esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In order to conduct research across multiple time scales, commonly used coarse-graining techniques may result in the loss of local variations within the signals. When the scale factor is large, the original data length is significantly reduced. This can lead to issues such as insufficient smoothness in feature generation, bias in feature extraction, and the neglect of certain signal features. 3. The widely used multiscale entropy calculates the average entropy values of coarse-grained sequences at different scale factors, which can lead to overfitting [18]. Due to the presence of many similar patterns within vibration signals, averaging the entropy values of various subsequences may introduce redundant information. This causes the model to become dependent on these redundant features, hindering its ability to accurately capture subtle changes and trends in the signals, thereby affecting the algorithm's generalization capability. To address the aforementioned challenges, the main contributions of this article are outlined as follows:  
1. FE is extended to multiple time scales through the method of shifted coarse-graining to handle data uncertainty and fuzziness. The advantage of this shifted coarse-graining approach is that the vibration signals are utilized multiple times. Consequently, each window can capture more subtle changes and trends, thereby reducing abrupt variations between different windows and accurately reflecting the local features of the original signal. 2. To avoid overfitting caused by redundant features, a non-parametric feature extraction method named improved multiscale fuzzy entropy (IMFE) is proposed. This method utilizes the nonlinear dynamic model of IMFE to quantify temporal complexity, circumventing the issue of hyperparameter tuning time. Comparative experimental results demonstrate that IMFE exhibits superior robustness. 3. An intelligent fault diagnosis method for rotating machinery is proposed, consisting of four modules: data decomposition, feature extraction, feature dimensionality reduction, and fault diagnosis. First, the empirical mode decomposition (EMD) method is used to perform three-layer decomposition on the data, yielding three intrinsic mode functions. Then, the signal features extracted using IMFE are input into linear discriminant analysis (LDA) for dimensionality reduction. Finally, the support vector machine (SVM) algorithm is employed as the classifier to achieve fault diagnosis, with \(K\)-fold cross-validation methods utilized to evaluate the diagnostic model's performance [19]. 4. The proposed method is validated using open-source data from rotating mechanical rolling bearings. The results indicate that the proposed method outperforms other comparative methods. The remainder of this article is organized as follows. A brief introduction to the foundational theories is provided in Section 2. The proposed method is presented in Section 3. Two case studies are discussed in Section 4, in which self-constructed signal data and the Case Western Reserve University (CWRU) bearing dataset are utilized for validation. The conclusions and future work are summarized in Section 5.
# LSTM Recurrent Neural Network for Hand Gesture Recognition Using EMG Signals
## 2 Methodology
### Data Preprocessing  
Once the EMG data were acquired, the four EMG signals were preprocessed so they could be used as inputs to the proposed model. The preprocessing is a fundamental step when working with EMG signals, since raw EMG signals are random and difficult to interpreter, the preprocessing step allows to extract valuable information to the raw EMG signal This information then can be used as input data to a classifier. For this step an EMG feature extraction process is generally conducted. According to scientific literature there are many known features of EMG signals [3]. However, research on EMG signal processing showed that there is not a single best feature extraction process or formula, and that it largely depends on the data and required characteristics of the system [52].  
With this in mind, the preprocessing step conducted in this research was mainly focused on smoothing the signal and removing the unuseful noise of it, leaving the feature extraction for the proposed neural model [53,54]. The preprocessing method applied to the signals can be seen in Figure 2. In this process, initially the mean of the signal is subtracted in order to center it at zero mean; then, a rectification step is performed in which the absolute value is applied to the zero mean signal, this is a useful step during the preprocessing of the EMG signal [46]; then, a moving average filter was applied in order to reduce the noise of the signal [13] and to obtain its envelope, which is an useful characteristic when working with neural network models; finally the signal was normalized between 0 and 1 as recommended in the Deep Learning literature [55].  
In general, the moving average is the most common filter in digital signal processing (DSP) due to its ease of implementation. In spite of its simplicity, the moving average filter is optimal for reducing random noise while retaining the signal, making it an outstanding filter for time domain encoded signals [56], thus allowing to obtain useful information about the EMG signal in the time domain, while keeping the computational cost low. Its formula is given by Equation (1).  
\[y(i)=\frac{1}{M}\sum_{j=0}^{M-1}x(i-j) \tag{1}\]  
For the moving average filter, a total of 20 delays for the filter period N was selected. This period allowed to smooth the signal reducing the noise, making it easier for the proposed LSTM model to identify the signal patterns that represent a hand gesture. Figure 3 shows the steps of the signal pre-processing for a window of 200 samples.  
Figure 2: Data processing block diagram.
# Impact of Atmospheric Pressure Microwave Plasma Treatment on Quality of Selected Spices
## 3 Results and Discussion
### Color  
Table 1 shows the optical properties of spices exposed to cold plasma treatment. The lightness (\(L^{*}\)) of black pepper seeds and juniper berries decreased with treatment time. No such behavior was found for allspices berries in which the changes of the \(L^{*}\) parameter were significant only in the case of the treatment that lasted 45 s. The values of the parameters which represent the share of red-green colors were higher for plasma treated materials in comparison to the reference samples. For example, \(a^{*}\) chromametric values were equal to 2.43 and 3.14 for untreated and 60 s irradiated black pepper seeds, respectively. In turn, \(b^{*}\) color parameter changes depended on the type of investigated raw materials. Plasma treatment caused an increase of b* in the case of black pepper seeds and allspices berries, but the changes registered for juniper berries were ambiguous. Similar findings were reported for black pepper seeds treated by direct plasma generated with a radio frequency jet by [PERSON] et al. [29]. The values of \(\Delta E\) indicated that, in most cases, the change in color was distinct (\(\Delta E>3.5\)) but in some cases plasma treatment led to smaller color changes, which, nevertheless, could be noticed even by an inexperienced observer (\(2<\Delta E<3.5\)) [44]. Literature data about the impact of plasma on color of different food materials is ambiguous. For instance, [PERSON] et al. [45] reported that exposition of chili pepper samples to atmospheric air plasma for 60 s did not cause any significant change of \(L^{*}\) or \(b^{*}\) but led to a decrease of the red/green chromatic parameter. Moreover, [PERSON] et al. [46], who studied the impact of plasma treatment on the color of blueberries, reported that longer plasma treatment times may lead to darkening of blueberries instead of the bleaching that was expected due to anthocyanin degradation. In turn, [PERSON] et al. [47] demonstrated that cold plasma did not cause any significant changes of color of blueberries. The research performed by [PERSON] et al. [48] on microwave-powered cold plasma treatment of mandarin showed that color of the tissue did not change after plasma treatment The effect of plasma on color seems to depend strongly on the type of plasma treatment and the properties of the material, among which the moisture content and the type of pigment play the most import role.
# Multi-Channel Expression Recognition Network Based on Channel Weighting
## 3 Experimental Results and Analysis
### Experimental Data  
In order to verify the effectiveness of the network proposed in this paper, this paper selected the public datasets FER2013, Oulu_CASIA and RAF-DB for experimental verification of seven types of basic expressions. Figure 8 shows some pictures from the dataset. The selected dataset covers images of faces in different lighting, skin tones, poses and occlusions.  
The FER2013 dataset is a facial expression dataset provided by the Kaggle Facial Expression Recognition Challenge. The dataset has been divided into three parts by the challenge organizers: 28,709 training sets, 3589 public test sets, and 3589 private test sets. The dataset contains facial expressions of different ages and angles, and the resolution is relatively low. Many pictures are also occluded by hands, hair, scarves, etc., which are very challenging and meet the conditions in the real environment.  
The Oulu_CASIA dataset contains various expression categories of 80 people divided into two different environmental types, infrared and visible light. The photos were taken under three lighting conditions, ranging from 18 to 30 pictures in each case. In this study, the last six images of each category are taken for training and validation.  
The RAF-DB dataset includes 29,672 face pictures in real scenes. Specifically, the RAF-DB dataset has two subsets. The first is a single-label expression dataset, with a total of seven types of expressions; the second is a compound expression dataset, with a total of twelve compound expression categories. This paper studies the problem of single-label facial expression recognition, so the single-label images in the RAF-DB dataset were used to verify the method.  
Figure 8: Partial dataset image display.
## 2 CET Systems and EV
### Main Features of CET Devices  
The acoustic CET Systems [5; 6; 7; 8] use two piezoelectric transmitting and receiving transducers converting electric-acoustic (pressure)-electric energy. These systems have different advantages. They can be used where electromagnetic (EM) fields are not allowed and they have smaller dimensions when high directionality is required. Normally, their efficiency is less than that of the inductive ones; but, when the distance transmitter to receiver is much larger than their radii (ranges), their efficiency can be higher [5; 9]. Representative applications of acoustic CET systems involve biomedical (with a power up to 100 mW and efficiency up to about 40%), across-wall sensors for metal inserts (nuclear systems, vacuum chambers, gas cylinders...) transferring 1 kW at 84% efficiency [10].  
Light CET Systems use Laser diodes to produce an optical power ray and photovoltaic diodes to transform it back into electrical energy. Their efficiency and practical applications turn out to be limited when operated over long spaces because of diffractive losses. The power level is very small (1-10 W), and optical to electrical conversion efficiency is 20-30% [11; 12; 13; 14]. Applications principally embrace spacecraft platforms and terrestrial technologies [11; 12].  
Capacitive CET Systems use two primary metal plates and two isolated secondary plates connected to electronic converters to transfer electric energy. The surfaces of the coupling metal plates are layered with dielectric to offer isolation and to rise the coupling capacitance [15; 16; 17; 18]. Opposing to inductively coupled CET, the capacitive CET uses an electric field and metal barriers. Owing to the constricted character of the electric field between metal plates, it also has the capacity to bring down EM interference (EMI). The power level and efficiency are between 5-50 W and 50-80%, respectively. Their usual uses include power sources to light-emitting diode (LED) lamps, mobile phones, sensors for respiratory machines [15; 16].  
Inductive CET Systems consist mainly of a big air gap transformer and resonant converter. The transformer possesses isolated sides (primary transmitter and secondary receiver) that can be mobile. Between the source and the primary, a dc/ac high frequency resonant converter is inserted. Linking the secondary to the load, an ac/ac resonant converter with load-adapted frequency is employed.  
For more details, concerning figures illustrate different CETs; one can consult e.g., [1], for acoustics, light, capacitive and inductive, see respectively, (Figures 2-5, pp. 48-50 in [1]). Also in the same reference, a table to compare and contrast different CETs is given, (Table 1, p. 49 in [1]).
## 3 System Design
### Sensing Glove  
#### 3.2.1 Pressure Sensor Specifications  
Figure 5 shows the prototype of the sensing glove. The built-in pressure sensor used in the sensing glove was adopted from the pressure-sensitive resistance method developed by [PERSON] et al. [6]. In their study the pressure sensors were installed at an interval of 2 mm, and the number of pressure detection points was 5 \(\times\) 10 = 50 points. In this study, the number of sensors was expanded from 50 points to 10 \(\times\) 10 = 100 points per finger. The sensing glove covers three fingers: the thumb, index finger, and middle finger. The glove can measure at approximately 300 Hz when transmitting with 8-bit resolution and can measure at approximately 900 Hz when transmitting information from only one finger. Because the two-point discrimination threshold of the distal phalanx of the finger is approximately 1.5 mm at the tip and approximately 2 to 3 mm at the fingertip [1], the resolution of the pressure sensor is approximately the same as the number of pressure points that the fingertip can discriminate. The thickness of the glove was approximately 1.2 mm. This thickness protects the sensor and acts as an appropriate spatial low-pass filter. The thickness of this glove is approximately the same as that of a general leather glove. As will be revealed later in experiment 2, the thickness of the glove considerably impairs the sense of touch and causes difficulty in understanding the shape of the object. As mentioned in the introduction, this is thought to simulate the decline in tactile sensation due to ageing and the decline in tactile sensation in the case where gloves are necessary.
# Whole-Genome Alignment: Methods, Challenges, and Future Directions
## 2 Classification of Whole-Genome Alignment Algorithms
### Anchor-Based Methods
#### 2.2.7 Minimap2  
Minimap2 is a versatile alignment tool commonly used in bioinformatics and genomics research, being designed to efficiently align DNA sequences, typically whole genome sequences, against a reference genome or assembly [44] (Figure 7). It is widely used for tasks such as genome assembly, variant calling, structural variation detection, and transcriptome mapping. As an anchor-based approach, it uses a method based on hashing and chaining.  
Minimap2_Align(TargetSequences, QuerySequences):  
IndexTarget = SketchIndex(TargetSequences)  
For each Query in QuerySequences:  
SketchQuery = SketchIndex(Query)  
Identify Matching Sketches between IndexTarget and SketchQuery  
Chain Matches to identify potential alignments  
Refine alignments using dynamic programming  
Return High-confidence Alignments  
This technique involves identifying \"anchors\" (similar or identical sequence regions) between the sequences being compared, and then chaining these anchors together to create an alignment. The efficiency and speed of Minimap2 largely stem from its ability to quickly identify and utilize these anchors for alignment [44]. The algorithm proceeds in two main stages: indexing and alignment. During indexing, Minimap2 preprocesses the reference genome to create an index structure that facilitates rapid sequence comparison. During alignment, it uses the index to efficiently locate and extend sequence matches between the query and reference genomes. Minimap2's complexity depends on the size of the input sequences and the chosen parameters. It typically has a worst-case time complexity of \(O(n\lg n)\), where \(n\) is the length of the reference sequence.
## 5 Results
### Experimental Results and Analysis
#### 5.2.2 Comparison of Tracking MAE Distribution  
Assuming that both the process noises and measurement noises obey the Gaussian distribution with mean of 0, Figure 7 shows the simulation results of target tracking from one Monte Carlo run when the standard deviation of measurement noises is 100 m, and where the tracking mean absolute error (MAE) distribution over time can be found. Since the non-determinism of the CS-jerk model requires constant modification of the maneuvering noises to match real the motion state, whether in the initial uniform motion phase or the later variable acceleration phase, there is an equal magnitude of tracking errors.  
Among these algorithms, PF has the largest randomness of errors, while the error distribution of UPF is approximately close to that of MMNF, which shows the effectiveness of using UKF to establish the proposal distribution of PF. PS-PF, both in the error randomness and in the occurrences of error spikes, is lower than PF and UKF, and is close to UPF and MMNF. Relatively speaking, the MAE distributions of PS-UPF and PS-AUPF are more concentrated than that of others, with lower fluctuation range, randomness of error and occurrences of large error spikes, and the tracking accuracy of these two are also close. For further verification, Figure 8 shows the average distribution of tracking MAE in each numerical interval during 100 Monte Carlo runs. From PF to PS-AUPF, the MAE distribution appears to be concentrated to the left, and its randomness tends to decrease. For PS-UPF and PS-AUPF, the medians of MAE are reduced by about one interval compared with general PF, and the overall MAE distributions are more concentrated than UPF and MMNF. Especially for PS-AUPF, its 80% MAEs are less than 200, reflecting the improvement of accuracy.  
Figure 6: Relationship between sample size and tracking Root Mean Squared Error (RMSE).
# Effect of Process Parameters on Mode Conversion in Submicron Tapered Silicon Ridge Waveguides
###### Abstract  
The modal property and light propagation in tapered silicon ridge waveguides with different ridge heights are investigated for a silicon on insulator (SOI) platform with a 500 nm silicon (Si) thickness. Mode conversion between the transverse magnetic (TM) fundamental and higher-order transverse electric (TE) modes occurs when light is propagated in a waveguide taper. Such a conversion is due to mode hybridization resulting from the vertical asymmetry of the cross-section in the ridge waveguides. The influence of angled sidewalls and asymmetric cladding on mode conversion is also studied. It is shown that a very long taper length (adiabatic) is required for a complete conversion to take place. Conversely, such mode conversion could be suppressed by designing a short non-adiabatic taper. Our results show that significant improvement in performance metrics can be achieved by considering process parameters' effect on mode conversion. With an optimum selection of the etching depth and accounting asymmetries due to angled sidewalls and cladding, we demonstrate an 84.7% reduction in taper length (adiabatic) for mode conversion and a 97% efficiency TM preserving taper (ultra-short). The analysis is essential for applications such as compact polarizers, polarization splitters/rotators, and tapers for TM devices.  
photonic integrated circuits; silicon photonics; mode hybridization; mode converters; polarization splitter and rotators; tapered waveguides; adiabatic tapers; angled sidewalls +
Footnote †: journal: Applied Sciences
# ShuffleDetect: Detecting Adversarial Images against Convolutional Neural Networks
## 10 Performance Comparison of ShuffleDetect and Feature Squeezer (FS)
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c} \hline \hline
**Scenario** & **Attacks** & **Detectors** & \(\mathbf{C_{1}}\) & \(\mathbf{C_{2}}\) & \(\mathbf{C_{3}}\) & \(\mathbf{C_{4}}\) & \(\mathbf{C_{5}}\) & \(\mathbf{C_{6}}\) & \(\mathbf{C_{7}}\) & \(\mathbf{C_{8}}\) & \(\mathbf{C_{9}}\) & \(\mathbf{C_{10}}\) & **AVG** \\ \hline \multirow{8}{*}{Targeted} & \multirow{2}{*}{EA} & ShuffleDetect & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100.0 \\ \cline{2-13}  & & FS & 89.0 & 100.0 & 90.9 & 94.0 & 91.1 & 90.6 & 89.9 & 91.9 & 93.8 & 95.9 & 92.7 \\ \cline{2-13}  & & ShuffleDetect & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100.0 \\ \cline{2-13}  & & FS & 100.0 & 94.7 & 98.2 & 100.0 & 97.8 & 98.2 & 97.3 & 98.0 & 96.6 & 97.4 & 97.8 \\ \cline{2-13}  & & ShuffleDetect & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100.0 \\ \cline{2-13}  & & FS & 98.0 & 94.7 & 98.2 & 100.0 & 97.8 & 98.2 & 97.3 & 98.0 & 96.6 & 97.4 & 97.6 \\ \cline{2-13}  & & ShuffleDetect & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100 & 100.0 \\ \cline{2-13}  & & FS & 100.0 & 100.0 & 100.0 & 100.0 & 97.8 & 100.0 & 100.0 & 100.0 & 100.0 & 100.0 & 99.8 \\ \hline \multirow{8}{*}{Untargeted} & \multirow{2}{*}{EA} & ShuffleDetect & 88.5 & 90.7 & 93.9 & 96.9 & 96.9 & 95.9 & 96.9 & 97.9 & 93.9 & 93.9 & 94.5 \\ \cline{2-13}  & & FS & 53.1 & 43.3 & 45.5 & 39.8 & 40 & 33.3 & 33 & 35.7 & 47.5 & 35.4 & 40.6 \\ \cline{1-1} \cline{2-13}  & & ShuffleDetect & 80.7 & 89.0 & 92.5 & 86.2 & 87.2 & 85.7 & 92.5 & 85.8 & 86.5 & 87.3 \\ \cline{1-1} \cline{2-13}  & & FS & 81.9 & 86.6 & 86.4 & 85 & 76.7 & 83.1 & 77.5 & 88 & 85.4 & 83.4 \\ \cline{1-1} \cline{2-13}  & & ShuffleDetect & 66.6 & 72.5 & 86.4 & 93.7 & 86.0 & 83.6 & 88.4 & 92.6 & 84.2 & 84.0 & 83.8 \\ \cline{1-1} \cline{2-13}  & & FS & 96.8 & 90.1 & 94.8 & 96.9 & 98 & 90.8 & 96.8 & 94.7 & 94.7 & 93.6 & 94.7 \\ \cline{1-1} \cline{2-13}  & & ShuffleDetect & 66.6 & 72.5 & 86.4 & 93.7 & 86.0 & 83.6 & 88.4 & 92.6 & 84.2 & 84.0 & 83.8 \\ \cline{1-1} \cline{2-13}  & & FS & 96.8 & 90.1 & 94.8 & 96.9 & 98 & 90.8 & 96.8 & 94.7 & 94.7 & 93.6 & 94.7 \\ \cline{1-1} \cline{2-13}  & & ShuffleDetect & 66.6 & 72.5 & 86.4 & 93.7 & 86.0 & 83.6 & 88.4 & 92.6 & 84.2 & 84.0 & 83.8 \\ \cline{1-1} \cline{2-13}  & & FS & 96.8 & 90.1 & 94.8 & 96.9 & 98 & 90.8 & 96.8 & 94.7 & 94.7 & 93.6 & 94.7 \\ \cline{1-1} \cline{2-13}  & & FS & 96.8 & 90.1 & 94.8 & 96.9 & 98 & 90.8 & 96.8 & 94.7 & 94.7 & 93.6 & 94.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: Performance comparison of ShuffleDetect and FS regarding detection rates.
# Deep Learning in Financial Modeling: Predicting European Put Option Prices with Neural Networks
## 3 Materials and Methods
### Training and Evaluation  
#### 3.7.1 Training Strategy  
The proposed deep neural network (DNN) was trained using a supervised learning approach, where the predicted option prices were compared to the theoretical prices computed from the Black-Scholes model. The objective was to minimize the error between the predicted and actual values using the Mean Squared Error (MSE) [18] as the loss function. The MSE quantifies the average squared differences between predicted and true prices, ensuring that large deviations are penalized more than smaller ones  
To enhance generalization and prevent overfitting, the dataset was randomly split into 70% training data, 15% testing data, and 15% validation data. The training process included an early stopping mechanism [19], which monitored validation loss and halted training once it stabilized. This approach prevents unnecessary iterations that could lead to overfitting. Furthermore, extensive hyperparameter tuning was performed to determine an optimal configuration. The best results were achieved with a batch size of 32 and an initial learning rate of \(10^{-3}\), using the Adam optimizer, which demonstrated stable convergence and efficient weight updates.