esa-sceva/satcom-chunk-collection huggingface dataset:
# Estimation Method for Road Link Travel Time Considering the Heterogeneity of Driving Styles
## 3 Case Study  
### Data Preprocessing  
In Ningbo, Zhejiang Province, China, the ANPR system is installed at almost every intersection. The license plate recognition data contains the following information about each passing vehicle: The detector device number, license plate, time of passage, vehicle type, and the number of lanes. After checking the original data, we found that there are problems such as rapid vehicle speed, insufficient light, insufficient equipment recognition accuracy, etc. [13]. There is a considerable number of wrong recognition data or repeated recognition data.  
The license plate recognition data used in this study derive from the urban road data in the central metropolitan area of Ningbo on 5 June 2018. We screened all data within the study area by detector device numbers. Duplicate, erroneous, and unchecked data were removed. To reduce the computer memory space occupied by the long detector device number and vehicle number in the original data, they are renamed. The travel time of each road section is extracted from the preprocessed data.  
Initially, the population characteristics are analyzed based on travel time data. We selected three specific road links. The histograms of the travel time distribution frequency distribution of road links 1, 2, and 3 are shown in Figure 1. It can be seen from the figure that the travel time of most travelers is concentrated within a specific range. The peak frequency of travel time of road link 1 is concentrated at approximately 50 s, and the peak frequency of travel time of road link 2 is focused on about 40 s. The peak travel time-frequency of road link 3 is concentrated in 7  
Figure 2 shows the hourly distribution of travel time for road links 1, 2, and 3. It can be seen from the figure that there is a certain similarity in the full-day travel time between three road links. The travel time of each road link is the lowest from 0 a.m. to 5 a.m. The morning peak appears from 7 a.m. to 9 p.m., and the evening peak appears from 5 p.m. to 7 p.m. It can also be seen from Figure 2c that the travel time of road link 3 at 8 a.m. is significantly longer than other periods. The sharp increase in travel time may be due to an increase in the morning rush hour, changes in signal timing, or other traffic management controls. In the meantime, there are many discrete points in the figures. These points are exceedingly different from the travel time of most vehicles, which are abnormal values of the travel time and need to be corrected.
## 4 Numerical Example
### FNE Method
#### 4.3.3 Results--FNE Method  
The next stage of the _FNE method_, after preparing the approximation formulas (15) for the points marked on the balance path (Figure 8 and Table 4), is the reliability analysis and determination of the reliability index \(\beta\). In this case, NUMPRESS software was also used [35]. The previously mentioned FORM was selected for analysis. In the formula for limit state function (8), parameter \(\mu\)(X) was the function of random variables describing by explicit state functions (15, 16). Load multiplier \(\mu_{NN}\)(X) was directly computed by the reliability program. The analysis shows how the introduction of random variables affects the value of the Hasofer-Lind reliability index (Figure 11). Values of dimensionless load multiplier, \(\mu\), in Figure 7 are selected points on equilibrium path in Figure 6.  
The tendency in the reliability index results is the same as in the case of the _FE method_. The highest values of the reliability index are obtained when the random variable is only the load, \(P\) (Figure 11). The use of mathematical models with imperfections of node positions results in the reduction of the reliability index value.
# Stability Analysis of Jinchuan Hydropower Station Hydraulic Tunnels during Excavation and Unloading
## 4 Numerical Modeling of the Excavation of Adjacent Tunnels
### Constitutive Model and Parameters  
The Mohr-Coulomb constitutive model, which represents loose or cemented granular materials (such as concrete, soil, and rock), is a general geotechnical model for simulating underground excavation and slope instability. Moreover, the Mohr-Coulomb constitutive model and the Drucker-Prager model are the two plastic models with the highest calculation efficiency, whereas the other plastic constitutive models require more time and memory during the calculation process. The Mohr-Coulomb model was chosen for this analysis to accelerate the calculation because the surrounding rock of the spillway tunnel and diversion tunnel is loose.  
The calculation parameters were based on the geological report, which states that IV1 dominates the surrounding rock in the vicinity of the spillway tunnel and the diversion tunnel. The main faults (f31, f27, and f54) were primarily well-extended weak structural planes made up of uncomented schist, mylonite, fault gouge, and a few rock fragments. The technical parameters of the surrounding rock and faults in the model were selected based on the above description, as listed in Tables 1 and 2.
# Experimentation Environments for Data Center Routing Protocols: A Comprehensive Review
## 2 Routing in the Data Center
### RIFT: Routing in Fat Trees  
A basic reasoning behind the development of specific routing protocols for the data center is that the awareness of the underlying fat-tree topology may be used as an advantage to reduce control messages flooding. In this regard, given the North-South, East-West orientation, RIFT floods flat link-state information northbound only so that each level obtains the full topology of its South levels. However, link-state information is, with some exceptions, never flooded East-West or back South again. This characteristic defines RIFT as an anisotropic protocol (i.e., the information is not evenly distributed but summarized along the N-S gradient), where the nodes do not receive the same information from multiple directions simultaneously. Indeed, since there is an understanding of the topological dimension, reachability information is not received \"freely\" on any link. Therefore, under normal conditions, RIFT does not need to tie-break the same reachability information using some kind of distance metric, which leads ultimately to hop-by-hop forwarding to shortest paths only. Moreover, its computation principles (south forwarding direction is always preferred) lead to valley-free forwarding behavior, hence loop-free, allowing nodes to use all feasible paths (i.e., multi-path forwarding), using all the available bandwidth. Consequently, each fabric level obtains the full topology of its South levels and has one default route to the higher level. This allows a highly desirable aggregation of routes but can lead to the black-hole of traffic or even to partial network partitioning in case of misconfiguration or while failures are being resolved. RIFT addresses these problems by implementing an automatic disaggregation of prefixes in case of link and node failures. This mechanism is based on positive non-transitive disaggregation and negative transitive disaggregation. The former is used by a node that detects that its default IP prefix covers one or more prefixes that are reachable through it but not through one or more other nodes at the same level. Thus, it has to advertise those prefixes southbound to prevent traffic black-holing explicitly. It is non-transitive because the effects of this type of disaggregation are always contained in a single level of the fabric. The latter is used by a ToF node when it discovers that it cannot reach a fallen Leaf in a multi-plane topology. Thus, it has to disaggregate all the prefixes of such Leaf, sending them southbound. This type of disaggregation is transitive because if a node receives a negative disaggregation for a specific prefix from its parents, it has to propagate such disaggregation southbound to reach the Leaf. This is necessary since Leaves connected to multiple planes may have to choose the correct plane to prevent traffic black-holing. For details on RIFT mechanism and concepts, such as South Reflection, Flood Repeater, Bandwidth Adjusted Distance, and Thrift packet encoding, please refer to [10].
## 5 Simulation Experiments
### Connectivity Analysis  
After using the detection and repair method of the DHD-MEPO, the connection between the connectivity of the sensor nodes is analyzed. The orange ball in the figure is the static node, the green ball is the mobile node, and the blue ball is the node for repairing the hole. The candidate repair nodes for the target region of \(10\times 10\times 10\) m\({}^{3}\) and \(100\times 100\times 100\) m\({}^{3}\) are selected by the DHD-MEPO, and the initial location maps are shown in Figures 17a and 18a. The repair nodes use the emperor penguin optimized repair  
Figure 16: Plot of mobile node utilization versus number of mobile nodes: (**a**) Relationship map within the area \(10\times 10\times 10\) m\({}^{3}\); (**b**) Relationship map within the area \(100\times 100\times 100\) m\({}^{3}\).  
Figure 15: Algorithm’s graph of uniformity of energy consumption of mobile nodes: (**a**) Relationship map within the area \(10\times 10\times 10\) m\({}^{3}\); (**b**) Relationship map within the area \(100\times 100\times 100\) m\({}^{3}\).  
algorithm, which uses the lens-imaging mapping learning principle to perturb the position of the repair node to enhance the ability to search for the hole region in different directions. The position after repairing the hole is shown in Figures 17b and 18b, the optimal position of the repair node is found according to the multi-objective function as well as the constraints and Figures 17c and 18c verify the network connectivity after the repair node completes the task of repairing the hole. According to Equation (18), \(AM(s_{i},s_{j})=1\) represents the network connection between two nodes and the Kruskal method is used to construct the minimal spanning tree. The algorithm is proven to satisfy the condition that the nodes in the network repair the hole to meet the coverage requirement while also ensuring network connectivity, so that the repair node can instantly send the data information sensed in the hole area to the neighbor nodes and eventually hand over to the BS center for processing.  
Figure 17: The \(10\times 10\times 10\) m\({}^{3}\) area connectivity map: **(a)** Initial node location distribution; **(b)** Distribution of node positions after algorithm optimization; **(c)** Network connectivity.
## 3 State-of-the-Art Results on the Detection of Identity Deception
### Detection Features
#### 3.2.2 User Behavior Models  
User behavior models primarily focus on features that relate to an individual user's behavior, such as frequency of activities (e.g., number of tweets or posts per time interval), patterns of activity, and clickstream sequences [3; 6; 7; 8; 21; 22; 27]. This set of features can help identify malicious actors who may not interact with other users frequently, but still engage in unwanted online behaviors such as repeatedly posting spam or malware in the form of misleading URLs. With this perspective, a detection model can focus on features related to an individual's behavior rather than their behavior in a social context. These features are also essential to detect malicious users whose area of influence lies outside of the social interactions with other users. An effective example of this phenomenon is the predictability of a user's behavior as an indicator of being a malicious bot. A normal person who is using some social platform will likely have some degree of variation in the type, frequency, and duration of activities in which he/she engages. The sequence and the amount and time of activity would not be identical on a daily basis, but the same cannot be said for malicious bots. These bots are often designed to repeatedly perform a certain task to achieve a certain goal. They engage in predictable behaviors at relatively regular intervals. This predictability of malicious bot accounts provides a strong metric through which these accounts can be readily detected.
# Global IoT Mobility: A Path Based Forwarding Approach
## 3 Overview of Related Work
Performance measurements of their proxy design using both simulation and real implementation demonstrate that TCP can traverse ICN networks without significant additional delay or loss of goodput. The authors in [43] identify a common design space for providing IP and NDN mobility support and propose two knowledge-driven mobility support approaches. These approaches exploit knowledge such as network topology and movement trajectory to tweak the network for better mobility performance. Experiment results show that the knowledge driven approaches significantly improve mobility support performance. The authors in [44] propose a hybrid architecture to support video streaming simultaneously over TCP/IP and Named Data Networking (NDN)-based architecture via operating system and networking virtualization techniques. In addition, to relieve users from the burden of installing a new protocol stack (in the case of NDN) on their devices, the authors developed a lightweight solution in the form of a container that includes the network stack as well as the streaming application. A propotype evaluation demonstrates that, in the case of live streaming, NDN achieves better QoE per client than IP and can also utilize higher than allocated bandwidth through in-network caching. Notably, the proposals above deploy NDN/CCN cores that introduce large stateful Pending Interest Tables (PITs) that keep track of information requests, hence, imposing scalability challenges.
# Configuration Optimization of Mobile Photovoltaic-Diesel-Storage Microgrid System Based on CPS-MOEA
## 5 Experiments
### Algorithm Performance Comparison
#### 5.3.1 Convergence and Diversity  
To better evaluate the performance of CPS-MOEA, we choose generation distance (GD), spread and inverted generation distance (IGD) as the performance metrics. Given the randomness of MOEAs, the proposed method was independently executed 30 times. After 30 independent simulation runs, Table 3 provides the specific values of three measurement indicators.  
* Spread Metric Analysis  
The spread metric assesses the distribution of solutions along the Pareto front, focusing on how evenly the solutions are spread out. A lower spread value indicates a more uniform distribution. CPS-MOEA's average spread value matches that of PICEA-g and shows a noticeable improvement over MOPSO and GDE3, with differences of 0.01 and 0.02, respectively. This suggests that CPS-MOEA is capable of producing solutions that cover the Pareto front more uniformly than MOPSO and GDE3. The best spread value achieved by CPS-MOEA further underscores its superior performance in ensuring a more efficient distribution of solutions compared to the other algorithms.  
* Inverted Generational Distance (IGD) Metric Analysis  
The IGD metric provides insights into both the convergence to the true Pareto front and the distribution of solutions. It measures the distance between the true Pareto front and  
Figure 7: Box diagram of priority members.  
the obtained Pareto front, with lower values indicating better performance. CPS-MOEA's average IGD value is competitive with PICEA-g and outperforms MOPSO and GDE3 by margins of 0.043 and 0.004, respectively. This superior performance is evident in both the average and the best IGD values obtained by CPS-MOEA, highlighting its effectiveness in achieving closer convergence to the true Pareto front while maintaining a good distribution of solutions.  
* Comparative Performance  
The comparative analysis with PICEA-g, MOPSO, and GDE3 illustrates that CPS-MOEA not only ensures a more efficient distribution of solutions across the Pareto front but also achieves better convergence to the true Pareto front. The significant improvements in both spread and IGD metrics demonstrate CPS-MOEA's ability to balance the trade-offs between multiple objectives effectively, making it a more suitable choice for solving complex multi-objective optimization problems, such as those encountered in mobile photovoltaic-diesel-storage microgrid systems.  
In conclusion, the application of CPS-MOEA to the optimization of mobile microgrid systems showcases its superiority in navigating the complexities of multi-objective optimization, achieving a well-balanced and high-performing set of solutions that could significantly benefit decision-making processes in energy management and planning.
## 2 Materials and Methods  
### Data Preparation  
The training dataset, extracted from evaluation reports obtained from welding consumable manufacturers and shipyards, consisted of 160 data points. Various base metals, including high-manganese steel, stainless steel, and 9% nickel steel, were welded using an austenitic stainless-steel filler metal. The input parameters of the model include the chemical composition of the weld metal, including nine alloying elements (C, Si, Mn, P, S, Ni, Cr, Mo, and Cu), and the welding heat input per unit length. The output parameter of the model was the yield strength of the weld metal. The statistical characteristics of the collected data are summarized in Table 1.  
Additional data points for austenitic stainless-steel weld metals were collected from published papers [40; 41; 42; 43; 44] and mill test certificates from shipyards to validate the prediction models developed in this study, with 40 data points used for the model verification, accounting for 25% of the training data points. The ranges of each parameter in the validation dataset are listed in Table 2.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \multirow{2}{*}{**No.**} & \multicolumn{2}{c}{**Descriptive**} & \multicolumn{8}{c}{**Variable (Unit)**} \\ \cline{3-13}  & \multicolumn{2}{c}{**Statistics**} & \multicolumn{1}{c}{**C**} & **Si** & **Mn** & **P** & **S** & **Ni** & **Cr** & **Mo** & **Cu** & **Heat Input** & **Yield Strength** \\  & & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(wt\%)** & **(kJ/cm)** & **(MPa)** \\ \hline
1 & Min. & 0.014 & 0.15 & 1.01 & 0.001 & 0.001 & 0.014 & 1.21 & 0 & 0 & 9 & 335 \\
2 & Max. & 0.45 & 0.99 & 22 & 0.026 & 0.015 & 20.4 & 25.4 & 3.66 & 0.3 & 37.4 & 565 \\
3 & Mean & 0.18 & 0.49 & 4.48 & 0.013 & 0.005 & 12.5 & 15.5 & 1.92 & 0.07 & 14.8 & 451 \\
4 & Std. Dev & 0.12 & 0.17 & 3.63 & 0.004 & 0.003 & 4.0 & 4.96 & 1.25 & 0.05 & 5.22 & 51.11 \\ \hline \end{tabular}
\end{table}
Table 1: Descriptive statistics of the dataset for model training.
# Transformer-Based Cascading Reconstruction Network for Video Snapshot Compressive Imaging
## 6 Conclusions  
We propose a Transformer-based cascading reconstruction network for video snapshot compressive imaging. The network is configured as a cascade of two stages: overall structure reconstruction and incremental details reconstruction. In the first stage, a multi-scale Transformer network is designed to extract the multi-scale spatiotemporal features, and the residual measurement mechanism is used to calculate the measurement residual of the reconstruction of the first stage. The incremental details reconstruction network takes the measurement residual as the input and utilizes the dynamic feature fusion module to adaptively fuse the output features of the two stages to improve the reconstruction quality. Experiments show that the proposed network effectively utilizes the advantages of the Transformer and the convolution neural network. Compared with the current reconstruction methods based on convolution neural networks, the proposed network can obtain better reconstruction quality.  
J.W.: investigation, resources, methodology, writing--original draft. J.H.: software, validation. X.C.: formal analysis, data curation. K.H.: visualization. Y.S.: conceptualization, writing--review and editing, supervision, funding acquisition. All authors have read and agreed to the published version of the manuscript.  
This research was funded by The National Natural Science Foundation of China under Grant U2001211 and 62276139.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & **Time (s)** & **Frames per Second (FPS)** & **Parameters (\(\times\)10\({}^{6}\))** \\ \hline GAP-TV & 49.67 & 0.75 & - \\ GMM-TP & 184.84 & 0.20 & - \\ MMLE-MFA & 930.3 & 0.04 & - \\ MMLE-GMM & 3802.3 & 0.01 & - \\ PnP-FFDNet & 10.32 & 3.62 & - \\ E2E-CNN & 3.96 & 9.43 & 0.82 \\ RE\({}^{2}\)-Net & 4.25 & 8.78 & 3.49 \\ Ours & 6.73 & 4.75 & 2.16 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the reconstruction time of different methods.
## 3 Validation of MANTiS-32 Sensing Performance
### Calibration and Accuracy Assessment of the MANTiS-6050 Sensor
#### 3.1.1 Calibration of the MANTiS-6050 Sensor  
The MANTiS-6050 sensor outputs acceleration measurements internally in 16-bit signed integer format, spanning a range from \(-\)32,768 to +32,767. Under ideal conditions, this digital output is assumed to exhibit a linear correspondence with gravitational acceleration within the range of \(-\)1 g to +1 g, such that \(-\)32,768 maps to \(-\)1 g, +32,767 to +1 g, and 0 to 0 g. However, due to inherent variations in the manufacturing process and external environmental factors, the raw sensor output is susceptible to zero bias and sensitivity drift, necessitating a robust calibration procedure to ensure accurate translation into physical units. To compensate for these deviations, a three-point calibration method was applied, as depicted in Figure 6.  
This method involves positioning the sensor at three reference orientations corresponding to known static acceleration levels: \(-\)1 g (Low), 0 g (Mid), and +1 g (High). By capturing the sensor's digital output at each of these calibration points, a linear mapping is established between the raw A/D values and the actual gravitational acceleration. This mapping allows for precise correction of both offset and sensitivity errors.  
Figure 6: Schematic of the three-point calibration method.  
Following the application of this calibration procedure, the MANTiS-6050 sensor is capable of delivering accurate acceleration measurements in units of g, as further validated through empirical testing presented in Section 3.1.2.