esa-sceva/satcom-chunk-collection huggingface dataset:
# Particle Swarm Optimization of a Passivity-Based Controller for Dynamic Positioning of Ships
## 5 Numerical Simulations
### The PSO Tuning of the Proposed Controller  
Here, we show the procedures to tune the controller gains as discussed in Section 4. We use the same model parameters as in the previous subsection. The algorithm parameters are shown in Table 2. The iterations versus the fitness values for ISE are shown in Figure 7. The termination happened before the maximum iteration because of the steady value of the fitness functions. The optimal values resulted from the tuning iterations are obtained as  
\[K=\begin{bmatrix}10\\ 10\\ 4.125\end{bmatrix}.\]  
Figures 8-10 show the time histories of the ship's position and orientation using both our original proposed controller and the tuned one with PSO algorithm. It is clear from the plots that the states converge to the desired position and heading angle. The time response characteristics are shown in Table 3. We can see that PSO method improved the response of the system achieving a faster settling while reducing the overshoot (undershoot) as well as avoiding a long and time-consuming trial and error procedure to find the optimal gains. The improvement can be also shown from the trajectories for the two cases (with and without using the PSO method) in Figure 11 which clearly shows better trajectory profile and more convenient path in the case of PSO-tuned controller.  
Figure 6: The orientation (heading) of ship \(q_{3}\) (yaw angle \(\psi\)).  
Figure 8: The position of ship \(q_{1}\) in the \(x\)-coordinate with and without PSO.  
Figure 7: Integral square error (ISE) fitness values versus iterations.  
Figure 10: The orientation (heading) of ship \(q_{3}\) (yaw angle \(\psi\)) with and without PSO.  
Figure 9: The position of ship \(q_{2}\) in the \(y\)-coordinate with and without PSO.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter Name** & **Value** \\ \hline Maximum iteration & 500 \\ \hline Swarm Size & 30 \\ \hline Self-Weight acceleration coefficient (\(c_{1}\)) & 1.494 \\ \hline Social-Weight acceleration coefficient (\(c_{2}\)) & 1.494 \\ \hline Inertia weight (w) & 0.729 \\ \hline kq1 range & 0.001–10 \\ \hline kq2 range & 0.001–10 \\ \hline kq3 range & 0.001–10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: PSO algorithm parameters.  
Figure 11: The trajectories of the surface ship in the \(q_{1}-q_{2}\) plane.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Time Characteristics** & **Without PSO** & **with PSO** \\ \hline \(q_{1}\) Rise time(s) & 40.7935 & 17.0299 \\ \hline \(q_{1}\) Settling time(s) & 457.7632 & 277.9465 \\ \hline \(q_{1}\) Overshoot percentage & 41.0047 & 5.6116 \\ \hline \(q_{2}\) Rise time(s) & 47.6423 & 15.9123 \\ \hline \(q_{2}\) Settling time(s) & 480.8057 & 398.5471 \\ \hline \(q_{2}\) Overshoot percentage & 44.4818 & 7.9774 \\ \hline \(q_{3}\) Rise time(s) & 5.4699 & 3.4137 \\ \hline \(q_{3}\) Settling time(s) & 9.1445 & 6.2746 \\ \hline \(q_{3}\) Overshoot percentage & 1.3724 (under) & 0.6043 (under) \\ \hline \hline \end{tabular}
\end{table}
Table 3: Time performance characteristics for ISE and without PSO.
## 2 Benchmark Highway Bridge
### Earthquake Excitations  
In this study, for the nonlinear dynamic analysis of the bridge under earthquakes, the nonlinear response history analysis is carried out by selecting a set of ground motion time histories. For this purpose, researchers often select ground motion time histories from a database of past recorded earthquakes. However, recorded motions are not always available for all types of earthquakes and in all regions. Therefore, researchers often have to choose records from locations other than the project site and modify these records by scaling or spectrum matching techniques to fit their desired conditions. Sometimes, they are forced to scale recorded motions by factors as large as 10 or 20 or modify the frequency contents to achieve desired intensity or frequency characteristics ([PERSON], 2010). Such modified motions may not accurately characterize real earthquake ground motions. To tackle this deficiency, researchers have developed techniques for the generation of synthetic ground motions that simulate realistic ground motions for specific design scenarios ([PERSON], 2010; [PERSON], 2010).  
In the benchmark bridge package, six ground motions from past earthquakes were selected without scaling or spectrum matching ([PERSON], 2010). These earthquakes were North Palm Springs (1986), TCU084 component of Chi-Chi earthquake Taiwan (1999), El Centro component of Imperial Valley earthquake (1940), Rinaldi component of Northridge earthquake (1994), Bolu component of Duzce Turkey earthquake (1999), and Nishi-Akashi component of Kobe earthquake (1995). Because of the weaknesses of selecting ground motions from inappropriate locations with unreasonable scaling or spectrum matching, in this study, strong motion records are carefully selected to match site conditions.  
Figure 2: Passive Simulink model of the benchmark bridge.  
Besides, 10 synthetic motions are generated to be used along with the real records in the nonlinear response history analysis. The approach for selecting real records of strong ground motion and generating synthetic records are presented in the next section.
###### Abstract  
Cracked rock masses in cold regions are subjected to freeze-thaw cycles over extended periods, resulting in freeze-thaw deformation. The combined effects of freeze-thaw cycling and the depth of cracks significantly influence the stability and durability of underground rock engineering in these regions. In some cold regions with minimal annual rainfall, rock masses are unable to absorb external water during freeze-thaw cycles. As freeze-thaw deformation progresses, the rock transitions naturally from a saturated state to an unsaturated state. To investigate the deformation damage mechanisms and evolution patterns of saturated red sandstone with initial non-penetrating cracks of varying depths (20 mm, 30 mm, 40 mm) under freeze-thaw cycling conditions without external water replenishment and with naturally varying saturation levels, relevant freeze-thaw cycle experiments and strain monitoring were conducted. The results indicate that cracked red sandstone experiences residual strain in each freeze-thaw cycle, which gradually accumulates, leading to irreversible freeze-thaw damage deformation. The cumulative residual strain of the rock specimen after 45 freeze-thaw cycles was 40.69 times greater than the residual strain from the first cycle. Additionally, the freeze-thaw strain characteristic values exhibited a clear correlation with crack depth. These findings provide experimental methods and data references for analyzing the deformation and failure mechanisms of cracked rock induced by freeze-thaw damage in cold regions.  
cracked red sandstone; freeze-thaw cycles; freeze-thaw deformation; residual strain; freeze-thaw damage effects +
Footnote †: journal: applied sciences  
Article Experimental Study on Deformation and Damage Evolution of Cracked Red Sandstone Under Freeze-thaw Cycles
## 3 Applications
### Example Calculation
#### 3.2.5 Checking Calculation of Mortar Coating under Serviceability Limit States  
The maximum bending moment of the pipe at the spring-line was \(M_{pms}^{l}=\) -24.897 kN\(\cdot\)m/m. The value of \(M_{pms}^{l}\), is negative, indicating that the mortar coating was subjected to tension. The absolute value was taken when substituting the following equations. The axial tension of the pipe at the spring-line was \(N_{ps}^{l}=769.388\) kN/m The maximum tensile stress at the edge of the pipe at the spring-line was \(\sigma_{ss}^{l}=9.44\) N/mm\({}^{2}\). The strain of mortar coating was \(\varepsilon_{mt}=\frac{f_{mt,k}}{E_{m}}\geq\frac{0.52\sqrt{f_{m,k}}}{E_{m}}=0.0001444\). The design parameter of strain for the mortar coating was \(\alpha_{m}=5\). Thus, \(\left(\alpha_{m}\varepsilon_{mt}E_{m}=17.44\) N/mm\({}^{2}\right)\) > \(\left(\sigma_{ss}^{l}=9.44\) N/mm\({}^{2}\right)\), indicating that the area of prestressed strands is able to meet the tensile requirement of the mortar coating under the serviceability limit states.
# CEMB-YOLO: Efficient Detection Model of Maize Leaf Blight in Complex Field Environments
## 4 Experimental Results and Analyses  
### Data  
The NLB dataset [43] was created for detecting maize leaf blight disease and is the largest dataset of its kind, with each image annotated by one of two anthropologists. The dataset was divided into three parts: the first was taken with a handheld camera device, the second part was taken by mounting the camera on a 5 m long boom and the third part was taken with a DJI Matrice 600 sUAS camera on board, flying at an altitude of 6 m and a speed of 1 m/s, capturing images every two seconds. The handheld datasets, which include 1019 images with different angles and backgrounds and 7669 annotations, are used in this research study because it offers clear and training-friendly images. Figure 8 displays the dataset example image.  
We apply data augmentation techniques, such as overexposure, haze, rain, random rotation and random cropping, to the original dataset to mitigate the effects of a small dataset on the model training. This technique is randomly combined to generate a total of 9070 images. The dataset is split into training and validation sets at 7:3 ratio.
# A SqueezeSAR Spatially Adaptive Filtering Algorithm Based on Hadoop Distributed Cluster Environment
## 3 Experimental Results and Analysis
### Comparative Experiments and Analysis of Results
#### 3.3.1 Accuracy Evaluation  
In the comparison experiments, the first point worth considering is the consistency, in terms of accuracy, which is related to whether the parallelization algorithm is worth applying to practical application scenarios. In general, splitting data tends to affect the accuracy to different degrees, depending on the parallelization strategy used. However, in practice, because this study only splits the row and column files of the data and the SHP files corresponding to the pixel point identification, the entire process of the row and column and SHP correspond separately; thus, the parallelization process by splitting the data does not cause any loss in the accuracy of the filtering. To more intuitively observe the change in the interferogram phase information before and after filtering, we used the cpxiddle script of [PERSON] to generate the interferogram before and after the spatial adaptive filtering in the case of multiview 3/1. Figure 10 represents a certain differential interferogram generated in the study area before filtering. Figure 11 shows the differential interferogram after using spatially adaptive filtering, whereas Figure 11a represents the interferogram after filtering with conventional processing, and Figure 11b represents the interferogram after filtering with a parallel algorithm. From Figure 11, it is clear that there is no significant difference between the interferograms after filtering using the two methods. In fact, after filtering the phase of the interferogram using the two different processing methods, 100,000 pixels were sampled randomly for an accuracy comparison, which clearly shows that the phase values calculated by the two processing methods are exactly the same, which confirms our previous hypothesis. Broadcasting by the broadcast RDD can not only effectively solve the problem of the performance loss due to the large data transmission, but also ensures that accuracy will not be lost. A comparison of Figures 10 and 11 shows that when the interferogram has undergone spatial adaptive filtering, the speckle noise of  
Figure 9: Subsidence rate map of the study area obtained after time series analysis.  
the interferogram in non-urban areas is significantly reduced, and the overall quality of the interferogram is significantly improved.
# A New Algorithm Model Based on Extended Kalman Filter for Predicting Inter-Well Connectivity
## 5 Multi-Well System Connectivity Analysis
### Multi-Producer-One-Injector  
A significant decrease in the inter-well connectivity coefficient has been observed in the multi-well system. The range of connectivity coefficient in the multi-well system decreases from 0-1 to 0-0.4 (Figure 16) compared to that of the single-well model (Figure 6). This may be attributed to the intensified interference/competition among wells. In a multi-producer-one-injector system, the fluid suffers more friction and pressure losses due to the greater number of wells involved as it flows from the injectors to the producers. These additional flow resistances would reduce the inter-well connectivity.  
In addition, it is found that the inter-well connectivity coefficient for each of the four marginal injectors is different due to the pre-determined channel permeabilities. As the permeability increases from 30 mD to 120 mD, it is found that a higher channel permeability induces a larger inter-well connectivity coefficient, which follows the same pattern as that of a single-well model, which can be verified from the oil saturation distribution presented in Figure 17. The channel connected to the upper left corner has the lowest permeability (30 mD) against the matrix permeability (5 mD) and thus is characterized by non-obvious water migration in Figure 17a.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Scenario**} & \multirow{2}{*}{**Well Pattern**} & \multicolumn{2}{c}{**Channel**} & \multicolumn{1}{c}{**Time of**} & \multicolumn{1}{c}{**Well**} \\  & & **Permeability** & **Commissioning** & **Spacing** \\  & & **(mD)** & **(Month)** & **(m)** \\ \hline
1 & One-injector–four-producer & 30, 60, 90, 120 & 120 & 212.15 \\
2 & Four-injector–one-producer & 30, 60, 90, 120 & 120 & 212.15 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Basic setting of inter-well connectivity analysis in two well group configurations.  
Figure 16: Inter-well connectivity coefficient in a multi-producer–one-injector model.  
As shown in Figure 18, the time-lag coefficients under different permeability channels remain relatively stable at the initial stage and change little over a longer period of time. This indicates that the connectivity between the injector and four producers is stable in the early stages. However, in 2017, the time-lag coefficient of each well experienced a certain decline. This may be due to an increase in injection pressure differential in 2017, which resulted in a fluid dynamics change affecting the time-lag coefficient. Generally, the time-lag coefficients in high permeability channels show a consistent trend, although the values differ slightly.
# Path Loss Determination Using Linear and Cubic Regression Inside a Classic Tomato Greenhouse
## 3 Results and Discussions
### New Optimized Model with Verification of Values  
The Matlab program (R2018a) has been used to develop our optimized model through linear regression when the nodes were at 0.5 m from the ground. The mathematical equation used was: \(y=-2.0685d-19.252\) (where \(d\) is the distance between the nodes \(\mathrm{T_{x}}\) and \(\mathrm{R_{x}}\)). If the nodes \(\mathrm{T_{x}}\) and \(\mathrm{R_{x}}\) are placed at a 1.5m distance from the ground, the mathematical equation of the optimized model was obtained by cubic regression using the same software is this time: \(y=-0.056156d^{3}+1.6125d^{2}-17.006d\) \(-\) 0.56299. The Figures 6D and 7D show the best precision of our models with respect to the others. The other models with the closest approximation of values were two-ray and Weissberger a 0.5 m and COST235, UIT-R, FITU-R, FSPL for 1.5 m. Likewise, the percentage of error (% error) was found with the following formula: \(\mathrm{Abs}[[1-(\mathrm{X_{i\;empirical}}/\mathrm{X_{i\;model}})]\times 100\%]\), where \(\mathrm{X_{i}}\) is the measured value (\(\mathrm{X_{i\;empirical}}\)) or predicted (\(\mathrm{X
# Raman Spectroscopy as Noninvasive Method of Diagnosis of Pediatric Onset Inflammatory Bowel Disease
## 2 Materials and Methods
### Receiver Operating Characteristic (ROC) Curve and Youden Index (J)  
The ROC curve is the graph of sensitivity vs. 1-specificity, where the sensitivity represents the true positive rate, while (1-specificity) is the false positive rate [37]. In this study, sensitivity and specificity of the diagnostic test were determined considering positive and negative test in the two different groups (IBD vs. non-IBD subjects). Sensitivity referred to the ability of Raman spectroscopy to correctly identify those patients with the inflammatory process. Instead, specificity referred to the ability of Raman spectroscopy to correctly identify those patients without the inflammation. The area under the ROC curve (AUC) represents the probability Pr (Y \(\geq\) X), i.e., the probability of the marker value Y of a randomly selected subject from the diseased population to be higher than the marker value X of a randomly selected subject from the healthy population [38]. The obtained AUC result is an effective way to summarize the overall diagnostic accuracy of the test. It takes values from 0 to 1, where a value of 0 indicates a perfectly inaccurate test and a value of 1 reflects a perfectly accurate test. In general, an AUC value equal to 0.5 suggests no discrimination (i.e., ability to diagnose patients with and without the disease or condition based on the test), from 0.7 to 0.8 is considered acceptable, from 0.8 to 0.9 is considered excellent, and more than 0.9 is considered outstanding [39]. On the other hand, since AUC serves as a measure of a biomarker's global diagnostic accuracy over all possible cut-points, it cannot directly provide an 'optimal' threshold or cut-point needed by clinicians for making diagnosis, i.e., for classifying a subject as either diseased or healthy. For this reason, the cut-point was determined using the Youden index \(J\), defined as \(J\) = sensitivity + specificity - 1. This index represents the maximum vertical distance between the ROC curve and the diagonal, and is taken at the cut-point that optimizes the biomarker's differentiating ability when equal weight is given to sensitivity and specificity [40]. It offers the optimal cutoff-point and, at the same time, it is a direct measure of the diagnostic accuracy at the optimal cut-point, that is, the maximum overall correct classification rate a marker can achieve [41; 42].
# Firmware Updates over the Air via LoRa: Unicast and Broadcast Combination for Boosting Update Speed
## 6 Implementation and Tests with Real Hardware
### Tests of the Unicast Method for the OTA Update with Real Hardware  
A laboratory setup us used to carry out several tests of the OTA update. As a first example, Figure 24 shows the RSSI of each frame during a complete OTA procedure, with 812 chunks of 197 bytes each. The distance between the gateway and the node is 2 m, without any obstacle between them. Both the gateway and the node are out from their ATEX enclosures to reduce the signal attenuation.  
In this case, considering that the tests are run in an isolated place, the frames are sent in a continuous mode, not considering the 1% duty cycle. The total required time is 807 s (roughly 13 min and a half).  
As can be observed, the vast majority of the frames arrive on the first attempt (yellow dots), although some arrive on the second one (green dots). The RSSI is always around \(-49\) dBm, with reasonably stable behavior.  
One fact should be noted: in the simulation tests, frame loss occurred solely due to the RSSI level falling beneath the sensitivity threshold of the receiver. In the tests with real hardware, there may be other causes: interference with equipment in the same frequency bands and limitations of the hardware, etc. This is the reason why some frames are lost in spite of the big margin between the RSSI level (\(-49\) dBm) and the sensitivity of the receptor (\(-125\) dBm).  
Figure 23: Memory map of the flash memory.  
Another test is carried out between a gateway in the lab and a node on the upper floor. In this case, there is a significant attenuation level, mainly caused by the slab between the floors. As can be observed in Figure 25, the RSSI level ranges between \(-85\) and \(-110\). In addition, it can be observed that many frames require two attempts, and some even require three. However, the algorithm is solid and can complete the firmware transfer in 2450 s (more than 40 min).
# Interest Forwarding in Named Data Networking Using Reinforcement Learning
## 5 System and Scenario Description
### Content Popularity  
The choice of the popularity distribution is an important factor that determines the performance of in-network caching in ICN. The Zipf's law is the favoured choice to model popularity in ICN [19; 59; 60].  
[PERSON]'s law, popularized initially in Statistical Linguistics, predicts, for a catalog of \(N\) items, that the probability of referencing the \(i\)-th most popular item is:  
\[\rho_{N}(i)=\frac{\frac{1}{\mu}}{\sum_{i=1}^{N}\frac{1}{\mu}}, \tag{8}\]  
where the _skew factor_, \(\alpha\), is a constant characterising the distribution.  
The more general Mandelbrot-Zipf (Mzipf) distribution is also adopted in some analysis [61; 62]. In MZipf,  
\[\rho_{N}(i)=\frac{\frac{1}{(i+q)^{\alpha}}}{\sum_{i=1}^{N}\frac{1}{(i+q)^{ \alpha}}}, \tag{9}\]  
where \(\alpha\) and \(q\) both characterise the distribution. Equation (9) reduces to (8) when \(q=0\).  
There is also no consensus on the parameter settings for either distributions. However, the values used in the literature are informed by those arrived at from large-scale studies of actual traces observed at ISPs and CDNs [57; 63; 64; 65]. As a result, values of \(\alpha\in[0.6,2.5]\) and \(q\in[0,50]\) are common, while [67; 24; 6] include uniform popularity (\(\alpha=0\)) in their evaluations.  
In our tests, we adopt the MZipf distribution to represent content popularity, and we use values within these ranges which are realistic, considering the catalogue sizes we consider, to characterise the distribution.
# Sea Drift Trajectory Prediction Based on Quantum Convolutional Long Short-Term Memory Model
## 2 Related Work
### Previous Research Results
On the other hand, CNN typically consists of a series of two-dimensional convolutional layers and pooling layers, with relatively simpler connections between layers. CNN is more suitable for processing two-dimensional data and capturing spatial features, making it widely applicable in the field of computer vision. As the TCN network uses one-dimensional convolutional layers, it may be limited in capturing the features of time series data, such as periodic data with multiple time scales. Therefore, in some cases, TCN may not fully capture the characteristics of time series data. Some scholars have proposed a CNN-LSTM hybrid framework for prediction [26; 27; 28]. The LSTM network is an excellent variant model of RNN that can better handle time series tasks and solve the gradient explosion problem in RNN networks. However, although the LSTM part of the model has been partially resolved in terms of gradient problems, it is still challenging to solve for many sequences. In addition, the LSTM model architecture is relatively complex and time-consuming to train. Due to these issues, quantum computing provides a new approach to solving problems that traditional computers cannot handle, as it offers a different computing environment [29; 30]. In particular, superposition and entanglement, which are not initializable in classical computing environments, can achieve robust performance by utilizing the parallelism between quantum bits. Common quantum architectures proposed so far include Quantum Neural Networks (QNN) [31], Quantum Convolutional Neural Networks (QCNN) [32], and Quantum Long Short-Term Memory Networks (QLSTM) [33].