esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
### Extent of Irrigated Area  
The mapped irrigated area as a function of spatial resolutions is compared in Figure 3 for the three selected study sites. Generally, it shows a decrease in irrigated area with decreasing spatial resolution. Nevertheless, there are large differences in the relationship between resolution and area between the selected regions. This can be seen in Figure 3 in China, where the scaling effect is rather small, whereas Sudan and USA show a pronounced scaling effect.  
Table 1 shows the absolute values of the mapped irrigated area in the three study sites in km\({}^{2}\) for the selected spatial resolutions.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Spatial Resolution** & **China [km\({}^{2}\)]** & **Sudan [km\({}^{2}\)]** & **USA [km\({}^{2}\)]** \\ \hline
1000 m & 2872.95 & 2159.24 & 734.21 \\
600 m & 2904.48 & 2416.68 & 903.24 \\
300 m & 2908.71 & 2599.83 & 1021.41 \\
100 m & 2910.75 & 2832.09 & 1144.47 \\
60 m & 2905.35 & 2901.15 & 1233.78 \\
40 m & 2905.28 & 2945.68 & 1262.84 \\
20 m & 2919.77 & 3009.17 & 1305.00 \\
10 m & 2992.01 & 3044.93 & 1401.12 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Resulting irrigated area in the three different regions.  
Figure 3: Mapped irrigated area in the selected Sentinel-2 tiles in China, Sudan and USA at different spatial resolutions. In Sudan and USA, the mapped irrigated area decreases with decreasing spatial resolution while the mapped irrigated area in China is almost independent of resolution.  
Figure 3 and Table 1 show that the mapping result in China is hardly affected by the upscaling resolution from 10-1000 m. Reasons are the structure of the irrigated area in this region which consists of very large-scale cohesive irrigated plots. In this case, NDVI does not change significantly by averaging towards lower resolutions and a mix of different NDVI values hardly occurs in the high-resolution ensemble underlying the low-resolution pixels.  
Figure 4 shows the spatial distribution of the mapped irrigated area in the three selected Sentinel-2 tiles for a spatial resolution of 10 m, 300 m and 1000 m. Visually, the spatial irrigation patterns largely differ in the three regions: while irrigation in Sudan and USA is scattered, the irrigated area in China is more clumped in two large contiguous irrigation clusters. At the coarser spatial resolutions, the small and scattered irrigated areas in Sudan and USA disappear while the irrigated agglomerations in China prevail.
## 2 Materials and Methods
Figure 1: Grading curves for fine and coarse aggregates. The morphology of the investigated materials was examined with the scanning electron microscope SEM FEI Quanta 250 FEG, equipped with EDS. Five mix-proportions were designed, reference mix R and four mixes (R0, R, R+F, R+F+A, R+Z+F+A) containing mineral additions and chemical admixtures. They were designed according to [39], and the compressive strength of concrete was tested according to [40]. The mix-proportions are presented in Table 5.  
Samples have been tested according to a three-point bend scheme using a 200 ton hydraulic press, equipped with a power distribution ring as well as load sensor (strain gauge) and deflection gauge [41]. A general view of the test device is given in Figure 3a. Samples (100 \(\times\) 100 \(\times\) 400 mm) with an initial notch of 40 mm in depth and 2 mm in width have been used for testing. Two cycles of loading (up to 10% of the expected peak load) and unloading have been held before testing. Specimens have been loaded continuously until their fracture. Load-deflection curves were recorded using a computer-aided data acquisition system (Figure 3b). An estimate of the fracture energy was obtained from the L-d curves according to the RILEM method (work-of-fracture). All series consisted of four specimens. Cube specimens measuring 100 \(\times\) 100 \(\times\) 100 mm were used to determine the compressive strength. Specimens have been molded according to [40] and cured (temperature of 20 \(\pm\) 2 \({}^{\circ}\)C and relative humidity of 95%) for 28 days. Then, specimens were taken out from the curing tanks, and a central notch for samples (100 \(\times\) 100 \(\times\) 400 mm) was made using a diamond saw. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Mix Identification** & **R0** & **R** & **R+F** & **R+F+A** & **R+Z+F+A** \\ \hline Cement, kg/m\({}^{3}\) & 450 & 450 & 450 & 450 & 405 \\ Zeolitic tuff, kg/m\({}^{3}\) & - & - & - & - & 45 \\ Sand, kg/m\({}^{3}\) & 400 & 400 & 400 & 300 & 300 \\ Crushed granite sand (0.63-2 mm), kg/m\({}^{3}\) & - & 100 & 100 & 100 & 100 \\ Granite gravel (5.0–10 mm), kg/m\({}^{3}\) & 400 & 370 & 370 & 370 & 370 \\ Granite gravel (10.0–20 mm), kg/m\({}^{3}\) & 1000 & 930 & 930 & 930 & 930 \\ Superplasticizer, \% by mass & 0.8 & 0.8 & 1.5 & 1.2 & 1.5 \\ Air-entraining admixture, \% by mass & - & - & - & 0.4 & 0.4 \\ Fiber with deformed shape, kg/m\({}^{3}\) & - & - & 8 & 8 & 8 \\ Ordinary microfiber, kg/m\({}^{3}\) & - & - & 1.05 & 1.05 & 1.05 \\ The volume of the entrained air, \% & 1.5 & 1.5 & 2.0 & 6.0 & 6.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The mixture proportions (W/C = 0.36, slump class of fresh concrete—S4). Figure 2: Polypropylene fibers used in the study: (**a**) fibers with deformed shape; (**b**) microfibers. Load-deflection curves were plotted based on the obtained results of the specimens' tests. The physical and mechanical characteristics of the concrete and the energy characteristics of the fracture were calculated.
## 3 Results and Discussion
### Effect of theadsorbent Dose  
The effect of the mass of adsorbent (0.25 to 3.0 g/L) on the adsorption capacity and the percentage of dye removal showed (Figure 4) that the removal of each dye increased when the adsorbent amount increased, which could be attributed to the greater availability of adsorption sites; after reaching a plateau, one can observe the steady-state of the (pseudo) equilibrium, likely because of the saturation of the available adsorption sites as already reported in several papers [37; 38]. For the following investigations, equilibrium was attained with a selected adsorbent dosage of 1 g/L.  
On the other hand, the increase in the adsorbent mass led to a decrease in the amount of dye uptake per gram of adsorbent (q\({}_{\text{e}}\)). This drop in adsorption capacity is likely due to sites remaining unsaturated during adsorption [39; 40] and particle aggregation [41].
# Rapid Planning of an Assembly Path by Reusing the Prior Path
## 4 Examples and Comparison
### The Comparison of Results of RRT * and PPR  
(1) The comparison of average values of results  
The paths planned by using the two algorithms are random due to the small number of sampling points, resulting in different results for each algorithm when planning the same path. Therefore, the number of sampling points, the path length, and the running time when the path is successfully planned for the first time are calculated for the two algorithms, and the average values of 100 sets of planning results of Case Study 1 are compared, as shown in Table 1.  
Table 1 shows that the average number of sampling points of the PPR algorithm is only 1/4 that of the RRT * algorithm in path planning, and the path length and running time of the PPR algorithm are also less than those of the RRT * algorithm.  
(2) The comparison of success rates of results  
In the virtual assembly of complex products, the start positions of the parts are different; thus, 50 starting points are randomly generated in the configuration space, and the assembly path for each starting point is planned by using the RRT * algorithm and the PPR algorithm. The number of successful plans with different maximum numbers of sampling points is taken as the success rate of each algorithm, as shown in Figure 19. When the success rate equals 1, it means that every starting point finds an assembly path successfully.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** &
\begin{tabular}{c} **Number of** \\ **Sampling Points** \\ \end{tabular} & **Path Length** & **Running Time (s)** \\ \hline RRT * & 1104 & 384 & 1.26 \\ PPR & 330 & 248 & 0.356 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average values of the planning results obtained by the rapidly exploring random tree star (RRT *) algorithm and path planning algorithm based on prior path reuse (PPR).  
Figure 18: Assembly paths planned with different \(N\) of Case Study 2.  
Figure 19 shows that the success rate of both algorithms increases as the number of sampling points increases. However, the success rate of the PPR algorithm is much larger than that of the RRT * algorithm when the maximum number of sampling points is small.  
The curves in Figure 19 show the relationship between the maximum number of sampling points and the success rate. For example, when the maximum number of sampling points is 500, the success rates of PPR and RRT * are about 0.7 and 0.05, which means 0.7 and 0.05 of starting points find their assembly paths by performing PPR and RRT *, respectively. The number of sampling points need to be 12,000 and 2500 at least by RRT * and PPR to make the success rates become 1.  
The comparison of the minimum number of sampling points  
The above-mentioned path planning results presented in Table 2 show that the minimum number of sampling points and the calculation time required by the PPR algorithm are much smaller than those of the RRT * algorithm, which verifies the effectiveness of the PPR algorithm.
# A Seabed Real-Time Sensing System for In-Situ Long-Term Multi-Parameter Observation Applications
## 3 Key Design of SRSS/ILMO
### Low Power Design  
The seabed observation system of SRSS/ILMO works with limited power on the seabed. A low power consumption design is needed and its scheme is as follows.  
First, the system adopts low-power chips such as the ARM processor. Second, on the basis of using low power devices, the system adopts a modular design method based on operation function. ARM connects with the underwater acoustic communication module, PA200, TCM XB, pressure sensor, and penetration system needed in the placement and recovery process, seabed penetration and pull up process. Seabed in-situ long-term observation sensors are connected to FPGA. Finally, based on the function module division, the method of hierarchical control of the power supply is adopted to reduce the power consumption of the system. As shown in Figure 10, the power supply of the sensor can be controlled by the FPGA according to the observation task, ARM can control the total power supply of the part of the FPGA according to the work task, and ARM can control the power supply of the external equipment connected to itself according to the work task.  
The power consumption of the modules of the SRSS/ILMO, used in the project of seabed engineering geology in-situ long-term real-time surveying, is shown in Table 1. The minimum power consumption of the SRSS/ILMO is 0.96 W.
## 5 Conclusions  
The purpose of this work is to develop a novel IMCMR-UWSN technique for maximizing the energy efficiency and longevity of UWSN. The IMCMR-UWSN technique led to the invention the CKHA technique and formed the clusters effectively. Additionally, a new routing technique termed the SA-GSO technique was introduced and identified as a function for selecting the optimal routes to BS. The use of numerous CH input parameters and effective route selection contributes to the network's overall performance improvement. The experimental results analysis for the IMCMR-UWSN technique is validated, and the findings are examined under a variety of different circumstances. The complete comparison analysis demonstrated the IMCMR-UWSN technique's superior performance to other contemporary techniques. In the future, the IMCMR-UWSN technique's energy efficiency can be increased further through the design of data aggregation methodologies. Additionally, resource allocation strategies based on metaheuristic algorithms can be created to efficiently allocate resources.  
Conceptualization, N.S. and P.M.; Methodology, N.S. and P.M.; Validation, N.S. and Y.A.; Formal Analysis, S.A. and N.S.; Investigation, P.M. and N.S.; Resources, N.S. and S.U.; Data Curation, P.M. and S.U.; Writing Original Draft Preparation, P.M.; Writing Review and Editing, N.S. and Y.A.; Visualization, O.I.K.; Supervision, N.S. and Y.A.; Project Administration, N.S. and Y.A.; Funding Acquisition, S.A. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Taif University, grant number TURSP-2020/313.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The study did not report any data.  
Figure 7: NOPR analysis of the IMCMR-UWSN technique.  
**Acknowledgments:** We deeply acknowledge Taif University for supporting this research through the Taif University Researchers Supporting Project, Number (TURSP-2020/313), Taif University, Taif, Saudi Arabia.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Conclusions  
Under untrusted source conditions, the primary factors to consider are the security of the dual light-field protocol as well as sufficient secure bit rate and transmission. In this paper, we introduce UB light monitoring into the SNS protocol. The introduction of UB light-source monitoring in the SNS protocol provides a novel and effective solution. By externally monitoring the light source signals of the communicating parties, following filtering and phase randomization, the proportion of UB signals with photon numbers within a specified interval \([N_{\text{min}},N_{\text{max}}]\) can be determined. This allows for the accurate estimation of key parameters in the protocol's security bit rate formula, such as the probabilities of zero-photon, single-photon, and two-photon signals. Consequently, a compact lower bound for the security bit rate can be effectively determined. Furthermore, this scheme monitors the laser source signal before attenuation, bypassing the challenges of low detection efficiency and high costs commonly associated with single-photon detection for weak light monitoring.  
The simulation analysis demonstrates that this scheme can achieve 817.5 km of secure transmission under ideal simulation parameters, which closely matches the transmission performance of an ideal light source environment. Under actual simulation parameters, the scheme can achieve 490 km of secure transmission, matching over 92% of the performance in an ideal light source environment. Compared to other protocol schemes, this scheme demonstrates better tolerance to light source fluctuations and offers significant advantages in transmission distance and secure bit rate.  
Conceptualization, J.H. and W.L.; methodology, J.H. and W.L.; validation, J.H. and Y.Q.; formal analysis, J.H., Y.Q. and W.L.; investigation, J.H. and W.L.; resources, Y.Q.; writing--original draft preparation, J.H.; writing--review and editing, J.H. and W.L.; visualization, J.H.; funding acquisition, Y.Q. 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 (62301177) and Guangxi Science and Technology Program (GuiKeAD21220107).  
The authors declare no conflicts of interest.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\alpha\) & \(p_{d}\) & \(\eta_{D}\) & \(e_{det}\) & \(f\) \\ \hline
0.2 dB/km & \(1\times 10^{-8}\) & 0.6 & 2\% & 1.15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Values of parameters used in simulation (set as in Refs. [24; 46] for more practical conditions).  
Figure 3: Performance diagram of SNS protocol based on UB monitoring solution under actual parameters.
# Temperature State Awareness-Based Energy-Saving Routing Protocol for Wireless Body Area Network
## 4 TSAER: Protocol Description
### TSAER: Operational Steps
#### 4.3.1 Network Initialization  
In the initialization phase, the sink broadcasts an information packet to other nodes, including the location of the sink. After receiving this information packet, the other sensor nodes will obtain the location information of the sink and broadcast a HELLO message packet (HM) to the neighbor nodes. The HELLO message packet includes the node ID, remaining energy, temperature, and distance to the sink. The inside of each sensor node will build or update the neighbor table (NT) according to the information in the HELLO packet. The neighbor table includes the information of the local node and the neighbor node.  
The intercommunication of information between sensor nodes facilitates the routing of nodes based on the information in the neighbor table. Algorithm 1 presents the neighbor table generation process.  
```
Input: \(\text{HM}_{\text{j}}\) (HM from neighbor node j) Output: \(\text{NT}_{\text{i}}\) (The neighbor table of node i) Start
1. For each HM do
2. If (\(\text{HM}_{\text{j}}\
eq\text{NT}_{\text{i}}\)) then
3. Update \(\text{NT}_{\text{i}}\)
4. \(\text{NT}_{\text{i}}\)(T, \(\text{E}_{\text{res}}\), D) \(\leftarrow\text{HM}_{\text{j}}\)(T, \(\text{E}_{\text{res}}\), D)
5. else
6. Discard \(\text{HM}_{\text{j}}\)
7. End If
8. End For End
```  
**Algorithm 1** Neighbor table generation process
# Secure Watermarking Schemes and Their Approaches in the IoT Technology: An Overview
## 2 The Concept of Watermarking
### Embedding Domain
#### 2.1.4 Edge Detection  
The exploration and development of the Edge Detection Technique (EDT) represent an integral part of algorithms, which are based on object detection as the EDT introduces the object outline [42]. The EDT hides data, which represents an efficient approach that should be taken into account since it assures perceptual transparency when the embedding capacity is found sufficiently huge [43]. Many different edge detection algorithms are produced for applying them to solve issues related to data hiding as Sobel and Canny detectors [3].
# Study on Dynamic Loading Characteristics of Rock Containing Holes
## 2 Numerical Model
### Introduction to the Discrete Element Method and the Bonded Particle Model  
In 1979, [PERSON] and [PERSON] initiated the development of the Discrete Element Method (DEM) [44], establishing the basis for the Particle Flow Code (PFC). PFC simulations replicate the mesoscopic mechanical behavior of rocks by treating each particle as an independent computational entity, emphasizing the interactions and evolving dynamics at particle contacts. This method allows for a precise representation of mechanical failures observed in the macroscopic failure stages of rocks. By integrating various parameters from the specimen's failure process, PFC adeptly models the formulation of failure equations, accurately capturing discontinuities like cracking and separation. Such simulations reflect the fundamental mechanisms, procedures, and outcomes associated with rock mechanical failures, thus enabling their comparison and verification against field experiments. As a result, researchers increasingly utilize PFC to explore the mechanical attributes of discontinuous materials and the fracture behaviors of continuous media.  
PFC incorporates two primary bonding models: the Contact Bond Model (CBM) and the Parallel Bond Model (PBM). The CBM cannot resist frictional forces at the bonding interface, presenting only bonded and unbonded states without opposing relative rotation.  
Figure 3: Dynamic equilibrium state inspection.  
Figure 2: Schematic diagram of dynamic stress wave action.  
In contrast, the PBM operates like a collection of springs with both standard and shear stiffness, allowing it to withstand frictional forces and support the transfer of forces and moments. When the strength threshold is surpassed, the bond model fails, and the PBM temporarily resembles a linear model, As shown in Figure 4. This study employs PFC2D to replicate the mesoscopic mechanical behaviors and crack formation in rock masses containing voids under dynamic impact. Previous research suggests that the PBM yields more accurate results for homogeneous materials like rock [45], which has led to the selection of the PBM viscous model for this numerical analysis.
# Characterizing Perception Deep Learning Algorithms and Applications for Vehicular Edge Computing
## 4 Analysis and Key Insights
### Limitations  
There are several limitations that are noteworthy and go beyond the scope of a single paper. However, we believe that addressing these will be great future work directions to navigate.  
First is the partial consideration of AV dynamics. This paper zeroes in on memory contention and latency in deep learning workloads but does not explicitly account for vehicular mobility models or dynamic network conditions (e.g., handoff between edge nodes as a vehicle moves). While we believe the memory-contention insights are broadly applicable, real-time vehicular scenarios might involve intermittent connectivity or variable network latency that further affects resource allocation.  
Second is the limitations of open source datasets. It is a well-known fact that these publicly available datasets are just a very small representation of the conditions faced in real life. Any models created or studied within the scope of these publicly available datasets will run into the same issues of being narrow in scope and inapplicable to real-life needs. We believe that more diverse datasets are needed to refine the observations made in this paper.  
Third, we also see that certain layers (e.g., routing and shortcut layers in YOLO) exhibited disproportionate latency spikes under memory contention. While we hypothesize that these anomalies stem from architecture design and memory-access bottlenecks, our analysis does not fully isolate all possible causes (e.g., overhead from framework-level memory allocation, PCIe contention, or caching effects). A deeper hardware or machine-level study could offer more conclusive explanations.  
Finally, the current work does not quantitatively evaluate how encryption or obfuscation techniques impact memory contention. In safety-critical vehicular settings, ensuring data confidentiality might introduce extra overhead, which we have not yet modeled.
# A Method for Detecting Surface Defects in Railhead by Magnetic Flux Leakage
###### Abstract  
With the rapid development of the world's railways, rail is vital to ensure the safety of rail transit. This article focuses on the magnetic flux leakage (MFL) non-destructive detection technology of the surface defects in railhead. A Multi-sensors method is proposed. The main sensor and four auxiliary sensors are arranged in the detection direction. Firstly, the root mean square (RMS) of the \(x\)-component of the main sensor signal is calculated. In the data more significant than the threshold, the defects are determined by the relative values of the sensors signal. The optimal distances among these sensors are calculated to the size of a defect and the lift-off. From the finite element simulation and physical experiments, it is shown that this method can effectively suppress vibration interference and improve the detection accuracy of defects.  
rail defects; MFL detection; relative value; Multi-sensors; RMS Article