esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Pattern of Arriving Ship's Trajectory
#### 4.1.3 Breakwater Phase  
The breakwater phase corresponds to Turning Section 2 and is the process in which the waterway is exited and the breakwater passed through, as shown in Figure 11. At this time, the average SOC is 9.44 knots and the distribution of the frequency count is as shown in Figure 16. In general, it was analyzed with a high ratio of distribution of between 9 and 10.5 knots. The COG is shown in Figure 16, and the ship that initially moved at about 336 to 338 degrees (32 counts) changed course from 358 to 000 degrees (57 counts).  
As is shown in Figure 17, the time series analysis result of this phase shows the change as a change in course that occurs between boxplots 5 and 6. The SOC is shown in Figure 17, and the average starting point in the waterway phase was determined to be 10.25 knots. Beyond that, at the point of course change, the SOC decreased to an average of 8.86 knots, and at the last point of the phase, the average was analyzed to be 8.70 knots. In the case of COG, the change was evident. According to Figure 17, the degrees start at an average of 338.93, gradually change, then change to an average of 354.74 at the point of the course change, and then gradually approach toward 000. The summarized descriptive statistics are shown in Table 7.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Scaled**} & \multicolumn{4}{c}{**Speed Over Ground**} & \multicolumn{4}{c}{**Course Over Ground**} \\ \cline{2-10}  & **Mean** & **Std.** & **Min** & **Median** & **Max** & **Mean** & **Std.** & **Min** & **Median** & **Max** \\ \hline
0 & 7.78 & 1.67 & 5.30 & 7.75 & 12.60 & 329.27 & 46.13 & 0.20 & 335.80 & 359.50 \\
1 & 7.65 & 2.12 & 4.90 & 7.60 & 13.60 & 335.30 & 4.94 & 322.20 & 335.90 & 346.50 \\
2 & 7.97 & 2.08 & 4.50 & 7.30 & 14.10 & 332.48 & 38.50 & 1.80 & 337.00 & 359.50 \\
3 & 7.96 & 2.10 & 4.70 & 7.45 & 14.40 & 329.53 & 53.87 & 2.60 & 337.15 & 356.10 \\
4 & 8.61 & 2.22 & 4.80 & 8.30 & 14.40 & 338.21 & 2.87 & 331.30 & 338.00 & 349.70 \\
5 & 9.67 & 1.99 & 5.80 & 9.90 & 13.10 & 338.65 & 2.39 & 329.10 & 338.45 & 344.70 \\
6 & 10.35 & 1.69 & 5.70 & 10.50 & 13.40 & 338.51 & 2.33 & 327.40 & 338.50 & 346.60 \\
7 & 10.49 & 1.75 & 6.00 & 10.70 & 13.60 & 337.93 & 2.80 & 325.00 & 338.10 & 344.00 \\
8 & 10.77 & 1.77 & 6.90 & 10.60 & 14.00 & 337.70 & 1.59 & 333.90 & 337.80 & 342.40 \\
9 & 10.88 & 1.82 & 6.00 & 10.80 & 14.00 & 337.26 & 4.52 & 331.00 & 336.95 & 359.10 \\
10 & 10.59 & 1.47 & 5.30 & 10.50 & 13.80 & 331.49 & 43.78 & 0.20 & 336.95 & 355.10 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Descriptive statistics for the arrival waterway phase of the scaled time series.  
Figure 16: Frequency analysis of the arrival breakwater phase: (a) SOC; (b) COG.
## 3 Observations and Discussions
### Coefficient of Subgrade Reaction (\(k_{\rm s}\))  
Figure 7 presents the coefficient of subgrade reaction for raft foundations with different dimensions. The value of \(k_{\rm s}\) (Units in F/L3) was calculated dividing the contact pressure by the foundation settlement at the same node. Excluding the data shown in Figure 7a for the small raft in clay, \(k_{\rm s}\) was generally found to be smaller at the center and larger at the edges. Stiffer soils provide higher coefficients of the subgrade reactions. Sandy soils provide higher coefficients compared to those found in clay. For square rafts in sand, \(k_{\rm s}\) was affected considerably by the foundation width and the soil stiffness. For small rafts in clay, the values of \(k_{\rm s}\) became rather consistent. Corresponding \(k_{\rm s}\) values at the center of the foundation (\(k_{\rm sc}\)) are summarized in Table 3. It can be learnt that the values of \(k_{\rm sc}\) for the subgrade soils at the center of the foundations were affected by both the soils and the foundation dimension. However, the effects due to the dimensions of the foundation were relatively small in comparison to those caused by soil stiffness.  
The functional values of the normalized functions of \(f(x)\) for \(k_{\rm s}\) based on the values of \(k_{\rm sc}\) at the center of the foundation are shown in Figure 8. It can be found that the normalized values of \(k_{\rm s}\) in sand were nearly independent of soil stiffness (Figure 8d-f), whereas those  
Figure 7: Coefficient of subgrade reactions for square raft foundations with different dimensions: (a) 16 m, (b) 26 m, (c) 36 m in clays; (d) 16 m, (e) 26 m, (f) 36 m in sands.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Size**} & \multicolumn{3}{c}{**The Coefficients of Subgrade Reactions at the Center of the Foundation (\(k_{\rm sc}\))**} \\ \cline{2-7}  & **Clay 1** & **Clay 2** & **Clay 3** & **Sand 1** & **Sand 2** & **Sand 3** \\ \hline
**16 m** & 1186 & 2201 & 7877 & 5281 & 8793 & 13,075 \\
**26 m** & 1379 & 3732 & 8078 & 4948 & 7824 & 11,262 \\
**36 m** & 1768 & 3983 & 7456 & 5025 & 7808 & 11,278 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The coefficients of subgrade reactions obtained at the center of the foundation.  
found in clay exhibited some deviations at the edge according to the changes in soil stiffness (Figure 8a-c). Except for the smallest raft in clay, all the shapes of the normalized values of \(k_{\mathrm{s}}\) for the subgrade soils were about the same. This indicates that the normalized \(k_{\mathrm{s}}\) can be modelled by some mathematic functions which are useful to routine design practice.
# Reference Broadcast-Based Secure Time Synchronization for Industrial Wireless Sensor Networks
## 4 RSTS Protocol
### Communication Energy Cost  
RSTS improves synchronization security by making use of the public neighbor forwarding mechanism. This protocol gains an additional accuracy over FTSP due to shortening the synchronization path by nearly half. Unfortunately, the slave node cannot obtain the master's timestamps efficiently, which results in higher communication energy costs. We assume that each broadcast costs energy \(E\). In FTSP, each node updates its clock with \(n\) communications and costs energy \(nE\). For RSTS, it adds an information forwarding step with two communications, which results in energy \(3 nE\). In other words, RSTS secures time synchronization at the cost of increasing the communication load, which is a common issue for secure time synchronization protocols.
Sensors_**2015**, _15_, 24735-24770; doi:10.3390/s151024735  
_Sensors_  
**ISSN 1424-8220**  
www.mdpi.com/journal/sensors  
_Article_  
**Resilient Wireless Sensor Networks Using Topology Control: A Review**  
**[PERSON], [PERSON] *, [PERSON] and [PERSON]**  
Centro de Investigacion en Tecnologias Software y Sistemas Multimedia para la Sostenibilidad (CITSEM), Campus Sur Universidad Politecnica de Madrid (UPM),  
Ctra. de Valencia, km. 7. 28031 Madrid, Spain; E-Mails: [EMAIL_ADDRESS] (Y.H.);  
[EMAIL_ADDRESS] (J.S.); [EMAIL_ADDRESS] (L.L.)  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +34-91-336-5511; Fax: +34-91-336-7817.  
Academic Editor: [PERSON]  
_Received: 6 June 2015 / Accepted: 8 September 2015 / Published: 25 September 2015_  
**Abstract:** Wireless sensor networks (WSNs) may be deployed in failure-prone environments, and WSNs nodes easily fail due to unreliable wireless connections, malicious attacks and resource-constrained features. Nevertheless, if WSNs can tolerate at most losing \(k-1\) nodes while the rest of nodes remain connected, the network is called \(k-connected\). \(k\) is one of the most important indicators for WSNs' self-healing capability. Following a WSN design flow, this paper surveys resilience issues from the topology control and multi-path routing point of view. This paper provides a discussion on transmission and failure models, which have an important impact on research results. Afterwards, this paper reviews theoretical results and representative topology control approaches to guarantee WSNs to be \(k-connected\) at three different network deployment stages: pre-deployment, post-deployment and re-deployment. Multi-path routing protocols are discussed, and many NP-complete or NP-hard problems regarding topology control are identified. The challenging open issues are discussed at the end. This paper can serve as a guideline to design resilient WSNs.  
**Keywords:** wireless sensor networks; fault-tolerant; resilience; self-healing; fault tolerance; topology control
## 2 Materials and Methods
### Studied Case  
In the presented study, the Alavian dam was considered as a case study to provide a better understanding of the earth-fill dam's behavior during rapid drawdown conditions and its impact on the upstream and downstream slope of the dam. The Alavian dam (37\({}^{\circ}\)26\({}^{\prime}\)12\({}^{\prime\prime}\) N-46\({}^{\circ}\)15\({}^{\prime}\)23\({}^{\prime\prime}\) E) is situated on the Soofian-Chay River, on the southern hillside of Sahand Mountain southeast of Urmia Lake at a distance of 120 km from Tabriz city and 3 km from Maragheh city in East Azerbaijan province, north-western Iran [34]. The earth-fill dam was constructed between 1990 and 1995 and has been active since 1996 [35]. The geometrical characteristics of the Alavian dam and lake are presented in Table 1. The dam serves to provide flood control and water supply for municipal and irrigation uses. After construction, it was noticed that the dam had settled five times more than expected. The Alavian dam is a non-homogenous (layered) earthen dam with a central impervious core flanked by previous shells. To protect the upstream slope, a 1.5 m thick layer of stone riprap is used. An injection gallery was built with concrete blocks under the core and on the bedrock. The dam is mainly composed of two layers: the internal layer as the core is made of clay, and the outer layer is a semi-pervious region made of sand and gravel, which is shown in Figure 1. Table 2 provides the geotechnical properties of the embankment's geo-materials which are obtained by performing various geotechnical in situ and laboratory tests based on the American Society for Testing and Materials (ASTM) instructions. A number of standardized laboratory tests for the core, shell, and filter materials have been conducted in order to investigate the hydraulic and mechanical properties. The series of tests that are included in core materials are index tests, compaction, determination of specific gravity, permeability, triaxial, oedometer, and direct-shear tests. A large direct shear test was performed on shell and filter materials. The dam's embankment soils are assumed to be isotropic and homogeneous in the stability and seepage analyses.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Parameters** & **Unit** & **Value** \\ \hline Height from the river water table & m & 70 \\ Height from the river bed & m & 76.8 \\ Length of the dam crest & m & 935 \\ Width of the dam crest & m & 10 \\ The total volume of the embankment (dam body) & m\({}^{3}\) & 476,000 \\ The surface area of the reservoir & ha & 262 \\ The main water level in the reservoir & m & 1568 \\ The maximum water level in the reservoir & m & 1572 \\ The minimum water level in the reservoir & m & 1525 \\ The main total water volume of the reservoir & m\({}^{3}\) & \(60\times 10^{6}\) \\ The maximum total water volume of the reservoir & m\({}^{3}\) & \(73\times 10^{6}\) \\ The minimum total water volume of the reservoir & m\({}^{3}\) & \(3\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Geometrical characteristics for the Alavian dam and lake.
## 5 Conclusions  
Aiming at the issue of island partitioning of a distribution system with multiple distributed generators, most of the existing studies fail to consider the influence of island partition results on system stability in later power restoration. Therefore, this paper constructs an island partition method considering the protection of vulnerable nodes and mainly focuses on two points: (1) identifying the vulnerable nodes in distribution system, which will impact the stability of the grid most; and (2) designing the island partition strategy considering the protection of both the primary load and vulnerable node. To identify the vulnerable nodes in the distribution system, we proposed a coupling feature analysis method of neighboring nodes. Additionally, we also established a kind of modified PageRank algorithm to realize the bidirectional transmission of coupling features to calculate the transient coupling between individual nodes and other nodes in the distribution system. Thus, the vulnerable node which has the ability of impacting the stability of the power grid the most is identified. Then, we construct an island partition model and its constraints with important load restoration and vulnerable node protection taken as its objectives, and propose the mutually exclusive firefly algorithm to uplift its ability to search for the island partition's globally optimal solution. The simulation results show that the operation state of the distribution system during the power restoration process is stable. Thus, we conclude that the method proposed in this paper can effectively enhance the stability and safety of the distribution system during the power restoration process with a relatively high ratio of power supply for important loads and provide references for grid dispatchers and controllers in terms of power grid emergency control.  
**Acknowledgments:** This work was supported by Beijing Municipal Natural Science Foundation (Approaches on Interdependent Distribution System Reconfiguration Based on Key Node Protection) and the National High Technology Research and Development of China (2015 AA050203).  
**Author Contributions:** [PERSON] provided theoretical guidance. [PERSON] designed the algorithm for vulnerable nodes identification and did the simulations of island partition. [PERSON] optimized the algorithm of island partition.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 5 Test Results and Interpretation
### Air and Road Surface Temperature  
The air and road surface temperatures were monitored during all the tests mainly because they may have effects on the measured dynamic parameters [31]. The air temperature recording started at 8:30 a.m. and a measure each 15 min was performed, up to 2:30 p.m. For the road surface, measures started at 9:30 and were then performed every 15 min. The trends of the air and road surface temperatures are plotted in Figure 14; it can be noticed that the air temperature uniformly increased during all the test periods, starting from 13 \({}^{\circ}\)C, in the early morning, up to almost 30 \({}^{\circ}\)C, at the end of tests. As previously stated, in view of the temperature variation, some thermal effects on the displacement measurement cannot be excluded. On the contrary, the effects of temperature on the dynamic measures were revealed to be almost negligible for the case study at hand.
###### Abstract  
This paper reviews the variability of passenger service time (PST) at the platform-train interface (PTI), a critical performance indicator in metro systems shaped by the infrastructure design, affecting passenger behavior and accessibility. Despite its operational importance, PST remains underexplored in relation to crowd management strategies. This review synthesizes findings from empirical and experimental research to clarify the main factors influencing PST and their implications for platform-level interventions. Key contributors to PST variability include door width, gap dimensions, crowd density, and user characteristics such as mobility impairments. Design elements--such as platform edge doors, yellow safety lines, and vertical handrails--affect flow efficiency and spatial dynamics during boarding and alighting. Advanced tracking and simulation tools (e.g., PeTrack and YOLO-based systems) are identified as essential for evaluating pedestrian behavior and supporting Level of Service (LOS) analysis. To complement traditional LOS metrics, the paper introduces Level of Interaction (LOI) and a multidimensional LOS framework that captures spatial conflicts and user interaction zones. Control strategies such as platform signage, seating arrangements, and visual cues are also reviewed, with experimental evidence showing that targeted design interventions can reduce PST by up to 35%. The review highlights a persistent gap between academic knowledge and practical implementation. It calls for greater integration of empirical evidence into policy, infrastructure standards, and operational contracts. Ultimately, it advocates for human-centered, data-informed approaches to PTI planning that enhance efficiency, inclusivity, and resilience in high-demand transit environments.  
platform-train interface; passenger service time; crowd management; accessibility; level of service; experiments +
Footnote †: journal: Applied sciences
# Face Recognition Based on Deep Learning and FPGA for Ethnicity Identification
## 2 Background and Related Work
### Transfer Learning  
It is difficult to train a whole CNN from scratch owing to the limited undersized datasets. Alternatively, [PERSON] and [PERSON] [20] presented a CNN, namely VGG, which achieved an accuracy of 92.7% with ImageNet that has up to 1000 classes and over 14 million images. VGG-16 and VGG-19 are the two forms of the trained VGG model, where their parameters and structures are available online freely. Pre-trained models, fine-tuning CNNs, and a fixed feature extractor are the main three scenarios of the latter learning approach, named transfer learning (TL). In comparing these models, fine-tuning showed the most popular with different models such as ResNet50, DenseNet GoogLeNet, and VGG.  
The RR-VGG model, which used VGG-16 inside, is presented in the coming sections of this study. Several works leveraged the VGG structure for performing transfer learning in various challenges. For extracting useful features, [PERSON] et al. [37] utilized the trained VGG model to detect lung adenocarcinoma, while for image classification, different classifiers were used. [PERSON] et al. [38] adopted several pre-trained CNN models (GoogleNet, AlexNet, and VGG) inside a fully convolutional model. For segmentation purposes, they fine-tuned the model. Applying the pre-trained VGG models in problems of computer-aided detection was studied by [PERSON] et al. [39] and attained inspiring results. Transferring a pre-trained model such as VGG becomes more appropriate than using other techniques.  
On the other hand, multiple small filters (e.g., 3 \(\times\) 3) are used to build larger filters (e.g., 5 \(\times\) 5) in the VGG-16 model, which has three fully connected and 13 convolutional layers. Thus, the whole convolutional layers have only a 3 \(\times\) 3 filter size. Altogether, processing one 224 \(\times\) 224 input image needs a VGG-16 model of 15.5 G multiply-and-accumulates and 138 M weights [40].  
One of the most major disadvantages of DL is that it takes greater processing time due to a large amount of data it must deal with. As a consequence, to solve these challenges, they deploy different parallel hardware architectural platforms, such as FPGA and GPU.
## 3 Architecture of the Proposed Solution
### Generalized Overview of the Proposed Solution  
We utilized an OpenStack-based cloud monitoring solution consisting of the cloud controller node and the compute node. This monitoring solution effectively captures the stolen time data information from the VMs on the cloud compute node while the controller node hosts the monitoring dashboard. Multiple VMs are launched on the cloud compute node, with each VM behaving independently and possessing a unique process ID. The current status of all the VMs of the host OS is accessible through the /procfs folder, enabling comprehensive monitoring capabilities.
# Numerical Study on the Yaw Control for Two Wind Turbines under Different Spacings
## 4 Results  
### Angular Speed of the Rotors  
From the previous studies about the wake redirection control [26; 30], the yaw angle is generally in the range of \(0^{\circ}\) to \(30^{\circ}\). Therefore, when we investigate the effect of the yaw condition, the yaw angles are taken as \(0^{\circ},10^{\circ},20^{\circ}\) and \(30^{\circ}\), respectively.  
The rotor speeds of two turbines are adjusted by the Five-Region Method and tend to a dynamic balance, respectively. Figure 6 shows the average rotor speed of each turbine in different cases. For a specific turbine spacing, with the increase in yaw angle, the rotor speed of WT1 (the upstream wind turbine) decreases while that of WT2 (the downstream wind turbine) increases. Under a specific yaw angle, the turbine spacing mainly affects the operation state of WT2. With the increase of turbine spacing, the rotor speed of WT2 also increases, but the growth rate of this speed with turbine spacing is not linear. In comparison, the effect of yaw angle on rotor speed is greater than that of turbine spacing and at the \(20^{\circ}\) yaw angle, the effect of spacing on WT2 is more obvious than at the other yaw angles.
# FLATSIM: The ForM@Ter LArge-Scale Multi-Temporal Sentinel-1 InterferoMetry Service
## 2 The NSBAS Processing Chain Used in the FLATSIM Service
### Interferogram Multi-Looking, Filtering, and Unwrapping  
After atmospheric correction, we take further looks to obtain 64 \(\times\) 16 or 32 \(\times\) 8 looks interferograms (this choice, fixed for a given region of interest, is left to the PIs of the scientific proposals). Filtering is performed by averaging on a 12 \(\times\) 12 triangular sliding windows weighted by spatial coherence.  
Unwrapping of multi-looked interferograms uses the coherence associated to the filter averaging process on complex values to guide the unwrapping integration path [19; 51]. The unwrapping path is also set to avoid crossing large topographic gradients in radar geometry. Its seed point is automatically chosen in an area with good coherence over the interferogram stack. These choices for the filtering and unwrapping procedure are quite suitable for an automatic processing over mountainous and vegetated areas with limited displacement gradients, but is not adapted to large deformation gradients.  
After unwrapping, coefficients of a linear phase ramp in range and azimuth are computed for each interferogram, together with the residual phase standard deviation. The coefficients are inverted over the interferometric network in order to get a consistent flattening of each interferogram. The phase standard deviation is also inverted for each time step, as in [18]. All interferograms are referenced by setting the averaged phase in a small area adjacent to the unwrapping seed point to zero.  
Figure 4: Example of a wrapped interferogram in 32 looks and in radar geometry, before (**a**) and after (**c**) correction by the prediction from ERA-5 atmospheric model (panel (**b**)). For the 250 km wide southern segment of track D135 at the border between the Tibetan plateau and the Sichuan basin. One color cycle = 2.6 cm of delay. First date: 8 June 2020; second date: 20 June 2020.  
Finally, the unwrapping fraction (proportion of the pixels unwrapped in one interferogram), the residual phase standard deviation per interferogram and the residual phase standard deviation per date are used to automatically discard anomalous interferograms or anomalous acquisitions. This step is very important in the automated FLATSIM procedure to ensure the quality of the final products. It typically discards from 0 to 20 interferograms and 0 to 3 acquisitions per processed track (see Figure 5).  
Figure 5: Processing steps for each interferogram.