esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Preliminary Background of the Proposed Control Method
### FOPID Tuning  
Despite their advantages, FOPID controllers' tuning is not straightforward. As such, numerous studies have focused on determining an appropriate tuning technique for FOPID controllers. The existing tuning methods for FOPID controllers are divided into  
Figure 4: The illustrations of the FOPID Controller.  
Figure 3: Stability regions of fractional-order systems \(0<x<1\).  
three categories: auto-tuning, robust tuning, and optimal tuning methods [40]. Although these auto-tuning technologies are extremely beneficial in practical applications, they are rarely employed in motion control applications, which require high bandwidths (crossover frequency) [43]. But, for process control (first-order plus delay plants), they are highly useful instruments. Some studies have led to the development of tuning techniques based on Ho constraints, whereby mathematical techniques like the graphical approach, the Newton-Raphson numerical iterative algorithm, and others are used to meet performance constraints including stability, bandwidth, resilience, and precision [44]. These techniques meet all of the designer's requirements; however, solving these nonlinear equations is extremely challenging and sometimes impossible. Furthermore, a number of issues arise since several of these methods need the determination of the plant's dynamic properties. Based on this, it can be stated that the majority of the tuning techniques currently in use are complicated, and each one is limited to a certain class of systems. However, some studies were conducted in an effort to develop a novel tuning technique using optimization techniques such as genetic algorithms and particle swarm optimization [45]. In this method, multiple objective functions and restrictions are addressed when tuning FOPID controllers. These strategies enable controllers to function at their best. This motivates the use of the MFPOA in turning the FOPID controller.
## 4 The Complementary Filter Method and Performance Analysis
### The Fusion Velocity  
Similarly, according to Equation (5), we can differentiate the CCD's signal and integrate the accelerometer's signal to get the fusion velocity as follows.  
\[\begin{array}{ll}\frac{v_{fusion}}{v_{real}}&=\ \frac{C_{filter}}{1+C_{filter}} \cdot se^{-\tau s}H_{CCD}+\frac{1}{1+C_{filter}}\cdot\frac{1}{s}H_{ACC}\\ &=\ B_{closed}\cdot se^{-\tau s}H_{CCD}+B_{restrain}\cdot\frac{1}{s}H_{ACC}\\ &=\ \frac{F_{c}(s)}{G_{\rm F}(s)}\end{array} \tag{22}\]  
In Equation (22), we still choose a one order filter to extract the signals of the CCD and the MEMS accelerometer. Since the fusion velocity is used in closed-loop control, we choose a high-pass filter not a band-pass to extract the MEMS accelerometer's signal, or it will result in the decrease of closed-loop bandwidth. Substitute the filters to Equation (22) as follows.  
\[\frac{v_{fusion}}{v_{real}}\ =\ \frac{1}{1+T_{1}s}\cdot e^{-\tau s}sH_{CCD}+ \frac{T_{1}s}{1+T_{1}s}\cdot\frac{1}{s}H_{ACC} \tag{23}\]  
In Equation (23), we can treat \(sH_{CCD}\approx\frac{1}{s}H_{ACC}\approx v_{real}\), then  
\[v_{fusion}\approx\ (\frac{1}{1+T_{1}s}\cdot e^{-\tau s}+\frac{T_{1}s}{1+T_{1}s} )v_{real} \tag{24}\]  
Substitute Equation (20) into Equation (24)  
\[\begin{array}{ll}v_{fusion}&=\ (\frac{1}{1+T_{1}s}\cdot e^{-\tau s}+\frac{T_{ 1}s}{1+T_{1}s})v_{real}\approx(\frac{1}{1+T_{1}s}\cdot\frac{1}{1+T_{1}s}+\frac {T_{1}s}{1+T_{1}s})v_{real}\\ &=\ \frac{T_{1}\tau s^{2}+T_{1}s+1}{T_{1}\tau s^{2}+(T_{1}+T)s+1}v_{real} \approx\ v_{real}(\because\tau<<T_{1})\end{array} \tag{25}\]  
From Equation (25), the fusion velocity would have the same form with the platform's real velocity but in fact, due to the influence of the delay, the natural frequency and damping coefficient may change slightly. The handover frequency \(1/T_{1}\) could be 2 Hz, which is same with the fusion acceleration's. With the spectrum fitting method, we can identify the detail parameters and give out the accurate transfer function.
# An IoT-Based System for Monitoring the Health of Guyed Towers in Overhead Power Lines
## 4 Analytical Model of the Tower
where \(\mathbf{v}\) represents the considered base vector parallel to the cable and \(\mathbf{v}\) represents the vector \(\mathbf{g}\). Thus, we define \(\theta_{ckx}\), according to Equation (2), as the angle between the \(x\)th-cable and the vector \(\mathbf{g}\). The analytical model allows us to know \(\mathbf{g}\) vectors and forces of each cable as a function of the tower inclination. Figure 16 illustrates a diagram showing the proposed simulation of the tower analytical model. Thus, we generate data by setting a displacement from the tower model's initial x and y center position according to a random variable. The chosen random variable is Gaussian distributed with zero mean and unitary variance. The gray dots represent the displacement around the initial position of the tower. Notice that as the top center of the tower changes from the initial position, the cable forces on positions 1, 2, 3, and 4 change accordingly to the promoted displacement of the tower center. The data of the forces, accelerometer axes, and \(\mathbf{g}\) relative to each cable are recorded to be used for analysis and in the machine learning training process. A MATLAB script calculates all related variables and stores them in a file that feeds the neural network for training. The tower's \(x\) and \(y\) displacements follow a Gaussian random variable of zero mean and unitary variance.
## 4 Results and Discussion
### PLSDA Loading Vectors
Though not displayed in a figure, the top loading vectors for the classification task associated with LIBS analysis carried out on soil samples from the Belmont and Lebanon, New Hampshire locations are, in order of importance, Ca, Al, Mg, Si, H, Fe, K, Ti, Na, C and O. The three Belmont location soil pits were distinguished on the basis of same elements in a similar order of relevance: Ca, Al, Mg, Si, H, Fe, Na, C, Ti, K and O. In these two cases, the contribution for the C line at 193.1 nm plays a more important role than for the other two soil suites, suggesting that varying amounts of organic matter aid  
Figure 5: Plot of loading vectors versus energy (keV) for the XRFS analysis of the New Mexico (**A**) and military installation (**B**) soil samples. Only the K\(\kappa\) lines are identified for the most significant loading values, except in the case of the Pb L\(\alpha\) and L\(\beta\) lines. Peaks associated with the Rh X-ray source and the broad Compton scattering peaks are also not labeled. in the discrimination. Likewise, the relative amount of moisture present in these samples contributed to the discrimination as evidenced by the comparably high loading value for the H line at 656.3 nm. Soil is a complex mixture in different proportions of rocks fragments and minerals, organic materials, and moisture and gases, the Raman bands associated with the mineral components are expected to overlap. In addition, there is significant fluorescence associated with the organic components of soil. Since no attempt was made to remove the fluorescence background or to deconvolve overlapping bands, few peaks can be confidently identified, and the plots are not included in this paper. The only distinct loading for the New Mexico soils is the symmetric stretching mode of sulfate that appears at 1010 cm\({}^{-1}\)[58]. This is the strongest of the sulfate stretching modes, as illustrated in the representative Raman spectrum of a gypsyiferous sample from White Sands, NM (Figure 2). The military installation soils likewise do not have distinct peaks but rather show broad, unidentified, overlapping bands. The loading values for the Raman data have a dynamic range that is one or two orders of magnitude smaller than those found for the XRFS and LIBS classification tasks respectively. This suggests that the differences between the Raman spectra of soils from different locations is not as marked as for the data associated with the elemental analysis techniques.
# Passive Planar Microwave Devices
## 1 Introduction  
Passive planar circuits play a key role in many RF/microwave applications, such as in wireless communications, medical instrumentation, and remote sensing. From their earliest developments during World War II to the present day, they have become indispensable for their low cost and low weight while maintaining high performance. As a result, they are still undergoing research and development. In recent years, multiple technologies have been proposed with the aim of combining the characteristics of traditional planar and nonplanar transmission lines, highlighting substrate integrated waveguide (SIW) technology as the most popular among them.  
This Special Issue is focused on highlighting recent contributions to microwave device development in planar technologies. A total of twelve papers have been published in this volume, each addressing several important research problems and advancements in the field of filters, multiport circuits, dividers, combiners, couplers, multiplexers, microwave sensors, and antennas. These articles provide a significant contribution to the state of the art.
###### Abstract  
To evaluate the effect of simultaneous liquid or gel sodium hypochlorite (NaOCl) irrigation on cyclic fatigue of F6 SkyTaper (F6 ST) and OneCurve (OC) single files, 180 new 25/0.06 F6 ST and OC files were divided into 6 groups (\(n=15\)) for each brand. Groups 1 and 4 included new instruments not exposed to NaOCl at 20 \({}^{\circ}\)C and 37 \({}^{\circ}\)C, respectively. Groups 2 and 5 included files activated with liquid NaCl at 20 \({}^{\circ}\)C and 37 \({}^{\circ}\)C, respectively. Groups 3 and 6 consisted of instruments tested with NaOCl gel at 20 \({}^{\circ}\)C and 37 \({}^{\circ}\)C, respectively. Instruments were subjected to a fatigue test using a novel customized device. Data were expressed as time to fracture (Ttf) and statistically analyzed (\(p<0.05\)) after checking their normality through the Shapiro-Wilk test. Because they were normally distributed, 2-way analyses of variance (ANOVA) and the Tukey multiple comparison post-hoc test were used. Time to fracture of all tested instruments decreased at 37 \({}^{\circ}\)C (\(p<0.05\)). At 20 \({}^{\circ}\)C, NaOCl improved Ttf of F6 ST and OC (\(p<0.05\)). NaOCl liquid increased Ttf of F6 ST (\(p<0.05\)) in comparison with gel, while there was no difference between the two formulations for OC. At 37 \({}^{\circ}\)C, both NaOCl formulations had no significant influence on Ttf for F6 ST, while they increased Ttf of OC (\(p<0.05\)). NaOCl improved the cyclic fatigue resistance of OC, independently of the temperature, while for F6 ST the negative impact of higher temperature reduced the irrigant benefits.  
**Keywords:** cyclic fatigue resistance; F6 SkyTaper; gel; OneCurve; sodium hypochlorite; temperature
# Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities
## 5 Discussion
This is due to the use of the GFA approach, which gives better results compared to the GF in the case of low mobility, and when the difference between the speeds of the vehicles is important. Therefore, the difference in the process used to elect vehicles and the use of the Dijkstra algorithm offer a high rate of convergence. The simulation results show that our approach has the best performance in terms of end-to-end delay. The EED of G-OLSR decreases more and more as the traffic increases, and this is due to the use of the GFA approach, which gives better results when the mobility and the traffic density increase. In addition, the use of the Dijkstra algorithm presents a high convergence rate and therefore a lower EED for high traffic. When the traffic density is situated between 200 and 400 vehicles, the EED increases for OLSR but remains stable for G-OLSR, and this sudden change is made because the nodes are abundant within the network. The traffic density increases progressively, and the probability to find nodes also increases, so the EED decreases beyond 400 vehicles. Therefore, G-OLSR protects the efficiency of the information transmitted in the vehicular Ad hoc Network as we reduce the EED and we increase the PDR. However, our approach presents some limits. When the traffic density is low or the nodes are abundant within the network, we note that G-OLSR becomes passive and behaves nearly as OLSR.
# C-V2X Centralized Resource Allocation with Spectrum Re-Partitioning in Highway Scenario
###### Abstract  
Cellular Vehicle-to-Everything communication is an important scenario of 5G technologies. Modes 3 and 4 of the wireless systems introduced in Release 14 of 3 GPP standards are intended to support vehicular communication with and without cellular infrastructure. In the case of Mode 3, dynamic resource selection and semi-persistent resource scheduling algorithms result in a signalling cost problem between vehicles and infrastructure, therefore, we propose a means to decrease it. This paper employs Re-selection Counter in centralized resource allocation as a decremental counter of new resource requests. Furthermore, two new spectrum re-partitioning and frequency reuse techniques in Roadside Units (RSUs) are considered to avoid resource collisions and diminish high interference impact via increasing the frequency reuse distance. The two techniques, full and partial frequency reuse, partition the bandwidth into two sub-bands. Two adjacent RSUs apply these sub-bands with the Full Frequency Reuse (FFR) technique. In the Partial Frequency Reuse (PFR) technique, the sub-bands are further re-partitioned among vehicles located in the central and edge parts of the RSU coverage. The sub-bands assignment in the nearest RSUs using the same sub-bands is inverted concerning the current RSU to increase the frequency reuse distance. The PFR technique shows promising results compared with the FFR technique. Both techniques are compared with the single band system for different vehicle densities.  
C-V2X; connected vehicles; full frequency reuse distance; partial frequency reuse distance; resource allocation; vehicular communication +
Footnote †: journal: Electronics  
electrnics  
electrnics
## 3 Results and Discussion
### Determination of Volatile Substances
#### 3.2.2 Results of GC-MS Measured Volatile Substances in Hairtail Surimi  
A total of 138 volatile substances were detected using GC-MS technology, including 20 alcohols, 7 aldehydes, 12 olefins, 4 phenols, 12 alkanes, 8 ketones, 15 esters, 6 acids, 16 benzenes, 4 ethers, and 8 amines, as well as 26 other types of compounds (Table 1). The flavor of the MFHS gel is significantly impacted by these variations in volatile chemicals. Statistics on the quantity and relative content of different volatile substances at distinct fermentation times revealed that Monascus fermentation increases the types of benzene and other volatile substances while decreasing the types of aldehydes, olefins, phenols, alkanes, and ethers in the surimi gel's volatile substances. Moreover, the kinds of alcohols and amines show a trend of first decreasing and then increasing with the fermentation period, and acids and esters exhibit an opposite tendency. The volatile substances of MFHS were mainly alcohols, esters, benzenes, aldehydes, and other classes. Moreover, the relative content of each category of volatile flavor exhibited various variations with the change in fermentation time. Compared with the changes in the volatile substances of hairtail surimi gel with Monascus fermentation at different times, we further examined the volatile substances that were reduced, increased, and added after fermentation.
# Neural Models for Measuring Confidence on Interactive Machine Translation Systems
## 3 Methodology  
This section briefly summarizes the information described in Sections 4 and 5. Those sections explain in more detail how we train and develop the CMs used in the project and the experimental setup we followed.  
MT systems use CMs to estimate the correctness of the translations, being able to obtain scores at different levels: document, sentence, and word. The system compares the confidence estimation with a threshold set by researchers to classify the different elements as correct or incorrect. For those elements with a confidence score lower than the threshold, the system classifies them as incorrect. In this project, we worked with word-level CMs and applied them in an IPNMT environment. With this implementation, the user only has to provide feedback about the first word classified as incorrect. By not having to correct those words classified as correct, the system reduces the human effort, but the final translations could have some misclassified words.  
The CMs developed in this project are recurrent neural networks (RNN). We trained them with word scores obtained from four of the most used metrics in MT: Bleu, Meteor, Chr-F, and TER.  
For the experimentation process, we used the EU corpus. With this corpus, we tested the robustness and the applicability of our CMs. We calculate the percentage of misclassified words they obtain to test their robustness. The dataset was extracted by performing a conventional IPNMT session, saving the correct words and mistakes of the MT system. Then, we applied them in an IPNMT environment to test their applicability by comparing the effort reduction with the translation quality obtained.  
Real translators are very costly and take much time for each experiment. For this reason, we opted to simulate the user in the IPNMT environment.
## 3 Multi-Dimensional Fusion Feature-Enhanced Prediction Method
### Deep Fusion Module
#### 3.2.2 Adaptive Weight Fusion Strategy  
In order to give full play to the advantages of CNN and XGBoost, an adaptive weight fusion strategy is adopted. Let \(y_{CNN}\) be the predictive output of the CNN model and \(y_{XGBoost}\) be the predictive output of the XGBoost model. The fused predictive output calculation formula is as follows:  
\[y=\alpha y_{CNN}+(1-\alpha)y_{XGBoost}. \tag{19}\]Among them, \(\alpha\) is the weight of the CNN model, and its value is dynamically adjusted according to the model performance during the training process to meet \(0\leq\alpha\leq 1\). In the early stage of training, CNN quickly extracted basic features, which contributed greatly and relatively high in the process of loss function decline. As the training progresses, XGBoost learns complex nonlinear relationships and increases the impact on prediction accuracy, and the model automatically increases its weight ratio. This strategy avoids the limitation of fixed weight and optimizes the prediction accuracy.  
In summary, by constructing a deep fusion module containing a three-layer CNN structure, combining batch normalization and the dropout mechanism, and adopting an adaptive weight fusion strategy, this study successfully combines the temporal feature extraction ability of CNN with the nonlinear mapping advantage of XGBoost to construct an end-to-end prediction framework. The hybrid framework shows excellent performance in the crude oil futures price prediction task. It substantially enhances the precision of forecasts and simultaneously minimizes prediction variability via the integration of multiple models. This approach introduces a novel framework that holds significant theoretical importance and practical relevance for financial market forecasting.
## 3 Results and Discussion
[PERSON] et al., proposed [11] an LSTM-CNN model for epileptic seizure detection using EEG signals, highlighting the need for automated techniques due to the lengthy process and shortage of specialists for the visual inspection of EEG reports. Their proposed model combined LSTM and CNN for feature extraction and classification, and they achieved promising results in detecting seizures. Thus, our findings provide valuable insights into the performance of outlier detection algorithms for epileptic seizure detection in EEG signals and highlight the potential of distance-based methods, particularly when combined with PCA, for achieving high performance in this task. These findings are consistent with current studies [9; 10; 11; 12]. Further research in this area could potentially lead to the development of more effective and efficient computer-aided tools for epilepsy diagnosis, reducing the interpretation load on specialists and improving patient care.
## 5 Experimental Evaluation
### Algorithm Performance Results  
#### Overall Performance Comparison  
Table 5 presents aggregate performance metrics across all simulation scenarios, demonstrating the effectiveness of our multi-algorithm approach through rigorous statistical analysis.  
The Analysis of Variance (ANOVA) analysis reveals statistically significant differences between algorithms (\(p<0.001\)), with HATC achieving 31.01% success rate--a 26.5% improvement over the KFTC baseline. The large effect size ([PERSON]'s d = 0.82) indicates practical significance beyond statistical significance.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Algorithm** & **Success Rate** & **95\% CI** & **Effect Size 1 ** & **Rank** \\ \hline KFTC & 24.51\% & [23.8\%, 25.2\%] & – & 4 \\ RLSTC & 26.94\% & [26.2\%, 27.7\%] & 0.51 & 3 \\ FLTC & 29.34\% & [28.6\%, 30.1\%] & 0.59 & 2 \\ HATC & 31.01\% & [30.2\%, 31.8\%] & 0.82 & 1 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Algorithm performance summary (N = 8000 tests per algorithm).
# Generation of Complete SAR Geometric Distortion Maps Based on DEM and Neighbor Gradient Algorithm
## 2 Methodology  
### Overview of the SGD  
For the slopes facing the SAR sensor, when the local terrain slope angle is smaller than the local incidence angle, such slopes have a shorter length in the SAR image than the flat terrain, which means the range resolution on such slopes becomes worse. This is the so-called foreshortening effect, which achieves the worst performance when the local terrain slope angle equals the local incidence angle (Figure 1a). Furthermore, when the local terrain slope angle exceeds the local incidence angle, the bottom and the top of such slopes are reverse imaged, which is known as the active layover effect. For the slopes facing away from the SAR sensor, when the local terrain slope angle is smaller than the complementary angle of the local incidence angle, the range resolution on such slopes becomes better in the SAR images than the flat terrain. Obviously, the resolution-enhancing effect is popular in InSAR measurements. However, when the local terrain slope angle exceeds the complementary angle of the local incidence angle, such steep slopes are completely prevented from receiving the SAR signal by the mountain itself. This is the active shadow effect that makes the slopes dark in the SAR image. For more details on geometric distortion analysis, see [14; 15]. In addition, in the object space, the layover region splits into active and passive subregions. Active layover regions are the sources for layover, whereas passive layover regions are induced by the actives lying over them (Figure 1b). The active layover region is embedded in two passive regions called \"near passive\" and \"far passive\". The near passive region is closer to the sensor than the far passive region. Generally, the layover regions are much brighter than other places on the SAR image. In contrast to the layover, where two passive regions occur, here we have only one passive region for the shadow, called the \"passive shadow\" region. It is located at the end of the active shadow, where it cannot be reached by the radar beam since it is blanked by the active shadow regions (Figure 1c).  
Figure 1: Illustrations of foreshortening (**a**), layover (**b**), and shadow (**c**).