esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Ship-to-shore (STS) container cranes are important pieces of equipment in container terminals for container handling, so they need to be properly designed to avoid damage or collapse due to natural hazards (i.e., storms or earthquakes). However, the dynamic analyses necessary for this can be cost- and time-consuming because of the need to consider the time history of ground motions and several sources of uncertainty. Thus, sensitivity analysis on the input parameters to the responses of the structures is needed to categorize which sources of uncertainty are significant enough to be considered as random variables. In this study, an investigation is carried out into the sensitivity of some sources of uncertainty to the seismic response of a Korean container crane structure. The input random variables studied include ground motion intensity, ground motion profiles, mass, damping, and elastic modulus of steel. Nonlinear dynamic analyses are conducted using a set of 20 natural ground motions scaled to three ground motion intensity levels, in compliance with the Korean Design Standard. The method of deterministic sensitivity analysis using the so-called tornado diagram is applied for the evaluation of structural systems. For the studied type of Korean container crane, it can be stated that the intensity of ground motions (i.e., spectral acceleration) is the most significant input parameter on the response of the structure, as measured in terms of portal drift, vertical reaction of the crane's legs, and total base shear. The next most significant influencing factors are the mass of the structure and the characteristics of every ground motion. Damping plays a relatively important role on the total base shear, while it shows almost no impact on the axial reaction of the crane's legs. Of the three engineering design parameters (portal drift, vertical reaction, and total base shear), the elastic modulus exhibits a low effect, but it should be considered a source of uncertainty in seismic analysis.  
Sensitivity analysis; sources of uncertainty; STS container crane; tornado diagram; Korean Design Standard; time-history acceleration analysis 2018 8 1667; doi:10.3390/app8091667
## 3 Results and Discussion
### Consistency of Phase Transition Enthalpies of Alkylbenzenes  
Some of alkylbenzenes, 1,2,4,5-tetra-iso-propylbenzene, 1,4-di-tert-butylbenzene, 1-methyl-3,5-di-tert-butylbenzene and 1,3,5-tri- tert-butylbenzene are solids at room temperature and their enthalpies of fusion, \(\Delta^{\rm d}_{\rm cr}H^{\rm o}_{\rm m}\)(\(T_{\rm fus}\)), are known from the literature. The enthalpies of fusion of 1,4-di-tert-butylbenzene were reported several times and agree well (see Table 4). To provide more confidence, weighted average values of \(\Delta^{\rm d}_{\rm cr}H^{\rm o}_{\rm m}\)(\(T_{\rm fus}\)) were calculated using experimental uncertainty as a weighting factor.  
The thermodynamics of their solid-liquid, liquid-gas and solid-gas phase transitions can be easily reconciled and validated using the following fundamental relationship:  
\[\Delta^{\rm g}_{\rm cr}H^{\rm o}_{\rm m}(298.15\;{\rm K})=\Delta^{\rm g}_{\rm I }H^{\rm o}_{\rm m}(298.15\;{\rm K})+\Delta^{\rm d}_{\rm cr}H^{\rm o}_{\rm m}( 298.15\;{\rm K}) \tag{6}\]  
Since the thermochemical calculations are commonly carried out at the reference temperature \(T\) = 298.15 K, the enthalpies of fusion have to be adjusted to this temperature. The adjustment of \(\Delta^{\rm d}_{\rm cr}H^{\rm o}_{\rm m}\)(\(T_{\rm fus}\)) was performed with the help of the equation [41]:  
\[\Delta^{\rm l}_{\rm cr}H^{\rm o}_{\rm m}(298.15\;{\rm K})/\left({\rm J}\cdot{ \rm mol}^{-1}\right)=\Delta^{\rm l}_{\rm cr}H^{\rm o}_{\rm m}(T_{\rm fus}/{\rm K })-\left(\Delta^{\rm g}_{\rm cr}C^{\rm o}_{p,\rm m}-\Delta^{\rm g}_{\rm I}C^{ \rm o}_{p,\rm m}\right)\ \times\ \left[(T_{\rm fus}/{\rm K})-298.15\;{\rm K}\right] \tag{7}\]  
where \(\Delta^{\rm g}_{\rm cr}C^{\rm o}_{p,\rm m}\) and \(\Delta^{\rm g}_{\rm I}C^{\rm o}_{p,\rm m}\) were taken from Table S2. With this adjustment, the molar enthalpies of fusion, \(\Delta^{\rm l}_{\rm cr}H^{\rm o}_{\rm m}\)(298.15 K), for four alkylbenzenes were calculated (see Table 4). Uncertainties in the temperature adjustment of fusion enthalpy from \(T_{\rm fus}\) to the reference temperature were estimated to account for 30 % of the total adjustment [42].  
Apparently, the vaporisation enthalpies of alkylbenzenes have been measured more frequently (see Tables 1 and 2) than the sublimation enthalpies (see Table 3). It therefore makes sense to derive additional sublimation enthalpies according to Equation (6) using the evaluated \(\Delta^{\rm g}_{\rm I}H^{\rm o}_{\rm m}\)(298.15 K)-values from Tables 1 and 2. The sublimation enthalpies calculated in this way are denoted by _PhT_ (calculated as the difference in _Phase_ Transitions) in Table 4. As it can be seen in Table 4, these values for all three compounds are hardly distinguishable from experimental results, proving consistency of the solid-liquid, solid-gas and liquid-gas phase-transition enthalpies evaluated in the current study.
## 2 Materials and Methods
### Study Selection and Data Extra Action  
Firstly, two authors created the criteria for eligibility. When both authors had accepted the search strategy, one of them searched the databases (last search: January 2021). Then, two researchers reviewed all the studies (title, abstract, and full text) independently, using an automation tool--Rayyan [24]. The results were compared and all occurring conflicts at every stage of study selection and data extraction were solved through discussion.  
Articles meeting the following criteria were included in the review: (1) published in the last 5 years (January 2015--December 2020); (2) English language; (3) randomized controlled trials (RCT); and (4) targeted for the healthy elderly, rehabilitation, and exergames. Studies were excluded due to: (1) publishing before 2015; (2) writing in a language other than English; (3) reports from books, case reports, observational, as well as interventional (non-randomized) clinical trials, and protocols of RCTs; and (4) do not concern the elderly, rehabilitation, and exergames.  
The data was extracted from articles by two authors. Quantitative variables were presented as mean, minimum, and maximum values. Qualitative variables were described as percentage frequencies.
# Permanent Deformation Mechanism of Steel Bridge Deck
## 1 Introduction  
### Research Background  
According to the 2023 Statistical Bulletin on the Development of the Transportation Industry released by the Ministry of Transport of China, the total number of highway bridges nationwide reached 1.0793 million by the end of 2023, demonstrating a consistent upward trend. Simultaneously, the number of urban traffic bridges also increased steadily, reaching 89,300, thereby contributing to the enhancement of urban transportation networks and improving overall operational efficiency and load-bearing performance. With the continual expansion of the bridge inventory and the increasing complexity of serviceenvironments, heightened attention has been directed toward the durability and functional performance of bridge structures.  
Steel bridges have been extensively employed in long-span structures--such as suspension bridges, cable-stayed bridges, and select overpass segments--owing to their distinctive advantages. As a vital structural component of steel bridges, the steel bridge deck pavement (SBDP) is directly installed on orthotropic steel deck plates, which possess relatively low flexural stiffness. In comparison with conventional asphalt pavements, SBDPs are more susceptible to temperature variations. Elevated temperatures have been shown to weaken the interfacial bonding between the pavement and the steel deck [1,2], thereby altering the mechanical response of the overall pavement system [3]. Consequently, the occurrence of rutting and other distress modes during service becomes more pronounced. Current research on rutting development in SBDP primarily focuses on two domains: experimental investigations and finite element method (FEM) analyses. These approaches are predominantly conducted at the macroscopic scale, which limits their capacity to capture particle-level interactions and kinematic behavior within the material. As a result, critical mesoscopic mechanisms remain inadequately understood. Therefore, it is of substantial importance to explore the rutting formation process of SBDP from a meso-mechanical perspective to bridge this knowledge gap and inform more effective design and maintenance strategies.
# An Audio Watermarking Algorithm Based on Adversarial Perturbation +
## 4 Experiments and Discussion
### Robustness Against Attacks  
In Equation (13), all attacks are assumed to be equally probable. However, the robustness of watermarking is inconsistent for various attacks in the experimental results of Table 2. Figures 7-9 are examples of experiments with a host audio. Figure 7 shows the waveform of the host audio and the embedded watermark. Figures 8 and 9 show experimental visualizations of algorithms AAW-CN and AAW-CL against different attacks, respectively.  
Figure 8: Experimental example of AAW-CN algorithm. Each subfigure represents an attack, with the waveform of the attacked audio at the top and the extracted watermark at the bottom, where the red indicates the bits that were extracted incorrectly.  
Figure 9: Experimental example of AAW-CL algorithm. Each subfigure represents an attack, with the waveform of the attacked audio at the top and the extracted watermark at the bottom, where the red indicates the bits that were extracted incorrectly.  
It can be seen that both DEN and REC attacks also contain WGN attacks yet achieve decent BDRs. It is argued that the filtered Gaussian noise produces a predictable effect on the audio, and thus the robustness to watermarking is weakened but not as drastically as with WGN alone.
###### Abstract
[4] achieved a good balance between accuracy and computational complexity by proposing an attention mechanism and class-aware edge information utilization. In this study, we propose an efficient atrous residual network (EAR-Net) to improve the segmentation accuracy while maintaining the computation costs of previous studies [8; 9; 22; 23]. Our model consists of three modules (residual learning, lightweight ASPP and decoder). First, we achieved high accuracy by utilizing residual learning that enhances the context information extraction ability. The implementation of residual learning is ResNet [24], and we prevented feature map resolution reduction by removing a pooling layer from a stem block pre-processing the input image of the ResNet. This method improves the accuracy with a simple operation, and maintains the residual learning structure, allowing reuse of pre-trained weights on ImageNet. In addition, there is an advantage that this method can be simply applied to other segmentation models. Second, we utilized lightweight ASPP to minimize computation costs. The conventional ASPP greatly contributes to the improvement of segmentation accuracy, but has a disadvantage in that the computation costs are large. Therefore, we minimized the computation costs by replacing traditional convolution with depthwise separable convolution (DSConv) [25] in ASPP. ASPP using DSConv has the advantage that there is almost no decrease in accuracy, which is a trade-off for reducing the computation costs. Third, we proposed a new decoder combining DSConv and interpolation to minimize the computation costs. The decoder performs feature restoration to generate a segmentation map, and a good balance between accuracy and computation costs is important. DSConv and interpolation significantly reduce computation costs while minimizing performance degradation. The decoder consists of three decoding blocks, and aims to further improve accuracy by gradually performing feature map expansion and restoration. Finally, we adopted focal loss [26] to alleviate the class imbalance between the background and the objects. The focal loss increased learning efficiency. The experimental results showed high accuracy with computation costs similar to those in the conventional segmentation methods. In an ablation study, we analyzed the contributions of the ASPP and DSConv in the EAR-Net. The main contributions of this paper are as follows. * We propose an encoder, utilizing residual learning with improved stem block. This method improves accuracy with a simple operation, can reuse pre-trained weights, and is applicable to other segmentation models;
* We propose a lightweight ASPP utilizing DSConv to minimize the computation costs without degrading accuracy;
* We propose a new efficient decoder combining DSConv and interpolation to achieve a good balance between accuracy and computation costs.
# Cellulose and Vanadium Plasmonic Sensor to Measure Ni\({}^{2+}\) Ions
###### Abstract
In spite of having its outstanding properties, CNCs were surface-modified with cetyltrimethylammonium bromide (CTAB) through hydroxyl groups to generate their moderate hydrophobic, simultaneously serves as a promising matrix for attaching nanoparticles [45; 46; 47]. On the other hand, metal salt nanostructures, namely vanadium salts--also known as ammonium metavanadate (NH4 VO3)--have played ideal reagents in the laboratory and analytical chemistry, which find widespread usage in the detection of drugs, gaseous, and N-acetyltransferase activity in humans [48]. Moreover, they also serve as precursors of various medicinal agents and production of catalysts on account of their low-cost, mild reaction conditions, high yield, and operational simplicity. Hence, it is expected that vanadium-cellulose composite-based thin films can further improve the sensing performance of the SPR sensor. The main of the present work is to enable the potential detection of Ni(II) ion using vanadium incorporated in modified cellulose nanocrystal. To the best of our knowledge, this is the first report that utilizes vanadium salts-CNCs in detecting nickel ions, with the obtained detection limit of nickel ion at 2 ppb.
## 4 Evaluation Metrics
### Mobility Models' Metrics
#### 4.1.3 Average Coverage  
Coverage is the percentage of the defined area that has been passed through by a node at any point in time. The area is divided into a virtual grid, represented by a two-dimensional array. During each cycle, each node increases the array value corresponding to its location by one. The coverage percentage is calculated by determining how many parts of the array have a value greater than zero, then dividing that by the total number of parts and multiplying by 100. The final result gives an easy visualization of the node distribution. This visualization allows us to see if the distribution is homogeneous and not concentrated around the center of the simulation area, for instance.  
Coverage is important in our analysis because it gives an indication of how suited the mobility model is to the type of application for which we want to use it. Our main concern in this paper is search missions; therefore, a mobility model with a large enough coverage of the entire region without major unbalanced clustering is preferred because it shows that the mobility pattern allows extensive searching of the entire region. Moreover, the faster most of the area is covered, the better the mobility model is.
# Remote Sensing Inversion and Mapping of Typical Forest Stand Age in the Loess Plateau
## 2 Materials and Methods
### Methods
#### 2.3.3 Inversion Model of Forest Stand Age  
Accurate input parameters are the key to accurate inversion of a remote sensing model of forest age. Principal component analysis (PCA) was selected as the data for the characteristic parameters of the model; this method removes noise and unimportant features from high-dimensional spectral data and realizes the selection of model input parameters [36].  
Two types of methods were chosen to map forest stand age, namely the random Forest (RF) algorithm and the multiple linear regression (MLR) model. The RF algorithm is a bagging algorithm, and a bagging algorithm is a kind of ensemble learning method. Ensemble learning is the process of training multiple weak models to form a strong model [37]. The performance of the strong model is much better than that of a single weak model. In the training phase, the random forest uses bootstrap sampling to collect multiple different sub-training datasets from the input training dataset to train multiple different decision trees in turn. In the prediction phase, the random forest averages the prediction results of multiple internal decision trees to obtain the final results [38]. The algorithm mainly considers the two processes of the training stage and the prediction stage to realize the forest stand age inversion based on remote sensing data. In the training stage, bootstrap sampling is used to collect several different sub-training datasets from the input training dataset to train several different decision trees in turn. In the prediction stage, the forest stand age inversion model is obtained by averaging the prediction results of multiple decision trees. MLR refers to a linear regression model containing multiple explanatory variables, which is used to explain the linear relationship between the explained variable and explanatory variables [39].  
Using the position information of forest stand age observation sample points, we extracted the corresponding sentinel data canopy reflectance, a total of 603 pairs of spectral and corresponding age data, and divided the data into a training set (accounting for 70% of the total dataset) and verification set (accounting for 30% of the total dataset).
# Fault Diagnosis of Wind Turbine Gearbox Based on Mel Spectrogram and Improved ResNeXt50 Model
## 2 Preprocessing
The calculation formula is as follows:  
\[E_{l}(k)=\sum_{m=0}^{\mathrm{M-1}}\left|X_{l}(k)\right.^{2} \tag{5}\]  
In Equation (5), \(E_{l}(k)\) is the energy of the \(l\)-th frame at the frequency index \(k\), and \(X_{l}(k)\) represents the result of the STFT of the \(l\)-th frame. The Mel frequency is linear when the actual frequency is below 1000 Hz, and above 1000 Hz, it becomes logarithmic in growth. By setting the upper and lower limits of the frequency, unwanted or noisy frequencies can be filtered out and then converted to the Mel frequency [21]. Then, a triangular filter bank with K channels is configured on the Mel frequency axis, and the frequency response of each filter is as follows:  
\[H_{m}(k)=\left\{ \begin{array}{ll}0&k<f(m-1)\\ \frac{k-f(m-1)}{f(m)-f(m-1)}\ f(m-1)\leq k\leq f(m)\\ \frac{f(m+1)-k}{f(m+1)-f(m)}f(m)\leq k\leq f(m+1)\\ 0&k>f(m+1)\end{array} \right. \tag{6}\]  
In Equation (6), \(H_{m}(k)\) is the frequency response of the \(m\)-th filter, representing the value of the frequency index \(k\), and \(m\) represents the filter number. \(f(m)\) is the frequency value of the \(m\)-th Mel frequency, and the frequency is usually converted to the Mel frequency using the Mel frequency scale. \[{F_{Mel}}^{-1}(f)=700(e^{(f/1125)}-1) \tag{7}\]  
\[f(m)=(\frac{\mathrm{N}}{f_{s}})F_{Mel}^{-1}[F_{Mel}(f_{l})+m\frac{F_{Mel}(f_{ h})-F_{Mel}(f_{l})}{\mathrm{M}+1}] \tag{8}\]  
In Equations (7) and (8), \(f_{h}\) and \(f_{l}\) represent the highest and lowest frequencies of the filter frequency, respectively; M is the number of Mel filters; \(f_{S}\) is the sampling frequency of the wind turbine gearbox, where \(f_{S}\) = 16 kHz; and N is the frame length for STFT. By using Mel filters to reduce the dimensionality of the data, the size of the data is reduced, and subsequent model training and recognition are simplified. The process of generating the Mel spectrogram is as follows: first, the audio signal undergoes FFT (Fast Fourier transformation); second, the power spectrum is obtained through pre-emphasis; finally, the Mel spectrogram is generated using Mel filter banks.
# A CNN-Based Downscaling Model for Macau Temperature Prediction Using ERA5 Reanalysis Data
## 1 Introduction
In the face of heterogeneous time series data from multiple sources, CNN can synchronously analyze the cross-modal interactions of temperature, barometric pressure, humidity, and other meteorological elements by constructing a multi-dimensional tensor input layer, and it breaks through the dependence of the classical time series model on the assumption of equidistant intervals by making use of the cavity convolution technique compatible with non-uniform sampling data [8]. This study focuses on improving the spatial applicability of ERA5 reanalysis data in the Macau region. Due to the special geographic location of Macau (22.16\({}^{\circ}\) N, 113.57\({}^{\circ}\) E), which does not overlap with the ERA5 grid points, there is a systematic bias in the direct calculation of traditional interpolation methods. For this reason, we systematically compare three benchmarking methods: directly using the true value of the Macau region (Observed) with the nearest-neighbor interpolation (Nearest), bilinear interpolation (Linear), and cubic spline interpolation (Cubic) [9], and constructing a convolutional neural network (CNN) model for the spatial refinement correction. The main contributions of this research are as follows:  
1. In this study, based on the 2012-2020 ERA5 hourly 2 m temperature data (VAR_2T), a 41 \(\times\) 41 grid dataset around Macao (17-27\({}^{\circ}\) N, 109-119\({}^{\circ}\) E) was constructed, and a CNN model was used for spatial and temporal feature extraction and prediction. The CNN model was used for spatial and temporal feature extraction and prediction, and the output values were compared with the measured temperatures at the Tai Tam Shan site of the Macau Meteorological Bureau (22.16\({}^{\circ}\) N, 113.57\({}^{\circ}\) E). 2. In the methodological framework, this study compares the CNN model with three traditional interpolation methods: the ERA5 Nearest directly uses nearest-neighbor grid point temperatures; the ERA5 Linear implements bilinear interpolation based on 4-neighbor grid points; and the ERA5 Cubic performs cubic spline interpolation through 16-neighbor grid points. A baseline reference is provided for subsequent model performance analysis. The rest of the paper is structured as follows: Section 2 briefly describes the data sources and processing methods, including the ERA5 reanalysis dataset, and the Macau ground station data; Section 3 introduces the traditional interpolation methods as well as our deep learning model; Section 4 demonstrates the experimental results and argues the advantages of deep learning over traditional methods and the potential for regional expansion; and finally, Section 5 concludes the results and looks forward to the actual business applications and multi-region extension.
# Security Performance Analysis of LEO Satellite Constellation Networks under DDoS Attack
## 1 Introduction
Therefore, the space-time graph model is firstly used to determine the key nodes in the satellite network, a DDoS attack is used as the main means to attack the key nodes in the link, and the security performance of the entire satellite network against DDoS attacks is analyzed. Finally, the co-simulation in the scenario is confirmed. It has certain reference value for analyzing the security of LSCNs. In the following sections, we first introduce related works on satellite key-node identification and satellite network DDoS attacks. Section 2 describes the methods used. Next, the simulated scenario and the parameter settings are given in Section 3. Section 4 analyzes the security performance of LSCNs after being attacked. Finally, we provide the conclusions.