esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental Setup
### Data Visualization  
By using the \(t\)-SNE approach, we plotted the data to 2D real vectors to find any hidden clustering in the data. Figure 4a shows the \(t\)-SNE plot for the GISAID dataset (before applying any feature selection). The goal here is to show how well different variants are forming clusters. Since the colors could change randomly because \(t\)-SNE is a non-deterministic algorithm just like \(k\)-means, we may not have same color for a given variant every time. Different variants in the scatter plot can be easily visualized. Even though we cannot see clear separate clusters for each of those variants, small clusters are obvious for different variants. This evaluation for such data reveals that using any clustering algorithm directly will give us good results, and some data preprocessing is curtailed for efficiently clustering the variants.  
By visualizing the GISAID dataset using \(t\)-SNE, more clear clusters are visible after applying three different feature selection methods. In Figure 4b-d, we apply different feature selection methods, namely Boruta, LASSO, and RFF, respectively. We can observe that the clustering is purer for Boruta and LASSO regression but not for RFF. This behavior shows that the supervised methods (LASSO regression and Boruta) are able to preserve the patterns in the data more effectively as compared to the unsupervised RFF. Note that, after feature selection, some data points are still isolated from the big clusters. This behavior shows that, although those isolated data points belong to a certain cluster, they may be potential candidates for developing into a new variant.
###### Abstract  
Fault diagnosis in rotating machinery is significant to avoid serious accidents; thus, an accurate and timely diagnosis method is necessary. With the breakthrough in deep learning algorithm, some intelligent methods, such as deep belief network (DBN) and deep convolution neural network (DCNN), have been developed with satisfactory performances to conduct machinery fault diagnosis. However, only a few of these methods consider properly dealing with noises that exist in practical situations and the denoising methods are in need of extensive professional experiences. Accordingly, rethinking the fault diagnosis method based on deep architectures is essential. Hence, this study proposes an automatic denoising and feature extraction method that inherently considers spatial and temporal correlations. In this study, an integrated deep fault recognizer model based on the stacked denoising autoencoder (SDAE) is applied to both denoise random noises in the raw signals and represent fault features in fault pattern diagnosis for both bearing rolling fault and gearbox fault, and trained in a greedy layer-wise fashion. Finally, the experimental validation demonstrates that the proposed method has better diagnosis accuracy than DBN, particularly in the existing situation of noises with superiority of approximately 7% in fault diagnosis accuracy.  
fault diagnosis; deep learning; stacked denoising autoencoder; feature extraction; integrated deep fault recognizer +
Footnote †: journal: Applied Sciences
## 3 Results and Discussion
### Multiplex Allele-Specific PCR Analysis  
In previous studies, the interpretation of results obtained via PCR was complicated as both the presence or absence of amplification of the InDel marker and the size difference in the PCR amplification products were considered. In this study, the allele-specific PCR including the InDel sequences allowed for an intuitive result interpretation based on the state of PCR amplification. Although the results can be confirmed more easily than with existing analysis methods, it still takes much time and labor to confirm the origin of one sample because, to do this, PCR must be performed on 24 single soybean grains and confirmed via electrophoresis. To solve this problem, we set up six groups of 11 markers to enable multiplex PCR. To validate the feasibility of allele-specific PCR for InDel markers 1 to 11 at a single annealing temperature, amplification was assessed across a temperature range of 46 to 60 \({}^{\circ}\)C. Among these temperatures, we found that only the target product was amplified at 56 \({}^{\circ}\)C, without any non-specific PCR products (Figure 2). We attempted to confirm 11 InDel markers via a single multiplex PCR; however, because of the size of the amplified product and non-specific reaction, we optimized a total of six groups (Figure 3). Accordingly, we were able to significantly reduce the time to determine the geographical origin through multiplex allele-specific PCR.
# Vertebral Center Points Locating and Cobb Angle Measurement Based on Deep Learning
## 3 Materials and Methods
### Post-Processing
#### 3.4.2 Spine Curve Fitting and Cobb Angle Calculation  
The Cobb angle is a measure of the degree of scoliosis. The end vertebra of a lateral curvature is used to determine the Cobb angle. In this experiment, the end vertebra is determined by fitting a 7 th-degree polynomial to the 17 vertebral center points predicted by the network, using the y-coordinate as an independent variable. The Cobb angle is then calculated by taking the included angle between the upper and lower end vertebrae, determined by the slope of the normal line at the fitted curve.  
The experiment found that the upper and lower edges of the vertebrae are not parallel to the normal line at the fitted curve due to the interaction force between the vertebrae. Furthermore, a positive correlation was observed between the error and the fitted spine curve. Thus, the angle error is calculated using Formula (4) to correct the normal slope.  
\[\Delta_{i}=\frac{d_{i}}{|d_{i}|}\cdot log_{\mathrm{x}}(1+d_{i}) \tag{4}\]  
where \(i\) is the index of the spine center, is the correction factor, \(d\) is the slope of the tangent line, and is the angle error. In the above correction process, the angle between the normal line and the positive direction of the X-axis is subtracted from the correction degree calculated by this formula. Figure 7 shows the results before and after correction:  
Figure 6: The result after filtering noisy points.
###### Abstract  
Incorporation of [18]Flluorine into PET radiotracer structure has traditionally been accomplished via nucleophilic pathways. The [18]Flluoride is generated in an aqueous solution via proton irradiation of oxygen-18 enriched water and must to be introduced into water-free organic solutions in order to generate reactive species. Thus nucleophilic [18]F-fluorination traditionally included steps for [18]Fl fluoride concentration on the anion exchange resin, followed by removal of residual water via azeotropic distillation with McN, a time-consuming process associated with radioactivity losses and difficult automation. To circumvent this, several adsorption/elution protocols were developed based on the minimization of water content in traditional kryptofix-based [18]Fl fluoride elements. The use of pre-dried KOH/kryptofix solutions, tertiary alcohols, and strong organic bases was found to be effective. Advances in transition metal-mediated [18]Ar approaches for radiolabeling of non-activated aromatic substrates have prompted development of alternative techniques for reactive [18]Fl fluoride species generation, such as organic solutions of non-basic alkyl ammonium and pyridinium sulfonates, etc. For radiofluorinations of iodonium salts precursors, a \"minimalist\" approach was introduced, avoiding the majority of pitfalls common to more complex methods. These innovations allowed the development of new time-efficient and convenient work-up procedures that are easily implementable in modern automated synthesizers. They will be the subject of this review.  
18]Fl fluoride; nucleophilic fluorinations; phase transfer catalysts; tetraalkylammonium salts; copper-mediated fluorination; synthesis automation
# Dynamic Impedance Estimation: Challenges and Considerations
## 4 Dynamic Impedance Estimation
### Active Impedance Estimation Method  
As previously mentioned, passive methods will only estimate the impedance value for harmonic h if this harmonic is present in the power signal. To overcome this limitation, some disturbance must be caused in the network in a controlled manner to excite the network at interest frequencies. These disturbances can, however, significantly affect the PQ.  
A solution proposed in [41; 42] is a sequential injection of small short-duration signals and low energy. In this way, it is possible to scan the network's frequency without significantly affecting the power quality. Another advantage of the injection method is that it is possible to find the harmonic transfers in the system, as long as GPS synchronizes the measurements. Note that depending on the application, one will need not the synchronized phasor but the synchronized waveform.  
In the method described in [41], signals called Gaussian Modulated Signal (GMS) are injected at a given point in the network, and the response to this disturbance is captured at other points in the network, determining the harmonic transfer. The GMS is mathematically described by,  
\[i_{d}(t)=Ge^{-k\pi hf_{1}\frac{\rho^{2}}{2}}\cos(2\pi hf_{1}t) \tag{12}\]  
where \(i_{d}(t)\) represents the current injection of the GMS form, \(f_{1}\) the fundamental frequency of the network, \(h\) is the harmonic order whose transfer wants to be estimated, \(k\) is a constant that defines the resolution in the frequency, and \(G\) controls the pulse energy. The challenge is the injection of the signal in the network. However, when there are electronic converters, it is relatively simple to insert the pulse through their control.
## 3 Sensor Technologies in Chemical and Elemental Detection
### Spectrometers  
Spectrometers are a valuable tool for analyzing the properties of leaves. Several types of spectrometers are mostly used for close-proximity measurements. Fiber optic spectrometers are used due to their flexibility and adaptability. These devices employ fiber optic cables for light transmission from the leaf to the device. This design is particularly suitable for field studies because it ensures minimal disturbance of the sample, and it is possible to measure in hard-to-reach areas [15]. Spectrometers provide the absorption spectrum of the target, which shows how much light is absorbed at each wavelength. By measuring multiple points on the leaf, users can have a good overview of the leaf's spectral data.
# Anomaly Detection of CAN Bus Messages Using a Deep Neural Network for Autonomous Vehicles
## 4 Experimental Results
### Datasets  
In this section, we simulate the scene of in-vehicle network communicating. The CAN bus data packets are created by CANoe. The used dataset is composed of three parts: the training set, validating set and test set. The number of the generated packets is about 200,000. Of this total number, 150,000 of them are normal CAN bus data and the rest of them are abnormal data. In order to avoid the over-fitting problem found in [8], 70% of normal packets are used to train and 30% of normal packets are used to validate. All data features have a special bit position in the data field. The proposed network architecture can regard mode information and value information as semantics, in which the mode information and the value information represent a special message corresponding to an ECU and the value of the mode, respectively. For example, the special command message from mode information can control the wheels, and the command message from value information can control wheel angle or the speed, as shown in Figure 5. The modal information is constant in a short time, and the value information may change with the change of noise. In our work, the valuable information is only used in the training stage, and the mode information is used in the detection stage. In the CAN syntax, the data field contains 64-bit positions (eight bytes), and the probability distribution of each kind of bit-symbol forms a special feature vector.
## 2 O-RAN Architecture and Development
### O-RAN Open Source Development Landscape
Therefore, research and development efforts, such as developing a specific SD-RAN for existing RIC implementation (for example, FlexRIC [33]), or supporting new 3 GPP specifications (for example, splitting of the CU into CU-UP and CU-CP [34]), are open for both research and industrial communities to contribute.
## 3 Results  
Meteorological conditions during the applications and over the next 7 days in REC Zlotnik are shown in the Table 3. Temperature during the applications in 2017 ranged from 17 to 20 \({}^{\circ}\)C, 15 to 18 \({}^{\circ}\)C in 2018, but in 2019 from 12 to 20 \({}^{\circ}\)C, whereas RH, respectively, in \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Herbicide**} & \multirow{2}{*}{**Trade Name**} & \multicolumn{2}{c}{**Rates**} \\ \cline{3-4}  & & **Recommended** & **Reduced** \\ \hline \hline \multirow{4}{*}{Phenmedipham a + ethofumesetamitron b  } & \multirow{2}{*}{\begin{tabular}{c} Powertwin 400 SC, Adama Polska \\ + ethofumesetamitron b  } & \multirow{2}{*}{\
\begin{tabular}{c} Powertwin 400 SC, Adama Polska \\ + ethofumesetamitron b  } & \multirow{2}{*}{
\begin{tabular}{c} 200 & 140 \\ +200 & +140 \\ 700 & 700 \\ (FEM 1) \\ \end{tabular} } \\ \hline \hline \multirow{2}{*}{**Adjuvant**} & \multirow{2}{*}{**Adjuvant type**} & **Rate** & **Abbreviation** \\ \cline{3-4}  & & **L ha\({}^{-1}\)** & **Abbreviation** \\ \hline \hline \multirow{2}{*}{Adjuvant 1} & rapeseed oil methyl esters of & \multirow{2}{*}{1.5} & EXP 1 \\  & fatty acids, & & \\  & surfactants, pH buffer, drift & & \\  & reducing agent & & \\  & rapeseed oil methyl esters of & 1.5 & EXP 2 \\  & fatty acids, & & \\  & surfactants, pH buffer & & \\  & organoidsilicone surfactant & 0.1\% \(v\)\(v^{-1}\) & S \\ \hline \hline \end{tabular}  
* \({}^{\mathrm{a}}\) phenmedipham + ethofumesetam (Powertwin 400 SC, Adama Deutschland GmbH, Koln, FRG); b metamitron (Goltix 700 SC, Adama Poland, Warsaw, Poland); EXP 1, ADJ 2, ADJ 3 experimental adjuvants provided by Agromix.  
\end{table} 
Table 2: Description of the tested and standard adjuvants added to herbicides. the years was 60-70, 68-80, and 58-75%. The average temperature after treatments in the year 2017 was 9.6-22.1 \({}^{\circ}\)C, 9.7-19.4 in 2018, and in 2019, it was 7.8-19.9 \({}^{\circ}\)C.  
The electrolytic conductivity (EC), surface tension (ST), contact angle (CA), and pH of solutions, and the untreated check versus the treatments were significant (\(p\) = 0.0001). The surface tension, contact angle, and pH of spray liquid droplets differed for treatments, and the ST, CA, EC, and pH of spray liquid droplets differed for treatments (Table 4). The presence of adjuvarants in solutions led to a reduction in ST, CA, and EC both in only water, and also water with FEM at limited rates. The addition of FEM at a full rate to water did not impact on the EC of the spray liquid, 794 and 787, which was similar to EXP 2 and EXP 3, but FEM at reduced rate led to lower
## 2 Related Works  
Positioning technology has been developing for decades. During this time, various location technologies have emerged. To summarize the previous technical work, in this section, we start by summarizing positioning techniques based on the common metrics of positioning. We then review recent research advancements in wireless positioning systems, which contain a detailed explanation of positioning technology for 5G cellular networks related to the paper's topic. Additionally, we provide an overview of AI-based indoor positioning methods and DSSL-based indoor positioning schemes.
# An Ensemble Stacked Convolutional Neural Network Model for Environmental Event Sound Recognition
## 1 Introduction
To address the obstacles for ESC as discussed above, we propose a new stacked convolutional neural network model to improve the recognition performance of both the logmel-CNN and raw-CNN models. A new Dempster-Shafer (DS) evidence theory-based fusion algorithm is also proposed to exploit the synergistic capabilities of the raw-CNN and logmel-CNN approaches, which achieved better fusion performance than the average fusion approach. DS evidence theory, as a classical fusion algorithm, has the ability to consider the basic credibility distribution (prediction results) of several models to achieve better fusion, and has been widely used in multisource information fusion for recognition, which has achieved favorable results [24; 25; 26; 27; 28]. The main contributions of this paper are as follows:  
1. We proposed a five-layer stacked CNN network for sound event recognition based on a special convolutional filter configuration with decreasing filter sizes and static and delta log-mel input features. The test results from three datasets, ESC-10, ESC-50 [1], and Urbansound8k [29], indicated that the recognition performance of our model is higher than those of previous logmel-CNN models including Picazk [4], Salamon and Bello [19], and EnvNet [5]. 2. We designed an end-to-end stacked CNN model for sound event recognition from raw waveforms without feature engineering. It has a special two-layer feature extraction convolution layer and convolutional filter configuration to directly learn features from raw waveforms. Our models achieve a 2% and 16% improvement in recognition accuracy on the datasets of ESC-50 and Urbansound8k [29], respectively, compared to the existing top end-to-end model EnvNet [5] and the 18-layer convolutional neural network [22]. 3. We developed a novel ensemble environmental event sound recognition model, DS-CNN, by fusing logmel-CNN and end-to-end raw-CNN models using DS evidence theory to exploit raw waveform features as well as the log-mel features. The experimental result indicated that the recognition accuracy of the DS-CNN had surpassed the recognition accuracy of human and other mainstream algorithms over the ESC-50 dataset and is better than other simple averaging and product of probability fusion methods. To our knowledge, this is the first instance of applying DS fusion to the environmental sound recognition problem with improved recognition performance. The remaining structure of this paper is as follows. Section 2 describes related works on environmental sound recognition. In Section 3, we first present the overall framework of the DS environmental recognition model. Then, we introduce the method of feature extraction and the network architecture. The detailed description of the experimental dataset and results are described in Section 4. Finally, we draw conclusions about our work in Section 5.
## 3 System Model and Problem Formation
### Assumptions and Calculations
#### 3.3.2 Close Bounded Network  
The maximum numbers of traffic flows are known, similar to [43]. The traffic demand is static, as private WMNs are assumed to be used only by their permanent or regular clients and not shared or used by other public users.
# Wavelet Time-Frequency Analysis on Bridge Resonance in Train-Track-Bridge Interactive System
## 4 Discussion  
The article mainly focuses on applying the wavelet time-frequency analysis to the initial study on the bridge resonance in a simple train-track-bridge interactive model. Therefore, to some extent, there exists a certain amount of limitations and deficiency which needs to be improved for the future scope of study.  
1. The train-track-bridge interactive model established in the article is 2D, only involving the vertical displacement and the torsional pitching. Hence, to make the results closer to the realistic project, the study will be extended in a 3D train-track-bridge interactive model with more DOFs.
2. The article is lacking in the data measured on site to validate the theoretical results and rectify the model. Thus, the future study will try to launch the real measurement both on the train and on the bridge.
3. The article neglects that the track irregularity is actually a type of random excitation which will randomize the response of the train-track-bridge interactive system. In this case, the future study will combine the random vibration theory with the wavelet time-frequency analysis so as to make this method feasible for the actual damage detection on the train-track-bridge interactive system.
## 7 Ethical Implications of Healthcare 4.0
### Potential for discrimination against certain groups of people:  
Healthcare 4.0 relies heavily on digital technologies that raise concerns about the fair and equitable treatment of individuals. The following are some key concerns:  
1. Data and algorithmic biasing: If the data used to train algorithms is biased or incomplete, the AI systems may make decisions that perpetuate existing healthcare disparities. For instance, if historical data disproportionately represents certain groups, the AI might provide less accurate or inadequate diagnoses and treatments for other underrepresented groups. Thus, if certain groups of people are underrepresented in the training data used to develop AI algorithms, the algorithms might not perform as accurately for those groups. This could lead to biased treatment recommendations or misdiagnoses [114]. In addition, if access to healthcare technologies or AI-based services is restricted based on socioeconomic status or geographic location, this could exacerbate existing healthcare disparities.  
Privacy and consent: As digital technologies collect vast amounts of personal health data; individuals' privacy becomes a major concern. Healthcare 4.0 should address many privacy and consent issues. Mitigating privacy risks and ensuring secure data management in Healthcare 4.0 systems can be achieved in several ways, as presented in Table 13. However, Table 14 presents the potential ways to ensure proper patient consent [115; 116].
3. Health insurance discrimination: Health data could potentially be used by insurance companies to assess risk profiles and adjust premiums accordingly, disadvantage individuals with specific health conditions or genetic predispositions.