esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Computational Results  
### Characterizing the Distance Measure  
In the first preprocessing step, we evaluate how well our distance metric captures known similarities and differences between protein structures by computing intra-family and inter-family distances. A good distance for structure comparison should pool similar structures, _i.e._, from the same family, whereas it should locate dissimilar structures from different families far apart from each other. In order to quantify such characteristics, we compute for each family with at least two members a central, representative structure according to Equation (4). Therefore, we compute the distance between any two structures that belong to the same family. Such intra-family distances should ideally be small. We observe that the distribution of intra-family distances differ between classes and are usually smaller than 0.5, except for class c. For the four major protein classes a to d, there is a distance peak close to zero and another one around 0.2. For the four major protein classes, they are visualized in Figure 3.  
We then compute a radius around the representative structure that encompasses all structures of the corresponding family. The number of families with a given radius decreases nearly linearly from zero to 0.6, with most families having a radius close to zero and almost no families having a radius greater than 0.6. The histogram of family radii is visualized in Figure 4.  
Figure 3: Histograms of intra-family distances divided by class: (**a**) corresponds to class a; (**b**) corresponds to class b; (**c**) corresponds to class c; (**d**) corresponds to class d.  
Figure 4: A histogram of the radii of the multi-member families.  
Figure 5: Histograms of overlap values between any two multi-member families for the four main classes a–d: (**a**) corresponds to class a; (**b**) corresponds to class b; (**c**) corresponds to class c; (**d**) corresponds to class d. The title gives an interval on the percentage of overlapping families, computed by using lower and upper bounds, respectively.  
Considering that the distance metric is bound to be within zero and one, intra-family distances and radii show that the distance overall captures the similarity between structures well. Further, we investigate the distance between protein families by computing their overlap value as defined by \(r_{\mathcal{F}_{1}}+r_{\mathcal{F}_{2}}-d(R_{\mathcal{F}_{1}},R_{\mathcal{F}_{2}})\); for a histogram, see Figure 5. Most families are not close to each other according to our distance metric. Families of the four most populated classes, which belong to different superfamilies, overlap in 23% to 25% of cases for class a, 11% to 18% for class b, 10% to 22% for class c and 11% to 18% for class d. These bounds on the number of overlapping families can be obtained by using the lower and upper bounds on the distances between representatives and the distances between family members appropriately.
# Research on Trajectory Planning and Tracking Methods for Coal Mine Mobile Robots
## 4 Experiments on Trajectory Planning and Tracking of CMRRs
### Experiments on Trajectory Planning of CMRRs
#### 4.1.1 Static Scene Planning Experiment
Figure 15:2 Experimental results of MINCO trajectory method in multiobstacle scenarios: (**a**) start planning; (**b**) trajectory execution; (**c**) entering the obstacle zone; (**d**) leaving the obstacle zone. corridor generation after the front-end search, and then carries out the generation of the optimal trajectory within the safe corridor, so the whole trajectory planning process avoids obstacles, and therefore, a better result can be obtained in the first planning. Because the safety threshold is too small, several replannings are carried out when judging whether the trajectory is safe or not, but the number of times is much smaller than the Fast-Planner algorithm.
# Energy Efficiency for Green Internet of Things (IoT) Networks: A Survey +
## 2 Motivation of the Research  
The framework of the IoT is based on several enabling technologies including WSNs, cloud computing, machine learning, and peer-to-peer systems [15]. WSN is the most crucial part of the communication process of the IoT networks. In WSNs, sensor nodes capable of detecting the required information, performing some processing and communicating with other connected nodes are the main component of these networks. However, the life of these nodes is often restricted by being powered by a battery with a limited life, constraining processing ability, memory, and radio communications [16]. Energy efficiency is one of the most crucial issues for WSNs. Most of the energy is consumed in data processing and transmissions [17]. Therefore, this paper provides a brief description of the IoT, and WSNs technology that enabled the IoT revolution. The paper also focuses on energy conservation as one of the major challenges facing IoT and WSNs, and identifies factors that affect energy consumption in such networks.
# Solution Merging in Matheuristics for Resource Constrained Job Scheduling
## 5 Experiments and Results  
C++ was used to implement the algorithms, and the implementations were compiled with GCC-5.2.0. The mixed integer programming (MIP) component was implemented using Gurobi 8.0.0 [39] and the parallel ant colony optimisation (ACO) component using OpenMP [40]. The experiments were conducted on Monash University's Campus Cluster with nodes of 24 cores and 256 GB RAM. Each physical core consisted of two hyper-threaded cores with Intel Xeon E5-2680 v3 2.5 GHz, 30M Cache, 9.60 GT/s QPI, Turbo, HT, 12C/24T (120W).  
The experiments were conducted on a dataset from [23]. This dataset consists of problem instances with 3 to 20 machines, with three instances per machine size. There is an average of 10.5 jobs per machine. This means that an instance with 3 machines has approximately 32 jobs. Further details of the problem instances, and how the job characteristics (processing times, release times, weights, etc.) were determined, can be obtained from the original study.  
To compare against existing methods for resource constrained job scheduling (RCJS), we ran column generation and ACO (CGACO) of [28], column generation and differential evolution (CGDELS) of [41], the MIP (Model 2--which is most efficient), column generation (CG) on its own and parallel ACO. The results for the MIP, CG and ACO are presented in Appendix D and are not discussed in the following sections as they prove not to be competitive.  
Thirty runs per instance were conducted and each run was allowed one hour of wall-clock time. Based on the results obtained in Section 5.4, 15 cores were allowed for each run (that is, Gurobi uses 15 cores when solving the involved MIPs and the ACO component is run with \(n_{s}=15\)). To allow a fair comparison with CGACO and CGDELS, the same algorithm was run on the same infrastructure using 15 cores per run. The parameter settings for the individual merge search (MS) and construct, merge, solve and adapt (CMSA) runs were obtained by systematic testing (see Appendices B and C). The detailed results are provided in the following sections. The parameter settings for each individual ACO colony were the same as those used in [27; 29]: \(\rho=0.1\), \(q_{0}=0.9\) and \(n_{ants}=10\).  
The result tables presented in the next sections are in the following format. The first column shows the name of the problem instance (e.g., 3-5 is an instance with 3 machines and id 5). For each algorithm we report the value of the best solution found in 25 runs (_Best_), the average solution quality across 25 runs (_Mean_), and the corresponding standard deviation (\(SD\)). The number of performed iterations, as an average across the 25 runs, is also provided (_Iter._). The best results in each table are marked in boldface. Moreover, all statistically significant results, obtained by conducting a pairwise \(t\)-test and using a confidence interval of 95%, are marked in italics.
## 2 Materials and Methods
### Methodology
#### 2.3.2 Set Up of the Algorithm at Six Intra-Plot Spatial Scales  
The geo-location of texture measurements was used to extract the SAR satellite signals by considering six buffers, with a radius ranging from 5 to 30 m (corresponding to areas from ~80 to ~2800 m\({}^{2}\)), and to select the top soil moisture measurements performed within these areas of interest (Figure 1). Above 30 m, there is too much overlap between zones and the radar coefficients are not independent enough. One independent RF statistical algorithm was implemented for each spatial area and each satellite configuration (distinguishing frequency and polarization states). Whatever the considered case, the dataset was randomly divided into training and validation sets, each containing half of the data. The number of available points varied according to the frequency and with the considered size of the buffer, from 725 (for the 5 m buffer) to 1014 (for other buffers) in the X-band and from 593 (for the 5 m buffer) to 798 (for other buffers) in the C-band. For each considered satellite configuration and spatial scale, the statistical algorithm was calibrated and then validated on independent datasets, repeating the procedure ten times.
## 5 Discussion
### About the Type of Instrumentation  
In order to have a greater knowledge of the structure to be studied and to allow the adequate implementation of an SHM system, the advantages of having continuous instrumentation compared to a discontinuous one are clear [4; 5; 6; 7]. Furthermore, as indicated in the introduction, new technologies offer great possibilities that have yet to be explored [8; 9; 10; 11; 12; 13; 14].  
However, excessive instrumentation, which generates a large amount of information but whose data have no practical interest in the installed bridge SHM system, should also be avoided. In that case, the investment made in the system would not be useful. Technical professionals with knowledge and experience must plan in advance which instrumentation is necessary for the desired objective.  
Therefore, a balanced situation must be reached so that the instrumentation installed in the structures is truly useful for the projected SHM system at a reasonable cost.
# Aerodynamic Efficiency Improvement on a NACA-8412 Airfoil via Active Flow Control Implementation
###### Abstract  
The present paper introduces a parametric optimization of several Active Flow Control (AFC) parameters applied to a NACA-8412 airfoil at a single post-stall Angle of Attack (AoA) of \(15^{\circ}\) and Reynolds number \(Re=68.5\times 10^{3}\). The aim is to enhance the airfoil efficiency and to maximize its lift. The boundary layer separation point was modified using Synthetic Jet Actuators (SJA), and the airfoil optimization was carried on by systematically changing the pulsating frequency, momentum coefficient and jet inclination angle. Each case has been evaluated using Computational Fluid Dynamic (CFD) simulations, being the Reynolds Averaged Navier-Stokes equations (RANS) turbulence model employed the Spalart Allmaras (SA) one. The results clarify which are the optimum AFC parameters to maximize the airfoil efficiency. It also clarifies which improvement in efficiency is to be expected under the operating working conditions. An energy balance is presented at the end of the paper, showing that for the optimum conditions studied the energy saved is higher than the one needed for the actuation. The paper clarifies how a parametric analysis has to be performed and which AFC parameters can be initially set as constant providing sufficient previous knowledge of the flow field is already known. A maximum efficiency increase versus the baseline case of around 275% is obtained from the present simulations.
## 5 Conclusions  
This paper proposed a method for extending a phoneme set using a large amount of broadcast data to improve the performance of a Korean spontaneous speech recognizer. In this process, we described a method to extract the fixed-length latent vectors from the variable-length phoneme-level segments, as well as a method to derive subword units using decision tree-based clustering. As a result, the proposed unit shows about 17.8% relative performance improvement over the phoneme unit in the News data. Here, the News evaluation data consist of read speech and are similar to the speech data used for the subword unit derivation and the acoustic model training. On the other hand, in the Web data consisting of read speech and different domains, the proposed unit showed a performance improvement of about 11.1%. Finally, in the SPT data consisting of spontaneous speech, the proposed unit showed a performance improvement of about 11.1%. As a result, we confirmed that the proposed unit is more suitable for spontaneous speech recognition than the phoneme unit with the acoustically low discrimination problem in spontaneous speech.  
We expect that the proposed method for deriving new acoustic units from speech data will contribute to building a decoding unit with high discrimination. To do this, we plan to use all the broadcast speech data to train the acoustic model and verify the performance of the subword unit, derived from the grapheme unit, rather than the phoneme unit. We then plan to use the proposed unit as a decoding unit for end-to-end speech recognition systems, after which we will compare our results with those of other decoding units, such as byte-pair encoding and word piece model, to confirm the performance of the spontaneous speech system. In addition, although this study focused on subword units, in the future, we plan to study how speech recognition performance can be improved by applying discriminative training to the proposed unit.  
Data curation, J.-U.B. and S.-H.K.; conceptualization, J.-U.B.; Methodology, J.-U.B. and O.-W.K.; writing--original draft preparation, J.-U.B.; resources, S.-H.K.; writing--review and editing, O.-W.K.; supervision, O.-W.K.; all of the authors participated in the project. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This work was supported by the Electronics and Telecommunications Research Institute (ETRI) grant funded by the Korea government [20 ZS1140 Development of Core Conversational AI Technologies]. The authors declare no conflict of interest.
# A Practical Neighbor Discovery Framework for Wireless Sensor Networks
## 7 Evaluations
### Effectiveness of Spear Components  
In Spear, the communication module evidently increases the discovery rate as compared to the bare protocols, as demonstrated by the evaluations in Section 7.1. The energy module significantly extends a node's lifetime, as described in Section 7.2. Though we did not evaluate the application module separately, the evaluations targeting at discovery latency and discovery rate imply that Spear could adjust the related parameters (such as duty cycle, \(p_{1}\) of PPR, and \(p_{2}\) of DPR) to reduce the discovery latency. The algorithmic module computes an optimal schedule automatically as shown in Section 7.3.
## 2 Materials and Methods
### Data Preprocessing  
#### 2.3.1 Elimination of Direct Current (DC) Component  
When addressing tooth helix deviation, a crucial factor to consider is the direct current (DC) component or average value of the waveform. Removing the DC component is prioritized as it has a significant impact on pitch deviation but does not affect the phase shift of the helix deviation. Following this, the helix deviation curve after removing the DC component for \(i^{th}\) tooth number, \(f_{\beta i}(x)\), is calculated with the following Equation (1) in this paper.  
\[f_{\beta i}(x)=\widehat{f_{\beta i}}(x)-f_{\beta\;DC\;i}(x) \tag{1}\]  
The helix deviation curve obtained from the data of gear inspection for \(i^{th}\) tooth number is regarded as \(\widehat{f_{\beta i}}(x)\), and the DC component or average value of the curve for \(i^{th}\) tooth number is defined as \(f_{\beta\;DC\;i}(x)\). Additionally, \(x\) denotes the position of the evaluation length of helix deviation.
# Conversion of Stearic Acid into Bio-Gasoline over Pd/ZSM-5 Catalysts with Enhanced Accessibility
## 2 Experimental Procedure
### Analysis of the Reaction Products  
A Varian CP-4900 MicroGC (Varian, Palo Alto, CA, USA) equipped with two injection lines with columns Molsieve 5 A PLOT and ParaPLOT U/Q, respectively, and TCDs was employed for the analyses of the gaseous phase. Both channels allowed detecting and measuring H\({}_{2}\), CO, CO\({}_{2}\), and hydrocarbons from C\({}_{1}\) to C\({}_{4}\). The liquid products were measured in a Brucker SCIEN 456-GC furnished with a fused silica BR 5 column (0.25 mm internal diameter and 30 m length) and a flame ionization detector (FID). This Gas Cromatography (GC) enabled the separation of compounds in the C\({}_{4}\)-C\({}_{20}\) interval. Prior to the analyses, the samples were silylated using a method adapted from the EN-14105 standard. For each 0.86 g of sample, 30 \(\upmu\)L of pyridine, 90 \(\upmu\)L of N-methyl-N-(trimethylsilyl)-trifluoracetamide (MSTFA), and 10 \(\upmu\)L of 1,2,3,4-tetrahydronaphthalene as internal standard were mixed and strongly shaken. This mixture was held at ambient temperature for 15 min before analysis.
## 5 Discussion  
Inverse problems are remarkably challenging because of their ill-posed phenomenon. Although mesh-based numerical approaches combined with numerical techniques, such as the Tikhonov's regularization technique and the L-curve method, have been widely used to mitigate the ill-posed phenomenon of inverse problems, shortcomings in terms of stability and accuracy still exist. In this study, a newly developed MSMM that is capable of solving inverse heat conduction problems in two dimensions is presented. To the best of the authors' knowledge, the pioneering work using the proposed MSMM to solve the inverse heat conduction problems has not been reported in previous studies yet. The contributions and limitations of the proposed MSMM are discussed below.  
Figure 22: Comparison between the temperature fields of the proposed method and the generalized finite difference method. (**a**,**b**) the computed results without the noise and the exact solution and (**c**,**d**) the computed results with a noise level of \(s=3\) and the exact solution.  
This study first adopts the MSMM to solve the inverse heat conduction problem. The MSMM is a highly accurate numerical method because specific solutions are adopted. Non-singular basis functions are derived from the general solutions of the governing equation and provide a highly accurate numerical solution. The method is especially advantageous for solving inverse problems because it can mitigate the ill-posed phenomenon and obtain results with higher accuracy. Moreover, regularization techniques are not required for solving the inverse problems. The MSMM maintains the same superiorities of the CTM, such as the boundary discretization only, and is advantageous for solving inverse problems.  
However, limitations still exist. For example, the MSMM approximates the solution using many source points by the addition theorem, it can only be applied for linear governing equations. Five examples are carried out in this study. Result comparisons with previous published data are also conducted. The results from Examples 1-3 demonstrate that the MSMM may obtain better accuracy than the MFS. In addition, the results from Examples 4 and 5 depict that the MSMM is more accurate than the weighted RKCM and the GFDM. Finally, the results from all examples show that the proposed method may recover the unknown data with very high accuracy.
# CFAM: Estimating 3D Hand Poses from a Single RGB Image with Attention
## 4 Experiments
### Estimating 3D Hand Poses from a Single RGB Image
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Method** & **Wrist** & **Thumb** & **Index** & **Middle** & **Ring** & **Little** \\ \hline \multirow{2}{*}{RHD} & [PERSON] [16] & **0** & 24.57 & 28.58 & 25.5 & 24.46 & 25.33 \\  & [PERSON] & 0.13 & **18.46** & **20.01** & **18.1** & **16.83** & **18.11** \\ \hline \multirow{2}{*}{STB} & [PERSON] [16] & 0 & 9.41 & 10.06 & 9.62 & 8.61 & 8.51 \\  & [PERSON] & 0 & **9.11** & **9.04** & **10.04** & **8.97** & **8.75** \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Mean error of the 3D hand pose estimation from the GT-cropped RGB image (mm). The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Methods** & **Wrist** & **Thumb** & **Index** & **Middle** & **Ring** & **Little** \\ \hline \multirow{2}{*}{RHD} & [PERSON] [16] & **0** & 33.81 & 35.55 & 30.33 & 28.5 & 31.2 \\  & Ours & 0.14 & **24.2** & **25.7** & **22.35** & **20.65** & **22.48** \\ \hline \multirow{2}{*}{STB} & [PERSON] [16] & 0 & 8.45 & 8.83 & **8.16** & **7.19** & **7.17** \\  & Ours & 0 & **8.24** & **8.29** & 8.79 & 7.89 & 7.62 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Median error of the 3D hand pose estimation from the GT-cropped RGB image (mm). The best results are highlighted in bold. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Datasets** & **Methods** & **Wrist** & **Thumb** & **Index** & **Middle** & **Ring** & **Little** \\ \hline \multirow{2}{*}{RHD} & [PERSON] [16] & 0.97 & 0.47 & 0.41 & 0.46 & 0.48 & 0.45 \\  & [PERSON] & 0.97 & **0.58** & **0.54** & **0.59** & **0.61** & **0.58** \\ \hline \multirow{2}{*}{STB} & [PERSON] [16] & 0.97 & 0.81 & 0.8 & **0.81** & **0.83** & **0.83** \\  & [PERSON] & 0.97 & **0.82** & **0.82** & 0.8 & 0.82 & 0.82 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: AUC of the 3D hand pose estimation from the GT-cropped RGB image (0–50 mm). The best results are highlighted in bold. Figure 6: AUC of the 3D hand pose estimation on the RHD dataset from the GT-cropped RGB images (**left**) and the RGB images (**right**). Although our method performed better on most fingers, in some experiments, [PERSON]'s method obtained a better result for the thumb. Our method focuses more on the global optimum, while [PERSON]'s method pays more attention to the accuracy of single fingers. In general, our method worked best, but [PERSON]'s method performed better on the thumb.