esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
As global warming accelerates in recent years, the frequency of droughts has increased and water management at the national level has become very important. In particular, accurate understanding and management of the forest is essential as the water storage capacity of forest is determined by forest structure. Typically, data on forest vertical structure have been constructed from field surveys that are both costly and time-consuming. In addition, machine learning techniques could be applied to analyze, classify, and predict the uncertainties of internal structures in forest. Therefore, this study aims to map the forest vertical structure for estimating forest water storage capacity from multi-seasonal optical satellite image and topographic data using artificial neural network (ANN) in Gongju-si, South Korea. For this purpose, the 14 input neurons of normalized difference vegetation index (NDVI), two types of normalized difference water index (NDWI), two types of Normalized Difference Red Edge Index (NDre), principal component analysis (PCA) texture, and canopy height average and standard deviation maps were generated from Sentinel-2 optical images obtained in spring and fall season and topographic height maps such as digital terrain models (DTM) and digital surface models (DSM). The training/validation and test datasets for the ANN model were derived from forest vertical structures based on field surveys. Finally, the forest vertical classification map, the result of ANN application, was evaluated by creating an error matrix compared with the field survey results. The result showed an overall test accuracy of \(\sim\)65.7% based on the number of pixels. The result shows that forest vertical structure in Gong-ju, Korea can be efficiently classified by using multi-seasonal Sentinel-2 satellite images and the ANN approach.  
forest vertical structure; Sentinel-2; WorldDEM; artificial neural network +
Footnote †: journal: Applied Sciences  
1
# Sound Event Detection Using Derivative Features in Deep Neural Networks
## 3 Network Architecture
### Basic CRNN  
The architecture of the basic CRNN is shown in Figure 2. It is similar to other CRNNs commonly used for SED [11], but the output of the network is fed to a global average pooling (GAP) layer for training using weakly labeled and unlabeled data. The GAP is used to compute the clip-level output for each class by time-averaging the frame-level sigmoid output of the classification layer. Three convolution blocks (ConvBlocks) consisting of two-dimensional CNNs, one bidirectional GRU layer, and one classification layer implemented as an FNN are cascaded in series. The GAP layer averages the frame-level output of the classification layer for the 240 frames corresponding to the 10 s audio clip. The GPA layer is not used for strongly labeled data.  
The 64-dimensional log-mel filterbank and its first and second derivative values are used as input to the basic CRNN. They are independently constructed as (\(240\times 64\))-dimensional feature maps. Although the number of weights increases with the additional feature maps due to the first and second derivative features at the input layer of CRNN, this increase is relatively small compared with the total number of weights of the basic CRNN. The number of weights of the proposed model is about 127,000 compared to 126,000 without derivative features at the input layer.  
In ConvBlock, a \(3\times 3\) convolutional filter is applied to the context window of the input feature map, and batch normalization is used to normalize the filter output to zero mean and unit variance. A rectified linear unit (ReLU) activation function is applied after batch normalization. Non-overlapping \(1\times 4\) max pooling is applied only in the frequency domain to reduce the dimensionality of the data and  
Figure 1: Extraction process of log-mel filterbank (LMFB).  
to improve frequency invariance. We preserve the time dimension to make use of the time-correlation information of the sound signal, which will be exploited in the following GRU layer. Dropout is used after max pooling to reduce overfitting in the training.  
The output of the last ConvBlock is used as an input to the bidirectional GRU, which has 64 units in each direction and feeds its output into the classification layer, which has 10 units corresponding to the sound classes. These units have a sigmoid activation function, the output of which denotes the posterior probability of the classes for each frame of the sound signal.
## 1 Introduction
Our first goal in this paper is to identify if the anamorphic and frequency dependent phenomenon affects our particular application, which in the present case is the application of PA-LCoS microdisplays to holographic data storage. We also need to test other limitations such as the maximum resolution of the optical system, aberrations, etc. We want to know the origin of these limitations to change the setup to improve the performance and to know our practical data storage capacity. In this paper, we focus in the complete optical system. Thus, we evaluate the limitations due to the lenses, the ones added by the PA-LCoS microdisplay, and the illumination. All this work is necessary prior to the introduction of the holographic material, which adds additional degradation effects. The methodology that we propose is not only able to provide a global characterization of the HDSS, but also to perform a local and quantitative characterization across the aperture of the system. This local characterization is very useful to evaluate nonuniformity of the illumination, spatial variant response of the LCoS device, and misalignments of the elements in the system. As we are managing a binary data storage system, to study the influence of the different elements, we measured the Bit Error Rate (BER) or the Quality factor (Q-factor), figures of merit defined for digital data transmission systems. We analyzed the errors retrieved in the reconstructed image by means of a CCD camera. We show how the introduction of the material affects the performance. We used Polyvinyl Alcohol Acrylamide (PVA/AA) that was produced, characterized and modeled by our group [23]. These preliminary results show us how to improve the process of producing and depositing PVA/AA for holographic data storage applications.
# Bridge Assessment under Earthquake and Flood-Induced Scour
###### Abstract  
Earthquakes and floods in Albania are devastating, but combining these two different hazards in terms of action on bridge structures may lead the bridge to collapse. This article presents a seismic risk assessment of a code-conforming precast reinforced concrete bridge located in a region prone to earthquakes and where local scour induced by floods is a significant concern. The seismic action is considered using a group of ground motion accelerograms generated by matching the accelerogram of the 29 November 2019 earthquake in Durres (M = 6.4), Albania, to the target response spectrum. The scouring effects on the bents of the bridge are characterized by the scour depths. A set of non-linear time-history analyses of the bridge are performed to assess the bridge's performance. The bridge fragility curves are generated and analyzed for multi-hazard scenarios at both element and system levels for different flow discharge values and PGA levels. The result shows a low seismic risk of the bridge with bearings when considering the local scour induced by flood events in the seismic analysis due to their flexibility to adapt to changes in structure geometry and significant foundation stiffness. This research also emphasizes the significance of choosing the right foundation type and depth for bridges located in areas prone to local scour induced by floods.  
seismic and flood hazards; local scour; failure probabilities; fragility curves; damage limit states +
Footnote †: journal: Applied sciences
## 3 Results
Because these indicators are sensitive to sample size, other standardized indices were used. The NFI showed a value of 0.999, the CFI obtained a value of 0.998, the IFI presented a value of 0.998 and the TLI showed a value of 1.031, which shows an excellent fit of the model. In addition, the RMSEA obtained a value of 0.000, which reveals an excellent fit for this parameter. Figure 3 and Table 2 show the regression weights obtained for the male gender. In boys, sleep duration was directly associated with self-concept (\(p<0.001\); r = 0.351) and indirectly with problematic use of videogames (\(p<0.001\); r = \(-0.549\)). Similarly, the practice of daily physical activity was positively related to self-concept (\(p<0.001\); r = 0.351) and negatively with problematic use of videogames (\(p<0.05\); r = \(-0.252\)). Thus, it was shown that in the male gender there was a positive and bidirectional association between the duration of sleep and the practice of physical activity (\(p<0.05\); r = 0.279). In addition, a direct effect of self-concept on adherence to the Mediterranean diet (\(p<0.05\); r = 0.286) and an indirect effect on problematic use with videogames (\(p<0.001\); r = \(-0.531\)) were highlighted. The model developed for the female gender showed a good fit for all the indices, for which the chi-square analysis showed significant values (X\({}^{2}\) = 0.039; \(p<0.001\); df = 1; pl = 0.844). In relation to the standardized indices, the NFI showed a value of 0.998, the CFI obtained a value of 0.996, the IFI presented a value of 0.997 and the TLI showed a value of 1.153, which shows an excellent fit of the model. In addition, the RMSEA obtained a value of 0.001, which reveals an excellent fit for this parameter. Figure 2: Structural equation for the theoretical model. Note: Sleep Duration (hours per day) (SD); Self-Concept (SC); Physical Activity (minutes per day) (PA); Mediterranean Diet (MD); Problematic Use of Videogames (PUVG). Figure 4 and Table 3 show the regression weights obtained for the female gender. In the case of girls, sleep duration was not statistically associated with self-concept (\(p>0.05\)); however, it was significantly and indirectly associated with the problematic use of videogames (\(p<0.001\); r = \(-0.358\)). It was highlighted that the practice of daily physical activity was positively related to self-concept (\(p<0.001\); r = \(0.433\)) and with the problematic use of videogames (\(p<0.01\); r = \(0.325\)) (Table 4). Along these lines, self-concept exerted an inverse effect with the problematic use of videogames (\(p<0.001\); r = \(-0.386\)). Finally, it should be noted that in girls, the bidirectional association between the duration of sleep and the practice of physical activity was indirect (\(p<0.01\); r = \(-0.319\)) (Table 4). By way of synthesizing comparative results, the structural equation models based on gender showed in both genders that the practice of physical activity was positively associated with self-concept and that sleep duration and self-concept were negatively associated with problematic use of videogames. In this sense, in boys there was a direct
## 4 Monitoring System Design
### Stress Measurement Points  
For this construction project, during incremental launching, the strategic placement of stress measurement points is crucial. Initial measurement points are set on the bottom side of the girder sections at temporary piers and at the midspan, with corresponding points on the top of the girder, especially at locations with varying cross-sections. As the steel box girder is incrementally launched, new measurement points are added to the new segments, continuously monitoring stress distributions to ensure safety. Additional points are also arranged on the temporary piers and midspan of the new steel box girder segments as they advance. Furthermore, measurement points at the connection between the guide girder and steel box girder ensure the reliability of this critical node. Comprehensively, the stress measurement points are specifically arranged as follows:  
Four stress measuring points are arranged at a height of 6 cm from the bottom of each temporary pier column, and for temperature compensation, the temperature-compensated strain-gauge method is adopted and stress compensation measuring points need to be arranged, with a total of 19 stress measurement points. The 60 m steel box girder spans four segments. A stress test section is set up in the middle of each segment. Five measuring points are arranged in each test section. Meanwhile, four temperature compensation ports are configured in the stress-strain acquisition device. A stress test section is also set up at the junction of the five- and six-section girders, a total of 29 stress measuring points. At the same time, two sections are arranged at the connection between the guide girder and the steel box girder. For each section, four measuring points are placed, and overall, eight stress measuring points are positioned. One interface is on the side of the guide girder, and the other section is on the side of the steel box girder. The layout of the measuring points is illustrated in Figure 10. \(Z_{0}\) is the compensation strain gauge. Through these stress measuring points, the stress of each unfavorable working condition throughout the incremental launching is monitored, and the data are recorded by a JM3816 static resistance strain gauge. Finally, the stress is determined using the material's stress-strain characteristics.  
Figure 9: Displacement monitoring points of incremental launching and guide girder.
## 2 Discussion
### Nuclear Factor Kappa B (NF-kB)  
Nuclear factor kappa B (NF-kB) is a transcription factor activated by two major signaling pathways: the classical (canonical) signaling pathway and the alternative pathway. NF-kB is known to regulate multiple aspects of adaptive and innate immune responses, as well as inflammation. For example, NF-kB helps induce pro-inflammatory cytokines and regulate T-cell activation and differentiation. It was also implicated in many other inflammatory diseases such as multiple sclerosis, rheumatoid arthritis, and asthma [14]. The genes associated with psoriasis that encode regulators of the NF-kB pathway include TNFAIP3, TNFRSF1B, TNIP1, TRAF1 IP2, and NF-\(\kappa\)-BIA. TNFAIP3 is induced by TNF, which inhibits NF-\(\kappa\)B activation and TNF-mediated apoptosis and is involved in cytokine-mediated immune and inflammatory responses. TNFRSF1B is a TNF-\(\alpha\) receptor that mediates the recruitment of antiapoptotic proteins. TNIP1 encodes TNFAIP3 interacting protein 1, which plays a role in the regulation of NF-\(\kappa\)B activation. TRAF1 IP2 encodes a protein that interacts with TRAF proteins and plays a central role in innate immunity in response to pathogens, inflammatory signals, and stress. NF-kBIA encodes a member of the NF-\(\kappa\)B inhibitor family, which interacts with REL dimers to inhibit NF-\(\kappa\)B/REL complexes involved in inflammatory responses [4].  
Several SNPs in the genes involved in the TNF signaling pathway were implicated in psoriasis susceptibility. The rs2230926 SNP in the TNFAIP3 gene, characterized by the G allele, was associated with an increased risk of psoriasis. This variant affects the regulation of TNF-alpha-induced signaling pathways, contributing to the dysregulation of the immune responses observed in psoriasis. Similarly, the rs1061624 SNP in the TNFRSF1B gene, featuring the T allele, was linked to psoriasis susceptibility, potentially disrupting the TNF receptor signaling and promoting chronic inflammation. Additionally, the rs7708392 SNP in the TNIP1 gene was associated with psoriasis, suggesting a role in modulating NF-\(\kappa\)B activity and immune responses. The rs929386 SNP in the TRAF1 IP2 gene was also implicated in psoriasis susceptibility, potentially affecting the NF-\(\kappa\)B signaling pathway. Furthermore, the rs696 SNP in the NF-\(\kappa\)BIA gene, characterized by the A allele, was linked to an increased risk of psoriasis, potentially altering the negative regulation of NF-\(\kappa\)B and promoting inflammation. These SNP variants shed light on the involvement of TNF signaling and NF-kB pathway dysregulation in the pathogenesis of psoriasis [3; 4; 9; 10; 14]. However, further research is needed to fully elucidate the extent of their role in disease development and progression.
# Research on Lateral Deformation Control Criteria of Metro Shield Tunnels with Excessive Ellipticity
## 3 Finite-Element Analysis
### Model Boundary Processing  
The model incorporates three-dimensional curved bolts and bolt handholes to simulate the contact relationship between concrete segments, bolts, and handholes. The size and relative positioning derive from construction design drawings. To reflect the yielding behavior of the steel, the Von Mises eigenstructure is used for the connecting bolts. The yield strength of 5.8-grade bolts used in this model is taken as 400 MPa, and the ultimate strength is taken as 500 MPa. The contact points between adjacent segments, bolts, and tunnel segments employ a surface-to-surface contact model. This model enforces hard contact in the normal direction, allowing pressure transfer and separation post-contact, and the tangential direction is modeled using the Coulomb friction method based on the penalty function approach. The finite-element model is shown in Figure 7.  
The surrounding rock's constraint on the tunnel segment's exterior is simulated using curved springs. The foundation reaction coefficients for the radial springs can be sourced from the table in accordance with the standard penetration strikes of the strata [20], or they can be determined using established empirical values or calculated per theoretical methods [21,22]. This research focuses on prevalent stratigraphic parameters in South China, choosing various foundation spring coefficients to represent common soil strata. Foundation spring coefficients of 5, 10, 20, and 30 MPa/m are applied, loading each instance based on real-world deformation. The remaining two tangential spring foundation reaction force coefficients are 1/3 of the radial coefficients [23].
# Challenges Associated with Implementing 5G in Manufacturing
## 4 Infrastructure Requirements for 5G
### Fog and Edge Computing  
Edge computing is currently being implemented in many industries utilizing the industry backbone for data management known as the programmable logic controller (PLC). The main task of a PLC is to acquire data from field devices, apply logic or arithmetic functions and set output values based on these computations. This typically requires round-trip times in the range of several microseconds to milliseconds. With the use of 5G networks, the rate at which instructions are processed by edge networks will increase with the flow of more considerable amounts of data. Fog computing brings intelligence to the shop floor network; edge computing brings intelligence, processing power, and communication capabilities directly into devices such as smart sensors and gateway devices [2].
## 2 Methodology  
Accurate power consumption forecasting is essential for optimizing energy distribution, improving efficiency, and maintaining grid stability. Reliable predictions allow energy suppliers to allocate resources more effectively, minimizing waste and preventing grid overloads [20, 21, 22]. By leveraging predictive models to assess energy demand, power plants can generate only the required amount of electricity, reducing resource consumption. At the same time, grid operators can mitigate overload risks, while consumers can optimize their electricity usage.  
Traditional forecasting methods primarily rely on statistical models that analyze historical data to predict future consumption trends. However, these models struggle to incorporate external factors such as weather variations, social events, and complex user behavior. Additionally, they often fail to capture the nonlinear characteristics of power consumption data, limiting their accuracy and scalability.  
To address these limitations, this study introduces an AI-driven approach called the MC-LSTM model. This method integrates k-means clustering with LSTM neural networks to enhance prediction reliability and efficiency. The k-means clustering algorithm categorizes power consumption data based on seasonal and time-dependent characteristics, identifying structural consumption patterns. Separate LSTM models are then trained for each cluster,enabling them to learn long-term dependencies in time-series data with greater specificity. By combining clustering with deep learning, the MC-LSTM model effectively incorporates diverse external factors and delivers highly accurate power consumption forecasts.
## 2 Modeling of the Taper-Leaf and the Multi-Leaf Spring
### The Mechanical Model of the Taper-Leaf Spring Including \(n\) Pieces  
The half of the taper-leaf spring including \(n\) pieces (\(n\) = 2 or 3) is taken as the research object. Its mechanical model is shown in Figure 3. Similarly, the half of the single taper-leaf spring also can be regarded as an elastic beam. It is fixed at one end. A concentrated load \(F\) is applied at the other end. The geometric parameters and the coordinate system are marked in Figure 3. The end displacement of the taper-leaf spring is \(y\). \(\gamma_{i}\) is defined as the thickness ratio and \(\gamma_{i}=h_{11}/h_{i2}\). Moreover, all the design values of \(\gamma_{i}\) are the same in practical engineering application and \(\gamma_{i}=\gamma\).  
Under the load \(F\), each piece does not separate from each other at the free end. The free ends of the \(n\) pieces for the taper-leaf spring are equivalent to a whole body and the vertical displacement occurs under the load \(F\). Thus, each piece has the same vertical displacement \(y\) at the free end, that is:  
\[y_{i}=y \tag{10}\]  
where \(y_{i}\) the displacement of the \(i\)th piece at the free end (\(i\) = 1, 2 or \(i\) = 1, 2, 3).  
According to Equation (7), \(y_{i}\) can be expressed as:  
\[y_{i}=\frac{4F_{i}\left[L^{3}+l_{2}^{3}\left(1-\gamma^{3}\right)\right]}{Ebl_{ 2}^{3}} \tag{11}\]  
where \(F_{i}\) is the load shared by the \(i\)th piece.  
Substituting Equation (11) into Equation (10), obtain the following:  
\[\frac{F_{1}}{h_{12}^{3}}=\frac{F_{2}}{h_{22}^{3}}=\cdots=\frac{F_{i}}{h_{22}^{3 }}=\cdots=\frac{F_{n}}{h_{n2}^{3}}=\frac{F}{h_{e2}^{3}} \tag{12}\]  
where \(h_{e2}\) is the equivalent thickness.  
According to the model in Figure 3, the sum of the concentrated forces is equal to the concentrated load \(F\), that is:  
\[F_{1}+F_{2}+\cdots+F_{n}=F \tag{13}\]  
According to Equations (12) and (13), \(h_{e2}\) can be calculated by:  
\[h_{e2}=\sqrt[3]{h_{12}^{3}+h_{22}^{3}+\cdots+h_{n2}^{3}} \tag{14}\]  
Figure 3: The mechanical model of the taper-leaf spring including \(n\) pieces.  
According to Equation (12), \(F_{i}\) can be calculated by:  
\[F_{i}=\frac{h_{i2}^{3}}{h_{e2}^{3}}F \tag{15}\]  
Under the load \(F\), the maximum normal stress of the \(i\)th piece can be expressed as:  
\[\sigma_{\text{imax}}=\frac{6F_{i}L}{bh_{i2}^{2}} \tag{16}\]  
Substituting Equation (15) into Equation (16), obtain the following:  
\[\sigma_{\text{imax}}=\frac{6 FLh_{i2}}{bh_{e2}^{3}} \tag{17}\]