esa-sceva/satcom-chunk-collection huggingface dataset:
# Strengthening Polymer Concrete with Carbon and Basalt Fibres
## 3 Results and Discussion
### Mechanical Tests of the Samples for Compression  
In addition to examining the bending strength of the reinforced concrete samples, compressive tests were performed using the samples. Figure 7 shows a plot of the compressive strength of the samples as a function of the percentage of the fibre reinforcement in the polymer concrete beams.  
The results of the analysis of the graph, presented in Figure 7, allow one to conclude that, in the range of reinforcement percentage values ranging from 0.1 to 1.0%, a linear dependence of bending strength on the reinforcement percentage is observed for carbon fibre. For basalt fibre, the linear dependence is observed in the range of 0.1 to 1.5% of the reinforcement. Maximum strengthening is achieved for the carbon fibre reinforcement of 1.0% and for the basalt fibre reinforcement of 1.5%. During such strengthening with basalt  
Figure 6: The graph of the dependence of the ultimate bending strength on the reinforcement percentage.  
fibres, an increase in compressive strength by 15.4% is observed. During the reinforcement with carbon fibres, the increase in compressive strength is 10.1%.
## 3 Results
### Deployment Site-Keelung Harbor Offshore Cage  
1. Geographic Location: Offshore of Keelung Harbor, Northern Taiwan.
2. Experiment Purpose: To investigate the effects of cage aquaculture technology in marine environments and analyze the associated environmental challenges.
3. Experiment Content: The ROV monitored environmental parameters, such as temperature and salinity, while assessing the stability of the cage structure. Figure 9 shows the installation of cage nets in the open sea environment off Keelung Harbor, representing the challenging offshore testing conditions. The integration of multiple remote control technologies enabled operators to adjust the ROV's path in real-time, allowing for adaptation to dynamic changes in the marine environment.  
The Keelung offshore test showcased the system's performance in more challenging environments. Operators were able to select the most suitable control interface based on the prevailing communication conditions. The system successfully transmitted critical environmental parameters, such as temperature and salinity, in real-time, enabling operatorsto adjust monitoring strategies immediately based on this data. These results demonstrated the system's reliability and flexibility in offshore environments.
# Smart Inverters for Microgrid Applications: A Review
## 1 Introduction
What is more, the system can be more efficient and simple as there is no more need for AC-DC-AC converters. These converters are commonly used for wind or hydro energy conversion and they consist of two back-to-back bidirectional AC-DC converters with a DC link. This DC link decouples two 3-phase AC systems with different parameters enabling the maximization of the output power by using maximum power point tracking (MPPT) algorithms and the synchronization with the grid [12; 13; 23; 24; 25; 26]. The hierarchical control for DC microgrids is similar to the one proposed for AC but simpler. At the primary level, the droop control of DC microgrids only consists of voltage versus current droop in contrast to AC microgrids where active and reactive power droop controls must be provided. The secondary control regulates voltage deviations resulted from the primary control and restores the values to nominal levels, which is only applied at islanded mode. In grid-connected mode, the references comes from the grid parameters. Similar to AC microgrids the tertiary control regulates the power flow with a stiff DC grid at energy management level [13; 27; 28]. The power converters are able to play different roles when incorporated into a microgrid, namely grid forming, grid feeding and grid following. These are defined in [10] alongside with control structures at the converter level. Another fact that should be considered is that DGs in a microgrid are commonly placed far away from each other. Even though using various droop control schemes reduces the requirement of communication between inverters, a link is unavoidable if a good power sharing control scheme is desired. However, to implement fast communication links which can fulfill the requirements of this kind of applications and be sufficiently reliable can be very costly [10; 29]. Therefore, finding a long range and preferably low operating cost communication protocol to fill in for the current lack of effective bidirectional communication between inverters can be considered a very promising field for research and development. In this paper, a literature review on \"smart inverters\" and their application to microgrids is conducted. The \"smartness\" features are introduced and explained in detail. In each section, different methods and challenges regarding each of these indicators are addressed and are empowered by equations and tabular or illustrative information where required. The technological achievements, as well as present gaps, are introduced throughout the article. Finally, the discussion section presents the main conclusions drawn from the study and provides insights into possible paths for further research and development.
# Numerical Investigation of Thermal-Flow Characteristics in Heat Exchanger with Various Tube Shapes
## 4 Findings and Analysis
### Impact of Tube Shape on Flow Pattern and Local Temperature Distribution
For all Reynolds numbers, the \(\Delta\)P values decrease as the axis ratio increase where the amount of \(\Delta\)P for the circular tube is considered to be 17-23% higher than that for the O3 tube and about 38-48% higher than that for the O1 tube design, depending on the  
Figure 8: Comparison of oval tube shapes at varying Res: (**a**) thermal exchange rate, (**b**) convective heat transfer coefficient, and (**c**) Colburn factor. Reynolds number. This is due to the fact that the increase of tube axis ratio causes less energy to be received from the fluid pressure; thus, the value of the pressure drag force is reduced. Figure 9b depicts the variation of the friction factor \(f\) for all of the inspected shapes of oval tubes at various Reynolds numbers. For all oval tube patterns, a rise in the Reynolds number results in a drop in \(f\) values. The maximum velocity and pressure loss augment with increased Re, but the resulting \(f\) decreases due to the dependence of \(f\) on the ratio of the pressure drop to the square of the maximum velocity (see Equation (14)). In addition, we can observe that O4 shows the higher values and O1 shows the lower values of \(f\) in comparison to other configurations. In fact, the O4 tube shape provides a 20-23% higher friction factor compared to the O1.
# A Robust Localization System Fusion Vision-CNN
## 5 Experiments  
### Experimental Platform and Measuring Equipment  
In order to verify the performance of the algorithm, we built an autonomous mobile robot platform, Mir-UR5. The experimental platform is shown in Figure 14. Its hardware mainly included: a mobile robot MiR100, a six-degree-of-freedom manipulator UR5, and a depth camera Kinect V2. The mobile controller device was an Alienware computer, which was configured with a GTX1080 GPU, 32G RAM and an I9 CPU processor. The operating system was Ubuntu 18.04 and the Robot Operating System (ROS) framework was also used. The external measurement system is shown in Figure 14, which contains a Leica Geosystems Absolute Tracker (AT960) and laser tracker targets, with an absolute positioning accuracy that could reach 0.01 mm.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Case 1**} & **AMCL** & **The System** & \multirow{2}{*}{**Time–Cost Reduction**} \\ \cline{2-3}  & **Mean Time–Cost (ms)** & & \\ \hline case 1 & 10.442 & 7.258 & 30.49\% \\ case 2 & 10.883 & 7.286 & 33.05\% \\ \hline \end{tabular}
\end{table}
Table 3: The comparison of mean time–cost in case 1 and case 2, respectively.  
Figure 13: The comparison of time–cost. (**a**) case 1; (**b**) case 2.
## 3 Pavement Self-Sensing Technology Based on Conductive Polymer Composite Materials
### Factors Affecting the Electrical Conductivity of Polymer Composites  
#### 3.1.1 Polymer Matrix  
Polymer matrices commonly used in polymer matrix composites are broadly categorized into thermosets (e.g., resins [98]), thermoplastics, and elastomers (e.g., rubbers [99] and PDMS [100]). In the preparation of composite materials, a rational choice can be made based on the scope of application of the material and the purpose of research and development. Among them, resins are often used as the matrix because of their better force-sensitive properties and durability. Resin polymers mainly include epoxy resins, nylon (PA), polypropylene (PP), polyethylene (PE), high-density polyethylene (HDPE), ethylene glycol ethanediolic acid ester, polyethylene terephthalate (PET), and so on [101]. [PERSON] et al. [88] prepared two types of polymer composites using carbon fiber mats as the conductive material and two types of resins, flexible resin and 3201 vinyl resin (rigid resin), as the polymer matrix and comparatively investigated the effects of resin types on strain resistivity. The results show that due to the difference in Poisson's ratio, the spacing between the transverse joints of the flexible resin decreases to a greater extent, which slows down the magnitude of the increase in resistivity. Because epoxy resin has the advantages of high reactivity and strong cohesion, and its strength is adjustable and controllable, it can coordinate with the stiffness of asphalt pavements. Therefore, epoxy resin is an ideal matrix of sensitive polymer composites for road-monitoring applications.
## 3 A Wiener Process-Based Approach of RUL Prediction with Unequal Interval Data
### Linear Random Wear Modeling and RUL Prediction  
In this article, a linear wear model is established based on the Wiener process. The random wear process \(X(T)\) is described as:  
\[X(T)=X(0)+\theta T+\sigma B(T) \tag{8}\]  
where \(\theta\) is the coefficient of wear rate, \(\sigma\) is the diffusion coefficient, \(X(0)\) is the initial wear loss, \(B(T)\) is standard BM. In general, \(X(0)=0\).  
In order to predict the RUL of the device accurately, the FHT [15] is used to define the life of the device. \(X_{0:k}=X(T_{0:k})\) indicates the wear series of PCCS at \(T_{k}\). The RUL of PCCS is defined as:  
\[L_{k}=\inf[l_{k}:X(l_{k}+T_{k})\geq w|X_{0:k}] \tag{9}\]  
where \(w\) is failure threshold, \(X_{0:k}=\{X_{0},X_{1},X_{2},\ldots,X_{k}\}\) is the wear loss at \(T_{k}\).  
The model assumptions are widely used in [6; 16  
The Equation (12) has a detailed proof process in [8], it will not be repeated here. It can be seen that the RUL distribution predicted by the method in this article is an exact closed solution. The expectation of RUL prediction is described as:  
\[\begin{array}{l}E(L_{k}|X_{0\pm})=E(\frac{w-X_{k}}{\theta}|X_{0\pm})=\frac{w-X _{k}}{P_{ijk}}\exp(-\frac{\theta_{kk}}{2P_{ijk}})\int_{0}^{\hat{\theta}_{ijk}} \exp(-\frac{n^{2}}{2P_{ijk}})\\ =\frac{\sqrt{2}(w-X_{k})}{\sqrt{P_{ijk}}}D(\frac{\hat{\theta}_{ijk}}{\sqrt{2P_ {ijk}}})\end{array} \tag{13}\]  
where \(D(z)=\exp(-z^{2})\int_{0}^{z}\exp(u^{2})du\) is Dawson integral. In particular, if \(\Pr(\theta<0)=0\), the \(D(z)\approx 1/2z\), then \(E(L_{k}|X_{0\pm})=(w-_{k})/\hat{\theta}_{kk}\).  
The model parameters \(\mu_{\theta},\sigma_{\theta}^{2},Q,\sigma^{2}\) are unknown. In order to obtain a more accurate RUL prediction for PCCS, the wear data \(X_{0\pm}\) are used to update the model parameters continuously.
## 1 Introduction
The supervised-learning models (MLC, SVM, KNN, MDC, CART, LR, ANN, RFC, SAM, MDM, PLC, MARS, MC, CA, CNN, RNN, and LSTM) are considered to be more accurate than the unsupervised-learning models (KM, FCM, and ISODATA). The unsupervised learning is performed with no prior information about the data, and there are no training data available for training the unsupervised algorithms. It performs the LU/LC classification by learning the data without any class labels. The advantage of unsupervised algorithms helps in finding the unknown patterns in the image, which are more difficult to find by using the normal method. The results of the unsupervised classification algorithms were used as the input training data for the supervised algorithms. The advantage of using the unsupervised methods (KM, FCM, and ISODATA) is that they help in separating the similar and dissimilar pixels into clusters through the distance functions. The disadvantage of the unsupervised-learning model is the high computational time when the data are unstructured. The main disadvantage is that unsupervised algorithms are not used during the process of LU/LC prediction analysis since it requires both past and present training data. The supervised learning depends on the user-defined training data for classifying the LU/LC classes. The MLC, SVM, KNN, MDC, SAM, MDM, CART, MARS, and PLC techniques were widely used for classifying the LU/LC classes. The models based on LR, ANN, MC, CA, CNN, RNN, GRU, and LSTM were widely used during the process of LU/LC prediction analysis. The supervised classifiers help in providing the results by using previous experiences. The real-world computation problems were solved by using supervised-learning methods. It performs the classification and prediction with the knowledge of class labels. The supervised-learning models were used during the process of LU/LC prediction analysis. The past and present training data have been analyzed and processed in supervised learning. The accuracy results of the standard classification and prediction algorithms differ for each study area. The results mainly depend on the training parameters and the complexity of the input data. In terms of LU/LC analysis, the misclassification rate has been observed due to the overlapping of pixels in the satellite image. In all the neural network models, the time taken for training and validation is more for massive datasets. The disadvantage of the standard LU/LC machine-learning model is lacking knowledge of the predicted map, resulting in difficulties for the urban planners when further processing the data [44; 45; 46]. The rest of the paper is given as follows: Section 2 explains the motivation and contribution of this work. Section 3 explains the proposed methodology of this research work. Section 4 explains the materials and methods proposed in our research work, Section 5 provides the training parameters and validation results of each method used in this research work, Section 6 explains the comparative analysis of our LU/LC prediction model, and Section 7 delivers the conclusion of this research work.
## 5 Discussion
### Comparison with State-of-the-Art Models  
In order to emphasize the originality and effectiveness of our CNN-ResBiGRU model, we performed a comparative assessment of its performance against other cutting-edge models often used for time-series classification tasks, including InceptionTime [42], DeepConvLSTM [43], and ResNet [44]. InceptionTime is a deep learning framework that draws inspiration from the Inception-v4 model, which is well known for its outstanding performance in various time-series categorization tasks. This model has several convolutional layers with varied kernel sizes and mixes their outputs to collect characteristics at different time scales. The DeepConvLSTM model is a well-recognized architecture that integrates convolutional layers with LSTM cells. This allows it to effectively collect spatial and temporal features from time-series data. ResNet, first developed for image classification, has been modified for time-series classification by substituting 2D convolutions with 1D convolutions. The incorporation of residual connections effectively tackles the issue of the vanishing gradient problem, enabling the successful training of networks with increased depth.  
In order to guarantee an equitable evaluation, we included these cutting-edge models and trained them on the same Myogym dataset, employing the five-fold cross-validation  
Figure 7: Comparative results of the proposed CNN-ResBiGRU using different types of sensor data.  
procedure outlined in Section 3.3.4. The effectiveness assessment of our CNN-ResBiGRU model in comparison with InceptionTime, DeepConvLSTM, and ResNet on the Myogym dataset is shown in Table 9.  
The data shown in Table 9 illustrate the exceptional efficacy of our CNN-ResBiGRU model in comparison to cutting-edge models such as InceptionTime, DeepConvLSTM, and ResNet when considering different combinations of sensor modalities. CNN-ResBiGRU routinely achieves superior performance compared to these models in situations that include accelerometer, gyroscope, IMU, EMG, and the combination of IMU and EMG data fusion. Our model efficiently preserves spatial and temporal interdependence in the sensor data by integrating convolutional layers and residual BiGRU blocks. This is achieved by the architecture's design, which incorporates residual connections and layer normalization. Furthermore, incorporating IMU and EMG data offers further information, enabling our model to acquire additional distinctive characteristics for precisely identifying gym exercises. The findings demonstrate the originality and efficiency of CNN-ResBiGRU in using multimodal sensor data to achieve reliable and precise categorization of gym workouts. This establishes it as a promising method in this field.
# Fuzzy Multivariate Regression Models for Seismic Assessment of Rocking Structures
## 2 Methods  
### Examined Structural Systems  
Various types of structural and nonstructural elements, such as bridge piers, ancient columns, and electrical equipment, can exhibit a rocking response when subjected to seismic excitation. The rocking response is described by the following equation of motion [9]:  
\[\ddot{\theta}=-\mathrm{p}^{2}\bigg{[}\sin(\mathrm{sgn}(\theta)\alpha-\theta) +\frac{\alpha_{\mathrm{g}}}{\mathrm{g}}\cos(\mathrm{sgn}(\theta)\alpha-\theta )\bigg{]} \tag{1}\]  
where \(\alpha_{\mathrm{g}}\) represents seismic acceleration, g is the gravitational acceleration, and \(\alpha\) is the slenderness of the rocking blocks. The \(\mathrm{sgn}(0)\) represents the signum function applied to the ground acceleration. Finally, the frequency parameter \(\mathrm{p}\) is defined as follows:  
\[\mathrm{p}=\sqrt{\frac{3\mathrm{g}}{4\mathrm{R}}}\sqrt{\frac{1+2\gamma}{1+3 \gamma}} \tag{2}\]  
where R represents the semi-diagonal length of the piers, and the variable \(\gamma=\mathrm{m_{b}}/\mathrm{Nm_{c}}\) indicates the relative mass of the deck, where \(\mathrm{m_{b}}\) is the mass of the deck, N is the number of rocking piers, and \(\mathrm{m_{c}}\) is the mass of an individual rocking block in the absence of a cap beam, i.e., the case of a single rocking block \(\gamma=0\).  
Throughout the rocking response, energy dissipation is instantaneous and takes place solely at impact moments when the tilt angle of the rocking piers changes sign. Solving the impact problem yields the ratio between the post and pre-impact rotational velocity. For the rocking frame, this ratio, denoted as the coefficient of restitution (COR), is expressed as follows [18]:  
\[\mathrm{COR}=1-2\sin^{2}\alpha\frac{1+4\gamma}{\frac{3}{3}+4\gamma} \tag{3}\]  
In the present study, three representative structures of each category are examined. \"Structure 1\" represents a bridge pier consisting of rocking columns with a height (H) of 9.6 m and a width (B) of 1.6 m [9]. \"Structure 2\" corresponds to the columns of the Temple of Aphaia, located in Aegeina, with H = 5.27 m and B = 0.99 m [44]. Lastly, \"structure 3\" refers to a refrigerator with H = 2.13 m and B = 0.635 m [45]. In Figure 1, the examined free-standing structures are illustrated. Moreover, the model parameters are provided in the same figure.  
It is worth noting that if the rocking rotation exceeds the slenderness of the rocking block, the system overturns. The ratio of the maximum developed rocking rotation to the slenderness of the rigid block consists of a representative and interpretable damage index.
# Validation of Cable-Driven Experimental Setup to Assess Movements Made with Elbow Joint Assistance
## 1 Introduction and Literature
Considering the low cost and strength, an experimentally tested [30] nylon-braided cable is used in the setup for joint actuation. The study focuses on recording the tension in the cable and validating the results with a static model. In the context of rehabilitation, where device actuation occurs at low speed, the effects of inertial forces can be neglected. Thus, the current work predominantly considered a static model instead of a dynamic model to present force characteristics. The model assumes that the cable is always in tension and inelastic. This model is then used to assess the effects of reaction forces at the elbow and moments at the anchor points. The model assumes elbow flexion occurs in the sagittal plane, excluding any shoulder movement, which is also reflected in the experimental setup. Key inputs include the total weight supported by the exosuit (including external load), forearm length, and the distances from the joint to the anchor points. The model calculates the required holding torque for various flexion angles, cable tension, and cable length, as well as reaction forces at the elbow joint and moments at the anchor points caused by cable tension. Studies on bearable limb compression pressures and forces [31; 32; 33] help to ensure that the forces predicted by the model result in a comfortable exosuit design. Additionally, the calculated torque values can guide motor selection for the actuation mechanism, while the relationship between cable length and flexion angle informs motor control for precise elbow positioning. The findings of this study can improve the design of cable-driven exosuits, which can assist stroke patients in performing repetitive joint exercises necessary for recovery and functional independence. Based on the results, the study also investigated optimal anchor point positions for various loads, aiming to improve tension, reaction forces, moments, and human comfort. It is expected that optimized physical variables will also improve the overall efficiency and performance of the suit [34]. The research objectives are  
* To validate the experimental cable tension with a mathematical model during cable-driven elbow actuation for different anchor point positions. * To comprehend reaction forces experienced at the elbow joint and moments at the anchor point attachments with the observed tension values. * To optimize the anchor point position from the elbow joint to achieve better performance and wearer's comfort. In Section 2, the experimental setup and the static model are presented. In Section 3, the experimental data are presented. Also, from the results and data, an optimization is performed to identify suitable anchor point positions. In Section 4, the significance of these results in the development of a cable-driven exosuit is emphasized. Finally, in the conclusions, Section 5, the key insights are summarized.