esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Validating the theoretical models of seismic wave velocity, attenuation, and anisotropy in fractured rocks in a laboratory setting remains an important goal. Previous studies have been conducted in high-porosity ranges that represent very different physical properties compared with those of unconventional reservoirs. There is a lack of experimental validations of equivalent medium theories in fractured tight rocks.  
This study aimed to investigate the feasibility of applying the Galvin model in fractured tight rocks, by observing P- and S-wave velocities and anisotropy in a set of low-porosity artificial fractured sandstones. The measurements showed that the Galvin model significantly underestimated P- and S-wave velocities, but overestimated P-wave anisotropy in water-saturated conditions. In dry conditions, the Galvin model severely underestimated P-wave velocity in the 0\({}^{\circ}\) and 45\({}^{\circ}\) directions, but overestimated P-wave anisotropy. Meanwhile, in both dry and water-saturated conditions, the predicted S-wave anisotropies were comparatively close to the measured results.  
Based on the comparison between measured data and theoretical predictions, we modified the Galvin model by (1) using the [PERSON] (1995) squirt flow model to calculate the frequency-dependent moduli of the background matrix, thus adding a squirt flow effect into the Galvin model; and (2) using the Thomsen model to calculate the elastic moduli of the fractured rocks in low- and high-frequency limits. In water saturation, predictions of the modified model fitted well with the measured data. However, in dry conditions, the predicted P-wave anisotropy of the modified model was still significantly higher than the measured results. This might be due to the interaction between fractures, which acts to reduce overall anisotropy in dry conditions but has little effect on anisotropy in water-saturated conditions. Neither the Galvin model nor the modified model take the interaction between fractures into account. In both dry and water-saturated conditions, the predicted S-wave anisotropy of the modified model was slightly higher than the measured results.  
Sample manufacturing, ultrasonic measurement, data analysis, Y.Z.; supervision, B.D. All authors have read and agreed to the published version of the manuscript. This research received no external funding.  
**Institutional Review Board Statement:** Not applicable.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & \multicolumn{3}{c}{**P-Wave Anisotropy Parameter \(\varepsilon\)**} \\ \cline{2-4}  & **High Frequency** & **Low Frequency** & **Measured Frequency** \\  & **Limit** & **Limit** & **(0.5 MHz)** \\ \hline Galvin model & 0.49 & 4.3 & 1.1 \\ \hline Modified model & 2.07 & 4.9 & 2.7 \\ \hline \end{tabular}
\end{table}
Table 3: Calculated P-wave anisotropy parameter, \(\varepsilon\), in high-, low-, and measured-frequency limits in sample 2#.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 4 Experimental Results
### Feature Extraction  
In the process of feature extraction, the size of the image block is a critical factor that affects performance. Calculating the gradient orientation histogram for a larger image block can capture the overall structure of the block, but at the cost of losing more local details. On the other hand, calculating the gradient orientation histogram for a smaller image block can capture more details but is also too sensitive to deformations. We segment the image into different block sizes by setting the maximum pixel size of the image block. Figure 5 illustrates the feature extraction result of different maximum pixel size settings on an image from the KITTI07 dataset, where each local region feature is indicated by a different color. It can be observed that larger maximum pixel sizes result in fewer image blocks for clustering, leading to a loss of more image information. Conversely, smaller maximum pixel sizes increase the amount of less important information and significantly increase execution time.  
After segmenting the image into image blocks, the core distance \(\epsilon_{c}\) and the reachability distance \(\epsilon_{r}\) are the two most critical parameters for clustering the image blocks. The \(\epsilon_{c}\) determines the beginning of a cluster, while \(\epsilon_{r}\) affects the size of the cluster, and the two parameters work together to generate a cluster. Figure 6 demonstrates the feature  
Figure 5: The feature extraction result of different maximum pixel size settings on an image from the KITTI07 dataset, where each local region feature is indicated by a different color.  
extraction result of different \(\epsilon_{c}\) and \(\epsilon_{r}\) settings on the image shown in Figure 5. It is important to note that \(\epsilon_{r}\) cannot be smaller than \(\epsilon_{c}\). From Figure 6, we can observe that when \(\epsilon_{c}\) is small, the number of clusters is small, while a large \(\epsilon_{c}\) leads to an increase in the number of clusters, but some less important information is also extracted as a cluster. When \(\epsilon_{r}\) is small, the size of the clusters is relatively small, and a larger \(\epsilon_{r}\) results in the merging of adjacent clusters, which ultimately reduces the number of clusters obtained.
## 4 Inspection Results
### 3D Scanning Technology  
The 3D scanner successfully detected any deviations from the gold standard, i.e., 100% defect detection. The full blade scan and processing of the scan data took just under a minute (approx. 55 s). Repeated scanning resulted in the same inspection outcome, i.e., blades rejected in the first round of inspection were also rejected in the second round. The same applied for accepted blades. Thus, the scan repeatability was 100%. While it is known that any measurement systems including 3D scanning have a measurement error, this did not affect the inspection results of this study.  
The results can be visualised in different ways using colour coding to highlight the deviation based on (a) metric measurements, (b) severity levels and (c) as accept/reject criteria. An example of each representation is given in Figure 4. For the purpose of this study, the accept/reject representation was chosen, since it allows comparison of the results  
Figure 3: Sample output of borescope inspection software: (**a**) Correctly detected airfoil dents, (**b**) correctly detected leading edge dent, (**c**) detected deposit, (**d**) detected deposit and missed dent on leading edge, (**e**) missed nick on trailing edge, (**f**) missed dent in leading edge. Software detections are indicated by red bounding boxes. Missed defects are highlighted by blue circles.  
with the IP and AI software. In the scan output, any deviations between the scanned and nominal blade are coloured in red. This corresponds to the bounding boxes in the borescope and piece-part inspection software.  
The scanner was successful in detecting a variety of defects including tears (Figure 4a), chicks and dents (Figures 4b and 5c), bends (Figure 4c), tip curls (Figure 5a), as well as airfoil dents (Figure 5b). The effect of deposit build-up on the edges--a common cause for false positives in human inspection--was not tested in the present study. Accepting 'offsets' to the ground truth model might lead to better inspection results since deposits are being accepted (avoiding false positives), or contrarily, it could decrease the inspection performance as other defects (e.g., corrosion) might be incorrectly accepted (increasing false negatives). This would be an interesting assessment for future work.
###### Abstract  
Postural imbalance is a leading cause of injury in older adults. Our study investigated the effectiveness of virtual reality (VR)-based interventions on balance ability in this population. Here, we examined 21 older, healthy adults (75.8 \(\pm\) 5.2 years old). Participants performed 6 weeks of balance training, twice per week for 30 min; the experimental group donated on Oculus VR headset during the training while control participants did not. To assess balance ability, a force platform measured displacement of the center of pressure (COP) during quiet standing in double-leg, tandem, and single-leg stances with eyes closed pre- and post-assessment. COP measurements included mediolateral (ML) and anterior-posterior (AP) directions for root mean square (RMS), peak-to-peak displacement (MAXD), total excursion (TE), and 95% confidence area ellipse (AE) parameters. Post-training assessments showed improvements (significant decreases) in the COP parameters. Control group COP parameters improved in various stances ranging from a 3% to 40% decrease on average. The VR group improved MAXD, TE, and 95% AE ranging from a 5% to 47% decrease, on average, across various stances post- compared to pre-training. VR-based exercise training programs may encourage older adults to engage in mobility exercises, leading to a reduced risk of falls or injuries.  
aging; elderly; virtual reality; balance training; center of pressure; falls Article
## 2 Study Area and Data Processing
### Construction of Evaluation Index System
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Factors** & **Source Data Description** & **Source** \\ \hline Elevation & GDEMV3 30 m & [[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) (accessed on 22 April 2010) \\ Slope & Derivative dataset & Extracted from DEM \\ Aspect & Derivative dataset & Extracted from DEM \\ Plane curvature & Derivative dataset & Extracted from DEM \\ Profile curvature & Derivative dataset & Extracted from DEM \\ TRI & Derivative dataset & Extracted from DEM \\ TWI & Derivative dataset & Extracted from DEM \\ Stream density & River network distribution vector data & [[http://www.geodata.cn](http://www.geodata.cn)]([http://www.geodata.cn](http://www.geodata.cn)) (accessed on 2 April 2010) \\ NDVI & Remote sensing image & [[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) (accessed on 20 April 2010) \\ LDWI & Remote sensing image & [[https://www.gscloud.cn/](https://www.gscloud.cn/)]([https://www.gscloud.cn/](https://www.gscloud.cn/)) (accessed on 20 April 2010) \\ Land cover type & 30 m resolution global land cover & [[http://www.resdc.cn/](http://www.resdc.cn/)]([http://www.resdc.cn/](http://www.resdc.cn/)) (accessed on 20 April 2010) \\ Road density & Road network data & [[http://www.webmap.cn](http://www.webmap.cn)]([http://www.webmap.cn](http://www.webmap.cn)) (accessed on 20 April 2010) \\ Distance to fault & National active fault vector data & [[https://data.earthquake.cn/](https://data.earthquake.cn/)]([https://data.earthquake.cn/](https://data.earthquake.cn/)) (accessed on 2 April 2010) \\ Fault density & & [[https://developers.google.cn/earth-engine/](https://developers.google.cn/earth-engine/)]([https://developers.google.cn/earth-engine/](https://developers.google.cn/earth-engine/)) (accessed on 22 April 2010) \\ Rainfall & CHIRPS Pentad & [[https://developers.google.cn/earth-engine/](https://developers.google.cn/earth-engine/)]([https://developers.google.cn/earth-engine/](https://developers.google.cn/earth-engine/)) (accessed on 22 April 2010) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Data sources for evaluation factors.
## 2 Materials and Methods
### Surface-Enhanced Raman Spectroscopy for Detection Limit Test  
Different concentrations (10\({}^{-3}\) M-10\({}^{-10}\) M) of RhB solutions were prepared. The SERS substrates Ag/PDA/ZnO@GMF, having different deposition times, were then used to investigate effective SERS enhancement by adding 10 \(\mu\)L aliquots from each RhB solution. The SERS signal of RhB on each substrate was recorded and compared. The laser wavelength used in this experiment was 532 nm.
# Evaluation of Optimal Visible Wavelengths for Free-Space Optical Communications
## 2 Theoretical Background
### Modulation Techniques  
Effective data transmission in FSO systems requires that information be encoded onto the optical carrier. Common modulation schemes include amplitude, phase, and frequency modulation, as well as pulse-based techniques such as pulse-width modulation (PWM) and on-off keying (OOK). Each method offers trade-offs in terms of implementation complexity, spectral efficiency, and resilience to noise [16; 17; 18].  
In this study, frequency modulation (FM) was employed due to its robustness and ease of implementation using a microcontroller. The Atmega328p generated a square-wave control signal to vary the modulation frequency between 0.6 MHz and 6 MHz. FM was selected for its low susceptibility to flicker noise and ambient light interference, contributing to stable signal transmission even in the presence of environmental disturbances. These characteristics made FM a practical and reliable choice for our short-range visible-light FSO platform.
## 2 Related Work
### Robustness Evaluation
#### 2.2.1 Adverse Factors  
In real traffic environments, adverse conditions are always diverse and overlapping, such as adverse weather, sensor damage, occlusion, and blur caused by motion. There are some studies that have analyzed adverse factors that may affect the robustness of object detection. There is evidence [21; 22] that adverse weather and light strength can impair the robustness of models, making them ineffective in previously unseen datasets. An experimental study [23] found that visibility can adversely affect detection results in a foggy environment, and four quantitative criteria were developed: no fog, light fog, medium fog, and heavy fog. The performance of detecting traffic signs in bad weather was examined [24], and the results indicated that detection performance degraded as weather conditions worsened. A lightweight object detection framework was proposed [25], and its effectiveness was demonstrated through the use of a dataset with image corruption caused by rain and fog. It has been shown [26] that blur, white noise, Gaussian noise, distortion, and extreme contrast can affect object detection algorithms. An analysis ofvibration-blurred images [27] explored the environmental adaptability of the visual perception system; moreover, the experiment quantitatively confirmed that mechanical vibration stress affects model performance. As a result of a detailed evaluation of semantic segmentation models in response to real-world image corruption, they [28] found that some architecture properties were significantly related to the robustness of their algorithms, like a dense prediction cell. However, currently most studies have only analyzed a few factors and have not analyzed comprehensively and in detail the adverse factors that affect vehicle detection.
# An Integrated Thermal-Electrical Model for Simulations of Battery Behavior in CubeSats
## 2 Methodology
### Thermal Model  
To simulate the heat transfer over the satellite, the classical energy balance is conducted along the CubeSat's full orbit, which is an environment where radiation and conduction are the main processes of heat transfer. In this work, the lumped parameter approach will be adopted, then parts of the satellite will be represented by nodes whose temperature gradient inside each node is negligible. To account for temperature gradients among the nodes is used the finite difference method. Therefore, the energy equation in each part of the satellite is indicated in Equation (31) (\(\forall w=1,\dots,7\)):  
\[Q_{T_{w}}(t)+Q_{T_{w}}(t)+Q_{\text{Int}_{w}}(t)=m_{w}v_{w}\frac{dT_{w}}{dt}(t) \tag{31}\]  
The sub-index \(w\) represents the main parts, or a node of the CubeSat, and each node has mass equal to \(m\), specific heat \(v\), temperature \(T\), thermal radiation rate \(Q_{r}\), net heat transfer between internal parts \(Q_{T}\), and internal heat generation \(Q_{\text{int}}\). Notice from previous section that \(Q_{T_{7}}=0\). The system is solved by the Newton-Raphson method, and the time derivative in Equation (31) is discretized iteratively--by iterations \"it\"--at \(it\) and \(it-1\).  
The net heat transfer between internal parts occurs by conduction and radiation processes. To keep the model simple, this term will be given by Equation (32), where \(R_{T_{w}}\) is a thermal resistance of node \(w\).  
\[Q_{T_{w}}(t)=\begin{cases}\frac{T_{7}(t)-T_{w}(t)}{R_{T_{w}}}&\text{if $w\leq 6$}\\ \sum\limits_{w=1}^{6}\frac{T_{w}(t)-T_{7}(t)}{R_{T_{w}}}&\text{if $w=7$}\end{cases} \tag{32}\]  
The internal heat generation \(Q_{\text{int}}(t)\) is non-zero only for the battery node, where an electrical heater with power \(P_{H}(t)\) is introduced, ruled by Equation (33).  
\[Q_{\text{int}_{w}}(t)=\begin{cases}0&\text{if $w\leq 6$}\\ P_{H}(t)&\text{if $w=7$}\end{cases} \tag{33}\]  
The remaining parameters used in the thermal model are summarized in Table 2. Except for the arbitrary value for \(R_{T_{w}}\), these values are approximations of numbers found in the literature--for example, in [19; 30; 31].  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline \(Q_{Sun}^{\prime\prime}\) & 1360 [W/m\({}^{2}\)] \\ \(Q_{r}^{\prime\prime}\) & 237 [W/m\({}^{2}\)] \\ \(\alpha_{w}\) & 0.3 [-] \\ \(\varepsilon_{w}\) & 0.3 [-] \\ \(A_{T_{w}}\) & 0.01 m\({}^{2}\) \\ \(a\) & 0.3 [-] \\ \(T_{\infty}\) & 2.7 K \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of the irradiance model.
## 3 Experimental Results and Analysis
### AE Activity and Threshold Stress
As shown in Table 4, the \(\sigma_{\rm c}\) of the sample C-3 was 23.89% of the \(\sigma_{\rm p}\), whilst that of the samples containing inverted U-shaped openings was within the range of (12.50%-17.83%)\(\sigma_{\rm p}\). The reason may be that the excavation of the openings at the center of the samples gives rise to the decrease in the numbers of natural defects. In respect of the \(\sigma_{\rm i}\), it corresponded to the axial stress when the crack 1\({}^{\rm a}\) started to initiate. The value of the sample C-3 was 0.44\(\sigma_{\rm p}\). In contrast, the values of the other samples ranged from 0.27\(\sigma_{\rm p}\) to 0.30\(\sigma_{\rm p}\). The order of the initiation stress of the samples with openings could be ranked as: sample V-3 > sample H-3 > sample S-2 > sample D-1. This is because the tensile cracks occurred more easily around the openings than in the intact sample, and the crack initiation stress was attributed to the stress state of the roof or floor of the opening, which would be illustrated in detail in the next section. With regard to the \(\sigma_{\rm d}\), for the samples with openings, it corresponded to the stress when the slabbing crack or the posterior tensile crack emerged, and their values were 0.65-0.78 times the peak stresses. This would also be deeply analyzed in Section 4.
###### Abstract  
Alert dwell time, defined as the time elapsed from the generation of an interruptive alert to its closure, has rarely been used to describe the time required by clinicians to respond to interruptive alerts. Our study aimed to develop a tool to retrieve alert dwell times from a homegrown CPOE (computerized physician order entry) system, and to conduct exploratory analysis on the impact of various alert characteristics on alert dwell time. Additionally, we compared this impact between various professional groups. With these aims, a dominant window detector was developed using the Golang programming language and was implemented to collect all alert dwell times from the homegrown CPOE system of a 726-bed, Taiwanese academic medical center from December 2019 to February 2021. Overall, 3,737,697 interruptive alerts were collected. Correlation analysis was performed for alerts corresponding to the 100 most frequent alert categories. Our results showed that there was a negative correlation (\(\rho=-0.244\), \(p=0.015\)) between the number of alerts and alert dwell times. Alert dwell times were strongly correlated between different professional groups (physician vs. nurse, \(p=0.739\), \(p<0.001\)). A tool that retrieves alert dwell times can provide important insights to hospitals attempting to improve clinical workflows.  
computerized physician order entry; interruptive alert; alert dwell time; alert fatigue
# DARAL: A Dynamic and Adaptive Routing Algorithm for Wireless Sensor Networks
## 4 Simulation
### Metrics Overview
#### 4.1.1 Convergence Time  
The convergence time is a key parameter in the present work, and it has been calculated differently for each routing protocol, granting a fair comparison between different algorithms such as \(AODV\) and \(O\)-\(RPL\).  
For \(AODV\), the convergence time for each node is calculated independently as the elapsed time since a random node sends a Route Request (RREQ) message to an objective node until a Route Reply (RREP) message is received by the sender. The average convergence time of the whole network is calculated after all the nodes in the network have calculated their own convergence times, as the average of the individual convergence times of all the nodes in the network.  
For \(O\)-\(RPL\), it has been used the mechanism implemented by [PERSON] et al. [7], where the convergence time of a network is measured as the time elapsed, since a new \(DODAG\) root is set until all the nodes have joined that \(DODAG\). To obtain an average measure, 10 different routes have been  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Size** & **Number of Nodes** & **Area Size** & **Node Degree** \\ \hline Small & 100 & \(250\times 250\) m & 5 \\ Small & 100 & \(175\times 175\) m & 10 \\ Small & 100 & \(145\times 145\) m & 15 \\ Medium & 200 & \(350\times 350\) m & 5 \\ Medium & 200 & \(250\times 250\) m & 10 \\ Medium & 200 & \(200\times 200\) m & 15 \\ Large & 400 & \(500\times 500\) m & 5 \\ Large & 400 & \(350\times 350\) m & 10 \\ Large & 400 & \(290\times 290\) m & 15 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different simulation scenarios.  
Figure 11: Samples of a small (**a**), medium (**b**), and large (**c**) scenarios.  
calculated, changing the \(DODAG\) root for each one. Then, the average convergence time has been estimated as the mean of the convergence times obtained in these 10 initializations of the network.  
Finally, for \(DARAL\), the convergence time of each node is calculated as the elapsed time since the node is started until its status changes to \(CONNECTED\) or \(AWAITING\), as both statuses allow a node to send and receive data messages. Then, the network convergence time is obtained as the average of all the node convergence times in the network.