esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Model Test Validation  
### Grid Independence  
By modifying the grid size while keeping the same grid structure, various grid refinement systems were created to examine the impact of grid size variation on computational output. The number of grids varied from 1.4 million to 6 million in deep water settings and from 2.6 million to 7.9 million grids in shallow water (\(h\)/\(T_{m}\) = 1.18) conditions. Without jetting, simulations were run for both the deep water and shallow water conditions. Investigations were done into how varying grid quantities affected resistance. In both deep  
Figure 4: The computational domain. (**a**) deep water; (**b**) shallow water.  
Figure 5: Local grids and refinement.  
water and shallow water situations, Table 3 shows the fluctuation in model resistance about the number of grids with a model velocity (\(V_{m}\)) of 0.878 m/s (Froude number \(F_{r}\) = 0.113). The change rate listed in the table shows how much the resistance value has changed concerning the prior grid layout.  
Table 3 shows that when the number of computing grids rises, the computed resistance eventually tends to stabilize in both deep and shallow water. When the total number of grids reaches 2.2 million or greater under deep water circumstances, continued grid expansion leads to a resistance change rate of less than 0.5%. Similar to this, in extremely shallow water depths, the resistance begins to stabilize and shows a variation rate of less than 0.43% concerning the grid amount when the number of grids exceeds about 3.7 million. As a result, grid systems with 2.2 million and 3.7 million grids, respectively, are chosen for further research into situations involving deep water and shallow water.
## 3 System Framework and Algorithm Procedure
### Contention Period Optimization  
It is worth noting that more optimal throughput can be obtained by tuning the length of the CP. Therefore, we also design a sophisticated mechanism to adaptively adjust it. In this section, we first borrow a mathematical analytical model derived in [2] and numerically analyze the theoretical optimal solution about the contention period. Let \(\tau\) be the probability that a node sends a packet in a random discrete time slot and then \(\tau\) can be obtained, as follows:  
\[\tau=\frac{2}{1+W} \tag{1}\]  
where \(W\) is the length of the CP. When \(n\) nodes attempt to compete the channel, let \(P_{tr}\) to denote the probability that at least one transmission occurs in a time slot. Let \(P_{s}\) denote the probability that a successful transmission occurs on the channel. Therefore, \(P_{tr}\) and \(P_{s}\) can be calculated, as follows:  
\[p_{tr}=1-\left(1-\tau\right)^{n} \tag{2}\]\[p_{s}=\frac{n\tau(1-\tau)^{n-1}}{p_{tr}}=\frac{n\tau(1-\tau)^{n-1}}{1-\left(1- \tau\right)^{n}} \tag{3}\]  
In addition, in the CP, the system throughput can be calculated as follows:  
\[S=\frac{P_{s}P_{tr}E[P]}{(1-P_{tr})\delta+P_{s}P_{tr}T_{succ}+P_{tr}(1-P_{s})T_ {fail}} \tag{4}\]  
where \(E[P]\) is the average packet payload, which can be substituted by the length of DTR, since only DTR packet is transmitted during the CP. The \(\sigma\) is the duration of an empty slot, \(T_{succ}\) and \(T_{fail}\) are the average time of successful transmission and collision, respectively, and they are given by:  
\[\left\{\begin{array}{l}T_{succ}=T_{DIFS}+T_{DTR}\\ T_{fail}=T_{DIFS}+T_{DTR}\end{array}\right. \tag{5}\]  
where \(T_{DIFS}\) and \(T_{DTR}\) are the time of DIFS and transmitting a DTR frame, respectively. Therefore, The Equation (4) can be rewritten, as follows:  
\[S=\frac{E[P]}{T_{succ}-T_{fail}+\frac{\left(1-P_{tr}\right)\delta/P_{tr}+T_{ fail}}{P_{s}}} \tag{6}\]  
We can obtain the highest throughput by maximizing the following expression, since \(E[P]\), \(\sigma\), \(T_{succ}\), and \(T_{fail}\) are fixed for all nodes.  
\[\frac{P_{s}}{(1-P_{tr})\delta/P_{tr}+T_{fail}}=\frac{n\tau(1-\tau)^{n-1}}{T^{ \prime}_{fail}-\left(1-\tau\right)^{n}\!\!\left(T^{*}_{fail}-1\right)} \tag{7}\]  
where \(T^{*}_{fail}=T_{fail}/\sigma\) is the collision duration measured in unit time slot \(\sigma\). After some simplifications, it yields the following equation.  
\[\left(1-\tau\right)^{n}-T^{*}_{fail}\!\!\left[n\tau-\left[1-\left(1-\tau\right) ^{n}\right]\right]=0 \tag{8}\]  
Assuming \(\tau<<1\), it results in the following.  
\[\left(1-\tau\right)^{n}\approx 1-n\tau+\frac{n(n-1)}{2}\tau^{2} \tag{9}\]  
Thus, \(\tau\) can be approximately derived from the following equation.  
\[\tau=\frac{\sqrt{\left[n+2(n-1)\!\left(T^{*}_{fail}-1\right)\right]/n-1}}{(n- 1)\!\left(T^{*}_{fail}-1\right)}\approx\frac{1}{n\sqrt{T^{*}_{fail}/2}} \tag{10}\]  
Finally, we obtain an approximately optimal contention period configuration by substituting the above into Equation (1).  
\[W\approx n\sqrt{2T^{*}_{fail}} \tag{11}\]
# Assessment of Mechanical Properties of Corroded Prestressing Strands
## 4 Prestressed Concrete Beams with Corroded Strands
### Test Result  
Figure 16 shows the load-displacement curves of the specimens and an image of the wire fractures after the tests were conducted. Table 3 summarizes the strength, displacement, and failure modes of the specimens. Considering the material properties in Table 2, the non-corroded RB's nominal strength was 159.3 kN. The strength and displacement in the experiment were 163.9 kN and 18.40 mm, respectively. After the yielding of the strand and rebar, the compressed concrete reached a crushing failure mode. After the QB member experienced one wire fracture, it then reached a crushing failure. The strength and displacement at the moment of fracture were 161.0 kN and 17.89 mm, respectively. The corrosion reduced the strength and flexural capacity by 1.77% and 2.77% compared to the RB member. This means that the corroded strand had a limited influence on the strength of the member due to sufficient bond between the strand and surrounding grout.  
MB1 and MB2, which had corrosion in the center of the span length, showed strength decreases of 10.68% and 17.27% and decreases in the displacement capacity of 12.28% and 13.15%, respectively. Both specimens experienced a compression failure after two wire fractures. Additionally, four wires failed in MB2, which means that several wires can fail at one time. The corrosion in the strands at the location of the maximum moment significantly influences the ultimate behavior of a PSC beam.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
###### Abstract
Resource management techniques could be static or dynamic depending on whether the environment is static or dynamic. In the latter case, the dynamicity of traffic load, user positions, user mobility, QoS requirement, and base station density are considered. Different RRM strategies for improving spectral efficiency have been proposed, such as efficient static or dynamic channel allocation, transmit power control, spectrum management, cache management, link adaptation, user association, beamforming, and handover criteria.
## 3 Results and Discussion
### Multivariate Modeling for Universal Calibration
\begin{table}
\begin{tabular}{c c c c c} \hline
**Element** & **Algorithm** & **Parameters** & **RMSEP** & \(\mathbf{r}\) \\ \hline \multirow{3}{*}{\begin{tabular}{c} Si \\ (0.03–36.00\%) \\ \end{tabular} } & FP & 1.42 & 0.98 \\ \cline{2-5}  & KRLS & \(\lambda=0.010\) & 1.82 & 0.96 \\ \cline{2-5}  & IC-PLS & LV\({}_{1}=2\), LV\({}_{2}=1\) & 1.52 & 0.91 \\ \hline \multirow{3}{*}{\begin{tabular}{c} Al \\ (0.17–11.40\%) \\ \end{tabular} } & FP & 3.88 & 0.92 \\ \cline{2-5}  & KRLS & \(\Lambda=0.023\) & 1.57 & 0.68 \\ \cline{2-5}  & IC-PLS & LV\({}_{1}=2\), LV\({}_{2}=2\) & 0.96 & 0.90 \\ \hline \multirow{3}{*}{\
\begin{tabular}{c} Ti \\ (0.01–1.00\%) \\ \end{tabular} } & FP & 0.09 & 0.89 \\ \cline{2-5}  & KRLS & \(\Lambda=0.002\) & 0.10 & 0.65 \\ \cline{2-5}  & IC-PLS & LV\({}_{1}=2\), LV\({}_{2}=1\) & 0.05 & 0.94 \\ \hline \multirow{3}{*}{
\begin{tabular}{c} Mn \\ (0.01–1.75\%) \\ \end{tabular} } & FP & 0.11 & 0.92 \\ \cline{2-5}  & KRLS & \(\Lambda=0.001\) & 0.16 & 0.83 \\ \cline{1-1} \cline{2-5}  & IC-PLS & LV\({}_{1}=5\), LV\({}_{2}=2\) & 0.13 & 0.88 \\ \hline \end{tabular}
\end{table} 
Table 2: The results of the algorithms’ implementation for different elements.
# 3D Convolutional Neural Networks for Remote Pulse Rate Measurement and Mapping from Facial Video
## 2 Related Works
### Imaging Photoplethysmography
#### 2.1.2 Image Processing  
The face is the most exploited region of interest (ROI) [2]. Different automatic face-tracking algorithms have been exploited over the past years. [PERSON] et al. proposed to resize the bounding box provided by the Viola-Jones face detector [44]. [PERSON] et al. proposed to select only skin pixels by prior skin detection [45] and to define custom sub-regions from the face lightness distribution [46]. The cheeks and forehead correspond to other custom regions that were particularly tracked [46; 47] using deformable model fitting. Block-based (or grid) methods that ensure spatial subdivision were proposed to increase the signal-to-noise ratio by retaining only the most relevant cells [48].  
Some authors proposed to work with color spaces different from the standard RGB. Color spaces like \(L^{*}u^{*}v^{*}\) or \(L^{*}a^{*}b^{*}\) (developed by the International Commission on Illumination) that allow luminance-chrominance representation were particularly employed [45]. Spatial averaging is ultimately performed to transform 2D images into a 1D signal, each image being transformed to a scalar value.
# Predicting Geotechnical Parameters from Seismic Wave Velocity Using Artificial Neural Networks
## 4 Artificial Neural Networks Approach  
During the past few years, artificial neural networks (ANNs) based modeling has been gaining popularity in the field of geotechnical engineering [53]. ANNs can learn complex nonlinear relationships between parameters from many data [54]. The methodology of ANN is based on the human brain activity of processing data. Much like the human brain, ANN has a large number of interconnected cells called neurons [55]. There are connection links between the neurons to transfer signals from one neuron to the other. ANN consists of three different layers (i.e., input layer, hidden layers, and output layer). Information is passed from the input layer through hidden layers to the output layer. The hidden  
\begin{table}
\begin{tabular}{c c c} \hline
**Soil Type** & **Liquid Limit** & **Plasticity Index** \\ \hline Clay loam & 23 & 9 \\ Silty clay (gray) & 30 & 13 \\ Silty clay (brown) & 40 & 18 \\ Silty loam & 32 & 6 \\ Loess & 32 & 6 \\ \hline \end{tabular}
\end{table}
Table 1: Type of soil used for the tests.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Type of Data** & **Parameters** & \multicolumn{3}{c}{**Ranges**} \\ \cline{3-4}  & & **Max** & **Min** \\ \hline General & Plasticity Index & 21.87 & 2.03 \\  & LL (\%) & 45.43 & 17.39 \\  & Dry density (kg/m\({}^{3}\)) & 2240.58 & 1465.69 \\ Field & Water content (\%) & 18 & 3.87 \\  & Velocity (m/s) & 1128.99 & 67.12 \\  & Dry density (kg/m\({}^{3}\)) & 2240.58 & 1465.69 \\ Lab & Water content (\%) & 20.53 & 4.74 \\  & Velocity (m/s) & 1675.07 & 57.48 \\  & Dry density (kg/m\({}^{3}\)) & 2246.59 & 1484.71 \\ Field plus Lab & Water content (\%) & 20.96 & 3.13 \\  & Velocity (m/s) & 1705.07 & 7.23 \\ \hline \end{tabular}
\end{table}
Table 2: Parameters and ranges.  
layers process the received signals from the input layer then transmit the information to the output layer. The output layer receives the processed information from the hidden layer and executes the outputs. ANN is capable of learning highly complex relationships which are difficult to solve by traditional computational techniques. The performance of an ANN model depends on the quality and the size of the database. Erroneous and too small databases affect the accuracy of the model performance. ANN can be of different types, i.e., feed-forward neural networks, recurrent networks, and stochastic neural networks depending on the number of layers, the activation function and training algorithm [55]. The vital part of ANNs approach is the activation function, which introduces nonlinearity to the network to solve complex problems. Examples of different activation functions that can be used are linear, binary step, sigmoidal, and hyperbolic tangent sigmoid.
# Deep Learning-Based Applications for Safety Management in the AEC Industry: A Review
## 3 Review of DL and ML Safety Applications
### Jsm  
#### 3.2.1 Workers' Unsafe Behavior Detection  
On-site surveillance videos or images have been used for automated unsafe behavior detection in recent years. Variables such as hard hats, safety vests and workers can be detected by using certain computer vision techniques (e.g., a background subtraction algorithm [89], the histograms of oriented gradients (HOG) method [90], and the scale-invariant feature transform (SIFT) [91]). Nowadays, such methods which require much work for feature extraction are being replaced by DL gradually.  
[PERSON] et al. [92] developed a CNN-based framework that could determine if workers (even they are moving) were wearing hard hats on the construction site. [PERSON] et al. [93] modified a CNN to detect workers' hard hats, and the model produced excellent results in the mean average precision (mAP) performance metric. Similarly, the Faster R-CNN [94] and SSD methods [95] were also employed to detect hard hats.  
[PERSON] et al. [96] modified the Faster RCNN to identify if workers equipped harnesses properly. [PERSON] et al. [97] employed a VGG-16 model to detect if safety guardrails were installed correctly to prevent workers from falling from heights. [PERSON] et al. [98] integrated a Gaussian mixture model (GMM) with CNNs to detect roofers on roof construction sites. This research can alleviate roof site fall risks.  
In the unsafe activities identification area, [PERSON] et al. [19] coupled a long short-term memory (LSTM) model [99] with CNNs to identify if the worker would climb a ladder unsafely [19]. [PERSON] et al. [100] developed an image-based risk prevention system to display the safety-related information of each construction worker on a wearable augmented reality (AR) device. [PERSON] et al. [101] utilized a Faster R-CNN to determine workers' activities based on construction site images. Considering that temporal information is necessary for dynamic activities detection, [PERSON] et al. [102] later improved the framework for video-based worker activity recognition by helping the temporal information emerge. Some researchers have also investigated construction vehicle detection using DL. [PERSON] et al. [103] employed a region-based FCN to detect construction vehicles. [PERSON] et al. [104] used a Faster R-CNN to identify the spatial relationship of workers and excavators on construction sites. This study provided a basic prototype of the site safety alert system, which can prevent workers from being hit by heavy equipment. [PERSON] et al. [105] used a Faster R-CNN to identify on-site workers in diverse poses against complex backgrounds.
## 4 Discussion
### Behavior of Residual Space Charges  
The main difference between repetitive breakdowns and single ones is that numerous residual space charges generated during the preceding discharge remain in the electrode gap, thereby affecting subsequent breakdown processes [13; 14; 15; 27; 28; 29]. The impact of residual space charges on subsequent breakdown can be roughly divided into two aspects: (1) distorting the re-applied electric field, and (2) affecting the initiation and development of streamers [30; 31]. Therefore, this section analyzes the impact of switch parameters on repetitive breakdowns based on these two aspects as follows.
# Image Resolution Enhancement of Highly Compressively Sensed CT/PET Signals
###### Abstract  
One of the most challenging aspects of medical modalities such as Computed Tomography (CT) as well hybrid techniques such as CT/PET (Computed Tomography/Positron emission tomography) and PET/MRI is finding a balance between examination time, radiation dose, and image quality. The need for a dense sampling grid is associated with two major factors: image resolution enhancement, which leads to a strengthening of human perception, and image features interpretation. All these aspects make an unsupervised image processing much easier. The presented algorithm employs super-resolution-reconstruction with high accuracy motion fields' estimation at its core for Computed Tomography/Positron Emission Tomography (CT/PET) images enhancement. The suggested method starts with processing compressively sensed input signals. This paper shows that it is possible to achieve higher image resolution while keeping the same radiation dose. The purpose of this paper is to propose a highly effective CT/PET image reconstruction strategy, allowing for simultaneous resolution enhancing and scanning time minimisation. The algorithm aims to overcome two major obstacles--image resolution limitation and algorithm reconstruction time efficiency-by combining a highly-sparse Ridgelet analysis based sampling pattern as well as PET signal sensing with super-resolution (SR) image enhancement. Due to the diverse nature of Computed Tomography, the applied Ridgelet analysis arguing its usability turned out to be efficient in reducing acquisition times in regard to maintaining satisfying scan quality. This paper presents a super-resolution image enhancement algorithm designed for handling highly sensitively compressed hybrid CT/PET scanners raw data. The presented technique allows for improving image resolution while reducing motion artefacts and keeping scanning times at pretty low levels.  
CT; PET; super-resolution; compressed sensing +
Footnote †: journal:
## 2 System Model
### Adaptive Threshold Technique  
In the single input single output (SISO), an optical wireless communication system information is received with a negligible amount of interference. However, in the proposed visual MIMO system, an ambient light and adjacent channel interference degrade the signal to interference noise ratio (SINR), which leads to a significant decrease in the BER performance. This problem can be addressed with the use of an adaptive threshold technique as illustrated in Figure 10. In the proposed technique, the LEDs  
Figure 8: Flow chart of the particle filter tracking.  
Figure 9: Estimation of (QR)Quick response position code using Particle filter tracking.  
present in the LEA are divided into three subgroups, namely group-1, group-2 and group-3 based on their spatial distribution. LEDs placed in the interior, intermediate, and outer region of LEA are selected to form group-3, -2 and-1, respectively. The LED subgroups are highlighted with a yellow, green, and blue color marker as illustrated in Figure 11. This grouping of LEDs provides a reliable tool to minimize the interference created by adjacent LEDs.  
The proposed ATT involves two steps. First, estimate the LED status with a fixed threshold and the second step adaptively changes the threshold value based on the adjacent LED status. In the first step, the status of each LED in the LEA is calculated by finding the average intensity of all the pixels belonging to that LED. The status of an LED is determined by setting the threshold value for this average intensity. By experimental analysis, the threshold is set to 220 in the grayscale level in the absence of interference. In the second step, the threshold value for each LED is altered based on the status of its immediate horizontal and vertical neighbor LEDs. If group-1 LEDs are OFF, then the status of their immediate horizontal and vertical neighbor LEDs is noted. If those LEDs are ON, then the threshold is increased to 235 and the status of the group-1 LEDs is corrected. Similarly, a new threshold of value 240 and 245 are set based on adjacent LEDs for group-2 and -3, respectively and their status are corrected.
## 5 Results
### Vision-Based Versus Prompt-Based Approach: Results
#### 5.2.2 Usability  
As Figure 6b shows, questions 3 to 4 asked the participants to rate the overall performance of the approaches. For Q3, participants showed no statistically significant difference between the two approaches (\(Z=-0.51\), \(p=0.61\)), with vision-based (\(M=5.50\), \(SD=1.31\)) and prompt-based (\(M=5.67\), \(SD=1.30\)) approaches being relatively similar. Also, for Q4, participants showed no statistically significant difference between the two approaches (\(Z=-1.14\), \(p=0.25\)), with vision-based (\(M=4.83\), \(SD=1.85\)) and prompt-based (\(M=5.58\), \(SD=0.90\)) approaches being relatively similar.  
Figure 6: Questionnaire results from comparing perceived user experience between vision-based and prompt-based approaches. The significance levels are as follows: * \(p<0.05\); ** \(p<0.01\).
## 7 Conclusions  
Sub-surface deployment of passive wireless sensors probed from the surface by using a GPR (Ground Penetrating RADAR) provides a compelling scenario meeting user expectations and radiofrequency emission compliance. However, using GPR beyond its expected design reaches the limitation of the timebase stability with a local sampling rate drift well above the sensor delay variation with the sensed environment conditions. Furthermore, most commercial GPRs operate in pulsed mode in which the wavelength is fixed and frequency varies with surrounding permittivity, as opposed to surface acoustic wave transducers operating at a fixed frequency range defined by the spatial period of electrodes patterned on a piezoelectric substrate. Knowing these limitations, mitigation strategies include replacing the drifting analog timebase generator with a stable digital solution and using a wideband antenna in addition to deploying sensors in environment with stable permittivity such as concrete, so that the antenna operating frequency remains within the transducer passband. This prospective study must now be completed with a practical deployment scenario in which the benefit of the embedded sensor complementing the non-destructive GPR characterization is to be addressed.
# Passivity-Based Control for a Micro Air Vehicle Using Unit Quaternions
## 2 Preliminaries
### Passivity-Based Control Approach  
Passivity is a fundamental property exhibited by many physical systems which involves energy dissipation and transformation. It is related to the notion of stability in an input-output sense, that is, a system is stable if bounded input energy supplied to the system produces bounded output energy. Passivity-based control methodology relies on synthesizing control law which render the closed loop system passive [26].  
Let us consider a system described as follows  
\[\left\{\begin{array}{l}\dot{x}(t)=f(x)+g(x)u(t)\\ \dot{g}(t)=h(x)\end{array}\right. \tag{3}\]  
where \(x\in\mathbb{R}^{n}\), \(u\in\mathbb{R}^{m}\) and \(\ddot{y}\in\mathbb{R}^{m}\), \(f(x)\), \(g(x)\) and \(h(x)\) are smooth functions. There is a function \(w\left[u,\dot{y}\right]\) called supply rate, such that is locally integrable, i.e.,  
\[\int\limits_{t_{0}}^{t_{1}}w\left[u\left(t\right),\dot{y}\left(t\right)\right] dt<\infty,\forall t_{0}\leq t_{1}\]  
If there is a function \(H(x)\geq 0\), \(H(0)=0\), such that  
\[H(x(t_{1}))-H\left(x(t_{0})\right)=\int\limits_{t_{0}}^{t_{1}}w\left[u\left(t \right),\dot{y}\left(t\right)\right]dt-d(t) \tag{4}\]  
then, (3) is said to be a dissipative system. When \(w\left[u\left(t\right),\dot{g}\left(t\right)\right]=\ddot{y}^{T}\left(t\right) u\left(t\right)\), (3) represents a passive system. Here, \(H(x)\) represents the storage function and \(d(t)\) is the dissipated energy function. For the system (3), passivity is equivalent to the existence of a scalar \(H(x)\) such that,  
\[(\
abla_{x}H)^{T}f(x)\leq 0,\ h(x)=g^{T}(x)\
abla_{x}H\]  
Figure 1: Axis-angle representation of a rotation.