esa-sceva/satcom-chunk-collection huggingface dataset:
# Research on Key Technology of Wind Turbine Drive Train Fault Diagnosis System Based on Digital Twin
## 1 Introduction
However, studies on fault diagnosis of wind turbine transmission systems using this method have yet to be reported. As the core of the wind turbine, the drive train often makes it difficult to monitor its real-time working conditions, and the downtime caused by drive train failure is also the longest [14]. How to effectively monitor the drivetrain and fault diagnosis is also a hot research topic nowadays. The above shows that digital twin technology has excellent development prospects and practical value in the field of fault diagnosis. Therefore, this paper proposes a wind turbine drive train fault diagnosis system based on digital twin, and the following steps were conducted in this research: (1) Deeply planning of the construction and processing of the digital twin model, including lightweighting and other processing of the built 3D model of the wind turbine generator, and, at the same time, writing scripts according to the actual operation data to complete the accurate behavior mapping between the physical entity and the virtual model. (2) Proposing a fault diagnosis model based on the IVMD-PSO-LSSVM fault diagnosis model, which is characterized by fast computational efficiency, high accuracy, and fast response speed, which is proved by the experiments listed in the paper. (3) Combining the fault diagnosis with the digital twin system, a wind turbine drive train fault diagnosis system based on the digital twin was developed, and the system was verified with the actual wind turbine data. The experiments show that the method can effectively monitor the actual operating conditions of the wind turbine drive train and restore them through the digital twin model, and carry out online fault diagnosis and fault localization, which realizes the high efficiency and intelligence of the wind turbine drive train fault diagnosis and solves the problems of untimely discovery of the traditional fault diagnosis and complex maintenance. The rest of this article is organized as follows: Section 2 introduces the components of the digital twin system. Section 3 introduces the construction of digital twin models, including the construction of twin models, lightweight, and kinematic mechanism models. Section 4 introduces the IVMD-PSO-LSSVM-based fault diagnosis model applied to digital twin systems. The performance of the model is also experimentally verified. Section 5 presents a digital twin-based fault diagnosis methodology and the validation of a digital twin-based fault diagnosis system for wind turbine drive trains. Section 6 discusses the contributions of this paper and future research directions. Section 7 provides the conclusions of the report.
# A BiGRU Model Based on the DBO Algorithm for Cloud-Edge Communication Networks
## 4 Simulation Experiment and Result Analysis
### Introduction to the Dataset  
The publicly available dataset chosen for this chapter is the Aggregated Traffic in the United Kingdom Academic Network Backbone dataset, hereafter referred to as the \(AT_{\textit{UK}}\) dataset [43].  
The \(AT_{\textit{UK}}\) dataset is from the UK academic backbone and provides aggregated traffic information for about 2 months, starting at 9:30 on 19 November 2004 and ending at 10:45 on 27 January 2005. The data were collected every 5 min, and the data consisted of the sum of the accumulated traffic during that 5-min period. The dataset consists of 19,888 rows of data, with only two columns per row for time information and cumulative traffic information.  
The application scenario for the DBO-BiGRU model described in this chapter is the prediction of cumulative flow over a future time period. This scenario fits extremely  
Figure 5: Overall architecture of DBO-BiGRU prediction model.  
well with the \(AT_{UK}\) dataset and therefore it was chosen for the validation of the model's prediction accuracy.  
The first 16,000 rows of data from \(AT_{UK}\) are extracted for the experiment, and a 16,000 \(\times\) 2 data matrix is used as the input to the model; the validation set and the training set are divided in a ratio of 1:4.
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 3 Network Model, Q-Learning, and Fuzzy Logic
### Fuzzy Logic  
A fuzzy logic system consists of three components: _fuzzification_, _fuzzy logic controller_, and _defuzzification_. The first component converts the crisp values of the variable into their fuzzy form using some membership functions. The second one is responsible for simulating the human reasoning process by making _fuzzy inference_ based on inputs and a set of defined **IF-THEN** rules. The module itself can be separated into two subcomponents, namely _Knowledge Base_ and _Inference Engine_. _Knowledge Base_ is a set of specifically designed rules so that together with the input states of variables, they will produce consistent results. Each rule's form is \"**IF** [set of input] **THEN** [output]\". More explicitly, a fuzzy rule \(R_{i}\) with \(k\)-inputs and 1-output has the following form.  
\[\begin{split} R_{i}:&\textbf{IF }(I_{1}\text{ is }A_{i1})\Theta(I_{2}\text{ is }A_{i2})\Theta\ldots\Theta(I_{k}\text{ is }A_{ik})\\ &\textbf{THEN }(O\text{ is }B_{i}),\end{split} \tag{2}\]  
Figure 2: Q-learning overview.  
where \(\{I_{1},\ldots,I_{k}\}\) represents the crisp inputs to the rule. \(\{A_{i1},\ldots,A_{ik}\}\) and \(B_{i}\) are linguistic variables. The operator \(\Theta\) can be **AND**, **OR**, or **NOT**. The _Inference Engine_ is in charge of the estimation of the Fuzzy output set. It calculates the membership degree (\(\mu\)) of the output for all linguistic variables by applying the rule set described in the _Knowledge Base_. For Fuzzy rules with lots of inputs, the output calculation depends on the operators used inside it, i.e., **AND**, **OR**, or **NOT**. The calculation for each type of operator is described as follows:  
\[(I_{i}\text{ is }A_{i}\text{ }\textbf{AND}\ I_{j}\text{ is }A_{j}):\] \[\mu_{A_{i}\cap A_{j}}(I_{ij}) =\min{(\mu_{A_{i}}(I_{i}),\mu_{A_{j}}(I_{j}))},\] \[(I_{i}\text{ is }A_{i}\text{ }\textbf{OR}\ I_{j}\text{ is }A_{j}):\] \[\mu_{A_{i}\cup A_{j}}(I_{ij}) =\max{(\mu_{A_{i}}(I_{i}),\mu_{A_{j}}(I_{j}))},\] \[(\textbf{NOT}\ I_{i}\text{ is }A_{i}):\] \[\mu_{A_{i}}(I_{i}) =1-\mu_{A_{i}}(I_{i}).\]  
The last component helps to convert the fuzzy output set from the linguistic variables into a crisp value. The most popular fuzzy solution is a methodology called the centroid technique, described as follows:  
\[\textit{Center of Gravity of B (CoG_{B})}=\frac{\int_{-\infty}^{+\infty}\mu_{B}(z)z \text{d}z}{\int_{-\infty}^{+\infty}\mu_{B}(z)\text{d}z}, \tag{3}\]  
where \(\mu_{B}(z)\) is the output membership function of the linguistic variable \(B\).
## 3 Large Arrays Fabrication
### Thermal Coupling to the Bath  
A TES film kept at its transition temperature \(T_{c}\) by the electro-thermal feedback, must be weakly connected to the thermal bath stabilized at a temperature \(T_{bath}<T_{c}\). The thermal link must be properly engineered to be in accordance with the application requirements. A low-stress, silicon-rich \(Si_{x}N_{y}\) membrane is widely used, with a few exceptions, to achieve the thermal isolation because it is mechanically strong and compatible with the other processes used in TES fabrication. The thermal conductance can be controlled by changing the thickness of the membrane and its geometric design, by introducing, for example, slit holes or other phonon scattering structures. In Figure 8a, we show a photograph of an array of ultra-low-noise TES bolometers suspended with 4 diagonal thin and long legs with dimensions 340 \(\upmu\)m \(\times\) 0.5 \(\upmu\)m \(\times\) 0.25 \(\upmu\)m [101], giving a good example of how the SiN membrane is mechanically stable and flexible in its design. For X-ray calorimeters, the membrane has a rather simple squared geometry, typically without etched holes or slots. As an example, Figure 8b shows a microphotograph of thousands of squared pockets vertically etched through the Si carrier wafer of 300 \(\upmu\)m by using a deep-Reactive Ion Etching (RIE) process. It is possible, in this way, to create a uniform large array of the 0.5-\(\upmu\)m-thick SiN membranes with very high yield. The scalability towards an array of 100-kilo pockets is not considered a bottleneck [102].  
Figure 7: (**a**) Microphotograph of a 100\(\times\) 30 \(\upmu\)m\({}^{2}\) Ti/Au TES connected to the Nb superconducting leads and a schematic of a cutting view along with the dotted line. The separated leads are overlapped near the TES film but electrically separated with an oxide insulating layer, forming a microstripline. The figure was reprinted from [89] by permission from Springer Nature. Copyright ©2019, Springer Science Business Media. (**b**) Microphotograph of an Au X-ray absorber. The absorber is an overhanging structure supported by several stems that can be seen as dots on top of the absorber surface. The two of them located close to the center of the absorbers are thermally connected to the TES thermometer, not visible in the photo.  
A critical issue however remains the proper thermalization of the Si carrier structure to minimize the cross talk between the nearest neighbor pixels located on each pocket. This could become critical when large arrays need to be built. For a k-pixel array under development for X-IFU, it was show that the thermal crosstalk between pixels can be made smaller than \(2\times 10^{-4}\) (well within the requirements derived from the scientific goals of X-IFU) by coating the back-side and sidewalls of the Si grid structure with a layer of Cu or Au with an appropriate thickness [103].
## 2 Stochastic Modeling of Roof Snow Load  
According to EN1991-1-3:2003 [12], the snow load on roof is given by the following general expression:  
\[s=\mu_{i}C_{e}C_{t}s_{k}, \tag{1}\]  
where  
* \(s\) is the snow load on the roof;
* \(s_{k}\), depending on the location of the construction site, is the characteristic value of the ground snow load, having a probability of exceedance \(p=0.02\) in one year. \(s_{k}\) is derived from the ground snow load map of the pertinent European country, as provided in the National Annex to the Eurocode;
* \(C_{e}\) is the exposure coefficient, which defines the reduction or increase in the snow load on a roof of an unheated building caused by wind erosion. The exposure coefficient, depending on the roof's exposure to wind, is the ratio between the snow load remaining on the roof in the actual exposure condition and the snow load on a normally exposed roof. As already mentioned, three different wind exposure conditions are considered: sheltered (\(C_{e}=1.2\)), normal (\(C_{e}=1.0\)), and exposed (\(C_{e}=0.8\)). These depend not only on the terrain's roughness (e.g., the terrain category [20]) but also on the wind velocity at the construction site during the winter season [21].
* \(C_{t}\) is the thermal coefficient accounting for the variation of roof snow load as a function of the heat flux through the roof, which, in general, results in a melting of the snow cover and in a reduction in the roof load;
* \(\mu_{i}\) is the snow load shape coefficient, i.e., the ratio of the snow load on the roof to the undrifted snow load on the ground, disregarding the influence of exposure and thermal effects and depending on the roof's shape: flat, pitched, or curved  
According to [16], the shape coefficient \(\mu_{1}\) can also be considered as a function of the site's exposure conditions. A new formulation of \(\mu_{i}\) depending on \(C_{e}\) has been proposed in the new draft of prEN1991-1-3:2020 [17], leading to the following expression:  
\[s=\mu_{i}(C_{e})C_{t}s_{k}. \tag{2}\]  
The aim is to obtain a model better representing the snow load redistribution process due to the wind transport of snow particles from the windward to the leeward side [16], thus avoiding the inconsistencies resulting from the application of the current version of the standard in the case of exposed conditions, which leads to a general reduction of snow loads for both the leeward and windward sides of the roofs.  
When using Equation (1) or Equation (2), the exposure coefficient \(C_{e}\) and the thermal coefficient \(C_{t}\) are usually considered as deterministic parameters, while the ground snow load (for which measurements are available) and the shape coefficient are described by random variables. Figure 1 illustrates the stochastic distribution of the parameters in the chain defining the actions induced by snow on roofs.
# An Efficient and Accurate Adaptive Time-Stepping Method for the Landau-Lifshitz Equation
###### Abstract
[30] presented a family of high-order computation methods for the Landau-Lifshitz-Gilbert equation, using Gauss-Legendre quadrature to achieve arbitrary-order accuracy while preserving geometrical properties such as constant magnetization magnitude and Lyapunov structure, validated through theoretical analysis and numerical experiments. [PERSON] et al. [20] analyzed a second-order accurate, linear computational method for the LL equation with large damping parameters, and they provided a rigorous error estimate and addressed the stability challenges of the non-linear projection step, which ensures efficiency by solving only a linear system with constant coefficients at each time step. The primary purpose of this study is to propose an efficient and simple adaptive time-stepping FDM for solving the LL equation by strategically selecting time steps to maintain displacement within a tolerance, which enables larger steps near equilibrium for faster computations. Unlike traditional adaptive time-stepping methods, the proposed approach does not rely on an iterative process to determine the appropriate time step within a specified tolerance. Instead, the proposed method is a single-step method. [PERSON] and [PERSON] [29] developed two classes of length-preserving methods for the LL equation using distinct Lagrange multiplier approaches, including efficient higher-order predictor-corrector methods and energy-dissipative schemes, validated through numerical experiments and comparisons with existing methods. The structure of this paper is delineated as follows: Section 2 introduces the algorithm proposed in this study for the numerical solution. Computational experiments conducted with the proposed method are detailed in Section 3. The paper concludes with summarizing remarks in Section 4.
## 3 Results and Discussion
### Topic 1: Health Status and Needs
#### 3.2.2 Impacts of FIFO Work Arrangements on Health Behaviours  
In terms of physical health, it has been reported that while working away (on-shift), FIFO workers in the resource sector may have limited access to healthy food or make poor dietary choices and may engage in limited physical activity. In the resource sector, there have also been reports of risky behaviours, such as excessive alcohol [9] and tobacco consumption [10] at levels that are above those of the general population. A 2020 study [11] of oil and gas workers in the United States reported that the prevalence of excessive alcohol use was 30%. Furthermore, a systematic review of health behaviours in the resource sector, inclusive of the oil and gas industry, found that while on-shift, workers were more likely to drink alcohol excessively, smoke tobacco, and have a poor diet [2]. Frequent travel and being away from home are also related to fatigue and sleep disturbances, with more than 20% of FIFO workers reporting moderate-severe sleep disturbances [12]. FIFO workers generally sleep less and have lower-quality sleep while on-shift compared to when at home [2], which may impact their work performance and safety.
# EDM of D2 Steel: Performance Comparison of EDM Die Sinking Electrode Designs
###### Abstract  
Electric discharge machining (EDM) of tool steel (D2 grade) has been performed using different tool designs to produce through-holes. Machining performance has been gauged with reference to machining time, hole taper angle, overcut, and surface roughness. Inaccuracies and slow machining rate are considered as the most common limitations of the electric discharge machining (die-sinking). Traditionally, a cylindrical tool is used to form circular holes through EDM. In this study, the hole formation is carried out by changing the tool design which is the novelty of the research. Two-stage experimentation was performed. The newly designed tools substantially outperformed a traditional cylindrical tool, especially in terms of machining time. The main reason for the better machining results of modified tools is the sparking area that differs from the traditional sparking. Comparing against the performance of a traditional cylindrical tool, the newly designed tools offer a considerable reduction in the machining time, radial overcut, and roughness of the inside surfaces of machined holes, amounting to be approximately 50%, 30.6%, and 38.7%, respectively. The drop in the machining time along with a condensed level of radial overcut and surface roughness can shrink the EDM limitations and make the process relatively faster with low machining inaccuracies.  
EDM; D2 steel; reliefangle; land thickness; modified tools; machining time; hole taper angle; overcut; surface roughness +
Footnote †: journal: Applied Sciences
# Education and Training in Applied Remote Sensing in Africa: The ARCSSTE-E Experience
## 1 Introduction
### The Post Graduate Diploma (PGD) Programme
In a previous \"post-training\" survey that was conducted in 2012 by ARCSSTE-E, it was observed that a large percentage of alumni reside in Africa, while a few that are abroad are either working with international organisations or undertaking further studies [10]. They all attested to the positive impact of the PGD programme on their careers. Figure 2: Annual distribution of students in the PGD programme of African Regional Centre for Space Science and Technology Education in English (ARCSSTE-E) from 2001 to 2018. Each bar shows the number of females relative to males. Figure 3: Distribution of participants of the Postgraduate Diploma (PGD) programme of ARCSSTE-E according to countries from 2001 to 2018. A strong component of the course content is to meet regional and global interests and development agendas. At the regional level, ARCSSTE-E is contributing to the 'Agenda 2063' programme [11] of the African Union, particularly Goal 2 (well-educated citizens and skills revolution underpinned by science, technology, and innovation), Goal 6 (blue/ocean economy for accelerated economic growth), and Goal 7 (environmentally sustainable and climate resilient economies and communities) [12]. Further, a key component of the African Space Policy [13] and the African Space Strategy [14] is capacity development in earth observation, as well as other thematic areas. ARCSSTE-E has also positioned itself to meet this need. At the global level, ARCSSTE-E is contributing to meeting the Sustainable Development Goals (SDGs) of the United Nations through indigenous capacity development. Specific SDG-related courses that are offered in the PGD programme include Sustainable Development and Carrying Capacity; Urban Planning; and, Cost-Benefit Analysis. Furthermore, ARCSSTE-E is contributing to the Sendai Framework for Disaster Risk Reduction 2015-2030, through its course on 'Natural Disasters and Management'.
# Motion Characteristics of Self-Sensing Piezoelectric Actuator for Yarn Micro-Gripper
## 2 Structure and Modeling of the Self-Sensing Micro-Gripper
### Design and Modeling of Self-Sensing Circuits
#### 2.3.3 Self-Sensing Parameter Identification  
For the circuit designed in this paper, the feedback capacitor \(C_{0}=1\)\(\mu\)F was chosen. The other parameters (\(i_{BIAS}\), \(R_{FP}\)) of Equations (17) and (18) need to be further determined, and the following steps describe the identification process.  
When the piezoelectric ceramic is not subjected to an external force and no external voltage is applied, no current passes through the piezoelectric material; the rise in output voltage at this point is caused by the bias current of the operational amplifier chip. Measuring the rate of change of \(V_{out}\) over a few tens of seconds gives \(i_{BIAS}\approx 47\) pA.  
From the electrical model of the BPBA, it can be seen that in the case of an external force \(F=0\) and a constant voltage \(V_{in}\
eq 0\), the electrical model of the piezoelectric ceramic can be simplified to a leakage resistance. An external resistor is connected in series with the piezoelectric ceramic. After a few hundred seconds, when the creep effect of the piezoelectric ceramic itself becomes negligible, the value of \(R_{FP}\) can be measured by the  
Figure 5: Electronic circuit schematic of self-sensing.  
voltage division method. The identification can be repeated and averaged for different values of the leakage resistance and the external resistance. For our actuator, we found \(R_{FP}\) = 1.02 M\(\Omega\).
## 3 Proposed Methodology
### Performance Evaluation  
The cross-validation strategy is carried out to improve the robustness and validity of our performance evaluation and determine the result of each dataset. The performance metrics, namely sensitivity (Sn), specificity (Sp), accuracy (AC), and F-measure, are used to evaluate the classification efficacy of this study.  
The sensitivity (true positive rate) measure (Equation (15)) is the proportion of the number of correct powerful texture signs for classification, which enhances and segments breast cancer mammogram images found in the dataset and heavily relies on false negatives. The ratio of identified positive cases to actual positive cases increases and the rate of false negatives decreases when sensitivity increases. The specificity (true negative rate) measure (Equation (16)) denotes the proportion of the number of correct powerful texture signs for classification that enhances and segments breast cancer mammogram images to the total number of real images found in the dataset. Accuracy (classification rate) (Equation (17)) denotes the correctness of the proposed detection method. Accuracy indicates the correctness of the predicted evaluation results. Finally, the F-measure is calculated using Equation (18).  
\[Sensitivity=\frac{TP}{TP+FN}\times 100\% \tag{15}\]  
\[Specificity=\frac{TN}{TN+FP}\times 100\% \tag{16}\]  
\[Accuracy=\frac{TN+TP}{TN+TP+FN+FP}\times 100\% \tag{17}\]\[F-measure=\frac{2 TP}{2 TP+FN+FP}\times 100\% \tag{18}\]  
Here, true positives (TP) are correctly identified disease cases, false positives (FP) are incorrectly recognized disease cases, true negatives (TN) are correctly recognized healthy cases, and false negatives (FN) are incorrectly recognized healthy cases.  
A receiver operating characteristic (ROC) graph is used as a representation of the comparative trade-off between costs (false positives) and benefits (true positives). A point on the ROC curve represents sensitivity and specificity corresponding to a decision. The following equations show the way of calculating the FP rate and the TP rate:  
\[FP\ Rate=FP/(FP+TN) \tag{19}\]  
\[TP\ Rate=TP/(TP+FN) \tag{20}\]