esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Principles of the Inverse Velocity Method (INV)  
Under constant load conditions, the inverse deformation velocity (\(1/v\) or \(\Lambda\)) can be described by the following equation [2]:  
\[\frac{1}{V}=\left[A(\alpha-1)\right]^{\frac{1}{\alpha-1}}\left(t_{f}-t\right) ^{\frac{1}{\alpha-1}} \tag{1}\]  
where \(V\) represents deformation velocity, \(t\) denotes time, \(t_{f}\) corresponds to predicted failure time, and \(A\) and \(\alpha\) are material-specific constants. Following [PERSON]'s [16; 17] empirical validation, the exponent \(\alpha\) typically assumes a value of 2 for geological materials, which yields the simplified formulation:  
\[\frac{1}{V}=A\left(t_{f}-t\right) \tag{2}\]  
Equation (2) serves as the foundational predictive formula of the inverse velocity method for landslide time prediction, which is used in this study. The operational workflow is summarized in Figure 1.
## 4 Model Description  
### Medium-Voltage Distribution Test Networks  
The first analyzed network is an MV semi-urban distribution test network. The scheme of the studied part of this semi-urban system is presented in Figure 3. The model assumes that the supply points, the normally open circuit breaker (CB), and all transformers (TRs) in the 10 kV/0.4 kV transformer stations (TSs) are ideally reliable. The entire network consists of underground cable lines, with each section having an equal length of 0.3 km. The dashed lines indicate power lines that do not exist in the baseline scenario. A disconnector is positioned at both ends of every section. In total, the network includes 40 TSs. Figure 3 highlights the nodes with different consumption types: nodes with 100% commercial load,  
Figure 2: The algorithm for selecting the optimal investment options.  
mixed nodes with both commercial and residential consumption, and industrial nodes. All other nodes in the network are classified as residential.  
The second analyzed network is an MV rural distribution test network. Consumer nodes, representing 10 kV/0.4 kV TSs, are marked with arrows. The network consists of 29 consumer nodes in total. All lines are overhead and numbered from 1 to 49, with their lengths provided in Figure 4. The dashed lines indicate power lines and automatic reclosers that do not exist in the baseline scenario. Figure 4 highlights the nodes with different consumption types, specifically marking nodes with 100% commercial consumption and mixed nodes with both commercial and residential consumption. All other nodes in the network are classified as residential.  
An overview of the consumer distribution in both the MV semi-urban and rural test networks, including consumption type, node locations, total load supplied by each TS 10 kV/0.4 kV, and the number of connected consumers, is presented in Tables 2 and 3, respectively.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Consumption Type** & **Node(s)** & **Each Node Load** &
\begin{tabular}{c} **Number of** \\ **Consumers** \\ \end{tabular} \\ \hline Industrial consumption & 25 & 540 kW, \(\cos\varphi\) = 0.9 & 1 \\ \hline
100\% commercial consumption & 23, 30, 40 & 500 kW & 2, 9, 5 \\ \hline Mixed commercial and residential consumption & 2, 6, 38 & \
\begin{tabular}{c} commercial: 204.3 kW, \\ residential: 136.2 kW \\ \end{tabular} &
\begin{tabular}{c} commercial: 3, \\ residential: 60 \\ \end{tabular} \\ \hline
100\% residential consumption & all other load nodes & 227 kW & 100 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Consumer data in the semi-urban test network.  
Figure 3: MV semi-urban test network configuration.
# NR5G-SAM: A SLAM Framework for Field Robot Applications Based on 5G New Radio
## 3 Methodology
### Front-End Block (FEB)
#### 3.3.1 Prior Factor  
A prior factor is required in Factor Graph SLAM to initialize the starting pose of the robot and to keep track of its states during the mapping process. It is a constraint that represents the known information about the robot's pose or the landmark locations in the map.  
The prior factor defines the initial values of the NR5G pose, RSSI pose, and IMU variables in the factor graph and constrains the subsequent observations based on these values. It contains the prior belief about the robot's initial pose and/or the known locations of certain landmarks in the environment. It plays a crucial role as without the prior factor, the optimization process would not be properly initialized and the solution could converge to a wrong estimate.
## 4 Discussion
### Future Directions  
Future studies should focus on standardizing cueing protocols to optimize their efficacy. Longitudinal studies with larger sample sizes are needed to evaluate the long-term benefits of cueing on gait, balance, and quality of life. Additionally, exploring the synergistic effects of cueing combined with balance-specific exercises, cognitive-motor training, or technology-based solutions (e.g., virtual reality) may yield more comprehensive improvements for PD patients.  
The absence of long-term follow-up data in most included studies represents a critical gap. While short-term benefits of cueing interventions on gait and dynamic balance are evident, the sustainability of these effects remains uncertain. Future research should prioritize longitudinal trials to evaluate the long-term impact of cueing on functional outcomes and fall risk in Parkinson's disease. Additionally, understanding the role of continuous and sustained cueing programs in rehabilitation settings will be essential for integrating these interventions into routine clinical practice.
## 4 Experiments and Results
### Time Efficiency of Applied Sensitivity Analysis  
In terms of the time efficiency of the application of EventTracker SA in this paper, according to Equation (3), using a 100 trigger data series (i.e., \(N_{ Trigger}=100\)), 6 event data series (i.e., \(N_{Event}=6\)), and a search slot period of 100 s (i.e., \(SS=100\) s), the maximum period allowed for generating 1 series of Sensitivity Indices (i.e., \(T_{Score_{i}}\)) was 160 ms. Generation of SIs took fractions of milliseconds that left plenty of time before the next bunch of data samples filled one search slot.
## 3 Methodology and Experiments
### Study 2: EPI Sequence  
In the second study, a standard axial, multi-slice GE EPI sequence (repetition time (TR) = 2 s, echo time (TE) = 40 ms, 84 \(\times\) 84 matrix, 3 \(\times\) 3 mm\({}^{2}\) in-plane resolution, 4 mm slice thickness, flip angle = 85\({}^{\circ}\), fold-over direction = AP, SENSE factor = 2, i.e., a two-fold reduction in the number of lines of k-space acquired) was executed while EEG data were recorded. Twenty equidistant slices were acquired within a 2 s TR period, with a resulting slice acquisition frequency of 10 Hz. This standard GE EPI sequence is popular among researchers for characterizing the GA in combined EEG-fMRI data acquisition. Seventy volumes of EEG data were recorded on the phantoms, and 150 volumes were acquired on the human subjects. In this study, the EEG data were recorded with a typical bandwidth  
Figure 6: Representation of the three gradient pulses which were applied along the three orthogonal directions (right–left, RL, anterior–posterior, AP, and foot–head, FH) (**A**) and the corresponding induced artefact voltages (in \(\upmu\)V) in two example leads (Fp1 and F3) for the head-shaped phantom (**B**).  
(0.016-250 Hz) conventionally used in combined EEG-fMRI studies to keep the EEG signal amplitude in the dynamic range of the EEG amplifier.  
EEG data were recorded using the spherical and head-shaped phantoms for six trials in a single recording session and six human subjects in different sessions. Table 1 shows the main characteristics of the human test subjects. The subjects were asked to stay as stable as possible to avoid any motion artefacts in the EEG data. Subjects' EEG data were visually inspected during the EEG recording, and any particular dataset was discarded and re-recorded if any noticeable motion artefacts were observed. In each session, the subjects were placed in the same axial position (+4 cm axial [8]) inside the MRI scanner to avoid any variation due to subject positioning. However, there was some obvious variation due to the head shape and orientation. In the case of the phantoms, in each trial, the phantom was taken out of the scanner and then replaced inside it to mimic the possible variation experienced with the subjects in different recording sessions.
## Results and Discussion
### Study of the Interferences  
In order to evaluate the specificity of the suggested approach, the sensor response in the presence of several potentially interfering species was investigated. In particular, the response to Pb(II) was evaluated in solutions containing 10 umol/L of the following cations: Fe\({}^{2+}\), Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), Zn\({}^{2+}\), Al\({}^{3+}\), Mn\({}^{2+}\), Cu\({}^{2+}\), and Cd\({}^{2+}\). No additional signals were recorded when Fe\({}^{2+}\), Na\({}^{+}\), K\({}^{+}\), Ca\({}^{2+}\), Mg\({}^{2+}\), Zn\({}^{2+}\), Al\({}^{3+}\), Mn\({}^{2+}\), or Cu\({}^{2+}\) ions were present in solution during the determination of Pb at different concentrations (Figure 5). Conversely, a well-defined peak signal for Cd\({}^{2+}\) was observed at \(-\)0.8 V. However, no changes in the Pb current peak were observed for the GrRAC-70% composite sensor in the presence of Cd\({}^{2+}\) ions in solution (Figure 5). Therefore, the GrRAC-70% sensor can be used to detect Pb(II) even in the presence of other metals.
## 6 Conclusions  
This article conducted adhesion experiments of carbon particles in oil on the surface of epoxy resin under direct current voltage and studied the effects of factors such as electric field strength, particle size, and insulation oil flow velocity on particle adhesion. The adhesion experimental data of carbon particles under different experimental conditions were obtained through digital image processing technology. Combined with the micro particle force analysis method and JKR particle contact model, the adhesion characteristics of carbon particles in insulation oil on the surface of epoxy resin were analyzed. The analysis results were consistent with the experimental data, and the validity of the results was verified. The main conclusions are as follows: (1) The direct current electric field is the main factor driving the adhesion of carbon particles in oil to the surface of epoxy resin. As the electric field strength increases, the degree of carbon particle adhesion first increases and then decreases, indicating the existence of a peak adhesion field strength. Therefore, when designing the insulation structure of the valve-side bushing, it is necessary to focus on the area where the electric field strength is close to the peak adhesion field strength. (2) The smaller the particle size, the greater the overall adhesion degree of carbon particles; the peak adhesion field strength of carbon particles with different particle sizes is also different. Small particle sizes are more likely to adhere in high field strength areas, which may cause more serious harm to the insulation performance inside the valve-side bushing. (3) The flow velocity of transformer oil has a significant impact on the adhesion behavior of carbon particles. In the lower flow velocity range, as the oil flow velocity increases, the degree of carbon particle adhesion also increases. However, as the flow velocity further increases, the degree of particle adhesion will gradually decrease. Therefore, increasing the circulation speed of transformer oil appropriately will help reduce the degree of particle adhesion.  
Methodology, formal analysis, writing--original draft preparation, J.Z.; project administration, supervision, Z.Z.; writing--review and editing, conceptualization, Z.D.; investigation, Z.X.; resources, J.D.; validation, J.C. and F.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the National Natural Science Foundation of China, grant number 52077012. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Data are contained within the article. We thank the National Natural Science Foundation of China (Grant No. 52077012) for supporting this research.  
[PERSON] is employed in Maintenance and Test Centre, EHV, China Southern Power Grid and all authors declare no conflict of interest.
## 5 Experimental Results and Discussion  
### Basic Analysis of the Measured Deflections and Deformations  
The tested SLBs and TLBs were loaded, including the elastic-plastic stage. The cracks propagated almost up to the compressed zone border. Figure 4 presents the relationship between the force and mid-span deflection in both tested beams. As evident from the figure, the maximum load measured in the SLB was about 320 kN; for the TLB, it was more than 440 kN (i.e., 37.5% higher). The TLB mid-span deflections under the same loads for the elastic-plastic deformation range were about 37% lower compared with the SLB. This was due to the positive effect of using SFHSC in the TLB compressed zone relative to the SLB, which had no steel fibers in the compressed zone.  
The typical relationships between the load and longitudinal compression deformations in concrete measured in the middle of the investigated SLBs and TLBs are shown in Figure 5. The maximum compression deformations in the SLB were about 1.5%; in the TLB, they were about 2.4% at the maximum measured loads. In other words, the deformability of SFHSC in the compressed zone of the TLB was 60% higher compared with the ordinary concrete without fibers in the SLB.  
The typical relationships between the load and transverse tensile deformations measured in the middle of the investigated SLBs and TLBs are shown in Figure 6. The maximum  
Figure 4: Load–mid-span deflection (LVDT 2) diagram.  
Figure 5: Typical load–longitudinal deformation diagram.  
values of the tensile deformations in the SLB were about 0.55%; in the TLB, they were about 0.75% at the maximum measured loads. In other words, the transverse deformability of SFHSC in the TLB was 36% higher compared with the SLB.
## 3 Results and Discussion
### Taste Substances in TOT and OOT  
The water extract of tea (i.e., the total amount of substances soluble in hot water), which determines the thickness of tea soup and the quality and intensity of its taste, is considered to be one of the important indicators of tea quality [43; 44]. The main substances in the water extract, including polyphenols such as caffeine and catechins (EC, ECG, EGC, EGCG and C) and free amino acids, were significantly higher in TOT than in OOT (Figure 5, Supplemental Table S3). Total free amino acids, which endow sweet and umami flavors and are important precursors to aroma compounds [45], were significantly higher in TOT than OOT.  
Tea polyphenols are one of the main substances contained in tea, accounting for 20-35% of the dry weight of tea leaves, and catechins account for more than 70% of the total polyphenols [45]. The four substances EGCG, EGC, ECG, and EC account for about 90% of total catechins [28]. As one of the most important functional components of tea, catechins have strong antioxidant effects and can effectively reduce the risk of cardiovascular disease and cancer [2; 32]. The content of total polyphenols was significantly higher in TOT than in OOT. Most of the catechins were significantly higher in TOT, except EC, which was not significantly different between two tea samples. The only component that was higher in OOT than TOT was caffeine. Caffeine is one of the most bitter components of tea, being about twice as bitter as EGCG [3]. Taken together, the taste of TOT is expected to be better than the taste of OOT, due to the lower content of caffeine and the higher amount of free amino acids that likely balance the bitter effect of the polyphenols.  
Figure 4: PCA biplot of volatile substance of TOT and OOT. Each point represents a volatile substance: 1, hotrienol; 2, benzyl alcohol; 3, nerolidol; 4, \(\alpha\)-farnesene; 5, 1,3,5,7-cyclooctatetraene; 6, linalool; 7, \(\beta\)-ocimene; 8, \(\beta\)-myrrene; 9, indole; 10, linalool oxide III.
# Three-Dimensional Probabilistic Semi-Explicit Cracking Model for Concrete Structures
## 3 Estimation of the Coefficients of Variation of Fracture Energy
### Strategy to Choose the Best Value of \(\sigma_{L}(G_{IC})\) for Each Mesh Refinement  
The approach for selecting the optimal \(\sigma_{L}(G_{IC})\) value for each mesh refinement relies on two mechanical outcomes from the experimental study: (1) the load-notch opening  
Figure 5: Indication of the locations where prestressing forces and imposed displacement are applied-frontal view of Mesh 3.  
displacement (load-NOD) curve, and (2) the equivalent crack propagation length (ECL). To determine the appropriate \(\sigma_{L}(G_{IC})\) values, the strategy involves the following two steps:  
1. First step: Comparison between numerical and experimental load-NOD curve. During this step, the following criteria are considered: 1. The experimental curve must lie within the range of numerical curves. 2. Minimization of the variability within the bundle of the numerical curves as possible. This emphasis on minimizing variability is crucial since the load-NOD curve characterizes a structural behavior (global) rather than material behavior (local), which typically exhibits more variability. At the conclusion of this initial, two \(\sigma_{L}(G_{IC})\) values are selected for each mesh refinement. These values align most closely with the two aforementioned criteria and will proceed to the subsequent analysis in the second step. 2. Second step: Comparison between numerical and experimental macrocrack propagation length (in terms of ECL). Determining the macrocrack propagation length is the primary objective of the proposed model, thus serving as the final criterion for the optimal solution. In this comparison, it is essential to clarify some points: 1. The comparison is made concerning the last notch opening displacement (NOD) for which an equivalent crack length has been calculated. In the experimental work of [PERSON] [28], the last NOD is 1560 \(\upmu\)m, and the corresponding last ECL is 2.09 m. 2. Since the model defines the macrocrack as a series of fully damaged elements, the numerical macrocrack should generally be smaller than the experimental one (represented by the ECL) for identical notch openings. This size difference is expected to decrease with mesh refinement. 3. Numerically, the macrocrack tip is identified by the last fully damaged finite element. However, in experimental studies, surface cracks might appear larger than those within the specimen due to different stress situations. This discrepancy might lead to the equivalent crack being smaller than the visible surface crack. Additionally, numerical cracks could be larger than the equivalent crack for meshes comprising multiple elements in the specimen's thickness. This scenario is particularly observed in Mesh 3 within the scope of this study.
# Offloaded Computation for QoS Routing in Wireless Sensor Networks
## 2 Related Work  
Various solutions have been proposed across different levels of communication networks to effectively manage Quality of Service (QoS). For instance, Traffic Engineering (TE) offers tools for classifying flows, utilizing class-based or priority-based frameworks and policy-based scheduling to manage queues, as well as flow rate limitations to mitigate congestion (e.g., flow control in TCP and traffic shaping). Among the most recognized QoS-aware solutions are Integrated Services (IntServ) and Differentiated Services (DiffServ) [22].
# Comparison of L1 and L5 Bands GNSS Signals Acquisition
## 4 Application to GPS and Galileo Signals
### Comparison of Galileo E1 and E5 Signals Acquisition
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Context** & **Signal** & \(T_{\rm C}\) & \(N_{\rm FB}\) & \(N_{\rm CB}\) & \(N_{\rm CELL}\) & \(P_{\rm FA,C}\) & **SNR** \\ \hline \multirow{3}{*}{no} & E1 (4.096) & 100 ms & 1200 & 409,600 & 491,520,000 & 2.04 \(\times\) 10\({}^{-12}\) & 18.30 dB \\  & E1 (6.144) & 100 ms & 1200 & 614,400 & 737,280,000 & 1.36 \(\times\) 10\({}^{-12}\) & 18.35 dB \\  & E5 & 100 ms & 900 & 2,048,000 & 1,843,200,000 & 5.40 \(\times\) 10\({}^{-13}\) & 18.49 dB \\ \hline \multirow{3}{*}{assistance} & E1 (4.096) & 100 ms & 32 & 16,384 & 524,288 & 1.91 \(\times\) 10\({}^{-9}\) & 17.12 dB \\  & E1 (6.144) & 100 ms & 32 & 24,576 & 786,432 & 1.27 \(\times\) 10\({}^{-9}\) & 17.20 dB \\ \cline{1-1}  & E5 & 100 ms & 24 & 20,480 & 491,520 & 2.04 \(\times\) 10\({}^{-9}\) & 17.10 dB \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Search space and statistical parameters for \(P_{\rm FA,G}=10^{-3}\) and \(P_{\rm D}=0.9\) for different contexts with Galileo signals. The same values are considered for unlimited \(T_{\rm C}\). integration time, especially for weak signals, but that having assistance still helps to reduce the integration time in a non-negligible way. Then, Tables 19 and 20 and Figure 14 summarize the product of ratios for all the sensitivities, both taking into account the input memory and not consider this factor--comparing E1 with itself for different sampling frequencies, and comparing E5 with E1, respectively. Table 19 shows that using a sampling frequency of 6.144 MHz instead of 4.096 MHz for the E1 signal triples the complexity most of the time. Therefore, it can be concluded it is certainly more efficient to use a sampling frequency of 4.096 MHz and a technique that modifies the local code to get rid of the side peak [34; 35], rather than using a sampling frequency of 6.144 MHz. Then, Table 20 and Figure 14 show that when there is no assistance, the E5 acquisition is approximately 25 to 45 times more complex. This is due to the very long secondary code to synchronize with (100 chips), which dramatically impacts the complexity. When there is assistance, without considering the input memory, the E5 acquisition is still more complex, but by a lower ratio, around 6 for coherent only implementation and around 11 when non-coherent accumulation is used. One may note that if the ratio between E5 and E1 with a sampling frequency of 6.144 MHz was calculated (by simply diving the ratio of Table 20 by those of Table 19), the E5 could be only twice more complex than E1 for coherent only implementation. This is impressive, noting that the sampling frequency of the E5 is more than three times higher and the primary code is more than twice longer. Finally, still with assistance, if the input memory is considered, the complexity increases and increases with the sensitivity, because at this time the sampling frequency of 20.48 MHz of the E5 signal becomes a huge burden.