esa-sceva/satcom-chunk-collection huggingface dataset:
# Calculation of Nonlimit Active Earth Pressure against Rigid Retaining Wall Rotating about Base
## 6 Parameter Sensitivity Analysis
### Analysis of the Effect of Interface Friction Angle \(\delta\) on the Earth Pressure Distribution  
An angle of rotation of \(1\times 10^{-4}\) rad in the wall was considered for the analysis for \(\delta=0.2\varphi\) to \(\varphi\).  
As can be observed in Figure 6, the soil pressure distribution curve in the upper half of the soil mass was linear as \(\delta\) changed. A further increase in \(\delta\) resulted in the curve in the lower half of the soil shifting toward the negative direction of the \(x\)-axis. For the same soil depth, the earth pressure did not vary significantly in the upper half of the soil as the depth increased, but it decreased sharply in the lower half of the soil as \(\delta\) increased. Moreover, the decrease in earth pressure became more pronounced as the soil depth increased. This observation can be explained by the fact that, in RB mode, the fill behind the wall has a tendency to slide downwards relative to the retaining wall. Further, when the friction angle between the wall and the soil is considered, the wall surface will exert an upward frictional force on the fill to prevent it from sliding downward, resulting in a deflection in the principal stresses and the soil arch effect. This friction exerts an upward restraining force on the soil, counteracting the gravity of the fill to some extent and lowering the soil weight. Thus, the earth pressure in the soil decreases as \(\delta\) increases.  
Figure 5: Horizontal active earth pressure distribution along wall depth with varying rotation angle.  
Figure 6: Horizontal active earth pressure distribution along wall depth with varying interface friction angles \(\delta\).
# Optical Flow Odometry with Panoramic Image Based on Spherical Congruence Projection
###### Abstract  
Panoramic images provide distinct advantages in odometry applications, which are largely due to their extensive field of view and higher information density captured in a single frame. Traditional odometry methods often rely on mapping panoramic images onto the planar structure for feature tracking. However, this process introduces uneven distortions of features, which diminish the accuracy of feature tracking and odometry, particularly in scenarios involving large displacements. In this work, we address this challenge by introducing a novel approach, named spherical congruence projection (SCP), that maps panoramic images onto a spherical structure and projects the spherical pixels onto a two-dimensional data format while preserving the spherical pixel topology. SCP effectively eliminates the distortion across the panoramic image. Additionally, we present the optical flow odometry on the panoramic image in the spherical structure and integrate it with the proposed SCP method for the first time. The experimental results in public and custom-built datasets demonstrate that the proposed SCP-based odometry method reliably tracks features and maintains accurate odometry performance, even in fast-moving scenarios.  
optical flow tracking; spherical mapping; feature extraction +
Footnote †: journal: Applied sciences
# Time-Aware and Feature Similarity Self-Attention in Vessel Fuel Consumption Prediction
## 3 Methodology
### Sequence Model  
#### 3.1.1 Sequence Processing  
Ship data have sequential property as the data acquisition occurs consecutively during the sailing. Before introducing the sequence model in this study, it is necessary to describe how we convert sailings into sequences, which are inputs of the model. Although we can roughly define sequences based on sailings, sequence-length has a large deviation depending on sailings. The deviation occurs owing to different sailing distances or preprocessing of noise and missing data. Thus, we use the truncated sequence per sailing, \(S=\{s_{1},s_{2},\cdots,s_{n}\}\), where \(n\) indicates the fixed max-length to truncate the sequence. If the length of \(S\) does not satisfy fixed max-length, we pad them to zeros for guaranteeing the equal sequence length. Each \(s\) consists of the \(k\) number of features (e.g., speed, heading, and draft).
## 3 Numerical Simulations and Discussion
### Effect of Sensor Resolution
#### 3.4.1 Results
From 1 to 461 Hz, k4.yy and c4.yy, k4.yx and c4.yx cannot be identified. From 1 to 301 Hz, k5.yy and c5.yy, k5.yx and c5.yx cannot be identified. From 1 to 331 Hz, k6.yy and c6.yy, k6.yx and c6.yx cannot be identified. From 1 to 115 Hz, 119 to 255 Hz, k7.yy and c7.yy, k7.yx and c7.yx cannot be identified. From 1 to 793 Hz and 869 to 1285 Hz, k8.yy and c8.yy, k8.yx and c8.yx cannot be identified. Similar results can be obtained from simulations of h1.1 and h1.4, whose results are shown in Figures A11 and A12 in Appendix C.
# A Fault Detection Framework for Rotating Machinery with a Spectrogram and Convolutional Autoencoder
## 3 Proposed Framework
### Two-Dimensional Convolutional Autoencoder with Spectrogram
In the 2D-CAE structure, the input spectrogram is embedded into smaller latent feature vectors by passing through multiple convolutional blocks within the encoder part, and thus, the encoder of the 2D-CAE structures can be regarded as a feature extractor. Hence, the latent feature vectors obtained from the encoder of the 2D-CAE are used as inputs of the one-class classification step. Because the 2D-CAE structure is trained with data collected only from the normal operating state, the weights of the trained 2D-CAE can incorporate information on only the normal signals. Conversely, the fault signal's spectrogram cannot be properly involved in its latent feature vector, and the fault signal's features might be significantly deviated from those of the normal signal. **Remark**. Please note that, in the current study, the input and output of the 2D-CAE model are the same spectrograms, which are transformed from the original normal signals, not noise added. As a feature extractor, denoising the 2D-CAE model [66] can also be considered in order to deal with the intrinsic noises in the normal signals. In the denoising 2D-CAE model, the inputs of the 2D-CAE model are random noise-added spectrograms, whereas the outputs are the original spectrograms. In addition, by formulating the loss function as MSE between input and output, the latent features can capture the intrinsic properties of signals by removing the noise elements. In the current study, we conducted an experiment to compare the standard 2D-CAE (proposed) with the denoising 2D-CAE, and the comparative results are provided in Section 4. According to the comparative results, the fault detection performance of the denoising 2D-CAE is comparable to that of the original 2D-CAE. However, the denoising 2D-CAE might not be appropriate for the fault signal detection problems because the optimal level of noise added to input spectrograms is not easy to be determined. Furthermore, the standard 2D-CAE also can partially remove some of the noise because it attempts to compress the most useful features of input signals as much as possible, and the noise elements are not such informative [67]. Therefore, in this study, we propose to employ the standard 2D-CAE as the feature extractor of the input signals.
# Optimizing Healthcare Delivery: A Model for Staffing, Patient Assignment, and Resource Allocation
## 3 Model Mathematical Formulation
### Objective Function  
Our objective is to minimize the total cost of staff assignments, patient scheduling, and resource allocation while maximizing the efficiency of healthcare delivery. The following objective function represents this:  
\[\text{minimize}\sum_{i=1}^{N}\sum_{t=1}^{T}W_{it}x_{it}+\sum_{i=1 }^{N}\sum_{j=1}^{N}\sum_{t=1}^{T}I_{ijt}x_{it}x_{it}+\sum_{p=1}^{P}\sum_{t=1}^ {T}C_{pt}y_{pt}+\sum_{r=1}^{R}\sum_{t=1}^{T}D_{rt}z_{rt}\] \[+\sum_{p=1}^{P}\sum_{t=1}^{T}P_{pt}y_{pt}+\sum_{i=1}^{N}O_{i}o_{i} +\sum_{p=1}^{P}\sum_{i=1}^{N}\sum_{k=1}^{K}\sum_{t=1}^{T}U_{p}v_{ip}+\sum_{i=1 }^{N}\sum_{t=1}^{T}S_{i}x_{it}.\]  
Objective Function Terms  
1. \(\sum_{i=1}^{N}\sum_{t=1}^{T}W_{it}x_{it}\): This term represents the weight associated with assigning staff member \(i\) to time slot \(t\).  
2. \(\sum_{i=1}^{N}\sum_{j=1}^{N}\sum_{t=1}^{T}I_{ijt}x_{it}x_{jt}\): This term represents the interaction weight between staff members \(i\) and \(j\) at time slot \(t\) when both are assigned to work.
3. \(\sum_{p=1}^{P}\sum_{t=1}^{T}C_{pt}y_{pt}\): This term represents the cost associated with assigning patient \(p\) to time slot \(t\).
4. \(\sum_{r=1}^{R}\sum_{t=1}^{T}D_{rt}z_{rt}\): This term represents the cost associated with allocating resource \(r\) at time slot \(t\).
5. \(\sum_{p=1}^{P}\sum_{t=1}^{T}P_{pt}y_{pt}\): This term represents the penalty associated with patient \(p\) not receiving service during time slot \(t\).
6. \(\sum_{i=1}^{N}O_{i0}i\): This term represents the overtime weight for staff member \(i\) when they work overtime.
7. \(\sum_{p=1}^{P}\sum_{i=1}^{N}\sum_{k=1}^{K}\sum_{t=1}^{T}U_{p}v_{ip}\): This term represents the weight associated with patient \(p\) not receiving service when staff member \(i\) serves them.
8. \(\sum_{i=1}^{N}\sum_{t=1}^{T}S_{i}x_{it}\): This term represents the skill level of staff member \(i\) required for serving patients during time slot \(t\).
## 1 Introduction
However, its performance is highly sensitive to the selection of the penalty factor \(c\) and the kernel function parameter \(g\). Particle Swarm Optimization (PSO) is a swarm optimization algorithm [16]. PSO is particularly suitable for solving highly complex optimization problems due to its advantages of a fast convergence speed and good search performance. Therefore, PSO is used to iteratively optimize \(c\) and \(g\) to improve the accuracy of the final pattern recognition. In summary, aiming at the fault identification problem of reciprocating compressors, a fault diagnosis method based on optimized RSSD and optimized-parameter RCMDE is proposed. The main research contents include the following: The high-quality and low-quality factors of RSSD are optimized through ASO. When the kurtosis of the low-resonance component reaches the maximum value, the corresponding high-quality and low-quality factors are selected. The core parameters of RCMDE are optimized by GA to obtain the most suitable eigenvalues. PSO is used to iteratively optimize the key parameters \(c\) and \(g\) of SVM to improve the accuracy of the final pattern recognition. The obtained entropy value is calculated by improved SVM to obtain the fault diagnosis result. The method was validated through simulated and experimental signals of valve faults in reciprocating compressors. The rest of this paper is arranged as follows. Section 2 details the background of the ASO optimization RSSD and GA optimization RCMDE. Section 3 describes the simulation signal processing of reciprocating compressor valve failure by ASO-RSSD and compares these results with the RSSD processing without quality factors' optimization. Section 4 explains the experimental setup, the process of using RSSD signal processing, and the extraction of RCMDE eigenvalues. The conclusion is presented in Section 5.
## Appendix A Equations and the FEM Numerical Method  
### Equations for Electromagnetic and Thermic Problems  
The total electromagnetic field \(\mathbf{E}(\mathbf{x})\) is the solution of the Helmholz equation, given by:  
\[\
abla\times(\
abla\times\mathbf{E}(\mathbf{x}))-\frac{\omega^{2}}{c^{2}} \epsilon_{r}(\mathbf{x})\mathbf{E}(\mathbf{x})=\mathbf{0},\quad\forall\mathbf{ x}\in\Omega, \tag{1}\]  
with the velocity of light denoted by \(c\) and the curl operator by \((\
abla\times)\). To solve Equation (1), a radiation boundary condition (Equation (1)) is applied on the external border \(\Gamma\) of the computational domain \(\Omega\)[20; 22]:  
\[\hat{n}\times(\
abla\times\mathbf{E}(\mathbf{x}))=-\frac{\omega}{j}\hat{n} \times\hat{n}\times((\mathbf{E}(\mathbf{x})-\mathbf{E}_{i}(\mathbf{x}))+\hat{ n}\times(\
abla\times\mathbf{E}_{i}(\mathbf{x}))\quad\forall\mathbf{x}\in\Gamma, \tag{2}\]with the normal vector to the surface \(\hat{n}\), \(\hat{n}^{2}=-1\), and the incident electromagnetic field \(\mathbf{E}_{i}\). Such a boundary condition is used to describe the free propagation of the total electromagnetic field. The amplitude of the illumination field is given by \(E_{0}=\sqrt{2c\mu_{0}P_{s}}\), with the permeability of a vacuum \(\mu_{0}\) and the surface laser power density \(P_{s}\). Such a surface laser power density \(P_{s}\) is a function of the laser power \(P_{w}\) and the diameter of the laser beam \(D_{beam}\) (i.e., \(P_{s}=4P_{w}/(\pi D_{beam}^{2})\)).  
For the thermic problem, the temperature elevation is governed by the heat equation (Equation (2)) with the external boundary condition (Equation (A3)):  
\[\hat{n}\cdot\left(\frac{\kappa(\mathbf{x})}{\rho(\mathbf{x})C_{p}(\mathbf{x})} \
abla\cdot T(\mathbf{x},t)\right)+\frac{\alpha(\mathbf{x})}{\rho(\mathbf{x}) C_{p}(\mathbf{x})}(T(\mathbf{x},t)-T_{0})=0,\ \ \ \ \ \forall\mathbf{x}\in\Gamma,\] (A3)  
with the initial temperature \(T_{0}\) and the heat transfer coefficient \(\alpha\). Such an external boundary is the Fourier boundary and the initial condition \(T(\mathbf{x},0)=T_{0}\)[33].
###### Abstract  
Tangerine peel, rich in moisture (75-90%) and medicinal value, requires drying to prevent spoilage and extend shelf life. Traditional heat pump drying often causes uneven airflow, leading to inconsistent drying and nutrient loss, compromising product quality and storage stability. In this study, a prediction model of drying moisture ratio of tangerine peel based on Kolmogorov-Arnold network bidirectional long short-term memory (KAN-BiLSTM) and multimodal feature fusion is proposed. A pre-trained visual geometry group U-shaped network (VGG-UNet) is employed to segment tangerine peel images and extract color, contour, and texture features, while airflow distribution is simulated using finite element analysis (FEA) to obtain spatial location information. These multimodal features are fused and input into a KAN-BiLSTM model, where the KAN layer enhances nonlinear feature representation and a multi-head attention (MHA) mechanism highlights critical temporal and spatial features to improve prediction accuracy. Experimental validation was conducted on a dataset comprising 432 tangerine peel samples collected across six drying batches over a 480 min period, with image acquisition and mass measurement performed every 20 min. The results showed that the pre-trained VGG-UNet achieved a mean intersection over union (MIoU) of 93.58%, outperforming the untrained model by 9.41%. Incorporating spatial features improved the coefficient of determination (R\({}^{2}\)) of the time series model by 0.08 \(\pm\) 0.04. The proposed KAN-BiLSTM model achieved a mean absolute error (MAE) of 0.024 and R\({}^{2}\) of 0.9908, significantly surpassing baseline models such as BiLSTM (R\({}^{2}\) = 0.9049, MAE = 0.0476) and LSTM (R\({}^{2}\) = 0.8306, MAE = 0.0766), demonstrating superior performance in moisture ratio prediction.  
Keywords:image segmentation; finite element analysis; moisture ratio prediction; KAN-BiLSTM; feature fusion +
Footnote †: journal: Applied Sciences
# Underwater Biomimetic Covert Acoustic Communications Mimicking Multiple Dolphin Whistles
## 4 Analysis of Experimental Results  
In this section, the BER performance of the proposed method was compared to conventional methods through computational simulations and real ocean experiments using underwater channels. Finally, the MOS test was performed to evaluate the similarity of the bio-mimicking signals generated by the proposed method to actual dolphin whistle sounds.
# A Simple Experimental Stand for Studying the Network Security Level of Power Electronic Devices
## 2 Types of Cyber-Attacks and Vulnerabilities Applicable to Power Electronic Devices
### Vulnerabilities Specific to the PED  
Vulnerabilities are a broad category of flaws, potential \"cracks\" and weaknesses in the system hardware, software of a network device [20; 21; 22; 23; 24; 25]. These vulnerabilities can be classified as follows:  
* Physical--means that the device is easily accessible by anyone who wants to harm it. Examples of such vulnerabilities are the physical manipulation of inverters or PV controllers installed in some unattended solar farm. If measures are not taken to physically protect these PEDs, the same can be very easily manipulated, resulting in the operation of the target park being suspended;
* Network vulnerabilities--this type of vulnerability is related to the interaction between the PED and the IP network. When a PED connects to an IP network which is connected to the Internet, it immediately becomes a potential victim of attack. If the PED is not secured from attacks, it can very easily be taken under remote control. These vulnerabilities make it very easy for a PED to be remotely taken over and controlled.  
The vulnerabilities that are specific to standard network devices can be said to be applied to PEDs as well. Of course, PEDs are not network devices and they have more limited network functionalities. In general, the communication capabilities are limited to a simple web server (http server) through which the PED is monitored and managed, i.e., vulnerabilities related to the operating system such as malware are not common for PEDs. Therefore, it can be argued that mainly network vulnerabilities related to web servers are the most common for PEDs. Examples of such vulnerabilities are the use of http instead of https; the use of weak passwords; unnecessarily open ports; and vulnerability to DoS attacks, etc.
## 4 Evolution of Cadmium Transporters and Their Functions in Cadmium Accumulation
### Function of Cd Transporters
#### 4.2.1 NRAMPs  
NRAMPs represent a family of metal transporters that are located at the membranes of root cells and are evolutionarily conserved in a wide range of organisms, including bacteria, fungi, plants, and animals [95]. In plants, _NRAMP_ genes participate in the uptake of divalent cations, such as Fe\({}^{2+}\), Mn\({}^{2+}\), Cu\({}^{2+}\), Zn\({}^{2+}\), and Cd\({}^{2+}\) (Table 2). In Arabidopsis plants, six NRAMP family members have been identified (Table 1), with _AtNRAMP3_ and _AtNRAMP4_ being localized at the tonoplast and responsible for the Cd\({}^{2+}\) efflux from vacuoles to the cytosol [103]. In rice, OsNRAMP1 and OsNRAMP5 are plasma membrane (PM)-localized transporters involved in taking up Cd from the external solution to root cells [65; 104]. The knockdown or CRISPR/Cas9-mediated editing of _OsNRAMP5_ causes a dramatic reduction in Cd and Mn concentrations in both rice roots and shoots [65; 105]. However, OsNRAMP3, OsNRAMP4, and OsNRAMP6 have no Cd transportation capacity [106; 107; 108]. In barley, HvNRAMP5, which is 84% identical with OsNRAMP5, can also mediate Cd uptake [109]. In the hyperaccumulator _Nocaea caerulescens_, NcNRAMP1 is one of the main transporters involved in the influx of Cd\({}^{2+}\) across the endodermal PM, thus playing a key role in Cd\({}^{2+}\) influx into the stele and contributing to Cd root-to-shoot transport [110].
## 4 Q3: What Are the Methodologies Used to Overcome the Challenges in UIoT?
### Methods to Overcome the Technical Challenges of UIoT  
#### 4.1.1 Low Battery Consumption Methods  
In [54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64], the existing techniques for solving the battery problem in UIoT are discussed, and some methods are indicated herewith. In [56], [PERSON] et al. proposed a powerful and rechargeable module using Panasonic (CGR18650E) to provide sufficient energy, and the experiment result shows that it is reliable and safe in the underwater environment. In [58], [PERSON] et al. designed a battery-less underwater node that can recharge via an acoustic signal from a short or long distance. In [59], [PERSON] proposed a named data networking (NDN) approach for relay network topology in underwater acoustic sensor networks to identify the node's power consumption in a shallow sea and deep-sea environment. Finally, in [60], [PERSON], a two-level Redundant Transmission Control (RTC) was proposed to control the communication in underwater acoustic sensor networks, and the performance result shows that energy consumption is lower for the RTC approach.
## 3 Properties of Multifunctional Cemertitious Composites
### Conductive Fillers
#### 3.1.2 Carbon Nanotubes (CNTs)  
Carbon nanotubes (CNTs) are formed by the rolling of graphene sheets. Depending on the structure of the rolled graphene, CNTs are classified into single-walled carbon nanotubes (SWCNTs) and multi-walled carbon nanotubes (MWCNTs) [46]. The incorporation of CNTs not only enhances the mechanical strength but also significantly improves the functional properties of multifunctional cementitious composites [47]. However, several challenges arise when using CNTs as conductive fillers in cementitious composites. One of the primary challenges is the difficulty in dispersing CNTs, as they tend to agglomerate due to the strong van der Waals forces on their surfaces [48]. To address this issue, various dispersion techniques, such as sonication and the use of chemical surfactants, have been employed [49]. Additionally, the high cost of CNTs presents another barrier to their widespread use in cementitious composites [46]. To mitigate this challenge, combined conductive fillers, such as mixtures of CFs and CNTs, have been explored in previous studies, offering a cost-effective solution while enhancing both the mechanical and electrical properties of the composites [18]. The optimal proportion of CNTs in multifunctional cementitious composites is influenced by several factors, including the type of CNTs, the quality of dispersion, and the desired mechanical and functional properties [20].
## 2 Materials and Experimental Testing
### Experimental Testing
#### 2.3.3 Compressive Strength Test  
Dry Compressive Strength  
The compressive strength was conducted on three samples from each mixture prepared. This test consisted of applying a compressive load continuously and without jerking to the hardened block until failure (Figure 8). The maximum load supported by the specimen during the test was recorded by using a Hydraulic IGM--France machine with a maximum load capacity of 3000 KN. Tests were carried out at a pressure increase of 0.15 MPa/s until the complete rupture of the specimen. Then, the compressive strength was calculated on each block according to Equation (4):  
\[\sigma\text{c}=\frac{\text{Fc}}{\text{S}} \tag{4}\]  
where \(\sigma\text{c}\) (MPa), Fc (N), and S (mm\({}^{2}\)) are the compressive strength, the maximum failure force and the contact surface between the CEB and plates used in the test, respectively.  
Figure 7: Capillarity absorption test.  
Wet Compressive Strength  
This test was identical to the dry compression test, the only difference being that the test specimens were previously moistened according to the following preparation method:  
1. We immersed the block completely in water;
2. After 2 h of immersion, we removed the block from the water and wiped it with a damp cloth;
3. We stored the removed blocks in normal laboratory conditions at 20 \(\pm\) 2 \({}^{\circ}\)C for at least 48 h before testing.