esa-sceva/satcom-chunk-collection huggingface dataset:
# Use of Reservoir Sediments to Improve Engineering Properties of Dune Sand in Oman
## 3 Results and Discussions  
### Index Properties  
The maximum (\(\rm{e_{max}}\)) and minimum (\(\rm{e_{min}}\)) void ratios of sand increased by adding sediments (Figure 3). Based on the test data, regression relationships were evaluated to assess \(\rm{e_{max}}\) and \(\rm{e_{min}}\) based on the sediment contents (s). The observations made in this study validate the findings of [29; 33] about the void ratio range (\(\rm{\Delta e=e_{max}-e_{min}}\)) of sands treated with different percentages of fines as shown in Figure 4. Likewise, the void ratio range (\(\rm{\Delta e=e_{max}-e_{min}}\)) of sand decreased up to 8.8% and then increased with the fine contents, \(\rm{F_{c}}\) (Figure 5) and the respective regression models for [29] have also been provided.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Material**} & **Specimen** & **Sand Contents** & **Sediment** & \multirow{2}{*}{**F\({}_{\text{c}}\) (\%)**} & \multirow{2}{*}{**G\({}_{\text{s}}\)**} & \multirow{2}{*}{**USCS**} \\  & **Designation** & **(\%)** & & & & \\ \hline Dune sand (DS) & DS & 100 & 0 & 3.5 & 2.62 & SP: poorly graded sand \\ \hline \multirow{8}{*}{Mixes (DS-RS)} & DS-RS5 & 95 & 5 & 5.5 & 2.6 & SP: poorly graded sand \\  & DS-RS10 & 90 & 10 & 8.8 & 2.58 & SP-SM: poorly graded sand to \\  & DS-RS20 & 80 & 20 & 15.7 & 2.56 & SC: clayey silt \\  & DS-RS50 & 50 & 50 & 38.4 & 2.49 & SC: clayy silt \\  & DS-RS75 & 25 & 75 & 59.4 & 2.43 & CL: low plasticity clay \\  & DS-RS90 & 10 & 90 & 69.1 & 2.41 & CL: low plasticity clay \\  & DS-RS95 & 5 & 95 & 70.4 & 2.39 & CH: high plasticity clay \\ \hline Reservoir & RS & 0 & 100 & 74.1 & 2.38 & CH: high plasticity clay \\ sediment (RS) & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Index properties of dune sand–reservoir sediment mixtures.  
Figure 2: Particle size distributions of dune sand (DS), reservoir sediment (RS), and the prepared mixes.  
The consistency (liquid limit (w\({}_{\rm L}\)), plastic limit (w\({}_{\rm P}\)), and plasticity index (I\({}_{\rm P}\))) of sand-sediment mixtures was determined as per ASTM standard [34]. The effect of sediment contents on the consistency limits and the respective linear regression models have been presented in Figure 6. In general, the addition of sediments increased the plasticity of the dune sand.  
Figure 4: Comparison of void ratio range, \(\Delta\)e.  
Figure 3: Regressions of sediment content and void ratios.
## 4 Mechanical Analysis and Strength Check of the Backfilling Hydraulic Support
### Mechanical Analysis of the Backfilling Hydraulic Support  
The biggest difference between the modified backfilling hydraulic support and the original top-coal caving hydraulic support is that it adds a rear top beam and increases the roof control area, which requires the stress analysis of the top beam [35]. The mechanical model is established by taking the reformed top beam of the ZC4800/16/30 backfilling hydraulic support. Points \(B\), \(C\), and \(D\) are the constraints of the front, middle and rear columns on the top beam, respectively; \(F_{1}\), \(F_{2}\), and \(F_{3}\) the supporting force of the front, middle and rear columns, respectively; \(f\) is the friction force on the top beam; \(q\) the load on the top beam; point \(O\) the hinge point of the front and rear top beams (see Figure 8).  
The overall uniformly distributed load form of the front and rear top beams is \(q(x)=q\). The equation is constructed from the support in equilibrium.  
\[\left\{\begin{array}{l}\sum F_{x}=0\ -\sin\alpha F_{1}-\sin\beta F_{2}+\sin \theta F_{3}+f=0\\ \sum F_{y}=0\ \ \cos\alpha F_{1}+\cos\beta F_{2}+\cos\theta F_{3}-q(l_{1}+l_{2}+l_ {3}+l_{4}+l_{5})=0\\ \sum M_{o-left}=0\ \ \cos\alpha F_{1}(l_{2}+l_{3})+\cos\beta F_{2}l_{3}-\frac{q}{2} (l_{1}+l_{2}+l_{3})^{2}=0\\ \sum M_{o-right}=0\ \cos\theta F_{3}l_{4}-\frac{q}{2}(l_{4}+l_{5})^{2}=0\end{array}\right. \tag{5}\]  
The dimensions of the top beam of the backfilling hydraulic support are as follows: \(l_{1}=0.72\), \(l_{2}=1.302\), \(l_{3}=0.383\), \(l_{4}=1.019\), \(l_{5}=0.977\). According to measurement data, when the support height is at 1.6-3.0 m, the angles between the front, middle, and rear columns and the vertical direction are as follows: \(a=10.5\)-\(23.7^{\circ}\), \(\beta=2.1\)-\(4.2^{\circ}\), \(\theta=23.6\)-\(51.1^{\circ}\). The support state at a height of 2.5 m is taken to solve the load ratio, and corresponding angles of columns are as follows: \(a=11.6^{\circ}\), \(\beta=3.4^{\circ}\), \(\theta=29.5^{\circ}\). Maple is used to write the corresponding calculation program.  
\[\begin{array}{l}F_{1}=1.53q;\\ F_{2}=0.95q;\\ F_{3}=2.25q;\\ f=-0.74q\end{array} \tag{6}\]  
Loads of the front, middle and rear columns are \(F_{1}\): \(F_{2}\): \(F_{3}=1\):0.62:1.47. At the uniform load, the stress of the rear column is the largest, followed by that of the front and middle columns. The modified hydraulic support should be manufactured with appropriate material strength combined with the stress characteristics of the front, middle and rear columns. Necessary reinforcement is required for columns bearing relatively large loads.  
Figure 8: Backfilling hydraulic support with a uniform external load on the top beam.
# Performance Analysis of Amplitude Comparison Monopulse Direction-of-Arrival Estimation
## 2 Angular Tracking of Amplitude Comparison Monopulse
\begin{array}{l}G\left(\theta_{A}\right)=G\left(\theta_{\text{azi}},\theta_ {\text{ele}}\right)\\ =G\left(\theta_{S}\right)\left[1+\frac{k_{m}}{\theta_{\text{3 dB}}}\left(\theta _{\text{azi}}+\theta_{\text{ele}}\right)\right]\\ =G_{0}\left[1+\frac{k_{m}}{\theta_{\text{3 dB}}}\left(\theta_{\text{azi}}+ \theta_{\text{ele}}\right)\right].\end{array}  \tag{7}\]  
The gain of the four antenna beams is obtained by (7), and the difference pattern (\(\Delta\)) \(\varepsilon_{\text{azi}}\) and \(\varepsilon_{\text{ele}}\) of the azimuth and elevation shown in Figure 2 can be written as  
\[ \begin{array}{l}\varepsilon_{\text{azi}}=A\left[\left\{G\left(\theta_{A} \right)+G\left(\theta_{D}\right)\right\}-\left\{G\left(\theta_{B}\right)+G \left(\theta_{C}\right)\right\}\right]\\ =4 AG_{0}k_{m}\frac{\theta_{\text{azi}}}{\theta_{\text{3 dB}}}\end{array}  \tag{8}\]  
In addition, the sum pattern of the four beams (\(\Sigma\)) can be expressed as  
\[ \begin{array}{l}\sum=A\left[G(\theta_{A})+G(\theta_{B})+G(\theta_{C})+G( \theta_{D})\right]\\ =4 AG_{0}.\end{array}  \tag{9}\]  
Figure 2: Track axis and angular error of monopulse radar beam. The azimuth error (\(\epsilon_{\rm azi}\)) and the angle error (\(\epsilon_{\rm ele}\)) on the track center axis are  
\[\frac{\theta_{\rm 3 dB}\epsilon_{\rm azi}}{k_{m}\sum}=\hat{\theta}_{\rm azi},\frac{ \theta_{\rm 3 dB}\epsilon_{\rm ele}}{k_{m}\sum}=\hat{\theta}_{\rm ele} \tag{10}\]
# Global and Local Information Adjustment for Semantic Similarity Evaluation
## 2 Related Works  
Sentence similarity evaluation is used in various fields, and recently, it has shown good performance by utilizing deep learning [2; 3; 4]. Ref. [2] evaluates the similarity by applying the Siamese network structure to LSTM [13], a family of recurrent neural networks (RNN) that perform well in sequential data. The Siamese network is a structure in which two inputs are entered simultaneously into a single neural network [2]. Words constituting sentences are represented as vectors through word embedding. The two sentences composed of word vectors are entered into the LSTM and learning proceeds. The similarity is evaluated using Manhattan distance calculation, which is \(\exp\left(-\parallel h^{t}{}_{L}-h^{r}{}_{L}\|_{1}\right)\), where \(h^{t}{}_{L}\) and \(h^{r}{}_{L}\) are the output vectors of the last hidden states representing each sentence.  
Ref. [3] evaluates the similarity by applying the Siamese network structure to CNN and LSTM. Ref. [3] converts two sentences into two vectors. Then, local features are extracted through CNN, which extracts information on adjacent words. The extracted local features are entered as an LSTM, and LSTM is trained. The similarity between the two sentences is calculated by applying the Manhattan distance to the LSTM output vectors.  
Ref. [4] evaluates the similarity by applying the Siamese network structure to group CNN and bidirectional GRU (Bi-GRU), a family of RNNs. [4] converts two sentences into two vectors. Then, multi-local features are extracted through group CNN, and the most representative local features are obtained by applying max-pooling to the multi-local features. These extracted local features are concatenated with the vector, then it enters the input of Bi-GRU. The similarity is determined by applying Manhattan distance to the Bi-GRU output vectors.  
To consider whole sentence information, our proposed model in this study uses the last hidden state as a global feature by applying the Siamese network structure to Bi-LSTM. Additionally, to use localized sentence information representing contextual information, after applying self-attention the feature extracted from the capsule network is used as a local feature. Finally, sentence similarity is evaluated by applying Manhattan distance to global and local features.
## 5 Industrial Testing
### Stress On-Line Monitoring  
Deep and shallow stress meters are arranged on both sides of the roadway to realize real-time online monitoring of the internal stress of the coal body. Specific monitoring program: In the 19,111 auxiliary transport roadway from the cutting eye 50 m from the two sides of the roadway, the first group of stress measurement points is set up and then a group every 25 m, the depth of the holes is 8 m and 15 m, the spacing of the holes is 1.0-1.5 m, a total of 10 groups of arrangements. When the distance between the measuring point and the working face is less than 30 m, it starts to retreat, and as the working face  
Figure 16: The trend of pulverized coal quantity: (**a**) solid coal side and (**b**) coal pillar side.  
Figure 15: Layout of variable diameter borehole: (**a**) plane figure and (**b**) sectional drawing.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Depth/m** & **1\(-\)5** & **5\(-\)10** & **10\(-\)15** \\ \hline \multirow{2}{*}{Hazard criteria} & Quality of pulverized coal & 3.6 kg/m & 5.5 kg/m & 7.5 kg/m \\  & Dynamic phenomenon & Stuck drill, suction drill, against drill, strange noise, impact in the hole \\ \hline \hline \end{tabular}
\end{table}
Table 5: The early warning indicators of pulverized coal quantity.  
advances, the measuring point moves forward in turn, always keeping the monitoring range of 300 m in front of the coal wall of the working face until the end of the mining. The basis of early warning judgment of stress online monitoring is shown in Table 6.  
The borehole stress meters were arranged in the middle of the variable diameter boreholes at depths of 8 m and 15 m. From Figure 17, it can be seen that the stress change at 8 m between the solid coal side and the coal pillar side is small, with a small increase and a maximum value of about 7.10 MPa, which indicates that the pressure relief effect is good in the area. About 15 m has a larger change in stress, with a peak value of 11.5 MPa on the solid coal side on 15 December 2022, and a rapid decrease in stress to 5.1 MPa thereafter. The stress at 8 m is similar to that of the solid coal side, with a limited increase and the maximum value is about 7.11 MPa, which indicates that the pressure relief effect is better in this area; the coal pillar side reaches the peak value of 11.8 MPa on 17 December 2021, and then the stress decreases rapidly to 5.5 MPa, which indicates that the borehole collapses due to the high stress, and the coal undergoes yielding damage, and the high stresses are released.  
From the above analysis, it can be seen that after pressure relief of variable diameter drilling in the 19,111 auxiliary transport roadway, the stress concentration in the shallow surrounding rock was greatly reduced, and the stress was transferred to the deep rock layer, which verified the principle of the pressure relief of the variable diameter drilling and realized the safety and stability of the roadway.
# Semantic Image Segmentation Using Scant Pixel Annotations
## 4 Evaluation of the SSPA Approach
### Datasets
#### 4.1.3 Biofilm Dataset  
The biofilm dataset (Figure 2E,F) consists of scanning electron microscope (SEM) images of _Desulfoibrio alaskensis_ G20 (DA-G20, a sulfate reducing bacteria (SRB), and their biofilm grown on bare mild steel that was used as a working electrode (working specimen) in microbiologically influenced corrosion (MIC) experiments [48]. The details of the growth procedures and biocorrosion tests were discussed in [49]. Owing to its high ductility, weldability, and low cost, mild steel remains a popular choice of metal in civil infrastructure, transportation and oil and gas industry applications, and routine applications. However, under aqueous conditions, mild steel is susceptible to microbially infused corrosion caused by microorganisms, including SRB [50]. The goal of segmentation of the biofilm dataset is to identify the shape and size of each bacterial cell or a cluster of cells to detect metal corrosion. A \"1\" or white indicates a pixel in a bacterial cell, and \"0\" indicates a pixel at the boundary between bacteria.
###### Abstract  
Land surface phenology (LSP) products that are derived from different data sources have different definitions and biophysical meanings. Discrepancies among these products and their linkages with carbon fluxes across plant functional types and climatic regions remain somewhat unclear. In this study, to differentiate LSP related to gross primary production (GPP) from LSP related to remote sensing data, we defined the former as vegetation photosynthetic phenology (VPP), including the starting and ending days of GPP (SOG and EOG, respectively). Specifically, we estimated VPP based on a combination of observed VPP from 145 flux-measured GPP sites together with the vegetation index and temperature data from MODIS products using multiple linear regression models. We then compared VPP estimates with MODIS LSP on a global scale. Our results show that the VPP provided better estimates of SOG and EOG than MODIS LSP, with a root mean square error (RMSE) for SOC of 12.7 days and a RMSE for EOG of 10.5 days. The RMSE was approximately three weeks for both SOC and EOG estimates of the non-forest type. Discrepancies between VPP and LSP estimates varied across plant functional types (PFTs) and climatic regions. A high correlation was observed between VPP and LSP estimates for deciduous forest. For most PFTs, using VPP estimates rather than LSP improved the estimation of GPP. This study presents a useful method for modeling global VPP, investigates in detail the discrepancies between VPP and LSP, and provides a more effective global vegetation phenology product for carbon cycle modeling than the existing ones.  
vegetation photosynthetic phenology; land surface phenology; MODIS product; climatic region; starting day of GPP; ending day of GPP
# Integrating a LiDAR Sensor in a UAV Platform to Obtain a Georeferenced Point Cloud
## 2 Materials and Methods  
This section discusses the materials used to implement the developed system as part of this study. The system is configured using an Odroid XU4 Mini Computer connected to a Wi-Fi network using a Wi-Fi/USB module. The system is powered using a 2200 mAh/11.1 V LiPo battery connected directly to the VLP-16 sensor and a UBEC 5V regulator connected to the Mini Computer components. Below is a graphic of the integration scheme with the connections between the different parts (Figure 1), and specifications of the main equipment are outlined in Section 2.1.  
The LiDAR sensor chosen is the Class 1 Puck Lite LiDAR VLP-16 LiDAR Laser Sensor with a 100 m range that generates up to 600,000 points/s through a 360\({}^{\circ}\) horizontal field of view and a 30\({}^{\circ}\) vertical field of view [30]. The GARMIN 18\(\times\) LVC GNSS receiver is used for data synchronization. The LiDAR was integrated into the UAV platform Matrice 600 Pro [31], a hexacopter approximately 1.5 m in diameter with a maximum take-off weight of 15.5 kg. Six 3-cell LiPo batteries power the drone with a capacity of 4.5 Ah each. These batteries allow for approximately 15 min of flight time based on the configuration of the proposed LiDAR system, which carries a payload of 1300 g. Finally, an Odroid XU4 [32] Mini Computer with Linux operating system is used to collect the information from the VLP-16 through a direct Ethernet connection. Position and orientation information is obtained from the UAV Flight Control System and sent to the Minicomputer through a  
To validate the obtained point cloud, we surveyed six reference control points using a total station [33]. In addition, a GNSS (Global Navigation Satellite System) [34] was used to survey two additional control points to correct point coordinates for increased accuracy. Finally, the points were placed in a georeferenced orthophoto previously obtained using a DJI Phantom 4 Pro UAV [35] for area recognition and comparison.  
In addition, this section also explains the application procedure used to generate the final point cloud with this system. The flow presented in Figure 2 is divided into four main stages: Data Collection, Data Processing, Validation and Final Results. Each step is described below in its corresponding section.
# A PID Parameter Tuning Method Based on the Improved QUATRE Algorithm
## 6 Conclusions  
In this paper, there is a lot of room to improve the accuracy of the original algorithm. Therefore, an improved QUATRE algorithm was proposed. The idea is the adaptive change of matrix \(B\); scale factor \(F\); matrix \(M\); and the population size reduction scheme. In general, programs to reduce population size can strike a balance between the diversity of test vectors and the number of generations. Therefore, this paper used the linear population reduction strategy to improve the overall ability of the algorithm. After tests of the cec213 test function, through the comparison with PSO and the standard QUATRE algorithm, the superiority of the improved algorithm was proven.  
After the algorithm was improved, this paper proposed a PID parameter tuning method based on an improved QUATRE algorithm, which used the excellent search capability of the improved algorithm to determine the best parameter combination of the PID controller. Through our experimental simulation, from the unit step response diagram, it can be concluded that our new approach is better than the PID parameter tuning of POS and the original QUATRE algorithm smaller overshoot and rise time make the whole control more stable, which also shows that the PID parameter tuning method based on improved QUATRE algorithm is more effective.  
Figure 8: The unit response curve of the third-order controlled object.  
**Author Contributions:** Writing--original draft, Z.-Q.Z.; Writing--review & editing, S.-J.L. and J.-S.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Institutional Review Board Statement:** Not Applicable.  
**Informed Consent Statement:** Not Applicable.  
**Data Availability Statement:** Not Applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction
Subsequently, the signal traversing the OFDM communication system is used to compute the BER, which serves to assess the quality of the channel. In the context of a variable underwater environment, the CLCQ designs a reward function that considers channel quality, node buffer state, and residual energy. The reinforcement learning method enables the CLCQ to adjust the next hop node selection dynamically. Simulation results indicate that the CLCQ exhibits a higher PDR in the context of complex underwater acoustic channels. The main contributions made by this paper are the following:  
1. The cross-layer routing protocol is designed by combining the physical layer and data link layer parameters to realize the information exchange between different layers. Simulation results show that the protocol can effectively improve the network performance. 2. The channel impulse response is calculated using winter sound speed profile data from a specific sea area, and the results are applied to the OFDM communication system to obtain the BER of underwater acoustic channels under specific marine environments, different transceiver positions, and fixed modulation modes. The BER serves as a channel quality evaluation index, which provides an important basis for designing cross-layer routing protocols. 3. The reward function for reinforcement learning was designed by considering channel quality, node buffer state, and remaining energy to select reliable links and avoid congestion, thereby enhancing the PDR and reducing end-to-end delay. Additionally, a forwarding candidate selection method based on node depth, remaining energy, and buffer state was proposed to accelerate algorithm convergence. The rest of the paper is organized as follows: Section 2 reviews related research. The system model, including the network model, energy model, and communication model,is presented in Section 3. The proposed CLCQ protocol is described in detail in Section 4. Section 5 evaluates the performance of the CLCQ protocol through simulation. Section 6 summarizes the research results and concludes.
###### Abstract  
Surface electromyography (sEMG) plays a crucial role in several applications, such as for prosthetic controls, human-machine interfaces (HMI), rehabilitation, and disease diagnosis. These applications are usually occurring in real-time, so the classifier tends to run on a wearable device. This edge processing paradigm imposes strict requirements on the complexity classifier. To date, research on hand gesture recognition (GR) based on sEMG uses discriminant classifiers, such as support vector machines and neural networks. These classifiers can achieve good precision; they cannot detect when an error in classification has happened. This paper proposes a novel hand gesture multiclass model based on partial least square (PLS) class modelling that uses an encoding matrix called error correcting output codes (ECOC). A dataset of eight different gestures was classified using this method where all errors were detected, proving the feasibility of PLS-ECOC as a fault-tolerant classifier. Considering the PLS-ECOC model as a classifier, its accuracy, precision, and F1 are 87.5, 91.87, and 86.34%, respectively, similar to those obtained by other authors. The strength of our work lies in the extra information provided by the PLS-ECOC that allows the application to be fault tolerant while keeping a small-size model and low complexity, making it suitable for embedded real-time classification.  
electromyography; fault tolerant; gesture recognition; wearable; edge computing; sensitivity; specificity; class modelling; partial least squares; error correcting output; diagonal modified confusion entropy