esa-sceva/satcom-chunk-collection huggingface dataset:
# Vision-Based Concrete-Crack Detection on Railway Sleepers Using Dense U-Net Model
## 4 Results and Discussions
### Quantitative Results  
In this section, we present the quantitative results achieved by our proposed Dense U-net model. For verifying the performance of our proposed model, we calculated _Accuracy_, _Recall_, _Precision_, F1-_score_, _IoU_ as the assessment metrics by using the following equations.  
\[Accuracy=\frac{TP+TN}{TP+TN+FP+FN} \tag{9}\]  
\[Precision=\frac{TP}{TP+FP} \tag{10}\]  
\[Recall=\frac{TP}{TP+FN} \tag{11}\]\[F1\text{-}score=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{12}\]  
\[IoU=\frac{TP}{TP+FP+FN} \tag{13}\]  
where, \(TP\), \(TN\), \(FP\), and \(FN\) denote the number of true positives, true negatives, false positives, and false negatives respectively. Table 1 presents the test results of both the U-net and the Dense U-net model. We can see from Table 1 that the segmentation result of the Dense U-net model is better than the result of the original U-net model. Though the Precision value of the Dense U-net model is 0.9% lower than the U-net model, the Recall value is increased by 4.7%; hence, the overall F1-_score_ (an important evaluation metric for segmentation task) is also increased by 2.15%. In this work, we considered one more important metric, IoU, which increased by 3.28% in the Dense U-net model. Thus, the experimental results show that our proposed Dense U-net model is more effective than the original U-net model in the case of segmenting the railway sleeper images.
## 4 Conclusions  
Due to the differences in mechanisms causing in-orbit failure of a spacecraft by the Space Electrostatic Discharge Effect and the Single Event Effect, as well as the principal mechanisms by which the effects are brought about on the device, these effects may be simulated by an ESD generator and a pulsed laser facility, respectively. In this study, experimental simulations of the Space Electrostatic Discharge Effect and the Single Event Effect were conducted. The following conclusions were obtained by comparing the similarities and differences of the two effects on SRAM devices:  
1. In both tests, the number of soft errors within the SRAM device increases as the discharge voltage and pulsed laser energy increase. This SRAM device has an upset threshold of 814 pJ for SEE and an upset threshold of 12.5 kV for SESD;
2. In contrast, SEE causes essentially single-bit upsets (SBU), whereas SESD produces mostly multi-bit upsets (MBU). Apparently, the radiation-hardening design for SEE is no longer applicable to SESD;
3. The soft error in the circuit is associated with a drop in supply current, so it can be detected by monitoring the current value to determine whether it is abnormal.  
The findings presented above provide experimental evidence for screening SESD and SEE, as well as guidelines for subsequent protection design. Further work is required to  
Figure 11: The curve of the lowest current value at Vcc port. (**a**) The curve of minimum current with the variation of pulsed laser energy. (**b**) The curve of minimum current with the variation of discharge voltage.  
complement the simulation and theoretical analyses for the specific details of the distinction and the deep-seated mechanism study.  
Conceptualization, R.C.; Investigation, X.W. and R.Y.; Methodology, X.W.; Resources, J.H.; Supervision, J.H.; Validation, Q.C.; Writing--original draft, X.W.; Writing--review & editing, X.W., R.C., Y.L. and J.H. All authors have read and agreed to the published version of the manuscript.  
This project was supported by the National Natural Science Foundation of China under Grant 11875060 and the Foundation of Key Laboratory of Chinese Academy of Sciences under Grant E12130101S.  
Not applicable.  
The data used to support the findings of this study are available from the corresponding author upon request.  
The authors declare no conflict of interest.
## 1 Introduction
Treatment approaches include both nonoperative and surgical methods, contingent upon the acuteness of the injury and associated findings. Nonoperative intervention remains a subject of debate, with the literature suggesting a success rate of less than 50% [17; 18]. This approach involves physiotherapy, the use of nonsteroidal anti-inflammatory drugs, and immobilization in a short cast for a duration of 6 weeks to facilitate the healing of the superior peroneal retinaculum (SPR). Neglecting to address peroneal tendon disorders can result in enduring lateral ankle discomfort and significant functional challenges. Repeated dislocations necessitate surgical intervention [19; 20]. Numerous authors have detailed various techniques for surgical treatment, such as groove deepening, tendon rerouting, bone block procedures, and tendon graft reconstruction. Groove deepening can be achieved through direct or indirect methods. The direct method involves elevating the cartilaginous floor of the retromalleolar groove, creating sufficient concavity for tendon movement. On the other hand, the indirect method uses a 3.5 mm drill to deepen the groove without disturbing the floor, helping to stabilize the peroneal tendons. Rerouting the peroneal tendons under the calcaneofibrular ligament has been attempted, but it comes with high complication rates like sural nerve issues and ankle instability, making it unpopular. Alternatively, distal fibula rotation or translation following a sagittal osteotomy can act as a physical barrier against peroneal dislocation, but it is associated with complications like bone displacement and tendon problems. Some sur geons have explored using the Achilles, peroneus brevis, and plantaris tendons to reinforce the SPR through a transosseous fibular tunnel, with varying degrees of success [21; 22]. The aim of this study was to retrospectively evaluate the short- and medium-term functional outcomes of the retromalleolar groove deepening technique, as described by [PERSON] and [PERSON] [23], in the management of recurrent peroneal tendon dislocation.
# A Review on Styrene Substitutes in Thermosets and Their Composites
## 2 Traditionally Crosslinkable Monomers
### Vinyl Monomers  
#### 2.1.1 Divinylbenzene  
As a derivative of benzene, divinylbenzene (DVB) is similar to styrene in structure and is a well-studied comonomer that can partially or completely replace styrene since early this century. DVB has two unsaturated double bonds, low molecular weight, and high reactivity (Table 1). However, DVB can easily occur self-polymerization and is not easy to blend with the resins, producing white particles because of too high reactivity. Also, the toxicity of DVB used as crosslinking agent is not entirely clear [24].  
[PERSON] et al. [25] investigated the free-radical copolymerization of AESO with styrene and DVB to prepare soybean oil-based thermosets. It was found that DVB could reduce resin viscosity, promote gel process, and increase crosslinking degree. DVB addition resulted in a 14-24 \({}^{\circ}\)C increase in the glass transition temperature (\(T_{\text{g}}\)) of the resulting resins. Also, the addition of DVB increased the modulus of AESO resin and widened the glass transition zone. The curing kinetics of styrene mixed with DVB as crosslinking agents in AESO resin was further studied [26].  
Moreover, DVB has been used in other oil-based resins including fish oil and tung oil [27]. The mechanical properties of the cationically polymerized DVB-based resins were similar to those of styrene-polymerized resins. [PERSON] et al. studied bulk cationic copolymerization of styrene and DVB with conjugated natural oil for the production of agricultural fibers-reinforced composites. The composites were much more brittle, but stiffer than the pure resins. The prepared composite has a renewable content of 88%, and thus has great potential applications in automotive, construction and furniture industries [27].  
Other styrene derivatives include \(\alpha\)-methylstyrene, fluorostyrene and vinyltoluene (Table 1). Compared to styrene, \(\alpha\)-methylstyrene shows a lower reactivity and thus lower crosslinking efficiency of unsaturated double bond in resin system. Fluorostyrene-crosslinked polyesters had better heat resistance and electrical properties. Due to its high boiling point and less volatility, vinyltoluene is an ideal crosslinker, but its price is too high and thus has not been widely used [28].
# Optimization of Signal Detection Using Deep CNN in Ultra-Massive MIMO
## 2 Materials and Methods
### Machine Learning Method
#### 2.3.3 Outlier-Robust Extreme Learning Machine (ORELM)  
Recent modifications to the ORELM aimed to enhance its proficiency in the \(l_{1}\) norm for outlier-producing techniques. Such advancements can be realized through the application of the ELM [23], as elaborated by  
\[\hat{\beta}=\underset{\beta}{\mathrm{argmin}}\|\tau H\beta-t\|_{1} \tag{17}\]  
The optimal solution to the outlined optimization problem is obtained utilizing the standard \(l_{2}\) norm.  
\[\hat{\beta}=\underset{\beta}{\mathrm{argmin}}\|\tau H\beta-t\|_{1}+\frac{1}{2 C}\|\beta\|_{2}^{2} \tag{18}\]  
The specific processes of the ELMx-based algorithm are summarized in Algorithm 1. Noting, the ELMx based algorithm is used to obtain the estimated channel matrix in Algorithm 1.  
```
1:Input: Real and Imaginary number \(X\in\mathbb{R}^{n\times l}\), \(Y\in\mathbb{R}^{n\times l}\)
2:Output:\(\widetilde{\mathbf{X}}\).
3:Initialization: Randomly assign input weight \(c_{i}\) and bias \(V_{i}\) for each hidden neuron \(i\), where \(i=1,2,\ldots,L\).
4:Hidden Layer Output Matrix Calculation:
5:for\(i=1\) to \(N\)do
6: Calculate the hidden layer output vector \(t_{i}\) for each input \(X_{i}\) using the activation function \(o(x)\):
7:\(t_{i}=o(c_{i}\cdot x_{i}+V_{i})\)
8:endfor
9: Form the hidden layer output matrix \(H\) using all \(H_{i}\).
10: Output using Equations (14), (16) and (18).
```  
**Algorithm 1** ELM, RELM and ORELM algorithm.
## 3 Results and Discussion
### Changes in Leaf Flavonoid Distribution  
In order to study the functional role of flavonoids in natural sunlight acclimation of grapevine leaf, confocal laser scanning microscopy (CLSM) was used for micro-localization of these secondary metabolites. Leaves of Goher altering have been chosen for analysis, because it displayed the highest flavonoid content. Leaves were pre-treated with the Naturstoff reagent (NS, see 3.6 for details) to enhance fluorescence of flavonoids. The adaxial side of lamina directly facing sunlight irradiation were used in microscopy analyses. Significant changes were found in flavonoid content of epidermal layer, with markedly high accumulation of NS-conjugated flavonoids in cell nuclei and cell walls in sun leaves compared to half shade leaves (Figure 1, insets). Similar changes were observed in the palisade parenchyma cells and in their chloroplasts (Figure 1).  
Studies have shown that flavonoids are associated with the chloroplast envelope membrane and may limit the diffusion of reactive oxygen species out of the chloroplast [33, 34, 35]. It has also been shown in different plants that they are capable of quenching hydroxyl radicals, hence protecting cells against nuclear DNA damage [36,37]. Flavonoids indeed may protect membranes by enhancing membrane rigidity and therefore preserving them against oxidative damage [38,39]. In our case, there was no vacuolar fluorescence in the palisade cells of the grapevine, while [PERSON] [40,41] successfully detected NS-conjugated flavonoids in the vacuoles of mesophyll cells in _Ligustrum vulgare_ under 100% natural sunlight, but failed to visualize flavonoids in other cellular compartments. They detected light-responsive quercetin and luteolin derivatives, while hydroxycinnamates, which serve specific UV-B screening functions [42], and monohydroxy-B-ring flavonoids were unresponsive to excess light. Our study showed similar results in so far as flavonoid derivatives accumulated to a greater extent than _trans_-caftaric acid did, which provides support for their antioxidant role prior to screening properties in photoproduction. This conclusion is further corroborated by distribution of NS-conjugated flavonoids, not only in the epidermis, but also in the mesophyll. _L. vulgare_ and _Phillyrea latifolia_ with the same flavonoid composition, but with different morpho-anatomical traits, showed different metabolic plasticity in the acclimation process to high solar radiation, namely quercetin- and luteolin-glycos accumulated to a much greater extent in _L. vulgare_ than in _P. latifolia_, and in different microlocations [43]. Together with the dihydroxy-B-ring substituted quercetin-glycos, trihydroxy myricetin-glycos accumulated significantly in two Mediterranean shrubs (_Myrtus communis, Pistacia lentiscus_) in response to different abiotic stresses (light, salt) [13]. Our studies confirm that changes in the flavonoid content of the leaves, and their micro-localization in the leaf tissues, have a key role in the acclimation mechanisms of the grapevine, even in a less contrasting light environment.
## 3 Results and Discussions
### Cutter Force
Increases in the BTS were found to result in contradictory contributions to B1 and B4. Tensile strength is considered an important parameter in rock cutting because tensile fracturing is of importance in the rock fragmentation process. This is because rock chipping highly depends on tensile cracks' propagating towards adjacent cutting lines. Consequently, the differences discussed above affected the results for the correlations between the brittleness indices and cutter forces. The prediction models used for the cutter forces obtained from the statistical regression analysis are summarized in Table 5. For generalized force prediction, the penetration depth and cutter spacing of the disc cutter were considered in the force prediction equations. The results indicated that B1 and the UCS and BTS models gave a better prediction for the cutter force compared to the other brittleness indices models. The results indicated that the brittleness indices had positive relationships with the normal cutter forces based on the power function, meaning that the cutting resistance or required energy increased with the brittleness of the rock. These findings coincide with those of previous studies [4; 27; 40]. Further, the UCS was found to have a positive relationship with the cutter force, while the BTS had a negative relationship with the cutter force from the regression model. If it is not possible to perform the LCM test for a given rock formation in the early stage design of TBM tunneling, the cutter force, which is an essential parameter for the specification design of TBM, can be approximately estimated using the proposed equations in Table 5, based on the basic mechanical rock properties, i.e., the brittleness or rock strengths.
## 3 System Performance
### The End-To-End SNDR of the System
#### 3.1.1 The SNDR for AF Protocol  
Now, by taking the \(i\)-th transmitted link for an example, we first provide the expression of the \(y_{1i}\left(t\right)\) which is the signal received by \(S_{1}\) from the \(i\)-th R. By substituting (1) and (3) into (2), \(y_{1i}\left(t\right)  
To get better system performance, MRC and maximum ratio transmission (MRT) technologies are used at the \(i\)-th R in the received and transmitted slot, respectively. After setting \(\lambda_{1i}=\frac{|\mathbf{h}_{1i}|^{2}P_{1}}{\delta_{1}^{2}}\) and \(\lambda_{2i}=\frac{|\mathbf{h}_{2i}|^{2}P_{2}}{\delta_{1}^{2}}\), the SNDR at \(S_{1}\) is given by  
\[\gamma_{1i}=\frac{\lambda_{1i}\lambda_{2i}}{\lambda_{1i}\lambda_{2i}A_{1}+ \lambda_{1i}^{2}B_{1}+\lambda_{1i}C_{1}+\lambda_{2i}D_{1}+E_{1}}, \tag{8}\]  
where \(A_{1}=\frac{1-k_{2}k_{3}}{k_{2}k_{3}}\), \(B_{1}=\frac{\delta_{1}^{2}(1-k_{1}k_{3})}{\delta_{1}^{2}k_{2}k_{3}}\), \(C_{1}=\frac{P_{1}\delta_{1}^{2}+P_{2}\delta_{1}^{2}}{\delta_{1}^{2}P_{2}k_{3} k_{2}}\), \(D_{1}=\frac{P_{1}}{P_{1}k_{2}k_{3}}\), and \(E_{1}=\frac{P_{1}}{\delta_{1}^{2}\delta_{1}^{2}k_{3}k_{2}P}\).  
With the same method, the SNDR of \(y_{2i}\left(t\right)\) is given by  
\[\gamma_{2i}=\frac{\lambda_{1i}\lambda_{2i}}{\lambda_{1i}\lambda_{2i}A_{2}+ \lambda_{2i}^{2}B_{2}+\lambda_{2i}C_{2}+\lambda_{1i}D_{2}+E_{2}}, \tag{9}\]  
where \(A_{2}=\frac{1-k_{1}k_{3}}{k_{1}k_{3}}\), \(B_{2}=\frac{\delta_{1}^{2}(1-k_{2}k_{3})}{\delta_{1}^{2}k_{1}k_{3}}\), \(C_{2}=\frac{P_{2}\delta_{2}^{2}+P_{2}\delta_{1}^{\[\gamma_{r4i} =\frac{\left|\mathbf{h}_{2i}\right|^{2}k_{3}P_{1}}{\left|\mathbf{h}_ {2i}\right|^{2}\left(P_{1}+P_{2}\right)\left(1-k_{3}\right)+\delta_{2}^{2}}\] \[=\frac{\lambda_{2i}}{\lambda_{2i}F_{4}+L_{4}}, \tag{14}\]  
where \(F_{4}=\frac{\left(P_{1}+P_{2}\right)\left(1-k_{3}\right)}{P_{1}k_{3}}\) and \(L_{4}=\frac{P_{3}}{k_{3}P_{1}}\).  
The DF protocol is used in the network, hence the corresponding SNDRs are respectively, expressed as  
\[\gamma_{DF1i} =\min\left(\gamma_{r1i},\gamma_{r2i}\right), \tag{15}\]  
\[\gamma_{DF2i} =\min\left(\gamma_{r3i},\gamma_{r4i}\right). \tag{16}\]  
Similar to AF protocol, the opportunistic relay selection scheme is also used; the final SNDR of DF protocol is given by  
\[\gamma_{de}=\max_{i\in\left\{1,\ldots,N\right\}}\left\{\min\left(\gamma_{DF1i},\gamma_{DF2i}\right)\right\}. \tag{17}\]
## 2 Bearing Failure Modes
### Intelligent Detection  
Intelligent detection, as a popular research direction, utilizes machine learning, deep learning, and data mining technologies to achieve automatic feature extraction of massive quantities of data and assist in bearing detection. The principle of intelligent detection is shown in Figure 10.  
Traditional machine learning relies on manual feature extraction, such as frequency domain and time domain features, and then uses classification algorithms such as support vector machines (SVMs) and random forests to identify faults [70; 71; 72; 73]. Deep learning methods improve detection efficiency by automatically extracting data features [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91]. For example, convolutional neural networks (CNNs) are suitable for extracting spatiotemporal features of vibration signals, long short-term memory (LSTM) networks excel at capturing temporal features in time series, and autoencoders are used for unsupervised anomaly detection. Further combination models, such as CNN-LSTM, combine the advantages of spatiotemporal feature extraction and temporal analysis. In addition, multimodal data fusion provides support for more accurate detection by combining data sources such as vibration, temperature, current, images, and sound signals [92; 93]. The continuous development of these intelligent detection technologies has driven the development of bearing fault detection towards automation and intelligence.  
Although bearing fault detection methods based on machine learning and deep learning have advantages in automation, accuracy, and unsupervised anomaly detection, they also face challenges in practical applications. The main issues include the demand for a large quantity of high-quality annotated data, high computational resource consump  
Figure 10: Principle of intelligent detection.  
tion, insufficient interpretability of the model, poor robustness under variable operating conditions, as well as overfitting and high maintenance costs. These limitations make it difficult for intelligent bearing detection technology to be widely promoted in industrial environments, promoting further research on transfer learning, semi-supervised learning, and interpretable models to improve its applicability.
## 3 CCD Assault Detection Using Euclidean Distance-Based Anomaly Detection Schemes
### Euclidean Distance-Based Anomaly Detection Scheme 1  
For a large number of given data points, the datasets that vary significantly from the average of the data are called outliers or anomalies. Anomaly detection is a class of machine learning applications, and it has many areas of utilization, such as data cleaning, diagnosis, fraud detection, intrusion detection, and so on. Different types of anomaly detection techniques have been proposed in the literature, such as model-based, distance-based and statistical-based methods [37]. Considering the scenario where the class labels are not provided in the SE-MF dataset, we propose an ED-based anomaly detection scheme (EDADS-1), depicted in Figure 3. The measurement samples are periodically collected at the PCC via the RTUs installed in different locations of the electrical power network. The historical dataset is  
Figure 2: The concept of compactness and separability.  
formulated at the beginning. During the FS process, the optimal features of the SE-MF dataset are selected through GA. Then, the optimal features dataset is divided into training and testing subsets. It is worth mentioning that the training and testing subsets carry both normal and compromised data. In Step 1, centroid vector \(C_{d}=\{\mu_{1},\mu_{2},...,\mu_{n}\}\) is calculated by finding the mean, \(\mu_{r}\), of all the features of the training data subset, where \(\mu_{r}=\frac{a}{\sum\limits_{i=1}^{n}\frac{X_{i}^{(i)}}{a},\forall r\in\{1,2,...,n\}\). In Step 2, the Euclidean distance between each sample and the centroid is calculated to form a distance vector, \(D_{R}=\{D_{R1},D_{R2},...,D_{Rn}\}\), where \(D_{Rp}=\sqrt{\sum\limits_{i=1}^{n}\Big{\{}\big{(}\mu_{i}-X^{(i)}{}_{Rp}\big{)} ^{2}\Big{\}}},\forall p\in\{1,2,...,a\}\). In Step 3, average distance \(D_{R,avg}=\sum\limits_{i=1}^{a}\frac{D_{Rp}}{a}\) is calculated. The average distance can be considered a virtual boundary around the normal dataset. In the testing phase, the Euclidean distance of each sample is calculated from its centroid to form distance vector \(D_{T}=\{D_{T1},D_{T2},...,D_{Tb}\}\), where \(D_{Tq}=\sqrt{\sum\limits_{i=1}^{n}\Big{\{}\big{(}\mu_{i}-X^{(i)}{}_{Tq}\big{)} ^{2}\Big{\}}},\forall q\in\{1,2,...,b\}\). Finally, the test is performed to identify the new test point as being normal (if \(D_{Tq}<D_{R,avg}\)) or an outlier (if \(D_{Tq}>D_{R,avg}\)).  
The proposed EDADS-1 algorithm has appreciably low computational complexity grounded in the fact that calculation of the distance of one sample is required to identify the new data point as being normal or a potential CCD assault. Additionally, it has the ability to identify the outliers in the SE-MF dataset as a potential assault even though the class labels are provided. Moreover, with the increased historical SE-MF dataset, the average distance \(D_{R,avg}\) becomes closer to the actual value, and the detection is more accurate.
## 4 Numerical Results
### Algorithms Performance  
Firstly, a set of several widely-known benchmark functions is applied. These benchmark functions include two uni-modal functions, namely the Sphere function and Rosenbrock's function. Eight multi-modal functions are also included for the evaluation of the SI algorithms, namely the Ackley's, Griewanks's, Weierstrass, Rastrigin's, Noncontinuous Rastrigin's, Schwefel's, Generalized Penalized, and Generalized Penalized 2 functions. The global minima of all the above functions are zero. The definitions of these functions are given in the literature [60; 83]. The dimensionality \(D\) of all benchmark functions is set to 60. For all algorithms, we apply the number of iterations \(MaxIt\) to 1000, the population size \(NP\) to 100, and the number of independent trials to 50.  
Table 2 lists the results (average values of 50 independent trials) of the comparative study. We can easily notice that WOA finds the best results in eight out of the 10 test functions. GWO performs a higher score, as compared to the rest of the algorithms, in two test functions; only one test function achieves better results with PSO.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Opt. Problem** & **SI Algorithm** & **References** \\ \hline Antenna array & GWO, WOA, SSA & [47; 48; 49; 52; 54; 55; 56; 57; 58; 59; 64; 65; 66; 68; 70; 77; 79; 81] \\ \hline Radiation pattern & GWO, WOA & [50; 69] \\ \hline Antenna design & GWO, WOA, SSA & [56; 58; 59; 60; 62; 63; 72; 73; 74; 75; 82] \\ \hline MIMO system & WOA, SSA & [67; 71; 80] \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of optimization problems and the corresponding algorithms.