esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Outage Probabilities
### Outage Probabilities
 \tag{21}\]  
where \(Interference_{RD}\) is the circular area of radius \(r_{O2D,m}\) centered at the destination position \((x_{D},y_{D})\), and \(\widetilde{f}_{O,m}^{RD}(x_{D},y_{D})\) is the average interferer density of \(Interference_{RD}\), defined as:  
\[\widetilde{f}_{O,m}^{RD}(x_{D},y_{D})\equiv\frac{1}{\pi\,r_{O2D,m}}\iint_{ Interference_{RD}}f_{O,m}(x,y)dxdy. \tag{22}\]  
If interferers are distributed in a uniform distribution over the cell area, then Equation (21) can be rewritten as follows:  
\[P_{RD,m}^{Outage}\approx\min\left(1,\frac{r_{O2D,m}}{R^{2}}\right)=\min\left\{ 1,\frac{d_{RD}2}{R^{2}}\left(\frac{\Gamma_{R}\lambda_{m,n}P_{O2,m}^{TX}}{P_{ R}^{TX}}\right)^{\frac{2}{\alpha}}\right\}. \tag{23}\]  
Hence, the outage probability for relay-destination link can be written as follows:  
\[P_{RD}^{Outage}=1-\prod_{m=1,m\
eq n}^{M}\left(1-P_{RD,m}^{Outage}\right) \approx 1-\prod_{m=1,m\
eq n}^{M}\left(1-\min\left\{1,\frac{d_{RD}2}{R^{2}} \left(\frac{\Gamma_{D}\lambda_{m,n}P_{O2,m}^{TX}}{P_{R}^{TX}}\right)^{\frac{ 2}{\alpha}}\right\}\right). \tag{24}\]  
In this paper, we considered three different scenarios of D2D relaying, and the outage probability calculated using Equations (11), (19), and (24) according to the target scenario. Note that \(P_{SD}^{Outage}\) in Equation (11) is a function of \(d_{SD}\), \(P_{SR}^{Outage}\) in Equation (19) is a function of \(d_{SR}\), and \(P_{RD}^{Outage}\) in Equation (24) is a function of \(d_{RD}\). If other parameters are fixed, the outage probabilities can be calculated based on the source, relay, and destination positions.
## 3 Results and Discussion
### Three-Dimensional Diffusion Models
On the basis of diesel oil diffusion shown in Figures 6 and 8, we can calculate that the longitudinal penetration depths of diesel oil in different soil samples with the moisture of 0%, 5%, 10% are 5.05 cm, 5.10 cm, 4.11 cm, which are shown in Table 1, and the lateral diffusion breadths of diesel oil are 5.36 cm, 6.44 cm, 10.11 cm, when 5.3 g diesel oil was quickly dropped on the surface of soil by a pipette. Figure 9 shows the diffusion mechanisms of diesel oil at the surface of the soil and longitudinal cut sides through the central point of the diesel oil dropped. As shown in Figure 9, the diesel oil diffuses around the central point like a circle, and the soil color becomes shallow gradually with the departing from the dropping center of diesel oil. On the longitudinal section, the diesel oil diffuses in accordance with parabolic-like line form, and the soil color becomes shallow towards periphery. This results show that the diesel oil evenly distributes in all lateral directions, combined with the longitudinal section, the permeation-diffusion model of diesel oil in soils can be viewed as a rotating body formed by rotating a parabola around an axis which was crossing the central point and vertical to the soil surface. Then the three-dimensional diffusion models of diesel oil in different moisture soils are established and shown in Figure 10. For estimating the total amounts of diesel oil which were dropped in soils, the three-dimensional diffusion models and the calibration curve were used. Assuming that the diffusion laws in longitudinal direction are consistent, to validate the accuracies of the calibration curve and the three-dimensional diffusion models of diesel oil in soils, a way of calculating triple integral by the use of symmetry is utilized. The calculated weights of diesel oil in different soil samples with the humidity of 0%, 5%, 10% are 5.57 g, 5.81 g, and 5.72 g. These results gained through the three-dimensional diffusion models show that the relative standard deviations of the predictions for \begin{table}
\begin{tabular}{c c c} \hline
**Soil Moisture** & **Lateral Diffusion Breadth(cm)** & **Longitudinal Penetration Depth(cm)** \\ \hline
0\% & 5.43 & 5.05 \\
5\% & 6.44 & 5.01 \\
10\% & 10.11 & 4.11 \\ \hline \end{tabular}
\end{table} 
Table 1: Diffusion range of diesel oil in different soil moisture. Figure 8: The concentrations of diesel oil in different lateral diffusion breadths. Figure 7: The fluorescence intensity of diesel oil in different lateral diffusion breadths. As shown in Table 2, we calculated the polluted volumes of diesel oil in soils with different soil moistures, and the polluted volumes of soils by diesel oil are 233.90 cm\({}^{3}\), 332.70 cm\({}^{3}\), and 660.05 cm\({}^{3}\). These results express that the soil moisture can extend the diesel oil pollution to the soil. Therefore, LIF technique can be considered as a fast and useful tool for analyzing the three-dimensional content distribution of diesel oil and evaluating the soil environmental quality.
# Overall Design of Satellite Networks for Internet Services with QoS Support
## 2 Proposed Design of Satellite Networks
### Other Considerations  
In the satellite communication, to alleviate the effect of the long propagation delay and use the frequency resource efficiently, the usage of the performance enhancing proxy (PEP) should be considered in the transport and the application layers [21; 36]. Generally, the TCP accelerator, the data compression, and the caching are used in the PEP in the satellite network [21; 36]. Another concern  
Figure 3: System model of the proposed design (QoS support). DAMA, demand assigned multiple access; UT, user terminals.  
is the support of the OTM terminal in the satellite network [37; 38]. In the OTM terminal, the active control system for the antenna pointing toward a satellite should be addressed [37]. Since temporary outage can occur by channel blockage due to pointing errors of the antenna and obstructions such as tunnels, high buildings, and trees, a data transfer mechanism such as an application layer forward error correction is also addressed in the satellite network to compensate the effect of temporary channel outage [38; 39; 40].
# Hybrid Impedance-Admittance Control for Upper Limb Exoskeleton using Electromyography
## 4 Materials and Methods
### Calibration  
#### 4.6.1 Normalization  
We employed a variation of the MVC (Maximum Voluntary Contraction) method to calibrate the system. MVC measures the maximum EMG value that the user can generate [32]. The proposed variation, which we call contraction for maximum angle (CMA), measures the maximum value for the EMG signal the user generates when contracting a muscle to the joint's limit in the shortest time. This measure becomes the reference for normalizing intermediate values. CMA method makes the participants produce acceptable entries for the system, even if they have anatomical differences. The average error is lower than 4.5%, and the robotic manages to mimic the movements of the upper limbs of a human in real-time.  
The experiment carried out consisted of performing the elbow and shoulder extension and flexion movements; these movements being ordered in biceps, triceps, anterior deltoid, and latissimus dorsi. In the first execution, the elbow flexion and extension movements were performed as quickly as possible. In the second execution, the user extended and flexed the shoulder.
# Satellite On-Board Change Detection via Auto-Associative Neural Networks
## 2 Materials and Methods
### Dissimilarity Measurements  
Figure 10 shows the main procedure steps for change detection at the patch level. The smaller representation, i.e., the patch vectorial representation (PVR), of the input patches associated with time t1 was obtained by means of the AANN or the DWT techniques. In  
Figure 8: (**a**) Original patch; (**b**) 2-level DWT; (**c**) DWT level and sub-bands.  
Figure 9: Comparison between the reconstructed images: (**a**) original patch at time t1; (**b**) patch reconstruction through the AANN model; (**c**) patch reconstruction through DWT; (**d**) original patch at time t2; (**e**) patch reconstruction through the AANN model; (**f**) patch reconstruction through DWT.  
particular, only the encoder stage of the AANN is involved in this step, with the output represented by the bottleneck layer. Thus, the AANN input is a vector of length 1024, which is compressed into a smaller vector of length 32. Afterwards, the same procedure was done for the patch associated with time \(\text{t}_{2}\). Then, the dissimilarity between the two obtained PVRs was computed.  
In particular, the Euclidean distance (ED), defined according to the following expression, was considered:  
\[\text{ED}=\sqrt{\sum_{\text{k}=1}^{32}\left(\text{o}_{\text{t}_{1}\text{k}}- \text{o}_{\text{t}_{2}\text{k}}\right)^{2}} \tag{1}\]  
where k is the index of the vector element, and o\({}_{\text{t}_{1}\text{k}}\), o\({}_{\text{t}_{2}\text{k}}\) are the k-th patches associated with time \(\text{t}_{1}\) and with time \(\text{t}_{2}\), respectively.
# Path Optimization of Two-Posture Manipulator of Apple Packing Robots
## 3 Materials and Methods
### Path Optimization Method for the Manipulator of Apple Packing Robots
#### 3.2.4 Packing Path Optimization Algorithm
The original grasping matrix is recorded as _Grasp_, where \(\textit{Grasp}=[-11\)\(-12\)\(-21\)\(-22\)\(-31\)\(-32\)\(-41\)\(-42\)\(\ldots\)\(-m1\)\(-m2]\). The original placing matrix is recorded as _Pos_, where _Pos_ = [\(11\)\(12\)\(21\)\(22\)\(31\)\(32\)\(\ldots\)\(n1\)\(n2]\). Figure 10: Process of comparison and substitution operator. The chromosome codes are selected in accordance with the path order of the end effector. The chromosome is defined as a linear sequence, which is composed of codes randomly extracted from matrixes _Grasp_ and _Pos_. A placing point will not repeatedly place other apples when an apple already exists. Taking 4 grasping points and 14 placing points as an example, a complete chromosome can be created as Chrome \(A\), shown in Figure 8, which is [0 \(-\)11 12 \(-\)22 21 \(-\)31 32 \(-\)42 41 \(-\)12 52 \(-\)21 61 \(-\)32 72 \(-\)41 81 \(-\)11 92 \(-\)22 101 \(-\)31 112 \(-\)42 121 \(-\)12 132 \(-\)21 141], when apples in the grasping points are continuously supplied. these marked placing points are removed from the initial placing point matrix _Pos_, and these unmarked placing points form a new matrix _Pos_New_. Subsequently, every number of 200 in the chromosome is randomly replaced with the marked number of any rotation angle solution for any unmarked placing points. The same placing points cannot be repeatedly selected. The progeny chromosomes are updated to be \(A3\) and \(B3\). The process of the crossover operator is shown in Figure 8, where the values of \(acr\_node\) and \(acr\_len\) are 6 and 12, respectively. This region crossover operator preserves the excellent genes of the paternal chromosomes and guarantees the randomness of the crossover results. Therefore, the population can comprehensively develop by maintaining excellent genes. The region crossover operator is valuable in selecting the most efficient solution globally among various posture solutions of position points.
## 4 Experiments
### Visualization Analysis  
To visually and conveniently demonstrate the detection capability of the proposed model, comparative experiments were conducted on the VisDrone-DET2021 dataset, using confusion matrices and inference results to evaluate the model's performance.  
To visualize RPS-YOLO's ability to predict object classes, confusion matrices for both RPS-YOLO and YOLOv8s were plotted, as shown in Figure 12. Each row of the matrix corresponds to a ground truth class, while each column represents a predicted class. The upper triangular area highlights the number of false positive detections (false positives, FPs), the lower triangular area shows the proportion of missed detections (false negatives, FNs), and along the main diagonal, the proportion of correctly detected samples (true positives, TPs) is shown.  
As shown in Figure 12, the region along the diagonal of the confusion matrix for RPS-YOLO is more pronounced than that of YOLOv8s, signifying that RPS-YOLO has improved accuracy in correctly predicting object classes. YOLOv8s shows a higher rate of misclassifications for smaller objects, such as tricylc  
Figure 12: (**a**) Confusion matrix plot of YOLOv8s. (**b**) Confusion matrix plot of RPS-YOLO.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Network** & **mAP0.5 (\%)** & **Params (M)** & **GFLOPs** & **FPS** \\ \hline YOLOv3-tiny & 26.9 & 12.1 & 18.9 & **86** \\ YOLOv5s & 29.8 & 9.1 & 23.8 & 60 \\ YOLOv6s & 29.9 & 16.3 & 44.0 & 52 \\ YOLOv8s & 31.9 & 11.1 & 28.5 & 56 \\ PP-PicoDet-L & 31.1 & **3.30** & **8.9** & 67 \\ YOLOv10s & 31.2 & 8.1 & 24.5 & 50 \\
**RPS-YOLO** & **35.3** & 13.3 & 37.7 & 49 \\ \hline \hline \end{tabular}  
* The best result is presented in bold.  
\end{table}
Table 3: Comparison experiment results of RPS-YOLO and lightweight networks on the UAVDT dataset.  
erable number of missed detections or false positives for these categories. The enhanced model mitigates the misdetection rate for these objects, ultimately improving the overall detection performance.  
To visually demonstrate the detection performance, inference experiments were conducted on RPS-YOLO and YOLOv8s. Nighttime and densely populated scenes were selected as the experimental datasets. These scenes feature numerous small and varied objects, making them ideal for inference evaluation. The results are presented in Figure 13.  
As shown in the circled regions in Figure 13a, the proposed method demonstrates superior detection accuracy for small objects located in distant areas of the field of view when compared to YOLOv8s while also improving the recognition accuracy for occluded objects. Similarly, as observed in the circled regions in Figure 13b, the proposed method reduces the misdetection rate for densely packed small objects.
# The Latest Advances in Wireless Communication in Aviation, Wind Turbines and Bridges
## 2 Wireless Communication and Protocols' Characteristics
Because signals are transmitted in the open air, signals and confidential information can be intercepted and copied by an intruder. There are also health aspects. Being continuously exposed to any type of radiation can be possibly dangerous. Although RF energy levels that can cause damage are not accurately determined, it is recommended that RF radiation be avoided to the maximum [3].
## Materials and Methods
### In Situ Sensor Module  
A printed circuit board was developed that incorporates a sensor concept to measure the vibration of the gear in situ. The in situ sensor module (ISM) is shown in Figure 1. It consists of two MEMS acceleration sensors (ADXL1005, Analog Devices, Wilmington, MA, USA) which allow a compact design. More details about the sensors are listed in Table 3. They measure the tangential acceleration on the gear. The sampling rate of these sensors is 16 kHz. In addition, the ISM was fitted with two temperature sensors (TMP1075, Texas Instruments, Dallas, TX, USA). These sensors are connected to copper thermal pads on the underside of the ISM to transfer the heat from the gear. The thermal conductivity of the copper thermal pads is 380 W/m\(\cdot\)K. Therefore, the sensors were able to measure the temperature of the gear wheels core surface every 5 min. The ISM is controlled by a microcontroller (STM32 WB55 RG, ST microelectronics, Geneva, Switzerland). The recorded data is stored in flash memory on the ISM. The storing is due to the sampling rate of the accelerometers, which results in a large amount of measurement data. The microcontroller also allows data to be sent via Bluetooth Low Energy (BLE), which is used to send the data to a host computer for final storage and analysis. The BLE is also used to control the ISM via commands. This control allows data acquisition from the sensors to be started dynamically and with variable measurement durations. In addition, a special command can be used to check the status of the ISM and stop the test bench if an error occurs. All components are mounted directly on the module's circuit board. The circuit board and the microcontroller are powered by two LiPo 120 mAh batteries that are mounted on top of this board using a 3D-printed holder. This design allows the complete ISM to be mounted directly and flush to the gearwheel using screws that pass through the gear, as shown in Figure 1. Without the need for an additional module on the shaft or bearing block, it is more compact than the designs of [PERSON] et al. [11], [PERSON] et al. [7] and [PERSON] et al. [12].  
Figure 1: Pictures of the ISM on the POM gear: (**a**) without batteries (white: microcontroller; blue: flash memory; yellow: accelerometer and direction of measurement; red: temperature sensor); (**b**) with the battery holder and batteries.
## 3 Routing Issues and Challenges in WBSNs
### Limited Resources  
Along with limited energy source, WBSNs also have short Radio Frequency (RF) transmission range, poor computation capabilities, limited storage capacity, as well as low bandwidth--which may keep on changing due to noise and other interferences [19]. Researchers must be aware of the limited resources when designing routing protocols for WBSNs.
## 4 Results and Discussion
### Comparison with the Existing Approaches by Varying Source and Target Locations
The proposed method resolves these space complexity related issues through reduction in search space, modeling tasks with an ESWG, producing far lower but relevant edges and vertices, and reducing the visibility checks between vertices by incorporating far-reachability and direction-guidance concepts while making an ESWG. The proposed PP method is complete, and it can be used for many UAV practical applications in urban environments. The proposed method gives good performance due to two main concepts: (i) a new space reduction concept is proposed, which not only assists in lowering the time complexity by restricting the path exploration in the space of highest priority, but also assists in finding low-cost paths in most cases; and (ii) an ESWG, which models the tasks with far lower edges and vertices that curtails the computing time of path searching significantly by making a direction-guided search of the target location. It effectively resolves the trade-off between optimality and efficiency in pathfinding from an urban environment populated by various obstacles.
## 2 Methodology
\[\Delta\phi_{diff}=\phi_{def}+\phi_{atm}+\phi_{noi} \tag{4}\]  
\[\phi_{def}=\phi_{lin}+\phi_{non} \tag{5}\]  
where \(\Delta\phi_{diff}\) represents the interferometric phase after removing the flat-earth and topographic phases; and \(\phi_{lin}\) and \(\phi_{non}\) denote the linear and nonlinear surface deformation phases, respectively. \(\phi_{def}\) refers to the deformation phase. (5) The deformation phase can be obtained by adding the nonlinear and linear deformation, and then the deformation information in the study area can be solved. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Serial** & **Image** & **Spatial** & **Temporal** & **Serial** & **Image** & **Spatial** & **Temporal** \\
**Number** & **Baseline/m** & **Baseline/d** & **Number** & **Baseline/m** & **Baseline/d** \\ \hline
1 & 17 January 2018 & 67.09 & \(-528\) & 19 & 25 March 2019 & \(-63.73\) & \(-96\) \\
2 & 10 February 2018 & 6.41 & \(-504\) & 20 & 6 April 2019 & \(-14.19\) & \(-84\) \\
3 & 6 March 2018 & 13.28 & \(-480\) & 21 & 30 April 2019 & \(-89.44\) & \(-60\) \\
4 & 30 March 2018 & 48.10 & \(-456\) & 22 & 12 May 2019 & \(-19.04\) & \(-48\) \\
5 & 23 April 2018 & \(-6.24\) & \(-432\) & 23 & 5 June 2019 & 28.04 & \(-24\) \\
6 & 17 May 2018 & 19.20 & \(-408\) & 24 & 29 June 2019 & 0 & 0 \\
7 & 29 May 2018 & 29.99 & \(-396\) & 25 & 23 July 2019 & \(-52.51\) & 24 \\
8 & 22 June 2018 & \(-5.17\) & \(-372\) & 26 & 4 August 2019 & 14.74 & 36 \\
9 & 16 July 2018 & 20.67 & \(-348\) & 27 & 28 August 2019 & 18.82 & 60 \\
10 & 21 August 2018 & \(-19.90\) & \(-312\) & 28 & 21 September 2019 & \(-65.90\) & 84 \\
11 & 14 September 2018 & 2.30 & \(-288\) & 29 & 15 October 2019 & 73.27 & 108 \\
12 & 8 October 2018 & 69.68 & \(-264\) & 30 & 20 November 2019 & \(-54.98\) & 144 \\
13 & 1 November 2018 & \(-50.82\) & \(-240\) & 31 & 14 December 2019 & 31.26 & 168 \\
14 & 25 November 2018 & 75.00 & \(-216\) & 32 & 7 January 2020 & 68.47 & 192 \\
15 & 19 December 2018 & 106.11 & \(-192\) & 33 & 31 January 2020 & 25.88 & 216 \\
16 & 12 January 2019 & \(-13.09\) & \(-168\) & 34 & 12 February 2020 & 10.44 & 228 \\
17 & 5 February 2019 & 134.60 & \(-144\) & 35 & 7 March 2020 & \(-7.76\) & 252 \\
18 & 1 March 2019 & \(-38.77\) & \(-120\) & 36 & 31 March 2020 & \(-41.35\) & 276 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Spatial–temporal baseline distribution parameters of Sentinel-1A. Figure 1: (**a**) Time-position of Sentinel-1A image interferometric pairs; and (**b**) time-baseline of image interferometric pairs. The red lines depict interferometric pairs. The yellow and green diamonds correspond to the reference image and secondary images, respectively.
# Generative Music with Partitioned Quantum Cellular Automata
## 4 Music Mapping
### Two-Dimensional PQCA Mapping
#### 4.2.1 Calculating the Pitch of a Cell  
The system is programmed with four sources of _reference pitches_, labelled as 00, 01, 10 and 11, respectively (Figure 23). The sources can hold any number of pitches. In this example, they hold four pitches each.  
To establish which source to pick a reference pitch from, the system builds a two-bit long code \((xy)\) with the values of the top right \((x)\) and bottom left \((y)\) neighbours of the respective cell (Figure 25). For instance, in Figure 22, the cell at coordinates \((5,2)\) yields the code \((10)\): \(x=1\) and \(y=0\). Thus, the system retrieves the first pitch, \(A3\), from source 10. The system loops through a source sequentially; the next time it needs to retrieve a reference pitch from source 10, it will pick the second one, \(E4\), and so on.  
In addition to the sources, the system holds a list of transposition coefficients \(\Delta=[\delta_{1},\delta_{2},...,\delta_{n}]\). These coefficients correspond to semitones for transposing a given reference pitch. That is, the system transposes the selected reference pitch by adding to (or subtracting from) a transposition coefficient \(\delta\).  
Let us consider the following example: \(\Delta=[7,-5,0,12,4,0,5,-7]\). \(\Delta\) must be of the same length as the horizontal dimension of the grid; in this case, equal to eight. This is because the \(x\) coordinate of the cell defines the index \(i\) of \(\Delta[i]\) to retrieve the respective coefficient to transpose the pitch. Thus, for cell \((5,2)\), it will add four semitones (\(\Delta[5]=4\)) to pitch \(A3\), resulting in C\(\sharp\)4 (Figure 26).
# Seismic Reliability Assessment of Water Supply Systems Considering Critical Paths
## 2 Methodology
### Critical Paths for Earthquake Resistance and Assumptions of the Model  
The definition of critical paths, on which it is most important to increase the seismic reliability of the water supply system, may vary depending on the presence or absence of certain types of pipe  
Figure 1: Schematic illustration of the research methodology. REVAS.NET: Reliability Evaluation model of Seismic hazard for water supply NETwork.  
network (branched and looped), the topological level of the network, and the presence or absence of important social infrastructure (hospitals, fire stations, public facilities, etc.). Since this study investigates water supply in an industrial water supply system, which suffers enormous economic loss if the water supply is stopped for even a few hours, the paths that independently supply water from the reservoir to the customer were defined as critical paths. Since the purpose of this study is to link the characteristics of the supply line with the evaluation of the water supply reliability, in accordance with the earthquake resistance of the major supply lines, all possible pipelines were included as critical paths. In addition, without reclassifying and assessing the 1 st and 2 nd hierarchy levels within each critical path in detail, it was assumed that each critical path is completely seismic-resistant and the joints and other facilities in the corresponding path were also changed to earthquake-resistant structures.