esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
This study builds on existing research by providing a comprehensive analysis of solar irradiance on curved surfaces of commercial vehicles, factoring in self-shadowing and dynamic environmental conditions. Previously, we discussed the differences between traditional PV power generation and VIPV applications [3]. In a conventional PV panel, it was considered that the flat surface and uniform hemisphere sky model was used. The angular response was primarily determined by trigonometric relationships. In contrast, for VIPVs, it was necessary to expand the flat surface into a curved surface, resulting in non-uniformity. Additionally, a differential geometry description was used, employing vector expressions of a unit element, rather than cosine loss due to incident angles. Therefore, we focus on the effect of the incident angle distribution for the curved surface of commercial vehicles. The following issues are not discussed in this study:  
* The interaction between curved surfaces and non-uniform solar irradiance affected by the shading environment [14; 15]. * The scalar-based simplified correction of curved surfaces [16]. * The effects of partial and dynamic shading on curved surfaces [10]. * The impacts of spectrum variation, rather than irradiance, on the performance of tandem solar cells [17; 18]. * The angular response of curved photovoltaic devices [5]. Testing methods for curved photovoltaic devices [3,19]. * Advanced interconnection [20]. These factors are important in the broader context of solar energy, but are outside the scope of this research, which focuses specifically on incident light and its effect on solar power generation in vehicle-integrated systems. Our approach can be extended to vehicles in motion, accounting for partial and dynamic shading effects, and applies to various types of mobility platforms, such as trucks, trains, and high-altitude pseudo-satellites (HAPS), provided that the three-dimensional surface geometry is known.
## 3 Results
### Acceleration Correlates with Distance  
We regressed the acceleration data measured in dermatome C7 onto distance from the source vibration and found a negative correlation (r = 0.943, \(p<0.05\); Figure 2: gray shading). As the distance from the source increased, the measured acceleration decreased. By contrast, we found no correlation between measured acceleration and participant arm circumference, weight, or gender (\(p>0.05\) in all cases). Participant anthropometrics showed no influence on vibration propagation.  
Figure 2: Mean acceleration (across participants) at different distances from a source vibration at various frequencies. The _black dashed horizontal line_ shows the amplitude value of the vibrotactile intensity discrimination threshold for dermatome C7. The _gray vertical bar_ marks the highest intensity vibration (241 Hz) acceleration values. Error bars show SEM.  
Figure 3 shows the percent residual acceleration across the four measurement distances in dermatome C7 (i.e., the relative amount of acceleration that remains after accounting for the magnitude of the source vibration). We found that a decaying exponential function reasonably describes the change in acceleration \(y\) as a function of measurement distance \(x\) (Equation (2)):  
\[y=a*e^{(-b*x)} \tag{2}\]  
where \(a\) and \(b\) are constant scaling and rate coefficients, respectively. Across the 12 stimulus intensities, the average goodness of fit was high [R\({}^{2}\) = 0.927 \(\pm\) 0.051 (mean \(\pm\) SD); \(a=87.66\)\(\pm\) 9.97; \(b=0.360\)\(\pm\) 0.041]. The standard error of the non-linear model estimate was 1.29% at 4 cm, 0.84% at 8 cm, 0.45% at 12 cm, and 0.12% at 16 cm (Figure 3: gray shading).  
Finally, we regressed the percent residual acceleration at 4 cm and 8 cm onto source vibration intensity and found a significant correlation at both locations (4 cm: r = 0.911, \(p<\) 0.05; 8 cm: r = 0.991, \(p<\) 0.05). Because the relative amount of vibration varies as a function of source intensity at 4 and 8 cm--even after normalizing (dividing) by the source intensity--these results show that vibration propagation varies as a function of the source's frequency, and not just its magnitude.
# Blur Kernel Estimation by Structure Sparse Prior
## 3 Method
### Optimization Procedure  
The optimization problem in the proposed objective function in Equation (8) involves multiple variables, which is non-convex and hard to minimize directly. Fortunately, it is convex with respect to any one of the variables while fixing the other variables. Hence, we adopt the alternating minimization strategy by an iterative framework. The proposed objective function involves multiple variables, namely \(x,k\), and \(\alpha\), thus is hard to solve directly. An alternating minimization scheme is adopted, which reduces the original problem into three simpler sub-problems and alternately optimizes them. These three sub-problems of \(x,k,\alpha\) are described as follows:  
\[\hat{k}=\underset{k}{argmin}\;||y-k*x||_{2}^{2}+\lambda_{1}||k||_{1}, \tag{9}\]  
\[\hat{\alpha}=\underset{\alpha}{argmin}\;||x-D\alpha||_{2}^{2}+\lambda_{2}||d \odot\alpha||_{2}^{2}, \tag{10}\]  
\[\hat{x}=\underset{x}{argmin}\;||y-k*x||_{2}^{2}+\eta||x-D\alpha||_{2}^{2}+ \lambda_{2}||d\odot\alpha||_{2}^{2}. \tag{11}\]  
Following this scheme, the sub-problems for optimizing each variable are addressed in an alternating fashion. In this case, the optimization problem can be solved by the following steps:
# Artificial Intelligence in Ophthalmology: Advantages and Limits
## 5 AI and Different Applications in Ophthalmology and Other Fields
### Retinoblastoma and Leukocoria  
Retinoblastoma is the most common source of primary intraocular cancer in childhood and is responsible for 10-15% of cancers occurring in the first year of life. This tumor can grow and metastasize rapidly.  
AI can be used to improve screening and identification of retinoblastoma.  
CRADLE (Computer-Assisted Leukocoria Detector) is a smartphone-based application dedicated to screening children for leukocoria [123]. Patients had retinoblastoma, Coats disease, amblyopia, hypermetropia, and cataracts. CRADLE has been redesigned and is now based on a built-in CNN that was formerly designed to find leukocoria in nonclinical environments. A total of 52,982 photos were utilized to train and test the specific algorithm, and they were all acquired occasionally by the patients' relatives. Retroactive research for CRADLE's abilities showed that the application was suitable for identifying leukocoria in 80% of patients with eye conditions. Additionally, it found leukocoria in images that were acquired an average of 1.3 years before clinical diagnosis [123].
# Machine Learning Algorithm for Delay Prediction in IoT and Tactile Internet
## 4 Problem Formulation and System Model  
Recently, various ML algorithms were used for network traffic prediction, such as random forest [32], ARIMA [33], LSTM [7; 11; 12], etc. In this work, the NARX-RNN was proposed for IoT delay prediction as proven in many applications. Moreover, we used the \(k\)-step ahead prediction approach with NARX neural network for IoT traffic prediction.  
First, we built the IoT system to generate the dataset; we also modeled the IoT system using the AnyLogic simulator. Second, after collecting, analyzing, and processing the dataset, we used it as input to ANN for the prediction process. After loading the dataset as input for the network, the dataset was divided into two subsets in the columns Input (I) and Output (O) and then split into training, testing, and validation subsets accordingly. The normalization of the input data must be in the interval [\(-\)1, 1] compatible with the actual maximum or minimum values.  
The model ML was trained with four different training algorithms: Trainbr, Trainlm, Traincgf, and Trainrp. In addition, the prediction accuracy was measured using ML-based functions: MSE, RMSE, and MAPE.
# Fire Risk Assessment of Urban Utility Tunnels Based on Improved Cloud Model and Evidence Theory
## 3 Fire Risk Assessment Model of Urban Utility Tunnels
### Evidence Theory  
Evidence theory can be used for confidence reasoning based on multiple data information. It is an effective method to deal with uncertainty problems and has many applications in fault diagnosis and risk assessment [26; 27]. In evidential reasoning, \(R\) is said to be the identification framework, assuming that \(R\) is the universal set of all research objects and the elements in \(R\) are mutually exclusive. \(2^{R}\) is the power set of \(R\), and \(A\) is any subset of \(2^{R}\) and belongs to the interval [0, 1]. If the function \(m\) satisfies \(m(\phi)=0\) and \(\sum\limits_{A\in R}m(A)=1\), \(m\) is called the basic probability distribution function of \(R\). \(A\) is called the focal element of the basic probability distribution function \(m\). \(m(A)\) indicates the degree of support of the relevant evidence for the event \(A\), which can also be called the confidence or membership degree.  
It is assumed that \(m_{1}\) and \(m_{2}\) are two separate data sources on the same identification framework \(R\) and that \(A_{1}\), \(A_{2}\), \(\ldots\), \(A_{i}\) and \(B_{1}\), \(B_{2}\), \(\ldots\), \(B_{j}\) are their corresponding focal elements. Then, the synthetic rule of evidence theory can be expressed as:  
\[m(\sigma)=\left\{\begin{array}{l}0\,,\,\,\,\,\sigma=\phi\\ \sum\limits_{A_{i}\cap B_{j}=\sigma}\frac{{m_{1}(A_{i})m_{2}(B_{j})}}{K}\end{array}\right. \tag{7}\]  
\[K=\sum\limits_{A_{i}\cap B_{j}\
eq\phi}m_{1}(A_{i})m_{2}(B_{j}) \tag{8}\]  
where \(m(\sigma)\) is the membership degree after fusion, and \(K\) indicates the degree of conflict between evidence. The larger the \(K^{-1}\), the greater the degree of conflict.
## 3 The Proposed Framework for Anomalous Event Recognition
### Proposed Joint Learning Model and Multiple Ranking Measures (MRMs)
#### 3.3.2 Deep Multiple Instance Learning Block  
The joint learning of both concatenated features is conducted in the DMIL block with the help of the proposed MRMs in a weakly-supervised manner, as shown in Figure 1. Anomalous events are infrequent in the real-world, and annotating all videos at the temporal-level is laborious and time-consuming; therefore, utilizing video-level annotations is an essential advantage of using DMIL. Moreover, training a deep neural network with any available information is advantageous. Therefore, a DMIL approach [19; 20] is used similar to [PERSON] et al. [14] work in the absence of temporal annotations.  
As introduced in Section 3.2, the group with positive (anomalous) samples is represented as \(G_{a}\), while the group with negative (normal) samples is \(G_{n}\). Both of them are then updated with their concatenated extracted features. Each group has \(N\) number of samples. Exploiting video-level annotations, and with the help of the proposed MRMs, the DMIL block trains the network to predict the abnormality score of each sample, given that at least one of the positively annotated samples contains an abnormality in it. Since normal video samples have no abnormalities, this result also indicates the accurate annotations of negative samples.  
Therefore, in the absence of accurate annotations of positive samples, an optimization function, shown in Equation (1), can be used with the highest scored instance from each group or video. Note that we use an optimization function that is different from the optimization function of the standard supervised binary classification (where instance-level annotations are available) [20].  
\[\min_{\mathbf{w}}\frac{1}{v}\sum_{l=1}^{v}\overbrace{\max\left(0,1-Y_{G_{l}} \left(\underset{s\in G_{l}}{\max}(\mathbf{w}.\phi(x_{s}))-b\right)\right)}^{ \widehat{h}}+\frac{1}{2}\parallel\mathbf{w}\parallel^{2} \tag{1}\]  
where \(v\) is the total number of training videos, \(Y_{G_{l}}\) represents the video-level or group-level annotation (groups \(G_{n}\) and \(G_{a}\)), \(\phi(x_{s})\) stands for feature representation of a sample (an instance), \(\widehat{h}\) denotes the hinge loss function, \(b\) stands for a bias term, and \(\mathbf{w}\) denotes model weights. The following section describes the proposed objective function for DMIL.
## 5 Conclusions  
In the present paper, a novel SMC based FTC scheme with RADP-based SS for a category of uncertain cascade systems is proposed. In particular, a novel idea for the ADP-based SS design is proposed to cope with the mismatched uncertainties in the SMC theory and application. For the practical implementing, the off-line design of RADP-based SS avoids the time-consuming training of neural network weights, and the adaptive SMC is also helpful to implement. In addition, the simulation part demonstrates a practical example that is the attitude control system of the spacecraft. Note that the uncertainty is also mismatched for the subsystem in this paper. Furthermore, matched uncertainties including certain faults for the subsystem are worth researching in the future. In addition, more new sliding surfaces based on the ADP algorithm treasure house could be designed, which can be combined with the powerful SMC mechanism to solve more difficult FTC problems.  
Y.D. provided methodology, validation, and writing--original draft preparation; B.J., Y.M. and Y.C. provided conceptualization, writing--review & editing, and supervision; and B.J. provided funding support. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China under Grant 62020106003, Grant 61922042 and Grant 61773201, the Fundamental Research Funds for the Central Universities of China under Grant NC2020002, and Grant NP2020103, the 111 Project of the Programme of Introducing Talents of Discipline to Universities of China under Grant B20007, and the 13 th Five-Year Equipment Pre-research Projects of China under Grant 30501050403.  
Figure 6: Comparison of angular velocity of ACS without and with input delay. (**a**) Evolution of angular velocity of the ACS. (**b**) Evolution of attitude with input delay.  
Figure 7: Comparison of inputs of ACS without and with input delay. (**a**) Evolution of control input of the ACS. (**b**) Evolution of control input with input delay.
## 2 Numerical Simulations and Wind Tunnel Tests
### Turbulence Model
#### 2.2.2 LES Method  
The LES model was proposed by [PERSON] in 1963 [35]. The fundamental concept of LES is to filter turbulent eddies using a characteristic grid. Eddies larger than the spatial filter grid are solved directly using the Navier-Stokes equation, while eddies smaller than the spatial grid are modeled using a subgrid-scale model. In this case, the standard Smagorinsky-Lilly (SM) subgrid-scale model is employed, as shown in the following equation.  
\[\tau_{ij}-\frac{1}{3}\tau_{kk}\delta_{ij}=-2(C_{S}\Delta)^{2}|\overline{S}| \overline{S}_{ij} \tag{3}\]  
where \(\tau_{ij}\) denotes the sublattice scale stress, \(\tau_{ij}=\overline{u_{i}u_{j}}-\overline{u_{i}}\overline{u_{j}}\), and \(v_{SGS}\) denotes the sublattice vortex viscosity coefficient, and the expression is  
\[v_{SGS}=l_{S}|\overline{S}|=(C_{S}\Delta)^{2}|\overline{S}| \tag{4}\]  
where \(\overline{S}_{ij}=\frac{1}{2}\Big{(}\frac{\partial\overline{u}_{i}}{\partial x _{j}}+\frac{\partial\overline{u}_{j}}{\partial x_{i}}\Big{)}\); \(|\overline{S}|=\sqrt{2\overline{S}_{ij}\overline{S}_{ij}}\), \(\Delta=\big{(}\Delta_{x}\Delta_{y}\Delta_{z}\big{)}^{\frac{1}{3}}\); \(l_{s}\) is the Smagorinsky length scale; \(|\overline{S}|\) is the norm of the strain rate after filtering; \(\overline{u}_{i}\), \(\overline{u}_{j}\) indicates the filtered velocity; \(\Delta_{i}\) denotes the grid size in direction \(I\); and \(C_{S}\) is the Smagorinsky constant, which takes the value of 0.1 in this paper.
_applied sciences_  
_Article_ Study on the Interference Distribution Characteristics of the HVDC Grounding Electrode Current with Buried Pipelines Based on MoM and FEM  
[PERSON] 1,*, [PERSON] 1, [PERSON] 2 and [PERSON] 2  
1 State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Beijing 102206, China; [EMAIL_ADDRESS]
2 China Electric Power Research Institute, Beijing 100192, China; [EMAIL_ADDRESS] (F.B.); [EMAIL_ADDRESS] (F.C.)  
* **Correspondence: [EMAIL_ADDRESS]**  
**Abstract:** It is very important to calculate the interference of high voltage direct current (HVDC) grounding current with pipelines accurately and take proper protective measures to ensure energy transmission safety. There is still a lack of systematic research into related prediction methods that consider the nonlinear polarization on the interface between the soil and pipelines. In this paper, a methodology is proposed for calculating the coupling voltage and current on buried pipelines induced by grounding currents that accounts for nonlinear polarization based on the method of moments (MoM) and the finite element method (FEM). The validity of the proposed mathematical model is verified by a scale experiment. The interference distribution characteristics under different parameters are analyzed using the proposed method. The results show that the relationship between the maximum leakage current density and the electrode grounding current satisfy the linear function. The corrosion area becomes more concentrated as the grounding current increases. The corrosion area range increases substantially as the distance increases, whereas the influence degree decreases substantially. Both the maximum leakage current density and corrosion risk area are positively correlated with the soil resistivity.  
**Keywords:** HVDC grounding electrode; buried pipelines; nonlinear polarization; method of moments (MoM); finite element method (FEM); pipe-to-soil potential; leakage current density; distribution characteristics
# Opportunities and Limitations in Recycling Fossil Polymers from Textiles
## Recycling Fossil Polymers
### Non-Woven Production Using Recycled Fibres  
Non-woven fabrics are broadly defined as web structures bonded together by entangling fibres mechanically, thermally fusing the fibres or chemically bonding the fibres [124]. Typical raw materials are natural fibres (cotton, jute, linen), regenerated fibres (Bamboo fibre, viscous fibre, Tencel, modal fibre), synthetic polymer: PP, PE, PET, PA, polyester, PCL, PLA). The various methods for bonding are:  
1. Adding an adhesive.
2. Thermally fusing the fibres to each other or to the other thermoplastic fibres or powders.
3. Fusing fibres by first dissolving and then re-solidifying their surfaces.
4. Creating physical tangles or tuft among the fibres.
5. Stitching the fibres or filaments in place.  
The non-woven fabrics can be divided into eight types according to different manufacturing processes:  
1. spunlace non-wovens;
2. Heat-bonded non-woven fabrics
3. pulp air-laid non-wovens
4. Wet-laid non-woven
5. Spunbond non-wovens
6. Acupuncture non-wovens
7. Stitch non-wovens  
They are used in numerous applications, including baby diapers, adult incontinence products, wet wipes, surgical drapes and covers, liquid cartridge and bag filters, face masks, air-conditioning filters, soil stabilizers and roadway underlayment, erosion control, drainage systems, insulation (fibreglass batting), pillows, cushions, and upholstery padding, carpet backing, automotive headliners and upholstery, house wraps, and disposable clothing (foot coverings, coveralls).  
The non-woven market size is forecast to reach US$55.1 billion by 2026, after growing at a CAGR of 3.8% during 2021-2026 [125]. The impact of the COVID-19 pandemic was minimal on the growth of the non-woven fabric market. As governments provided economic supports for SMEs and other benefits, consumers worldwide could access essential commodities and personal protective equipment. The grow of the non-woven fabric market is mainly attributed to the increase in demand for non-woven products and solutions for hygiene, medical, construction and filtration applications.  
In the contest of recycling synthetic fibres, the production of non-wovens is very important because mechanically recycled fabric usually results in fibres with a lower length that is often suitable for the production of non-wovens [126]. Thus, the non-woven market, driven by a significant growth, could partially absorb fossil polymer fibres.
# Generative Oversampling Method for Imbalanced Data on Bearing Fault Detection and Diagnosis
## 2 Data-Driven Bearing Fault Detection
### Fault Classification Using CNN  
Using BF-NSP, solving FDD problems by time-series data analysis becomes an image recognition problem. We employ a CNN classifier because it provides outstanding performance in image recognition and classification [11]. The structure of the proposed CNN classifier, the CNN-based bearing fault detector (CBFD), is shown in Figure 4. There are three convolution (Conv) layers with kernel size 10 \(\times\) 10, 5 \(\times\) 5, and 3 \(\times\) 3, respectively. Batch normalization was applied to the first Conv layer only. Two fully connected (FC) layers followed the Conv layers. Output nodes reflected fault types; there were four output nodes for the normal condition and three bearing fault types (inner race, outer race, and contaminant fault). The activation functions for all layers were ReLU.  
In the CNN architecture, the selectable hyper parameters were the number of filters in each Conv layer, the size of each FC layer, and dropout rate. To determine the hyper parameters, the criteria for the fault classifier under data imbalanced condition was defined as accuracy over 95% for test sets in mild imbalance ratio less than 20:1 and high accuracy even in cases of severe imbalance ratio over 50:1.  
We tested varying the number of filters in each Conv layer and found the optimal numbers that met the criteria of training and test accuracy. The results of the number of filters should range from 10 to 50 because underfitting occurred when the number of filters was less than 10 and overfitting occurred when it was more than 50. The size of the FC layer affected the expressiveness of the networks and the training time. A large FC layer size increased the risk of overfitting and training time but could increase the accuracy. Through our experimentation, two FC layers of sizes less than 200 and 20 could not meet our performance criteria. Considering the overfitting and the training time, the optimal size  
Figure 3: Spectral density difference in resonance bandwidths.  
of each FC layer was determined as 500 and 50. Finally, a dropout layer with 75% keep probability was applied to the first FC layer to mitigate overfitting in the training phase. The CBFD model is described in Figure 4.