esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
The rail industry is constantly facing challenges related to safety with regard to the detection of surface cracks and internal defects within rail tracks. Significant focus has been placed on developing sensor technologies that would facilitate the detection of flaws that compromise rail safety. In parallel, robot automation has demonstrated significant advancements in the integration of sensor technologies within end effectors. This study investigates the novel integration of an ultrasonic sensor within a robotic platform specifically for the application of detecting surface cracks and internal defects within rail tracks. The performance of the robotic sensor system was assessed on a rail track specimen containing sacrificial surface cracks and internal defects and then compared against a manual detection system. The investigation concludes that the robotic sensor system successfully identified internal defects in the web region of the rail track when utilising a 60\({}^{\circ}\) and 70\({}^{\circ}\) wedged probe, with a frequency range between 4 MHz and 5 MHz. However, the surface crack investigation proved that the transducer was insensitive to the detection of cracks, possibly due to the inadequate angle of the wedged probe. The overall outcome of the study highlights the potential that robotic sensor systems have in the detection of internal defects and characterises the limitations of surface crack identification to assist in enhancing rail safety.  
Keywords:flaw detection; rail inspection; robotic system; ultrasonic testing +
Footnote †: journal: Applied sciences
## 3 Sample Environments
### Humidity Control  
In a range of fields, the control of humidity is important. For biological materials that have to be studied in their natural state, this is an obvious requirement, but industrial processes and the filling of the pores of porous materials to influence the scattering contrast should also be mentioned.  
The simplest method is to place the samples in a closed environment with a reservoir containing a saturated salt solution. However, to change the relative humidity one has to use different salt solutions, which makes this method somewhat cumbersome for on-line experiments [125].  
An elaborate system using mass flow controllers with the possibility to mix two different vapors to control the relative humidity between 0 and 90% has been used to study semicrystalline polymers, porous materials, and polyelectrolyte membranes [126]. The advantage of having two independent streams of vapor that can be mixed is that it offers the possibility to vary the H2O/D2O ratio, which in the case of porous materials, allows one to find the matching point for contrast variations very rapidly. The same system can be used to create a vapor pressure of organic solvents.  
Developments in commercial humidity generators allow for the construction of rather simplified humidity-controlled cells, which were used in studies of forest products [127] where structural changes as function of moisture were observed. A commercial humidity generator was also used to study novel phases of lipid bilayers that are important for membrane fusion [128].  
Relative humidity control of membranes in fuel cells during SANS experimentation was reported. However, in order to gain insights into the performance of nafion membranes in fuel cells under normal operating conditions, a total immersion of the membranes in H2O/D2O, whilst the material was uniaxially deformed, was required. To avoid a complicated experimental set-up with multiple mechanical feedthroughs to the liquid reservoir, U-shaped load beams were used, allowing for the material of interest to be immersed in a temperature-controlled D2O reservoir [129]. See Figure 14.
## 5 Conclusions and Discussion  
Based on the first phase of the Pinglu Canal Youth Hub Project, through field blasting tests, it was found that there are certain errors in predicting the peak vibration velocity of rock slopes with weak interlayers using the traditional Sadowsky formula and the height difference correction formula. By employing dimensional analysis, a blasting vibration prediction model that takes the influence of weak interlayers into account has been derived, along with a modified formula that includes the inclination factor of weak interlayers. Comparative analysis shows that the new prediction model improves the fitting decision coefficient in the X direction from 0.848 to 0.9928, in the Y direction from 0.875 to 0.9862,  
Figure 8: (**A**) Result of Mean Absolute Error; (**B**) Result of Root Mean Square Error.  
and in the \(Z\) direction from 0.847 to 0.9946. Moreover, the new prediction model reduces the root mean square error in the vertical \(Z\) direction by 96% compared to the traditional model, demonstrating a significant advantage.  
Although the model has been effectively validated through this case study, further research and exploration are needed to assess applicability and generalizability in other regions. The next step is numerical simulation, which is remarkable for its convenience and economy. It can be used for parametric analyses of various influencing factors, such as the thickness and number of weak interlayers and the physical properties of the rock. This can effectively compensate for the limitations of field tests. The resulting dynamic response data will further improve the universality of the modified prediction modeling method presented in this paper.  
Methodology, S.C.; Formal analysis, J.P.; Investigation, [PERSON] and L.H.; Data curation, J.G.; Writing--original draft, J.Z.; Writing--review & editing, [PERSON]; Supervision, N.J. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Natural Science Foundation of Hubei Province grant number No. 2024 AFA092, the Key Research and Development Program of Hubei Province grant number No. 2021 BAD004, as well as by the National Natural Science Foundation of China grant number No. 42102329, and No. 52478525.  
Not applicable.  
The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding author.  
Authors [PERSON] and [PERSON] were employed by the company CCCC Fourth Harbor Engineering Institute Co., Ltd. Author [PERSON] was employed by the company Guangxi Pinglu Canal Construction Co., Ltd. Author [PERSON] was employed by the company CCCC Fourth Harbor Engineering Bureau Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.  
The following abbreviation is used in this manuscript:
# Overview of Compressed Sensing: Sensing Model, Reconstruction Algorithm, and Its Applications
## 4 Compressed-Sensing Applications
CS provides a new perspective for channel estimation by using channel sparsity. [PERSON] et al. proposed a novel spectrum-sensing algorithm based on STP-CS to judge the state of channel occupancy in wireless networks [82], which is a generalization of a traditional spectrum-sensing algorithm. They took advantage of the sparsity of channel energy in wireless networks, and only needed to reconstruct the energy vector of the occupied channels instead of recovering the entire spectral signals. [PERSON] et al. addressed the sparse channel-estimation problem in multiple-input-multiple-output orthogonal frequency-division multiplexing systems with the help of distributed CS [83]. There is a spatiotemporal union channel-estimation algorithm based on structured compressive sensing to reduce the required pilot overhead. This method utilizes common sparse spatiotemporal characteristics of delay-domain MIMO channels [84]. With the help of compressed sensing, [PERSON] et al. proposed a method to reduce the memory footprint of a Monte Carlo simulation where the scalar flux over the entire problem is desired [85]. They prescribed to randomly select the Monte Carlo particle tallies that were not contiguous in space, and used a small number of these tallies for partial reconstruction on the basis of minimizing the reconstructed total variation norm. Results for a TRIGA reactor simulation indicated that their method could give accurate flux maps for thermal and fast fluxes by using about 10% the total number of tallies. In addition, some CS applications include analog-to-information conversion [86], channel coding [87], radar reconstruction [88], and radar remote sensing [89; 90]. There are still many CS application fields waiting to be explored.
# From Discourse Relations to Network Edges: A Network Theory Approach to Discourse Analysis
## Appendix A  
### Basic Statistics for a Number of Real-World Networks  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Network** & _Nsize_ & _Nedge_ & **C** & **r** \\ \hline Film actors & 449,913 & 25,516,482 & 0.78 & 0.208 \\ Company Directors & 7673 & 55,392 & 0.88 & 0.276 \\ Math coauthorship & 253,339 & 496,489 & 0.34 & 0.120 \\ Physics coauthorship & 52,909 & 245,300 & 0.56 & 0.363 \\ Biology coauthorship & 1,520,251 & 11,803,064 & 0.60 & 0.127 \\ Email address books & 16,881 & 57,029 & 0.13 & 0.092 \\ Student dating & 573 & 477 & 0.001 & \(-\)0.029 \\ \hline WWW nd.edu & 269,504 & 1,497,135 & 0.29 & \(-\)0.067 \\ [PERSON] thesaurus & 1022 & 5103 & 0.15 & 0.157 \\ \hline Internet & 10,697 & 31,992 & 0.039 & \(-\)0.189 \\ Power grid & 4941 & 6594 & 0.080 & \(-\)0.003 \\ Train routes & 587 & 19,603 & 0.69 & \(-\)0.033 \\ Software packages & 1439 & 1723 & 0.082 & \(-\)0.016 \\ Software classes & 1376 & 213 & 0.012 & \(-\)0.119 \\ Electronic circuits & 24,097 & 53,248 & 0.030 & \(-\)0.154 \\ Peer-to-Peer network & 880 & 1269 & 0.011 & \(-\)0.366 \\ \hline Metabolic network & 765 & 3686 & 0.67 & \(-\)0.240 \\ Protein interactions & 2115 & 2240 & 0.071 & \(-\)0.156 \\ Marine food web & 134 & 598 & 0.23 & \(-\)0.263 \\ Freshwater food web & 92 & 997 & 0.087 & \(-\)0.326 \\ Neural network & 307 & 2359 & 0.28 & \(-\)0.226 \\ \hline \end{tabular}
\end{table}
Table 1: Network motifs and antimotifs of the two corpora, C58 and STAC, for three-node subgraph patterns.  
Network theory allows for the comparison of several different complex systems that would otherwise be considered completely isolated and beyond comparison. A recent and rather interesting case of the application of network theory in a novel class of social networks, i.e., the follower-following graph, can be found in [79].
## 3 Analysis of Literature Resources
### Metaheuristic Techniques
#### 3.3.2 Population-Based Metaheuristics
Ants initially explore randomly, but over time, they converge on shorter, more efficient paths as stronger pheromone trails reinforce better solutions [29; 62]. Among the Swarm Intelligence methods, ACO is the most effective and efficient solution for high-complexity combinatorial optimization problems [67; 71]. ACO is especially well-suited for high-complexity scheduling problems, which resemble routing problems where tasks must be optimized based on a defined \"distance\" metric, such as penalty costs \(c_{ij}\)[38; 67]. Solutions are built incrementally in chronological order, allowing the algorithm to preserve high-quality partial sequences during its search, rather than relying on fixed sequences. In a scheduling context, each ant represents a potential solution, and the paths they take correspond to different scheduling strategies. These are evaluated using a fitness function, typically the makespan, where shorter makespans receive more pheromone reinforcement, increasing their selection likelihood in future iterations [51; 62]. Particle Swarm Optimization (PSO) is a population-based algorithm designed for solving continuous optimization problems. Inspired by the collective behavior of bird flocks and fish schools, PSO models particles explore a solution space by combining cognitive learning (based on individual experience) and social learning (influenced by neighbors) [63; 72; 73]. This dual-learning approach enables PSO to converge efficiently toward high-quality solutions. Indeed, it seems to be more effective than single-state metaheuristics like GAs [63]. PSO is widely applied in Flexible Job Shop Scheduling Problems (FJSSPs), especially in multi-objective settings. Particles adjust their positions and velocities based on both their own best-known solutions (local optima) and the global best-known solution within the swarm. Velocity bounds are set to keep particles within the solution space, and the process continues until a stopping criterion--like iteration limit or stagnation--is reached [73]. While PSO's strength lies in its hybrid search strategy, combining exploration and exploitation, it can suffer from premature convergence and a loss of diversity. To counter this, duplicate particles are detected and mutated using a roulette wheel selection method, promoting diversity [73]. Although PSO is considered one of the most effective Swarm Intelligence techniques, it faces challenges such as slow convergence, high computational cost, and limited global-local search balance. To improve performance, hybrid variants--such as Hybrid PSO--have been proposed, combining PSO with other metaheuristics for better optimization [63; 72]. Therefore, hybrid techniques will be discussed in the next section.
# Evaluation of Emotions from Brain Signals on 3D VAD Space via Artificial Intelligence Techniques
###### Abstract  
Recent achievements have made emotion studies a rising field contributing to many areas, such as health technologies, brain-computer interfaces, psychology, etc. Emotional states can be evaluated in valence, arousal, and dominance (VAD) domains. Most of the work uses only VA due to the easiness of differentiation; however, very few studies use VAD like this study. Similarly, segment comparisons of emotion analysis with handcrafted features also use VA space. At this point, we primarily focused on VAD space to evaluate emotions and segmentations. The DEAP dataset is used in this study. A comprehensive analytical approach is implemented with two sub-studies: first, segmentation (Segments I-VIII), and second, binary cross-comparisons and evaluations of eight emotional states, in addition to comparisons of selected segments (III, IV, and V), class separation levels (5, 4-6, and 3-7), and unbalanced and balanced data with SMOTE. In both sub-studies, Wavelet Transform is applied to electroencephalography signals to separate the brain waves into their bands (\(\alpha\), \(\beta\), \(\gamma\), and \(\theta\) bands), twenty-four attributes are extracted, and Sequential Minimum Optimization, K-Nearest Neighbors, Fuzzy Unordered Rule Induction Algorithm, Random Forest, Optimized Forest, Bagging, Random Committee, and Random Subspace are used for classification. In our study, we have obtained high accuracy results, which can be seen in the figures in the second part. The best accuracy result in this study for unbalanced data is obtained for Low Arousal-Low Valence-High Dominance and High Arousal-High Valence-Low Dominance emotion comparisons (Segment III and 4.5-5.5 class separation), and an accuracy rate of 98.94% is obtained with the IBK classifier. Data-balanced results mostly seem to outperform unbalanced results.  
emotion evaluation; VAD space; artificial intelligence; EEG; SMOTE
# Textile Flexible Job-Shop Scheduling Based on a Modified Ant Colony Optimization Algorithm
## 2 Textile Flexible Job-Shop Scheduling Problem
### Model Building  
Each machine only corresponds to one production operation in the T-FJSP, which is different from other FJSPs, where each machine can correspond to one or more operations. Based on the symbols provided above, the mathematical model of the T-FJSP is as follows [25]: \begin{table}
\begin{tabular}{c l l l} \hline \hline
**Symbol** & **Meaning** & **Symbol** & **Meaning** \\ \hline \(n\) & Total textile products (jobs) & \(c_{jh}\) & The operating completion time of \\  & & & operation \(h\) of job \(j\) \\ \(m\) & Total machines & \(c_{jh}(i)\) & The operating completion time of \\  & & & operation \(h\) of job \(j\) on machine \(i\) \\ \(i,e\) & Machine index, \(i,e=1,2,3,\ldots,m\) & \(m_{jh}\) & The number of machines that can be selected \\  & & & for operation \(h\) of job \(j\) \\ \(j,k\) & Job index, \(j,k=1,2,3,\ldots,n\) & \(L\) & A large positive number \\ \(h_{j}\) & The total operations of job \(j\) & \(C_{j}\) & The completion time of each textile product (job) \\ \(h,l\) & Operation index, \(h,l=1,2,3,\ldots,h_{j}\) & \(C_{max}\) & The makespan of the textile workshop \\ \(O_{jh}\) & The operation \(h\) of job \(j\) & \(T_{m}^{i}\) & The total machine operating time of machine \(i\) \\ \(M_{ijh}\) & The operation \(h\) of job \(j\) & & \\  & processed on machine \(i\) & \(T_{p}\) & The total production time of textile product (job) \\  & & The operating time of & \\ \(p_{ijh}\) & operation \(h\) of job \(j\) & \(T\) & The total completion time of the workshop \\  & & processed on machine i & \\ \(s_{jh}\) & The operating start time of & & \\ \(s_{jh}\) & The operating \(h\) of job \(j\) & \(T^{*}\) & The optimal total completion time of the workshop \\ \hline \hline \end{tabular}
\end{table} 
Table 1: T-FJSP symbols and their meanings. Select function \(\delta_{ijh}\), which represents the selection relationship between operations and jobs: \[\delta_{ijh}=\left\{ \begin{array}{ll}1&\textit{select }M_{ijh}\\ 0&\textit{otherwise}\end{array} \right.\] (1) Indicator function \(x_{ih}(j)_{il}(k)\) represents the priority of work on the same machine: \[x_{ih}(j)_{il}(k)=\left\{ \begin{array}{ll}1&\textit{select }M_{ijh}\textit{ be fore }M_{ikl}\\ 0&\textit{otherwise}\end{array} \right.\] (2) When operation h of job j selects machine i: \[s_{jh}+\delta_{ijh}\cdot p_{ijh}=c_{jh}\] (3) The completion time of the previous operation must be smaller than the start time of the next operation: \[c_{jh}\leq s_{j(h+1)}\] (4) The completion time of each workpiece cannot exceed the total completion time: \[c_{jh_{j}}\leq C_{\max}\] (5) When \(M_{ijh}\) precedes \(M_{ikl}\), select the start s\({}_{kl}\): if the current \(c_{jh}(i)\) is greater than the current \(c_{k(l-1)}(e)\), the \(s_{kl}\) on machine \(i\) is the current \(c_{jh}(i)\); otherwise, the \(s_{kl}\) on machine i is the current \(c_{k(l-1)}(e)\): \[\left\{ \begin{array}{ll}s_{kl}=s_{jh}+p_{ijh}&\textit{if }c_{jh}(i)>c_{k(l-1)}(e)\\ s_{kl}=c_{k(l-1)}(e)&\textit{if }c_{jh}(i)\leq c_{k(l-1)}(e)\end{array}
# Predicting the Evolution of Syntenies--An Algorithmic Review
## 5 Accounting for Gene Trees  
The aforementioned methods for inferring the evolution of a set of syntenies only consider syntenies' contents and gene arrangements, while ignoring the evolution of each gene family through nucleotide and amino acid substitutions and indels affecting their sequences. A plethora of methods exist for reconstructing gene trees from sequence divergence. Classical methods use a distance, maximum likelihood or Bayesian approach for inferring the gene tree best representing a sequence alignment (e.g., PhyML [76], RAxML [77], MrBayes [78]), while others use a species tree, in addition to a multiple sequence alignment, to model gene gains and losses inferred from the reconciliation between gene and species trees (e.g., TreeBeST [79], PhyIDog [80], ALE [81]). Several gene tree databases from whole genomes are available, including Ensembl Compara [82], PhylomeDB [83] or Panther [84].  
In the following, we review the computational approaches that have been considered to integrate gene trees, in addition to gene order and/or gene gain and loss, in a unifying framework for synteny evolution.
# Opportunistic In-Flight INS Alignment Using LEO Satellites and a Rotatory IMU Platform
## 4 Iridium-Next and Orbcomm Downlink Signal Specification  
Usually, LEO satellites use quadrature phase shift keying (QPSK) or code-division multiple access (CDMA) downlink signals, which can be of two main types: continuous (such as Orbcomm and Globalstar) and burst-based (such as Iridium, etc.). In this study, we concentrated on two outstanding constellations, namely, Orbcomm and Iridium-NEXT. The orbital data of each satellite in all three constellations were updated every day in a TLE file, which is freely downloadable from the North American Aerospace Defense Command (NORAD) website. The data are represented with one of the simplified perturbations models such as SGP, SGP4, and SGP8.  
All the Orbcomm satellites propagate a continuous packed data in 1 MHz of the VHF bandwidth frequency between 137 to 138 MHz. The bit rate of the transmitted downlinks is 4800 bps. Technically, a major frame of Orbcomm downlinks includes 16 minor frames. Each 600 words is defined as a minor frame. These satellites transmit on 15 different center frequencies separated by a 25 kHz protected band among them. Although not all of the Orbcomm satellites are functional, there are eleven channels that definitely transmit downlink signals. Every single Orbcomm satellite vehicle transmits on two downlink frequencies in the mentioned VHF band [39]. On the other side, the Iridium-Next constellation works on the L-band frequency range. The frequency of their transmitted signals is designed on 1616 to 1626.5 MHz for both duplex and simplex channels. These downlinks are being propagated in 30 duplex sub-bands between 1616 and 1626 MHz. Additionally, there are 12 simplex channels between 1626 and 1626.5 MHz, with 90 ms time division multiple-access (TDMA) frames. Both simplex and duplex signals are based on burst messages; however, the simplex bursts are more suitable for navigation applications. The reason is the fact that they are always being transmitted from Iridium satellites. The center frequency of the Iridium-NEXT ring alert is 1626.27833 MHz and those of messaging channels are 1626.437500 MHz, 1626.395833 MHz, 1626.145833 MHz, and 1626.104167 MHz, for primary, secondary, tertiary, and quaternary messaging channels, respectively [40].