterized policy as the probability distribution of selecting power \(P_{t,l}\) for the \(l\)th channel in time-step \(t\), and from the state \((h_{t,l},\theta)\)[13; 14]:  
\[\pi(h,\theta)=\Pr[P_{t,l}|h_{t,l},\,\theta] \tag{11}\]  
where \(h_{t,l}\) represents recorded CSI data of \(l\)th channel, and \(P_{t,l}\in[0,P_{s}]\) is the allocated power as derived from \(\pi(h,\theta)\). The agent's reward-return \(r_{t+1}\) at the next time-step \(t+1\) is the following expression [13; 14]:  
\[r_{t+1} =L(\theta,v)_{0\leq\pi(h_{t,l})\
eq\rho_{s}}=\sum\limits_{l=1}^{L}C_{t,l} (\pi(h_{t,l},\theta),h_{t,l})-v\left(\sum\limits_{l=1}^{L}\pi(h_{t,l},\theta) -P_{av}\right) \tag{12}\]  
Then, the discounted cumulative returns \(G_{t}\) are defined as  
\[G_{t}=r_{t+1}+\gamma r_{t+2}+\gamma^{2}r_{t+3}+\ldots=\sum\limits_{t^{\prime} =t+1}^{T}\gamma^{t^{\prime}-t-1}r_{t^{\prime}} \tag{13}\]  
where \(T\) is the duration of an episode, \(t=0\ldots T-1\), and \(\gamma\in[0,\,1)\) is the discount factor [20; 21; 22]. The objective function is the long-term discounted rewards [13; 14]:  
\[J(\theta)=\mathrm{E}[G_{t}|\pi(h_{t},\theta)] \tag{14}\]  
In general, the gradient for a whole episode with duration \(T\) is  
\[\
abla_{\theta}J(\theta)=\int_{T}\
abla_{\theta}\pi_{\theta}(T)G(T)dT \tag{15}\]  
If we apply the log-derivative trick \(\
abla_{\theta}\pi_{\theta}=\pi_{\theta}\
abla_{\theta}\log\pi_{\theta}\)[13; 14], then:  
\[\
abla_{\theta}J(\theta)=\mathrm{E}[\
abla_{\theta}\log\pi_{\theta}(T)G(T)] \tag{16}\]  
Then, we obtain a Monte-Carlo approximation of \(\mathrm{E}[\,.\,]\) in (16) for all the \(t=0,1,\,\ldots\), \(T-1\) time-steps and \(l=0,1,\,\ldots,\,L-1\) channels [31]:  
\[\
abla_{\theta}J(\theta)=\frac{1}{T}\sum\limits_{l=0}^{T-1}\sum\limits_{l=0}^{ L-1}[\
abla_{\theta}\log\pi(h_{t,l},\theta)G_{t}] \tag{17}\]To summarize, the proposed model-free, DRL methodology requires only \(h_{l}\) samples that can be observed, \(C_{l}\) values that can be experimentally measured, and a differentiable policy to be optimized. The proposed methodology is summarized in Algorithm 1.
## 2 Related Works
### Linear Discriminant Analysis
* Projection Vector: To find the optimal projection vector w, LDA solves the generalized eigenvalue problem: \[SBw=\lambda SWw,\] (7) where \(\lambda\) is the eigenvalue corresponding to w.
* Dimensional Reduction: LDA selects the top k eigenvectors corresponding to the largest eigenvalues as the projection vectors. These projection vectors form a lower-dimensional subspace that preserves the most discriminating information between classes. * Classification: To classify a new observation, LDA projects it onto the subspace spanned by the projection vectors and assigns it to the class with the closest mean in the projected space. LDA assumes that the features follow a multivariate normal distribution and that the covariance matrices are equal across classes. It seeks to find a linear decision boundary that maximizes the separation between classes while minimizing the overlap within each class. By capturing the underlying structure of the data and finding an optimal projection, LDA provides a powerful tool for classification problems, especially when the classes are well-separated. It is important to note that there are variations of LDA, such as Regularized Discriminant Analysis (RDA) and Quadratic Discriminant Analysis (QDA), which relax certain assumptions and extend the applicability of the method to different types of data distributions.
## 3 Methodology
### Latency Evaluation of the Camera Feed Across Different Network Scenarios  
Three tests were conducted for latency measurements with the camera feed, each measuring the latency of a full frame being received by the client from the host network. These tests were conducted with the camera pointed at a 100 \(\upmu\)s precision timer displayed on the screen via a command line interface (CLI) tool. The client computer then connected to the host's video feed to display the timer video feed in an adjacent window. One hundred screenshots were then taken for each test, displaying the total latency introduced from transmitting a frame over the network. As previously mentioned, the frame latency required for extracting a frame from the USB camera was 100 ms, so any latency above this  
Figure 5: Network diagram of Mobile Hotspot-to-University network connection scenario including VPN.  
Figure 6: Network diagram of overseas network connection scenario including VPN.  
time was added by the network. Since this measurement of camera latency was restricted to local network configurations, tests of a virtual network connection through a loopback interface on the same client machine, a local wired network, and Wi-Fi connection through the VPN were conducted. Our tests utilizing the mobile hotspot were highly inconsistent, likely due to the limited 4G performance experienced by the mobile hotspot itself, thus resulting in us discarding these tests from consideration.
# A Statistical Model Updating Method of Beam Structures with Random Parameters under Static Load
## 5 Conclusions  
A new statistical model updating method has been developed for modifying the beam structures with random parameters under static load. The new updating method considered the uncertainty of structural parameters and measurement errors. A static condensation technique is used to reduce the unmeasured degrees of freedom in the finite element model. A statistical model updating equation with respect to element updated factors has been established afterwards. Using the high-order perturbation technique, the statistical model updating equation has been solved to obtain the statistics of the element updated factors. The results of two numerical examples show that the accuracy of the proposed method is very good. The static responses obtained by the updated finite element model coincide with the  
Figure 8: The statistical results of the element updated factors of the tested beam from the proposed HP-SMUM. (**a**) Means of element updated factors; (**b**) Standard deviations of element updated factors.  
Figure 9: The statistics of deflections of the tested beam based on the updated finite element model. (**a**) Means of deflections; (**b**) Standard deviations of deflections; (**c**) Probability density functions of the deflection at the 5 th node.  
measured results very well. Further, the updated results of the concrete beam based on static load tests testified the effectiveness of the proposed method.  
The authors would like to acknowledge the financial support by the National Natural Science Foundation of China (Project No. 51378407 and 51578431).  
[PERSON], [PERSON] presented the model updating method; [PERSON], [PERSON], [PERSON] completed the numerical examples, designed and performed the experiments; [PERSON] and [PERSON] wrote the paper.  
The authors declare no conflict of interest.
## 2 Background
Other types of attacks are related to the communication protocol used between EV and EVCS or vehicle-to-grid communication, where an attacker could intercept or manipulate the communication between the EV and EVSE or even the V2G communication. Finally, EVCS can be an entry point for attackers to target the overall electric grid infrastructure. To mitigate these risks, EVCS manufacturers and operators can implement security measures such as encryption, firewalls, and intrusion detection systems, as well as regularly update the firmware and software of the charging stations. Additionally, following the guidelines and standards such as ISO/SAE 21434 and ISO 15118 can help to improve the overall cybersecurity of EVCS. The authors in [8] discussed the security requirements in EVCSs and their associated challenges. The main challenges revolve around the extended CIA triad, which includes Confidentiality, Integrity, Availability, Nonrepudiation, authenticity (message freshness), and Authentication. Figure 2 depicts the security requirements and their challenges in EVCSs. In addition, the corresponding attacks mapped to the main security requirements are illustrated in Figure 3.  
Figure 2: Security Requirements and their challenges in EVCSs.
# A High-Resolution Spectral Analysis Method Based on Fast Iterative Least Squares Constraints
## 5 Discussion  
Time-frequency analysis can highlight the detailed characteristics of stratigraphy, while frequency attribute analysis can reveal subtle structural features in broadband amplitude maps. Ideally, time-frequency analysis aims to achieve high temporal resolution while also obtaining high frequency resolution through the use of short windows.  
In traditional spectral analysis methods, CWT struggles to perform local analysis because it does not use time windows to isolate seismic events, resulting in very low time resolution at low frequencies. The results from STFT are difficult to distinguish in detail due to the effects of frequency decay and severe window tailing. However, the least squares constrained spectral analysis method significantly reduces spectral leakage by solving the non-zero diagonal term information. With short windows, it can emphasize various frequency features, highlighting thicker layers in low-frequency profiles and thinner layers in high-frequency profiles. By incorporating prior information constraints, it weakens the spectral smoothness of windowed data, allowing short windows to achieve a high time resolution and more accurately invert the amplitude spectrum of seismic reflection waves.  
The Greedy FISTA spectral analysis method used in this study accelerates convergence speed through large-step iterations. To prevent divergence, a strictly less-than-1 parameter shrinkage step size is employed, ensuring that each iteration uses a locally optimal step size strategy to improve inversion efficiency. Additionally, the Greedy FISTA can eliminate high  
Figure 7: CLSSA time-frequency analysis characteristic frequency profile based on Greedy FISTA. (**a**) 10 Hz profile, (**b**) 20 Hz profile, (**c**) 40 Hz profile, (**d**) 60 Hz profile, Greedy FISTA uses 30 ms time window.  
frequency artifacts caused by noise in the CLSSA method, providing greater robustness and accuracy in resolution.  
The numerical model and its application to field data prove that our proposed method can achieve a higher resolution than traditional methods, such as wavelet transform, short-time Fourier transform, and generalized S-transform. This will effectively predict thicker sedimentary layers in low-frequency ranges and accurately identify thinner sedimentary layers in high-frequency ranges.
## A Cycle-Based Data Aggregation Scheme for Grid-Based Wireless Sensor Networks  
**[PERSON], [PERSON] * and [PERSON]**  
Department of Computer Science and Information Engineering, National United University,  
Miaoli 36003, Taiwan; E-Mails: [EMAIL_ADDRESS] (Y.-K.C.); [EMAIL_ADDRESS] (C.-H.H.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +886-37-381899; Fax: +886-37-354326.  
_Received: 4 December 2013; in revised form: 1 May 2014 / Accepted: 5 May 2014 / Published: 13 May 2014_  
**Abstract:** In a wireless sensor network (WSN), a great number of sensor nodes are deployed to gather sensed data. These sensor nodes are typically powered by batteries so their energy is restricted. Sensor nodes mainly consume energy in data transmission, especially over a long distance. Since the location of the base station (BS) is remote, the energy consumed by each node to directly transmit its data to the BS is considerable and the node will die very soon. A well-designed routing protocol is thus essential to reduce the energy consumption. In this paper, we propose a Cycle-Based Data Aggregation Scheme (CBDAS) for grid-based WSNs. In CBDAS, the whole sensor field is divided into a grid of cells, each with a head. We prolong the network lifetime by linking all cell heads together to form a cyclic chain so that the gathered data can move in two directions. For data gathering in each round, the gathered data moves from node to node along the chain, getting aggregated. Finally, a designated cell head, the cycle leader, directly transmits to the BS. CBDAS performs data aggregation at every cell head so as to substantially reduce the amount of data that must be transmitted to the BS. Only cell heads need disseminate data so that the number of data transmissions is greatly diminished. Sensor nodes of each cell take turns as the cell head, and all cell heads on the cyclic chain also take turns being cycle leader. The energy depletion is evenly distributed so that the nodes' lifetime is extended. As a result, the lifetime of the whole sensor network is extended. Simulation results show that CBDAS outperforms protocols like Direct, PEGASIS, and PBDAS.  
**Keywords:** base station; cell head; cycle leader; grid-based; wireless sensor networks
## 1 Introduction
A practical multicast clustering and power controlling strategy with theoretical analysis using game theory is proposed in [19] for the target of decreasing the data distribution time and achieving the QoS requirement of requested uses. However, cellular channel allocation is ignored in this scheme. The authors in [20] present a joint power and channel allocation algorithm to maximize the overall throughput while preserving certain level of QoS for both CUs and D2D groups. However, in this research, each D2D group can reuse the spectrum resource of at most one CU. This paper focus on the radio resource allocation problem for D2D multicast communications in cellular networks, where one multicast group can reuse multiple cellular spectrum resources. In this case of content dissemination application, the data requests from all UE should be satisfied as much as possible. Nevertheless, with the increase in the number of admitted D2D links, the power consumption of D2D transmitter becomes higher and cannot be ignored. Therefore, optimizing multiple objectives at the same time has a more practical significance in the resource allocation problem. As an important tool, multi-objective optimization has been studied in several recent works. The authors in [21] propose a robust resource allocation strategy to maximize energy harvesting efficiency while minimizing total transmit power in a cognitive network. Similarly, A. multiobjective framework is proposed in [22] to simultaneously improve the network energy efficiency and system throughput. Inspired by these studies, we employ a new multiple-objective optimization framework with the aim to jointly minimize the power consumption of D2D communication and maximize the system capacity of D2D groups. In particular, the main contributions of our work are as follows: (1) We formulate a simple but flexible multi-objective optimization framework via introducing weighted factors for power consumption and system capacity. In addition, we consider a certain level of SINR to guarantee the QoS requirements for both CUs and D2D groups. The framework allows network operators to adjust the tradeoff between the two objectives with various preferences; (2) We use the way of one D2D group sharing sub-channels with multiple CUs to allocate cellular resources, which further enhances the performance of our optimization; (3) We develop an efficient resource allocation scheme to achieve the desired tradeoff for given preferences (i.e., coefficients for power consumption and system capacity). The remaining of the paper is organized as follows. In Section 2, we describe the system model and problem optimization. In Section 3, we derive and propose the resource allocation algorithm for D2D multicast communication. Simulation results are shown and discussed in Section 4. Finally, conclusion is drawn in Section 5.
## 3 Use Case: (Com)2 INet for Levee Piping Hazard Inspection via Remote Sensing
The obtained scheduling decisions are then translated into network configuration files, guiding optimal network components to host the required SC to accomplish the computing service in a serial or parallel manner. In addition, to ensure continuous data transmission, uninterrupted critical services, and undegraded service quality, dynamic fault-tolerance mechanisms such as intelligent rerouting, dynamic load balancing, and flexible migration of network functions are employed.
## 3 Results
### Response of WUE and Climate Environmental Factors in Gansu Province
The areas exhibiting a positive correlation with vegetation WUE constituted 62.74% of the total, of which 9.74% passed the test of significance (\(p<0.05\)); they were mainly found in the western regions with a mean annual soil moisture of 0.15-0.25 m\({}^{3}\)/m\({}^{3}\) and in the eastern areas with a surface soil moisture of 0.25-0.35 m\({}^{3}\)/m\({}^{3}\). The areas with a negative correlation accounted for 36.96% of the total area and were primarily distributed in Gannan Tibetan Autonomous Prefecture in the southern part of the study area, where soil moisture exceeds 0.35 m\({}^{3}\)/m\({}^{3}\). The proportions of positive correlations between vegetation WUE and mean air temperature, precipitation, mean VPD, and mean surface soil moisture which passed the significance test at \(p<0.05\) were 14.56%, 25.33%, 19.62%, and 9.74%, respectively. Furthermore, the significant positive correlations with precipitation and VPD were greater than those with temperature and soil moisture, indicating that the impact of the former on the vegetation WUE in Gansu Province was more pronounced than that of the latter. Specifically, the four factors were ranked as follows: precipitation \(>\) VPD \(>\) temperature \(>\) soil moisture. However, these factors only showed significant positive correlations with WUE in some regions of Gansu Province, not in the whole province. The random forest algorithm offers unique advantages in quantifying the importance of factors and can be used to identify and rank the variables that have a large impact on the results. In this study, the random forest algorithm was used to quantify the climatic environmental factors of vegetation water utilization in Gansu Province to assess the importance of each. Figure 10 indicates that the importance scores of precipitation, VPD, temperature, and soil moisture were 0.332, 0.268, 0.221, and 0.179, respectively, with precipitation being the most important contributing factor, followed by VPD, which is consistent with the results obtained with the partial correlation analysis. By employing the random forest-based importance ranking analysis, we effectively determined the degree of contribution of different climatic environmental factors to the change in WUE. This study lays a foundation for future in-depth exploration of water-carbon coupling and its response to climate environment changes in Gansu Province.