esa-sceva/satcom-chunk-collection huggingface dataset:
# Applications of Microseismic Monitoring Technique in Coal Mines: A State-of-the-Art Review
## 4 Applications of Microseismic Monitoring in Coal Mine
### Rockburst
[106] established a rockburst monitoring and early warning method, integrating microseismic monitoring and electromagnetic radiation, which uses microseismic indices to predict the dynamic stress in the regional space and electromagnetic radiation indices to evaluate the concentration of static stress in the local mining space.
## 1 Introduction  
### 1.1 NiTi Shape Memory Alloys and Their Applications  
It has been more than 60 years since the discovery of an approximately equiatomic NiTi alloy called Nitinol. The last three letters in the alloy's name stand for Naval Ordnance Laboratory, the place of the discovery [1]. During these six decades, NiTi shape memory alloys (SMAs), well known for the shape memory effect and superelasticity due to the reversible solid phase martensitic transformation [2, 3, 4], have found many applications in industry and medicine. These unique properties allow NiTi alloys to recover their original shape after deformation and to exhibit high elasticity, making them invaluable in various engineering and biomedical applications.  
Some examples of NiTi applications include NiTi frames of reading glasses [5], an NiTi switch-off element in electric tea kettles [6], and NiTi pipe couplers used, among other things, to connect titanium hydraulic tubing in the Grumman F-14 aircraft [7]. Additionally, NiTi is used in various actuators and sensors [8, 9], fasteners such as Velcro-type NiTi hook fasteners [10], vibration and seismic applications [11], NiTi torque tubes installed in smart wings for wing morphing [12], NiTi self-expandable stents [13], and NiTi elements used in endoscopes [14]. The versatility of NiTi SMAs arises from their ability to perform reliably under cyclic loading conditions, which is critical for dynamic applications.  
However, some of the mentioned applications, and not only those, are limited by the fatigue life of cyclically loaded NiTi elements (e.g., Velcro-type NiTi hook fasteners or elastocaloric active regenerators [15]). The degradation of mechanical properties due to repeated stress cycles can lead to premature failure, posing safety risks and reducing the operational lifespan of components. Hence, fatigue-life studies of cyclically loaded NiTi parts have received much attention [16; 17]. Understanding the mechanisms of fatigue failure and identifying methods to enhance fatigue resistance are essential for extending the durability and reliability of NiTi-based components. Not long ago, some techniques, such as laser shock peening and shot peening [18; 19; 20], were proposed to improve the fatigue strength of alloy parts, which are also applicable to NiTi wires by generating compressive stresses on the surface.
# Feature-Selection-Based DDoS Attack Detection Using AI Algorithms
## 2 Related Works and Contribution
### ML Deployment for DDoS Attack Detection
In the detection step, a Deep Convolutional Neural Network (CNN) is used for classification and an Improved-Update-oriented Rider Optimization Algorithm (IU-ROA) for feature selection. A bait detection approach is used in the mitigation step to neutralize malicious nodes. The KDD Cup 99 dataset is used for testing, and the results demonstrate efficient DDoS attack detection and mitigation [23]. The goal of this paper is to use a variety of deep learning algorithms to categorize the traffic into normal and harmful classes based on features extracted from the dataset [24]. The authors of this study [25] have created a novel deep learning architecture called the Cybernet model, concentrating on 1D CNN and LSTM architectures. Learning the essential behaviors of cyberattacks in the realm of cybersecurity and accurately identifying and detecting various forms of DDoS attacks were the objectives.
## 1 Introduction
Here, the secondary conversion stage is a stage in which the motion converted in the primary conversion stage is used to operate a generator with a turbine or a motor to convert it into electric energy. The primary conversion stage is a stage in which the oscillatory motion of the waves is converted into useful motion, such as air- and water-flow, and a mechanical system can use that for power generation. The study on power generation characteristics when there is a structure behind WECs is substantially limited apart from the studies of a mathematical analysis based on linear theories [39; 40]. Since the hydrodynamic nonlinearity around WECs owing to the shape of WECs, a reflecting wall, and the effect of a PTO damping is predicted, it is necessary to evaluate the effects of non-linearity and consistency with linear theory by hydraulic experiments. Therefore, this study focuses on the effects of the installation positions of WECs on the behavioral characteristics of WECs and other hydraulic characteristics, which result in WECs being unstable before the improvement of wave power generation efficiency by utilizing coastal structures. This study examines the behavioral characteristics of a bottom-hinged flap-type WEC with a vertical wall behind it, as an additional facility for breakwaters, via laboratory experiments in unidirectional regular waves. The flap has the shape of an inverted triangular prism with an empty interior, which is similar to the hull intending to increase the restoring force to return to the original position by buoyancy. For evaluating power performance according to the WEC behavior, the PTO system adopts a rotary generator with a simple gear system that generates power in directions adhering to the rotational direction of the flap. Details of the WEC and the PTO system are presented in Section 2. The experimental analysis method is described in Section 3. Section 4 presents the results of the behavioral characteristics and the power generation performance under each condition, such as initial water depth, distance from the reflection wall, and wave steepness. Moreover, the effects of the flap motion on the power generation efficiency are discussed. Finally, the summary of the experimental results is presented in Section 4.
# Intra-Domain Transfer Learning for Fault Diagnosis with Small Samples
## 1 Introduction
The model is then transferred to the target task via shallow-layer freezing and finetuning. The intra-domain transfer learning strategy is the primary novelty of this research. We validate the proposed strategy in a case study with varying levels of small sample ratios and observe its efficacy over alternatives in terms of convergence speed and diagnostic accuracy. This study empirically proves the soundness and merit of transferring among distribution-alike domains in the field of fault diagnosis. We argue cautions against a direct transfer among inter-domains should be issued to reduce the risk of committing negative transfer. To have an intuitive understanding of the learned model, we produce heat maps to visualize the learned high-level features of the input data. Unlike traditional Class Activation Mapping methods, our novel method uses a channel-wise average over the final convolutional layer and up-sampling with interpolation. Heat map visualization verifies the proposed strategy can extract fault-related characteristics. The contributions of this research are twofold: (1) we propose an intra-domain transfer learning strategy and a fault diagnosis model for rotating equipment under the scenario of small samples; (2) we propose a novel heat map visualization method to demystify the transferred deep learning model. The remaining sections are organized as follows. Section 2 explains the intra-domain transfer learning strategy and introduces a rotating equipment fault diagnosis model based on vibration data. Section 3 validates the proposed strategy and model in a case study of a gearbox dataset. Section 4 demonstrates the heat map method and provides a visual portrayal of the features extracted by the transferred deep learning model. Section 5 concludes the work.
# A Novel Model for Accurate Daily Urban Gas Load Prediction Using Genetic Algorithms
## 1 Introduction
### Contributions  
In order to make up for the gap of previous research, the future research trend will focus on more complex models and more influencing factors. The following are this paper's primary contributions:  
1. The establishment and integration of a multiple weather parameter (MWP) prediction framework that incorporates cumulative temperature, solar radiation, wind speed, and building foundation temperature breaks through the simplification limitations of traditional single variable models and captures the complex coupling relationship between environmental factors and gas demand. The results show that the maximum relative error of the model prediction was 8.2%, and the mean absolute percentage error (MAPE) was 2.68%, effectively depicting the load fluctuation characteristics driven by multiple factors.
2. Based on the MWP, the effects of cumulative temperature, solar radiation, wind power, and building foundation temperature on the daily gas load were considered, and the coefficients of cumulative temperature, solar radiation. and wind power were modified to change the STD value. This mechanism enhanced the model's adaptability to temperature changes in different seasons and between day and night. In scenarios of extreme sudden drops in temperature, the maximum deviation between the predicted value and the actual value always remained at a relatively low level. It significantly improved the model's response ability to sudden load changes.
3. Taking a city as the research object, the MWP-DLP model was established, and the genetic algorithm was used to predict and solve the daily gas load. The initial population size was 60, with 1100 iterations. GA optimization converged the model variance to 5379, and the prediction accuracy was significantly improved compared with traditional methods. Finally, the accuracy of the model was verified by the measured data.
## 3 Results and Discussion
### Mechanical Resistance to Compression at 2, 7, 28 and 90 Days  
Figure 6 shows the results obtained in the test of mechanical resistance to compression done on the specimens made with the formulation PC/PAAZ and PCS-R, with percentages of substitution of 75:25% at 2, 7, 28 and 90 days.  
Figure 5: Evolution of the pozzolanic behavior of the samples: a and c correspond to 8-day test periods, whereas b and d represent 15-day periods.  
Among the most notable features highlighted is a trend in the exponential growth of mechanical resistance from 2 to 90 days. Note that all samples made with PC/PAAZ show only slight differences in their resistances between 2 and 28 days of curing; however, this difference is accentuated in later periods (90 days) in which the PAAZ-01 and PAAZ-02 samples reach values of 72.1 and 71.4 MPa, respectively, which not only exceed the two remaining samples (PAAZ-03 and PAAZ-04), but also the reference specimen (PCS-R). [PERSON] and [PERSON] [35] have shown that mortar and concrete specimens made with different types of pozzolans, such as volcanic ash, can match the rate of resistant activity of mortars above 90 days of setting. Another noteworthy highlight from this analysis is the influence of the chemical and mineral composition of the samples on the pozzolanic and hydraulic activity that takes place inside the mortar; thus we see that the samples PAAZ-01 and PAAZ-02, which have undergone a more intense process of zeotilization, are more reactive and develop greater mechanical strength than the samples PAAZ-03 and PAAZ-04, which come from areas of the deposit with a lower influence of the zeotilization process.  
Figure 7 represents the variations in the mechanical compressive strength determined by testing mortar specimens manufactured with a proportion of PC-PAAZ and PCS-R, where part of the PC has been replaced by 30%. As can be seen, the gain of mechanical resistance follows a temporal pattern that increases as the curing time passes; with few exceptions, the resistance values do not differ much from each other, nor do they differ much from the behavior of the resistance values observed in Figure 6. It seems that the substitution of Portland cement at both 25% and 30% does not significantly affect the development in mechanical strength of this type of specimen. [PERSON] et al. [36] have opted to increase in a controlled manner the replacement of cement with natural aggregates above 70% with satisfactory results.  
According to the data observed in Figure 6, we calculated that the increase in initial mechanical resistances (2 and 7 days) in the most reactive samples is 11.5% and between 18.8 and 26.9% for normal resistance (28 days), when the PC/PAAZ formulation is 75:25%. In the case of the formulation PC/PAAZ-70:30% (Figure 7), the initial mechanical resistance increases by 8.6%, while the normal resistance increases by 22.8%.  
Figure 6: Behavior of mechanical resistances at different ages of curing, with the formulation PC: 75%/PAAZ: 25%.
# Hydraulic Analysis of a Passive Wedge Wire Water Intake Screen for Ichthyofauna Protection
###### Abstract  
A passive wedge screen, thanks to its many functional and environmental advantages, has recently become a popular type of surface water intake for municipal and industrial purposes. The design solutions proposed in this paper for a passive wedge wire screen intake model and two different deflectors have been experimentally tested under conditions that can be considered as no-flow conditions at the hydraulic flume. There was only a slight flow associated with the operation of the screen, while there was almost no flow in the hydraulic channel itself, such that it would be considered a watercourse. A hydraulic analysis was carried out, including velocity distribution around the screen as well as the determination of head losses with or without deflectors installed inside the screen. Lower inlet and inflow velocities to the surface of the water intake reduce the risk of injury or death to small fish and fry as well as attracting pollutants understood as sediments, debris, and plant remains floating in the river. In order to achieve the lowest possible maximum inlet and inflow velocities at the highest possible intake capacity, it was necessary to equalize the approach velocity distributions. It was shown that by using the proposed deflectors, the approach velocity distributions were equalized and the maximum values of inflow and inlet velocities were reduced. A water intake screen with a deflector with an uneven porosity distribution equalized the approach velocities better than a deflector with equal openings, but the differences were small. Installing the wedge screen model reduced the maximum inlet velocity from exceeding 2 m/s to a value of 0.08 m/s, and after installing deflectors with equal and unequal openings to values of 0.06 m/s and 0.05 m/s, respectively. In addition to laboratory tests, the paper describes the numerical simulations performed in ANSYS Fluent software. The results of the simulations made it possible to obtain a broader study, as well as to compare the velocity values obtained at the measuring points during the laboratory tests.  
water intake; approach velocity; ichthyofauna conservation; protection of the aquatic environment; numerical simulation; Computational Fluid Dynamics +
Footnote †: journal: Applied sciences
# An Autoencoder-Based Task-Oriented Semantic Communication System for M2M Communication
## 3 Proposed Autoencoder-Based Image Transmission System
### Proposed End-to-End M2M Communication System  
Figure 4 illustrates the proposed E2E M2M communication system. As explained earlier, the semantic encoder and the semantic decoder of the proposed autoencoder are placed at the transmitter and the receiver, respectively. The transmitter consists of an  
Figure 3: The proposed Decoder in the Autoencoder.  
encoder followed by a polar channel encoder and a BPSK modulator. The modulated signal is transmitted over an AWGN channel and demodulated by a BPSK demodulator and a polar channel decoder followed by the decoder. The selection of the channel coding and modulation scheme is independent of the proposed autoencoder design and its E2E performance analysis since the objective of this paper is to consider the E2E autoencoder-based semantic image transmission system for M2M applications. Finally, the transmitted image and the received images are compared with an image objective quality metric (PSNR). The following subsections explain the other main details which are relevant to the proposed design.
## 2 Numerical Analysis of Creep Behavior  
### Integral Formulation  
Using the principle of superposition and knowing the compliance function \(J(t,t_{0})\) it is possible to determine the uniaxial strain evolution from any general uniaxial stress history, \(\sigma(t)\), in a integral-type form  
\[\epsilon(t)=\int_{0}^{t}J(t,\tau)\mathrm{d}\sigma(\tau) \tag{2}\]  
where \(t\) is the current time, \(t_{0}\) is the historic time and \(t-t_{0}\) is the elapsed time and the integrals are understood in the Stieltjes sense, so that they can be evaluated even for discontinuous stress and strain evolutions. Equation (2) is the classical Volterra integral equation of creep that can be easily extended for multiaxial stress/strain considering that creep does not affect the Poisson's ratio and, for this reason, it can be assumed as a constant. Under this assumption only the Young modulus is affected by creep and therefore Equation (2) can be extended for 3D general analysis as  
\[\epsilon(t)=\int_{0}^{t}J(t,\tau)\mathbf{G}\mathrm{d}\sigma(\tau) \tag{3}\]  
where matrix \(\mathbf{G}\) is constant over time  
\[\mathbf{G}=\left[\begin{array}{cccccc}1&-\
u&-\
u&0&0&0\\ -\
u&1&-\
u&0&0&0\\ -\
u&-\
u&1&0&0&0\\ 0&0&0&2(1+\
u)&0&0\\ 0&0&0&0&2(1+\
u)&0\\ 0&0&0&0&2(1+\
u)\end{array}\right] \tag{4}\]  
The value \(\
u=0.18\) will be used in all numerical simulations presented in this paper. Let us express Equation (2) in the incremental discrete form for finite element applications. In the time interval \(\Delta t=t_{i+1}-t_{i}\) we can define the stress and strain increments:  
\[\Delta\epsilon=\epsilon(t_{i+1})-\epsilon(t_{i})\quad\text{and}\quad\Delta \sigma=\sigma(t_{i+1})-\sigma(t_{i}) \tag{5}\]  
Using Equation (2), the strain increment in the time step \(\Delta t\) is  
\[\Delta\epsilon=\epsilon(t_{i+1})-\epsilon(t_{i})=\int_{0}^{t_{i+1}}J(t_{i+1}, \tau)\dot{\sigma}\mathrm{d}\tau-\int_{0}^{t_{i}}J(t_{i},\tau)\dot{\sigma} \mathrm{d}\tau \tag{6}\]and rearranged as  
\[\Delta\epsilon=\int_{0}^{t_{i}}\left[J(t_{i+1},\tau)-J(t_{i},\tau)\right]\sigma \mathrm{d}\tau+\int_{t_{i}}^{t_{i+1}}J(t_{i+1},\tau)\dot{\sigma}\mathrm{d}\tau \tag{7}\]  
Assuming a linear stress variation in the time interval, i.e., \(\dot{\sigma}=\Delta\sigma/\Delta t\), we can rewrite Equation (7) as  
\[\Delta\sigma=\overline{E}\Delta\epsilon-\widehat{\sigma}(t_{i}) \tag{8}\]  
where  
\[\widehat{E}=\frac{\Delta t}{\int_{t_{i}}^{t_{i+1}}J(t_{i+1},\tau)\mathrm{d} \tau}\quad\text{and}\quad\widehat{\sigma}(t_{i})=\overline{E}\int_{0}^{t_{i}} \left[J(t_{i+1},\tau)-J(t_{i},\tau)\right]\dot{\sigma}(\tau)\mathrm{d}\tau \tag{9}\]  
To calculate the second expression in Equation (9) one needs to know the entire load history in each material point and in each time interval. This requires to store during the analysis a huge amount of information that increases drastically with the length of time interval considered. This makes the integral approach for creep prohibitive in a finite element program for long-term analysis, while it might be utilized for short-term processes such as early-age analyses, although this is not the most efficient procedure.
## 2 Methodology
### Emotion Recognition Method Based on Sparse Auto-Encoder and Support Vector Machine  
We investigated an effective method for mapping users' physiological signals to their emotional states. As shown in Figure 9, we use an SAE to transform raw signals into extracted features. The sparse representations learned by the SAE were then used as inputs to train the SVM as a classifier for recognizing emotional states.  
First, an SAE was trained on the raw input x(k) to learn its primary features h1(k) of the raw input. These primary features were then used as the input for another SAE to learn secondary features h2(k). These secondary features were then used as inputs for an SVM classifier, which was trained to map the secondary features to the data labels. Finally, all three layers were combined to form a stacked SAE with two hidden layers and an SVM classifier layer that can classify the collected RSP signal  
Figure 8: Respiration signal segments after processing.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Features** & **Meaning** & **High Valence and High Arousal** & **Low Valence and Low Arousal** & **High Valence and Low Arousal** \\ \hline RSP rate & Respiration rate & 19.16 \(\pm\) 3.32 & 20.32 \(\pm\) 3.21 & 24.23 \(\pm\) 4.64 & 21.82 \(\pm\) 3.42 \\ MEAN\({}_{\text{BB}}\) (ms) & Mean value & 1179.41 \(\pm\) 216.24 & 1011.56 \(\pm\) 253.86 & 1090.87 \(\pm\) 327.02 & 1221.02 \(\pm\) 262.93 \\ SDNN\({}_{\text{BB}}\) (ms) & Standard deviation & 962.69 \(\pm\) 3129.23 & 989.69 \(\pm\) 1726.07 & 1118.32 \(\pm\) 2836.30 & 1070.03 \(\pm\) 625.21 \\ S2 & Variance & 9267.75 \(\pm\) 123.81 & 9893.90 \(\pm\) 367.42 & 12,507.51 \(\pm\) 382.11 & 11,450.23 \(\pm\) 368.37 \\ W\({}_{\text{w}}\) (ms) & The waveform width & 25.13 \(\pm\) 3.01 & 23.82 \(\pm\) 4.12 & 24.31 \(\pm\) 4.68 & 26.10 \(\pm\) 1.48 \\ T\({}_{\text{p}}\) (ms) & Time between peaks & 622.28 \(\pm\) 112.70 & 589.43 \(\pm\) 147.87 & 500.18 \(\pm\) 162.66 & 546.29 \(\pm\) 54.31 \\ SK & Skewness & 0.16 \(\pm\) 0.02 & 0.44 \(\pm\) 0.02 & 0.31 \(\pm\) 0.02 & 0.09 \(\pm\) 0.01 \\ BK & Kurtosis & \(-\)1.72 \(\pm\) 0.17 & \(-\)1.46 \(\pm\) 0.16 & \(-\)1.83 \(\pm\) 0.26 & \(-\)1.87 \(\pm\) 0.13 \\ F & RSP frequency & 0.29 \(\pm\) 0.08 & 0.18 \(\pm\) 0.08 & 0.13 \(\pm\) 0.05 & 0.11 \(\pm\) 0.08 \\ PSD & density & 18,334.09 & 19,662.05 & 24,708.53 & 22,666.06 \\ ApEn & Approximate entropy & 1.48 \(\pm\) 0.16 & 1.21 \(\pm\) 0.19 & 0.98 \(\pm\) 0.16 & 1.20 \(\pm\) 0.14 \\ SampEn & Sample entropy & 1.36 \(\pm\) 0.38 & 0.96 \(\pm\) 0.82 & 0.89 \(\pm\) 0.36 & 1.06 \(\pm\) 0.32 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Statistical results of selected respiration features for different emotions.  
This combined approach of using an SAE and an SVM leverages the strengths of both methods to enhance the generalization ability and performance of the model, particularly when the data have high dimensionality, a small sample size, or an unclear feature representation. Our hypothesis is that by automatically extracting features through deep learning, we can produce a more powerful and accurate emotion recognition model.