esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Active Fault-Tolerant Control Method for Configuration Transformation
### Fault-Tolerant Interval Inversion Method  
This section proposes an interval inversion model suitable for active fault-tolerant control during the configuration transformation. The method is based on reliability-based design optimization (RBDO), aiming to enlarge the tolerance range of controllable parameters while ensuring that structural reliability, design optimality, system redundancy, and local damage resistance criteria are all satisfied. To evaluate the tolerance range of parameters, we introduce a dimensionless indicator \(W\). This indicator reflects the constructability of the main cable configuration transformation process. The tolerance range of the controllable parameters is directly proportional to \(W\), meaning that a larger value of \(W\) results in a wider tolerance range for the parameters, ultimately enhancing the feasibility of the construction process. However, as the tolerance range increases, the design objective optimality (represented by performance indicators such as hanger tension uniformity and main cable shape accuracy) may slightly decrease. Therefore, a trade-off is necessary between constructability and design optimality. The proposed interval inversion model is shown as follows:  
\[\begin{array}{ll}find&D^{l}=\left[d_{1}^{l},\ d_{2}^{l}\ \ldots\,\ d_{n}^{l}\right]^{T},i=1,2,\ \ldots\ n\\ obj.&min\ f_{l}(D^{l}),j=1,2,\ldots,m\\ &min\ (-W)\\ s.t.&\beta(G_{k}(X,D^{l}))\geq\beta_{k}^{T},k=1,2,\ldots,r\\ &D_{l}\leq D^{l}\leq D_{u}\\ &W=\sqrt[n]{\prod_{k=1}^{n}\left(d_{k}^{w}/d_{k}^{c}\right)}\end{array} \tag{1}\]In the proposed interval inversion model, the assembly controllable parameters are represented as a set of interval variables \(D^{I}\), with each parameter having a lower bound \(D_{1}\) and an upper bound \(D_{\text{u}}\). These bounds are determined based on the tolerance requirements and the design space. \(X=[x_{1},\,x_{2},\,\ldots,\,x_{m}]^{T}\) is the \(m\)-dimensional vector of random variables accounting for stochastic uncertainties. Here, \(G_{k}\) is the limit state function; \(\beta\) and \(\beta^{T}\) are the corresponding reliability indexes and reliability constraint thresholds, respectively. For the \(k\)-th tolerance parameter \(d_{k}{}^{i}\), the parameters \(d_{k}{}^{w}\) and \(d_{k}{}^{c}\) refer to the midpoint and radius of the tolerance interval, respectively. The index \(f_{j}\) represents the performance of the inversion model. After introducing interval uncertainty, the reliability index fluctuates within the range \([\beta^{\text{L}},\,\beta^{\text{R}}]\). The model is solved iteratively, with each step determining the optimal values for the midpoint and radius of the tolerance intervals of the controllable parameters, ensuring that the system operates within the required reliability and safety margins.
# DEU-Net: A Multi-Scale Fusion Staged Network for Magnetic Tile Defect Detection
## 4 Experimental Setup
### Implementation Details  
1. Training Setup: the proposed network is built using the PyTorch framework 2.3. It was trained using the SGD optimizer with a momentum term of 0.9 and weight decay of 1 \(\times\) 10\({}^{-4}\) to train the model. Additionally, we set the initial learning rate to 0.01 and adopted a \"Poly\" decay strategy. All experiments were conducted on an NVIDIA GeForce RTX 3090 Ti 24 GB GPU (Produced by NVIDIA in Santa Clara, CA, USA, and purchase in China). The batch size was set to 8, and the maximum number of epochs was 160.
2. Evaluation Metrics: we assess the model performance using the Intersection over Union (IoU) and F1-Score. IoU assesses how well the predicted defect regions overlap with the true defect regions. This is crucial in defect detection where the precise localization of each defect can significantly impact the subsequent decision-making processes in quality control systems. These metrics are based on a confusion matrix, which includes four elements: True Positive (TP), False Positive (FP), True Negative (TN), and False Negative (FN). For each class, the IoU is defined as the intersection over the union of the predicted and true values, calculated as follows: \[IoU=\frac{TP}{TP+FP+FN}\] (6) The F1 scores for each category are calculated as follows: \[F1=2\cdot\frac{precision\times recall}{precision+recall}\] (7) where precision = \(TP\)/(\(TP+FP\)), and recall = \(TP\)/(\(TP+FN\)). In addition, MIoU represents the average \(IoU\) for all categories, and the \(F1\)Avg score is an average of the \(F1\) score for all categories. Flops (floating-point operations) denote the number of floating-point operations.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**BlowHole** & **Break** & **Crack** & **Fray** & **Uneven** & **Free** \\ \hline
115 & 85 & 57 & 32 & 103 & 952 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The number of various types of defects in the used dataset.  
Figure 6: Examples of magnetic tile surface defects, labeled with pixel-level ground truths (GTs).  
It reflects the operational efficiency of a model. In the experiments in this paper, Flops are compared among different models using an input image of size 320 \(\times\) 320. Smaller Flop values indicate better computational efficiency and lower time complexity for the model. Params represent the number of parameters included in the algorithm, reflecting the memory footprint of the algorithm. Smaller parameter values make it easier to deploy the algorithm on some mobile or embedded devices.
# Launch Dynamic Simulation of a Compressed-Air Launcher for Fire Suppression
## 4 Results and Discussion
### Influence of Initial Parameters
#### 4.2.3 Launch Angle  
A series of similar calculations were performed to discuss the effects of the launch angle. The calculation results show that the interior ballistic process is unaffected by the launch angles. The peak pressure was 9.3 MPa. The muzzle velocity was 189.89 m/s. The total barrel time was 9.1 ms. The gas mass increment in the barrel was 70.97 g. The gas transfer efficiency \(\eta_{gas}\) and launch efficiency \(\eta_{total}\) were 61.1% and 19.6%, respectively, when the nozzle diameter \(d\) was 30 mm, the gas cylinder volume \(V_{0}\) was 1.0 L, the initial pressure in the gas cylinder \(p_{0}\) was 10 MPa, the loading position \(l_{0}\) was 30 mm, and the launch angle \(\theta\) was between 20\({}^{\circ}\) and 70\({}^{\circ}\). The details of the main parameters at different launch angles are shown in Table 2.  
Therefore, it can be concluded that the nozzle diameter and gas cylinder volume have significant impacts on the launching process. A larger nozzle diameter contributes to a higher muzzle velocity and improved launch efficiency. Similarly, a larger gas cylinder volume leads to a higher muzzle velocity, but at the expense of a lower launch efficiency. The initial pressure in the gas cylinder demonstrates an approximate proportional relationship with the muzzle velocity. Meanwhile, the loading position has a slightly inverse effect on the muzzle velocity. Interestingly, the launch angle has a minimal influence on the overall launch process.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(\theta\) (\({}^{\circ}\)) & \(v_{0}\) (m/s) & \(p_{m}\) (MPa) & \(t_{g}\) (ms) & \(\Delta m\) (g) & \(m_{0}\) (g) & \(\eta_{gas}\) (\%) & \(E_{k}\) (J) & \(Q\) (J) & \(\eta_{total}\) (\%) \\ \hline
20 & 189.83 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9008.9 & 46,051.7 & 19.6 \\
25 & 189.90 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9015.5 & 46,051.7 & 19.6 \\
30 & 189.96 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9021.2 & 46,051.7 & 19.6 \\
35 & 189.92 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9017.4 & 46,051.7 & 19.6 \\
40 & 189.84 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9009.8 & 46,051.7 & 19.6 \\
45 & 189.84 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9009.8 & 46,051.7 & 19.6 \\
50 & 189.91 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9016.5 & 46,051.7 & 19.6 \\
55 & 189.96 & 9.3 & 9.1 & 70.98 & 116.1 & 61.1 & 9021.2 & 46,051.7 & 19.6 \\
60 & 189.91 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9016.5 & 46,051.7 & 19.6 \\
65 & 189.84 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9009.8 & 46,051.7 & 19.6 \\
70 & 189.84 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9009.8 & 46,051.7 & 19.6 \\ \hline Average value & 189.89 & 9.3 & 9.1 & 70.97 & 116.1 & 61.1 & 9014.2 & 46,051.7 & 19.6 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The main parameters at different launch angles when \(d\) = 30 mm, \(V_{0}\) = 1.0 L, \(p_{0}\) = 10.0 MPa, and \(l_{0}\) = 30 mm.  
Figure 12: Changes in the gas transfer efficiency and launch efficiency with different loading positions.
## 3 Results and Discussion
### Analysis of the Degree of Filler Dispersion  
In order to determine the values of the degree of dispersion (\(L\)) based on the torque property data provided in Table 2, we used Equation (5). Figure 2 illustrates the degree of MWCNT dispersion in the NR matrix. In comparison with the neat VNR matrix, lower values of L indicate better dispersion of the MWCNTs. According to Figure 2, the pure VNR sample has an \(L\) value equal to zero; the lower the \(L\) value, in comparison with the pure matrix, the better the dispersion of MWCNTs in the VNR-based nanocomposite. The L values for the different VNR/MWCNT nanocomposite specimens were below the value for the NVR matrix, demonstrating good dispersion of the MWCNTs. As compared to the pure VNR matrix, the NVR/MWCNT1 samples have a lower \(L\) value (\(-\)0.21) compared to the VNR/MWCNT5 samples (\(-\)0.06). MWCNTs have a greater tendency to agglomerate because of van der Waals forces as the MWCNT concentration increases in the VNR matrix. A lower viscosity tends to reduce the minimum torque, facilitating the dispersion of fillers and intensifying the interaction between MWCNTs and NR. Physical crosslinks are created between the filler and the matrix as a result of their interaction, which increases the total crosslink density [62].  
\begin{table}
\begin{tabular}{l c} \hline
**Specimens** & **Flory–Rehner (10\({}^{-4}\) (mol cm\({}^{-3}\)))** \\ \hline
**VNR** & 1.86 \(\pm\) 0.03 \\
**VNR/MWCNT1** & 1.86 \(\pm\) 0.02 \\
**VNR/MWCNT2** & 2.11 \(\pm\) 0.02 \\
**VNR/MWCNT3** & 2.22 \(\pm\) 0.04 \\
**VNR/MWCNT4** & 2.42 \(\pm\) 0.04 \\
**VNR/MWCNT5** & 2.45 \(\pm\) 0.04 \\ \hline \end{tabular}
\end{table}
Table 3: Crosslink density of NR/MWCNT composites (as determined via the Flory–Rehner method).  
Figure 1: (**a**) Variation in \(\rm Q_{f}/Q_{g}\) as a function of \(\rm e^{-Z}\) for VNR/MWCNT nanocomposites. (**b**) Effect of MWCNTs on \(\rm Q_{f}/Q_{g}\) of VNR/MWCNT nanocomposite specimens.
## 2 Preparation and Applications of Chitosan-Based Heterogeneous Catalysts
### Chemical Activation and Conversion
#### 2.1.1 Metal Catalysts
Satisfactory yields of biaryl product were obtained under both microwave and ultrasound irradiations in the catalysis reaction with 4-bromoacetphnone and phenylboronic acid. However, a decrease of catalytic activity was observed after the third reuse. A similar approach was also developed by [PERSON] _et al._[39]. His research group proposed the use of hexamethylendiisocyanate to cross-link chitosan and load Pd. The as-prepared catalysts were then employed for the hydrogenation of substrates of cyclohex-2-enone, benzalacetophenone, 1,2-diphenylacetylene, and \(N\)-benzylideneaniline at mild conditions using a microwave reactor. Catalysis test results showed that the solvent used in reactions had nearly no influence on the selectivity of the reaction but it showed a great dependence on the conversion. The catalyst could be reused for up to ten times for hydrogenation of cyclohex-2-enone without showing a dramatic loss in immobilized metal content. As magnetic material supported catalysts have revealed its insoluble and paramagnetic nature to enable easy and efficient separation from reaction mixture with an external magnetic field [5, 30, 35, 40], a novel magnetic chitosan-based catalyst was proposed by [PERSON] _et al._[5]. They used chitosan magnetite nanoparticles (chitosan-Fe\({}_{2}\)O\({}_{4}\)) to form Pd-based magnetic catalysts for fast hydrogenation of various aromatic nitro and acetalization of acrylonitrile under mild conditions. The catalyst was prepared by mixing the chitosan-Fe\({}_{3}\)O\({}_{4}\) nanoparticles with PdCl\({}_{2}\) in ethanol and then reduced by adding NaBH\({}_{4}\). While using methanol or NaBH\({}_{4}\) as reducing agents, it was expected to control the Pd particles onto chitosan within the nanoparticle range of 1-2 nm [24]. Catalysis results confirmed high activity of the as-prepared catalysts for the hydrogenation and acetalization reaction with almost complete conversion and 100% selectivity. Moreover, the catalyst could be recovered and recycled several times without significant loss in its activity, mainly due to the efficient stabilization of the active Pd species in the amine groups on the chitosan.
## 1 Introduction
Backstepping depicts another nonlinear structure, known for its robustness in terms of unmodeled dynamics and uncertainties, based on a Lyapunov approach by splitting the main system into subdivisions to achieve a suitable design [22]. In practical terms, backstepping has been implemented in a boost converter by the authors of [23], where the robustness and settling time have been improved; nevertheless, one of the main drawbacks of the backstepping is the complex design of the control law. Another non-linear technique is FLC, a practical, ruled-based approach in which the configuration is dependent on the operator knowledge with regards to the main system [24; 25]. FLC has been used in adaptive experimental DC-DC boost converters, where the inductor features changed to achieve high efficiencies and the outcomes showed significant enhancements [26]. In terms of PV, the research conducted by the authors of [27] was based on an analysis of the power forecast over a year, where they employed an adaptive neural fuzzy inference system and accurate results were achieved. Concerning MPPT for PV systems linked with boost converters, FLC was used by the authors of [28], where they showed that the outcomes developed a fast dynamic response and ensured the robustness. FLC has also been embedded for a PV battery system in a study based on an optimisation [29]; in this case, the state of charge and consumption were studied and the results revealed significant improvements. This research is focused on a type-1 FLC, which is known for its higher performance compared to conventional controllers due to its capability to manage uncertainties through rules [30; 31]. A VRE was designed with real data from the PV panel and was assembled with a type-1 FLC, an error-based controller used for tracking. The first comparison was performed against a P&O, as it is commonly used for the comparison of new MPPT controllers [32; 33]. The second contrast was executed with an SMC combined with the VRE to highlight the benefits of the proposed FLC. The drawbacks are related to sudden fluctuations around the maximum power point (MPP) due to sudden fluctuations in the irradiation or under partial shadowing conditions [33; 34]. This research paper is structured as follows: Section 2 describes the hardware used and its interconnection, and presents a theoretical explanation of the models applied for the PV and the boost converter with further details about the controllers handled for the MPPTtracking. Section 3 presents an experimental analysis of the PV features such as voltage-current and voltage-power curves, which are significant for generating a VRE. This section also concludes with the outcomes gathered with the implemented controllers. Finally, Section 4 comprises a summary of the highlights accomplished throughout this research.
## 3 BILSTM-Based Deep Neural Network Rock-Mass Classification Method
### Deep Neural Network Structure
#### 3.1.4 BILSTM-Based Deep Neural Network Structure Design  
Considering the influence of pre- and post-sequence MWD data on rock-mass classification, we designed a deep neural network with BILSTM as the core, as shown in Figure 7, supplemented by a fully connected layer to extract information. Batch normalization (BN) solves the internal covariate shift (ICS) problem and improves the model prediction performance [32]. The designed window selector facilitates learning more information and improves the model's generalization performance, while avoiding overfitting. The rock-class probability is output through the classifier module and the final prediction result is obtained through the 'Voter' module to ensure the accuracy of rock classification.  
The training and prediction processes of the neural network require taking a sample with a size of (\(n\), \(m\)), where \(n\) is the random window size of the window selector and m is the number of parameters, from the dataset. First, \(n\) eigenvectors with sizes of m are input into the two fully connected layers in turn; at the two fully connected layers, the activation function 'Relu' performs operations such as dimension reduction, rotation, translating, etc., thereby purifying the information related to the rock classes, and outputs (\(n\), \(k\)) samples, i.e., \(n\) vectors with sizes of \(k\), where \(k\) is the number of neurons in the output layer of the fully connected layer. It should be noted that the two fully connected layers share weights in a training batch, meaning that data at each depth in the training batch pass through the fully connected layer with the same weight. Next, the (\(n\), \(k\)) samples are input into the BILSTM hidden layer, and the information related to the depth-sequence in the learning samples is learned through bidirectional information transfer. Finally, the BILSTM output is passed into the last fully connected layer, whose activation function 'Softmax' obtains the rock-class probability; the final prediction result is obtained through 'Voter'.
# Survey on Wireless Technology Trade-Offs for the Industrial Internet of Things
## 4 Wireless Technologies for Industrial Applications
### Long Range Networks
#### 4.1.1 LoRa
In conclusion, the LoRa PHY allows long range, but the low network capacity and current class A MAC design restricts use cases to those with a combination of low data requirements, a majority of uplink traffic and overall traffic volumes that remain far below the theoretical capacity.
## 2 Materials and Methods
### Statistical Analysis  
Data were tested for normal distribution and variance homogeneity by Shapiro-Wilk test. After were subjected to a nested one-way analysis of variance (ANOVA) while using the SAS program. The independent variable was the aging time (1, 3, 6, 9, and 14 days). The mean values and standard error of the means (SEM) were calculated. When a significant effect (\(p<0.05\)) was detected, means were compared while using the Tukey's test.
# A Feasibility Study of the Drive-By Method for Damage Detection in Railway Bridges
## 4 Simulation Results
### Further Research Required  
The results reported in the previous section confirm that the drive-by method shows, when relying on numerical simulations, good performances in damage localization, and the highest amplitudes of the damage signal in the case of short bridges. Nevertheless, a few challenging items must be tackled for applications in a real railway environment, before this method can be tested in practice:  
* the difference between the accelerations generated on the train by healthy and unhealthy structures is in the order of \(10^{-2}\) m/s\({}^{2}\) or even \(10^{-3}\) m/s\({}^{2}\), which leads to potential operational difficulties [5]. The required sensitivity indeed conflicts with the full-scale range that is needed for the acceleration sensors placed on the wheelsets, whereas this issue is less severe for sensors  
Figure 21: Accelerations of the leading bogie; train speed 120 km/h, bridge #1. (**a**) Reduced ballast stiffness out of the bridge. (**b**) Constant ballast stiffness.  
Figure 20: Accelerations of the leading bogie in simulations with different track irregularities for the healthy and damaged bridge. TSR train, train speed 120 km/h. (**a**) Full-spectrum signal. (**b**) Damage signal.  
placed on the bogie frame. To overcome this drawback, high sensitive accelerometers should be placed on the mechanical suspensions (e.g., silicon pad or rubber layers), to reduce acceleration peak values by filtering all of the frequencies that are outside the range of interest. Such a configuration has already been adopted for selecting a specific frequency range in acceleration measurements on the axlebox [17].
* The method relies on the comparison between the current data acquisition and a reference \"healthy\" signal, so that the actual position of the train along the bridge must be known. A first solution may be to place a transponder at the beginning and at the end of the bridge, so that the actual position of the measuring point can be calculated at each time, based on the train speed value. A second solution may be the adoption of a geo-localization system for the train, based on high-performance GPS and odometry, which would allow for correlating acceleration data with the actual position of the train at the instant that they were acquired. This system is being currently developed in Europe for rolling-stock based diagnostics of the railway infrastructure [16; 18].  
Further investigation will also consider the development of a more refined bridge model, where a defect that is closer to reality can be introduced. This would allow for the quantification of the damage extent, which in this work has been simply considered as a stiffness reduction to perform a sensitivity analysis. Finally, it should be considered that deck torsion that associated with a left/right asymmetry of the defect calls for the consideration of the roll motion of train's vehicle as well.
# Augmented Reality in Precision Farming: Concepts and Applications
## 4 Findings
### How Is AR Currently within an Agricultural Setting?  
A limitation of this research is that, at the time of writing this article, there is a relatively low number of articles discussing the use of AR within an agricultural setting. Yet, while there is a reduced number of applied examples, there is a clear connections in the nature in which AR is adopted. It is important to reflect on the synergies as the deployment of AR solutions grow with the increasing use of digital twin and precision farming applications.  
Figure 5: MAR agricultural application framework adapted from [12].  
In terms of how AR is currently deployed within an agricultural setting, it is evident that applications are used to monitor, improve efficiency and to facilitate the scalability of operations within the domain. To summarise, Figure 6 displays a comparison of the discussed related works (both crop and livestock-based articles), the application and the corresponding cross-over in technology between the two within the aforementioned application domains.  
In short, AR has been deployed within 7 documented settings, namely to relay data back to the farmer concerning autonomous machinery [15], to provide crop overlay information [12] and crop disease identification [31], viticulture digitalisation [26], IoT data visualisation overlay onto crops [10], cow tracking within livestock management [13] and for use with information overlay onto smart glasses for general livestock management [16].