esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Sources and Processing of Fishery Data  
Fishery data were collected from the statistics of China's Northwest Indian Ocean fishing vessels between 2016 and 2021. The dataset included the vessel name, date of fishing operations (year, month, day), number of nets, operation location (latitude and longitude), and species of catches. The type of operation was light-purse seine fishing in the high seas of the Northwest Indian Ocean (40-80\({}^{\circ}\) E, 5\({}^{\circ}\) S-35\({}^{\circ}\) N) from January to May and September to December during the years 2016 to 2021. The spatial distribution of average catch per unit effort (CPUE) of purpleback flying squid in the Northwest Indian Ocean is illustrated in Figure 1.  
During our data analysis, we encountered several types of anomalies. Abnormal catch data included records with negative reported yields, which were removed to ensure the accuracy of the analysis. Temporal anomalies were entries where the recorded year was outside the relevant operational years of our study by Excel. Such out-of-range time values could skew our temporal analysis, so we excluded them. Spatial anomalies were data points suggesting fishing activities on land. To uphold the integrity of our spatial  
Figure 1: Distribution of average CPUE of purpleback flying squid in the Northwest Indian Ocean from 2016 to 2021.  
analysis, these were also removed using ArcGIS Pro [4]. The nominal _CPUE_ was calculated to establish a standard for determining whether a location qualifies as a central fishing ground. Subsequently, the fishery data were gridded at a spatial resolution of 0.5\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\), namely the fishing zone. The formula for calculating nominal _CPUE_, measured in units of t/net, was calculated as follows:  
\[CPUE=\frac{catch}{effort} \tag{1}\]  
where _catch_ (t) is the total weight of the purpleback flying squid in the current fishing zone, and _effort_ (net) is the total number of nets operated by all fishing vessels in the same fishing zone.  
Three distinct scenarios were utilized to segment the dataset for the training samples, with the months of June to August being excluded due to the influence of the monsoon season (Table 1). The median value of the _CPUE_ was employed as a criterion to categorize the dataset into fishing grounds, designated as 1, and non-fishing grounds, designated as 0. A summary of the experimental group numbers, the models employed, and the datasets was provided for comparative analyses (Table 2).
# Transitioning Broadcast to Cloud
## 3 Technologies Needed to Support Convergence
### Broadcast-Compliant Encoding  
As discussed earlier, broadcast and streaming workflows use encoders that are significantly different in their feature sets and tuning/stream constraints. Perhaps the most extreme example of such differences is a statmux regime, where encoders are operating under control of a multiplexer--an operating regime that has no parallel in streaming.  
Consequently, if cloud workflows are intended to be used for producing streams going back to broadcast distribution, the tuning or upgrade of existing cloud encoders will be needed. For implementation of statmux, the multiplexer should also be natively implemented in cloud and integrated with encoders.
## 3 Experimental Results  
The objective of this section is to check the proper operation of the adapted electric ALO. In previous works (see Annexes 2, 3, and 4), the different flight modes for this UAV were determined, as well as the energy needed for them. Table 10 shows a summary of the energy needs of each of the phases of the main flight plan that will have a cruising speed and altitude of 32 m/s and 720 m, respectively.  
Phase 1 of ascent: The first phase is immediate to the launching of the aircraft from the ramp and will consist of a rectilinear ascent with constant acceleration, until reaching 32 m/s, with an angle of ascent of 10\({}^{\circ}\). The height reached is around 120 m.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **V0 (m/s)** & **Vf (m/s)** & \(\gamma\) (\({}^{\circ}\)) & **t (min)** & **Max Power (kW)** & **E (J)** \\ \hline Phase 1 of ascent & 15 & 32 & 10 & 0,6 & 3.910 & \(1047\times 10^{5}\) \\ Phase 2 of ascent & 32 & 32 & 10 & 1.78 & 2.706 & \(4843\times 10^{5}\) \\ Cruise & 32 & 32 & 0 & 90 & 0.544 & \(2927\times 10^{6}\) \\ Descent phase & 32 & 28.5 & 2.54 & 6.75 & 0 & 0 \\ TOTAL & - & - & - & 98.89 & - & \(3516\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Energy requirements for each phase of the main flight plan.  
Figure 14: Detail of the QinetiQ® Energy Catapult launcher with a dummy on the ramp.  
Phase 2 of ascent: Once reached the previous level of 120 m and the speed of 32 m/s, the ascent continues. Due to the need to stay close to the coast, it is necessary to carry out the ascent to the height of 720 m cruise flight in a small space. Therefore, an ascent will be made following a helical path.  
Cruise: The phase of flight corresponding to cruise will take place at an altitude of 720 m, and at a speed of 32 m/s. With the aim of causing turns in the aircraft, both right and left, the layout of an eight-shaped circuit was chosen.  
Descent phase: This last phase of the flight will consist of a gliding flight from an altitude of 720 m and a speed of 32 m/s, to a height of 200 m and an approximate speed of 25 m/s, at which time the parachute will be deployed prior to recovery.  
Regarding the different phases of the flight plan described above, the experimental tests must be focused on (1) testing the capacity of the electric power supply system (battery test), (2) checking the dynamometer value and power consumption with different throttle demands (thrust test), and (3) determining the number of dual elastic bands needed to appropriately launch the RPA (launching dummy tests). Next, the experimental results obtained from each category test are presented.
## 7 Conclusions  
In this paper, the symmetry between satellite networks and terrestrial networks, and the asymmetry between their resources are considered. We research the satellite-terrestrial cooperation in MEC-enabled STN. Different from a bent pipe satellite and a regenerative satellite, we take into account a satellite, which functions as both a relay and a computing unit to assist UEs in computing tasks. Most existing researches ignore on-board processing capabilities of satellites, whereas this paper makes full and effective use of the capabilities. We design a system model of MEC-enabled STN. Because tasks are indivisible, computation can be performed at the UE, the satellite, or the BS. Aiming to minimize all UEs' task completion delay, the corresponding optimization problem is formulated, and then decomposed into a task offloading decision problem and a computing resource allocation problem. A joint offloading decision and resource allocation scheme based on potential game and Lagrange multiplier method is proposed to solve these problems. Simulation results show that compared with other baseline schemes, the proposed scheme can obtain lower completion delay of all UEs' tasks. Although on-board processing capabilities of satellites are currently limited, with the continuous development of hardware and software  
Figure 6: Delay vs. computing resources of the satellite.  
Figure 7: Delay vs. computing resources of the BS.  
technology, the capabilities will be improved gradually. The research of this paper is a theoretical exploration of a new research direction, which may be a reference for researchers in this research direction or related research directions. If the research contributes in any way to academia and industry, we will be honored and happy. There is still work to be done in the future, e.g., considering inter-satellite cooperation, which uses computing resources of MEC servers deployed at adjacent satellites to compute tasks.  
Conceptualization, M.T.; Data curation, M.T.; Formal analysis, M.T.; Funding acquisition, X.W.; Investigation, M.T.; Methodology, M.T. and S.L.; Project administration, X.W. and S.L.; Resources, X.W.; Software, M.T.; Supervision, S.L.; Validation, M.T.; Visualization, M.T.; Writing--original draft preparation, M.T.; writing--review and editing, M.T., X.W., S.L. and L.P. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Key Research and Development Program of China grant number 2019 YFB1406500.  
Not applicable.  
The authors would like to thank the editors and the reviewers for their helpful suggestions and constructive comments.  
The authors declare no conflict of interest.
## 4 Discussion
### Limitations of the Study  
A first limitation is related to neglecting the menstrual phase of the female participants. It has been shown that the phase of the menstrual cycle can influence the activation of the sympathetic nervous system under conditions of hypovolemia induced via LBNP [43; 44]. However, other studies have failed to evidence significant effects of menstrual phase on orthostatic tolerance [45] as well as on cardiovascular variables such as HR, CO, TPR, and stroke volume, both at rest and in response to hypovolemic stress, induced via LBNP or head-up tilt [37; 45; 46].  
An additional limitation concerns the fact that the influence of adipose tissue thickness (ATT) was not accounted for. It is known that ATT may affect NIRS measurements and complicate the comparison between subject groups [47]. In particular, [PERSON] et al. reported that tissue oxygenation changes during ischemia, as assessed by the slope of the BL index Hbdiff, defined as the difference between O2 Hb and HHb, negatively correlated with ATT. On this basis, a hypothetically larger ATT in females than in males could have contributed to the lower TOI and HHb slopes observed in the present study. However, several considerations point to excluding a major influence of ATT on the present results: (1) spatially resolved NIRS variables like TOI are intrinsically less affected by superficial tissue layers [12; 13], including the adipose tissue, although some residual influence remains [23]; (2) the difference in ATT between males and females in the present study was presumably small, given the similarity in BMI between the two groups (24 vs. 21 kg/m\({}^{2}\)); (3) ATT is considered to predominantly affect the blood volume rather than oxygenation indices [47; 48]; however, no main effect of sex on HHb and THI was observed here. These considerations support the concept that the observed differences in TOI and HHb slopes truly indicate a sex difference in blood flow control.
# Concealed Object Detection and Recognition System Based on Millimeter Wave FMCW Radar
## 2 Materials and Methods
### Synthetic Aperture Radar (SAR) and Multiple-Input Multiple-Output (MIMO) Radar Antennas Technique
#### 2.2.2 Actual Measurement Parameter Setting  
By using the MIMO-SAR radar, the horizontal equivalent antenna aperture is extended when the mechanical slide rail moves at a uniform speed of 20 mm/s. The radar starts with three transmitting antennas and four receiving antennas; 8 virtual channels are used in this paper, and they are arranged linearly; each step in the longitudinal direction is 7.590 mm, and the longitudinal equivalent antenna aperture is extended. The parameters set in this paper can measure the target with a distance of 90 mm from the radar. The number of sampling points in the horizontal direction is 180, and the number of sampling points in the longitudinal direction is 104. Scanning time and image resolution can be well guaranteed. Detailed parameters are shown in Tables 2 and 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value/Unit** \\ \hline Num\_horizontalScan & 180 points \\ Num\_longitudinalScan & 13 points \\ Horizontal\_scanSize\_mm & 90 mm \\ Longitudinal\_scanSize\_mm & 98.67 mm \\ Horizontal\_stepSize\_mm & 0.500 mm \\ Longitudinal\_stepSize\_mm & 7.590 mm \\ Platform\_Speed\_mmps & 20 mm/s \\ Z0 & 90 mm \\ \hline \hline \end{tabular}
\end{table}
Table 2: \(X,Y\) axis mechanical control parameters and target distance.  
Figure 4: (**a**) Displacement map of virtual channel in \(Y\)-direction; (**b**) the antenna of single transmitter and single receiver is compared with that of multiple transmitter and multiple receiver.
# Collecting, Processing and Secondary Using Personal and (Pseudo)Anonymized Data in Smart Cities
## 5 Application of Anonymization Tools and Privacy-Preserving Techniques
### Privacy-Preserving Techniques
#### 5.2.9 Other Techniques Related to Location  
In this section, we will investigate alternative techniques that are pertinent to location privacy preservation and can be adapted to such use cases.  
One widely studied approach is the implementation of dummy locations to safeguard the real mobility trace of individuals. Methods such as SybilQuery [171] aim to preserve privacy while enabling the analysis of mobility patterns.  
In the context of smart cities, a plethora of sensor data is generated and stored. In the case of location data, which is considered private information, a set of mobility traces can be collected by a trusted third party or sensors. However, this data must be sanitized before it is shared with other parties, such as a data analytics application running on an untrusted cloud platform.  
In the evaluation of patterns, there are lists of users' points of interests (POIs), which are specific places where a user has stopped for a given duration. Promesse [172] eliminates clusters of points that correspond to user stops by utilizing a speed-smoothing algorithm, thus erasing sensitive information about the users. [PERSON] and [PERSON] [173] propose the generation of fake traces that share statistical properties with the real traces as a replacement strategy.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 1 Introduction
Consequently, the challenge lies in obtaining the standard deviation value for \(G_{IC}\), with the quantification of this size-dependent variation being the primary objective of the present paper. Another contribution of the present work is the improvement in the implementation of the Monte Carlo (MC) method. This method is used due to the probabilistic nature of the numerical model, and involves conducting numerous numerical simulations of a structural problem using distinct spatial distributions of mechanical material properties. However, executing multiple finite element analyses leads to significant computational cost. To address this issue, a parallelization strategy is adopted in the implementation of the method, providing a significant decrease in the execution time of three-dimensional simulations. This approach overcomes the computational time constraints associated with the intensive computational cost related to the inverse analysis process, ensuring more efficient and timely results in large-scale simulations. Given these considerations, this study showcases significant progress in both the scale of application, particularly for large-scale concrete structures, and the methodology for determining the probabilistic properties of the 3D semi-explicit model. To achieve this, a comprehensive explanation of the inverse analysis procedure used to ascertain the standard deviation of \(G_{IC}\) based on element size is provided. Additionally, the results of three-dimensional simulations conducted on a large Double Cantilever Beam (DCB) specimen, depicting macrocrack propagation extending up to 2 m, are presented.
## 4 Result and Discussion
### End to End Delay  
It is time takes to transport data from source to destination node. A protocol that has the shortest transmission delay is considered to be effective, the comparisons are given in Table 3.  
In Figure 3, end-to-end delay during data transmission for various numbers of nodes is illustrated. E2E delay of the proposed technique is minimal than existing approaches, namely DWMA, CBR-RPL, WEMER and PEGASIS.  
Figure 3: Comparison of end-to-end delay.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No of Nodes** & **DWMA** & **CBR-RPL** & **WEMER** & **GG-Conv\_Clus-FSO** \\ \hline
0 & 0 & 0 & 0 & 0 \\
20 & 72.49 & 51.34 & 15.46 & 9.13 \\
40 & 125.44 & 71.66 & 43.21 & 29.47 \\
60 & 163.27 & 116.77 & 79.34 & 63.06 \\
80 & 255.79 & 166.06 & 134.29 & 103.22 \\
100 & 321.44 & 233.45 & 179.11 & 121.19 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of end-to-end delay.  
Figure 2: Comparison of energy consumption.
###### Abstract  
The utilization of photovoltaic (PV) cleaning robots has proven to be an effective method for maintaining the conversion efficiency of utility-scale PV power plants by mitigating the impact of dust accumulation. However, ensuring the safe operation of these robots, resembling tanks in appearance, particularly in wet working conditions, relies heavily on their adherence to PV panels. This study focuses on assessing the slip resistance of candidate materials coated on endless polyurethane timing belts, which are equipped on PV cleaning robots to enable the efficient cleaning of uneven and misaligned PV arrays. A novel apparatus is proposed to evaluate the coefficient of static friction (COSF) of slip specimens, considering factors such as outside patterns, area density, and shore hardness. The results highlight the significant influence of shore hardness and area density on the slip resistance of the specimens. Based on the findings, it is recommended to design track grooves with hexagon or zigzag patterns and maintain a low area density (e.g., 0.44 g-mm\({}^{-2}\)) to ensure the safe operation of PV cleaning robots, irrespective of the working conditions they encounter. By addressing the slip resistance challenge, this research contributes to the overall efficiency and reliability of PV cleaning robots, enhancing their performance in maintaining clean and optimal PV panel surfaces.  
photovoltaic cleaning robot; slip resistance; tank track grooves; shore hardness; outlook pattern +
Footnote †: journal: Applied Sciences
###### Abstract  
As the main noise source in the hydrogen fuel cell system, the noise level of the centrifugal air compressor greatly affects the comfort of the hydrogen fuel cell vehicle. For reducing the noise level of centrifugal air compressors, the noise characteristics and control of a high-speed two-stage compressor prototype are studied in this paper. Firstly, the near-field noise measurement, along with the independent component analysis, is carried out to identify the noise source of the developed compressor. Results showed that the \"buzz-saw\" noise at the rotating fundamental frequency and its low order harmonic frequency in the aerodynamic noise is prominent in the noise spectrum. Thus, the aerodynamic noise characteristics are predicted and analyzed using the CFD-BEM coupling aeroacoustic calculation model. Based on the analysis results, a noise control method coupling the structure optimization and perforated muffler is proposed. The results show that the sound pressure level of the air compressor at 1 m away from the surface is reduced by 4.1 dBA after the structural optimization. A perforated muffler applied in the pipe system of the air compressor can accomplish a reduction of 5.8 dBA in the sound pressure level of the air compressor by impeding the noise transmission on the path. With the coupled noise control methods above, the sound pressure level of the air compressor is reduced from 78.8 dBA to 68.9 dBA under the rated condition.  
fuel cell; two-stage centrifugal air compressor; aerodynamic noise; CFD-BEM coupling analysis; independent component analysis; perforated muffler
## 2 System and Channel Model  
Following the same system model (this system is a good example of state-of-the-art technology to demonstrate our approach and contribution; our analysis, however, is not limited to it) in [23; 24], we assumed \(M\) single-antenna transmitters and one single-antenna receiver. For the d-CDD-based CP-SC system, only \(K(<\!M)\) CDD transmitters are selected for data transmission. By using known pilot symbols, this selection process is performed at the receiver by measuring each received SNR, denoted by \(\gamma_{k}(1\leq k\leq M)\), from the \(k\)th transmitter and then selecting the \(K\) largest ones.  
Let \(S\) be the combined received SNR from the \(K\) selected transmitters; then, \(S\) is the sum of the \(K\) largest SNRs among \(M\) ones, that is, \(S=\sum_{k=1}^{K}S_{k:M}\) where \(S_{k:M}\) is the \(k\)th-order statistic (see [29] for terminology) such that \(S_{1:M}\geq S_{2:M}\geq\cdots S_{K:M}\geq\cdots\geq S_{M:M}\). Based on the definition of \(S\), we obtain  
\[S=\sum_{k=1}^{K}S_{k:M}=\sum_{k=1}^{K}\mathbb{I}_{k}\gamma_{k}, \tag{1}\]where \(\mathbb{I}_{k}\) is the indication function given by  
\[\Pr(\mathbb{I}_{k}=1)=p_{k}\quad\text{and}\quad\Pr(\mathbb{I}_{k}=0)=1-p_{k}. \tag{2}\]  
Based on the Bernoulli process, the PDF of \(S_{k:M}(=\mathbb{I}_{k}\gamma_{k})\) is given by Equation (A.2) in [45]  
\[f_{S_{k:M}}(x)=(1-p_{k})\delta(x)+p_{k}f_{\gamma_{k}}(x), \tag{3}\]  
where \(\delta(\cdot)\) is the Dirac delta function.  
As we consider distributed systems, different path losses are assumed in a large-scale fading model. As long as each channel experiences i.n.i.d frequency-selective fading, each \(\gamma_{k}\) is composed of a different number of multipath components. Hence, for a frequency-selective fading channel, the PDF of \(\gamma_{k}\), \(f_{\gamma_{k}}(\cdot)\) in (3), can be expressed as Equation (13) in [23]  
\[f_{\gamma_{k}}(x)=\frac{x^{m_{k}-1}\exp\Bigl{(}-\frac{x}{\eta_{k}}\Bigr{)}}{ \Gamma(m_{k})\eta_{k}^{m_{k}}}, \tag{4}\]  
where \(m_{k}\) is the number of multipath components of the \(k\)th channel, \(\Gamma(\cdot)\) is the complete gamma function [46] (Section (8.310)), and \(\eta_{k}=\frac{P_{T}\eta_{k}}{\sigma_{2}^{2}}\)[23] where \(P_{T}\) is the transmission power of each transmitter, \(a_{k}\) is the path loss component of the \(k\)th channel, and \(\sigma_{z}^{2}\) is the variance of noise. Note that, hereafter, for the mathematical tractability, we first consider the i.i.d. case where \(m_{k}\) and \(\eta_{k}\) in (4) do not depend on \(k\).
## 3 Orthogonal Test Scheme and Numerical Model
### Model Establishment Based on RFPA3D  
The sensitivity of fracture geometrical parameters to the elastic modulus and UCS of the fractured rock mass was investigated in the sloping rock of the Lianghekou Hydropower Station dam site. However, only the geometric parameters of the 3# fracture group changed during the study. The geometric parameters of the 1# and 2# fracture groups remained unchanged.  
The model size of 10 m \(\times\) 10 m \(\times\) 10 m and the element number of 120 \(\times\) 120 \(\times\) 120 were chosen to build the equivalent rock mass DFN model based on the RFPA3D software [30]. The input mechanical parameters of the RFPA software are the mesoscopic parameters of rock mass, which is related to the element size to reduce the mesh effect of the model. Nine numerical models of the orthogonal arrays of the test scheme were generated based on Tables 1 and 3, as shown in Figure 3. The red lines in the numerical model represent the intersections of the fractures and the rocks.  
Figure 3: \(Cont.\)  
Uniaxial, quasi triaxial, and true triaxial compression test of fractured rock mass can be implemented and simulated based on the RFPA3D software. However, the confining pressure may make the calculation results more complicated and diverse. To understand the sensitivity of the mechanical parameters of rock mass to geometric parameters under simple conditions, only the uniaxial compression test was adopted in this section.  
During the simulation, the mechanical parameters and boundary conditions were kept constant during the tests except for the factors considered to enhance the reliability of the conclusions. The bottom surface (YOZ) of the model was fixed, and a displacement load (0.2 mm/step) was applied to the upper surface of the model parallel to the X-axis direction until the model failed. The remaining boundaries of the model are free.