esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion  
### Compositional and Effective Volume  
The volume fractions are expressed as compositional volumes (V\({}_{\text{f}}\)) of filler in the bitumen. The effective volume (V\({}_{\text{e}}\)) of bitumen considers the effect of packing using Rigden voids. The effective volume is calculated using Equations (16) and (17) [6; 34; 40].  
\[\text{V}_{\text{f}}=\frac{\frac{\text{M}_{\text{f}}}{\text{S}_{\text{f}}}}{ \frac{\text{M}_{\text{f}}}{\text{S}_{\text{f}}}+\frac{\text{M}_{\text{b}}}{ \text{S}_{\text{b}}}} \tag{16}\]  
\[\text{V}_{\text{e}}=\frac{100}{(1-e)}\,\text{V}_{\text{f}} \tag{17}\]  
where, M\({}_{\text{f}}\) and S\({}_{\text{f}}\) refer to mass and specific gravity of filler, respectively, while M\({}_{\text{b}}\) is the mass of bitumen and S\({}_{\text{b}}\) is the specific gravity. \(\varepsilon\) indicates the Rigden voids of mineral filler. The calculations for one filler concentration for effective volume (V\({}_{\text{e}}\)) and free bitumen volume (FBV) are presented in Table 3. The results show that the same volume content of the filler result has different effective volumes. This will result in a difference in the stiffening effect.
###### Abstract
[20] proposes an OFDM modulation recognition (MR) architecture based on a scattering transform MR-network, considering constraints of radio frequency nonlinear distortion. Ref. [29] employs a blind channel equalization strategy to mitigate the effects of fading channels. Ref. [30] classifies OFDM symbol modulation based on convolutional neural networks. Implementing constellation mapping through the neural network structure enables the discovery of optimal symbol constellations that balance spectral efficiency, power efficiency, and PN robustness [31; 32]. Against this background, this paper proposes a neural network-empowered constellation-optimized IM-OFDM (CO-IM-OFDM) architecture, which can derive optimal constellations for index-modulated subcarriers under dual constraints of low PAPR and PN resilience. The framework jointly optimizes communication and sensing performance through weighted metric training. The main contributions are summarized as follows:  
* A neural network-based CO-IM-OFDM framework is proposed, which jointly optimizes subcarrier indices and constellation diagrams. By incorporating PAPR constraints, the trained constellations are suitable for IM-OFDM systems. * A neural network tailored for IM-OFDM constellations in ISAC systems is proposed. Using Adam optimizer, the network generates constellations considering PN robustness and PA nonlinearity constraints, which jointly optimize spectral efficiency and ranging accuracy. * We analyze constellation designs and performance metrics under varying trade-off weights via numerical simulations. With bit error rate (BER) and Cramer-Rao bound (CRB) as performance metrics, a weighting factor is defined to characterize joint performance, enabling maximization of the overall system efficiency.
## 2 Mathematical Model  
### System Description and Basic Assumptions  
Figure 1 illustrates the three-dimensional configuration of the TB-TSS. The structure comprises a main satellite and two subsatellites connected by two straight, massless, and inextensible tethers, L\({}_{1}\) and L\({}_{2}\). Before constructing the dynamic model, several reasonable assumptions were made with reference to [22]. Since the mass of the main satellite is significantly greater than the combined mass of the two subsatellites, the mass center of the system was assumed to be located at the main satellite and the system was assumed to be traveling in a Keplerian orbit. The inextensible and massless rod model used for the tether is always tensioned in the following sections and the satellite's mass is much greater than the tether's. Consequently, the three satellites are treated as lumped mass points due to the significant length of the tether. It is important to note that, in mathematical models, the central gravitational field plays a dominant role. All external perturbations, such as solar radiation pressure and atmospheric drag force, are neglected.  
Generalized coordinates were defined to develop the dynamic model of the system. Figure 1 illustrates the introduction of the Earth's inertial coordinate system, denoted as E-XYZ, with its origin situated at the center of the Earth. Additionally, the E\({}_{Z}\)-axis aligns with the Earth's rotation axis and points toward the north, the E\({}_{X}\)-axis points to the vernal equinox, and the E\({}_{Y}\)-axis completes the right-hand coordinate system. The local vertical local horizontal (LVLH) frame, represented by o-xyz, is a rotating frame. Its origin is located at the mass center of the main satellite. The o\({}_{X}\)-axis extends radially outward from the Earth along the local vertical, the o\({}_{Y}\)-axis represents the direction of the local horizontal, and the  
Figure 1: Schematic of the three-body tethered satellite system.  
o\({}_{x}\)-axis is determined by the right-hand rule. Two tethered coordinate systems (o-x\({}_{i}\)y\({}_{i}\)z\({}_{i}\) (i = 1, 2)) are utilized, with their origins at the mass center of the main satellite and subsatellite 1, respectively; o\({}_{xi}\) points in the direction of satellite i + 1 and rotates \(\theta_{1}\) (in-plane angle of each tether) around the o\({}_{x}\)-axis of orbit coordinate oxyz. Then, it rotates \(\alpha_{i}\) (out-of-plane angle of each tether) around the o\({}_{y}\)-axis to obtain the tether coordinate system.  
We assume that the mass center of the system resides in a circular orbit, with the position of the main satellite determined by the radial coordinate r and the true anomaly \(\phi\). Tether L\({}_{1}\) connects the main satellite (m\({}_{0}\)) to the subsatellite (m\({}_{1}\)), with \(\theta_{1}\) representing the in-plane rotation of the tether and \(\alpha_{1}\) its out-of-plane rotation. Similarly, tether L\({}_{2}\) connects satellite m\({}_{1}\) to satellite m\({}_{2}\), where \(\theta_{2}\) represents the in-plane rotation of the tether and \(\alpha_{2}\) its out-of-plane rotation.
## 3 Results and Discussion
### Compression Strain Rate for Quasi-Static Loading  
Usually, in the experimental uniaxial compression test, the velocity of the compressing plates is lower than that in DEM simulations because of computing time limitations. However, computations performed with inappropriately high values of compression velocity, which can be expressed by the strain rate, are not able to properly simulate the quasi-static nature of the compression process.  
Figure 6 presents the stress dependencies on strain obtained by using different values of the compression strain rate that vary from 53 s\({}^{-1}\) to 1058 s\({}^{-1}\). The curve \"EXP\" obtained from the experimental measurements is plotted for completeness reasons. All curves obtained for different values of the compression strain rate almost coincide up to the strain value of 0.025, which indicates the end of the time interval of medium microcrack propagation. However, some differences can be observed for higher strain values closer to the stress peaks. Thus, the compression strain rate can influence the numerical results in the time intervals of microcrack transfer to macro cracks and fast macro crack propagation. The curve obtained by using the highest value of the compression strain rate, which equals 1058 s\({}^{-1}\), highly differs from the other curves. However, the differences between the curves obtained by using the small values of 53 s\({}^{-1}\) and 106 s\({}^{-1}\) can be treated as negligible. Thus, the computations performed by using the compression strain rate, which is close to 53 s\({}^{-1}\), approach the quasi-static state and achieve the acceptable accuracy within the limits of available computational resources.
## 5 Conclusions  
In this paper, we proposed a fault detection and classification method in a multi-core cable using the ANN and hierarchy clustering based on TFDR results. The fault and cable junction can be detected and differentiated considering the location of the fault and the unbalance level of the reflected signals. Our classification and clustering results which were verified in four fault scenarios in multi-core cables (automotive lines and C&I multi-core cables), achieved the differentiation between faulty and normal line pairs along with the identification of the fault in a multi-core cable regardless of cable length.  
This method can be used for monitoring the overall condition of a multi-core cable through quantitatively tracking for changes in clustering results which enables condition-based maintenance. In short, this method can be applied for the health index of cables by analyzing the degradation tendency in a multi-core cable using feature changes of the TFDR results.  
Conceptualization, C.-K.L. and S.J.C.; methodology, C.-K.L.; validation, C.-K.L.; investigation, C.-K.L. and S.J.C.; data curation, C.-K.L.; writing-original draft preparation, C.-K.L.; writing-review and editing, S.J.C.; visualization, C.-K.L.; supervision, S.J.C.; project administration, S.J.C.; funding acquisition, S.J.C. All authors have read and agreed to the published version of the manuscript.  
This research was supported by newly appointed professor research fund of Hanbat National University in 2018.  
The authors declare no conflicts of interest. The funding sponsors had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.  
Figure 9: Faulty line classification results: (**a**) hierarchy clustering results when Lines 3, 4 are opened and (**b**) hierarchy clustering results when Lines 1, 2, 3, 4, and 5 are opened.
# GazeGuide: An Eye-Gaze-Guided Active Immersive UAV Camera
## 4 User Study
### Apparatus, Experiment, and Experimental Design
#### 4.1.2 Experiment  
We conducted the user study with two different experiments.  
**Experiment 1**: A static target gazing experiment was conducted for the evaluation with an objective to bring the targets to the center of view (Figure 4). During the experiment, the user was asked to gaze at the seven static targets, which were positioned in the user's field of view. The user was instructed to gaze at the center of the numbers with four different variations in directions (Table 3). These variations were introduced to evaluate the performance of the GazeGuide in simple and complex operations. During the experiment, the user was instructed to hold his/her gaze at the target for a gaze duration of at least 1 s, therefore ensuring that the camera maneuvered in the direction of the fixation.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Values** \\ \hline Weight & 247 g \\ Field of View & 94\({}^{\circ}\) \\ Controllable Range & Pan: \(\pm\)320\({}^{\circ}\) \\ Maximum Controllable Speed & Pan: 180\({}^{\circ}\); Tilt: 120\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Technical specifications of DJI Zenmuse-X3 gimbal camera.  
**Experiment 2**: A moving target-based experiment with a total track length of 37.1 m was designed in an outdoor environment (Figure 5) by considering three ToIs with an objective to maintain the ToI in the center of the view. Three ToIs moved in their unique predefined pattern (Figure 6). The experiment was carried out with two variations:  
**Variation 1:** ToIs were asked to move over the track individually one after another until they completed their patterns (here, the start and the end points were the same).  
**Variation 2:** ToIs were asked to move simultaneously in their respective patterns. During the experiment, each ToI was tracked for a particular duration before switching to another ToI; the process was continued for a specific time duration (here, 3 min, divided into 30 s for each ToI and tracked twice).
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 2 Core Principles of SLAM: Front-End and Back-End
### SLAM Front-End
#### 2.1.2 Direct SLAM  
Direct SLAM methods, in contrast to feature-based approaches, operate directly on the intensity values of pixels within images. Instead of extracting and tracking features, these methods minimize the photometric error, which refers to the difference in intensity values between corresponding pixels in consecutive images. This approach can be advantageous in environments with low texture or repetitive patterns.  
An example of a state-of-the-art direct SLAM method is direct sparse odometry (DSO) [13], whose architecture is presented in Figure 5. DSO offers several advantages:  
* **Robustness in Featureless Environments:** By directly analyzing pixel intensities, DSO is effective even in environments with few features, which can be common underwater.  
Figure 4: ORB-SLAM3 system architecture illustrating its key components and data flow. The system comprises the tracking module for camera pose estimation using ORB features, the local mapping module to construct and optimize the local map with new keyframes, and the loop closure module to detect loop closures and reduce drift through pose-graph optimization. (Adopted from [12]).  
* **Full Photometric Calibration:** The method includes calibration for factors such as exposure time, lens vignetting, and non-linear camera responses, improving the accuracy of photometric error minimization.
* **Efficient Pixel Sampling:** DSO uniformly samples pixels across the image, capturing information from regions with sharp edges and smooth intensity variations.  
Although both feature-based and direct SLAM methods have advantages and disadvantages, the choice of approach in underwater applications often depends on the specific environment and sensor configuration. Feature-based methods may be preferred when apparent features are present, while direct methods can offer robustness in feature-depleted environments.
## 2 Related Works
### Statistical Prediction Methods  
Prediction methods have been studied deeply based on statistical theory. The methods are established in the solution to the data rule description and relation fitting. The autocorrelation function and exponential decays are essential for studying time series variations [14]. In the generalized view, the traditional prediction methods include exponential smoothing [15], the state-space model represented by the Kalman filter [16], ARIMA [10], and ARCH [11]. The methods in the system of ARIMA are the most representative of the extensive studies and applications. The typical models include AR (Auto Regression), MA (moving average), and hybrid models. The AR model can fit the relationship of the regressor variable itself. The iteration relationship in the adjacent variables is expressed with a linear combination of the historical data. The MA model introduces the sliding window to extract the change features. The synthesis of AR and the MA can model the time series more accurately, in which ARIMA develops fast in the nonstationary regressive issue.  
The primary statistical model is presented with the expression of ARIMA:  
\[\left(1-\sum_{i=1}^{p}\alpha_{i}L^{i}\right)\left(1-L\right)^{d}x_{t}=\left(1+ \sum_{i=1}^{q}\beta_{i}L^{i}\right)\varepsilon_{t} \tag{1}\]  
where \(x_{t}\) is the value of time series at \(t\). \(p\) is the autoregressive order, \(d\) is the differential order, and \(q\) is the moving average order. \(\varepsilon_{t}\) is the error term conforming to the normal distribution. \(L\) is the Lag operator. \(\alpha_{i}\) and \(\beta_{i}\) are the autoregressive parameters and the shifted moving average parameter.  
The primary support for the prediction methods above is the statistical theory, which does not require a strict data size. The usual data size of dozens and hundreds can meet the modeling demand. Some methods have been proposed for the small-scale data of time series. [PERSON] [17] proposed the local extrapolation method to model a few samples that may be less than 10. The time series variables were derived based on the Kolmogorov-Gavor polynomial, in which the parameter identification and data sensibility were analyzed for short-term predictions. [PERSON] et al. [18] built a preprocessing model for small samples to enhance their stationary time series. The simulated annealing algorithm and support vector machine were introduced to predict samples. Furthermore, the grey theory and method [19; 20] are also studied in the traditional methods for small sample predictions.  
The methods above solve the small sample issue based on mathematical descriptions in the statistical framework. The model can fit the changing trend in the available data, but one wonders if it can accord with new data outside of the existing scope. The methods can predict a small-scale time series that is stable in the short term. It may be invalid for the long-term prediction of complex nonstationary time series.
## 3 Dynamic Forecasting Model Based on VMD-SLSTM Network
### Comparison with Other Forecasting Models  
To demonstrate the superiority of the proposed VMD-SLSTM network, two advanced forecasting models--the EMD-LSTM network [21] and the LSTM network [32]--are used for comparison. Figure 10 shows the forecast results of landslide displacement using the three forecasting models. It can be seen that the forecast results of the LSTM network are the worst. This is because the original landslide displacement-time curve is a non-stationary oscillation curve, and it is difficult to learn the law of  
Figure 8: Forecast results of displacement subsequences using SLSTM networks.  
Figure 9: Forecast results and errors of landslide displacement.  
displacement change if the LSTM network is used directly. The forecast curve of EMD-LSTM network fluctuates regularly, while that of VMD-SLSTM network can follow the observed curve well.  
Table 1 reports the mean absolute error (MAE) and root mean square error (RMSE) of the forecast results using the three forecasting models. It can be seen that the MAE of the VMD-SLSTM network is 1.99 mm, lower than the 3.99 mm of the LSTM network and 2.25 mm of the EMD-LSTM network. On the other hand, the RMSE of the EMD-SLSTM network is 2.50 mm, lower than the 4.15 mm of the LSTM network and 2.76 mm of the EMD-SLSTM. These results show that the VMD-SLSTM network outperforms the LSTM network and EMD-LSTM network.
# Dwarf Mongoose Optimization-Based Secure Clustering with Routing Technique in Internet of Drones
## 3 Results and Discussion
Concurrently, with 25 drones, the DMOSC-MHRS method rendered a higher REL of 94.98% whereas the SCA-ITV, BICSF, and HSCS approaches attained lower REL of 89.56%, 90.91%, and 92.52%, correspondingly. Finally, the experimental values demonstrated the enhanced performance of the DMOSC-MHRS model compared to recent models. \begin{table}
\begin{tabular}{c c c c c} \hline  & \multicolumn{4}{c}{**Reliability (\%)**} \\ \hline
**No. of Drones** & **SCA-ITV** & **BICSF** & **HSCS** & **DMOSC-MHRS** \\ \hline
15 & 87.36 & 89.13 & 90.43 & 94.29 \\ \hline
20 & 88.05 & 89.60 & 91.06 & 94.90 \\ \hline
25 & 89.56 & 90.91 & 92.52 & 94.98 \\ \hline
30 & 90.63 & 92.05 & 93.77 & 95.56 \\ \hline
35 & 91.90 & 93.66 & 95.11 & 96.99 \\ \hline \end{tabular}
\end{table} 
Table 4: Reliability analysis of DMOSC-MHRS approach with existing algorithms under distinct drones. Figure 8: CLT analysis of DMOSC-MHRS approach under Scenario-3.
# Multi-Scale Feature Learning for Language Identification of Overlapped Speech
## 3 Algorithm Model Structure
### Algorithm Module
#### 3.1.7 Convolutional Block Attention Module (CBAM)  
We use the CBAM [35] attention layer after SE-res2 Net to focus on the features related to language and generate distinctive feature representations for language identification. The advantage is that we use attention to measure how important each high-level feature is for the language difference instead of simply aggregating a bunch of features over time.  
The attention layer is located after the bidirectional LSTM. The output of the bidirectional LSTM is first passed through a softmax function to calculate the normalized weight \(\alpha_{t}\), calculated as shown in (2). The normalized weight \(\alpha_{t}\) is then weighted and summed over \(h_{t}\) to obtain the language representation \(c\), as shown in (3).  
\[\alpha_{t}=\frac{\exp(W\cdot h_{t})}{\sum_{l=1}^{T}\exp(W\cdot h_{t})} \tag{2}\]  
\[c=\sum_{t=1}^{T}\alpha_{t}h_{t} \tag{3}\]  
where \(W\) denotes the weight value, \(h_{t}\) is the state at the current moment, and the language representation \(c\) is derived by computing Equations (2) and (3) and then passed to the all-connected layer to obtain a more profound representation of the language. A softmax classifier maps the language representation to N different spaces for classification, where N denotes the number of classes of the language.  
The general network architecture of the convolutional chunk attention module is shown in Figure 8.  
Given a feature map, the CBAM can serially generate attentional feature map information in both the channel and spatial dimensions, and then the information from the two feature maps is multiplied with the previous original input feature map for adaptive feature correction to produce the final feature map.  
As shown in the figure above, there is an input, a channel attention module, a spatial attention module, and an output. The input features \(F\in R^{\mathbb{C}\times H\times W}\), followed by the channel attention module \(M_{c}\in R^{\mathbb{C}\times 1\times 1}\), multiply the result of the convolution by the original image, and the output of the channel attention module is used as input for the two-dimensional convolution of the spatial attention module \(M_{s}\in R^{1\times H\times W}\), and then the output is multiplied by the original image.  
\[F^{\prime}=M_{c}(F)\otimes F \tag{4}\]  
\[F^{\prime\prime}=M_{s}(F^{\prime})\otimes F^{\prime} \tag{5}\]  
Equation (4) focuses on the features of the channel by keeping the channel dimension constant and compressing the spatial dimension, focusing on the meaningful information in the input image. Moreover, Equation (5) focuses on the features in the space by keeping the spatial dimension constant, compressing the channel dimension, and focusing on the location information of the target.  
Figure 8: Convolutional block attention module.
## 4 Results and Discussion
### Bradening Coefficient, He  
Figure 6a shows the measured NO absorption spectrum at pressures of 929.1 mbar and 294.3 mbar. The corresponding Lorentzian widths exhibit excellent pressure linearity. The experiments yielded the NO-He broadening coefficients \(\gamma_{\rm He}\) = 44.17(23) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm and 37.79(22) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm for Peak 1 and Peak 2, respectively.