esa-sceva/satcom-chunk-collection huggingface dataset:
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Metaheuristics Require Experiments to Evaluate Algorithms' Performance  
The performance evaluation is an imperative criterion for metaheuristic. Experiments support the determining of how well a metaheuristic (MH) investigates the search space. Additionally, considering the consequences of the \"No Free Lunch\" theorems, it is hard to know in advance which MH will perform better than others. Moreover, since the most successful MH are stochastic--due to their probabilistic nature (uncertainty)--the results of MH tend to vary. Therefore, by considering an instance of a problem, the same algorithm may have variances in its performance. Consequently, to compare a set of metaheuristics' performances and determine which is the best, it is necessary to design and execute an experiment to conduct an appropriate evaluation.  
An experiment for metaheuristics evaluation involves a set of coordinated steps in a controlled environment, and an experiment should guarantee its reproducibility and comparability. In a summarized way, an experiment considers the following steps: (1) objectives definition, (2) design, (3) execution, and (4) conclusions [5; 58; 87; 88; 89].  
The first step, (1) objectives definition, aims to define the experiments' drivers, goals, and research questions. The design step (2) intends to specify, plan, and prepare the experiment. It commonly considers the following activities: defining the measures and terminology, selecting the metaheuristics, defining the parametrization tuning strategy, determining the report format, etc. Each algorithm may have several possible parameters configuration, and the parametrization tuning strategy defines how these configurations will be tested. It is essential to highlight that parameter setting directly influences the algorithm's performance [90] and it varies by the given problem. Generally speaking, it can happen manually by defining a set of possible configurations, or it can be conducted automatically, supported by an optimization method. Furthermore, when it is not a real problem, it should be necessary to choose benchmark problems and their \"data\" to compare performance among designed problems.  
The step experiment execution (3) is dedicated to performing the experiment, running each configuration, and collecting its data. It is necessary to emphasize that each configuration must be executed enough times to conclude whether a specific configuration is effectively better than others (e.g., thirty times may be sufficient to make conclusions statistically). Consequently, the data analysis must consider a statistical approach to understand the variance to determine whether the best performances are statistically. Consequently, it regards dispersion statistics measures (e.g., mean, median, standard deviation, and outliers' analysis) organized in visualizations or reports to support the decision. The final step, (4) conclusions, aims to analyze the results and organize the conclusions of the experiment.
# Evaluation of the Impact of Various Functional Fillers on Key Properties of Dental Composites
## 5 Conclusions  
The studies that were conducted demonstrate the impact of fillers (hydroxyapatite, calcium fluoride, and nanosilver) on the mechanical properties of flowable dental composites. The best properties were exhibited by the composite containing 2 wt% of hydroxyapatite, calcium fluoride, and nanosilver, as follows: hardness at the level of 89 (Shore D), compressive strength of 264 (MPa), impact strength of 0.128 (J/cm\({}^{2}\)), bending strength of 76.48 (MPa), and tribological wear of 1.24 (\(\times\)10\({}^{-4}\) Mm\({}^{3}\)/Nm). The findings suggest that the combination of fillers promoted higher hardness (\(p\) < 0.001) and lower tribological wear (\(p\) = 0.01) than the non-filler specimens, showing similar results on the other properties. However, the individual inclusion of nanosilver as a filler in the composite matrix was found to significantly deteriorate the impact, compressive, and bending properties of the material. Overall, the modified resins met the adequate mechanical requirements for flowable composites, except for the composite containing only nanosilver. Further experiments should be conducted in the future to improve these materials' performance.
# Research on Satellite Selection Strategy for Receiver Autonomous Integrity Monitoring Applications
###### Abstract  
Satellite selection is an effective way to overcome the challenges for the processing capability and channel limitation of the receivers due to superabundant satellites in view. The satellite selection strategies have been widely investigated to construct the subset with high accuracy but deserve further studies when applied to safety-critical applications such as the receiver autonomous integrity monitoring (RAIM) technique. In this study, the impacts of subset size on the accuracy and integrity of the subset and computation load are analyzed at first to confirm the importance of the satellite selection strategy for the RAIM process. Then the integrated performance impact of a single satellite on the current subset is evaluated according to the performance requirement of the flight phase. Subsequently, a performance-requirement-driven fast satellite selection algorithm is proposed based on the impact evaluation to construct a relatively small subset that satisfies the accuracy and integrity requirements. Comparison simulations show that the proposed algorithm can keep similar accuracy and better integrity performances than the geometric algorithm and the downdate algorithm when the subset size is fixed to 12, and can achieve an average 1.0 to 2.0 satellites smaller subset in the Lateral Navigation (LNAV) and approach procedures with vertical guidance (APV-I) horizontal requirement trial. Thus, it is suitable for real-time RAIM applications and low-cost navigation devices.  
satellite selection; multi-constellation; receiver autonomous integrity monitoring; impact evaluation; Lateral Navigation 2021131725[[https://doi.org/10.3390/rs13091725](https://doi.org/10.3390/rs13091725)]([https://doi.org/10.3390/rs13091725](https://doi.org/10.3390/rs13091725))
## 3 Results and Discussion
### Raman Spectroscopy Analysis  
Raman spectroscopy was performed to characterize the samples and elucidate the carbon structure. In Figure 2, all the samples possessed two characteristic peaks at \(\sim\)1348 (D band) and \(\sim\)1598 (G band) cm\({}^{-1}\). The 2D band intensity of rGO and GO normally appears at around 2700 cm\({}^{-1}\) but it was completely absent in all the three samples in this study. The absence of the 2D band intensity confirmed that the structure had high amount of defects. The 2D peak intensity depends on electron doping and structural disorders [32; 33]. In our study, the D band resulted from structural imperfection caused by the presence of the disordered carbon and the G band was attributed to the E\({}_{2g}\) vibration of carbon atoms. The intensity ratio of the D and G bands (I\({}_{\rm D}\)/I\({}_{\rm G}\)) was used to estimate the defects of the samples. Our results revealed that 10% Fe/N-rGO, 15% Fe/N-rGO and 20% Fe/N-rGO had I\({}_{\rm D}\)/I\({}_{\rm G}\) of 0.69, 0.65 and 0.71, respectively, suggesting that 20% Fe/N-rGO had the highest amount of defects [18]. I\({}_{\rm D}\)/I\({}_{\rm G}\) is closely related to the sp\({}^{3}\)/sp\({}^{2}\) carbon hybridization ratio. The sp\({}^{2}\)-hybridized carbon is assigned to the carbon graphitic structure, whereas the sp\({}^{3}\) carbon hybrid is assigned to the bond between carbon and atoms such as nitrogen, oxygen and iron anchored on the carbon surface. Furthermore, 15% Fe/N-rGO showed the lowest ID/IG, suggesting that the low amount of Fe or few point defects [34; 35; 36; 37] were introduced to the rGO structure. The result was consistent with the XRD results, which demonstrated that the lowest peak intensity among the three samples and the semi-quantitative EDS results reported in Table 1 reaffirmed the lowest Fe content of 15% Fe/N-rGO.  
Figure 2: Raman spectra of D and G bands of 10% Fe/N-rGO, 15% Fe/N-rGO and 20% Fe/N-rGO.
_Algorithms_**2015**, \(8\), 1052-1075; doi:10.3390/a8041052  
_algorithms_  
ISSN 1999-4893  
www.mdpi.com/journal/algorithms  
_Article_  
A New Approach for Automatic Removal of Movement Artifacts in Near-Infrared Spectroscopy Time Series by Means of Acceleration Data  
**[PERSON] 1,2,3,4**, [PERSON] 1,5**, [PERSON] 5,6** and [PERSON] 1,*  
1 Biomedical Optics Research Laboratory, Department of Neonatology, University Hospital Zurich, University of Zurich, 8091 Zurich, Switzerland; E-Mails: [EMAIL_ADDRESS] (A.J.M.); [PERSON]ESS] (M.W.)  
2 Institute for Biomedical Engineering, ETH Zurich, 8092 Zurich, Switzerland  
3 Member of the PhD Program \"Integrative Molecular Medicine\", University of Zurich, 8057 Zurich, Switzerland  
4 Institute for Complementary Medicine, University of Bern, 3012 Bern, Switzerland  
5 Zurich Center for Integrative Human Physiology Zurich, University Zurich, 8057 Zurich, Switzerland; E-Mail: [EMAIL_ADDRESS]  
6 Institute for Pharmacology and Toxicology, Chronobiology and Sleep Research, University of Zurich, 8057 Zurich, Switzerland  
* Author to whom correspondence should be addressed; E-Mail: [PERSON] Tel.: +41-44-2559326.  
Academic Editor: [PERSON]  
_Received: 6 July 2015 / Accepted: 28 October 2015 / Published: 19 November 2015_  
**Abstract:** Near-infrared spectroscopy (NIRS) enables the non-invasive measurement of changes in hemodynamics and oxygenation in tissue. Changes in light-coupling due to movement of the subject can cause movement artifacts (MAs) in the recorded signals. Several methods have been developed so far that facilitate the detection and reduction of MAs in the data. However, due to fixed parameter values (e.g., global threshold) none of these methods are perfectly suitable for long-term (_i.e._, hours) recordings or were not time-effective when applied to large datasets. We aimed to overcome these limitations by automation, _i.e._, data adaptive thresholding specifically designed for long-term measurements, and by introducing a stable long-term signal reconstruction. Our new technique (\"acceleration-based movement artifact reduction algorithm\", AMARA) is based on combining two methods: the \"movement artifact reduction algorithm\" (MARA,Scholkmann _et al.__Phys. Meas._ 2010, _31_, 649-662), and the \"accelerometer-based motion artifact removal\" (ABAMAR, [PERSON] _et al.__J. Biomed. Opt._ 2011, _16_, 087005). We describe AMARA in detail and report about successful validation of the algorithm using empirical NIRS data, measured over the prefrontal cortex in adolescents during sleep. In addition, we compared the performance of AMARA to that of MARA and ABAMAR based on validation data.  
Keywords:movement artifact reduction algorithm (MARA); acceleration-based motion artifact removal (ABAMAR); acceleration-based movement artifact reduction algorithm (AMARA); motion artifacts; movement artifacts; near-infrared spectroscopy (NIRS); functional-near infrared spectroscopy (fNIRS)
## 5 Simulation Results
### Verification of Performance Analysis  
In RDUR, the ERSR radius in each AA affects the number of URs. Under different ERSR radius sets, the ratio that MG user was selected as RU is plotted in Figure 5a. It shows that the analytical UR selection ratio was closed to the simulated one under different ERSR radius sets. When \(M_{i}\) was greater than about 120, the analytical result (23) was bigger than the simulated one. Because in the AAs near the BS the number of SU was large, \(R^{A_{n}}\) is larger than 1. Even though ARR was introduced to reduce the number of repeated UR, in these AAs some URs are repeatedly calculated. Besides, UR selection ratio increased as \(M_{i}\) increased. This was because that the density of candidate UR in the ERSR of USU becomes large.  
Obviously, as a result of small ERSR, the UR selection ratio under \(\theta_{1}\) was smaller than that under the other three sets. It can be seen that the UR selection ratio under \(\theta_{2}\) was larger than that under \(\theta_{3}\). When large ERSR was allocated to those AAs close to cell edge (e.g., \(r_{4}=r_{5}=100\) m), owing to the fact that there were more USUs than cell-center AAs, the large ERSR contributes to select more URs than the situation that large ERSR is allocated to the cell-center AAs (e.g., \(r_{1}=r_{2}=100\) m). Furthermore, after a thorough comparison between \(\theta_{2}\) and \(\theta_{4}\), it was found that UR selection ratios were almost equal. This also verifies that different ERSR allocations can influence the number of UR.  
The second-stage coverage ratio performance is presented in Figure 5b. As expected, the analytical result matches well with simulated result. The the gap between them was less than 1%, i.e., the maximum difference between the two results was less than two SUs. Furthermore, the coverage ratio under \(\theta_{3}\) was is smaller than that under \(\theta_{2}\). The reason was that more USU locate near the cell edge. Large ERSR was allocated to \(AA_{4}\) and \(AA_{5}\) to select UR. It better guarantees successful reception of USU. In addition, the second-stage coverage ratio also increased with the increase of \(M_{i}\).
## 3 Material and Methods
### Evaluation Metrics  
Given the imbalanced nature of our dataset, accuracy alone is not a suitable metric for evaluating model performance [52; 53]. Therefore, we prioritize precision, recall, and F1 score. Precision measures the proportion of true positive results among all positive predictions, recall measures the proportion of actual positives correctly identified, and the F1 score balances precision and recall. This approach provides a more accurate and reliable evaluation of the model's effectiveness in handling imbalanced data. We consider that true positive (TP) is the number of positive cases predicted as positive, True Negative (TN) is the number of negative cases predicted as negative, False Negative (FN) is the number of positive cases predicted as negative, and False Positive (FP) is the number of negative cases predicted as positive. Here are the metrics upon which our evaluations rely.  
* Precision measures the percentage of accurate positive predictions compared to the total number of samples classified as positive.  
Precision = TP/(TP + FP)
* Recall measures the percentage of accurate positive predictions compared to the total number of actual positives.  
Recall = TP/(TP + FN)
* The F1 score is a trade-off between precision and recall, which combines precision and recall assessing the performance of the model. It represents a better metric in the case of imbalanced classes.  
F1 score = 2 \(\times\) (Precision \(\times\) Recall)/(Precision + Recall)
# Fuzzy Risk Evaluation and Collision Avoidance Control of Unmanned Surface Vessels
## 3 Design of Smart Collision Avoidance Control System for USVs
### Decision-Making Procedure  
#### 3.2.1 Fuzzy Collision Risk Indicator  
In this section, a fuzzy based collision risk index, \(\mu_{CRI}\in\) [0,1], will be developed for evaluating the probability of the controlled USV with respect to arbitrary randomly incoming ships. When \(\mu_{CRI}=0\), this represents that a collision between two vessels will not occur. In contrast, \(\mu_{CRI}=1\), represent that collision between two vessels will occur, even if all possible actions have been made.  
Initially, the proposed fuzzy collision risk indicator was designed based on DCPA and TCPA. However, the maneuverability of the controlled USVs was inherently affected by their length in practice. Taking this issue into account, the DCPA of this proposed collision risk indicator for different types of USVs should be modified. Assuming that the lengths of the controlled USV is \(L_{O}\) and target ship is \(L_{T}\), the modified DCPA with respect to these two lengths can be represented as:  
\[\text{DCPA}_{L}=\frac{\text{DCPA}}{L_{S}} \tag{8}\]  
where \(L_{S}\) denotes the total lengths of the two ships with adjustable design weights 1.5 and 0.5 for the controlled USV and target ship, respectively.  
This leads to:  
\[L_{S}=1.5L_{O}+0.5L_{T} \tag{9}\]  
In this study, a real USV measuring 1.72 m is used as the research object. The universe of discourse of DCPA\({}_{L}\) is [\(-\)3, 20] and the universe of input of TCPA is [\(-\)5, 350]. Based on these two parameters, the 3D profile of the fuzzy collision risk identifier, which can be used to determine the level of collision risk, \(\mu_{CRI}\), when two ships are under an encounter situation is shown in Figure 6.  
Figure 5: Geometric relation between two encountered ships.
## 1 Introduction
Additionally, it should be noted that there were mainly two approaches to obtain FKE at 1.55 \(\upmu\)m from Ge, which were either by the use of Ge-rich GeSi such as Ge\({}_{0.992}\)Si\({}_{0.0075}\) (adding a very small amount of Si) [28], or by the application of compressive strain on pure Ge [29]. The proposed coupling structure should render a very similar performance in both cases as the refractive index of the Ge-rich GeSi and Ge are almost identical. We found that the proposed coupling structure could be employed to enable Si\({}_{3}\)N\({}_{4}\) waveguide integrated Ge-based optical modulator with a viable performance in term of extinction ratio and insertion loss with a compact footprint. The analysis of performance variation with respect to fabrication variation is also presented here with promising prospects. Also, it should be noted that although good optical coupling performance was demonstrated experimentally [30] and theoretically [31] from Ge-on-Si photodetector integrated directly with a silicon nitride waveguide, there is no need to maintain the fundamental mode inside and at the output of a photodetector, as required in the case of an optical modulator; therefore, for photodetector Ge can be made relatively large to accommodate optical coupling with silicon nitride. In addition, for Ref. 24 the integration of the Si\({}_{3}\)N\({}_{4}\) waveguide with Ge-on-Si photodetector was performed via SOI waveguide; thus, the SOI wafer was employed.
# Design of Edge-IoMT Network Architecture with Weight-Based Scheduling
## 2 Related Works
### Data Collection
#### 3.2.1 Dataset Description  
For healthcare professionals and patients, the long-term monitoring of physiological signals is beneficial to the rational assessment of health status and disease evolution. As we know, physiological signals reflect the electrical activity of a specific body part and  
Figure 2: A four-layer IoMT architecture.  
provide valuable information about mood, cognition, and many other health issues, thus any deviation from the norm in patterns may indicate an underlying health problem. For instance, arrhythmias can be picked up by ECG signals while epilepsy, which is a common neurological disorder, manifests itself as abnormalities in EEG signals during epileptic seizure. Monitoring and analyzing these physiological signals form the basis of biomedical devices used for the diagnosis, detection, and treatment of various diseases [30]. Moreover, in recent years, stress and mental health have been considered as important worldwide concerns. Stress detection using physiological signals such as ECG, EMG, and EEG is a traditional approach [31]. Therefore, three critical physiological signals are selected, namely ECG, EEG, and EMG.
* ECG: Since cardiovascular diseases may lead to sudden cardiac arrest or even death and are difficult for patients to detect and handle them effectively in time, it is necessary to monitor the ECG signal in real-time to prevent this situation. Here, we apply the MIT-BIH dataset as one of the input data, which is publicly available at PhysioNet [32]. The dataset provides data collected from 48 patients with recordings of 30 min each. The recordings are digitized at 360 samples per second per channel with 11-bit resolution over a 10 mV range.
* EEG: Many studies have shown that patients with head trauma or cerebrovascular disease can be managed effectively with continuous EEG monitoring, and there is also a lot of usage of EEG data analysis for effective diagnosis of certain diseases such as epilepsy. This work applies the EEGLAB dataset [33], which uses a single structure to store data, acquisition parameters, events, channel locations, and epoch information. The sample dataset consists of 32 channels, and the sampling frequency is 128 Hz. Channel 1 (FP1) and channel 2 (FP2) in the frontal area are adopted as inputs, which are commonly used for EEG analysis.
* EMG: Depending on the position of the patch, the collected EMG data can be used for different applications. The EMG dataset in bio-signals Repository [34] is used to control the prosthesis movements. The required finger movements are performed through 8 participants with ten categories of individual and combined finger movements (e.g., thumb, index, middle, ring, little, thumb-index, thumb-middle, thumb-ring, thumb-little, and hand-close finger movements). The data were collected using two EMG channels and the sample rate is 450 Hz. In this work, the hand-close dataset is selected as the EMG signal input.
# Radio Frequency Interference, Its Mitigation and Its Implications for the Civil Aviation Industry
###### Abstract  
Radio Frequency Interference has emerged as a growing challenge for aviation safety and system integrity due to the increasing spectral overlap between communication technologies and aviation systems. This paper investigates the sources, types, and consequences of RFI in Global Navigation Satellite Systems, Instrument Landing Systems, and altimeters used in civil aviation. A detailed examination of both intentional and unintentional interference is presented, highlighting real-world incidents and simulated impact models. The study analyzes technical mechanisms such as receiver desensitization, intermodulation, and cross-modulation, and further explores UAV-based interference detection frameworks. Mitigation strategies are reviewed, including regulatory practices, spectrum filters, shielding architectures, and dynamic UAV sensing systems. Comparative insights into simulation results, shielding techniques, and regulatory gaps are discussed. The paper concludes with recommendations for enhancing current aviation standards and suggests a hybrid validation model combining in-flight measurements with simulation-based assessments. This research contributes to the understanding of electromagnetic vulnerabilities in aviation and provides a basis for future mitigation protocols.  
radio frequency; UAV; VHF; UHF; navigation; aviation; flights; ILS 2025