esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results  
We simulated the hydraulic response of operand flows for six hours for the design storm given in Section 2.2.2, i.e., for a spatially-uniform rainfall \(\bar{P}=30\) mm\(\cdot\)h\({}^{-1}\) of three-hours duration. We ran fifteen simulations corresponding to the combinations of the three CNs \(\{64,89,100\}\) in the olive-grove area and the A-to-E scenarios that varied the Manning roughness or altered the digital elevation model in the retention basin as follows:  
Figure 5: Map and histogram of the local values of the Curve Number (CN) for the simulation setups CN64 (**a**,**b**) and CN89 (**c**,**d**) in the Arquillos Basin, computed and drawn by the authors using the Methods detailed in Section 2.2.4.  
* Without NbS, i.e., as in the present day with the Manning roughness mapping described in Section 2.2.3;
* Macro-roughness effects in the olive-grove region and the drainage network, i.e., \(n=0.2\) in the blue and green areas of Figure 4a;
* Macro-roughness effects only in the olive-grove region (i.e., \(n=0.2\)), keeping the drainage network as in the present day (i.e., \(n=0.025\));
* The artificial wetland, see the blue area in Figure 4b, by constructing a barrier at the level of 406 m with a maximum local height of 6 m, which yields an inundated area of 0.044 km\({}^{2}\) with a storage capacity of \(V_{w}=0.097\) km\({}^{3}\). The optimal location (dashed square in Figure 4a) catches three streams that drain 3.9 km\({}^{2}\) (56% of the total basin area);
* A reservoir pond with a uniform depth of 6 m to store storm runoff. The trapezoidal shape of the reservoir (red area in Figure 4a,b), surrounding the ground line at 406 m, has a perimeter of 1.5 km, a surface of 0.065 km\({}^{2}\), the bottom carved at 397 m, and a pond capacity of \(V_{p}=0.39\) km\({}^{3}\).  
The following subsections summarise the results for the fifteen scenarios simulated numerically. In Section 3.1, we start describing the dynamics of overland flows for the actual situation of the draining basin. Regarding the flood risk analysis, we shall verify the outputs of the computational model with available paleohydrological information on the simulated flood event. Further, we discuss the effects of surface waters on soil erosion risk at the catchment scale. Subsequently, in Section 3.2, we identify the most beneficial physical parameters concerning the mitigation of urban floods. Additionally, in Section 3.3, we analyse the simulated shear stresses to select the optimal model parameters for mitigating soil erosion.  
Lastly, in Section 3.4, we site and select the optimal nature-based solutions that provide the most benefits for minimising the peak streamflow, the runoff depth, and the erosion-prone area, which is the desired state. The fifteen scenarios simulated previously serve to delimit the specific locations where a change of the model's physical parameters is required to achieve the optimal values. Next, the particular NbS for the targeted placement is selected according to the simulation results.
## Appendix B Near-Surface Model Generation Method  
For an elastic wave numerical simulation, the model is established mainly to determine the spatial distribution of the elastic parameters. It includes five parameters of the medium involved: the P-wave velocity (\(v_{p}\)), S-wave velocity (\(v_{s}\)), density (\(\rho\)), Lame constant \(\mu\) and \(\lambda\). The relation among these parameters can be given by:  
\[v_{p}=\sqrt{\frac{\lambda+2\mu}{\rho}},\;v_{s}=\sqrt{\frac{\mu}{\rho}} \tag{14}\]  
in which three of the five parameters must be chosen to determine the other two parameters. Here, we control \(v_{p}\), \(v_{s}\), and \(\rho\) and determine the Lame constant according to these parameters.
# Thermoelectric Generator-Powered Long-Range Wireless Real-Time Steam Leak Detection in Steam Traps
## 5 Main Implementation and Results
### Thermoelectric Module Characterization  
Initial tests were conducted to characterize the thermoelectric module, located at the lower part of the device. To achieve this, a test bench was developed, consisting of a 2-inch pipeline heated by a 150 W resistor and a support structure for the pipeline (Figure 13, left). The test bench includes a PID controller and a precise PT100 temperature sensor to ensure accurate temperature control of the pipeline surface.  
Figure 12: Main algorithm.  
Subsequently, a preliminary version of the all-in-one IIoT system was tested for thermoelectric characterization. The housing for the electronic PCB was fabricated using additive manufacturing (PLA). In the right image of the figure, the device is appropriately positioned on the test bench for measurement. For the measurements, the temperature was increased in increments of 10 \({}^{\circ}\)C, with a 20 min stabilization period for the thermoelectric generator (TEG) surface temperature at each step.  
The initial test (Figure 14) demonstrates excellent linearity of the open-circuit voltage concerning the temperature of the hot surface on the test bench. The test was conducted over a range of pipeline surface temperatures from 50 \({}^{\circ}\)C to 170 \({}^{\circ}\)C, across five ambient temperatures (from 15 \({}^{\circ}\)C to 40 \({}^{\circ}\)C). A maximum voltage of 3.687 V was recorded when the pipeline surface reached 170 \({}^{\circ}\)C and the ambient temperature was 15 \({}^{\circ}\)C.  
In addition, a series of performance tests were carried out to determine the maximum power output of the device. The thermoelectric generator (TEG) produced a peak power output of 1.034 W under the same conditions (170 \({}^{\circ}\)C surface temperature and 15 \({}^{\circ}\)C  
Figure 14: Open-circuit voltage characteristics with a set of hot and cold surface temperatures.  
Figure 13: (**a**) The test bench. (**b**) Device under test.  
ambient temperature), which is sufficient to ensure the real-time operation of the IIoT device. The results are shown in Figure 15.
## 4 Experiments and Discussions
### Synthetic Data Experiments
#### 4.1.2 Experiments with the FMAR  
The 5 FECGs, 21 MECGs, and random white noise in Table 1 were superposed to produce synthetic ECG signals. When synthesising these signals, the FECG energy was maintained at 1 (0 dB) and the FNR at 10 dB. The FMAR was used to change the amplitude of the MECG to implement the experiments. Figure 8 shows the SNR improvement and correlation coefficient results of different amplitude ratios for the 105 synthetic signals using the proposed method. The red line represents the maximum value of the experimental results, while the blue line represents the minimum value.  
Figure 7: Correlation coefficient and signal–noise ratio (SNR) improvement results against the FECG to noise energy ratio (\(FNR\)) using the proposed method for 105 synthetic signals. The red, blue, and black lines present the maximum, minimum, and average values, respectively.  
The black line represents the average value. As can be seen from Figure 8, with increasing FMAR, the \(SNR_{improve}\) decreases correspondingly. This is because with increasing FMAR, the energy of the corresponding MECG will decrease, and the SNR improvement generated by MECG elimination will also decrease accordingly. However, when FMAR increases, the correlation coefficient \(C_{R}\) increases, indicating that the quality of the extracted FECG is improving. It is noted that when FMAR is small, the MECG residue is relatively large, which will affect the identification of the FECG. When FMAR is less than 0.1, FECG identification becomes difficult. On the contrary, when FMAR increases, the R peak of the FECG is close to the R peak of the MECG, which will interfere with the identification of the MECG. When FMAR is larger than 0.9, MECG extraction errors will occur. Fortunately, this is rarely the case in actual abdomen mixed signals.
# Compacted Area with Effective Links (CAEL) for Data Dissemination in VANETs
## 5 Settings for Performance Assessment
### Performance Metrics  
The following metrics are used to evaluate the proposed routing protocol's performance:  
1. Packet delivery ratio (PDR): The ratio of packets that are transmitted from the source to the destination.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Settings** \\ \hline Mobility & Manhattan \\ MAC Protocol & IEEE 802.11p \\ Network Simulator & NS-2 \\ Channel Type & Wireless \\ Propagation Model & Two Ray Ground \\ Antenna & Omni-Antenna \\ Transmission Range & 250 m \\ Traffic Type & CBR \\ Protocol Type & UDP \\ Max. Speed & 50 Km/h \\ Buffer Length & 50 packets \\ Number of Vehicles & 25, 50, 75, 100, 125, 150, 175, 200, 225, 250, 275, 300 \\ Number of Simulations & 15 \\ Simulation Area & 2500 m \(\times\) 2000 m \\ Simulation Time & 300 s \\ Routing Protocols & COOP, NCA(MPR), CACA, DyTE, RGoV, and CAEL \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation Settings.  
Routing overhead: This is defined as the additional number of routing packets sent across all nodes to reach the destination. A lower count of overhead packets indicates better performance.
3. Average end-to-end (E2E) delay: This indicates the average time interval between packet transmission and reception.
## 2 Materials and Methods
### Methods of Analysis of Physico-Chemical Characteristics and Tools  
The analysis of soil samples and materials included a number of measurements, including soil texture, pH, _TOC_, _HS_, _HA_, _FA_, _QHS_, and selected heavy metals using AAS (atomic absorption spectroscopy) analysis (Scheme 1).  
The materials of soil included 180 samples from two chosen locations (three different soil types) that were collected and analysed. Samples of soil were collected at various depths (up to 0.60 m; refer to Table 1). All soil sample analyses were performed three times, including those for pH and _TOC_ and heavy metals (Cd and Pb). Additional analyses were performed twice, including those for silt, sand, and clay fractions. The arithmetic mean values are the data that are displayed in the tables.  
The total amounts and bioaccessibility forms of cadmium and lead in the examined soil samples were the outcome. Furthermore, a comparison of the metallic element contents under various land uses, soil types, and depths was conducted. To prevent combining two distinct soil strata, soil samples were taken from each soil horizon using an Edelman soil auger and open soil probes. In order to detect contamination, at least one average sample (i.e., nine sampling points or places) was taken from each of the 10 ha in the area under investigation [32].  
This study employed UV-Vis spectroscopy as a key method for determining the colour quotients of humic substances, which is instrumental in assessing the quality of humus in the soil. This spectroscopic technique allows for the quantitative analysis of the absorbance values at specific wavelengths, such as 465 nm and 665 nm, to characterise the composition and maturity of humic substances. Additionally, this study utilised methods for extracting and quantifying HSs, containing HAs and FAs, to assess their content and composition in the soil. These methods involved group composition analysis and the determination of colour quotients for HAs and FAs, contributing to a comprehensive characterisation of the humic substances present in the soil samples. Furthermore, the research involved quantitatively determining heavy metals, such as lead and cadmium, using the ET-AAS technique on an atomic absorption spectrometer. This analytical method allowed for assessing thetotal and bioaccessible types of selected soil metals, providing crucial data for understanding the sorption and circulation of cadmium and lead in different soil types. Overall, the combination of UV-Vis spectroscopy (Specord 50 Plus, Analytik Jena, Germany), the extraction and quantification of humic substances, and the quantitative determination of heavy metals enabled a comprehensive analysis of the connections between the characteristics of the soil, humic substances, and the sorption of analysed metals in the study area.
# A Novel Hybrid Edge Detection and LBP Code-Based Robust Image Steganography Method
## 2 Related Works
### LBP-Based Image Steganography Method  
In 2020, [PERSON] et al. [24] proposed an LBP-based image steganography method. They take a cover image and divide it into \(3\times 3\) non-overlapping blocks. They collect LBP code from each block and XORed with secret message bits. Then, they shuffled those XORed values. At the same time, they also performed synchronization operations to preserve the local neighborhood relationship.
# Improved YOLOv7 for Small Object Detection Algorithm Based on Attention and Dynamic Convolution
## 2 Related Work
### Study of Improvements Related to YOLOv7  
As the latest algorithmic model of the YOLO series, there has been more and more research to improve it as the benchmark model to achieve specific effects, among which there are many classical research results in small target detection. For example, an underwater small-target-detection method based on YOLOv7 was proposed by [PERSON] et al. In this article [24], YOLOv7 was used as the benchmark model, and in order to reduce the loss of information brought about by underwater detection, a human SE attention mechanism was added to the model with the integration of the EIoU loss function and the FPN structure to make the model's detection more accurate. In order to solve the small-target-detection problem arising in satellite surveillance, [PERSON] et al. [25] proposed an improved algorithm of YOLOv7 based on HorNet convolution and the BoTNet attention mechanism, which overcame the problem of size change and strong noise interference arising in ultra-small target detection well. In addition, there are many research results based on YOLOv7 improvements that have informed this research.
## 2 Numerical Simulation
### Design of Monitoring Points and Conditions  
To achieve real-time monitoring of the entire subgrade for internal defects and to detect the specific locations where defects may occur, monitoring points were arranged on the left side of the subgrade. Four survey lines were laid out parallel to the subgrade direction at distances of 0 m, 5 m, 10 m, and 15 m from the toe of the slope, designated as survey lines 1 to 4. Fifteen rows of monitoring points were arranged perpendicular to the subgrade direction, with intervals of 4 m. The soil cavity was set at the centerline of the subgrade and the middle of the model. For ease of description, the center of the soil cavity was considered the origin. The left side of the soil cavity was taken as the negative direction and the right side as the positive direction. The layout of the monitoring points is shown in Figure 7.  
Figure 6: Comparison of vibration acceleration amplitude between field monitoring and numerical simulation.  
For the train load, the axle weight was 16t and the train speed was 250 km/h. In the dynamic response analysis model of the subgrade, under the condition of a normal subgrade without soil holes, the subgrade filling height was 4 m. There was an underlying soil hole in the subgrade in karst areas, and the distance from the top plate of the soil hole to the subgrade filling body was 2 m. The soil hole was modeled as an ellipsoid with a long semi-axis of 1.5 m and a short semi-axis of 1 m. The modeled soil hole is shown in Figure 8. The two conditions simulated the passage of the train over a normal healthy subgrade and the subgrade in karst areas, respectively, to compare the vibration responses at the monitoring points and analyze the dynamic response characteristics of the high-speed railway subgrade.
## 4 Micro-Cracking Behaviors of Shale under Different Bedding Parameters
### Effects of Shear-to-Tensile Strength Ratio of Beddings  
#### 4.2.1 Stress-Strain Responses and Micro-Cracking Evolution  
The effects of shear-to-tensile strength ratio (STR) on the stress-strain responses and micro-cracking behaviors are analyzed in this section. The tensile strength of beddings \(\overline{\sigma}_{c}^{gj}\) was kept to 15 MPa, while the shear strength of beddings was set to 12 MPa, 15 MPa and 18 MPa. Therefore, the STR in this section was set to 0.8, 1.0 and 1.2, respectively. Figure 11 presents the stress-strain curves and crack growth curves with dip angles of 0\({}^{\circ}\), 30\({}^{\circ}\), 60\({}^{\circ}\) and 90\({}^{\circ}\) when the STR is 1.0. Generally, the stress-strain curves in uniaxial compression have three stages: the linear elastic stage, the plastic deformation stage and the brittle failure stage. They are similar to those in Figure 7. This suggests that both the JSR and STR have a negligible influence on the shape of the stress-strain curves, while the dip angle of beddings makes a difference in the trend of the crack growth curves.  
Figure 9: Effects of the JSR on the crack types in uniaxial compression (Sís denotes shear crack of beddings; Sí denotes tensile crack of beddings; Pbs denotes shear crack of matrix; Pbt denotes tensile crack of matrix). (**a**) JSR = 0.8; (**b**) JSR = 1.0; (**c**) JSR = 1.2; (**d**) comprehensive comparison of matrix cracks  
Figure 12 shows that the STR alters the downward trend of the curves for the dip angle of 60\({}^{\circ}\), but the UCS of the shale specimen varies little at other dip angles. When the dip angle is 60\({}^{\circ}\), the UCS increases from 29 MPa to 37 MPa and subsequently to 44 MPa as the STR increases from 0.8 to 1.0 and then to 1.2, respectively. The UCS grows consistently with the increase of the STR.  
Figure 10: Distributions of crack type at different JSRs: (**a**) JSR = 0.8; (**b**) JSR = 1.0 (datum case); (**c**) JSR = 1.2.  
Figure 11: Stress-strain curves and evolution of micro-cracks simulated at different dip angles and in uniaxial compression (STR is 1.0). (**a**) dip angle = 0\({}^{\circ}\); (**b**) dip angle = 30\({}^{\circ}\); (**c**) dip angle = 60\({}^{\circ}\); (**d**) dip angle = 90\({}^{\circ}\)  
Figure 12: Variation of uniaxial compressive strength of shale at different STR.
# Route Availability as a Communication Quality Metric of a Mobile Ad Hoc Network
## 3 Route Nonuniformity Path Modelling  
The processes of modeling route nonuniformity in a MANET and of deriving route availability (RA) are described below. Methods of measuring connected time and disconnected time--which are needed for the calculation of RA--for AODV and OLSR are also presented.
# Performance of 5G Trials for Industrial Automation
## 2 Industrial Use Cases and Requirements
### Aachen Trial Site  
The Aachen trial site is part of the 5G-Industry Campus Europe. It is located on the shopfloor of Fraunhofer IPT, which is equipped with multiple different machine tools. The infrastructure reflects a production landscape that is representative of many manufacturing companies, ranging from small and medium enterprises to large enterprises. Trial activities are focused on using 5G non-public networks for monitoring critical manufacturing processes, contributing to the paradigm of networked, adaptive production. One of the use cases is the monitoring of the tool condition in cutting processes such as milling using a 5G wireless acoustic emission (AE) sensor system. These kinds of systems are widely applied today to detect critical incidents such as tool breakage, collisions, and even tool wear, helping companies to better use their tools throughout the tool's lifetime. A wireless AE system can be applied closer to the tool without the need to feed cables through the complete machine, making it ideally suited for easy retrofitting. The AE sensor system uses a probe that is attached close to the machining area, which is monitored by the sensor system at a 1 MHz sampling rate. The wireless sensor system also contains an FPGA-based platform for signal processing. The measurement data are transmitted over 5G at 8 Mbit/s. Low latency with high reliability provided by 5G is crucial since short reaction times upon critical incidents are needed. The requirements for the bounded communication latency and communication service availability for the workpiece-monitoring use case are listed in Table 1. More details of the use case can be found in [8; 10].
## 4 Results and Discussion
### Simulation Experiments and Analysis
The results show that when the field of view increases, the camera position error and target position error gradually decrease and that the field of view has a greater impact on the accuracy of the camera position. The feature count also has an impact on the target localization results. We used random sampling to uniformly select different numbers of feature points from an image for comparative experiments. The figure shows that when the feature count decreases, the RMS error of target localization increases continuously. Notably, when the feature count is less than 50, the RMS error of localization greatly increases. Figure 11: Influence of virtual scene error on 2D-3D registration accuracy: (**a**) influence on camera pose accuracy; and (**b**) influence on object positioning accuracy.
# A Mobile Solution for Enhancing Tourist Safety in Warm and Humid Destinations
## 5 Discussion  
In this section, we present the study's findings and implications of implementing a real-time information system for tourists at destinations. The system provides data on temperature, humidity, weather conditions, and safety tool preparation. The implications of this system are wide-ranging, encompassing tourism management, policy effectiveness, impact on tourist behavior, destination management, and practical implications. Additionally, the system contributes to sustainable tourism practices and opens up potential future directions.
## 7 Results and Discussion
### Validation Using UK-TDS-1 Data  
Following the success of the UK-DMC mission, the UK-TDS-1 satellite which embarked on a new generation GNSS-R receiver SGR-ReSI was launched on 8 July 2014 [4]. The UK-TDS-1 data products range from Level 0 (raw intermediate frequency samples) to Level 2 (DDM). The Level 2 products have been incoherently averaged on-board. This paper uses the Level 0 data of the RD16 which could be downloaded free from the website [[http://www.merrrbys.org/atlas/atlas](http://www.merrrbys.org/atlas/atlas)]([http://www.merrrbys.org/atlas/atlas](http://www.merrrbys.org/atlas/atlas)). By Equation (10), the delay difference change rate could be estimated as 0.78 chip/s. As shown in Figure 11, compared to the distortion of delay waveform in Figure 10, the less delay difference change rate results in the lower distortion of delay waveform. The recovered waveforms have higher similarity degree with the  
Figure 9: On-ground retracking processing of distorted waveform.  
aligned waveform than the one before retracking. The MSEs reduce to 70.10 and 61.80 from 161.02 and 227.34 for the waveform with the TRP of 1 s and 3 s. Although for the UK-TDS-1 data selected to demonstrate retracking approach here, the retracking performance of the waveform with the TRP of 3 s is better than the one with the TRP of 1 s, it still should be noted that the waveform with the shorter TRP have better retracking performance, which will be further proofed in the following section.