esa-sceva/satcom-chunk-collection huggingface dataset:
## Abbreviations  
\begin{tabular}{l l} Symbol & Description \\ \(gi\) & reflection attenuation \\ \(A(f,di)\) & line attenuation \\ \(N\) and \(e^{-j2\pi fri}\) & number of delays due to distance \\ \(W1\) & forward waves \\ \(W2\) & reflected waves \\ \(Zk\) & load impedance \\ \(Zo\) & characteristic impedance \\ \(e^{-\gamma l1}\) & attenuation of the line \\ \(kv\) & constant \\ \(\mu_{o}\) & constant \\ \(\mu_{1}\) & constant \\ \(c_{0}\) & speed of light \\ \(l_{i}\) & length of the route \\ \(\epsilon_{r}\) & relative permittivity of conductor \\ \(n\) & number of samples \\ \end{tabular}  
\begin{tabular}{l l} \(x\) and \(y\) & Means \\ \(Sx\) and \(Sy\) & Standard deviations \\ \(Tn\) & Normalized temperature \\ \(T\) & Actual temperature \\ \(T_{mean}\) & Mean temperature \\ \(H_{n}\) & Normalized humidity \\ \(H\) & Actual humidity \\ \(H_{mean}\) & Mean humidity \\ \(P_{n}\) & Normalized power consumption of consumers \\ \(P\) & Actual power \\ \(Pmean\) & Mean power \\ \(\alpha\), \(\beta\), and \(\gamma\) & Coefficients of regression model \\ \(t\alpha/2r\) & Confidence interval \\ \(\triangle T_{n}\) & Change in normalized temperature \\ \(P_{R}\) & Base power demands of residential \\ \(P_{C}\) & Base power demands of commercial \\ \(P_{I}\) & Base power demands of industrial consumers \\ \(\triangle T\) & Change in actual temperature. \\ \(T_{S}\) & Guard time \\ \(E_{S}\) & Guard energy \\ \(F_{c}\) & Carrier frequency \\ \(SNR\) & Signal to noise ratio \\ \(P_{S}\) & Power of the signal \\ \(P_{N}\) & The power of noise \\ \(A\) & Amplitude \\ \(\sigma\) & Standard deviation \\ \(e\) & Data rate \\ \(Teq\) & Average energy of transmitted bits \\ \(BER\) & Bit error rate \\ \(CAPEX\) & Capital expenditures \\ \(CCD\) & Charged-coupled device \\ \(DG\) & Distributed generation \\ \(DLC\) & Direct load control \\ \(DR\) & Demand response \\ \(DSM\) & Demand side management \\ \(EMI\) & Enhanced immunity \\ \(FEC\) & Forward error correction \\ \(G3-PLC\) & Genetic algorithm \\ \(GA\) & Genetic algorithm \\ \(GUI\) & Graphical user interface \\ \(LV\) & Low voltage \\ \(MCR\) & Master control room \\ \(MVPLC\) & Medium voltage power line communication \\ \(M-PSK\) & M-phase shift keying \\ \(MCR\) & Master Control Room \\ \(MV\) & Medium voltage \\ \(NB-PLC\) & Narrow band power line communications \\ \(OPEX\) & Operating expenses \\ \(OFDM\) & Orthogonal frequency division multiplexing \\ \(PLC\) & Power line communication \\ \(PAR\) & Peak to average ratio \\ \(QAM\) & Quadrature amplitude modulation \\ \(PV\) & Photovoltaic \\ \(RES\) & Renewable energy systems \\ \(SACS\) & Smart Air Conditioners \\ \(SFSK\) & Spaced Frequency Shift Keying \\ \end{tabular}
# Influence of Cracking on Oxygen Transport in UHPFRC Using Stainless Steel Sensors
## Materials and Methods  
### Mix Design  
The four concrete types studied in this work are: Standard Quality Concrete (C30/37, named SQC), High Performance Concrete (C70/85, named HPC), an Ultra High Performance Fiber Reinforced Concrete (C135, named UHPFRC) with steel fibers, and C125 modified to include alumina nanofibers and a self-healing promoter (named UHPFRC-n). UHPFRCs incorporated short straight-shaped steel fibers 13/0.2 (length 13 mm, diameter 0.2 mm, l/d/\({}_{t}\) = 13/0.2 = 65, tensile strength \(>\) 2000 MPa), to guarantee ductility and multiple micro-cracking. Table 1 displays the compositions of these four concretes. Superplasticizer A, Sika Viscocrete 5970, was used for SQC and HPC. Superplasticizer B, Sika Viscocrete 20 HE, was used for UHPFRC and UHPFRC-n.  
ANF Development provided the alumina nanofibers used in this work, and they have diameter of 4-11 nm, length of 100-900 nm, and surface area of 155 m\({}^{2}\)/g. Alumina nanofibers were added in a dosage of 0.25% by the binder weight. This material was added to verify its potential to control crack growth at early stages. A crystalline admixture from Penetron, as a self-healing promoter, was added. Crystalline admixtures are a type of permeability reducing admixture that are thought to enhance the  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Kg/m\({}^{3}\)** & **SQC** & **HPC** & **UHPFRC** & **UHPFRC-n** \\ \hline Cement 1 42.5 R-SR5 & 350 & 450 & 800 & 800 \\ Silica Fume & & 50 & 175 & 175 \\ Water & 192 & 160 & 160 & 165.0 \\ _n/c_ & _0.591_ & _0.356_ & _0.200_ & _0.206_ \\ _n/b_ & _0.591_ & _0.320_ & _0.164_ & _0.169_ \\ Aggregate 7/12 & 600 & 200 & & \\ Aggregate 4/7 & 300 & 600 & & \\ Natural sand & 950 & 950 & & \\ Limestone filler & 60 & & & \\ Siliceous sand–medium 0/1.6 mm & & & 565 & 565 \\ Siliceous sand–fine 0/0.5 mm & & & 302 & 302 \\ Siliceous sand–flour (\textless{50}\) \(\textless{50}\) \(\text{\SIUnitSymbolMicro m}\) & & & 225 & 225 \\ Short steel fibers 13/0.2 & & & 160 & 160 \\ Superplasticizer A & 0.7 & & & \\ Superplasticizer B & & 5.5 & 30 & 30 \\ Crystalline admixture & & & & 7.8 \\ Alumina nanofibers & & & & 2.438 \\ \hline Slump or [slump flow] (mm) & 180 & 190 & 700 & 620 \\ \hline Compressive strength at 28d (MPa) & 40.2 & 79.8 & 144.7 & 133.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Composition of the four concrete types studied and results of control tests.  
self-healing capability of concrete [18; 19; 20]. An additional 3% of water was needed in UHPFRC-n, due to the addition of alumina nanofibers and crystalline admixtures, to maintain the same workability of UHPFRC and ensure self-compacting properties.
# Adsorption of Cadmium and Methylene Blue Using Highly Porous Carbon from Hemp Seeds
## 4 Results and Discussion
### Adsorption Data
#### 4.2.2 Contact Time Effect  
The effectiveness of PHSs, CHS-500 and CHS-700 in adsorbing cadmium and methylene blue was assessed at time intervals between 5 and 720 min (Figure 4a,b). Figure 4a,b display a similar trend showing that the sorption capacities of all the adsorbents increased as contact time increased. The adsorption occurred in two stages. Stage 1 is where a fast adsorption rate occurs at the beginning of the adsorption process. This was attributed to the readily available adsorption sites, pores and functional groups on the surfaces of the adsorbents, which caused rapid adsorption; a similar result was observed by [PERSON] et al. (2022) [41]. Stage 2 involves a slower increment due to the saturation of the active sites as contact time advances. PHSs, CHS-500 and CHS-700 reached equilibrium after 100 min, and CHS-500 and CHS-700 showed higher sorption capacities than PHSs.
# User Mobility Modeling in Crowdsourcing Application to Prevent Inference Attacks
## 4 Proposed Approach
### Graph Generation  
Efficient graph generation is needed so to capture the spatial and temporal pathways between nodes, which correspond to POIs. To generate our mobility graph \(G\), we use the following pseudo-code in Algorithm 2 relying on the output of Algorithm 1 (which generates significant locations with metadata). The Algorithm 2 starts by initializing the empty sets \(\mathcal{V}\), \(\mathcal{E}\), and \(\mathcal{W}\) (line 5). We recall that the sets \(\mathcal{V}\) and \(\mathcal{E}\) represent the vertices and edges of the graph \(G\), respectively, while \(\mathcal{W}\) contains the weights associated with edges. The algorithm iterates through each location in \(\mathcal{P}\) (line 6) to create vertices (line 7), checking for and assigning any contextual metadata if present (lines 8-10) and adding the vertex to the set of vertices (line 11). For each pair of locations (s and t), the algorithm ensures they are not the same (line 14) and checks if a relation exists between them using the _RelationExists_ function (line 15). It classifies the timestamp and day using _ClassifyTime-byIndex_ (line 16) and _ClassifyDaybyIndex_ (line 17), respectively. The visit frequency, stay duration, and total duration are calculated using _VisitFrequencyGroupbyDayAndTime_ (line 18), _StayDurationGroupbyDayAndTime_ (line 19), and _DurationGroupbyDayAndTime_ (line 20). The data are then marked as processed using the _MarkAsProcessed_ function (line 21). If an edge between the locations does not already exist, a new edge is created with the calculated attributes and added to the graph (lines 23-30); otherwise, the existing edge is updated with the new attributes (lines 31-32). Finally, the algorithm returns the generated graph \(G\). Figure 4 illustrates a segment of a directed graph \(G\), representing the moves between three POIs with identifiers A, B, C, and D for the user [PERSON]. The nodes are linked by a directed edge, indicating the direction of travel. Each node is enriched with metadata, providing information about the POI it represents. In addition, the edge between the nodes contains a weighted attribute matrix that provides insight into the temporal and spatial aspects of the movement. In addition, we have contextual metadata such as 'opening hours', which are associated with nodes and edges. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Address** & **Timestamp** & **Category** & **Coordinates** \\ \hline Av. [PERSON], 75007 Paris & 2023-01-01 10:00:00 & historical, landmark & [48.858370, 2.294481, 48.858370, 2.294481] \\ Hotel Muguet, 11 Rue Chevert, 75007 Paris & 2023-01-01 23:56:00 & Accommodation & [48.8572669, 2.2261038, 48.8572669, 2.2261038] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Example output coordinates. Figure 4: Example of [PERSON]’s graph generation.
# A Survey of Visible-Light-Communication-Based Indoor Positioning Systems
## 5 Algorithms for VLC-Based IPS
### Trilateration and Triangulation Algorithms
#### 5.1.2 Triangulation Algorithms  
Similar to trilateration-based algorithms, triangulation also requires multiple visible light sources with known positions for 3D localization as shown in Figure 3(b). We can create the triangle for locating the mobile device by measuring the angles between the receiver and at least three light sources. The known positions of the light sources act as the vertices of the triangle, and the position of the receiver is determined by finding the intersection point of the lines or angles formed by the receiver and the light sources. The authors in [103] propose the indoor robot positioning algorithm based on triangulation using a multi-frequency approach. In [104], a triangulation-based method is proposed to estimate the receiver position using RSS with a minimum of three distance estimates.  
The triangulation algorithm is commonly used in VLC-based IPSs since it can provide accurate location estimation. However, it often requires precise angle measurements and knowledge of the light source positions, which may be challenging in real-world environments due to factors like noise, interference, and multipath effects.
# Characteristics of Coherent Optical Phonons in a Hexagonal \(\mathbf{YMnO_{3}}\) Thin Film
## 4 Results and Discussions
### Ultrafast Incoherent Responses of Excited Electron States  
We discuss the temperature dependence of incoherent responses of excited electron states. [PERSON] et al. studied the temperature dependence of a time-resolved transmittivity change in a YMnO\({}_{3}\) film [37]. The signals were fitted by a double exponential decay curve, where the faster and slower decay components were interpreted as electron-lattice thermalization and spin-lattice interaction, respectively. The time constant of the slower component show a peculiar behaviour at \(\sim\)120 K, which was considered to be due to a short-range spin ordering. [PERSON] et al. reported the temperature dependence of the \(\Delta R/R_{0}\) signal in a HoMnO\({}_{3}\) single crystal [25]. They fitted the \(\Delta R/R_{0}\) signal by using an equation that is similar to Equation (1). Most important finding is that the amplitude of the negative slow component shows a clear correlation to the position shift of Mn atoms as a function of temperature under the isostructural transition. Accordingly, ultrafast incoherent responses also offer a clue for deeper understanding of coupling phenomena. As shown in Figure 7, the fast and slow components have strong temperature dependences. In a lower temperature range, the signal has a V-shaped profile, while it gradually becomes L-shaped profile with an increase in temperature. The temperature at which the signal profile completely changes to L shape is \(\sim\)80 K. This behaviour seems like the emergence of the correlation with the antiferromagnetic ordering. However, at 70 K, the signal profile recovers from the L shape  
Figure 11: (**a**) Temperature dependence of the \(\Delta R/R_{0}\) signal in a LuMnO\({}_{3}\) film. (**b**) Image map of the temperature dependence of the FT amplitude spectrum of the oscillatory structure in (**a**) [24]. \(\copyright\) IOP Publishing and Deutsche Physikalische Gesellschaft. Reproduced by permission of IOP Publishing. CC BY-NC-SA.  
to V shape by decreasing the pump photon energy as shown in Figure 10a. It is noted that the profile of the 1.67 eV-pumping signal at 70 K is roughly consistent with that of the 1.72 eV-pumping signal at 10 K shown in Figure 5a. As described above, the peak energy of the absorption band is decreased by ~50 meV as increasing temperature from 10 to 70 K. These facts indicate that the signal profile is determined by the energy difference between the pump photon energy and absorption band peak. As described in Figure 2, the absorption band suggests multiple components. Therefore, the distribution of photogenerated electrons strongly depends on the pump photon energy around the absorption band peak. Thus, the strong temperature dependence in Figure 7 is dominated by the temperature-dependent shift of the absorption band. The systematic study on the pump photon energy relative to the absorption band peak is essential to the investigation of incoherent response dynamics.
## 3 The Proposed Scheme
### Data Embedding
#### 3.3.1. Speeding up the Modified Method  
In this subsection, we describe MGEMD features and then use these characteristics to speed up the embedding process. The MGEMD scheme groups the cover pixels into three categories for computation, _i.e._, \(D<2^{nk}\), \(D=2^{nk}\) and \(D>2^{nk}\). In order to speed up the embedding speed, we propose the embedding formulas shown as Tables 1 and 2 for \(D<2^{nk}\) and \(D>2^{nk}\), respectively. Now, we assume that \(s_{1}=5429\) and \(s_{2}=6643\) when \(k=3\), \(n=4\) and have both cover pixels of \((10,19,5,9)\) in Tables 1 and 2, respectively.
## 3 Results and Discussion
This study also revealed that the biophysical variables of the vegetation types obtained from Sentinel-2 are useful in the forest \(AGB\) calculation using local regression. However, this study assumed that the whole study area is composed of a single tree species, whereas in reality forests are composed of multiple tree species with geospatially varying amounts over undulating terrain conditions. In comparison with available datasets and existing techniques, however, the proposed method is highly useful in rapid forest \(AGB\) assessment. In addition, the availability of high quality optical multispectral remote sensing data on a global scale, free or at low cost from multiple sources, may help global society for forest \(AGB\) estimation. Finally, these results lead to conclude that the model obtained quickly produces the forest \(AGB\) from optical remote sensing images directly with high accuracy. The use of optical remote sensing data is rapid and simple compared to microwave remote sensing data, which need complex data processing for vegetation biophysical parameters extraction in \(AGB\) estimation. Further, the previous methods [9; 47] and microwave data in the forest AGB calculations depend on multiple factors, such as ecological, biotic, and topographic factors, including actual tree height, diameter, stem density, and shape of tree crown information.
# Metabolomics of _Pseudomonas aeruginosa_ Treated by Atmospheric-Pressure Cold Plasma
## 3 Results
### Detection and Analysis of Bacterial Metabolites
#### 3.2.2 Analysis of Differential Metabolites  
The different metabolites obtained by the above analysis are often biologically similar or complementary in results and functions, or are positively or negatively regulated by the same metabolic pathway, showing similar or opposite expression characteristics among different experimental groups. Hierarchical clustering analysis of these characteristics is helpful for us to to classify metabolites with the same characteristics and find the variation characteristics of metabolites between experimental groups.  
For each group comparison, we calculated the correlation coefficient of the quantitative value of the differential metabolites using [PERSON]'s method, which was presented in the form of a thermal map (Figure 7). In the figure, the color blocks at different positions represent the correlation coefficient between the metabolites at corresponding positions. Red represents a positive correlation, while blue represents a negative correlation. Non-significant correlations were marked with crosses.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Name of Metabololite**} & \multicolumn{2}{c}{**Kyoto Encyclopedia**} & \multirow{2}{*}{**1T**} & \multirow{2}{*}{**C**} & \multirow{2}{*}{**VIP**} & \multirow{2}{*}{**_p_} & \multirow{2}{*}{**Fold Change**} & \multirow{2}{*}{**Variation**} \\  & **of Genes and** & & & & & & **Trend** \\ \hline Monostearin & — & 0.01078 & 0.00780 & 1.87020 & 0.00942 & 1.38200 & up \\
1-Monopalmitin & — & 0.01756 & 0.01171 & 2.32257 & 0.00019 & 1.50025 & up \\ Melatonin & C01598 & 0.60762 & 0.13105 & 2.43275 & 0.00004 & 4.63664 & up \\ Thioctamide & C00579 & 0.70170 & 0.15122 & 2.42728 & 0.00005 & 4.64036 & up \\ Xanthotoxin & C01864 & 0.03646 & 0.00905 & 2.42773 & 0.00001 & 4.03036 & up \\ Androsterone & C00523 & 0.03348 & 0.00734 & 2.42492 & 0.00001 & 4.56092 & up \\ Putrescine & C00134 & 0.04506 & 0.02701 & 1.68454 & 0.04890 & 1.66810 & up \\ Leucine & C00123 & 0.07612 & 0.03049 & 1.32756 & 0.02636 & 2.49651 & up \\ Elaidic acid & C01712 & 0.00815 & 0.00348 & 2.33537 & 0.00113 & 2.34486 & up \\ D-(glycerol 1-phosphate) & C00093 & 0.00459 & 0.00268 & 2.06144 & 0.00654 & 1.71301 & up \\
1,4-Cyclohexanedione & C08063 & 0.00604 & 0.00767 & 1.83149 & 0.02327 & 0.78668 & down \\ Cellobiose & C06422 & 0.00153 & 0.00330 & 1.13819 & 0.00447 & 0.46277 & down \\ \hline \hline \end{tabular}
\end{table}
Table 3: Differential metabolites: 1T and C were the relative quantitative values of the substance in the treatment group and control group, respectively. The VIP is to compare this group of substances with the OPLS-DA model obtained by predicting significance of variables. The \(p\)-value is obtained by the \(t\)-test for the substance in the group comparison. \(p<0.05\).
###### Abstract
Similarly, gait training at an improper speed could cause a compensatory muscle activity pattern, which hampers neural restoration [14]. In the evaluation of neurophysiological muscle activity, electromyography (EMG) is frequently used to demonstrate the results of gait studies and investigate the effects of training parameters, such as BWS, treadmill speed, and guidance force, in RAGT studies [5]. However, to date, only two studies have examined the muscle activity patterns at different impedance levels, showing that the modular output of the synergistic muscle groups is invariant over impedance levels [15] and that reductions in impedance force do not increase muscle activity [16]. Recently, [PERSON] et al. (2014) [6] showed that the nature and magnitude of the differences between RAGT and treadmill walking depend on complex interactions between BWS and gait speed. Evaluating EMG biofeedback during RAGT is important when investigating muscle activity and inspecting its active participation in neuroplasticity. However, muscle activity studies utilizing EMG are time-consuming and limited by their in vitro settings, showing poor implementation in real-time RAGT sessions. EMG could be replaced with other biomechanical parameters with similar patterns, such as kinetic (torque) variables. The Walkbot gait training system offers real-time visual and haptic biofeedback for torque, kinematics, and active and resistive stiffness of the hip, knee, and ankle joint movements during gait rehabilitation. To supplement the other parameters, the biofeedback must be accurate and reliable. Nevertheless, most recent studies on force/torque estimation were conducted on the upper limbs [17; 18; 19]. Such protocols should be developed in consideration of locomotor control and motor learning and shed light on how training parameters alter gait training task demands and gait control. However, this research has its novelty in the fact that similar studies [5; 6; 9] have never been carried on a Walkbot RAGT exoskeletal device. Therefore, to gain an insight into how the nature and magnitude of gait speed and BWS affect muscle activity and how this muscle activity pattern correlates with hip and knee torques, Walkbot RAGT, with varied BWS and gait speed, should be studied in non-neurological adults. The aim of the present preliminary study was to determine the optimal RAGT training parameters (BWS and treadmill speed) for EMG muscle activity and the relationship between EMG muscle activity and hip and knee torques during RAGT in non-neurological adults. The hypothesis was that lower-limb EMG muscle activities including vastus lateralis oblique, biceps femoris, tibialis anterior, and gastrocnemius would be correlated with hip and knee torques during RAGT. Clinically, this preliminary information would serve the baseline normative data for clinicians when utilizing RAGT for locomotor recovery in individuals with neurological impairments or conditions.