esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Models
### Adaptive Heuristic Function and Segment Parallel A*(SPA*)
_Step 2:_ Next, the standard paths are divided equally. The small segments obtained from the equalization of the standard paths are assigned to run A* on the CPU cores separately by a control algorithm. The control algorithm for the tasks on the CPU is already packaged in Python, C++, Java, and MATLAB. In this paper, we choose MATLAB software and use the \"parfor\" command to decompose the original serial cycle into multiple parallel cycles. _Step 3:_ Each small segment of the planned trajectory is stitched together to form a risk-minimized trajectory from origin to destination. For long-distance planning tasks, multiple constraints, and high-resolution data requirements, SPA* dramatically reduces computational time consumption while maintaining performance through parallel allocation. It is more compatible than some current parallel path planning algorithms. SPA* is easy to adapt and straightforward to use for both risk-minimization and shortest-circuit problems. See Algorithm 1. ```
Input:\(F_{1}\); \(F_{2}\); \(path_{o}\); // \(F_{1}\) and \(F_{2}\) are 3D arrays, with the first two dimensions being longitude and latitude, and the third dimension being time. \(F_{1}(i)\) and \(F_{2}(i)\) represents the risk data at time period \(i\). \(m\) is the number of segments. Output:\(path_{SPA*}\)
1\(\xi\leftarrow\)round(linspace(\(path_{o}\), \(m\)))
2// \"round()\" represents rounding all elements to the nearest whole number. 3// \"linspace(\(a\), \(b\))\" represents dividing vector \(a\) into \(b\) segments. 4\(S\) is the \(m\times 2\) array. Each row stores the start and end indexes of each segment of \(path_{o}\). 5 parfor\(i\)\(\leftarrow\)\(1\) to \(m\)do
6 par\(\_path(i)\)\(\leftarrow\)\(A_{shf}^{\ast}(S(i,1),S(i,2),F_{1}(i),F_{2}(i))\)
7// \"\(A_{shf}^{\ast}(s,e,F_{1},F_{2})\)\" represents using A* with an adaptive heuristic function for path planning, starting from \(s\) and ending at \(e\), where the risk data used are \(F_{1}\) and \(F_{2}\). 8 end for
9\(path_{SPA*}\)\(\leftarrow\)[par\(\_path(1)\), par\(\_path(2)\), \(\cdots\), par\(\_path(m)\)]
10 return\(path_{SPA*}\)
```  
**Algorithm 1** SPA*  
Figure 3: Heat map on terrain risk. Figure 4 depicts the advantages of SPA* when performing dynamic path planning. Conventional path planning requires a full route from the current position to the end point for each time period. Then only the part of the route that can be reached by the ship within the specified time period is taken, and the remaining routes are deleted. If the dynamic planning from the starting point to the end point is divided into four time periods, then three long and short redundant paths will appear at the end. They significantly increase the algorithm's running time. In combination with the algorithm pseudo-code, SPA* is able to assign routes to different time periods and plan them simultaneously. This results in zero redundant routes, so the running time of SPA* is very fast.
## 4 Experiments
### Experimental Setting
#### 4.1.1 Dataset  
Our experiments use the Hong Kong Polytechnic University public finger image dataset (version 1) [13], referred to as PolyU-DB. [PERSON] and [PERSON] [13] divided the collection process of this dataset into two sessions: the data collected in the first stage consist of 6 images of the index finger and middle finger of one hand of 156 people, a total of 1872 images; in the second session, for 105 of the 156 people in the stage, 6 images each of the index and middle fingers of the same hand were collected again, for a total of 1260 images. The final dataset contains 3132 FV and IKP each, belonging to 312 categories. Figure 5 shows some of the images in the PolyU-DB dataset.  
During the experiment, we divide 312 categories of the PolyU-DB dataset into training and test sets. Among them, the training set contained 210 categories (2520 FV and IKP image pairs), while the test set contained the remaining 102 categories (612 image pairs), as shown in Table 1. To alleviate the problem that the model may not converge due to the small training set, we adopt image augmentation techniques such as translation clipping and conversion on the training set images. Thus, the data volume of the final training set is five times that of the original training set (12,600 image pairs).
# Image Representation Using Stacked Colour Histogram
## 4 Experimental Evaluation
### Image Retrieval
Figure 7 presents the results of SCH together with that of the Textons implemented by [PERSON] et al. [29] on Corel-10K. Both experiments used retrieval values of 10 to 100 with a step of 10. The proposed SCH outperformed TCM, MTH, CTM, NRFUCTM, and CMTH. Figure 5: Average precision for various retrieved values for the four datasets. Figure 6: Average precision for various retrieved values for the four datasets. The results for our experiment on the retrieval efficiency of SCH is compared with that of the textons approach as proposed by [PERSON] et al. [29] for extracting textural features in an image. Textons were selected largely because the literature has demonstrated that they are very effective for extracting textural information from images [27; 35] and hence can be used as benchmark for evaluating similar feature extraction schemes. From Figure 7, we can see that SCH outperformed all the texton approaches that have been used to index the Corel 10K. The obtained result is a result of the transformational (rotational, scaling, translation and deformation) invariant nature SCH.
# Development Status and Service Performance Preliminary Analysis for BDSBAS
## 4 BDSBAS Service Performance Evaluation  
Currently, the BDSBAS SF service augments GPS L1 C/A signal, and BDSBAS DFMC service augments BDS B1C and B2a dual-frequency signals and GPS L1 C/A and L5 dual-frequency signals. Since only a few of GPS satellites broadcast L5 signal and can be monitored in the Chinese service area, the GPS dual-frequency service performance cannot be reasonably evaluated. In addition, the definitions of some degradation parameters in the BDSBAS DFMC protocol are under discussion for optimization, the integrity and availability of the DFMC service will be discussed in future work. The BDSBAS DFMC service only evaluates the accuracy performance based on BDS constellation.  
The performance accuracy, integrity and availability of the BDSBAS SF service as well as the accuracy of the BDSBAS DFMC service are preliminarily evaluated, using the observations of 30 monitoring receivers evenly distributed in China from 1 July to 10 July in 2020. These monitoring receivers are independent from those generating BDSBAS differential corrections.
# Force Analysis of Circular Diaphragm Wall Based on Circular Cylindrical Shell Theory
## 6 Site Monitoring
### Radial Displacement Monitoring  
The radial displacement monitoring was to monitor the deep horizontal displacement of the circular diaphragm wall at Section A, and the instruments required included the movable vertical clinometer, the readout instrument for clinometer, and the pipe for the clinometer, as shown in Figure 8. From Figure 8a, we can see that the movable vertical clinometer consisted of the probe and cable. The probe was equipped with a high-precision accelerometer, which had good sealing performance and could withstand certain water pressure. There were two sets of guide wheels on the upper and lower parts of the probe, to facilitate its lifting and sliding along the guide slots in the pipe. The cable, besides powering and transmitting signals to the internal accelerometer, was able to connect the probe to the readout instrument and serve as a scale for the probe test point and a rope for the probe rising and falling. There was a mark on the cable every 0.5 m and the distance is indicated by amark that started from the middle point of the two sets of guide wheels. As shown in Figure 8b, the readout instrument for the clinometer was composed of the display, working power supply, power switch and other devices. As shown in Figure 8c, the pipe for the clinometer was made of PVC material, with a diameter of 70 mm and a length of 2 m for each one. An entire pipe was formed at Section A by connecting each pipe together, and the top of the entire pipe should be 20 cm long over the cap beam. There were two pairs of orthogonal longitudinal guide slots in the pipe, and the probe guide wheels were located in a pair of guide slots during measurement to ensure they could slide up and down freely. When the reinforcement cage of the circular diaphragm wall was made, the enter pipe was fixed on the reinforcement cage and then placed in the right position, with the reinforcement cage being placed into the excavated slot. The pipe must be protected when the concrete of the circular diaphragm wall is poured, and its perpendicularity must also be ensured. The specific position of the pipe in the circular diaphragm wall after installation is shown in Figure 2, and the monitoring frequency of radial displacement in the construction process is shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{3}{c}{**Monitoring Frequency**} \\ \cline{2-4}
**Monitoring Item** & **Excavation** & **Excavation** & **Excavation** \\  & **0 m-15 m** & **15 m-30 m** & **30 m-45 m** \\ \hline Radial displacement monitoring & Once/3 d & Once/2 d & Once/d \\ Circumferential stress monitoring & Once/3 d & Once/2 d & Once/d \\ \hline \hline \end{tabular}
\end{table}
Table 3: Monitoring frequency.  
Figure 7: The position of Point A.
## 1 Introduction
For example, the Djikstra Algorithm locates the single-source shortest path. However, the algorithm also suffers from performance issues. Its extension, the A\({}^{\star}\) algorithm, solves this problem by taking the heuristic function into consideration [11]. This enables fast node search. However, as the method searches for the shortest path, the generated trajectory tends to lead the vehicle to be too close to the obstacle. In the case of vehicles operating indoors, driving too close to any objects may be hazardous, as accidents may occur due to the possibility of humans exiting doors. Thus, an obstacle-distancing method for the vehicle is needed. A common method to penalize proximity between vehicles and any known landmark in the map is by using a potential field [12]. One of them is the Artificial Potential Field (APF) method. Some studies employ APF to create the potential of road boundaries which allows the vehicle to maintain within its lane on the road, simultaneously allowing a safe distance for any sudden appearing object [13; 14]. It also has been used to define human personal space [15]. However, the method may also yield trajectory near wall proximity unless further parameter tuning is needed. At the same time, it is computationally expensive if the number of points is large. Nevertheless, it can be optimized by discretizing the field [16]. This paper introduces an alternative APF path planning consideration for an environment known a priori. The main objective of the research carried out is to develop an effective fast-planning method for indoor vehicle navigation. The proposed solution was verified by simulated studies. The main contribution of this paper is the consideration of a map transformation method when the path is not found during the primary planning stage.
## 5 Conclusions  
We have presented a novel PC-HBF architecture for massive MIMO systems that employs both VPSs and CPSs for analog precoding. To maximize the sum rate of this architecture, it is necessary to find the optimal allocation of VPSs and CPSs to transmit antennas, and we formulate the corresponding optimization as a combinatorial problem. However, the use of exhaustive search to find the optimal solution to this problem involves excessively large complexity for massive MIMO systems. Hence, we develop a greedy VPS/CPS allocation algorithm that provides a near-optimal solution. The simulation results show that the performance of the proposed HBF architecture is close to that of VPS-based PC-HBF scheme, but it requires substantially fewer VPSs in each subarray, which reduces energy consumption. In the simulation results, the energy efficiency of the proposed architecture is 19.0-27.3% higher than those of conventional architectures for \(N_{t}=128\) and \(U=8\).  
Data curation, G.M.G.; Methodology, G.M.G. and K.L.; Supervision, K.L.; Writing--original draft, G.M.G.; Writing--review & editing, G.M.G. and K.L.  
This research was supported in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education(NRF-2019R1A6A1A03032119). This research was also supported in part by Basic Science Research Program through the NRF funded by the Ministry of Education (NRF-2016R1D1A1B03933122).  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Antennas** & \
\begin{tabular}{c} **Proposed PC-HBF,** \\ **Exhaustive Search** \\ \end{tabular} &
\begin{tabular}{c} **Proposed PC-HBF,** \\ **Greedy Algorithm** \\ \end{tabular} \\ \hline
16 & \(6.2\times 10^{11}\) & \(5.6\times 10^{4}\) \\
32 & \(8.4\times 10^{28}\) & \(4.1\times 10^{5}\) \\
48 & \(1.2\times 10^{49}\) & \(1.4\times 10^{6}\) \\
64 & \(1.8\times 10^{71}\) & \(3.2\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Complexity analysis.  
Figure 4: Energy efficiency comparison against number of users (\(U=N_{RF}\)). \(N_{t}=\) (64, 128, 256) and SNR = 20 dB.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### Codebook Design
#### 4.4.3 Self-Designing Codebook  
We set \(\mathbf{B}=\mathbf{B}_{0}\mathbf{B}_{1}\) with \(\mathbf{B}_{0}=[\mathbf{I}_{N}\ \mathbf{0}_{N\times(K-N)}]\) and find the \(K\times K\) matrix \(\mathbf{B}_{1}\) via (14) for \(C=K\) interpreting it as wiring matrix for some given auxiliary target matrix \(\mathbf{\bar{T}}\). The auxiliary target matrix may, but need not, be identical to \(\mathbf{T}\). The codebook designs itself taking the auxiliary target matrix as a model.
## 5 Case Study: Autonomous Management of Battery Recharging  
We present an adaptable, fuzzy, multi-agent model in Figure 5 that addresses the challenges of energy management for AIVs [33]. The efficient management of AIVs requires a holistic approach that takes into account several factors, including operational availability, energy consumption [34], collaboration between AIVs and the dynamic infrastructure, and their adaptation to changing conditions. We aim to optimize recharging based on energy costs, as a low workload combined with frequent recharging can increase the overall energy consumption of the system. In addition, poor anticipation can limit system availability.  
AIV missions do not follow a uniform distribution in terms of frequency, creating periods of intense activity and others that are quieter. It is, therefore, essential to link the energy consumption of AIVs to the amount of work carried out and their operational availability.  
To avoid an overload of recharging requests due to too many simultaneous requests, the AIVs need to work together by communicating with each other or via the infrastructure. As for automatic recharging, although it solves the problem of the number of charges, it requires space and consumes energy. Even a 2 to 3% reduction in energy consumption is significant for certain warehouses and airports. For the introduction of fleets of autonomous vehicles in the industry of the future, it, therefore, seems necessary to fine-tune the number of recharging stations. This sizing can be improved by taking into account the possibilities for communication between the AIVs, which can collectively avoid critical (urgent) recharging.
# Applications of Plasma Produced with Electrical Discharges in Gases for Agriculture and Biomedicine
## 3 Review of Plasma Applications in Agriculture and Medicine
### Plasma Applications in Agriculture
#### 3.1.1 Water and Sewage Purification with Ozone and AOPs
disposal of pharmaceuticals with similar energy consumption (e.g., membrane techniques 0.5-1.0 kWh/m3), makes it much more advantageous. Recently, with the aid of using the SPID RS2 system [123], which is a variant of the mobile _SPID_ device, producing degassed high-ozone water (OWWO) in the amount of 4 tons per hour and 96 tons per day; the possibilities and effectiveness of OWWO for virus inactivation were investigated including, in particular, for SARS-CoV-2. As shown by previous studies, viruses are resistant to disinfectants, and the effectiveness of their inactivation depends on the type, structure, and physical form in which they occur, e.g., on the surface of solid particles or surrounded by impurities in the form of street dust and soot, as well as on various surfaces, such as plastics, metals, cardboard, and leather [123; 130]. The OWWO technology was developed in Poland 10 years ago. _SPID_ devices have been produced since 2016. The results of research on this technology in the inactivation of viruses in urban infrastructure (bus shelters and street routes), based on the example of Warsaw, show that it is a promising technology, allowing for the disinfection of places frequented by thousands of people. It is also safe for the environment, because OWWO technology does not use any chemicals, only water, air, and electricity. For the above reasons, it is a technology supported by the Polish Ministry of Climate to be used to counter the pandemic caused by the coronavirus [123]. In [122], it was shown that in a plasma reactor of the APPJ type (plasma nozzle), SARS-CoV-2 is inactivated quickly and efficiently on various surfaces by means of electric discharges in argon. The effectiveness of inactivation depends on the absorbency and roughness of the surface, and argon plasma has much more reactive oxygen and nitrogen molecules than helium plasma. The results showed that treatment of the tested surfaces with plasma generated by the APPJ reactor, powered by a sinusoidal voltage with an input power of 12 W, argon flow rate of 6.4 L/min, voltage of U = 6.8 kV, frequency f = 12.9 kHz, and at a distance of the reactor from the surface of \(\sim\)15 mm, led to an almost 100% inactivation of the SARS-CoV-2 virus [122].
# Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data
## 1 Introduction
[35; 36; 37] presented statistical algorithms for the retrieval of soil moisture over agricultural areas applying \(\sigma^{\circ}\) in HH and HV (Horizontal Transmit/Vertical Receive--cross polarisation) polarizations from ENVISAT-ASAR data based on the Integral Equation Model (IEM) and the artificial neural network (ANN). [PERSON] et al. [38] studied the potential of multi-temporal C- and L-band SAR data to map temporal changes of surface soil moisture. They found that low incidence angles (e.g., 20\({}^{\circ}\)-35\({}^{\circ}\)) and HH polarization are generally better suited to soil moisture retrieval underneath agricultural crops than VV polarization and higher incidence angles. [PERSON] et al. [39] investigated the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages the experimental results, allowed to retrieve wheat biomass and soil moisture using Advanced Synthetic Aperture Radar data. The aim of the study was to determine the spatial rate of CO2 flux and soil moisture within a wetland ecosystem by applying soil-vegetation parameters represented by Sentinel-1 VH (Vertical Transmit/Horizontal Receive--cross polarization) and VV data. The work focused on:  
* Classification of the wetland vegetation habitat types based on optical and microwave satellite images. * Developing models for soil moisture assessment applying \(\sigma^{\circ}\) calculated from Sentinel-1 data. * Examination of influence of LAI and soil moisture on \(\sigma^{\circ}\) calculated from Sentinel-1 data. * Developing NEE models applying \(\sigma^{\circ}\) calculated from Sentinel-1 VH and VV data. * Mapping spatial distribution of NEE over a test site area. The investigation was carried out over the Biebrza Wetland located in North-East Poland from April to October in 2013-2015. For the year 2013 in-situ data for the classification of vegetation habitats have been carried out, and for the years 2014-2015 ground measurements of soil-vegetation parameters and carbon fluxes were done.
## 4 System Design
### Optimized Routing Paths  
When the optimal routing path is calculated through a DQN, the routing path is transmitted through OpenFlow to the erasure coding network topology. OpenFlow has a flow table that contains information regarding the path and method of packet forwarding used to the transmit blocks. When a packet occurs, it first ensures that the flow table contains information about the packet. If information about a packet exists, the packet is processed accordingly, and if information does not exist, the OpenFlow controller requests control information about that packet. Upon receiving control information from the switch, the OpenFlow controller checks the packet control information that exists inside and forwards the result to the OpenFlow switch. Packet control information within the OpenFlow controller can be entered through an API in an external program. The OpenFlow switch stores the control information received from the controller in the flow table and then uses the information in the flow table to forward the packet when the same packet occurs. In the erasure coding network topology, the source nodes that transmit the data blocks and parity blocks required for decoding to the destination node are transmitted using optimized network routing paths. The process of calculating the optimized routing paths is shown in Algorithm 1. First, the input parameters stored in the database, the hyperparameters used to design the DQN neural network model, and the discount factors (step 1) are specified, and the elements that are entered into the DQN neural network model are initialized. An element is a replay memory used to solve the correlation problem, a Q-value of the action from the state, a Q-value of the target network to calculate the next action, a P list that stores routes from each source node to the destination node, and a 2d LP list that stores routes from all source nodes to destination nodes (step 2). The detailed algorithm works through a double loop (step 3) because the block transfer required for the decoding operation must consider all routing paths from each source node to the destination node (step 4). The following indicates the same e-greedy methodology as Q-Learning and selects an action on which link to select from the source node to the next node and acquires a reward value to move to the next state (step 5). To reduce the correlation problems described, previous records are stored in the replay memory, and mini-batch learning is applied (step 6). The action is determined, the value is calculated to estimate the Q-value of the target network, and the weight is updated to optimize the learning model by applying gradient detection (step 7). \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layers** & **Size** & **Activation Function** \\ \hline Input Layer & 18 & None \\ Hidden Layer 1 & 32 & Leaky ReLU \\ Hidden Layer 2 & 64 & Leaky ReLU \\ Hidden Layer 3 & 128 & Leaky ReLU \\ Output Layer & 16 & None \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Layer design of DQN neural network.