esa-sceva/satcom-chunk-collection huggingface dataset:
# Information-Aware Secure Routing in Wireless Sensor Networks
## 3 Model and Methods
### Status of Sensor Nodes  
In the WSN, the status of each node \(j\) is associated with two values: (1) The residual energy and (2) its distance to the sink node. The status of each node is used as a heuristic to find the optimal secure route. A node with more residual energy that is closer to the sink node is chosen as the next-hop node. To balance the energy consumption and transmission delay for a sensor node, we define a status value for each node:  
\[E_{j}=\frac{\eta e_{\langle j,sink\rangle}}{\eta e_{\langle j,sink\rangle}+e_{ rj}}, \tag{5}\]  
where \(e_{\langle j,sink\rangle}\) is the energy required to send a packet from \(j\) to the sink, and \(e_{rj}\) is the residual energy of node \(j\). We use a constant \(\eta\) to balance the weight of \(e_{\langle j,sink\rangle}\) and \(e_{rj}\).  
With Equation (5), we can designate a node as the next hop if it has more residual energy and is closer to the sink. We further define the value \(E_{L}\) for the entire path:  
\[E_{L}=\sum_{k=1}^{K}(\frac{\eta e_{\langle m_{k+1},sink\rangle}}{\eta e_{ \langle m_{k+1},sink\rangle}+e_{rm_{k+1}}}\times\frac{1}{K}). \tag{6}\]  
The value \(E_{L}\) of the entire path is obtained by adding the status values of each node. \(E_{L}\) is normalized to ensure that it is always less than 1.
# LESS: Link Estimation with Sparse Sampling in Intertidal WSNs
## 4 Utilization of Compressive Sensing
### Prime of Compressive Sensing  
Compressive sensing is an effective signal processing technique. By leveraging the presence of structure and redundancy in signals, it reconstructs signals with a sampling rate (SR) which is much lower than the Nyquist rate. Nowadays, it attracts lots of attention in statistics, approximation theory, information theory, and data analytics. It also has many applications in wireless and mobile networks, such as environmental sensing [46], data aggregation [23], and localization [47]. In this paper, we utilize compressive sensing to estimate link quality to decrease communication overhead, which is a first trial.  
Consider a time discrete signal \(x\) with a finite-length \(N\), \(x\in\mathbb{R}^{N}\). We measure \(x\) and observe:  
\[y=\Phi x+z=\Phi\Psi\theta+z \tag{4}\]  
where \(y\in\mathbb{R}^{M}\) are available measurements, \(\Phi\) is a known \(M\times N\) measurement matrix (corresponding to the scheduling strategy in Section 5), \(\Psi\) is a known \(N\times N\) representation matrix whose inverse converts \(x\) to a sparse representation \(\theta\), and \(z\) is an unknown noise term. When \(\theta\) is sufficiently sparse and \(\Phi\) obeys a condition known as the Restricted Isometry Property (RIP), \(x=\Psi\theta\) can be reconstructed by solving the convex optimization problem:  
\[\underset{\theta}{arg\!\min}\|y-\Phi\Psi\theta\|_{2}^{2}+\lambda\|\theta\|_ {1} \tag{5}\]  
where the parameter \(\lambda\) can be viewed as a Lagrange Multiplier making a trade-off between the estimation error and the sparsity of the solution. In this paper, we mainly focus on how to tailor compressive sensing for low-cost link quality estimation, i.e., the transform, measurement and constraint matrices. Interested readers can refer to [48] for technical details about the principle of compressive sensing.
# Investigation of Debonding Effect in Internal Replacement Pipe System Under Lateral Loading
## 3 Surface Preparation and Its Impact on Debonding
### Continuous IRP System with Different Levels of Adhesive Strength
#### 3.3.4 Maximum Stress and Failure Mechanism  
Figure 39 illustrates the equivalent stress distribution in the IRP of a continuous host pipe when subjected to a maximum load of 71.2 kN (16 kip). It can be observed that the maximum stress in the midspan region is approximately 1.106 MPa (0.160 ksi), which is well below the strength of ALTRA10 at 79.420 MPa (11.519 ksi). This indicates that the IRP does not experience any material failure or debonding from the host pipe under a maximum load of 71.2 kN (16 kip). This result suggests that debonding and material failure may not be significant concerns for an IRP within a continuous host pipe, but they become critical factors when it is used as a repair system in a pipeline with circumferential discontinuity.
# Evidence-Based Investigation of Coronary Calcium Score in Cardiac Computed Tomography
## 2 Materials and Methods
### Image Analysis
This allows us to finally calculate \(\alpha\), which corresponds to the Poisson noise, and \(\beta^{2}\), which represents the Gaussian noise. Table 2 presents the \(\alpha\) and \(\beta^{2}\) of all overweight and obese patients according to Equations (1) and (3). The mean and standard deviation values of \(\alpha\) and \(\beta^{2}\) in overweight patients were 0.19 \(\pm\) 0.03 and 8.36 \(\pm\) 0.13, respectively. The same values in obese patients were 0.26 \(\pm\) 0.08 and 11.41 \(\pm\) 1.17, respectively. The _innoise_ and _poissrad_ functions in the MATLABIM toolbox (R2022a; MathWorks, Inc., Natick, MA, USA) were then used to generate 30 sets of hypothetical overweight and 30 sets of hypothetical obese CT images for quantitative evaluation by adding noise components to CT images from 30 normal weight patients using the pre-acquired noise level parameters. Example CT images are presented in Figure 1. \begin{table}
\begin{tabular}{c c c} \hline
**Overweight** & **Poisson-Dependent Parameter (\(\alpha\))** & **Gaussian-Dependent Parameter (\(\beta^{2}\))** \\ \hline Patient 1 & 0.21 & 8.49 \\ Patient 2 & 0.16 & 8.35 \\ Patient 3 & 0.19 & 8.23 \\ \hline Mean \(\pm\) SD & 0.19 \(\pm\) 0.03 & 8.36 \(\pm\) 0.13 \\ Median & 0.19 & 8.35 \\ Minimum, maximum & (0.16, 0.21) & (82.3, 8.49) \\
95\% CI & (0.12, 0.25) & (8.03, 8.68) \\ \hline
**Obese** & **Poisson-Dependent Parameter (\(\alpha\))** & **Gaussian-Dependent Parameter (\(\beta^{2}\))** \\ \hline Patient 1 & 0.25 & 11.36 \\ Patient 2 & 0.19 & 10.29 \\ Patient 3 & 0.27 & 10.42 \\ Patient 4 & 0.33 & 12.27 \\ Patient 5 & 0.27 & 10.67 \\ Patient 6 & 0.23 & 11.10 \\ Patient 7 & 0.36 & 14.04 \\ Patient 8 & 0.39 & 12.60 \\ Patient 9 & 0.33 & 12.42 \\ Patient 10 & 0.38 & 12.57 \\ Patient 11 & 0.16 & 11.31 \\ Patient 12 & 0.15 & 9.74 \\ Patient 13 & 0.17 & 10.22 \\ Patient 14 & 0.21 & 11.62 \\ Patient 15 & 0.17 & 10.59 \\ \hline Mean \(\pm\) SD & 0.26 \(\pm\) 0.08 & 11.41 \(\pm\) 1.17 \\ Median & 0.25 & 11.31 \\ minimum, maximum & (0.15, 0.39) & (9.74, 14.04) \\
95\% CI & (0.21, 0.30) & (10.77, 12.06) \\ \hline \end{tabular}
\end{table} 
Table 2: Noise level descriptive statistics of CT images from overweight and obese patients.
# Fast Planar Detection System Using a GPU-Based 3D Hough Transform for LiDAR Point Clouds
## 3 Planar Detection System Using GPU-Based 3D Hough Transform
### CPU-GPU Hybrid System  
To enhance the speed performance in the peak value searching process, we optimise a traditional CPU-based searching process into a CPU-GPU hybrid architecture with high-speed performance. Figure 5 presents the flowchart of our proposed plane detection system, in which CPU and GPU devices are simultaneously utilised to improve the operation efficiency. The input datasets and output results of the system are raw point clouds and estimated plane parameters, respectively. The data transmission and computation occurring in the associated work between CPU and GPU devices are explained as follows.  
After dividing global point clouds into individual subsets, our proposed 3 DHT algorithm is applied to each point subset to generate their corresponding 3D Hough Space in parallel. Owing to the memory size and thread count of a CPU device, point subsets are subsequently copied from the  
Figure 4: Peaks searched from a 3D flag map.  
Figure 5: CPU–GPU hybrid architecture of fast planar detection system.  
CPU to the GPU to execute sequence procedures one by one. In the GPU memory, the 3D Hough space is created with three dimensions that represent the parameters theta \(\theta\), radius \(r\), and phi \(\varphi\), as the variables in Equation (1). The resolution, representing the defined size of each cell, of the Hough space is determined by the environmental condition during the initialisation process. The points in each subset are subsequently transformed into the Hough space in parallel. When completing the transformation and statistic processes of each subset, the Hough space is updated with complete mapping information. In each thread on the GPU device, variable r is computed based on different variable pair (\(\theta\), \(\varphi\)) and a point coordinate. These threads are computed simultaneously, so that the 3 DHT algorithm is processed in parallel.  
In individual Hough space buffer, the connected component labelling algorithm is applied to cluster cells with high values into separated clusters. Several peaks are extracted from the Hough space by parallel. These peaks' coordinates are converted to the parameter in Equation (1). All the extracted peak parameters are copied from GPU memory to CPU memory. During the plane merging process, if two planes are very similar or nearly belonging to a same plane, their plane parameters are merged together. If the distance of two peaks generated from two different subset \(P\) is less than a predefined threshold \(\kappa\), the two detected planes are considered as the same one and are merged into one plane.
## 2 Materials and Methods
### Assessments
#### 2.3.5 ECG Measurements  
The selection of the two exercise modalities--intermittent back muscle loading and cycling--was based on their relevance to real-life physical activities involving postural and aerobic demands. During the procedure, a 12-lead ECG was recorded for later analysis by means of using the CardioSout Multi ECG recorder. The analysed parameters were sBP, dBP, heart rate (HR), and ST-segment depression (sum of negative values of 12 leads).  
Study A (Figure 1): ECG signals were captured every 15 s at baseline, during graded exercise on a cycle ergometer at intensities of 50 W (light), 100 W (moderate), 150 W (heavy), and 200 W (maximal), and throughout a 3 min recovery phase. Prior to testing, participants were attached with ECG electrodes and instructed to remain at rest for 30 min to establish stable baseline readings. This was followed by an additional minute of rest while seated on the cycle ergometer. The exercise protocol required participants to pedal at a steady cadence of 60 rpm, with workload increasing by 50 W each minute, beginning at 50 W and progressing to 200 W. Post-exercise, participants remained seated for 3 min while ECG data were continuously collected to assess recovery. A 12-lead ECG and arm-cuff ABP readings were taken at the conclusion of each workload stage. Changes in muscle StO\({}_{2}\) in the muscle (vastus lateralis) were also monitored continuously throughout the protocol. ECG and ABP measurements continued during the recovery period as well.  
Study B (Figure 1): The subjects performed three sets of exercise for the back muscles, consisting of trunk extension exercise performed on a back extension bench supported by a trainer. Before the measurements began, the ECG electrodes were applied to the participant's body and participants sat and rested for 30 min to assure stable baseline readings. Resting parameters were collected at the end of 30 min. After the test, the participants rested for 3 min while ECG data were continuously recorded for recovery analysis. The torso-stretching movement was performed for 10 s and the return to the starting position (torso bending movement) for 20-25 s (Figure 1), with three repetitions. Between each repetition, the subjects rested for 3 min while standing. After the exercise, the subjects rested for 3 min to monitor recovery. ECG measurements and the change in \(\mathrm{StO_{2}}\) in the muscle (erector spinae) were recorded continuously throughout the study; ABP was measured at the end of each set.
# GenTrajRec: A Graph-Enhanced Trajectory Recovery Model Based on Signaling Data
## 6 Discussion  
Our GenTrajRec model achieves state-of-the-art performance and provides a novel way of utilizing mobile signaling data. It has been adopted by China Mobile Communications Corporation in their project \"Key Technology Project for Spatiotemporal Big Data Products\", to improve the positioning accuracy of their signaling data. We use a feature fusion part to combine the raw and normalized features to obtain better representations. Moreover, unlike most of the work that considers only the trajectory sequence and uses positions of the trajectories to construct the graph, to deal with the uneven time intervals, we construct the graph by considering the time intervals between the two positions in the trajectories, which greatly improves the performance of the model. However, there are still some problems with our model: as is shown in Figure 8's third picture, this case shows that our model still can capture the transition patterns of the mobile phone users. However, the predicted positions are not fixed, the possible reasons might be as follows: (1) the cell towers' embeddings are area embeddings, and the predicted position of the user might be affected by this; (2) human mobilities have the feature of uncertainty and all the positions in a certain area could all be reasonably predicted places. Moreover, our work can use more complicated modules to model the global and local features of the base stations. All these problems could be tasks in future work for improvement.
# Rail Diagnostics Based on Ultrasonic Guided Waves: An Overview
## 7 Land-Based Systems Implementation-Evolution: Early Rail Defect Detection Capability
### Signal Pre-Processing
#### 7.3.1 Acquired Signals  
The impact of noise in the received signal can be reduced by band-pass filtering, making easier the task of detecting the reflections in the steps to follow. The center frequency and bandwidth of the filter can be set to be the same of the excitation signal. During pre-processing, the signals are converted to the frequency domain.
## 8 Results  
### Coil Parameters  
In the first step of experimental evaluation, the coil parameters were determined by the measurement setup shown in Figure 17 and by the procedure detailed in [28], i.e., the S-parameters of the data coil link were acquired with a vector network analyzer, converted to Z-parameters and subsequently to self-inductance \(L_{i}=\text{Im}(Z_{ii})/\omega_{0}\) and mutual inductance \(M_{ij}=\text{Im}(Z_{ij})/\omega_{0}\). The measured coil and coupling parameters for various coil separation distances \(d_{34}\) acquired in air are listed in Table 3 together with the computed values according to (6) and (7). With a maximum relative deviation of 5.2 %, simulated and measured values are in close agreement.  
With the same setup, the coils were analyzed in the environment of homogenous pork muscle tissue. The frequency behavior of the effective coil resistances \(R_{i}=\text{Re}(Z_{ii})\) and the mutual reactance \(X_{12}=\text{Im}(Z_{12})\) are compared to the characteristics of an air coil system in Figure 18: While the resonance frequencies of both data coils being subject to tissue are decreased and the width of the resonance peak is increased due to additional losses, the parameters show only minor deviations at the target operational frequency of 13.56 MHz. This can be attributed to the low number of turns of the  
Figure 16: Prototype circuits implementing the custom NFC transceiver: A shield including the analog front-end and the coil connectors is mounted on a general purpose control unit including DC/DC converters, an RF microcontroller, and a low-power FPGA. The net area required for the NFC transceiver is enclosed by the colored contours.  
Figure 17: Measurement setup to determine the S-parameters of the coil systems in air and pork tissue environment.  
presented data coils, and must be reevaluated for deviating designs with increased overall dimensions, decreased trace pitch and increased number of turns. Consequently, the given system implementation shows comparable results in both media, and is further evaluated in air for the sake of simplicity.
## 3 Cooperative Schemes  
As previously mentioned, multi-cell VLC systems, i.e., composed of multiple optical APs, are being deployed to create a seamless and reliable network. However, when multiple VLC APs are deployed, the intrinsic overlapping of light coverage areas produces a harmful inter-cell interference as long as cooperative or precoding schemes are not used. On the other hand, this overlapping is required for guaranteeing a proper illumination level satisfying lighting standards.
# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
## 2 Data and Methods  
### MODIS Instrument  
The Moderate Resolution Imaging Spectrometer (MODIS) is one of the instruments on board the Terra and Aqua satellites [30]. It has a total of 36 spectral bands, of which 20 are reflective solar bands (RSBs) and the remaining 16 are thermal emissive bands (TEBs). Table 1 shows some of the MODIS channel specifications [30; 31], including band numbers, primary use, bandwidths, center wavelength, spectral radiance, and signal-to-noise-ratio (SNR) values. In Table 1, each of the bands 3 and 4 has 20 detectors and a nadir spatial resolution of 500 m, whereas each of the bands 8-16 has 10 detectors and a nadir spatial resolution of 1 km. [PERSON] and [PERSON] [30] discussed the MODIS radiometric calibration procedure, including pre-launch and in-orbit calibration, in-orbit monitoring of detector response and noise characterization, and solar diffuser degradation of the MODIS instrument. They concluded that for both the RSBs and TEBs the overall in-orbit performance of the instrument is satisfactory, and they identified less than 10 out of 490 detectors to be either noisy or inoperable. For working detectors the MODIS L1B algorithm converts the digital response from the sensors to TOA reflectance factors for the RSBs and in-band spectral radiances for both the RSBs and TEBs. For non-functional or noisy detectors the L1B algorithm uses interpolation from the nearest adjacent working detectors to derive the values. For a typical scene the instrument calibration accuracy requirements are \(\pm 2\%\) for RSB reflectance factors and \(\pm 5\%\) for the RSB radiance product.
## 3 Waste Heat Recovery  
A large proportion of fuel energy in an internal combustion engine is lost as exhaust gas heat, a fact that reduces the overall engine efficiency of conventional vehicles [22]. Typical state-of-the-art waste heat recovery (WHR) technologies include mechanical or electric turbocompounding, bottoming cycles, and thermoelectric generators [1; 5]. A mechanical turbocompound system uses the exhaust gas energy to spin a turbine, which can then be linked to the crankshaft, offering more output power and as much as 5% better fuel economy. On the other hand, an electric turbocompound setup can exploit the exhaust gas energy to gain electrical power that can be saved in a battery or used to support several electric components of the vehicle, increasing the fuel economy up to 10%. Currently, approximately a 2% reduction in fuel consumption can be offered by thermoelectric generators, which use the exhaust gas heat to produce electricity directly via thermoelectric conversion means [2; 17; 20]. This study focuses on the bottoming cycle type of waste heat recovery, which employs thermodynamic cycles to gain energy out of the exhaust gas heat. These cycles operate with a working fluid and heat exchangers, which absorb the heat of exhaust gases to change the fluid's state, which is then channeled towards a turbine for power generation. The most widely used thermodynamic cycles for this application are Rankine and Brayton, but in this particular work only the Rankine cycle is taken into consideration as the most widely-accepted and employed means of waste heat recovery [23; 24]. A typical parameter that can predict the efficiency of a waste heat recovery system is the temperature of the exhaust gases, and as this temperature is increased, the amount and quality of energy that can be exploited will increase. Specifically, in the automotive industry the main types of vehicles that can benefit from a waste heat recovery system are the heavy-duty vehicles. These vehicles not only consume fuel in an excessive manner, but also produce great amounts of hazardous emissions, including NOx and CO\({}_{2}\) gases. The implementation of a waste heat recovery system in such vehicles can offer energy recovery benefits in various forms that can further increase the efficiency of the powertrain by approximately 10-15% and also reduce harmful emissions [4; 9; 14; 25].
## 4 Methods
### Smoothing Profiles  
In this study, the dataset consists of \(m\) profiles, each of which has a length \(n\). Thus, the \(j\)th observed profile is comprised of \(n\) pairs of points \((x_{ij},y_{ij})\), \(i=1,2,\ldots,n\) and \(j=1,2,\ldots,m\). The IC relationship between \(y\) and \(x\) (i.e., \(f^{0}(x)\)) is unknown. However, we assume the random error \(\varepsilon\) is normally distributed with a mean of zero and a standard deviation of \(\sigma\). We also assume the dataset includes both IC and OOC profiles.  
Figure 5: Framework of the proposed method.  
The first step of the proposed method is to smooth the profile data using an appropriate spline smoothing method. Our proposed method incorporates a clustering phase to aid in determining the critical parameter known as the contamination rate. The clustering method is used to determine a so-called main cluster that represents profiles of the IC process. Statistical analysis is then conducted to get a rough estimate of the contamination rate on the basis of the distribution of the Euclidean distances calculated from the observed profiles and the baseline profile. The baseline profile is calculated from all the profiles that constitute the main cluster.  
In this study, B-spline or basis spline is selected as the smooth function. B-spline is a method of approximating a curve based on given coefficients. The B-spline function can be used to approximate the nonlinear function \(S(x)\).The nonlinear function \(S(x)\) can be represented by  
\[S(x)=\sum_{i=0}^{n-1}c_{i}B_{i,d,t}(x) \tag{5}\]  
where \(c_{i}\) denote spline coefficients, and \(B_{i,d,t}\) are B-spline basis functions of degree \(d\) and knots \(t\).  
B-spline basis elements can be expressed as follows:  
\[B_{i,0}(x)=\left\{\begin{array}{ll}1,\ t_{i}\leq x<t_{i+1}\\ 0,\ \text{otherwise}\end{array}\right. \tag{6}\]  
\[B_{i,d}(x)=\frac{x-t_{i}}{t_{i+d}-t_{i}}B_{i,d-1}(x)+\frac{t_{i+d+1}-x}{t_{i+d +1}-t_{i+1}}B_{i+1,d-1}(x) \tag{7}\]  
In the present study, we implemented B-spline using the Python-based SciPy library [51]. The profile data were fitted with a B-spline algorithm, and interpolation profiles were obtained by using the splrep() and BSpline() functions in SciPy. The splrep() function was employed to determine the B-spline representation of the observed profile. The major parameters affecting the interpolation include the degree \(d\) of the spline, the number of knots \(t\), and the smoothing condition \(s\). The parameter \(s\) determines the smoothness of the fit. A higher \(s\) value indicates greater smoothness, whereas a smaller \(s\) value represents lower smoothness. The BSpline() function was used to evaluate the knot points and coefficients to obtain the fitted values.