esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Generalized Additive Model (GAM) Implementation
\[\text{CORR}=\frac{\sum\left(X_{i}\text{--}\overline{X}\right)\left(Y_{i}\text{--} \overline{Y}\right)}{\sqrt{\sum\left(X_{i}\text{--}\overline{X}\right)^{2}} \cdot\sqrt{\sum\left(Y_{i}\text{--}\overline{Y}\right)^{2}}} \tag{5}\]  
where CORR is the Pearson's Correlation Coefficient, \(X_{i}\) are the actual data, \(\overline{X}\) is the mean of the actual data, \(Y_{i}\) is the predicted data, and \(\overline{Y}\) is the mean of the predicted data. \[\text{RMSE}=\sqrt{\frac{1}{n}\sum\limits_{i=1}^{n}\left(Y_{i}-\hat{Y}_{i} \right)^{2}} \tag{6}\]  
\[\text{MAE}=\frac{1}{n}\sum\limits_{i=1}^{n}\left|\hat{y_{i}}-y_{i}\right| \tag{7}\]  
\[\text{MAPE}=\frac{100}{n}\sum\limits_{i=1}^{n}\left|\frac{y_{i}\text{--}\hat{y _{i}}}{y_{i}}\right| \tag{8}\]  
where n is the number of observations, \(Y_{i}\) are the actual values and \(\hat{Y}_{i}\) are the predicted values. _Spatiotemporal Prediction:_ One of the major benefits of the proposed modeling approach is its ability to predict the number of vehicles at sites (shelters or lodging facilities) for which there are no historical data, and such predictions can be made into future time periods. To generate predictions, a spatiotemporal prediction grid was designed to cover the study area and time period. This grid allows vehicle counts at shelters and lodging facilities to be predicted across different spatial and temporal points. The predicted vehicle counts were then visualized using GIS tools, which helped identify hotspots of shelter demand. These visualizations are crucial for emergency management, enabling authorities to allocate resources more effectively and anticipate areas where shelter demand may exceed capacity. By integrating these methodologies--spatiotemporal modeling, GAM, and detailed data processing--the study presents a robust framework for predicting and planning for non-congregate and congregate shelter demand. This approach can be adapted to other regions and disaster scenarios, providing a valuable tool for emergency management and resource allocation.
# End-To-End Convolutional Neural Network Model for Fear Fault Diagnosis Based on Sound Signals
## 1 Introduction
Although all these sound signal based methods can work in fault diagnosis and have the capability to detect fault modes, they rely heavily on prior knowledge of signal processing techniques and diagnostic expertise, especially for the analysis of multiple channel measurement signals [23]. Deep learning models [24] can overcome this limitation. Pre-processed data can be directly fed as samples to train deep neural networks without manual feature extraction and selection. At present, deep learning models have been widely applied in speech recognition [25; 26], acoustic scene classification [27; 28], and environmental sound classification [29; 30]. Many researchers also applied deep learning models in vibration signal analysis to detect fault modes [1; 2; 4; 31]. However, there are few studies applying deep learning in ABD for two reasons: (1) the scarcity of publicly available acoustical signal fault diagnosis datasets like Case Western Reserve University bearing vibration signal datasets, and (2) acoustical signal acquisition requires a higher standard for environment and for equipment. To address the obstacles as discussed above, a novel ABD method for gear fault pattern recognition is proposed. In our model, convolutional neural networks (CNN) are established to adaptively mine available fault characteristics by directly using time and frequency domain signals without other manually constructed features and automatically detect the fault modes of gears. In addition, we have released our acoustical gear fault diagnosis dataset to promote open research in the field of ABD. To our knowledge, this is the first such public sound dataset for gear fault diagnosis. The main contributions of this paper are as follows:  
1. Build and release the first ever publicly available sound dataset of gear fault diagnosis based on acoustical signal. 2. We designed an end-to-end stacked CNN model for an ABD task, which obtained information directly from time and frequency domain without feature engineering. We are the first to use CNN instead of traditional fusion algorithms, such as Dempster-Shafer (D-S) evidence theory, to fuse multi-channel acoustical signals.
# Modeling and Reasoning of Contexts in Smart Spaces Based on Stochastic Analysis of Sensor Data +
## 4 Principles of Context Reasoning
### Context Graph Building Steps
#### 4.2.1. Deciding the Number of Contexts  
To find the number of contexts, we first capture the probability of the consecutive occurrence of each pair of different sensors in the datasets. The idea is that sensors in one context are related and, thus, the occurrence probability is fairly high [41]. In other words, if the occurrence probability of a pair of sensors is low, the sensors are not in a context. This probability can be accurately calculated from the frequency of occurrence of consecutive sensor events. These conditional probabilities are arranged as a \(\zeta\times\zeta\) table (\(\zeta\) being the number of sensors), which is called the conditional probability table (CPT). Figure 3 shows how to decide on a pair of consecutive sensor events and how to build a CPT.  
Figure 3: A conditional probability table (CPT) consists of conditional probability of each pair of sensor events. Conditional probability reveals how frequently a sensor event occurs in a given scenario. A conditional probability of 0 means the sensor event never happened.  
CPT is used as the probabilistic fingerprint of the entire dataset. A pair of sensor events with high conditional probability usually contains sensor events that are related and associated with each other. They could belong to the same context and, therefore, are highly likely to occur together in this order. On the other hand, if the pair has low conditional probability, its sensor events are considered to be unrelated and would rarely occur together. A pair of sensor events with low probability indicates the end of one context and the start of another. Therefore, we divide the dataset between sensor events \(\mathbf{e}_{i}\) and \(\mathbf{e}_{i+1}\) if the conditional probability of \(\mathbf{e}_{i}\) and \(\mathbf{e}_{i+1}\) satisfies the condition \(\mathbf{P}(\mathbf{e}_{i+1})\leq\mathbf{\theta}\times\mathbf{P}(\mathbf{e}_{i})\), where \(\mathbf{\theta}\) is a parameter that represents the extent to which \(\mathbf{e}_{i+1}\) relates to \(\mathbf{e}_{i}\). We set \(\mathbf{\theta}\) to 0.5 in the experiments. Using this method, we divide the dataset into \(k\) groups, which are considered the number of contexts.
## 2 Methodology  
### Coordinate Systems and Symbols  
To predict the behaviors of the submarine, body-fixed (_Oxyz_) and earth-fixed (\(O_{1}x_{0}y_{0}z_{0}\)) coordinate systems are used to represent the dynamic behavior of the vehicle and its trajectory. The origin of the body-fixed coordinate system is located at the center of the mid-section of the submarine. Figure 1 illustrates the two coordinate systems and definitions of the kinematics, forces, and moments of the submarine, in which \(u\), \(v\), and \(w\) stand for linear velocities along the \(x\)-, \(y\)-, and \(z\)-axes, while \(p\), \(q\), and \(r\) are angular velocities about these axes, respectively. Furthermore, \(X\), \(Y\), and \(Z\) indicate external forces of the submarine along the \(x\)-, \(y\)-, and \(z\)-axes and \(K\), \(M\), and \(N\) are the moments of the body about \(x\)-, \(y\)-, and \(z\)-axes, correspondingly.
## 6 Deflection Measurement
### Laser Tracker  
The laser tracker is most commonly used for tracking the robot's EE position. It projects a laser beam onto an optical target that is in contact with an object to be measured, and determines the three-dimensional position of the target. The optical targets, known as spherically mounted retroreflectors (SMRs), reflect the laser beam to its emitted origin point. As shown in Figure 13, an EE was built to hold three SMRs, which were tracked by a FARO laser tracker. Each SMR's three translational position coordinates referring to the laser tracker coordinate system were recorded. If a six-dimensional (three translational positions plus three rotational angles) pose needed to be measured, at least three SMRs were required to form the six-dimensional pose [109]. The layout of the SMRs on the EE should be considered when the robot moves to the planned poses, which could hinder the laser beam reflecting from SMRs. To ensure that the SMRs were in the range of the laser tracker, offline simulation was needed to plan the location of the laser tracker and the robot poses. In addition, more than three SMRs could be mounted on the EE to ensure that at least three SMRs could be tracked at arbitrary poses. As shown in Figure 14, four SMRs were symmetrically located on a square plate for position tracking. The laser tracker manufacturer also supplied a six-dimensional position tracking device, such as a Leica T-Mac (Leica, Wetzlar, Germany) [124]. Depending on the distance between the SMRs and laser tracker, the absolute measuring accuracy reached 10 \(\upmu\)m.  
Figure 13: Experimental setup for deformation measurement with laser tracker [109].
## 3 Coupling Mathematical Model for Tunnel Freezing-Frost Heave
### Mathematical Model for the Freezing Temperature Field
\begin{array}{ccc}\lambda_{f}&(T<T_{d})\\ \lambda_{f}+\frac{\lambda_{u}-\lambda_{f}}{T_{r}-T_{d}}(T-T_{d})&(T_{d}\leq T \leq T_{r})\\ \lambda_{u}&(T>T_{d})\end{array} \right. \tag{5}\]  
where  
\(C^{*}\)--Equivalent volumetric specific heat of soil  
\(\lambda^{*}\)--Equivalent thermal conductivity of soil The two-dimensional heat balance control differential equation of the freezing temperature field may be consistently stated by synthesizing Equations (1)-(5):  
\[C^{*}\frac{\partial T}{\partial t}=\frac{\partial}{\partial x}\left(\lambda^{*} \frac{\partial T}{\partial x}\right)+\frac{\partial}{\partial y}\left(\lambda^{* }\frac{\partial T}{\partial y}\right) \tag{6}\]  
Equation (6)'s initial temperature requirement:  
\[T|_{t=0}=T_{0} \tag{7}\]  
where \(T_{0}\) is the initial temperature of the soil, and a uniform temperature distribution is assumed. The temperature field in Equations (3)-(8) must meet continuity and energy conservation constraints at the phase transition boundary, i.e., at the frozen front \(S(t)\), namely:  
\[T_{f}[S(t),t]=T_{d} \tag{8}\]  
\[\lambda_{f}\frac{\partial T_{f}}{\partial n}-\lambda_{u}\frac{\partial T_{u}}{ \partial n}=L\frac{dS(t)}{dt} \tag{9}\]  
where \(n\) is the phase transition boundary's normal vector. Because the freezing tube is significantly smaller than the tunnel and stratum, it may be treated as a single low-temperature point source, and the boundary conditions at the freezing tube are as follows:  
\[T|_{(x_{d}y_{d})}=T_{b}(t) \tag{10}\]  
where  
\(x_{d},y_{d}\)--Center coordinate of the freezing tube, m  
\(T_{b}\)--Temperature of refrigerant inside the freezing tube, \({}^{\circ}\)C  
The boundary conditions for soil at an infinite distance from the tunnel section:  
\[T|_{(x=\infty,y=\infty)}=T_{0} \tag{11}\]  
When considering the heat exchange between the surface and the surrounding atmosphere, the surface may be seen as a convective heat transfer boundary, namely:  
\[\lambda^{*}\frac{\partial T}{\partial n}=\alpha(T_{a}-T) \tag{12}\]  
where  
\(T_{a}\)--Atmospheric temperature, \({}^{\circ}\)C  
\(\alpha\)--Convective heat transfer coefficient between atmosphere and surface, kJ/(m\({}^{2}\cdot\) s \(\cdot\)\({}^{\circ}\)C)  
The preceding two-dimensional heat balance control differential equation, as well as its initial and boundary conditions, provide a mathematical description of the stratum's transient freezing temperature field.
## 4 Results
Visual interpretation indicates that the eight NDWI images all clearly show open water body features as the result of enhancement. The main LSW distributions were detected in all eight LSW maps (Figure 4c,e,g,j,l,n,p,r). We selected the panchromatic images of ALI and ETM+ as the reference data because the ALI and TM data were taken on the same day. The quantitative assessment results (Table 3) show that the highest CV, BCV, OA, and Kappa for the five ALI NDWIs in Region III also come from NDWI\({}_{\mathrm{A4},\mathrm{9}}\), followed by NDWI\({}_{\mathrm{A4},\mathrm{8}}\), NDWI\({}_{\mathrm{A4},\mathrm{10}}\), NDWI\({}_{\mathrm{A4},\mathrm{7}}\), and NDWI\({}_{\mathrm{A4},\mathrm{6}}\). The ETM+ sensor yielded results in the following order: NDWI\({}_{\mathrm{E2},\mathrm{5}}\), NDWI\({}_{\mathrm{E2},\mathrm{7}}\), and NDWI\({}_{\mathrm{E2},\mathrm{4}}\). The results of the quantitative analysis of Region III were the same as in Regions I and II. The total areas covered by water bodies in Region III derived from the ETM+ data were 22.21 km\({}^{2}\) (NDWI\({}_{\mathrm{E2},\mathrm{4}}\)), 19.36 km\({}^{2}\) (NDWI\({}_{\mathrm{E2},\mathrm{5}}\)), and 19.60 km\({}^{2}\) (NDWI\({}_{\mathrm{E2},\mathrm{7}}\)), while those derived from the ALI data were 19.53 km\({}^{2}\) (NDWI\({}_{\mathrm{A4},\mathrm{6}}\)), 19.03 km\({}^{2}\) (NDWI\({}_{\mathrm{A4},\mathrm{7}}\)), 18.54 km\({}^{2}\) (NDWI\({}_{\mathrm{A4},\mathrm{8}}\)), 19.03 km\({}^{2}\) (NDWI\({}_{\mathrm{A4},\mathrm{9}}\)), and 19.11 km\({}^{2}\) (NDWI\({}_{\mathrm{A4},\mathrm{10}}\)) (Table 3).
## 3 Sliding-Mode Controller  
The correct operation of the PV system based on the NEC boost converter requires the following conditions:  
1. Stable operation of the NEC boost converter, which requires a stable \(v_{cb}\) voltage; hence, the stable relation between the inductor currents given in (21) must be ensured, i.e., \((1-d)\cdot i_{1}=d\cdot i_{2}\).
2. The regulation of the PV voltage is needed to impose the MPPT reference to the PV panel. In this case, it is proposed to regulate the PV current first, which corresponds to the input current of the NEC boost converter. An additional voltage controller will be designed, which will generate the current reference needed to regulate the PV voltage.  
Then, a sliding-mode controller (SMC) is designed to provide robustness and global stability for any feasible operation condition of the PV system. The first step to design the SMC is to define the switching function, which establishes the SMC sliding surface. Based on the previous two control requirements, the switching function given in (41) is proposed. The first component of the switching function (left) imposes the stable relation between the inductor currents, while the second component (right) forces the PV current \(i_{pv}\) to follow the desired reference \(i_{r}\).  
\[\psi=\underbrace{\left[i_{1}\cdot(1-d)-i_{2}\cdot d\right]}_{\text{1. Inductor current balance}}+\underbrace{\left[\left(i_{1}+i_{2}-i_{pv}\right)-i_{r}\right]}_{\text{2. Input current control}} \tag{41}\]  
The stability analysis of SMC for switching converters requires three analytical tests [25]--transversality condition, reachability conditions, and equivalent control; these analyses are provided in the following subsections.
# 2D Phase-Based RFID Localization for On-Site Landslide Monitoring
## 4 Hamaliere Landslide Monitoring
### Localization Error and Reference Measurements
#### 4.5.2 Perspective for Improving Data Availability  
In this investigation, the tags that yielded only partial data (i.e., less than two antenna readings, long time periods without data) were not used, although more complex data assimilation techniques could be of use [59; 60]. Exploiting both the knowledge of the landslide mechanics and the redundancy of information that the system yields could allow tag monitoring even in partial data scenarios, which are a common issue in outdoor environments. Such techniques will be implemented in future work.
## 2 Model Design
### Iterative Quasi-Dynamic FEA Model  
The initial values of internal bearing loads \(Q_{1 jk}\) and \(Q_{2 jk}\) are calculated using the modified quasi-dynamic model of noncircular bearing under the assumption of rigidity; the values of \(\delta_{1 jkh}\) and \(\delta_{2 jkh}\) are set as zero. These loads are inserted into the FEA model in order to calculate the local offset of each of the slice/raceway contact points (\(\delta_{1 jkh}\) and \(\delta_{2 jkh}\)). These offsets are substituted into the modified noncircular quasi-dynamic model via Equation (1) in order to recalculate raceway loads \(Q^{\prime}_{1 jk}\) and \(Q^{\prime}_{2 jk}\), and they are then inserted into the finite element model once again. These steps are repeated until the load values satisfy the convergence precision.  
The quasi-dynamic iterative FEA model analysis flow is shown in Figure 3. When the results converge, the load distribution of the bearing, with consideration to the structural deformation of the integrated squirrel-cage flexible support, can be obtained. Furthermore, the dynamic characteristics of the bearing, and the stiffness of the entire structure, including squirrel-cage stiffness and the deformed bearing stiffness, can be obtained. The fatigue life can be calculated by the [PERSON] improved  
Figure 2: Preprocessor of the FEA (finite element analysis) model: (**a**) Flexible support structure with a squirrel-cage; (**b**) refinement of the contact area; (**c**) loads on the outer ring; (**d**) loads on the contact line.  
Lundberg-Palmgren (L-P) theory. The [PERSON] improved L-P theory is based on the L-P theory and contains the load distribution and lubrication of the bearing [2].
## 5 Performance Analysis  
### Experimental Setup  
The EdgeGuard framework was evaluated using a comprehensive simulation environment built with PyTorch v1.9.0 for federated learning implementation, integrated with Ethereum Ganache v2.5.4 for blockchain simulation. The experiments were conducted on a server equipped with 2 Intel(r) Xeon(r) Silver 4114 CPUs (Santa Clara, CA, USA) (40 cores, 2.20 GHz), 128 GB RAM, running Ubuntu 20.04 LTS, and NVIDIA Tesla V100 GPU with 32 GB memory. The federated learning environment was implemented using PyTorch DistributedDataParallel, incorporating our custom adaptive aggregation mechanism with differential privacy support through PyTorch DP. The blockchain component utilized a private Ethereum network with smart contracts written in Solidity v0.8.0, specifically modified for IoMT requirements. Web3.py v5.28.0 facilitated smart contract interactions, while NumPy v1.21.0 and Pandas v1.3.0 were used for efficient data manipulation and numerical computations. To simulate the IoMT environment, we configured three types of edge devices with varying computational capabilities, as shown in Table 2. The network environment was configured to reflect real-world conditions, with bandwidth variationsfrom 1-10 Mbps, latency ranges of 10-100 ms, and packet loss rates of 0.1-1% in a star topology with a central aggregator.  
**Dataset:** We utilized the MIMIC-III dataset, performing comprehensive preprocessing including temporal alignment of vital signs, missing value imputation using forward fill, feature normalization, and time series segmentation into 24-h windows. The dataset was split into training (80%), validation (10%), and test (10%) sets. Performance monitoring was conducted using Linux perf-tools v5.15.0 for resource utilization, iperf3 v3.12 for network statistics, and Intel RAPL (Running Average Power Limit) through powercap-utils v0.6.0 for energy consumption measurements.
## 5 Discussion
### Comparison between the Two Versions of Modular Thumb Actuator  
The two versions of the CMC unit of the M-ACT have a similar actuator structure, only differing in the position of the fiber axis (bending axis) for restricting the longitudinal elongation.  
In line with the design purpose, version 2, with an off-center bending axis, showed a greater flexion RoM than version 1 (Figure 3). Nevertheless, it did not exhibit a significant advantage in flexion force output, even at the metacarpal bone (corresponding to the CMC joint, Figure 5). The force was measured by inflating all actuator modules simultaneously, including two flexion modules and the CMC unit. Therefore, the measured force was the two actuator modules' combined force before and after the measurement position. In contrast to the flexion module for the IP joint, which had both ends fixed, the connection part of the CMC unit and MCP flexion module was nearly impossible to fix on the hand's dorsal side (Figure 9). As a result, during large air inflation, the root of the MCP flexion module would elevate the head of the CMC unit, thus reducing the flexion effect of the CMC unit.  
Additionally, the two versions of the M-ACT differed solely in the CMC unit, but the result showed that version 2 had a slightly greater flexion RoM in the CMC joint (the root section) and the IP joint (the tip section). Since the surface of a finger extends with finger flexion, mostly as the extension of the knuckle creases, the actuator modules need to move or elongate to maintain an efficient bending torque. Version 2 flexed more intensely than version 1 (Figure 3), indicating a greater elongation of the top surface of version 2. The lengthened top part may push the two flexion modules toward the fingertip to some extent. Consequently, the mutual effects between adjacent modules may affect the bending performance of the whole actuator.  
In terms of thumb abduction support, both versions of the M-ACT could impel the thumb to abduct to a similar angle (Figure 4a) and give comparable magnitudes of abduction torque (Figure 6a). However, due to its off-center bending axis, version 2 had a larger RoM (Figure 4b-d) and force output (Figure 6b) in the flexion direction during the air inflation of the left module of the CMC unit (abduction).  
In general, though version 2 had an undesirable flexion during thumb abduction, it outperformed version 1 in thumb flexion support and may be helpful for the flexion of other joints (i.e., IP joint). Accordingly, the M-ACT version 2 was selected for the following comparison with the 3C-ACT.
# A Novel Traffic Classification Approach by Employing Deep Learning on Software-Defined Networking
## 3 Materials and Methods
### Modify
#### 3.3.3 Data Scaling  
Standardization is a process that transforms the features of a dataset in such a way that they have a uniform scale and follow a standard normal distribution with a mean of zero and a standard deviation of one. Equation (1) represents the mathematical formulation of the z-score scaling standardization transformation, which was used in this process. Normalization is a fundamental technique in developing deep learning models, as it eliminates differences in feature scales, ensuring that all features have an equal impact and, thereby, facilitating feature comparison and interpretation. We employed the StandardScaler library from sci-kit-learn to perform the feature scaling transformation on the dataset.  
\[Z=\frac{(xi-mean(x))}{stdev(x)} \tag{1}\]