esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 System Model
### Energy Cost Model  
According to the principle that the multi-modal transducer focuses its beam energy and emits toward a certain orientation spatially, the energy conservation theorem is used for modeling the energy cost model for simplicity. Although it can be modeled as a 2D plane if the nodes depths are identical, we have to take into consideration the actual situation that the multi-modal transducer works in a 3D environment. Thus, the following question is raised: compared with the omni-directional one, how does a transducer with \(\pi/3\) beamwidth perform regarding energy cost?  
Assuming that the radiant energy is uniformly distributed in the space that it covers, the volume of 3D space that a beam covers can be expressed as its energy cost. So the costs for an omni-directional transducer and directional transducer radiating radius \(r\) are individually denoted as \(C_{O}(r)\) and \(C_{D}(r)\), as depicted below in Figure 5.  
We know the volume of the whole sphere is \(C_{O}(r)=\frac{4}{3}\pi r^{3}\), while \(C_{D}(r)\) can be calculated by dividing the volume into two parts, a inverted wedge and a dome. Their volumes can be calculated by a definite integral operation as follows:  
Figure 4: Nodes connection feasibility of different operating modes. Solid lines with arrows represent active communication links.  
Figure 5: Spatial radiation range of omni-directional and directional models.  
\[\begin{split} V&=V_{\text{w}edget}+V_{\text{dome}}\\ &=\int_{0}^{\frac{\sqrt{3}}{2}r}\pi(\frac{1}{\sqrt{3}}z)^{2}dz+ \int_{\frac{\sqrt{3}}{2}r}^{r}\pi(r^{2}-z^{2})dz\\ &=\frac{\pi}{9}z^{3}|_{0}^{\frac{\sqrt{3}}{2}r}+\pi z(r^{2}- \frac{1}{3}z^{2})|_{\frac{\sqrt{3}}{2}r}^{r}\\ &=\frac{\sqrt{3}\pi}{24}r^{3}+\frac{2\pi}{3}r^{3}-\frac{3\sqrt{3 }\pi}{8}r^{3}\\ &=\frac{2-\sqrt{3}}{3}\pi r^{3}\end{split} \tag{16}\]  
Finally, the relative energy cost between the omni-directional and directional models are achieved, and the energy cost ratio (ECR) is defined as follows:  
\[ECR_{D}^{\text{O}}(r)=\frac{\frac{4}{3}\pi r^{3}}{\frac{2-\sqrt{3}}{3}\pi r^{3 }}=4(2+\sqrt{3}) \tag{17}\]  
This value is approximately 14.93, meaning that directional transducer saves about 93% energy on the condition that they transmit the same distance. The result is used for evaluating our algorithm in the next section.
## 3 Fabrication and Integration
### Structure  
The adopted structures for CubeSat designs have become quite mature in recent years. However, they inherit a fixed construction approach from the early CubeSat missions, where all subsystem  
\begin{table}
\begin{tabular}{c c} \hline \hline  & **Mass [g]** \\ \hline Antenna module & 30 \\ Attitude determination and control system & 400 \\ Secondary payload (OC-OBC) & 40 \\ Break-out board & 10 \\ Inter-stage boards & 45 \\ Electrical power supply & 200 \\ Transceiver & 50 \\ Onboard computer & 40 \\ Data concentrator & 45 \\ Break-out board & 10 \\ Primary payload (FIPEX) & 160 \\ Solar panels & 360 \\ Structure & 390 \\ Harnessing & 120 \\ TOTAL & 1900 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SE01’s mass budget.  
Figure 7: SE01 subsystem layout.  
boards have a unique way of being stacked. Figure 8 illustrates such a configuration for a 2U CubeSat structure. The materials used for structural purposes are basically aluminum alloys for aeronautical applications, 7075 and 6061, as well as austenitic stainless steel, A2 and A4. However, aluminum materials must be surface-treated with a hard anodize coating, which is non-conductive, in order to prevent cold welding and corrosion. If conductive surfaces are required, aluminum should be treated with chromate conversion coating. Moreover, the use of steel in CubeSats is restricted only to fasteners or mechanical parts, to avoid magnetic field disturbances.
## 2 Packet-Based Traversal Time Hop Count Analysis (TTHCA)  
The rationale behind TTHCA was to develop a wormhole detection algorithm able to manage all variants of wormholes and implementable for all types of MANET devices and network scenarios, without incurring significant computational and network costs. TTHCA has been designed as an extension to the AODV routing protocol allowing the source node to receive, in addition to HC information, the total routing packet processing delay \(\mathit{\Delta T}_{\mathit{TOT}}\) during an AODV route discovery phase. Using \(\mathit{\Delta T}_{\mathit{TOT}}\) together with the RTT of a route, the source node can calculate the PTT of the route and compare it with the HC to identify potential wormhole routes. If PTT in relation to HC is greater than a pre-set threshold, the route discovery procedure is repeated until a safe route is found. To obtain \(\mathit{\Delta T}_{\mathit{TOT}}\) for a route, each intermediate node must measure the packet processing times of both the RREQ (\(\mathit{\Delta T}_{\mathit{RREQ}}\)) and RREP messages (\(\mathit{\Delta T}_{\mathit{RREP}}\)) and include these values in a new parameter within the RREP packet.  
As alluded in Section 1.1, TTHCA seeks to integrate the core concepts of RTT-based measurement with HC analysis, and while these separate mechanisms have proven effectual as wormhole detection techniques, their underlying design imposes certain inherent limitations in both achievable detection rates for MANET scenarios and the wormhole types each approach can identify. Before presenting the new TTHCA algorithm in detail, the next section briefly explores some of these constraints which TTHCA was specifically designed to address.
## 2 Materials and Methods
### Data Analysis and Responses  
NEXUS v1.7.1 software (VICON, Hauppauge, NY, USA) was used to export data from each sensor/marker of the EMG, force plates, and motion capture systems in a single (.csv) file that represented 30 cycles of repetitive bending. We developed a custom MATLAB code to import data from the Excel file. Data obtained from the force plate and motion capture system were filtered using a 2 nd-order, lowpass, digital Butterworth filter with a normalized cutoff frequency of 10 Hz. Meanwhile, the EMG was filtered using a Butterworth filter in the band [4; 32]. To evaluate variations across the bending/retraction cycles, we segmented the cycles using the position in the z direction of the upper-back reflective marker by detecting the bending start/end and retraction start/end portions. To ensure consistency, the middle 40% of the range of movement, scaled from 0 to 1, was selected, as shown in Figure 3. Each repetitive-bending task was divided into 30 distinct bending (BD), retraction (RT), and transition (TS) portions. The TS portion was defined as the spatial movement from the detected end of bending to the detected start of retraction, which represents the portion during which participants switched from bending to retraction.  
After segmenting the cycles into distinct portions based on the spatial location of the trunk, we calculated the responses from the EMG, force plates, and motion data. As the raw EMG data could not be directly used for processing, correlation, or comparison, we calculated the Root Mean Square (RMS) of the signal. Using the RMS, we determined the peak amplitude for each portion (bending/transition/retraction). Peak values were reported as the average of 50 datapoints before and after the detected peak. Similarly, we determined the peak values of the norm of the velocity of the upper-back, lower-back, and hip markers for each portion of cycles. Regarding the stability analysis, the maximum  
Figure 2: Schematic depicting (**top**) the experimental protocol with tasks and sub-tasks arranged in modules, and (**bottom**) experimental setup for performing trunk flexion tasks.  
distance of the COP (Center-of-Pressure) location from the neutral position for each portion was determined using the combined COP co-ordinates of both force plates with the distance formula. We also calculated the mean velocity of the COP.  
For statistical analysis, we used standard least squares with an emphasis on effect leveraging using JMP Pro(r) v16.1.0 software (SAS Institute, Cary, NC, USA), with the statistical significance level at a \(p\)-value \(<0.05\). Significant results were then followed by post-hoc paired comparisons using [PERSON]'s Honest Significant Difference (HSD) test where relevant. All the parametric model assumptions were validated before providing the mean (SD) for the levels of statistically significant effects.
## 3 Experiments and Simulation
### Comparison and Discussion  
The data of the initial sample set directly affect the accuracy of the Sobol sensitivity analysis method based on the ALK-SS model, so it is essential to ensure the accuracy of the simulation model. The experimental results of carbon-fiber structures with different crack lengths L were compared with the simulation results, as shown in Table 3 and Figure 10.  
Figure 8: Cumulative Fatigue Damage Flow Chart.  
Figure 9: Stress Cloud of Cracked Carbon Fibers.  
According to the data in Table 3 and Figure 10, the relative error between the simulated fatigue life results and the experimental results of the cracked carbon-fiber structures is within 10%. This indicates that the simulation model can accurately predict fatigue life and is highly consistent with the actual experimental data. Therefore, the finite element simulation model prediction method not only generates reliable response values for the training sample set but also provides an accurate data foundation for subsequent sensitivity analysis. This further verifies the effectiveness of the simulation model in predicting fatigue life and ensures the reliability of the analysis results.
# Energy-Efficient Internet of Drones Path-Planning Study Using Meta-Heuristic Algorithms
## 3 Meta-Heuristic Algorithms for Energy-Efficient IoD Path-Planning Description
### IoD Path Representation and Obstacle Modeling
#### 3.2.2 Obstacle Model  
In this paper, the obstacle is modeled as a half sphere as in [32], where the \(q^{th}\) obstacle is represented as  
\[Obs_{q}=(x_{q},y_{q},z_{q},r_{q}), \tag{7}\]where \((x_{q},y_{q},z_{q})\) is the coordinate of the obstacle center and \(r_{q}\) is the radius of the obstacle.  
For the \(k^{th}\) obstacle, a 3D coordinate can be expressed as follows:  
\[x_{k}=r_{k}\cos(\theta)\sin(\phi)+x_{k0}, \tag{8}\]  
\[y_{k}=r_{k}\sin(\theta)\sin(\phi)+y_{k0}, \tag{9}\]  
\[z_{k}=r_{k}\cos(\phi)+z_{k0}, \tag{10}\]  
where \((x_{k0},y_{k0},z_{k0})\) is the coordinate of the \(k^{th}\) obstacle's center, while \((x_{k},y_{k},z_{k})\) is the coordinates of the \(k^{th}\) obstacle. \(r_{k}\) corresponds to the radius of the obstacle, with \(\theta\in[0\;2\pi]\), and \(\phi\in[0\;\frac{\pi}{2}]\).
# Changes of Air Pollution between Countries Because of Lockdowns to Face COVID-19 Pandemic
## 3 Results and Discussion
### North America Region  
Variations in air pollutant concentrations based on the pre- and post-COVID-19 period for the United States of America and Canada from the North American geoeconomic region are found in Figure 2.  
Figure 2: \(Cont.\)  
In the USA, a national emergency was announced in the second week of March 2020 with a closure of services and other activities [86]. In March 2020, reductions in the concentration of NO\({}_{2}\) (\(-23\%\)), SO\({}_{2}\) (\(-137\%\)), O\({}_{3}\) (\(-7\%\)), PM\({}_{2.5}\) (\(-16\%\)), and PM\({}_{10}\) (\(-47\%\)) were observed. The highest decreasing ratio was obtained for SO\({}_{2}\) emissions compared with the pre-COVID-19 period (see Figure 2, USA). [PERSON] et al. (2020) investigated the changes in the air pollutants containing CO, NO\({}_{2}\), SO\({}_{2}\), PM\({}_{2.5}\) and PM\({}_{10}\) in the USA, considering the data during restriction periods of 2019 and 2020. The results showed that CO, NO\({}_{2}\), and PM\({}_{2.5}\) concentrations showed a decreasing trend of 19.3%, 36.7%, and 1.10%, respectively, while PM\({}_{10}\) and SO\({}_{2}\) increased by 27.8% and 3.81%, respectively, in five states of the USA during the period of lockdown.  
In Canada, a national lockdown was declared from 22 March to 2 May 2020. Our results are found in Figure 2, Canada. [PERSON] et al. [87] investigated the impact of the COVID-19 lockdown measures on air quality in Canada for the largest cities, including Toronto, Montreal, Vancouver, and Calgary, comparing the values of March-May 2020 with the same months of the previous 10 years (2010-2019). Results indicated that NO\({}_{2}\) and PM\({}_{2.5}\) demonstrated a decreasing trend in the presence of lockdown measures, while O\({}_{3}\) surface concentrations showed an increase up to a maximum of 21%.
# Feasibility of Optical Flow Field Measurements of the Coolant in a Grinding Machine
## 4 Measurement Results
### Verification of SIV
Deformations of the characteristic flow structures explain the increased standard deviation  
Figure 8: Measured and averaged velocity fields for a volume flow of 26.6 L/min in the top view and the side view of the MWF jet with PIV and SIV: (**a**) Average 2d velocity field from the top view. (**b**) Mean velocity and standard deviation (STD) of the top view measurement along the \(x\)-axis. (**c**) Average 2d velocity field from the side view. (**d**) Mean velocity and standard deviation (STD) of the side view measurement along the \(x\)-axis. for SIV. The deformation of the structures produces a sum of two velocity components: the motion of the entire structure plus the velocity of the deformation at its edges. The combination of the two velocity components increases the standard deviation while maintaining the mean value consistent. For the measurement of the side view, the standard deviation of SIV increases further by additional 0.4 m/s to 1.0 m/s (10.8%). This is caused by the superposition of the flow characteristics resulting from the observation along the width of the MWF flow. The structures from different depths cannot be distinguished from each other and a misallocation of the structures results in outliers which increases the calculated standard deviations. This assumption is confirmed by the unchanged standard deviation for PIV, where the observation of particles is reduced to the plane of the laser light sheet. This demonstrates that the PIV measurement provides a more precise flow measurement, but the SIV is an effective alternative. Although the measurement with SIV is less precise than PIV, especially for the side view, the velocity fields of the MWF supply flow measured by PIV and SIV are nevertheless consistent within the limits of the measured standard deviations. In addition, the confidence intervals of the measured velocities for both measurement methods also agree with the theoretically expected velocities. This confirms the assumption of the applicability of the characteristic flow structures of the MWF supply flow for a velocity measurement.
## 1 Introduction
[1] proposed a novel sinusoidal placement of a single FOS, illustrating its potential for monitoring a larger area by providing multi-directional strain measurements. The same authors [3] further proposed an original dual-sinusoidal placement, where two sinusoidal FOSs were placed 180\({}^{\circ}\) out of phase to each other, for a finer SHM system. Numerical and analytical results suggested that both embedded sinusoidal FOSs retained the multi-axial strain-measuring capability and provided complementary strain-measuring  
Figure 1: Schematic of typical FOS cross-section. areas [3]. Besides optimizing the placement pattern for the largest monitoring coverage, care should also be taken in the influence of FOS embedment on structural mechanical behavior, as well as sensing functionality. It is worthwhile to note that current research is the follow-up to (3) and will use the analytical modeling previously developed in (3). Many works focused on the influence of embedded FOSs due to their characteristics (e.g., diameter and coating material) [1; 2; 3; 18; 19; 20; 21; 28]. Edge effects, amplitudes, and period sizes of the sinusoidal placement pattern were also checked for their effects on the resulting principal strains but only under uniaxial tensile loading [1; 2; 3]. The influence of the dual-sinusoidal placement on structural mechanical behavior and sensing functionality under various loading conditions has not been considered yet. Based on previous research works [1; 2; 3], this article moves one step toward real-life applications by extending the simulation of a uniaxial tensile loading case to bending and torsion. Because even though offshore wind turbine blades are subjected to complex combined loads in service, primary states can be reduced to a combination of axial tension/compression, bending, and torsion. Moreover, this work extends a meso-scale numerical model (distributed FOSs embedded inside an adhesive bond layer between two CFRP laminae of \(0^{\circ}\) fiber orientation [1; 2; 3]) to a coupon-level numerical model of bonded CFRP laminates. As a result, this work helps to recognize the resulting strain patterns (strain components, levels, concentrations, and distributions) and sensing capabilities from an optimized dual-sinusoidal placement of FOSs in different loading cases, which can be useful for further quantitative studies and detailed sensor placement designs in large smart composite structures. Ultimately, through this research work, answers to some key questions can be found. For example, is the concept of dual-sinusoidal placement pattern beneficial? Can the pattern provide extra valuable information? Is the pattern suitable for accurate SHM of smart composites under various load conditions?
## 3 Sensitivity Analysis of Influencing Factors on Stress and Deformation of CFRDs
### Finite Element Model  
#### 3.2.1 Geometric Model  
The characteristics of the calculation and analysis model for the concrete-faced rockfill dam are delineated as follows: the dam is segmented into the concrete face slab, cushion layer, transition layer, and rockfill zone. The crest width of the dam measures 10 m, the thickness of the face slab is 0.5 m, the cushion layer has a horizontal width of 2 m, the transition zone extends 3 m horizontally, and the downstream face of the dam has a slope of 1:1.4. The valley is symmetrical, and the reservoir's water level reaches up to 10 m below the crest of the dam. The coordinate system for the calculations is defined with the X-axis along the dam axis, positive towards the right bank; the Y-axis extends along the river, with the positive direction pointing downstream; and the Z-axis is vertical, with the positive direction upwards. The sign convention for the calculation results is standardized: settlement is positive when upwards and negative when downwards; horizontal displacement is considered positive when moving downstream and negative when moving upstream; deflection is positive when facing upwards in the normal direction of the face slab and negative when facing downwards; and stress is regarded as positive under tension and negative under compression.  
The model's components are predominantly fashioned from the eight-node spatial hexahedral element, albeit incorporating a minor subset of the three-prism six-node element type within the ABAQUS (6.14) finite element software. Taking the 250 m high typical 3D concrete-faced rockfill dam with the fifth calculation scheme as an example, the standard cross-section is depicted in Figure 2 and the 3D finite element model is illustrated in Figure 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**No.** & **Dam Height/m** & \
\begin{tabular}{c} **Bank Slope** \\ **Gradient** \\ \end{tabular} &
\begin{tabular}{c} **Panel Phased** \\ **Construction** \\ \end{tabular} & **Water Storage** \\ \hline
1 & 200 & 1:0.50 & 1 & 1 \\
2 & 200 & 1:1.00 & 3 & 2 \\
3 & 200 & 1:1.50 & 2 & 3 \\
4 & 250 & 1:0.50 & 3 & 3 \\
5 & 250 & 1:1.00 & 2 & 1 \\
6 & 250 & 1:1.50 & 1 & 2 \\
7 & 300 & 1:0.50 & 2 & 2 \\
8 & 300 & 1:1.00 & 1 & 3 \\
9 & 300 & 1:1.50 & 3 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Calculation scheme.  
The computational boundary conditions entail fixing the base surface of the dam and the lateral margins of the left and right bank slopes, with the remainder of the domain being governed by a free boundary condition.
# Driver Drowsiness Detection by Applying Deep Learning Techniques to Sequences of Images
## 3 Materials and Methods
### Alternative I: Recurrent and Convolutional Neural Network
#### 3.1.3 Alarm Activation  
The following conditions must be met to alert the driver:  
* The driver is considered to be drowsy at a specific moment when the output of the analysis module is greater than a threshold, called _drowsiness_threshold_. This value ranges between 0 and 1.
* The driver has to be considered drowsy for multiple instants of the last 60 s to raise an alarm. This is determined by the variable _min_time_, which represents how many seconds the driver has to be drowsy before alerting him or her. This value ranges between 0 and 60.  
This way, when the driver is considered drowsy for at least the established minimum time, an alarm is raised. If the condition to raise an alarm continues after alerting the driver, a second alarm is not raised. Instead, another alarm is raised only if the conditions for alerting the driver are no longer met, and after that, drowsiness is detected again.  
To calculate the optimal values of variables _drowsiness_threshold_ and _min_time_, multiple tests were performed, which are described in Section 3.3.
# Experimental Study of LoRa Transmission over Seawater
## 3 Materials, Experimental Setup and Methods
### Weather Conditions  
The experiments performed in LOS scenario were conducted during sunny days with an average temperature of 28 \({}^{\circ}\)C. On the other hand, measurements done in obstructed LOS test case were carried out during winter on clear days with an average temperature of 4 \({}^{\circ}\)C. The amplitude of sea waves in both cases did not exceed 45 cm.