esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Numerical Analysis
### Numerical Analysis Summary
#### 4.1.3 Numerical Analysis Model Considering Site Conditions  
The measured wheel load was decreased after rail vertical deflection occurred at the transition section, and at the train speed of 300 km/h, the wheel load was increased at the earthwork section beyond 49 \(\sim\) 58 m. A numerical analysis model was developed considering tracked settlements and track irregularities to verify the proposed vehicle and track model. The numerical modeling of the transition section, considering the track deformation that is the same as field measured track deformed profiles, is shown in Figure 16.  
The length of the numerical analysis model was set to 120 m to completely distribute the stress generated at 40-60 m where the impact load was expected to occur. The train speed was assumed to be 300 km/h, which was the same as the operating speed. As the time taken to run 120 m was 1.44 s, the analysis was conducted for 1.5 s in the unit of 0.01 s so that the train completely passed the model.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Model Type** & \(\gamma\) **(kN/m3)** & \(E\) **(Pa)** & \(\
u\) & \(C\) **(kPa)** & \(\phi\) **(c)** \\ \hline Rail & Elastic & 78.50 & \(2.1\times 10^{11}\) & 0.3 & - & 0 \\ TCL & Elastic & 20.58 & \(30.0\times 10^{9}\) & 0.21 & - & 0 \\ HSB & Elastic & 20.58 & \(13.0\times 10^{9}\) & 0.25 & - & - \\ Abutment & Elastic & 24.50 & \(20.0\times 10^{9}\) & 0.21 & - & - \\ Cement treated gravel & Mohr–Coulomb & 20.50 & \(2.5\times 10^{8}\) & 0.21 & 20 & 35 \\ Gravel & Mohr–Coulomb & 19.60 & \(1.6\times 10^{8}\) & 0.21 & & 35 \\ Reinforced tracked & Mohr–Coulomb & 19.60 & \(1.6\times 10^{8}\) & 0.21 & - & 35 \\ Upper tracked & Mohr–Coulomb & 17.64 & \(8.0\times 10^{7}\) & 0.2 & 3.0 & 32 \\ Lower tracked & Mohr–Coulomb & 17.64 & \(6.0\times 10^{7}\) & 0.2 & 5.0 & 30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material properties of track model.  
Figure 15: Diagram of track model.
# Determining the Optimal Configuration of the Multi-Ring Tree for Bluetooth Multi-Hop Networks
## 3 Routing Protocol
### A Self-Healing Scheme  
A self-healing protocol can be achieved, since the multi-ring tree address scheme enables the maintenance of self-routing networks. This work considers four cases in the proposed self-healing protocol: a new node joining the network, a slave node failure, a master failure, and failure of one or two roots. When a node joins or fails in a piconet, each master adds a new AM_ADDR address for the new slave or deletes the slave connection from its piconet list, respectively. If a master/slave node leaves, the first slave node with the smallest AM_ADDR address becomes the new master; it connects all the other slaves with the same ring-tree address. In addition, the new master provides an update status for its affiliated root node.  
When a root node in a ring subnet leaves, the slave node with the smallest AM_ADDR address in the piconet becomes a new root and sends out a root update message to its affiliated slaves and its directly connected neighboring roots. The topology maintenance for a single failed node can be applied for cases with two failed roots, since two failed roots are disconnected independently. For example, in Figure 3, if root R10 fails, then the candidate slave with the capability to connect with current directly connected roots in the R10 piconet becomes a new root, and the new R10 sends a root update message to its directly connected roots R6, R9, R11, and R14. When R1 and R6 both fail, their slaves become the new roots independently and coordinate their corresponding slaves. As a result, the multi-ring subnet still can be maintained locally.  
```
1:When an immediate node \(m\) receives a packet for destination node \(n\). If\(m\)(Ring-ID) = \(n\)(Ring-ID, Root-ID) then call intra_root(\(n\)) else call inter_root(\(n\)) endif A. intra_root(\(n\)): Init: The immediate node \(m\) receives a packet and checks the Tree-ID of the destination. If\(m\)(Tree-ID) = \(n\)(Tree-ID) then the destination node is reached else the \(m\) computes the location of the destination by a tree-addressing criterion and forwards the packet to its downstream piconet until \(m\)(Tree-ID) = \(n\)(Tree-ID) endif B. inter_root(\(n\)): Init: The immediate node \(m\) receives a packet and checks Ring-ID of the destination. root_search(\(n\)): If\(m\)(Ring-ID) = \(n\)(Ring-ID) then the node \(m\) computes the destination root by equation (3) and forwards packets until the \(m\)(Root-ID) = \(n\)(Root-ID) if\(m\)(Tree-ID) = \(n\)(Tree-ID) the destination node is reached else call intra_root(\(n\)) endif endif if\(m\)(Ring-ID) > \(n\)(Ring-ID) then forwards the packet to the inner ring until \(m\)(Ring-ID) = \(n\)(Ring-ID) and call root_search(\(n\)) elseif\(m\)(Ring-ID) < \(n\)(Ring-ID) then forwards the packet to the outer ring until \(m\)(Ring-ID) = \(n\)(Ring-ID) and call root_search(\(n\)) endif
```  
**Algorithm 2** The pseudo-code of the scatternet self-routing protocol
# Image Processing of UAV Imagery for River Feature Recognition of Kerian River, Malaysia
## 2 Materials and Methods
### Image Processing of UAV Images
#### 2.3.3 Thresholding of Clustered Images  
We use HSV thresholding for the analysis of clustered images post-k-means clustering. In vision and image processing, separating image intensity from color information is very important for many applications that are possible only in HSV color space. For this reason, we convert the clustered images from RGB to HSV color space.  
Image segmentation is significant to gain clear perceptibility of the region of interest (ROI) [38]. We compute limits of the segmented pairs of hue (\(H_{lower}\), \(H_{upper}\)), saturation (\(S_{lower}\), \(S_{upper}\)), and value (\(V_{lower}\), \(V_{upper}\)) to convert HSV color space to binary form using the equation:  
\[\begin{array}{l}C(x,y)=\left\{1,H_{lower}<H_{input}(x,y)<H_{upper}\,\right\}\\ \left\{S_{lower}<S_{input}(x,y)<S_{upper},\right\}\\ \left\{V_{lower}\leq V_{input}(x,y)\leq V_{upper},\right\}\\ \left\{0,\ Otherwise\right\}.\end{array} \tag{5}\]In Equation (5), \(C(x,y)\) is the thresholded part. Additionally, Equation (5) shows that if the HSV values for the pixels of the input image lie between the range of lower and upper bound values, then its associated output pixel belongs to class object 1, otherwise it is null (0). The segmented image is further analyzed to compute the area of white pixels. We calibrate the percentage of white pixels to calculate the ROI in desired units.
## 2 Literature Review
### Sensor Fusion and Data Processing in IoT Health Systems
#### 2.4.1 Importance of Sensor Fusion for Accurate Health Monitoring  
Sensor fusion enables a more comprehensive understanding of patient health, enabling activity monitoring through the integration [50] of multiple sensor modalities. For example, combining electrocardiography (ECG) with motion-tracking technologies enhances cardiac monitoring accuracy.  
Some examples of multi-sensor fusion applications are provided below:  
_Gait Analysis_: Combining wearable technology with data from vision-based systems, such as Kinect sensors, improves the comprehension of joint movements in gait analysis and results in more accurate evaluations of gait anomalies [51, 52].  
_Vital Signs Monitoring_: By combining information from many biomedical sensors, vital signs can be monitored more precisely, which enhances the identification of disorders including arrhythmias and sleep apnea [53, 54].  
_Activity Recognition_: Through activity tracking, multimodal fusion sensors in body-worn devices have enhanced physical activity monitoring, preserving or improving the quality of social and personal lives [55].
# Quaternion Processing Techniques for Color Synthesized NDT Thermography
## 3 Proposed Approach
### Description of the Evaluation Metrics  
The quality of the signals generated, processed or measured is a fundamental requirement in all engineering disciplines. Engineers and researchers characterize signals quantitatively to assess the quality of equipment and processes, and to act accordingly. For this purpose, a large number of signal quality metrics have been developed [36; 37].  
To control the quality of the images and to be able to maintain it, and even improve it, it is necessary to measure the quality in each of the stages they go through. Presently, Image Quality Assessment (IQA) is of great importance in visual communication and is essential for most image processing applications.  
In general, image quality metrics can be classified into subjective and objective. Subjective metrics present important practical limitations, mainly due to the high degree of variability of the response to certain factors. Among the different options available in objective evaluation, there are scalar metrics, which include bivariate measures such as the mean square error and the \(L_{p}\) norm, metrics that try to imitate the human visual system such as the Human Visual System (HVS), and also graphic metrics. Within objective evaluation, pixel-based techniques, such as Mean Square Error (MSE), Root Mean Square Error (RMSE), and signal-to-noise ratio (SNR), are widely applied due to its simple calculation methodology. In general, these metrics are defined only for monochromatic images, discarding information related to color.  
The physical meaning of the SNR metric depends on the magnitudes involved. The general purpose of the SNR is to compare the level of a desired signal to the level of the background noise, and is often calculated in decibel scales for magnitudes with wide dynamic range [13; 38]. The following definitions for SNR have been used in this study to evaluate the performance of the proposed analysis methodology and compare it to conventional processing algorithms [13]:  
\[SNR1=\frac{\mu_{S}}{\sigma_{N}} \tag{17}\]  
\[SNR2=\frac{\left|\mu_{S}-\mu_{N}\right|}{\sigma_{N}} \tag{18}\]  
\[SNR3=10\log_{10}\frac{\left|\mu_{S}-\mu_{N}\right|^{2}}{\sigma_{N}^{2}} \tag{19}\]  
\[SNR4=\frac{\sigma_{S}}{\sigma_{N}} \tag{20}\]  
\[SNR5=\frac{\sigma_{S}^{2}}{\sigma_{N}^{2}} \tag{21}\]  
\[SNR6=10\log_{10}\left(\frac{\sigma_{S}^{2}}{\sigma_{N}^{2}}\right) \tag{22}\]  
\[SNR7=\frac{\left|\mu_{S}-\mu_{N}\right|}{\sqrt{(\sigma_{S}^{2}+\sigma_{N}^{2} )/2}} \tag{23}\]  
where \(\sigma_{S}\) is the standard deviation of the signal in the defect region of the image, \(\sigma_{N}\) is the standard deviation of the noise in the reference or sound region of the image, \(\mu_{N}\) is the mean level of the noise in the reference or sound region of the image and \(\mu_{S}\) is the mean level of the signal in the defect region of the image.
## 4 Validation of the Established FE Models
### Failure Mode and Crack Pattern
#### 4.1.2 Concrete Crushing for GFRP-Reinforced Beam  
Specimen LC-G-5#13.77 was used to illustrate the failure of concrete crushing for GFRP-reinforced beams; the relevant crack patterns and failure modes are shown in Figure 13. Compared to the FE model for steel reinforced beams, the model for specimen LC-G-5#13.77 exhibited a larger possible cracking area along the span direction. Similar to the test results, diagonal cracks in the shear span were observed in the FE model, although their inclinations were relatively small. At the point that \(M_{u}\) was reached, the compressive damage initially occurred among the concrete elements at the top of the midspan. With further increasing of displacement, no significant decrease in the load was observed before total disintegration of the compressive concrete occurred, which was in good agreement with the  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{3}{c}{**Moment**} & \multicolumn{2}{c}{**Deflection**} & \multirow{2}{*}{**Failure Mode**} \\ \cline{2-2} \cline{4-6}  & \(M_{u}\) (kN-m) & \(M_{u,\text{Exp}}\)/\(M_{u,\text{FEM}}\) & \(\Delta_{s}\) (mm) & \(\Delta_{s,\text{Exp}}\)/\(\Delta_{s,\text{FEM}}\) & \\ \hline LC–G–5\#13.77 & 71.69 & 1.16 & 12.82 & 1.17 & C.C \\ SLC–G–5\#13.77 & 82.96 & 1.19 & 14.13 & 0.79 & B.F \\ LC–G–8\#13.77 & 84.04 & 1.17 & 12.79 & 0.97 & C.C \\ SLC–G–8\#13.77 & 105.45 & 1.14 & 14.60 & 0.89 & C.C \\ \hline LC–G–3\#13.77 & 58.38 & 1.09 & 10.91 & 1.22 & B.F \\ SLC–G–3\#13.77 & 63.26 & 1.16 & 6.39 & 1.11 & FRP.R \\ LC–G–6\#9.87 & 54.09 & 0.80 & 4.89 & 1.35 & FRP.R \\ SLC–G–6\#9.87 & 65.63 & 0.86 & 3.04 & 1.34 & FRP.R \\ \hline LC–G–4\#9.87 & 44.81 & 0.73 & 1.17 & 1.02  
experimental observation. In this stage, the strain in the GFRP bars continued to increase, as depicted in Figure 13b.
# An Interactive Method for Detection of Process Activity Executions from IoT Data +
## 2 Background and Motivation
### BPM Meets CPS and IoT
#### 2.1.1 Process-Awareness of IoT Data  
Given a set of IoT data from sensors and actuators of CPS components, we define _process-awareness_ of IoT data as the amount of information related to the execution of processes and activities that is contained within the data set (cf. Figure 1).  
_Full process-awareness_ is given if each sensor event is fully contextualized in the process execution, i.e., it can be clearly and directly associated with the execution of a specific instance of an activity within a specific process instance. _No process-awareness_ is given if only the raw sensor readings are available without any process-related information.  
The **goal** of this work is to increase the level of process-awareness in a given IoT data set with no process-awareness to full process-awareness based on a set of semi-automated steps and assumptions.  
Note that as partially shown in Figure 1 the relation between a sensor reading and an activity execution is not always a clear 1:1 relation. This increases the complexity of determining the relationships automatically. A sensor event may also be associated with:  
* More than one activity (e.g., in case of batch processing);
* No activity (e.g., in case of context data [12]);
* An activity only indirectly (e.g., as an effect of activity execution).
## 2 Background and Relevant Research
### Real-World Data  
RWD are often accompanied by various challenges and complications, spanning from data quality management to data analysis methods. A characteristic of RWD is that they are observational, in contrast to data gathered from RCTs, and we are able to induce various forms of bias in the analyses.
# Multi-Satellite Observation-Relay Transmission-Downloading Coupling Scheduling Method
## 6 Conclusions  
In this article, an improved tabu search method for the multi-satellite observation-relay transmission-downloading coupling scheduling problem is proposed. Based on the multi-satellite earth observation tabu search algorithm, the data transmission path planning method for observation data transmission and downloading planning is introduced. The satellite observation scheduling tabu search algorithm is improved, referring to the idea of the artificial potential field method. Additionally, in the data transmission path planning method, two strategy methods, including the \"min-node strategy\" and the \"min-download time strategy\", are proposed based on the Dijkstra data transmission path planning method. Based on the strategy Dijkstra method, the state-strategy selection rules are introduced, and the rule-based Dijkstra data transmission path planning method is proposed. The simulation results show that the proposed entire process coupling scheduling method can schedule the observation, relay transmission, and download processes of the satellite group, and the achievement of the entire process scheduling enhances the ability of the satellite cluster to obtain the observation data. The improved Dijkstra method enhances adaptability to the multi-subsystem coupled problem, and the improved local search in the tabu search method amplifies the algorithm's search capability.  
Conceptualization, Y.D. and C.H.; methodology, Y.D. and C.H.; software, C.H.; validation, C.H.; formal analysis, C.H.; investigation, C.H.; resources, C.H.; data curation, C.H.; writing--original draft preparation, C.H.; writing--review and editing, Y.D. and C.H.; visualization, C.H.; supervision, Y.D.; project administration, C.H.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data used to support the findings of this study are available from the corresponding author upon request.  
This work was partially supported by the Key Laboratory of Spacecraft Design Optimization and Dynamic Simulation Technologies, Ministry of Education.  
The authors declare no conflict of interest.
## 4 Bacterial Stringent Response
### Stringent Response and Selective Aminoacylation of tRNA Isoacceptors  
The rate of codon translation depends on a variety of factors, but the most important factor perhaps is the concentration and charged fractions of the aminoacyl-tRNA isoacceptor with the respective anticodon that reads the codon [55]. It has been estimated that during exponential growth, the charged fractions of all tRNAs are about 80% such that it is sufficient to maintain the rate of translation [85]. However, theoretical modeling and experimental measurement using microarrays and Northern blots analysis show that amino acid limitation results in the selective de-acylation of tRNA, where the charged fractions of some isoacceptors will be low while some will be high [86; 87]. Following amino acid limitation, the charged levels of tRNA are expected to first decrease within a few seconds as the cellular pools of aminoacyl-tRNAs turnover rapidly, and are expected to increase over a time period of about 10-50 min as the bacteria begins to biosynthesize amino acids and re-establish the new steady-state values [88]. Specifically, during leucine starvation (2-45 min) in ReIA+ auxotroph strain CP78, the aminoacylation levels of tRNA\({}^{\text{Leu}}\) isoacceptors have been measured and categorized: tRNA\({}^{\text{Leu}}\) (CAA) and (UAA) isoacceptors are insensitive to amino acid starvation with aminoacylation levels of 10%-30%, tRNA\({}^{\text{Leu}}\) (CAG) isoacceptor (which is the best substrate for L/F-transferase) is categorized as intermediately sensitive to starvation with an aminoacylation level of 8%, and tRNA\({}^{\text{Leu}}\) (GAG) and (UAG) isoacceptors are sensitive to starvation with an aminoacylation level of 2%-4% [86].
# Passive Detection of Low-Altitude Signal Sources Using an Improved Cross-Correlation Algorithm
## 1 Introduction
Communication signals from low-altitude signal sources are collected in real-world outdoor and indoor environments for the first time. 2. The signals are analyzed in the time-frequency domain, and a cross-correlation threshold method is proposed to distinguish whether a signal source is present or not. 3. An improved cross-correlation method is proposed to estimate the DOA and communication frequency of a low-altitude signal source. 4. The proposed method is compared with several well-known techniques in the literature. The results obtained show that this method provides better detection of low-altitude signal sources, particularly over long distances. The remainder of this paper is organized as follows. The low-altitude signal source system model and the noise reduction algorithm are presented in Section 2. The cross-correlation threshold classification method and parameter estimation for a low-altitude signal source are presented in Section 3. In Section 4, the performance of the proposed algorithm is evaluated using real-world outdoor and indoor data. Finally, some concluding remarks are given in Section 5.
## 2 Atmospheric Oxygen Transmittance Effects at Tower Scale
### The Atmospheric Inversion Problem at High Spectral Resolution  
Let us consider Equation (3), but now taking into account that radiance acquired by any sensor is provided according to its ISRF, i.e., \(\langle L\rangle\). Therefore, radiance at TOC, \(\langle\overline{L}\rangle\), could be estimated by computing:  
\[\langle\overline{L}\rangle=\frac{\langle L\rangle}{\langle t_{dir}^{\uparrow}\rangle} \tag{4}\]  
However, Equation (4) implies convolving the atmospheric transmittance and the at-sensor radiance terms individually. This mathematical inconsistency, i.e., \(\frac{\langle L\rangle}{\langle t_{dir}^{\uparrow}\rangle}\
eq\langle\frac{L} {t_{dir}^{\uparrow}}\rangle\), is quantified in Figure 3 for a range of target-sensor distances. Notice that higher relative differences occur at the longer target-sensor distance (blue regions), because of the deeper oxygen absorption. Similarly, higher relative differences are also found with higher spectral resolution, because the narrower bands access deeper absorption lines.  
Consequently, when compensating for the oxygen transmittance effects in the FLD and SFM family of retrieval strategies this fact must be taken into account, since a simple compensation of the oxygen transmittance will also introduce an error driven by the instrument spectral resolution (see Section 4).  
Figure 2: Simulated upward atmospheric transmittance from a 0 m surface elevation to a tower sensor at 10 m height, with nadir viewing geometry and sea–level pressure conditions. Transmittance has been simulated at 1 cm\({}^{-1}\) spectral resolution for the O\({}_{2}\)–B (**a**), i.e., \(\sim\)0.05 nm, and the O\({}_{2}\)–A (**b**), i.e., \(\sim\)0.06 nm, absorption regions. Note the different Y–axis ranges used to emphasize details in both O\({}_{2}\)–B and O\({}_{2}\)–A panels.