esa-sceva/satcom-chunk-collection huggingface dataset:
# Classification of Sleep Stage with Biosignal Images Using Convolutional Neural Networks
## 6 Discussion  
We compare the proposed method to the previous studies on sleep-stage classification. Table 6 shows the characteristics and accuracies of recent studies. The studies on sleep classification are difficult to directly compare because they do not use the same database, testing methods, or scoring rules. Even if the studies using the same dataset are comparable, the following limitations exist: (1) configuration of experimental data in the dataset (whether Wake and Movement stages are included or not); (2) sampling methods of training, validation, and testing of the experimental data; and (3) distribution between classes (class imbalance program). For example, the epochs of the Wake stage occupy a higher percentage than any other stages in the Sleep-EDFx database, because wake epochs before and after sleep are recorded. Some studies try to rebalance the number of epochs for each stage, but others do not, which leads to biased results.  
Therefore, the performance comparison is inappropriate unless the experiments use the same experimental environment. With respect to this, the proposed method outperforms those of other studies, which commonly used data only. We verified classification performance in line with the previous studies with 94.0% accuracy.  
Figure 8: Confusion matrix of the classification results (Exp3).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Time Domain** & **Freq. Domain** & **Time and Freq. Domain** \\ \hline Accuracy & 86\% (71.4–100) & 86\% (72.1–95.2) & 94\% (78.4–100) \\ Weighted F1 score & 85\% (59–100) & 86\% (60–97) & 94\% (84–100) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Summary of experiments: mean and range of the testing set.
## 2 Related Work
### MEC Architecture  
In this paper, the role and location of MEC are both themes. Related research and investigation results are discussed below. In [29], MEC is regarded as one type of edge computing, while architectures such as Edge/Fog Computing are proposed. While there is a concept-based discussion, it does not refer to the players. In the architecture shown by ETSI in [30; 31], it is possible to deploy MEC in front of the core functions. In [30], they authors mainly provide an example of use cases where the MEC holder is an existing operator, without specifying MEC location. In [31], the authors discuss the collaboration between multiple legacy operators without discussing other types of players, such as local players or third parties. Although [32] makes architectural proposals focusing on MEC's NFV capabilities, the authors do not delve into the specific component level. References [33; 34; 35] are summaries from the viewpoint of MEC functions. In [33], there is a discussion about how to operate DNS on the architecture in order to reduce the connection latency to MEC. There is a discussion focusing on ETSI architecture regarding MEC deployment in [34]. In addition, in [35], an architectural discussion combining Information-Centric Networking (ICN) and MEC is provided. However, the division of responsibilities is missing, because the role of the architecture is not identified. The differences between the above-mentioned works [33; 34; 35] and this paper involve the viewpoints of each application function (e.g., RAN, Core, MEC) that the owner holds. Most significantly, this paper answers the question of who owns the E2E system functions in MEC architecture.
# A Novel MPPT Based Reptile Search Algorithm for Photovoltaic System under Various Conditions
## 3 Reptile Search Optimization (RSA) Based MPPT Algorithm
### RSA Algorithm for MPPT
#### 3.1.2 Encircling Phase (Exploration)  
According to the behavior of crocodiles during the encircling phase, they use two different strategies to move in the search space: (high walking and belly walking). These two walking techniques are performed in this level of optimization to support the hunting phase in the search area and produce more distinct solutions and investigate several regions. During this phase of the search, the high walking transportation strategy is performed only when \(\mathbf{t}\leq\frac{T}{4}\) and belly walking strategy is performed only if \(\mathbf{t}>\frac{T}{4}\) and \(\mathbf{t}\leq\frac{2T}{4}\) where \(\mathbf{t}\) is  
Figure 3: RSA-MPPT based flowchart.  
the current iteration [33]. Then duty cycle in the exploration phase is updated according to Equation (2).  
\[\mathbf{D_{i}}(\mathbf{t}+\mathbf{1})=\left\{\begin{array}{ll}\mathbf{D_{Best}}(\mathbf{t})-\mathbf{ \eta_{i}}(\mathbf{t})\times\mathbf{\beta}-\mathbf{R_{i}}(\mathbf{t})\times\mathbf{rand}&\mathbf{t}\leq \frac{\mathbf{T}}{4}&(\mathbf{a})\\ \mathbf{D_{Best}}(\mathbf{t})\times\mathbf{D_{r_{1}}}\times\mathbf{ES}(\mathbf{t})\times\mathbf{rand}& \mathbf{t}\leq\mathbf{2}\frac{\mathbf{T}}{4}\mathbf{and}\mathbf{t}>\frac{\mathbf{T}}{4}&(\mathbf{b})\end{array}\right. \tag{2}\]  
where \(\mathbf{\eta_{i}}(\mathbf{t})\) is the hunting parameter; it is beneficial to ensure exploration continuity and avoid getting stuck in local optima, especially in the last iterations. It is calculated using Equation (3). \(R_{i}\) denotes Reduce function, which is used to reduce search area, and it is calculated using Equation (4). Evolutionary ratio \(\mathbf{ES}(\mathbf{t})\) is a probability ratio that decreases arbitrarily between [2, \(-2\)] over the number of iterations to maintain diversity among potential solutions. It is calculated using Equation (5).  
\[\mathbf{\eta_{i}}=\mathbf{D_{Best}}(\mathbf{t})\times\mathbf{\alpha} \tag{3}\]  
\[R_{i}=\frac{\mathbf{D_{Best}}(\mathbf{t})-\mathbf{D_{r_{2}}}}{\mathbf{D_{Best}}(\mathbf{t})+\epsilon} \tag{4}\]  
\[\mathbf{ES}(\mathbf{t})=\mathbf{2}\times\mathbf{r_{3}}\times(\mathbf{1}-\frac{\mathbf{t}}{\mathbf{T}}) \tag{5}\]  
\(\mathbf{D_{Best}}\) is the best duty cycle obtained so far, \(\mathbf{T}\) is maximum number of iterations, \(\mathbf{rand}\) is random value between 0 and 1, \(\mathbf{r_{1}}\) and \(\mathbf{r_{2}}\) are random numbers between \([1,\mathbf{N}]\) where \(\mathbf{N}\) is population size. \(\mathbf{D_{r_{1}}}\), \(\mathbf{D_{r_{2}}}\) are random duty cycle positions. \(\mathbf{\beta}\) is a sensitive parameter that regulates the encircling phase's exploration accuracy (high walking) during iterations; in this paper, it is fixed at value 0.005. \(\mathbf{r_{3}}\) is random integer number between \(-1\) and 1. \(\mathbf{\alpha}\) is also a critical parameter that determines the exploration accuracy for hunting partnership. It is fixed at value 0.1 in this paper.
## 2 Materials and Methods
### Materials  
The MWCNTs were supplied by Nanocyl (NC7000, Sambreville, Belgium). The nanotubes were analysed by scanning electron microscopy/energy dispersive spectroscopy (SEM/EDS) (FEI, Quanta 400 FEG, Hillsboro, Oregon, USA) to observe the morphology and obtain the elemental analysis to identify the catalyst attached to the MWCNTs. The PDMS and the curing agent (SYLGARD(tm) 184 Silicone Elastomer Kit) were supplied by Dow Corning Co. Ltd Midland, Michigan, USA. The neodymium magnets were bought at K&J Magnetics, Pipersville, USA. The magnets used for the rectangular pattern were N42 SH magnets with dimensions of 25.2 \(\times\) 19.1 \(\times\) 19.1 cm\({}^{3}\), and for the other patterns, N42 magnets were used with dimensions of 0.5 \(\times\) 0.5 \(\times\) 0.5 cm\({}^{3}\).
###### Abstract
More recently investigations have identified this limitation and have applied non-linear tools and advanced statistical methods (i.e., Statistical Parametric Mapping) in biomechanical analyses [11; 23; 24]. To execute even a basic shoulder movement, the muscles around the shoulder joint need to work together, synergistically to create a coordinated movement [25]. In addition to poor technique, muscular imbalance can potentially cause faulty shoulder mechanics, which has been linked to increased risk of shoulder injury [26; 27; 28]. The majority of biomechanical research on overhead sports has investigated the external biomechanical performance and injury-risk indicators [29]. The interpretation of the existing findings has some shortcomings as internal joint loadings are not determined [29]. Musculoskeletal modelling offers the potential to understand human movement through the prediction of the articular and tissue loading [30]. Over the years, efforts have been made to evaluate the sensitivity of musculoskeletal model predictions to lower limb muscle-tendon properties [31; 32] and the body of knowledge on lower limb musculoskeletal modeling especially for gait analysis is well-documented [33]. However, little is known about the shoulders and upper limbs [33] and to the best of the authors' knowledge, almost all shoulder and upper limbs musculoskeletal modeling studies have a clinical context. Existing studies normally report external moments obtained through kinetic analysis to reflect joint loading [29; 34; 35]. The findings are limited as intrinsic forces such as muscle forces are not accounted for and therefore the calculated external joint moments do not necessarily represent the actual mechanical burdens imposed on the articular interfaces [36; 37]. The aims of this systematic review were to critically review the published studies over the past 10 years that primarily investigated shoulder biomechanics during overhead sporting movements. This systematic review aimed to thoroughly assess the body of published studies spanning the last decade, focusing on shoulder biomechanics during overhead sporting movements. The primary objectives were to summarize the utilizedbiomechanical analysis approaches and the key findings related to sports, gender, level of experience, and the presence or absence of injury in these studies. The review also serves to identify a research gap in this area for future investigation. A better understanding of shoulder biomechanics will assist researchers and practitioners in planning their respective strategies for performance enhancement and/or injury prevention with greater specificity and transferability across overhead sports.
## 5 Discussion
### Location Accuracy  
In V2P communication, precise location information is needed to predict the crash probability accurately. However, real-world measurements show that GPS location inaccuracy is 3 m for [3] and 10 m for [4]. This requires crash prediction algorithms to accommodate GPS inaccuracies while calculating the crash probability. Efforts by Audi [17] use a Kalman filter mechanism in order to accommodate GPS inaccuracy for VRU positioning. Broadcom Inc. has developed a positioning solution for a smartphone with 30 cm accuracy [53], however its results in real-world remain unseen.  
Also, current GPS-equipped devices (OBU/smartphones) do not support differentiation in 3D plane. This limitation may lead to false positive collision warnings. For example, if a pedestrian is crossing a street using an overpass while a vehicle is passing, the V2P system may predict that the vehicle and the pedestrian are on the verge of collision. The V2P systems must be capable of identifying the situation when the involved entities are not at the same level.
## 3 Results and Discussion
### Clinker Characterization
#### 3.2.4 Effect of High Alkali Content on Mineral Phase of Clinker  
Figure 8 illustrates the effects of elevated alkali conditions on the mineralogical phases of cement clinker. As shown in Figure 8, in the samples with low potassium content (K\({}_{1}\)) and low sodium content (N\({}_{1}\)), the peak intensities of C\({}_{3}\)S and C\({}_{2}\)S are notably pronounced, suggesting that the dominant mineral phase in the clinker is calcium silicate. However, as the alkali content increases, the diffraction peaks of C\({}_{3}\)S weaken progressively, indicating that higher alkali content inhibits the formation of C\({}_{3}\)S. This occurs because, in a highly alkaline environment, the addition of alkalis significantly lowers the melting point of the liquid phase, facilitating its early formation. This process encapsulates partially reacted C\({}_{2}\)S particles, thereby impeding their further interaction with CaO. Furthermore, it reduces the melt concentration, enhances ion diffusion, and alters the relative concentration ratio of Ca\({}^{2+}\) and SiO\({}_{4}\)\({}^{4-}\) in the melt [56; 57]. Consequently, the reaction shifts toward the stable formation of C\({}_{2}\)S, thereby suppressing the production of C\({}_{3}\)S. Concurrently, the diffraction peak of free calcium oxide (f-CaO) becomes more pronounced. Clinker samples with elevated potassium and sodium content exhibit significantly higher f-CaO levels, underscoring the detrimental effect of excessive alkali on the sintering process. Alkalis react with other constituents in the clinker, such as SiO\({}_{2}\) and Al\({}_{2}\)O\({}_{3}\), to form by-products, such as alkali silicates. This process indirectly reduces the consumption of CaO, leading to  
Figure 7: Cl\({}^{-}\) content of clinker under different dosage of biomass alternative fuel.  
a higher proportion of free CaO remaining. Additionally, as the alkali content increases, the diffraction peaks of K\({}_{2}\)SO\({}_{4}\), Na\({}_{2}\)SO\({}_{4}\), and Na\({}_{2}\)Si\({}_{2}\)O\({}_{5}\) emerge and intensify, indicating that alkali facilitates the formation of sulfates and Na\({}_{2}\)Si\({}_{2}\)O\({}_{5}\).
# Digital Simulation and Analysis of Assembly-Deviation Prediction Based on Measurement Data
## 5 Conclusions  
In this paper, the assembly of micro gas turbine core engines is taken as the research object to establish an assembly-deviation-analysis model based on design tolerance. Moreover, the key factors for the occurrence of assembly deviation and the causes of error are analyzed through Monte Carlo operations, which solves the assembly interference and collision caused by unreasonable design through optimization. This adjustment effectively resolves the interference issue resulting from the design error, indicating that digital simulation of assembly deviation based on design tolerances will help industry and researchers reduce design errors in practice. The assembly-deviation-analysis model is established on the basis of measurement data and different feature parameter-extraction methods. Then, the results of simulation analysis of assembly deviation based on the least squares fitting plane and the three high point fitting plane are compared with the actual results of assembly deviation. It is found that the simulation results of assembly deviation based on the three high point fitting plane are basically consistent with the actual outcome of the assembly, and the three high point fitting will help industry and researchers predict assembly deviations.  
The method proposed in this paper to predict and analyze the assembly deviation based on the design tolerance and measurement data is applicable in practice.  
Notwithstanding its contributions, this study is not without certain limitations, such as analyzing the micro gas turbine parts as rigid components, not further analyzing the deformation of thin-walled parts, not further investigating the mathematical model of assembly and not further analyzing the assembly deformation in the thermal state, which can be further investigated in the future.  
Conceptualization, R.Y.; methodology, G.X.; software, N.Z.; validation, G.X. and N.Z.; data curation, N.Z.; writing--original draft preparation, N.Z.; writing--review and editing, G.X.; supervision, Y.W.; project administration, R.Y. and Y.W.; All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
The authors declare that they have no known competing financial interest or personal relationship that could have appeared to influence the work reported in this paper.
## 1 Introduction
Nilufer municipality (Bursa, Turkey) has prepared and presented these results in a report as a result of measurements and observations made at 74 points on 5-6 March 2007 to determine the level of electromagnetic pollution originating from high-voltage lines, transformers, and base stations within the borders of Bursa [20]. Within the coverage area of the base station located in the Bahcelieveler neighborhood in the city center of Ordu (Turkey), the effective value measurement of the broadband-sourced electric field was made at 49 points, starting from the point close to the base station and moving away from the base station, provided that it remains within the coverage area. Results are shown on the map using Google Earth [21]. Broadband-sourced electric field effective value measurement was made at 500 points in the city center of Ordu (Turkey). In addition, 24 h measurements were taken in the houses opposite the base stations in the measurement areas, and the results of the measurements were evaluated [22]. For the measurement of the exposure values of infants living in Turkey to the electromagnetic field originating from GSM, the city of Izmir was selected as the pilot region, and the results were evaluated by measuring the electromagnetic field in the environments where a total of 151 babies living in certain houses in this city lived [23]. The electromagnetic field created by transformers and high-voltage lines in certain public areas in Konya (Turkey) city center was measured, and the results were compared with the limit values [24]. To find the SAR value of the electromagnetic field emitted by mobile phones in the human heart region, the values obtained by electrically modeling the heart were analyzed using the finite difference time domain method (FDTD) using MATLAB and compared with the limit values [25]. It is known that exposure to a non-ionizing electromagnetic field increases the temperature of cells in the human body, and its limits are determined in this way. In this study, it was determined that the fact that exposure can go beyond temperature in 5G should be shared with the public because the determined limits only take into account the thermal heating of the cell [26]. An electrical voxel model was created to represent the SAR value of mobile phones on the human head for a family of seven people. In this model, it is assumed that the youngest individual is 8 months old and the two oldest individuals are 43 years old. The distance between the hand holding the mobile phone and the head was modeled using the CST program, starting from 10 mm in 10 mm increments up to 40 mm [27]. The studies carried out in this field so far show that even if the electric field values are below the limit values, they not only heat the cells but also pose health risks.
# Evaluation of Multi-Stream Fusion for Multi-View Image Set Comparison
## 3 Solution  
### Structure  
Our solution to image set similarity estimation is not limited by the number of images, and will be appropriate even for a single image pair. The core of this solution consists of four image processing streams for image-pair comparison (Figure 3):  
1. the classification of the foreground object type,
2. the classification and the foreground object view,
3. image-pair similarity based on semantic image segmentation (the DL stream), and
4. feature-based image pair similarity (classic technique stream).  
After accumulating similarity scores obtained for all image-pairs from the two analyzed image sets, the individual results are fused and further classified to obtain a final likelihood as to whether both sets contain the same object. The approach performs a multi-score \"late fusion\" (one possible approach to fuse multi-modal data, e.g., [14; 15]), where the following scores are fused:  
1. The same-object (car)-type score (class likelihoods),
2. The same-object (car)-model and -viewpoint score (class likelihoods),
3. BSS error index (dissimilarity) based on all ROI image-to-image comparisons, and
4. BSS error index based on the ROI-features comparison for all image pairs.
## 3 Methodology
### Calibration Methods  
Despite achieving high predictive accuracy, models often produce probability estimates that diverge from true empirical likelihoods--a critical concern when probabilistic outputs inform risk-sensitive decisions such as admission to ICU. For example, a predicted probability of \(\hat{p}_{i}=0.9\) might correspond to observed positive outcomes in only 70% of similar cases, indicating systematic overconfidence. Calibration rectifies such miscalibrations by post-processing raw scores to ensure reliability: calibrated probabilities \(\hat{p}_{i}^{\text{cal}}=\phi(\hat{p}_{i})\) must satisfy the statistical consistency condition:  
\[\mathbb{E}[y_{i}\mid\phi(\hat{p}_{i})=p]=p\quad\forall p\in[0,1] \tag{1}\]  
where \(\phi:[0,1]\to[0,1]\) is a calibration function learned from held-out data.  
The calibration workflow mandates three distinct partitions of the dataset \(\mathcal{D}\): \(\mathcal{D}_{\text{train}}\) trains the base model \(M\), \(\mathcal{D}_{\text{cal}}\) fits the calibration function \(\phi\), and \(\mathcal{D}_{\text{test}}\) evaluates the calibrated system. This strict separation prevents information leakage, as \(\phi\) adapts to \(M\)'s biases without overfitting to test data. Formally, the optimal \(\phi^{*}\) minimizes a calibration-specific loss:  
\[\phi^{*}=\operatorname*{arg\,min}_{\phi\in\Phi}\sum_{(\mathbf{x}_{j},y_{j}) \in\mathcal{D}_{\text{cal}}}\mathcal{L}_{\text{cal}}\left(y_{j},\phi(\hat{p} _{j})\right) \tag{2}\]  
where \(\Phi\) denotes the hypothesis space of calibration functions. Parametric families \(\Phi\) enforce interpretable mappings at the cost of rigid assumptions, while non-parametric approaches (e.g., isotonic regression) flexibly adapt to arbitrary miscalibration patterns.  
Calibration proves indispensable in operational settings where predicted probabilities directly influence critical decision-making. The selection of a probability calibration method is a critical, yet often overlooked, step in building trustworthy predictive models. There is no single method that is universally superior; parametric approaches are simple and robust to small calibration sets, while non-parametric methods offer greater flexibility to correct complex miscalibration patterns. Our study therefore, employs a comparative approach, evaluating a range of techniques. The rationale for this is to investigate our central hypothesis: that the choice of calibration method has significant and differing downstream consequences for the safety of uncertainty estimates (via CP) and the transparency of the model's reasoning (via XAI). By comparing multiple methods, we can expose the critical trade-offs between them, providing a more complete picture of how to build a reliable and interpretable system. The subsequent sections detail calibration paradigms that address distinct challenges, including handling class imbalance, ensuring robustness against rare but critical escalation events, and preserving temporal consistency in dynamic patient care pathways.