esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
This paper proposes an evaluation index of wind turbine generator operating health based on the relationships with SCADA (Supervisory Control and Data Acquisition) data. First, the relationship among the data from a wind turbine SCADA system is thoroughly analyzed. Then, a time based sliding window model is used to process the SCADA data by the bin method, and a running state model of the wind turbine is established by data fitting. Taking the normal operation state model of the wind turbine as the standard reference and based on the Euclidean distance between the state model curve and the standard model curve, the health index of the wind turbine operation state is proposed. Finally, using SCADA data from two 2 MW direct-drive wind turbines as examples for analysis and discussion, the results show that: (1) health indicators have good stability and sensitivity to wind turbine operating conditions; (2) the width of the data window in the sliding window model must cover all operating conditions of the wind turbine to ensure that the health index depicts the operating state of the wind turbine; (3) the data window width, window increment, and data fitting modeling all affect the health indicators, and thus, the selection of the sliding window model parameters and the data relationship modeling methods should consider the accuracy and real-time performance of the health indicators; and (4) the data acquisition cycle does not affect the health indicators. Once the basic characteristics of the data relations are known, direct data fitting modeling is more efficient than bin preprocessing modeling.  
wind turbine; SCADA data; operating condition; health index +
Footnote †: journal: Medical Engineering  
1
Footnote 1: School of Mechanical Engineering, Hunan University of Science and Technology, Xiangtan 411201, China; hnust_zife163.com (F.Z.); [EMAIL_ADDRESS] (D.L.); [EMAIL_ADDRESS] (J.J.); [EMAIL_ADDRESS] (H.W)
# Endodontic Management of Endo-Period Lesions
## 2 Materials and Methods  
### Clinical Case 1  
A healthy 49-year-old Caucasian male presented at the clinic with a chief complaint of persistent pain and discomfort in his second left mandibular molar. The patient reported previous endodontic treatment 7 years ago.  
**Intra-oral clinical examination**  
The second left mandibular molar had been restored with composite resin restoration. The tooth exhibited tenderness on percussion, with a probing depth within normal limits (<3 mm), except for the distal surface of the tooth, where it was 7 mm.  
**Radiographic examination**  
Preoperative cone-beam computed tomography (CBCT) imaging revealed previous root canal obturation, with a large radiolucent area in the distal aspect of the tooth. Furthermore, there was considerable resorption of the buccal and lingual bone plate on the distal aspect of the tooth (Figure 1a,b). The CBCT axial view revealed a C-shape root canal configuration.  
**Endodontic Diagnosis**  
Pulpal diagnosis--previously treated  
Periapical diagnosis--symptomatic apical periodontitis  
Primary endodontic--secondary periodontal lesion  
**Periodontal Diagnosis**  
Localized periodontitis, Stage III (according to the classification which was presented at the 2017 World Workshop by the American Academy of Periodontology (AAP) and the European Federation of Periodontology (EFP) [9].  
**Treatment**  
After discussing treatment options with the patient, a decision was made in favor of endodontic intervention as the first stage of a comprehensive treatment plan, followed by periodontal treatment.  
Treatment was performed under magnification with a dental operating microscope. After local anesthesia (2% lidocaine with 1:100,000 epinephrine) and rubber dam isolation, the old restoration was removed and the appropriate access cavity was refined, which revealed a C-shape root canal configuration: Category II (C2) according to [PERSON]'s anatomic classification (Figure 1c).  
Cleaning and shaping of the root canal system was performed using HyFlex EDM (Coltene, Switzerland) with abundant 5% sodium hypochlorite and 17% EDTA irrigation (Figure 1d).  
The working length was determined by an electronic apex locator (Propez II; [PERSON], Ballaigues, Switzerland).  
Root canal obturation was performed with the GuttaFlow Bioseal system (Coltene, Roeko, Germany) (Figure 1e). The endodontic access cavity was restored with the composite material, and a temporary filling with a monolithic zirconia crown was applied. Periodontal therapy followed the endodontic treatment as soon as possible.  
Postoperative CBCT showed complete root canal filling (Figure 1f,g).  
**Follow-up**  
At a clinical evaluation 12 months later, the tooth was restored with glass-ionomer cement. The tooth was asymptomatic, and the periodontal pocket depth did not exceed 3 mm.  
A CBCT radiographic inspection at the same time revealed advanced bone healing (Figure 1).
## Methods
### Comprehensive Semantic Similarity Calculation Method  
The workflow of the comprehensive semantic similarity calculation method based on the SHO-BP algorithm is depicted in Figure 3. Firstly, an transport infrastructure BIM model is converted into an ontology model by analyzing its structure. Then, two concepts are selected from the ontology model, and their positions in the ontology tree structure are determined, along with the retrieval of their properties and parameters, to conduct the calculation of three types of similarity: Edge-Counting, Feature-based, and Information-Content similarity. Finally, the SHO algorithm is used to optimize the weights and thresholds of the BP neural network. The optimized BP neural network is then employed to perform comprehensive weighting on the three types of similarity, resulting in the overall ontology-based semantic similarity.  
Figure 2: Flowchart of the SHO-BP algorithm.  
The process of calculating comprehensive similarity involves weighting and summing three types of similarity: Edge-Counting, Feature-based, and Information-Content. The weights for this summation can be obtained through the training of the BP neural network. In order to achieve the task of weights allocation, we set the Edge-Counting, Feature-based, and Information-Content similarities between different concepts as inputs to the BP neural network. Simultaneously, we use the comprehensive similarity from the training dataset as the output for training the neural network. During the training process, the network continuously adjusts its weights and biases using the Backpropagation algorithm to minimize the loss function. Upon completion of training, the weights of the output layer are extracted and used as the weights for the three similarity factors, which are subsequently employed in the comprehensive calculation task. However, the network is prone to becoming stuck in the local optimal solution during the aforementioned training, resulting in a calculation model with lower accuracy in its outcomes. Therefore, we employ the SHO-BP algorithm to facilitate the training process. Through the SHO algorithm, we optimize the initial weights and thresholds of the BP neural network. Leveraging the global search capability of the SHO algorithm prevents the network from falling into a local optimal solution. This ensures that the weights obtained during training enable the accurate computation of comprehensive similarity while reducing the time required for the training process.  
Figure 3: Flowchart of comprehensive semantic similarity calculation.  
BP neural network convergence. The following sections provide a detailed introduction to the three similarity calculation methods.
# Multi-User Scheduling for 6G V2X Ultra-Massive MIMO System
## 3 Ultra-Massive MIMO Multi-User Scheduling Based on BD Precoding
### BD-Based Precoding  
BD precoding is a linear precoding widely recognized in the downlink of ultra-massive MIMO systems; the core of its algorithm is to perform two Singular Value Decomposition (SVD) to obtain the precoding matrix \(\mathbf{F}\) in which the user's precoding matrix of user \(k\) is \(\mathbf{F}_{k}=\mathbf{F}_{k}^{1}\mathbf{F}_{k}^{2}\).  
Define the system channel matrix as  
\[\mathbf{H}=\left[\mathbf{H}_{1}^{H},\mathbf{H}_{2}^{H},\ldots,\mathbf{H}_{R}^{H }\right]^{H} \tag{10}\]  
The complement matrix \(\mathbf{\widetilde{H}_{k}}\) of user \(k\) is given as  
\[\mathbf{\widetilde{H}_{k}}=\left[\mathbf{H}_{1}^{H},\ldots,\mathbf{H}_{k-1}^{ H},\mathbf{H}_{k+1}^{H},\ldots,\mathbf{H}_{R}^{H}\right]^{H} \tag{11}\]  
Using SVD decomposition of \(\mathbf{\widetilde{H}_{k}}\), we can get  
\[\mathbf{\widetilde{H}_{k}}=\mathbf{\widetilde{U}_{k}}\left[\begin{array}{c} T_{k}\ 0\\ 0\ 0\end{array}\right]\left[\mathbf{\widetilde{V}_{k}^{(1)}}\ \mathbf{\widetilde{V}_{k}^{(0)}}\right]^{H} \tag{12}\]  
where \(\mathbf{\widetilde{V}_{k}^{(1)}}\in\mathbb{C}^{N_{t}\times\overline{L}_{k}}\) and \(\mathbf{\widetilde{V}_{k}^{(0)}}\in\mathbb{C}^{N_{t}\times(N_{t}-\overline{L} _{k})}\) are the first \(\overline{L}_{k}=rank\left(\mathbf{\widetilde{H}_{k}}\right)\) and the last \(\left(N_{t}-\overline{L}_{k}\right)\) right singular value vectors of \(\mathbf{\widetilde{H}_{k}}\), and \(\mathbf{\widetilde{V}_{k}^{(0)}}\) is in the zero space of the complement matrix \(\mathbf{\widetilde{H}_{k}}\).  
Let \(\mathbf{F}_{k}^{1}=\mathbf{\widetilde{V}_{k}^{(0)}}\), the system equivalent channel matrix \(\mathbf{\widetilde{H}_{obj}}\in\mathbb{C}^{N_{t}\times(N_{t}-\overline{L}_{k})}\) is obtained based on SVD decomposition  
\[\mathbf{\widetilde{H}_{obj}}=\mathbf{H}_{k}\mathbf{\widetilde{V}_{k}^{(0)}}= \mathbf{U}_{k}\left[\begin{array}{cc}\mathbf{\widetilde{T}_{k}}&0\\ 0&0\end{array}\right]\left[\begin{array}{cc}-(1)&-0\\ \mathbf{V}_{k}&\mathbf{V}_{k}\end{array}\right] \tag{13}\]  
Then, let \(\mathbf{F}_{k}^{2}=\mathbf{V}_{k}^{-(1)}\in\mathbb{C}^{(N_{t}-R_{1})\times N_ {t}}\) and \(\mathbf{U}_{k}\in\mathbb{C}^{N_{t}\times N_{t}}\); we can get the precoding matrix and combiner of user \(k\) as  
\[\mathbf{F}_{k}=\mathbf{\widetilde{V}_{k}^{(0)}}^{-(1)}\mathbf{V}_{k} \tag{14}\]  
\[\mathbf{W}_{k}=\mathbf{U}_{k} \tag{15}\]  
Therefore, the main objective in the BD precoding algorithm is to find the optimal precoding matrix of user \(k\) to satisfy \(\mathbf{H}_{i}\mathbf{F}_{k}=0(\forall i\
eq k)\), to achieve the goal of eliminating interference.
# Threat Analysis and Risk Assessment (TARA) Analysis of an Autonomous Emergency Braking (AEB) System
## Abbreviations \begin{tabular}{l l}
**ACU** & Airbag Control Unit \\
**ADAS** & Advanced driver-assistance systems \\
**AEB** & Autonomous emergency braking \\
**BIP** & Blended Intensive Program \\
**CAN** & Controller Area Network \\
**DoS** & Denial of service \\
**ECU** & Electronic control unit \\
**EMS** & Engine management system \\
**EoP** & Elevation of privilege \\
**ESC** & Electronic Stability Control \\
**ETH** & Ethernet \\
**ETTC** & Enhanced time-to-collision \\
**FOPS** & Financial Operational Privacy Security \\
**HARA** & Hazard analysis and risk assessment \\
**HIL** & Hardware-in-the-loop \\
**HMAC** & Hash-based Message Authentication Code \\
**HSM** & Hardware Security Module \\
**ISO** & International Organization for Standardization \\
**KPI** & Key Performance Indicator \\
**MFA** & Multi-factor authentication \\
**MTM** & Microsoft Threat Modeling Tool \\
**OBD** & On-Board Diagnostics \\
**OEM** & Original equipment manufacturer \\
**OTA** & Over-the-air \\
**SAE** & Society of Automotive Engineers \\
**SAS** & Steering angle control \\
**STRIDE** & Spoofing; tampering; repudiation; information disclosure; denial of service; elevation of privilege \\
**TARA** & Threat analysis and risk assessment \\ \end{tabular}   
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Risk Level** & **Threat Type** & **Description** \\ \hline \multirow{6}{*}{Medium (26.3\%)} & \multirow{6}{*}{Denial of Service} & An attacker may initiate a denial of service by transmitting an excessive volume of data to the CAN channel, exploiting the absence of a rate limiter on the radar sensor. This overload disrupts the radar’s functionality, leading to an interruption in its service and impairing the system’s overall performance. \\ \cline{2-3}  & & A malicious entity could exploit this information disclosure vulnerability by intercepting CAN messages through a device connected to accessible vehicle interfaces, such as the OBD port, or by obtaining unauthorized access to in-vehicle network interfaces. By utilizing these entry points, the attacker may perform packet-sniffing operations to capture and analyze CAN communications, exposing critical AEB data related to braking parameters, speed, or sensor information. These sensitive data, once disclosed, could be used to infer or manipulate AEB responses, potentially compromising vehicle operations or facilitating additional targeted attacks. \\ \cline{2-3}  & \multirow{6}{*}{Repudiation} & The exploitation of the Electronic Management System EMS component can enable an attacker to manipulate the status of the brake lamp. By generating unauthorized commands that traverse the Controller Area Network CAN associated with the Electronic Stability Control ESC, the malicious entity can activate or deactivate the brake lamps without creating traceable evidence. This lack of accountability poses significant risks, as it allows the perpetrator to repudiate responsibility for actions taken by the vehicle, ultimately endangering the safety of other road users.
## 1 Introduction
This treatment involves performing an endoscopic hemilaryngectomy to remove the primarily affected vocal fold, while preserving as much as possible of the healthy tissue and pliability of the superficial lamina propria on the contralateral side (and, thus, preserve the potential for vocal function). The side with the vocal fold removed is then surgically reconstructed to form a scar band that is medialized using implantable materials (e.g., adipose tissue) so that the vibrating contralateral vocal fold can achieve glottal closure during phonation. The reconstructed larynx is analogous to the excised hemilarynx setup that has been previously employed to study phonatory physiology, e.g., [13; 33; 34; 35]. Many of these patients have perceptually typical conversational voices that are much improved compared with their pre-surgical condition, when they often exhibit insufficient glottal closure that produces excessively inefficient voice production and a breathy voice quality [36]. The primary aim of this paper is to report on the first in vivo application of a recently developed transoral, dual-sensor intraglottal/subglottal pressure (ISP) probe [12; 13] in such an individual with a hemilaryngectomy. In these individuals, the tip of the ISP probe is designed to rest against the surgically medialized scar band to yield stable and reliable collision pressure measurements of the medial surface of the contralateral vocal fold (essentially acting as an excised hemilarynx configuration). The probe was designed to simultaneously measure the intraglottal pressure signal from a proximal sensor and sub-glottal pressure from a second, distal sensor at the tip of the probe to gain empirical insight into in vivo vocal fold collision and aerodynamic relationships during phonation. When positioned appropriately during phonation, the intraglottal pressure signal is expected to consist of the two expected components of an impulsive peak due to vocal fold collision and a more rounded peak due to intraglottal aerodynamic pressures during the open phase of the phonatory cycle. A secondary aim of this paper is to illustrate how numerical models of phonation can be validated using the in vivo measurement of vocal fold collision pressures as reference values. Numerical models allow clinicians and researchers to derive hard-to-measure parameters related to voice production (such as vocal fold collision pressures) from relatively easier-to-measure parameters (such as glottal area waveforms) since invasive direct measurements are generally not feasible in practice. Two numerical models are evaluated that enable the derivation of vocal fold collision pressures from high-speed videoendoscopic imaging data.
# Long-Term Occurrence and Fate of Microlastics in WWTPs: A Case Study in Southwest Europe
## 3 Results and Discussion
### Microplastics Entrapped in Sewage Sludge  
It has been found that during the warmest months (May-September) the MP concentrations in dry sludge (28-39 MPs/g) were higher than those detected the rest of the months (12-22 MPs/g) (Figure 2c). These values are expressed by dry weight considering the sludge moisture of the different samples analysed (78-86% \(w/w\)). In the sludge samples there was a mean concentration of 24.0 \(\pm\) 8.6 MPs/g dry sludge, value similar to those reported in literature for urban WWTPs [31,64,67,70,72]. According to Figure 2c, it can be observed that MP percentage retained in sludge varies between 47% and 100% with a mean value of 79%. These percentages are in agreement with those reported by different authors (8-92%) [16,28,23,76]. The removal of MPs in previous stages, i.e., during pre-treatment processes, can achieve notable values of elimination, for example, [PERSON] et al. [44] found that 45% of MPs that arrive at WWTP can be removed in grit and grease system. These percentages have been calculated based on the number of microplastics detected in the influent and the final effluent of the WWTP taking into account the daily flow (Table S5) in each sampling point. In addition, a trend between temperature and MPs retained in sewage sludge was observed, i.e., temperatures seem to favour the entrapping of MPs.  
Physical and chemical properties of MPs retained in sludge samples were also analysed and results are summarised in Figure 7. Most MPs found in sludge are fragments and fibres (57 \(\pm\) 18% and 33 \(\pm\) 11%, respectively). Foams represent the 9%, but it should be noted that this specific shape was only detected in three samples (September, October and November) with percentages of 2%, 26% and 81%, respectively. The majority of the published works reported a higher abundance of fibres than fragments, with higher percentages than those found here (50-84%) [16,17,28,33,40,44,70,72,76]. However, it is remarkable that other works are in accordance with the results obtained in this case study, i.e., reported a higher proportion of fragments with respect to fibres [19,34,77].  
As can be seen in Figure 7, no notable differences in abundance of MPs regarding chemical composition were found. The most predominant polymers in sludge samples were PET (36 \(\pm\) 4%), followed by PS (25 \(\pm\) 4%), PA (20 \(\pm\) 4%) and PVC (9 \(\pm\) 3%), in accordance with other studies, i.e., [PERSON] et al. [72] reported relative abundances of PS (25%), PET (20%), PA (10%) and PVC (5%) of the same order of magnitude than those found here. This agrees with the fact that the abundances of these polymers decreased throughout the wastewater treatment processes, as above commented. The high density of these polymers favours their sedimentation, being more easily entrapped in sludge. Regarding colour, around 82% of MPs found in sludge were white and black, as occurred in the wastewater samples.
# An Iris Image Super-Resolution Model Based on Swin Transformer and Generative Adversarial Network
## 1 Introduction
### Contributions and Paper Outline  
The main contributions of this research can be summarized as follows:  
1. The paper introduces a unique model by combining residual Swin Transformer blocks with sub-pixel convolutional progressive upsampling. The model effectively addresses the relationship between global information in images while minimizing information loss during the reconstruction process.
2. The method incorporates adversarial learning into the model to provide more effective constraints on texture recovery. This addition enables the generated images to exhibit higher-frequency details, resulting in more realistic and visually appealing results.
3. A combination of multiple loss functions was employed, including perceptual loss, adversarial loss and the loss function utilizing the \(L_{1}\) norm. This integrated loss function design aids in balancing the quality, structure, and visual perceptual aspects of the generated images during training. By merging multiple loss functions, this paper presents an effective training approach that enhances the precision and realism of generated images.  
This paper comprises five parts. Section 2 presents related work and analyzes in detail the advantages and limitations of each approach. Section 3 delineates the proposed method and presents how the iris image resolution task is achieved. Section 4 describes the experiments and results, including the description of the dataset, the data preprocessing process, the model parameters, the metrics, and the analysis of the results. Section 5 presents the conclusions and the limitations of the method.
# Engine Fault Detection by Sound Analysis and Machine Learning
## 3 Methodology
### Classification and Fine-Tuning  
For the classification of vehicle fault diagnostic classes, we employed ELM, a machine learning algorithm based on a single hidden layer feedforward neural network architecture [32; 33; 34; 35]. In the ELM algorithm, the input layer weights and thresholds are assigned randomly, while the output layer weights are calculated based on these assignments. ELM training consists of two parts: (1) generating random hidden layer parameters from a predefined range, and (2) calculating the generalized inverse output weight matrix [36]. ELM is popular due to its fast learning speed, generalization ability, and simplicity. As illustrated in Figure 4, the ELM inputs map the features to the hidden layer, which are then passed on to the output layer. The output from ELM learning can be used for various tasks such as classification, regression, and clustering.  
ELM transforms input vector \(x=[x_{1},x_{2},\dots,x_{n}]\) into the hidden layer representation using the weight matrix \(W\) and the bias vector \(b\). Each neuron in the hidden layer uses an activation function \(g(.)\). The connections between the hidden layer outputs and the output layer are represented by the weights \(\beta\). In ELM, the weights and bias values for the hidden layer are randomly assigned and kept fixed. The weights \(\beta\) for the output layer are learned using the least squares method. The relationship between the input and output of the ELM is calculated as summarized in Equations (7)-(9).  
\[H=g(W\cdot X+b) \tag{7}\]  
\[Y=H\cdot\beta \tag{8}\]  
\[\beta=H^{+}\cdot T \tag{9}\]where \(H\) is the matrix of hidden layer outputs, \(Y\) denotes the output vector, \(H^{+}\) represents the Moore-Penrose pseudoinverse of the matrix \(H\), and \(T\) is the target vector.  
To optimize the performance of our ELM classifier, we utilized Grid Search, a hyperparameter optimization technique [37]. Grid Search systematically explores a predefined set of hyperparameters, evaluating each combination using 5-fold cross-validation to determine the optimal parameters that yield the highest classification accuracy. This method allows for a thorough examination of the model's performance across various settings, ensuring the selection of the most effective parameter set. The specific parameters used for Grid Search, such as the number of hidden neurons and activation functions, are outlined in Table 4. This exhaustive search process ensures that our model is finely tuned, enhancing its robustness and reliability in delivering accurate fault diagnosis results.