esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods  
### Defects of WTBs and Detection Methods  
#### 2.1.1 Defects  
The main requirement for WTBs is the long fatigue life. Therefore, the structure has to sustain 108-109 load cycles before failure if the turbine is designed for 25 years of operation [33]. Taking that into account, the polymer composites, which are made of strong, stiff, lightweight, corrosion-resistant, dielectric materials, are the most practical and prevalent choice for the manufacture of the WTBs [33]. However, due to the ever-increasing WT dimensions of blades, the gravity and inertia loads keep dominating against aerodynamic loads. The gravity loads being reversible per each rotation cycle are significant sources of WTBs fatigue damage, while the aerodynamic loads are responsible for the generated energy. Therefore, to reduce the weight of the WTBs, the complex design from fibre composites with sandwich structures joined by adhesive bonding is used. Usually, WTBs are made from glass fibres/epoxy composites and polymer foams or balsa wood sandwich structures [17; 19; 33]. Natural composites and hybrid composites adding carbon, aramid, or basalt fibres are also possible [34]. Another important aspect worth paying attention to when choosing materials for blades is sustainability which affects the new trend to use more easily recyclable thermoplastic resins [35; 36]. Production defects, transportation and installation damages, and irregular loading, including cyclic stresses, moisture and temperature variations, lighting strikes, and bird impact, cause complex processes of damage formation and an increasing number of damages and a variety of failure mechanisms [37; 38]. Different degrees of blade failures take about 5% of total wind turbine faults [39]. [PERSON] [37] identified different types of damage of WTBs: skin/adhesive debonding, adhesive joint failure between skins, sandwich debonding, delamination driven by buckling, splitting along fibres, gelcoat cracking, and gelcoat/skin debonding. The damages of composite materials can be classified as surface erosion, nonstructural damages, and delamination; structural damages as adhesive layers debonding and fibre failure [33]. The common damage modes in WTBs are shown in Figure 2.  
Production defects such as wrinkles, fibre misalignments, and voids can cause independent or interactive damage growth and initiation of failure mechanisms such as matrix cracking with fibre bridging, interface cracking, fibre fractures, translaminar cracking, delamination, debonding, micro buckling, etc. [40]. Not all defects proceed with failure problems during the estimated lifetime; thus, the ability to reliably predict the damage growth mechanism remains important. Prediction of the damage accumulation and fatigue life can be based on different approaches such as empirical or micromechanical modelling, while the degradation depends on the stress-strain state at the defect zone.
## 2 OBS Networks
### 2.3. QoS Support  
Different applications have different requirements regarding the handling of their traffic in the network. For instance, applications such as voice and video cannot tolerate long queuing delays and may need to be given higher priority than regular email or browsing traffic, which are called _elastic_ applications. This is a significant issue for any supporting transport network since current IP provides only _best effort_ service, while some mission-critical and real-time applications might require high QoS support with low values of delay, jitter and loss. To provide a feasible paradigm for the next IP over optical backbones, it is desirable that OBS architectures support different classes of user traffic. In  
Figure 4: Reservation and release schemes in OBS.  
OBS it is not possible to adopt neither the solution used in wavelength routed (WR) networks, nor the QoS developments for IP networks. In fact, while in WR networks the optical connections between ingress and egress pairs are treated as virtual links where QoS mechanisms developed for IP networks can be directly applied, in OBS, wavelengths are statistically shared among many connections between different pairs of nodes, leading to a finite burst loss probability on the transmission path which renders the WR approach unusable. QoS developments for IP can hardly be adapted to OBS networks since they rely on intensive buffering and electronic processing for service class isolation, while in OBS buffering is minimal and optical processing is still in its infancy. Therefore, intrinsic OBS features present new challenges for QoS provisioning, requiring the participation of several network entities along the end-to-end paths. Considering the separation between control and data planes, several new algorithms were proposed in literature, either taking advantage of the offset times between CPs and data bursts along with reservation, scheduling and contention resolution policies, or by exploring traffic engineering approaches like routing and load balancing (see Figure 5). While the first are mainly node-based mechanisms used for QoS differentiation, the later are edge-to-edge mechanisms within the core network to facilitate end-to-end QoS provisioning [27]. The work reported here can be classified in this second category.
# Recent Progress on Irradiation-Induced Defect Engineering of Two-Dimensional 2H-MoS2 Few Layers
## 2 Charged Particle Irradiation
### Swift-Heavy Ion Irradiation
#### 2.1.8 C\({}_{60}\) Ion Irradiation  
[PERSON] et al. [104] irradiated MoS\({}_{2}\) bulks under 20-40 MeV C\({}_{60}\) ions at 300 K. Irradiation was performed at normal incidence and at high fluences (\(3\times 10^{11}\) ions/cm\({}^{2}\)), as well at a grazing incidence of 20\({}^{\circ}\) and a low fluence of 10\({}^{9}\) ions/cm\({}^{2}\). The structural modifications occurred in the vicinity of the projectile paths.
## 4 Experimental Results and Analysis
### Result of Cheating Tool Detection  
To verify the effectiveness of the proposed Lightweight-YOLOv5-CA object detection network, a 7600-image overhead perspective abnormal object dataset and an 8899-image horizontal perspective abnormal object detection dataset were divided into training, validation, and test sets in a 7:1:2 ratio. In the same experimental environment, a comparative experiment was conducted on the detection performance of traditional YOLOv5 and Lightweight-YOLOv5-CA models. Table 5 presents the abnormal object detection results of the two models under the two perspectives. Through comparison, it can be seen that the Lightweight-YOLOv5-CA model under overhead and horizontal perspectives has better mAP50:90 detection results for books, papers, people, and mobile phones than the traditional YOLOv5 model, with average accuracy being 0.5% and 1.1% higher, respectively. Compared to YOLOv5, the number of parameters has been reduced by 3.5%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type of Gaze Direction** & **Gaze Direction** & **Accuracy** & **Type of Gaze Direction** & **Gaze Direction** & **Accuracy** \\ \hline \multirow{3}{*}{\begin{tabular}{c} Abnormal \\ (overhead \\ Perspective) \\ \end{tabular} } & Left & 90\% & Abnormal & \begin{tabular}{c} Uplett \\ (horizontal perspective) \\ \end{tabular} & Up & 100\% \\  & \begin{tabular}{c} Down \\ 83\% \\ \end{tabular} & \begin{tabular}{c} 100\% \\ \end{tabular} & \begin{tabular}{c} Uplett \\ (horizontal perspective) \\ \end{tabular} & \begin{tabular}{c} 92\% \\ \end{tabular} \\  & \begin{tabular}{c} Downright \\ 83\% \\ \end{tabular} & \begin{tabular}{c} Normal \\ \end{tabular} & \begin{tabular}{c} Normal \\ \end{tabular} & \begin{tabular}{c} Normal \\ \end{tabular} & \begin{tabular}{c} 100\% \\ \end{tabular} \\  & \begin{tabular}{c} Downright \\ 79\% \\ \end{tabular} & \
\begin{tabular}{c} Average \\ \end{tabular} &
\begin{tabular}{c} 92.4\% \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy of different gaze directions.  
Figure 13: Confusion matrix of gaze direction recognition.  
Figure 14 shows an example of the detection result of the Lightweight-YOLOv5-CA model for cheating tools from the desktop perspective. From the detection results in Figure 10(a), it can be seen that the proposed cheating tool detection model for the overhead perspective can detect books, papers, and mobile phones that appear at different positions on the desktop, achieving the effective detection of cheating tools.
# Enzymatic Synthesis of Biobased Polyesters and Polyamides
## 3 Polyamides
For example, nylon 6,6 is produced by polycondensation of adipic acid and 1,6-hexanedamine, while nylon 6 is typically produced by ring-opening polymerization of \(\varepsilon\)-caprolactone. Regarding the equipment and the reaction conditions followed, the polymerization steps in polyester and polyamide synthesis are similar [57]. However, with respect to the formation of high molecular weight products, the polymerization of polyamides differs from that of polyesters to some extent. Firstly, the chemical equilibrium is favored for the amide formation but is less favored for the ester formation. Secondly, when dicarboxylic acids are used as starting materials, salts are formed in polyamide synthesis, but there is no salt formation in polyester synthesis. In this case, stoichiometric equivalence can be much more easily achieved in polyamide synthesis. Thirdly, the amide interchange reactions (transamidations) are much slower than the ester interchange reactions (transesterifications). Currently, some biobased polyamides are already commercially available, including fully biobased nylon 4,10, nylon 10,10 and nylon 11, and partially biobased nylon 6,10, nylon 10,12 and PA 10,T, and so on (Table 2).
## 3 Modeling and Simulation
### Simulation Results
#### 3.3.1 Simulation Data with Different Braking Intensity  
The braking intensity ranged from 0.3 to 0.5 at the interval of 0.05 when the initial pressure of the accumulator was kept as 19 MPa and the initial braking speed was kept as 300 rpm. Firstly, the accumulator was charged to 19 MPa. Secondly, the flywheel was separately driven to the speed of 300 rpm by the accumulator. The speed of 300 rpm of the flywheel was equivalent to the 120 km/h of the selected vehicle. Thirdly, the flywheel was braked with the different braking intensity of 0.3-0.5 at the interval of 0.05. The obtained evolutions of speed of the flywheel and those of pressure of the accumulator were shown in Figure 5a,b, respectively.  
It could be observed that the simulation process could be divided into three periods. In the first period, the flywheel was driven by the accumulator. Speed of the flywheel was increased along with release of the hydraulic energy in the accumulator. In the second period, the vehicle was braked. The mechanical energy of the flywheel was transferred to the hydraulic energy of the accumulator. In the third period, the flywheel stopped. Speed of the flywheel was back to 0 rpm and pressure of the accumulator was tending towards stability. The braking time, change of the kinetic energy, recycled  
\begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Operating Mode**} & \multicolumn{3}{c}{**Parameters**} \\ \cline{2-4}  & **Braking Intensity** & **Initial Pressure of the Accumulator (MPa)** & **Initial Braking Speed (rpm)** \\ \hline \hline \multirow{4}{*}{With different braking intensity} & 0.30 & 19 & 300 \\  & 0.35 & 19 & 300 \\  & 0.40 & 19 & 300 \\  & 0.45 & 19 & 300 \\  & 0.50 & 19 & 300 \\ \hline \multirow{4}{*}{With different initial pressure of the accumulator} & 0.40 & 19 & 300 \\  & 0.40 & 20 & 300 \\ \cline{1-1}  & 0.40 & 21 & 300 \\ \cline{1-1}  & 0.40 & 22 & 300 \\ \hline \multirow{4}{*}{With different initial braking speed} & 0.40 & 19 & 100 \\  & 0.40 & 19 & 150 \\ \cline{1-1}  & 0.40 & 19 & 200 \\ \cline{1-1}  & 0.40 & 19 & 250 \\ \cline{1-1}  & 0.40 & 19 & 300 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The corresponding parameters in the three series of operating modes.  
Figure 5: Simulation results with the different braking intensity: (**a**) Evolution of speed of the flywheel, (**b**) Evolution of pressure of the accumulator.  
energy amount, and the energy recovery rate with different braking intensity were calculated and summarized in Table 3.  
From Table 3, it could be found that effect of the braking intensity to the energy recovery rate was not linear, and energy recovery rate reached its peak value of 87.61% when the braking intensity was 0.4. Further increase of the braking intensity led to decrease of the energy recovery rate, because the target torque exceeded the maximum regenerative braking torque supplied by the accumulator, which indicated that the friction brake was introduced, and some kinetic energy was lost.
# Effects of Torso Pitch Motion on Energy Efficiency of Biped Robot Walking
## 1 Introduction
Then, the minimum total energy consumed by the joint actuators per unit distance is chosen as the optimization objective, and the gait parameters are determined by optimization. Finally, two sets of optimal gaits with different gait periods and different step lengths are generated. The main contributions of this paper are as follows:  
1. Propose a gait pattern generation method for biped robots with the torso pitch motion, define the cyclic gait of a biped robot with multiple gait parameters, choose the minimum total energy consumed by the joint actuators per unit distance as the optimization objective, and determine the gait parameters by optimization;
2. Generate the optimal gait of TPM and TKV for different step lengths and different gait periods and compare the energy consumption of the two gait patterns; the optimization results show that TPM is over 12% more energy-efficient than TKV;
3. Carry out parameter study. Although the parameters related to the torso motion with different gait periods and step lengths are various, the overall trend is that torso leans forward during the double support phase (DSP) and leans backward during the single support phase (SSP);
4. Compare the energy consumption of each joint and observe that the main factor of energy-saving in TPM compared with TKV is the reduction of energy consumption of the swing knee during DSP. The structure of the rest of this paper is as follows: The second section introduces the modeling of biped robots. In the third section, some gait parameters are designed to define the trajectories. Then, in the fourth section, the optimization of gait parameters is discussed. The optimization results of the optimal gait and the comparison between TPM and the TKV are given and discussed in the fifth section. Finally, the sixth section summarizes the paper.
# Developing a Digital Twin and Digital Thread Framework for an 'Industry 4.0' Shipyard
## 5 New Digital Twin and Digital Thread Framework Development
### Cyber Security Framework  
The next vital step is to set up and control cybersecurity for the twin/thread framework work to ensure cyber resilience (Figure 8). The cybersecurity protocol contains three essential elements: 1. robust policies to maintain safeguard; 2. technologies that comply with security control; and 3. training of staff to support organizational awareness [46]. Data security could be industry-specific and some industries might require more rigorous security measures than others. A measure that would ensure the safety of the informationin the twin/thread framework would be the implementation of ISO27001 [47]. ISO27001 is an international security standard developed to provide a model for establishing, implementing, operating, monitoring, reviewing, maintaining and improving an information security management system. These security measures could be implemented to all users who have access to the DTW on the server. Additional training is recommended to all users to ensure the utmost safety of the organizations and the information stored within the twin/thread framework. This is an integral stage in the framework's development, as this is what protects both the users and the suppliers from potential danger and IT crime [46].  
Identifying correct user access and the creation of an identity and access management protocol (IAM) for the user are the next stages of the framework's development. This involves setting up correct access and roles for the right users, ensuring that only the information and recourses that are needed by that user is accessed [48; 49]. User authorization needs a further authentication step to ensure the security of the data. This could be achieved through adapting strong or multi-factor authentication options such as the use of security questions or through email authorizations [48].
## 4 Evaluation Methods
### Performance Evaluation  
The proposed model is a regression model designed to predict numerical values and is evaluated using two metrics. The first metric is the adjusted coefficient of determination, which is one of the most well-known metrics for evaluating regression models. This metric indicates how well the model explains the variance of the dependent variable and is expressed as follows:  
\[R_{adj}^{2}=1-\frac{(1-R^{2})(n-1)}{n-k-1} \tag{5}\]  
\[R^{2}=\frac{\sum(y_{i}-\bar{y})^{2}}{\sum(y_{i}-\bar{y}_{i})^{2}} \tag{6}\]  
where \(y_{i}\) and \(\bar{y}\) are the target value and mean of the dataset, and \(\hat{y}\) is a prediction. \(n\) and \(k\) are the number of observation and predictors.  
The second metric used is the root mean square error (RMSE), which is expressed as follows:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\sum(y_{i}-\hat{y}_{i})^{2}} \tag{7}\]  
This metric represents the average squared difference between the actual values and the values predicted by the model, indicating how close the model's predictions are to the actual data.  
By using these two evaluation metrics, we aim to directly assess the model's explanatory power for the dependent variable and the prediction error.
## 2 Background
### Support Vector Machine with Linear Kernel  
Support Vector Machine (SVM) is a supervised learning algorithm for classification tasks. When using a linear kernel, SVM constructs a hyperplane that best separates the classes in the feature space. It maximizes the margin between the support vectors and the decision boundary [33]. In the case of SVM with a linear kernel, the decision function for classifying a new data point \(X\) is given by Equation (15):  
\[f(X)=\text{sign}(\sum_{i=1}^{n}\alpha_{i}y_{i}K(X,X_{i})+b) \tag{15}\]  
where \(n\) is the number of support vectors, \(\alpha_{i}\) are the Lagrange multipliers, \(y_{i}\) are the class labels, \(X_{i}\) are the support vectors, \(K\) is the linear kernel function, and \(b\) is the bias term. Detecting bolt loosening in rotating joints is a critical challenge in industrial settings [33]. This study employs Support Vector Machines (SVMs) for vibration-based detection in a custom sewer-cleaning vehicle transmission. The SVM proved highly effective, achieving a remarkable 92.4% accuracy [33]. Using information from accelerometers placed in strategic places, the SVM shows how reliable it is at finding faults in important industrial parts like rotating joints [33].
# TF-YOLO: An Improved Incremental Network for Real-Time Object Detection
## 3 Proposed TF-YOLO Network
### Multi-Scale Prediction Framework  
Small target detection is a significant challenge that commonly causes traditional detectors to fail. Multi-scale prediction is an important step towards understanding and inferring different objects, especially small targets, and their arrangements observed in a scene. This section presents an improved FPN-based multi-scale prediction framework and integrates it to a particular filter detector to address that problem. The major advantage of FPNs is that they produce a multi-scale feature representation in which all levels are semantically strong. As a result, predictions can be made on the finest level. In addition, it can be trained end-to-end with three scales and be used consistently during training and testing process. Therefore, FPNs are able to achieve higher accuracy without increasing testing time over the single-scale baseline.  
Figure 4: The workflow of connecting multiple layers.  
In the proposed work, several convolutional layers are added from the feature extractor. The last three predict a three-dimensional tensor encoding bounding box coordinates, object prediction, and class predictions [1]. Assuming that there are 10 classes in the experiment, and three boxes at each scale are predicted. Thus, the tensor is \(M\times M\times[3\times(4+1+10)]\), for four bounding box offsets, one object prediction, and ten class predictions.  
The proposed TF-YOLO network adopts the advances of the Darknet structure. The neural network is not deep, whereas, the features in different scales are merged in the proposed TF-YOLO network. It connects the feature maps with the same feature scale in the above mentioned Darknet structure. Meanwhile, the network extracts the feature map from two former convolution layers, followed by the up-sampling layer. Then the tensors above are connected together. In this way, the characteristics of the hidden layer, as well as the deep features can be extracted by the full-connection layer. Section 3.1 explains how to make hierarchical connections in detail. In the first layer, the size of the tensor is \(13\times 13\times 18\). Via two convolutional layers and an up-sampling layer, the tensor becomes \(26\times 26\times 18\), which predicts the second scale. This procedure repeats one more time, and the tensors becomes \(52\times 52\times 18\).  
In the proposed TF-YOLO network, these three scales are used to detect targets in various sizes. The network performs large-scale detection in \(13\times 13\) size map and detects the moderate-scale target in \(26\times 26\). The small target is detected in \(52\times 52\) size map. By connecting the multiple features of the same scale, the TF-YOLO network prominently promotes the ability to detect objects. The feature extraction workflow of the TF-YOLO network is shown in Figure 5.
# Matched-Filter Thermography
## 4 Matched Filtering in Thermography
### Thermal Coherence Tomography (TCT)
#### 4.2.2 Select TCT Experimental Results  
Figure 9a schematically shows the cross-section of a black plastic step wedge sample, simulating absorbers at different depths in a turbid medium. Figure 9b shows the conventional lock-in thermography phase image obtained at 3 Hz along with its mean profile over the steps. It can be seen that, although LI imaging can detect all the steps, it loses its depth resolution over deeper steps due to the diffuse nature of thermal waves. On the other hand, TCT imaging at the same modulation frequency and experimental conditions (averaging, laser power, etc.) maintains excellent resolution down to the deepest step in both peak delay time and phase images, Figure 9c,d respectively. This experiment clearly demonstrates that, compared to lock-in thermography, matched-filter thermography yields a more localized response in a diffusive field and improves the axial resolution while probing deep into the sample.  
Figure 9e depicts the optical image of the interrogated surface of a goat bone along with its cross section. The optical image shows that the spongy trabecular bone is covered by the more dense cortical bone on the surface. Figure 9f shows the TCT phase image obtained at 10 Hz. Due to the relatively short thermal diffusion length at this frequency, the phase image reveals the structure of the cortical bone. However, reducing the excitation frequency to 1 Hz, and due to the depth selective nature of matched-filter thermography, the underlying trabecular structure can clearly be revealed (Figure 9g).  
Figure 8: Theoretical CC (**a**) phase; (**b**) peak delay time (left and bottom axis) and amplitude (right and top axis) curves as a function of the subsurface absorber depth using properties of dental enamel: \(\mu_{a}\) = 100 [m\({}^{-1}\)], \(\mu_{s}\) = 6000 [m\({}^{-1}\)], \(g\) = 0.96, \(r\) = 0.65, \(k\) = 0.9 [Wm\({}^{-1}\)k\({}^{-1}\)], \(\alpha\) = 5 \(\times\) 10\({}^{-7}\) [m\({}^{2}\)s\({}^{-1}\)]. The numbers accompanying the curves determine the carrier frequency according to the inset of part (**a**) [22].