esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Experimental Setup  
The experimental scheme is shown in Figure 1. The first master-oscillator power fiber amplifier (MOPFA) consists of a compact distributed feedback (DFB) diode laser at 1560.5 nm and a 10 W EDFA, and another MOPFA consists of a compact DFB at 1878.0 nm and a 2 W TDFA. The optical isolator is used to restrain the laser feedback, thus ensuring the stability of EDFA and TDFA. The half-wave plate (\(\uplambda\)/2) and the polarization beam splitter (PBS) cube are used to control the power of the 1560.5 nm and 1878.0 nm lasers, while also transforming the polarization of the fundamental lasers to the s polarization to meet the requirement of the sum-frequency process. Then, a 50 mm-long PPMgO:LN crystal (HC Photonics; Taiwan; the thickness is 0.5 mm; poling period of 23.4 \(\upmu\)m; type 0 matching; both ends of the crystal have flat surfaces with anti-reflection coatings for the fundamental and sum-frequency laser, and the residual reflectivity R < 0.2%) is used as the SFG crystal. Additionally, the crystal is placed in a homemade oven, which is made of red copper and stabilized at a precise temperature using a temperature controller (Newport Corp., California, America, Model 350B). We can achieve optimized phase matching by adjusting the temperature of the crystal. Matching is an important factor in a single-pass configuration [21]. We choose f = 100 mm and f = 75 mm with anti-reflection coating for both fundamental and sum-frequency lasers as the matching lens. After the laser passes through the crystal, a 75 mm lens is used to collimate the output laser. Subsequently, the 852.3 nm laser is divided into two beams by PBS: one beam passes through Cs atomic vapor cells and another beam passes through the Fabry-Perot cavity to monitor its frequency tuning range.  
Before the SFG, we divide the 1560.5 nm laser into two beams: one beam for the above experiment and another beam for SHG. Single-pass cascaded 25 mm-long PPMgO:LN crystals (HC Photonics; Taiwan; the thickness is 1.0 mm; poling period of 19.48 \(\upmu\)m; type 0 matching; both ends of the crystals have flat surfaces with anti-reflection coatings for the fundamental and doubled laser; and the residual reflectivity R < 0.2%) are used as the SHG crystals. We chose f = 50 mm with an anti-reflection coating for both fundamental and second-harmonic lasers as matching lensse. The waist spot radius was \(\sim\)28 \(\upmu\)m, which is very close to the value determined by the optimum B-K focus factor \(\xi\) = 2.84 [22]
## 3 Proposed Real-Time 3D Mapping-Aided (3 DMA) GNSS-Positioning System
### 3 DMA GNSS Positioning Algorithm
#### 3.3.2 Integrated Solution of 3 DMA GNSS  
For each candidate \(\mathbf{p}_{j=1..J}\), the integrated likelihood score, \(S_{j,SDM+LBR}\), will be evaluated,  
\[S_{j,SDM+LBR}=\sqrt{S_{j,\text{LBR}}\times S_{j,\text{SDM}}} \tag{5}\]  
where \(S_{j,\text{LBR}}\) and \(S_{j,\text{SDM}}\) are the likelihood score of likelihood-based ranging 3 DMA GNSS and shadow matching, respectively. The detail of the calculation of the likelihood scores can be found at [35].  
Shadow matching evaluates the visibility consistency between the measured carrier-to-noise ratio (\(C/N_{0}\)) and prediction with skymask. Shadow matching requires all satellites in ephemerides to predict the non-received one. Implementation-wise, we automatically download ephemerides from the day prior. Additionally, we use the same receiving time but a day before to estimate the satellite's angular position for visibility prediction with skymask. If the internet is enabled for the execution platform this can be replaced by assisted GNSS (AGNSS) [47; 48] and provide the satellite data to determine position via standard protocol, such as secure user plane location (SUPL). This is more easily achieved with built-in AGNSS devices, such as smartphones [49].  
With likelihood-based ranging 3 DMA GNSS, we model the pseudorange at each candidate position and compare it with the received pseudorange measurements. The NLOS predicted satellite at a candidate, likelihood-based ranging 3 DMA GNSS remaps NLOS pseudorange difference to a LOS one using the distribution model.  
The integrated solution of 3 DMA GNSS, \(\mathbf{x}_{3\text{DMA}}\), is calculated by weighted averaging of the distributed candidates with their likelihood score,  
\[\mathbf{x}_{3\text{DMA}}=\frac{\sum_{j=1}^{J}\mathbf{p}_{j}S_{j,SDM+LBR}}{\sum _{j=1}^{J}S_{j,SDM+LBR}} \tag{6}\]  
The receiver location is then optimized via FGO as a loosely-coupled solution.
# Automatic Processing of Historical Japanese Mathematics (Wasan) Documents
## 2 Materials and Methods
### Algorithm Overview  
In this section, we introduce the techniques used in our image processing pipeline (see Figure 4 for a visual representation), we also mention the goal they achieved and how they relate to each other. The following sections provide further details on each step.  
Figure 3: Some kanji characters have changed the way they are written between the Edo period and modern times. The “ima” \(\oplus\) kanji character (top) is of particular interest for our study.  
* Preprocessing steps to improve image quality:
* Hough line detector [31] to find (almost) vertical lines in the documents. These lines are then compared to the vertical direction. By rotating the pages so that the lines coincide with the vertical direction in the image we can correct orientation misalignements introduced during the document scanning process. See Section 2.3 for details.
* Noise removal [32]. This step is performed to eliminate small regions produced by dirt in the original documents or minor scanning problems. See Section 2.4 for details.
* Blob detectors to determine regions candidate to containing kanji. See Section 2.5 for details.
* Preprocessed images are used as the input for blob detector algorithms. These algorithms output parts of each page that are likely to correspond to kanji characters and store them as separate images. Three blob detector algorithms were studied (see Section 3.1 for the comparison).
* DL-Based Classification. See Section 2.6 for details.
* In the last step of our pipeline, DL Networks are used to classify each possible kanji image into one of the kanji classes considered. Five different DL networks were studied for this purpose. See Section 3.2 for detailed results.
* The output of our algorithm is composed of the coordinates of each detected kanji along with its kanji category. We pay special attention to those regions classified as to  
Figure 4: Image Processing pipeline.  
belong to the \"ima\" kanji. See Section 3.3 for the details focusing on the results related to the \"ima\" kanji as well as the combined performance of the blob detector and kanji classification algorithms for this particular case.  
The techniques used are general-purpose, that can be used in other automatic Wasan document processing tools. These include the other uses of geometric primitives such as the vertical or horizontal lines detected for angle correction or the use of the same deep learning networks to classify salient features of Wasan documents such as equations (marked with distinctive rectangular frames) or the geometric shapes included in the geometric description of the problems.
## 2 Methodology
### Hilbert-Huang Transform  
The Hilbert-Huang transform (HHT) is a time-frequency analysis method tailored for non-stationary and nonlinear signals. Unlike traditional techniques such as the Fourier or wavelet transforms that rely on predefined basis functions, the HHT is data-driven and adaptive [42; 43]. It consists of two main stages:  
1. Empirical mode decomposition (EMD);
2. Hilbert spectral analysis (HSA) via the Hilbert transform.
# How Resilient Are Kolmogorov-Arnold Networks in Classification Tasks? A Robustness Investigation
## 4 Methodology
### Kolmogorov-Arnold Networks and Multilayer Perceptron  
Our research investigates the resilience of two neural network architectures, namely the KAN and MLP models, against various adversarial attacks and defense strategies. These models were evaluated on multiple classification datasets under different attack scenarios, including the FGSM, PGD, CW, and BIP attacks. Additionally, the models were tested under standard, randomized smoothing, and adversarial training conditions. We  
Figure 3: Class distributions of GTSRB, BTSD, and CTSD datasets.  
also integrated randomized smoothing as a defense mechanism to further assess and enhance robustness.  
These KANLinear layers are key to the model's flexibility. They use grid-based kernels with \(gridsize=5\) and \(splineorder=3\), which were scaled by 1.0. The grid was dynamically updated within a range of \(-1\) to \(1\) using a grid eps of 0.02, enhancing its adaptability to the data distribution. The activation function used in the KAN model is SiLU, which allows for smoother gradient flow during backpropagation. Regularization is enforced through adaptive B-splines, which helps control overfitting. Following the configuration outlined in Efficient KAN [27], see Equation (6).  
In contrast, the MLP model is a baseline comparison to the KAN architecture. The MLP consists of two fully connected hidden layers, identical in structure to the KAN model, with 256 units in the first layer and 128 units in the second layer. The activation function used in the MLP is ReLU, which is commonly used for its simplicity and computational efficiency. Both models' output layers consist of units corresponding to the number of classes in the datasets. For training, we used the AdamW optimizer with a learning rate of 0.001 and a weight decay of \(1\times 10^{-4}\), involving training over 100 epochs with a batch size of 64. The cross-entropy loss function was employed to evaluate classification performance. This consistent training setup ensured a fair comparison between the KAN and MLP models under various adversarial attack scenarios, including FGSM, PGD, CW, and BIM attacks, as well as under standard, randomized smoothing, and adversarial training conditions, with the integration of randomized smoothing as a defense mechanism.  
As shown in Figure 4, the left side illustrates how MLP employs ReLU activation functions, and on the right side, the KAN architecture is designed with B-spline-based activation functions.
## 3 Theoretical Background
### Proposed DCGOSM-Based Speech Enhancement Technique
#### 3.9.1 Process of Obtaining the Required Sensing Matrix  
The process of obtaining the required sensing matrix involves obtaining an optimized sensing matrix with orthogonalized dominant columns for clean speech and noise signals. The speech enhancement performed using greedy algorithms assumes that speech signals exhibit a structured temporal pattern and a relatively narrowband spectral content, whereas the noise signals lack these properties. Therefore, when reconstruction is performed, the noise components equally spread over all columns of the sensing matrix, whereas the speech components become concentrated over a small number of columns (dominant columns, see Section 3.8). Therefore, when reconstruction is performed through dominant columns, the amount of the noise significantly reduces in the reconstructed speech. However, such approaches have following limitations:  
1. They assume that the noise components spread equally over all columns or noise must be white noise. However, for sounds other than white noise, this distribution of components will not be possible. Therefore, for sounds other than white noise, the performance of these approaches will naturally decrease. 2. Since the dominant columns are not known initially, they need to be searched every time from all columns, which increases processing time. 3. The algorithms have no awareness of speech and noise signals, and the selection of dominant columns is done solely on the basis of contribution. The algorithms always assume that higher contribution is due to speech components. Therefore, during higher noise conditions, the algorithms may select columns with dominating noise components. The condition may worsen in non-white noise cases. To overcome these limitations, the proposed DCGOSM approach obtains the sensing matrix based on the type of noise present in the noisy speech. The obtained sensing matrix guarantees separate dominating columns for clean speech and noise components. During the generation of the required sensing matrix, the dominant columns for the speech components are also stored; hence, during the enhancement process, the iterations will be performed only through these columns. The algorithm identifies speech and non-speech frames and obtains the dominant columns separately for speech and non-speech frames. Therefore, the dominant columns selected for the speech components are guaranteed to contain only speech components. The effect of this can also be seen in the results presented in Tables 1-3, where the proposed DCGOSM gives better relative improvement at higher noise conditions (lower SNRs).
### Educational Data Mining (EDM) Procedures
Both \(k\)-means ++ and fuzzy \(k\)-means require setting the number of clusters a priori, which is admissible if there is good intuition about how many clusters are present in the dataset, but otherwise it can be a problem. Furthermore, these methods also assume that the clusters are globular, because the methods compute centroids and assign each and every one of these instances to the nearest centroid, there being the possibility of considering some instances which do not belong to a cluster. Density-based spatial clustering of applications with noise (DBSCAN) is a data clustering algorithm proposed by [PERSON] et al. [39]. The algorithm assumes that the clusters are regions with a high density of points. Unlike the previous algorithms, it does not assume that every point in the dataset must necessarily belong to a cluster, and some examples can be classified as noise. In DBSCAN, clusters do not need to be globular; although it is not necessary to establish a correct number of clusters, it is needed to set an Epsilon parameter used to determine which instances belong to a grouping or are on the contrary noise. Other advantages are its stability--DBSCAN is stable across different runs--and its scalability, with it being an algorithm capable of working with very large datasets. Based on the above-mentioned research, the objectives of this study were:  
1. Apply an external logs analysis tool in Moodle to detect students at risk over the course of a semester in different phases (initial, intermediate, and final);
2. Detect in the sample, with an external log analysis tool in Moodle, the clusters in different phases (initial, intermediate, and final) differentiating by type of algorithm (\(k\)-means ++, fuzzy \(k\)-means, DBSCAN);
3. Check if there were differences in the clusters found depending on the type of algorithm (\(k\)-means ++, fuzzy \(k\)-means, DBSCAN);
4. To check students' satisfaction with the teaching process and the monitoring of learning. Figure 1: Heat map of weekly student monitoring in Moodle using UBUMonitor [35]. The research questions (RQ) related to the objectives are:  
* [leftmargin=*] RQ1: There will be different activity clusters in the Moodle platform depending on the log collection phases (initial, intermediate, and final);
* RQ2: There will be differences in the groupings obtained in the clusters in the log collection phases (initial, intermediate, and final) depending on the algorithm applied (\(k\)-means ++, fuzzy \(k\)-means, DBSCAN);
* RQ3: There will be differences in the clustering obtained in the log collection phases (initial, intermediate, and final) depending on the applied algorithm (\(k\)-means ++, fuzzy \(k\)-means, DBSCAN) providing a better fit in the DBSCAN algorithm;
* RQ4: Students will perceive the monitoring of their learning performed with the UBUMonitor application as reflected in high levels of satisfaction in the Questionnaire of Student Opinion on Quality of Teaching (QSOQT).
## 3 Materials and Methods
### Methodology
#### 3.2.2 Revised Universal Soil Loss Equation (RUSLE)
The resulted \(K\) values were implemented on the spatial interpolation method Kriging to produce values covering the total surface of the study areas. The topographic effect on soil erosion is determined by the impact of the slope length and steepness factor (\(LS\) factor) [27]. \(LS\) factor constitutes the result of slope length (\(L\)) and slope steepness (\(S\)) multiplication. Increased slope steepness values determine increased soil erosion risk due to the increase in the velocity and erosivity of the accumulated runoff [2]. The calculation of the \(LS\) factor was based on Equation (7) of [PERSON] [52]:  
\[LS=\left(\frac{U}{L_{0}}\right)^{m}\times\left\{\left[\sin\left(\frac{\beta \times 0.01745}{S_{0}}\right)\right]^{n}\right\}\times(m+1) \tag{7}\]  
where \(LS\) is the topographic factor, \(U\) is the flow accumulation multiplied with the pixel size, \(L_{0}\) is the slope length (22.13 m), \(\beta\) is the slope in degrees, \(S_{0}\) is the slope percentage (9%), \(m\) is sheet erosion ranging from 0.4 to 0.6, and \(n\) is rill erosion ranging from 1 to 1.3. The \(LS\) factor was created based on data derived from a digital elevation model (DEM) into a GIS setting. The rill erosion values corresponded to \(n=1.1\) while the sheet erosion values ranged according to the examination area each time. Specifically, for the regions of Schinos, Vilia, and Varympompmi, the sheet erosion value was set as \(m=0.45\), whereas for the Keratea region the corresponding value was \(m=0.5\). \(C\) factor demonstrates the cover management factor providing a measure of soil erosion rate as controlled by the cropping and management practices within a region [23; 24]. The cover management factor was calculated based on satellite data acquisition corresponding to specific time periods. Specifically, the satellite data consisted of Sentinel-2 images for the regions of Schinos, Vilia, Varympompmi, and Keratea, corresponding to time periods before and after the fire. Particularly, through the acquired data, normalized derived vegetation index (NDVI) images were generated to produce the factor's values based on the formulation (Equation (8)) developed by [PERSON] et al. [53]:  
\[C=\frac{1-NDVI}{2} \tag{8}\]The support practice \(P\) factor represents the practices on agricultural land that affect the soil erosion processes [54]\(P\) factor was estimated based on the Corine Land Cover 2018 dataset. According to [PERSON] et al. [55], all Corine Land Cover classes were assigned the value of 1, except in agricultural regions where the \(P\) factor was given the value of 0.5.
# A Review of Medical Diagnostic Video Analysis Using Deep Learning Techniques
## 4 An Overview of Deep Learning Techniques
### Fully Convolutional Network  
A fully convolutional network (FCN) is used for semantic segmentation and consists of convolution, pooling, and upsampling layers [18], as shown in Figure 4. FCN performs in-network upsampling and pixel-to-pixel inference by using a fully convolutional approach, such that it can store the pixelwise spatial correspondence by transforming all fully connected layers to convolutions and enable per-pixel segmentation. Arbitrary-sized video clips can be given input to FCN for the segmentation of ROI. The output provided by the FCN would be in the form of corresponding probability maps. The probability map's values represent the network output of one subwindow in the input video clips. The FCN probability map approach is much more efficient than the sliding window method, which repeatedly crops overlapping samples [19].
## Materials and Methods
### Samples Preparation
The overall mean marginal gap value and standard deviation were \(53.45\pm 30.52\)\(\upmu\)m. The minimum mean value (\(40.04\pm 18.90\)\(\upmu\)m) was recorded by PlanScan(r) (Planmeca Oy, Helsinki, Finland), then 3D PROGRESS Plus(r) (MHT, Verona, Italy) (\(40.20\pm 21.91\)\(\upmu\)m), True Definition Scanner(r) (3M, St. Paul, MN, USA) (\(40.82\pm 26.19\)\(\upmu\)m), CS \(3500^{\text{\textregistered}}\) (\(54.82\pm 28.86\)\(\upmu\)m) CS \(3600^{\text{\textregistered}}\) (\(59.67\pm 28.72\)\(\upmu\)m), Omnicam(r) (Denstply Sirona, Verona, Italy) (\(61.57\pm 38.59\)\(\upmu\)m), dental wings intraoral scanners (DWIO(r), Dental Wings, Montreal, Quebec, Canada) (\(62.49\pm 31.54\)\(\upmu\)m), while the maximum mean value (\(67.95\pm 30.41\)\(\upmu\)m) was recorded by TRIOS(r) 3. The Kruskal-Wallis tests revealed a statistically significant difference (\(p\)-value \(<\)0.5) in the mean marginal gaps between copings produced by 3D PROGRESS Plus(r), PlanScan, True Definition Scanner, and the other evaluated IOS. The use of an IOS for digital impressions may be a viable alternative to analog techniques. Although in this in vitro study PlanScan(r), 3D PROGRESS Plus(r) and True Definition Scanner(r) may have showed the best performances, all IOS tested could provide clinically encouraging results, especially in terms of marginal accuracy, since mean marginal gap values were all within the clinically acceptable threshold of 120 \(\upmu\)m.
# Adaptive Pitch Control of Variable-Pitch PMSG Based Wind Turbine
###### Abstract  
This paper presents an adaptive pitch-angle control approach for a permanent magnet-synchronous generator-based wind turbine (PMSG-WT) connecting with a power grid to limit extracted power above the rated wind speed. In the proposed control approach, a designed perturbation observer is employed for estimating and compensating unknown parameter uncertainties, system nonlinearities, and unknown disturbances. The proposed control approach does not require full state measurements or the accurate system model. Simulation tests verify the effectiveness of the proposed control approach. The simulation results demonstrate that compared with the feedback linearizing controller, conventional vector controller with proportional-integral (PI) loops, and PI controller with gain scheduling, the proposed control approach can always maintain the extracted wind power around rated power, and has higher performance and robustness against disturbance and parameter uncertainties.  
pitch control; permanent magnet-synchronous generator (PMSG); limit extracted power; nonlinear adaptive control (NAC); perturbation observer
## 3 Model Complexity and Space Reduction Techniques for System-Level Optimization
### Parameter Criticality Assessment
#### 3.2.1 Sensitivity Analysis
The optimization process targets torque ripple, average torque, and efficiency as the primary performance metrics. The paper selects CD-NSGA-II for its superior performance in handling complex, high-dimensional multi-objective optimization problems. Key merits include enhanced population diversity through Sobol sequence initialization, improved convergence and robustness via directional mutation and normal crossover, and adaptive solution ranking using a weighted chi-square distance metric with gradient sensitivity. A comparative analysis between the two optimization algorithms is conducted, revealing that while CD-NSGA-II leads to a slight decrease in efficiency and an increase in losses, it achieves a significant reduction in torque ripple compared to NSGA-II. However, the study does not consider the effects of friction and windage losses in its analysis. The same team of authors of [57] extend their novel topology for the ERSRM featuring an asymmetric stator for application in E-Bikes. The optimization process employs the NSGA-II algorithm in conjunction with a sensitivity analysis and FEM simulations to minimize torque ripple. The analysis and results align with their previous findings, demonstrating a significant reduction in the torque ripple. However, similar trade-offs are observed, including a slight decrease in efficiency and a corresponding increase in losses.