esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results
### SHS in the Squamous Region and RFA Treatment Response  
Pre-ablation SHSs parameters were not significantly different between patients of different age groups (data not shown). Subsquamous SHSs with higher eccentricity is strongly correlated with larger reduction of post-ablation BE length for less elderly patients (\(\leq\)70 years old, R = \(-\)0.884, \(p\) = 0.001, Table 4), but not for elderly patients (>70 years old, R = 0.355, \(p\) = 0.284) (Table 4). More vertical and more regular subsquamous SHSs corresponded to less reduction of post-ablation BE length for less elderly patients. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Metrics** & **Segment 1** & **Segment 2** & **Segment 3** & **Segment 4** & \begin{tabular}{c} \(p\)**-Value** \\ **(All)** \\ \end{tabular} & \begin{tabular}{c} \(p\)**-Value** \\ **(I vs. 2)** \\ \end{tabular} & \
\begin{tabular}{c} \(p\)**-Value** \\ **(2 vs. 3)** \\ \end{tabular} &
\begin{tabular}{c} \(p\)**-Value** \\ **(3 vs. 4)** \\ \end{tabular} \\ \hline
2D Number & 0.98 \(\pm\) 0.71 & 1.05 \(\pm\) 0.59 & 1.17 \(\pm\) 0.64 & 1.14 \(\pm\) 0.87 & 0.653 & - & - & - \\ Volume (mm\({}^{3}\)) & 2.25 \(\pm\) 1.86 & 2.37 \(\pm\) 1.84 & 2.32 \(\pm\) 1.64 & 2.28 \(\pm\) 1.91 & 0.994 & - & - & - \\
3D Cluster & 8.51 \(\pm\) 7.40 & 11.17 \(\pm\) 7.73 & 12.44 \(\pm\) 7.72 & 11.17 \(\pm\) 9.66 & 0.221 & - & - & - \\ Size (mm\({}^{2}\)) & 0.22 \(\pm\) 0.07 & 0.24 \(\pm\) 0.14 & 0.19 \(\pm\) 0.06 & 0.19 \(\pm\) 0.07 & 0.115 & - & - & - \\ Depth (mm) & 0.83 \(\pm\) 0.15 & 0.98 \(\pm\) 0.20 & 0.95 \(\pm\) 0.12 & 0.87 \(\pm\) 0.25 & 0.003 \(\pm\) ** & 0.006 \(\pm\) ** & 0.936 & 0.234 \\ Intensity & 0.64 \(\pm\) 0.11 & 0.65 \(\pm\) 0.09 & 0.67 \(\pm\) 0.08 & 0.65 \(\pm\) 0.18 & 0.793 & - & - & - \\ STD & 0.08 \(\pm\) 0.01 & 0.08 \(\pm\) 0.01 & 0.08 \(\pm\) 0.01 & 0.08 \(\pm\) 0.02 & 0.913 & - & - & - \\ Orientation (degrees) & 10.00 \(\pm\) 4.6 & 7.9 \(\pm\) 4.7 & 6.4 \(\pm\) 2.2 & 7.0 \(\pm\) 4.5 & 0.002 \(\pm\) ** & 0.144 & 0.399 & 0.898 \\ Eccentricity (0–1) & 0.82 \(\pm\) 0.07 & 0.90 \(\pm\) 0.03 & 0.90 \(\pm\) 0.02 & 0.84 \(\pm\) 0.21 & 0.006 \(\pm\) ** & 0.029 \(\pm\) ** & 0.999 & 0.135 \\ Extent (0–1) & 0.58 \(\pm\) 0.05 & 0.54 \(\pm\) 0.04 & 0.54 \(\pm\) 0.03 & 0.50 \(\pm\) 0.13 & \(\pm\)0.001 \(\pm\) ** & 0.057 & 1.000 & 0.172 \\ Perimeter (mm) & 2.02 \(\pm\) 0.22 & 2.17 \(\pm\) 0.40 & 2.04 \(\pm\) 0.29 & 1.95 \(\pm\) 0.54 & 0.110 & - & - & - \\ Solidity (0–1) & 0.90 \(\pm\) 0.01 & 0.89 \(\pm\) 0.01 & 0.89 \(\pm\) 0.01 & 0.84 \(\pm\) 0.21 & 0.080 & - & - & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of SHS characteristics over the 4 cm longitudinal segments in BE.
# Empirical Study on 5G NR Cochannel Coexistence
## 3 The Cochannel Coexistence Problem  
The cochannel coexistence problem between two or more networks in 5G deployments is constrained to regional regulations. In this article, the coexistence scenario analysis is based on locally deployed networks for indoor industrial applications operating on the same spectrum as other neighborhood networks. As described in Chapter 5 in [2], there are three different scenarios that could be addressed in this coexistence study: (i) a local non-public network may have a license to operate in the same spectrum as other public 5G services in the wide-area, (ii) an indoor non-public network might make use of a local 5G license in close vicinity with another non-public network within the same spectrum with its own local license, and (iii) a local license may include multiple factory buildings but also an outdoor network section on the same industrial environment area. In the third scenario, the outdoor network requires coordination among other outdoor networks on the same or adjacent channels, thus the TDD configuration needs to be aligned with the public 5G networks.  
As per [23], the indoor and outdoor networks have a common clock reference. Hence, the TDD pattern start is always synchronized in our measurements. Then, when the indoor and outdoor network use the same TDD pattern, the only present interference is the so-called near-far interference. Such interference situation occurs when both networks are either in DL or UL transmission mode at the same time. From the network perspective, the DL near-far interference arises when the coexisting base station is transmitting to its UE and creates interference to the neighbor UE. Hence, the UL near-far interference is the interference perceived on the base station due to the transmission of the neighbor UE. However, indoor network deployments might not be constrained to the harmonized outdoor TDD pattern and might choose a more balanced or UL-oriented TDD pattern in order to meet the system requirements of the use-cases within the non-public network. For instance, in Figure 1, the indoor and outdoor network TDD patterns are referred to as DDDU and DDDDU, respectively, where D and U represent downlink and uplink time slots, respectively. As already mentioned, the interference impact of the DL-to-DL and UL-to-UL slots is measured in the near-far interference. Moreover, due to the nature of the unsynchronized TDD pattern (indoors: DDDU, outdoors: DDDDU) the DL-to-UL and UL-to-DL interference is called the cross-link interference. This interference might arise when the interference network is transmitting in the opposite direction of the victim network.
# Optimizing Rural Highway Maintenance Scheme with Mathematical Programming
## 4 Numerical Examples
### Eastern Massachusetts Network  
Finally, we investigate the computational feasibility of our rural road maintenance model using the Eastern Massachusetts network, a much larger-scale network. We also perform a sensitivity analysis to examine the relationship between the number of country roads and the computing time under a 24 h computational time limitation.  
The network topology of Eastern Massachusetts is shown in Figure 5, with the rural road segments highlighted. This network has a total of 74 nodes and 258 road segments. The brown circles in Figure 5 denote the nodes of the network with the nodes numbered. The nodes in orange circles belong to a zone with the same travel demand level. Ten of these segments are randomly selected for maintenance. We test the computing feasibility of the proposed method by varying the number of maintenance road segments. The input is the number of maintenance segments. The output is the computing time. The test results are presented in Table 5.  
Figure 4: Entire cost of maintenance for each maintenance scheme.  
Figure 5: Eastern Massachusetts network with its rural road segments.  
Table 5 and Figure 6 show the relationship between the number of road segments and the computing hours required to solve the problem. The results indicate that the computing time grows exponentially with the number of maintenance road segments.
# Thermal Image and Inverter Data Analysis for Fault Detection and Diagnosis of PV Systems
###### Abstract  
The world's energy demand is on the rise, leading to an increased focus on renewable energy options due to global warming and rising emissions from fossil fuels. To effectively monitor and maintain these renewable energy systems connected to electrical grids, efficient methods are needed. Early detection of PV faults is vital for enhancing the efficiency, reliability, and safety of PV systems. Thermal imaging emerges as an efficient and effective technique for inspection. On the other hand, evidence indicates that monitoring inverters within a solar energy farm reduces maintenance expenses and boosts production. Optimizing the efficiency of solar energy farms necessitates comprehensive analytics and data on every inverter, encompassing voltage, current, temperature, and power. In this study, our objective was to perform two distinct fault analyses utilizing image processing techniques with thermal images and machine learning techniques using inverter and other physical data. The results show that hotspot and bypass failures on the panels can be detected successfully using these methods.  
PV panels; diagnosis; fault detection; thermal image; machine learning Article
# Transient Linear Circuit Analysis Using Finite Element Technique
## 3 Numerical Example  
To present the basic principle of the developed FET-based algorithm and to perform transient analysis, the following coupled linear circuit was analyzed.  
A coupled linear electrical circuit, shown in Figure 2 has four branches and two global nodes.  
Figure 3 shows the entire coupled linear circuit as a unique finite element (FE) prepared for the FET procedure. It has eight local nodes and four branches. The first branch contains an electromotive force that is a ramp function (in the time interval 0-100 ms increases its value from 0 up to 10 V). The value of the time interval \(\Delta t\) was taken as 0.1 ms. The values of the circuit elements were assumed to be as follows: \(R_{2}=1\)\(\Omega\), \(R_{3}=2\)\(\Omega\), \(R_{4}=1\)\(\Omega\), \(C_{2}=1\) mF, \(L_{3}=0.1\) H, \(L_{4}=0.01\) H, \(L_{34}=0.1\) mH.  
Figure 2: Example of coupled linear electrical circuit.  
Figure 4 shows the current in the first branch during the 0-0.6 ms time interval as well as during the entire simulation while Figure 5 shows the second branch capacitor voltage during the 0-0.8 ms time interval, as well as during the entire simulation. Numerical solutions obtained using [PERSON]'s method and using the generalized trapezoidal rule were compared to the solution obtained by Matlab Simulink software.  
Figure 4: First branch current during the 0–0.6 ms time interval.  
Figure 3: Coupled linear circuit finite element.  
Absolute errors of numerical solutions related to the Matlab Simulink solution are shown in Figures 6 and 7. Maximum values of these errors are presented numerically in Table 1.  
Figure 5: Capacitor voltage during the 0–0.8 ms time interval.  
Figure 6: Absolute errors of the current in the first branch.  
It was shown that the accuracy of results depends on the type of time integration scheme when performing the FET procedure. The above examples clearly show that time integration obtained by [PERSON]'s integration scheme gives a higher accuracy of results compared with results obtained by the generalized trapezoidal rule (\(\theta\)-method). The problem when using the \(\theta\)-method is the fact that the accuracy of results highly depends on the chosen time integration parameter value.
## 2 Proposed Mixed Mode I/II Test Specim for Adhesive Bounded Joints
According to the well-known literature [22; 23; 24; 25], knowing these three fracture parameters (i.e., \(K_{\rm I}\), \(K_{\rm II}\) and \(T\)) is sufficient for describing the crack tip stress/strain field and evaluating the failure behavior of cracked components subjected to mixed mode I/II loading. These fracture parameters depend on the geometry and loading conditions of any given test specimen. For the BISBB specimen, \(K_{\rm I}\), \(K_{\rm II}\) and \(T\) can be written as:  
\[K_{\rm I}=\frac{P}{Wt}\sqrt{\pi a}\ Y_{I}\ \ \left(\frac{a}{W}\cdot\frac{S}{W} \cdot\alpha\right) \tag{2}\]  
\[K_{\rm II}=\frac{P}{Wt}\sqrt{\pi a}\ Y_{II}\ \ \left(\frac{a}{W}\cdot\frac{S}{W} \cdot\alpha\right) \tag{3}\]  
\[T=\frac{P}{Wt}\ T^{*}\ \ \left(\frac{a}{W}\cdot\frac{S}{W}\cdot\alpha\right) \tag{4}\]  
in which \(Y_{I}\), \(Y_{II}\) and \(T^{*}\) are the geometry factors corresponding to \(K_{\rm I}\), \(K_{\rm II}\) and \(T\), respectively. These geometry factors are functions of crack length ratio (\(a\)/\(W\)), loading span ratio (\(S\)/\(W\)) and crack inclination angle (\(\alpha\)) in the BISBB specimen. In order to use the BISBB specimen in fracture analysis of adhesive bounded joints and testing real cracked adhesive bounded joint materials, it is necessary to first determine these three fracture parameters. Since such parameters have not been reported in the literature for the BISBB specimen, the main aim of this research is to determine these three fracture parameters for the BISBB and investigate the influence of affecting parameters (such crack length ratio, loading span ratio and adherent types) on the variations of the fracture parameters. While there is no analytical solution available for determining the stress/strain field ahead of the crack tip in the BISBB specimen and extracting its fracture parameters (i.e., \(K_{\rm I}\), \(K_{\rm II}\) and \(T\)), numerical analyses such as finite element method provide powerful tools for this issue that can be used for any desired fracture specimen. In the next section, the finite element method is utilized for computing the geometry factors of the BISBB specimen.
# Parallelization of the Bison Algorithm Applied to Data Classification
## 3 Proposed Approach
### Bison Algorithm for Classification Implemented with Spark
It distributes the data across multiple worker nodes, processes it simultaneously, and aggregates the results using accumulators and broadcast variables. This parallelization allows the algorithm to scale efficiently and handle large datasets without sacrificing speed or accuracy. Spark ensures that the algorithm can take advantage of cluster resources, making the training and testing phases much faster than they would be on a single machine. The algorithm uses Apache Spark to parallelize both the data processing and the training of the Bison model by distributing tasks across multiple nodes in a cluster. When the dataset is loaded, it is stored as a DataFrame, which Spark automatically splits and distributes across different worker nodes. Each node processes a portion of the data in parallel. For example, feature scaling, label encoding, and data splitting are all performed concurrently on each partition of the dataset. After the initial data preparation, the DataFrame is converted into an RDD (Resilient Distributed Dataset), which allows the algorithm to perform custom operations such as mapping and reducing in parallel. In the training phase, the swarm of bisons (candidate solutions) is broadcast to all worker nodes, ensuring that every node has access to the current bison positions without redundant data transfers. Each worker node evaluates how well these bisons classify the data points in its partition and updates the accumulator, a Spark variable that aggregates the misclassification counts from all nodes. This allows the algorithm to compute fitness (classification accuracy) in parallel, as each node processes its data independently. Once the fitness of each bison is evaluated, the bisons are updated, and the next iteration begins. This parallel approach allows the algorithm to scale efficiently, making it capable of handling large datasets and complex computations faster than if it were run on a single machine. The algorithm description is provided in Algorithm 1. ```
1:Initialize the Bison group randomly
2:Initialize the Runner group around \(x_{best}\)
3:Initialize the run direction vector
4:Read the dataset into an RDD
5:Initialize Accumulator Accu
6:loop
7:Broadcast Bison group
8:for each element in RDD do
9: Assign the element to the closest centroids
10: Update Accu
11:endfor
12:Update the fitness of the bisons based on the values in Accu
13:Update \(x_{best}\)
14:Update Bison Group according to the original algorithm
15:Reset Accu
16:endloop
```  
**Algorithm 1**Bison Algorithm with RDD. Figure 2 shows how the dataset is partitioned and then used by the Spark framework. Each partition is sent to an executor, which performs the closest centroid calculations.
## 3 Results
### Studies of Fixed-Bed Column
#### 3.4.3 The Bed Depth Effect  
Curves of breakthrough at different bed depths are displayed in Figure 6. They demonstrated that their development usually take place faster at a reduced bed depth, and that the breakthrough slope decreases upon the increase in bed depth, which usually leads to an expanded zone of mass transfer. Table 2 shows that \(q_{\mathrm{\alpha f}}\) was boosted from 22.17 mg:g\({}^{-1}\) to 31.49 mg:g\({}^{-1}\) as the bed depth increased from 8.5 cm to 15.3 cm, respectively. The slope of breakthrough curve declined following the increase in the bed depth, which led to an expanded zone of mass transfer. At the ultimate bed depth, a higher uptake occurred due to the adsorbent's surface area growth, leading to additional sorption sites  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \(\mathbf{C_{0}}\) & \(\mathbf{v}\) & \(\mathbf{Z}\) & \(\mathbf{t_{b}}\) & \(\mathbf{t_{total}}\) & \(\mathbf{q_{total}}\) & \(\mathbf{q_{\mathrm{\alpha f}}}\) & \(\mathbf{V_{eff}}\) \\ (mg:L\({}^{-1}\)) & (mL:min\({}^{-1}\)) & (cm) & (min) & (min) & (mg) & (mg:g\({}^{-1}\)) & (mL) \\ \hline
400 & 10.0 & 8.50 & 153 & 193 & 87.45 & 17.49 & 1930 \\
1000 & 10.0 & 8.50 & 40 & 90 & 110.85 & 22.17 & 900 \\
2000 & 10.0 & 8.50 & 35 & 60 & 228.95 & 45.79 & 600 \\
1000 & 7.5 & 8.50 & 60 & 130 & 135.75 & 27.15 & 975 \\
1000 & 5.0 & 8.50 & 84 & 149 & 152.05 & 30.41 & 745 \\
1000 & 10.0 & 12.75 & 106 & 188 & 223.28 & 29.77 & 1880 \\
1000 & 10.0 & 15.30 & 125 & 195 & 283.41 & 31.49 & 1950 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Data on column and their acquired parameters at different influent phenol concentrations, rates of flow, and bed depths onto rice husk activated carbon (RHAC) (temperature = 23 \(\pm\) 1 \({}^{\circ}\)C; pH = 5.58).  
Figure 5: Effects of variable rates of flow on the adsorption’s curve of breakthrough for RHAC (bed depth = 8.5 cm; phenol concentration at inlet = 1000 mg:L\({}^{-1}\)).  
adherence. Pollutant delayed breakthrough then led to a volumetric intensification within the processed solution [28,40].
# A Survey on Change Detection and Time Series Analysis with Applications
## 3 Decomposition Methods into Time-Frequency Domain  
This section briefly reviews several popular time-frequency decomposition methods and eventually continues to recent techniques for processing unequally spaced and weighted time series. Two popular cross-wavelet methods for analyzing multiple time series together are also reviewed at the end of this section.
## 2 Feasibility on ODEON-Based Auditory Scene Simulation in Closed Spaces
### ODEON-Based Binaural Auditory Simulation in Virtual Rooms  
With ODEON 8.0, we draw a rectangular room model, set sound absorption coefficient (concrete and glass), select sound sources and the receiving points (the position coordinates are the same as in the real room), establish sound field models of Room A and Room B (shown in Figure 3) by means of sound ray tracking method (5000 sound rays), and calculate the eight binaural impulse responses (the response duration is 1 s for all points) in the virtual rooms. The reverberation times are 0.16 s for simulated room A and 0.2s for simulated room B, respectively. The original signal in Section 2.1 is convolved with the eight impulse responses, respectively. Finally, the convolved left and right aural signals are played back with a properly balanced and adjusted headphone to produce \"virtual hearing\" that correspond to each receiving point in the real rooms. Before the listening test, all the headphones are connected with the power amplifier and adjusted at the same output level, with which the experimenter feels comfortable.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Room No.** & **L/m** & **W/m** & **H/m** & **S/m\({}^{2}\)** & **V/m\({}^{3}\)** & **T\({}_{\text{60}}\)/s** & **Background Noise/dB(A)** \\ \hline A & 7.30 & 6.50 & 3.05 & 179.08 & 144.72 & 0.162 & 35 \\ B & 5.70 & 3.00 & 3.00 & 86.40 & 51.30 & 0.119 & 37 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The parameters of real rooms.  
Figure 1: Binaural signal collection scene in real rooms. (**a**) Room A (**b**) Room B.  
Figure 2: Sketch map of positions of sound source and receiving point in real rooms. (**a**) Room A (**b**) Room B.  
Evaluation Experiment of Auditory Perception Consistency between Real and Simulated Binaural Signals  
For the auditory experiment, 20 listeners (undergraduates, postgraduates, and doctoral students majoring in acoustics aged between 20 and 30), who are familiar with the procedure of subjective listening test due to their former experience, are organized and the binaural signals are played back in pairs (\"real\" and \"simulated\") through Sennheiser HD280 Pro headphones. The listeners are unaware of the difference between the stimuli in each pair, they are only instructed to judge whether each pair of sounds is aurally distinguishable (if the two stimuli sound different or same). According to the experimental results, the degree of aural consistency between \"real\" and \"simulated\" binaural signals is evaluated to test the feasibility and accuracy of using ODEON 8.0 for indoor sound field auditory simulation research.
# Backtracking-Based Iterative Regularization Method for Image Compressive Sensing Recovery
###### Abstract  
This paper presents a variant of the iterative shrinkage-thresholding (IST) algorithm, called backtracking-based adaptive IST (BAIST), for image compressive sensing (CS) reconstruction. For increasing iterations, IST usually yields a smoothing of the solution and runs into prematurity. To add back more details, the BAIST method backtracks to the previous noisy image using L2 norm minimization, i.e., minimizing the Euclidean distance between the current solution and the previous ones. Through this modification, the BAIST method achieves superior performance while maintaining the low complexity of IST-type methods. Also, BAIST takes a nonlocal regularization with an adaptive regularizor to automatically detect the sparsity level of an image. Experimental results show that our algorithm  
In this paper, a modified IST method, named backtracking-based adaptive IST (BAIST), is proposed for image CS reconstruction. Similar to IST-type methods, it recovers images with forward-backward iterations. However, unlike IST-type methods that generally yield an over-smoothed solution, BAIST incorporates a backtracking constraint to add back-filtered details and enforce a noisier image through the minimization of the distance between the current solution and the previous ones. Furthermore, the nonlocal regularization with an adaptive regularization parameter set according to the current residual is to adaptively estimate the sparsity level of the image. Experimental results show that our method impressively outperforms previous methods in terms of reconstruction accuracy, and also achieves a competitive advantage in computation complexity.