esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
Part one of the validation process involved adding sampled DBS noise to VEMP responses from a healthy subject known to have robust VEMP responses, then running the post-filter analysis. See Figure 3, which shows raw original AC and BCV cVEMP and oVEMP data traces of a healthy subject superimposed with the raw trace and added sampled DBS noise then low-pass filtered and averaged. The trace lines match closely for cVEMPs, though the smaller-amplitude oVEMPs are slightly less well matched, though the presence of VEMPs is evident as vestibular peaks remain visible. Part two of the validation of the filtering process involved recording the VEMP response in the same participant (P5) with their DBS device switched ON and OFF. cVEMP and oVEMP recordings are provided in Figures 4 and 5, respectively. Averaged VEMP traces with the DBS switched ON and filtered are superimposed with VEMP traces with the DBS OFF. To further validate the reproducibility of VEMP traces, first- and second-half recordings were double traced. See Supplementary Material Figures S1 and S2. Table S1 shows VEMP reflex latencies and peak-to-peak amplitudes and differences between DBS ON and filtered versus DBS OFF. All VEMP responses recorded as present post-filtering with DBS ON were also recorded as present with DBS switched OFF. All reflex latency response differences between ON and OFF were under 1 ms. Peak-to-peak amplitude difference between DBS filtered ON and OFF varies between 17.34 \(\upmu\)V and 57.88 \(\upmu\)V for cVEMP and between 2.81 \(\upmu\)V to 0.2 \(\upmu\)V for oVEMP. In this participant, P5, no significant tremor re-emerged during the short period of the VEMP testing protocol (10 min) when the DBS device was switched OFF. Figure 2: Graphs showing left and right ears’ oVEMP responses to ACS and BCV recorded from one participant (P2) with DBS ON. Raw data (unfiltered) are shown on the top row and filtered data are shown on the bottom row. Note the scale difference between the unfiltered and filtered graphs. Vertical axis: amplitude in \(\upmu\)V; horizontal axis: latency in ms. ACS—air-conducted stimulus, BCV—bone-conducted vibration, DBS—deep brain stimulation, oVEMP—ocular vestibular evoked myogenic potential. Figure 4: Graphs showing left (**left**-hand-side panel) and right (**right**-hand-side panel) ear cVEMP responses to ACS (**top** row) and BCV (**bottom** row) recorded from one participant (P5) with DBS ON after the signal was filtered (orange curve) and DBS was OFF (blue curve). Coloured arrows indicate first positive and negative vestibular reflex peaks. ACS—air-conducted stimulus, BCV—bone-conducted vibration, DBS—deep brain stimulation, cVEMP—cerical vestibular evoked myogenic potential. Figure 3: Graph showing cVEMP and oVEMP responses to BCV hammer and ACS clicks recorded from one healthy participant. Original data are shown by the grey curve and data after DBS noise combination and filtering are shown by the blue curve. Note the similarity between the two curves particularly for the cVEMPs. cVEMP—cerical vestibular evoked myogenic potential; BCV—bone-conducted vibration; DBS—deep brain stimulation.
# Solar Potential in Saudi Arabia for Flat-Plate Surfaces of Varying Tilt Tracking the Sun
## 2 Materials and Methods  
### Data Collection  
Hourly values of H\({}_{\text{b,0}}\) (direct horizontal solar irradiance in Wm\({}^{-2}\)) and H\({}_{\text{d,0}}\) (diffuse horizontal solar irradiance in Wm\({}^{-2}\)) were downloaded from the PV--Geographical Information System (PV-GIS) tool [21] using the latest Surface Solar Radiation Data Set--Heliostat (SARAH) 2005-2016 database (12 years) [22; 23]. This platform provides solar radiation data through a user-friendly tool for any location in Europe, Africa, the Middle East including Saudi Arabia, Central and Southeast Asia, and most parts of the Americas. Nevertheless, the platform provides solar maps for Europe, Africa, Turkey, and Central Asia only. The methodologies used for estimation of solar radiation from satellites by the PV-GIS tool are described in various works [24; 25; 26]. For the purpose of the present work, a set of 82 sites was arbitrarily chosen in order to cover the whole territory of Saudi Arabia. Table 1 provides the names and geographical coordinates of the sites; Figure 1 shows their location in the map of the country. The selection of the sites was based on the \"inhabited\" criterion (i.e., urban areas, 57 out of 82), while the additional 25 sites out of the 82 are uninhabited and have no name (marked as \"unnamed\" in Table 1). For this reason, the distribution of the 82 sites is not uniform within Saudi Arabia. It must be mentioned that the downloaded hourly solar horizontal radiation values refer to those for an unobstructed horizon. \begin{table}
\begin{tabular}{c c c c} \hline \hline \# & **Site** & \(\varphi\) **(Degrees N)** & \(\lambda\) **(Degrees E)** \\ \hline
1 & Dammam & 26.42 & 50.09 \\
2 & Al Jubail & 26.96 & 49.57 \\
3 & Ras Tanura & 26.77 & 50.00 \\
4 & Abqaiq & 25.92 & 49.67 \\
5 & Al Hofuf & 25.38 & 49.59 \\
6 & Arar & 30.96 & 41.06 \\
7 & Sakaka & 29.88 & 40.10 \\
8 & Tabuk & 28.38 & 36.57 \\
9 & Al Jawf & 29.89 & 39.32 \\
10 & Rivadh & 24.71 & 46.68 \\
11 & Al Qassim & 26.21 & 43.48 \\
12 & Hafar Al Batin & 28.38 & 45.96 \\
13 & Buraydah & 26.36 & 43.98 \\
14 & Al Majma’ah & 25.88 & 45.37 \\
15 & Hail & 27.51 & 41.72 \\
16 & Jeddah & 21.49 & 39.19 \\
17 & Jazan & 16.89 & 42.57 \\
18 & Mecca & 21.39 & 39.86 \\
19 & Medina & 24.52 & 39.57 \\
20 & Taif & 21.28 & 40.42 \\
21 & Yanbu & 24.02 & 38.19 \\
22 & King Abdullah Economic City & 22.45 & 39.13 \\
23 & Najran & 17.57 & 44.23 \\
24 & Abha & 18.25 & 42.51 \\
25 & Bisha & 19.98 & 42.59 \\
26 & Al Sahmah & 20.10 & 54.94 \\
27 & Thabhloten & 19.83 & 53.90 \\
28 & Ardah & 21.22 & 55.24 \\
29 & Shaybah & 22.52 & 54.00 \\
30 & Al Kharkhir & 18.87 & 51.13 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The 82 sites arbitrarily selected over Saudi Arabia to cover the whole area of the country; \(\varphi\) is the geographical latitude, and \(\lambda\) the geographical longitude in the WGS84 geodetic system. The “unnamed” sites refer to those away from known locations. This table is a reproduction of Table 1 in [1; 2]. N = North, E = East.
# Analysis of Crack Formation and Growth in Tunnel Linings Using Double-K Fracture Criterion
## 2 Double-K Fracture Criterion  
### Fracture Characteristics of Concrete  
As the main ingredient of tunnel lining, concrete has significantly different fracture characteristics compared with other homogeneous materials. At the material level, concrete is a porous material composed of fine aggregate, coarse aggregate, and cement paste. Due to the poor strength of the bonding interface between aggregate and cement paste, a large number of microcracks are generated in the bonding interface before concrete cracking. At the macro level, the microcracks are clustered at the front end of the crack (physical crack tip) and form the so-called fracture process zone, which plays an important role in the fracture process of concrete [20; 21; 22].  
The composition of concrete cracks is plotted in Figure 1, where the fracture process zone is located at the front of the physical crack tip. Due to a large number of micro-cracks clustered in the fracture process zone, the stiffness and strength of concrete are greatly reduced. This phenomenon is usually called stress softening. The stress softening behavior of concrete can be expressed by different stress-crack width softening curves, such as linear softening curves, bilinear softening curves, and exponential softening curves. When the stress at the tip of the fracture zone (fictitious crack tip) reaches tensile strength, the fracture process zone will further expand. When the open width of the physical tip increases to the critical value, the fracture process zone near the physical tip cannot transmit stress and fracture completely into free fractures [21; 23].  
Figure 1: The composition of a concrete crack [21].  
A typical load-crack width curve (\(P\)-\(\delta\) curve) is plotted in Figure 2. Generally, the fracture process of concrete consists of three stages, i.e., crack initiation, stable propagation, and unstable fracture, which correspond to the linear segment (OB), nonlinear segment (BC), and softening segment (CE) in Figure 2, respectively. In the crack initiation stage, the size of the fracture process zone can be neglected, and the crack width increases linearly with the increase of the load \(P\). In the stable propagation stage, the fracture process zone starts to extend steadily and the nonlinear characteristics of the \(P\)-\(\delta\) curve begin to appear. The approaches of linear elastic fracture theory are no longer available at this stage. In the unstable fracture stage, the free crack starts to cause rapid extension, and the load-bearing capacity of concrete decays drastically [24].
## 1 Introduction
For Ti-Zr-Nb SMAs, certain regularities have been established regarding the effect of TMT on their structure and properties. Thus, moderate cold plastic deformation with true strain \(e\) = 0.3, followed by PDA at 600 \({}^{\circ}\)C for 30 min, creates a nanosubgrained structure in the \(\beta\)-phase of the Ti-22 Nb-6 Zr SMA and leads to the best combination of functional properties: low Young's modulus, low residual strain accumulation, good superelasticity, and high cycling stability at human body temperature [29]. However, for the Ti-18 Zr-15 Nb SMA, the best combination of functional properties corresponds to post-deformation annealing at 550 \({}^{\circ}\)C for 30 min, while annealing at 600 \({}^{\circ}\)C leads to recrystallization, grain growth, and the deterioration of functional properties [24; 25]. Additionally, there was an attempt to obtain not only a nanosubgrained structure, but a predominantly nanogenized structure in Ti-18 Zr-15 Nb by the implementation of severe plastic deformation with \(e\) = 3.0; however, the functional properties were not extensively studied [28]. As for the relatively new Ti-41 Zr-12 Nb SMA, the effect of TMT on its structure and functional properties has not yet been studied, and only some standard thermomechanical treatment routes have been used [19; 26]. Consequently, there is lack of systematic studies of TMT effects on the structure formation and functional properties of Ti-Zr-Nb SMAs across a wide composition range. Therefore, this work provides a comprehensive comparative study on the effect of thermomechanical treatment, including cold rolling (CR) with various strains ranging from moderate to severe and subsequent post-deformation annealing (PDA), on the phase and structure states, crystallographic texture, and crystallographic limit of recovery strain of low-, medium-, and high-Zr Ti-Zr-Nb SMAs. It is important to note that this work does not include studying the effect of TMT on the mechanical and functional properties of Ti-Zr-Nb SMAs. The structure-property relationships essential for application will be established in future studies.
# Discrete Environment-Driven GPU-Based Ray Launching: Validation and Applications
## 3 Validation
### Validation of the DED-RL Algorithm
#### 3.2.3 Results for San Jose and Atlanta  
Table 4 shows a comparison of measurements and RL simulations for the 437 sites cell sites in Atlanta and San Jose.  
In the table, mean error (\(\mu_{E}\)) statistics are shaded in gray because they have low relevance, as nominal ERP values declared by the cellular operators turned out to be unreliable and exhaustive ERP survey was not possible. In particular, the nominal values were overestimated in several cases, so an unbiasing procedure was applied to the simulation results: this explains why the average \(\mu_{E}\) values before unbiasing in Table 4 are quite high and always positive.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**SCENARIO**} \\ \cline{2-3}
**Metric** & \
\begin{tabular}{c} **San Francisco** \\ **(DED-RL)** \\ \end{tabular} &
\begin{tabular}{c} **San Francisco** \\ **(RT reference)** \\ \end{tabular} \\ \hline Frequency & \multicolumn{2}{c}{850 and 1900 MHz} \\ Mean \(\mu_{E}\) [dB] & 0.1 & \(-\)1.2 \\ St.dev. of \(\mu_{E}\) [dB] & 5 & 3.6 \\ Mean \(\sigma_{E}\) [dB] & 9.0 & 9.3 \\ St.dev. of \(\sigma_{E}\) [dB] & 1.6 & 1.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Global statistics for the RL simulations vs. measurements in the San Francisco scenario. Global statistics of RT in the same scenario are reported as a reference.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**DED-RL** & **Ray Tracing** & **DED-RL** & **RAY Tracing** \\
**(Full Map)** & **(Full Map)** & **(Simplified Map)** & **(Simplified Map)** \\ \hline
212 s & 302,400 s & 145 s & 36,000 s \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of computation time and speed-up gain of DED-RL vs. ray tracing for site A000127C in the San Francisco scenario.  
Regarding the error standard deviation (\(\sigma_{E}\)), performance is better than for San Francisco, especially for San Jose, which is a more standard urban scenario, with a nearly flat terrain, and a preponderance of mid-size buildings with vegetated areas: Figure 6 shows a comparison of the measured and simulated RF coverage map for one of the 79 sites in San Jose. The vegetated areas were available in the San Jose database, represented as brown polygons in Figure 6, and through-vegetation attenuation was taken into account as in [30]. The average per-site computation time for San Jose was 2.5 min, shorter than for San Francisco. The computation times for the Atlanta scenario (north and south) were similar to those of San Francisco (about 7 min per-site).
## 3 Results
### The Drivers of ET  
This study employed partial correlation analysis to investigate the driving factors of _ET_ during the growth period of cabbages. Specifically, the environmental factors include net radiation (_Rn_), air temperature (_Ta_), soil moisture content at 20 cm depth (_SWC_), and vapor pressure deficit (_VPD_), and the physiological factors encompass the gross primary productivity (_GPP_), water use efficiency (_WUE_), and leaf area (_LA_). As shown in Figure 5, the _ET_ of cabbages is mainly related to _GPP_ (0.66), _Rn_ (0.48), and _WUE_ (-0.35). The importance of each factor to _ET_ ranked in a descending order is as follows: _GPP_ > _Rn_ > _WUE_ > _LA_ > _SWC_ > _VPD_ > _Ta_. Among the main driving factors that cause changes in _ET_, _GPP_ and _Rn_ are positively correlated with _ET_, while _WUE_ is negatively correlated with _ET_.  
Figure 4: Dynamics of daily ET (**a1–a3**) and average WUE (**b1–b3**) in different growth periods.  
Based on principal component analysis (PCA), the relationship between the \(ET\) of cabbages and its main driving factors was analyzed (Figure 6). The above factors were transformed into two orthogonal components, namely PC1 and PC2, which explained 69.8% of the total characteristic variance of \(ET\). To be specific, PC1 could explain 43.0% of the total variance, with large loadings for \(GPP\), \(Rn\) and \(Ta\), while PC2 could explain 26.8%, with large loadings for \(WUE\) and \(LA\). Among them, \(GPP\) and \(Rn\) are highly positively correlated with \(ET\), while \(WUE\) is negatively correlated with \(ET\). The results from principal component analysis are similar to those from partial correlation analysis.
# A Spatial-Temporal-Semantic Neural Network Algorithm for Location Prediction on Moving Objects
## 3 Methodology  
The core idea of location prediction algorithms is to model regular patterns hidden in historical data and find the most possible movements based on current observations. Due to the continuity of space and time, trajectory is not suitable to be directly imported to a prediction model. As shown in Figure 4, the courier travels along the same road every day, but the trajectories are not exactly the same. Simply representing a location by continuous coordinates may lead to high computational cost and may not achieve a better prediction result. Before using prediction models, each of the points in a trajectory is first preprocessed in order to convert the real continuous values associated to the geospatial coordinates of latitude and longitude, into discrete codes associated to specific regions. Existing methods use clustering-based algorithms to transform trajectory into discrete cells, clusters and stay points. However, the density of points on the road may be relatively low compared to frequent regions, making it hard for clustering algorithm to identify. Additionally, they cannot deal with points along the road.  
In Section 3.1, a spatial-temporal-semantic feature extraction algorithm is proposed to overcome the difficulties and to discretize trajectory into location sequences, namely, \((loc_{1},loc_{2},\ldots)\). As shown in Equation (1), given an object \(Obj_{i}\) and current time \(t\), the problem of location prediction can be formulated as estimating the probability of the next location \(loc_{t+1}\) based on the current locations \((loc_{t-k},\ldots,loc_{t})\):  
\[P(loc_{t+1}|obj_{i},(loc_{t-k},\ldots,loc_{t})) \tag{1}\]  
Figure 4: The necessity of transforming trajectories into discrete and fixed locations.  
Then location prediction with discrete locations is like the classification problem. During the model building process, location sequence \((loc_{t-k},\ldots,loc_{t})\) is used as features and \(loc_{t+1}\) is the label. However, \((loc_{t-k},\ldots,loc_{t})\) generated by the first step might be very long and the performance of traditional models, such as HMM and RNN, will decline. In Section 3.2, a LSTM-based prediction model is proposed to handle long sequences. The flowchart of the STS-LSTM is illustrated in Figure 5.
# Analysis of Cultivated Land Productivity in Southern China: Stability and Drivers
## 2 Materials and Methods
### Research Methodology  
#### 2.3.1 Theil-Sen Median Trend Analysis and Mann-Kendall Significance Test  
Theil-Sen median trend analysis is a non-parametric method that requires no specific sample distribution and is unaffected by outliers. It is often used to analyze the trend in long-term series changes and is calculated as follows:  
\[\rho=Median\left(\frac{x_{j}-x_{i}}{j-i}\right) \tag{1}\]  
where \(\rho\) is the median value of cultivated land NPP to slope. When \(\rho>0\), the vegetation change increases, and when \(\rho<0\), the vegetation change decreases. \(x_{i}\) and \(x_{i}\) are the values of years \(j\) and \(i\) in the cultivated land NPP time series, respectively, and the median was used [34]. The calculation was applied using the zyp package (version 0.1-1) in R language (version 4.3.1), called the zyp.sen function for median slope estimation.  
The Mann-Kendall significance test is a non-parametric method which supplements Theil-Sen Median slope estimation to test the significance of a time series. Considering that long-term series data may exhibit autocorrelations (such as lag effects caused by interannual climate fluctuations), we adopted the variance correction method proposed by [PERSON] and [PERSON] to reduce the risk of Type I errors by adjusting the effective sample size [35]. The modified test was implemented in R language using the Man-Kendall function of the Kendall package (version 2.2.1), and the effective sample size was calculated with autocorrelation correction. The MK significance test formula is:  
\[S=\sum_{i=1}^{n-1}\sum_{j=i+1}^{n}\text{sgn}\big{(}x_{j}-x_{i}\big{)} \tag{2}\]  
where \(x_{i}\) and \(x_{j}\) are the observed values corresponding to time series \(i\) and \(j\), respectively, and \(i<j\), sgn() is a symbolic function.
# A Cooja-Based Tool for Coverage and Lifetime Evaluation in an In-Building Sensor Network+
## 3 WSN-Maintain
With DGRM, the topology file contains received signal strength in dB and received success ratio in the range of [0, 1] for every pair of sensor nodes. **Run data gathering simulation**: WSN-Maintain is run in parallel with the collect-view tool of Contiki, which was integrated into the Cooja simulator, to gather data from the WSN. Figure 2: Sensor detection models [15]. (**a**) Binary detector. (**b**) [PERSON]’s model. **Export log file and calculate node remaining energy**: Cooja communicates with WSN-Maintain through its log file. WSN-Maintain reads the log file to calculate the nodes' remaining energy. In [16], a linear model is used to estimate sensor node energy consumption. The total energy consumption \(E\) is defined as:  
\[\frac{E}{V}=I_{m}t_{m}+l_{t}l_{t}+l_{t}t_{t}+l_{r}t_{r}+\sum_{i}I_{c_{i}}I_{c_{ i}} \tag{8}\]  
where \(V\) is the supply voltage, \(I_{m}\) the current draw of the microprocessor when running, \(t_{m}\) the time in which the microprocessor has been running, \(I_{l}\) and \(t_{l}\) the current draw and the time of the microprocessor in low power mode, \(I_{t}\) and \(t_{t}\) the current draw and the time of the communication device in transmit mode, \(I_{r}\) and \(t_{r}\) the current draw and time of the communication device in receive mode and \(I_{c_{i}}\) and \(t_{c_{i}}\) the current draw and time of other components, such as sensors and LEDs. Table 4 shows the energy consumption parameters used. The current draw of the communication device in transmit mode (\(I_{t}\)) for different power level has been shown in Table 1. Time measurement is implemented using the on-chip timers of the MSP430. The 32,768-Hz clock divided by eight is used, producing 4096 clock ticks per second. **If a node fails or runs out of energy**: When a node dies, the coverage percentage decreases or the network disconnects. If the coverage percentage is below a threshold or the connectivity is insufficient, WSN-Maintain will either automatically find and turn on non-overlapping redundant nodes to maintain the coverage and connectivity requirement or report failures that require physical maintenance. Users can interactively use WSN-Maintain to simulate node failures by killing nodes or just wait until a node runs out of energy to see the results of WSN restoration.