esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study of the Bond Strength and Mechanism between Basalt Fibers and Asphalt Binders
## 3 Results and Analysis
### Comprehensive Analysis of Bond Strength Based on the Grey Relational Method  
In this paper, the fiber embedment depth and asphalt softening point were chosen. The Gray relational method was employed according to the data (as shown in Table 9) on the bond strength of fibers at embedment depths of 6, 12, 20, and 30 mm and asphalt types of neat asphalt, SBS-modified asphalt, and rubber asphalt [41]. The reason for using the softening point data to represent the asphalt is that penetration corresponds to viscosity, which correlates precisely with the bond strength of the fiber asphalt. The correlation of bond strength with fiber embedment depth and asphalt softening point (denoted as \(A_{embedded\ depth}\) and \(A_{asphalt}\)) was determined according to the grey relational method. The data in each row were divided simultaneously by the data in the first row according to the normalization method. After choosing the bond strength as the reference series, the correlation coefficients of basalt fibers were calculated according to the correlation coefficient calculation method. Finally, the correlation of fiber bond strength was obtained by averaging the correlation coefficients of each row between each indicator. The correlation of the bond strength of the fibers with fiber embedment depth is \(A_{embedded\ depth}\) (0.5901). The correlation of the bond strength of the fibers with asphalt softening point is \(A_{asphalt}\) (0.8318).  
By comparing the correlations, it can be seen that the factors influencing bond strength were ranked as follows: \(A_{asphalt}>A_{embedded\ depth}\). The bond strength of the basalt fibers with asphalt binder was determined by the physicochemical properties of the fibers and the asphalt binder. And the change in the type of asphalt binder led to a change in the interfacial contact property between the fibers and asphalt binder. Therefore, the bond strength between basalt fibers and asphalt binder was highly dependent on the type of asphalt binder.
## 4 Performance Analysis of U-Shaped Frame Optimized Structure
### Mass Check  
The empirical design structure model was subjected to identical geometric modifications as the initial model, encompassing the removal of fillets, bolt holes, and analogous features. Upon assessment, the empirical design structure model's mass was determined to be 5.82 kg. Through topology optimization, the final design structure's mass was reduced to 5.568 kg, resulting in a weight reduction of 4.3%. This optimized U-shaped frame structure design not only meets lightweight design requirements but also simultaneously enhances its static and dynamic performance.
## 3 Experimental Results  
We selected three hyperspectral datasets: Pavia University [21], Kennedy Space Center [22], and HyRANK [23]. These hyperspectral datasets are public and open source. Pavia University is part of the hyperspectral data imaged in 2003 in the Italian city of Pavia. The size of the hyperspectral image is 610 \(\times\) 340 pixels, with a total of 115 bands. After removing water vapor noise, there are 103 bands, in the wavelength range of 0.43-0.86 \(\upmu\)m. The data contain nine types of labels, a total of 42,776 pixels. The Kennedy Space Center dataset was captured by the AVIRIS sensor on 23 March 1996. It contains 176 spectral bands after removing water vapor noise and 13 types of labels. The HyRANK dataset was acquired by the Hyperion sensor, and each image has 176 spectral bands. To test the generalizability of this feature extraction framework, two pictures with real labels in the dataset, Loukia and Dioni, were selected. The former includes 14 types of labels, and the latter includes 12 types of them. We randomly selected 5% of the pixels of each category in Pavia University and Kennedy Space Center, and 10% in HyRANK as training set, and used the rest as the test set.  
Each pixel in the hyperspectral remote sensing image represents a ground distance from about tens to hundreds of meters. In other words, each pixel can be regarded as a sample, and an image contains tens of thousands of such samples. Each sample contains the information of spectral band, and there is also a spatial dimension relationship between them. Therefore, the data of hyperspectral image are sufficient.  
The influence of different classifiers and feature types on the classification results are verified separately by controlling variables. In this process, we use the Pavia University dataset and the Kennedy Space Center dataset and compare the experimental results with other literature. To verify whether the proposed framework is equally effective for the new dataset in the feature extraction stage, we use the image Loukia in the HyRANK dataset as training set of DeepLab v3+ and the image Dioni as test set.
## 3 Mathematical Model of Thermal Balance in HCS
### Solution for Heat-Dissipation Power  
The heat-dissipation capacity of the HCS primarily consists of hydraulic rigid oil hoses and components, oil tanks, and coolers. Due to the poor heat conductivity of rubber, which is the primary material used in hydraulic soft hoses, their contribution to heat dissipation is disregarded in this context.  
The heat-dissipation power of the components and rigid tubes within the HCS can be calculated using Formula (13).  
\[P_{\rm s1}={\sum_{i=1}^{m}A_{i}k_{\rm q}\Delta t_{1}} \tag{13}\]  
where \(A_{i}\) is the surface area of each component and each section of the hard pipe, \(k_{\rm q}\) is the convective heat-transfer coefficient, and \(\Delta t_{1}\) is the temperature difference between the hydraulic oil and the cooling medium.  
The cooling power of the oil tank and the cooler can be calculated using Formula (14).  
\[P_{\rm s2}=KA_{t}\Delta t_{1} \tag{14}\]  
where \(K\) is the heat-dissipation coefficient. When a fan is used for cooling, \(K\) = 14-20, and when water cooling is used, \(K\) = 110-175. \(A_{t}\) is the oil tank heat-dissipation area. The entire surface area of the oil tank that comes into direct contact with the oil is calculated as the oil tank's heat-dissipation area, while only 50% of the surface area that does not come into direct contact with the oil is calculated as the oil tank's heat-dissipation area.  
The total heat-dissipation power of a closed hydraulic circuit can be calculated using Formula (15).  
\[P_{\rm s}=\sum_{i=1}^{2}P_{si} \tag{15}\]  
where \(P_{\rm s}\) denotes the total heat-dissipation power.
## 10 Comparative Analysis of Propagation Models
### Comparison of Propagation Models on 6 GHz--SUI Model, PEM, ITU-R P.452-17
In the mountainous region (TestBS_3), the SUI model continues to exhibit underestimation, while the ITU-R P.452-17 model significantly overestimates the field strength compared to the PEM, with a notably high relative error. Based on these findings, it can be concluded that due to its conservative field strength estimates, the SUI model may only be used for rapid, coarse network planning, as it produces results very quickly and its underestimation is unlikely to result in interference issues. Nevertheless, its use in its current form is not recommended. The ITU-R P.452-17 model proves to be a reliable and suitable alternative in flat terrain. However, in more complex terrains, due to its high error rates, it is better suited for interference analysis, where its optimistic field strength predictions can serve a conservative function. From this perspective, its tendency to overestimate can help ensure interference-free operation by over-protecting the system. This does not imply that such overestimation is always acceptable, but it may still be appropriate for basic interference assessment tasks. The differences in the test point results are illustrated in Figures 34 and 35. The standard deviations of terrain elevation and field strength levels, as well as the Pearson correlation coefficients, are presented in Table 14.  
The results show a strong positive correlation between the standard deviations of terrain elevation and field strength levels for both the PEM and the ITU-R P.452-17 models, confirming the terrain sensitivity of the deterministic and hybrid models in the 6 GHz frequency band as well. A low positive correlation is observed for the SUI model, which can be attributed to the fact that this model does not directly incorporate the terrain profile; instead, it relies on general terrain types and associated constants to locally tune the model. Figure 34: Differences in field strength levels at the test points (PEM – ITU-R P452-17, 6 GHz). Figure 35: Differences in field strength levels at the test points (PEM – SUI, 6 GHz). Nonetheless, it can be concluded that the SUI model does not exhibit sufficient terrain sensitivity at this frequency. Its application is therefore limited to coarse, approximate estimations in cases where the detailed terrain profile is unavailable and only general terrain-type classification is accessible.
## 3 Materials and Methods
### Class Encoding  
Error correcting code (ECOC) is the method of choice to encode the \(K\) classes by means of \(c\) binary learners. This codification is defined by the matrix \(\mathbf{M}\) of \(K\) rows and \(c\) columns whose elements are \(+1\) or \(-1\). A simple example, Equation (6), of \(\mathbf{M}\) matrix was built to encode 4 classes using a code of length \(c=7\).  
\[\mathbf{M}=\begin{pmatrix}+1&+1&+1&+1&+1&+1&+1\\ -1&-1&-1&-1&+1&+1&+1\\ -1&-1&+1&+1&-1&+1\\ -1&+1&-1&+1&-1&+1&-1\end{pmatrix}. \tag{6}\]  
Each row of \(\mathbf{M}\) represents the class code, so in Equation (6), the objects of the third class will be assigned the code or vector \(\begin{pmatrix}-1,&-1,&+1,&-1,&-1,&+1\end{pmatrix}\). The same can be performed for the other three classes. Using these codes, it is possible to obtain the matrix \(\mathbf{Y}\) formed by the class codes of all the objects. Each column of \(\mathbf{M}\) is a binary learner that can compare between two groups of classes or superclasses \(\mathbb{N}\) and \(\mathbb{R}\). Continuing with the example of Equation (6), the first column (binary learner) divides the classes into two superclasses: the first one comprises the class \(\mathbb{N}=C_{1}\) and left the rest in the second, \(\mathbb{R}=C_{2}\cup C_{3}\cup C_{4}\). Another example could be the second binary learner that groups \(C_{1}\) and \(C_{4}\) in the first superclass, \(\mathbb{N}=C_{1}\cup C_{4}\), and \(C_{2}\) and \(C_{3}\) in the second, \(\mathbb{R}=C_{2}\cup C_{3}\).  
A series of changes that do not affect the codification can be deduced from the aforementioned description of \(\mathbf{M}\): permutations of columns, sign change in the whole column, and whole columns of \(-1\) or \(+1\) can be discarded. Therefore, the maximum length to encode \(K\) classes is \(c_{max}=2^{K-1}-1\). For example, in the case of Equation (6), with \(K=4\) classes, \(c_{max}=7\).  
Selecting the adequate binary learners and their number \(c\) for a specific problem is challenging. As evaluated with several datasets in reference [46] and given that our dataset has \(K=8\) classes, the proposed approach was to use function 'designecoc' (Statistics and Machine Learning Toolbox of MATLAB [50]). This function assigns positive or negative elements with equal probability for each element in \(\mathbf{M}\) and uses a random generator to obtain the encoding, so the size of \(c\) is a random variable. However, its mean can be estimated by \(10\log_{2}(K)\), which in our case, means \(c\cong 30\). Applying this codification to the training label matrix \(\mathbf{L}_{TR}\) with dimensions \((56\times 1)\) will result in matrix \(\mathbf{Y}_{TR}\) with dimensions \((56\times c)\cong(56\times 30)\) containing the encoding for each gesture, see Figure 1.
# Estimation of Intelligent Commercial Vehicle Sideslip Angle Based on Steering Torque
## 2 Materials and Methods
### Dynamic Models of EHPS System
#### 2.1.1 Mechanical System Dynamic Model  
In the intelligent commercial vehicle steering system, the steering torque mainly comes from the driver's hands and the motor's assist torque. As shown in Figure 2, in order to reduce the computational burden and the difficulty of analysis, the mechanical system dynamics model of EHPS is simplified into a system containing PMSM, TAS, worm gear mechanism, and an HPS, which is connected by a torsion bar. The dynamic equations of mechanical systems are described in Equations (1)-(3).  
\[J_{s}\ddot{\theta}_{s}+B_{s}\dot{\theta}_{s}+T_{t}+T_{fri}=T_{i} \tag{1}\]  
\[M_{p}\ddot{x}_{p}+B_{p}\dot{x}_{p}+F_{fric}+F_{fres}=T_{t}\cdot R_{t}+F_{hy} \tag{2}\]  
\[T_{t}=K_{t}\left(\theta_{\omega}-\theta_{lg}\right) \tag{3}\]  
\[\theta_{lg}=R_{t}\cdot x_{p},\theta_{f}=x_{p}/r_{cs} \tag{4}\]  
where \(J_{s}\) is the moment of inertia of the steering wheel and the worm gear; \(B_{s}\) is the damping coefficient; \(T_{t}\) is the torque of the torsion bar; \(T_{fri}\) is static friction; \(T_{i}\) is motor assistance torque; \(\theta_{s}\) is steering wheel angle; \(M_{p}\) is the equivalent mass of the nut; \(B_{p}\) is the damping coefficient between the nut and the hydraulic oil; \(F_{fric}\) is friction; \(F_{fres}\) is hydraulic power; \(R_{t}\) steering gear ratio; \(K_{t}\) is torsion bar stiffness; \(x_{p}\) is the displacement of the recirculation ball; \(\theta_{s}\), \(\theta_{lg}\), and \(\theta_{f}\) are steering wheel angle, screw angle, and front wheel angle.  
Figure 1: Schematic diagram of the EHPS system.
## 6 Simulation and Results  
In this section, we evaluate the performance of the RWFS scheme against the work presented by [PERSON] et al. in [22]. We compare their performance regarding filtering capacity, average energy consumption, network lifetime, network overhead, and delay.  
The simulations of both schemes were done using MATLAB. A heterogeneous WSN containing of \(n=100\) nodes is considered in this simulation. The value of \(m~{}=~{}0.1\), which means that 10% of the total number of nodes are advanced nodes containing \(a\) times more energy than the normal nodes. The nodes were randomly deployed in a 100 m \(\times\) 100 m topological area with one access point located at the center of the area (50 m, 50 m). To unify the simulation of the two schemes, we used the same clustering algorithm (E-DEEC) [37]. Table 2 shows all of the parameters that were used in this simulation.
# Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling
## 2 AI for Propagation Modeling
### Comparison between DL and ML Approaches  
Reviewed studies indicate that AI-based models have delivered the prospect of increased accuracy compared to empirical models. DL-based models reduced run-time compared to ray tracing at the cost of time-consuming training phase [22]. AI-based models added adaptivity, where other impacting factors, such as LoRa's SF and topographical information from the environment, could be integrated. However, both methods (ML and DL) have their disadvantages. There are multiple limitations to the current DL-based models.
## 5 Results
### Stability Analysis of SAVOA Path Planning  
According to Figures 14 and 15, in 12 repeated experiments, the highest values of the optimal fitness of SAVOA, AVOA, SSA, ABC, WOA, and GA in the mountainous region 1 scenario were 463.799, 496.220, 739.612, 501.345, 592.912, and 633.083, respectively; the minimum values of optimal fitness were 460.729, 474.182, 486.660, 473.403, 473.589, and 479.604; the standard deviations of optimal fitness were 0.852, 5.871, 73.349, 7.249, 39.825, and 49.885.  
In the mountainous region 2 scenario, the highest values of optimal fitness for the six algorithms are 462.106, 511.813, 775.521, 503.359, 751.339, and 587.792, respectively; the minimum values of optimal fitness were 459.800, 474.635, 512.939, 474.443, 473.261, and 523.344; the standard deviations were 0.674, 13.792, 82.232, 10.203, 86.826, and 20.586.  
The highest values of optimal fitness for SAVOA, AVOA, SSA, ABC, WOA, and GA were 461.579, 560.919, 654.730, 531.821, 549.571, and 580.526, respectively, for the city 1 scenario; the lowest values were 460.392, 472.498, 506.979, 471.515, 475.985, and 491.416; the standard deviations were 0.385, 25.300, 39.284, 18.873, 19.500, and 27.187.  
The highest values of optimal fitness in the city 2 scenario were 458.763, 589.909, 737.192, 542.138, 675.592, and 620.824, respectively; the lowest values were 457.216, 478.428, 481.252, 475.628, 495.523, and 489.958; and the standard deviations were 0.509, 28.505, 68.256, 21.159, 50.948, and 36.761, respectively.  
In summary, the optimal fitness of SAVOA in all four environments fluctuates slightly above and below 460, the standard deviation is not only the smallest but also is much better than other algorithms, and the fitness value is the most stable, which is an experimental result that fully demonstrates the excellent stability of SAVOA in the global path-planning task.
## 1 Introduction
Monitoring bat activity offshore is challenging. Passive acoustic monitoring over ocean waters requires some type of infrastructure (platforms, buoys, lighthouses, etc.) to support acoustic detectors. Barrier islands and offshore structures offer an alternative approach to collecting acoustic data in the near \"offshore\" environment if located a considerable distance from the mainland shoreline. Yet, these sites are accessible and feasible as detector deployment infrastructure. A few studies have approached the problem in this way, deploying acoustic detectors on islands, structures at sea, and on the coastline [26; 27; 28]. However, most of this research has been concentrated in the Northeast. Further south in the mid-Atlantic, some degree of seasonally fluctuating barrier island use by bats has been observed [25] as has fall offshore flight [23; 24]. However, temporally, and geographically limited sample sizes somewhat constrain generalizability and the development of predictive models to describe the factors influencing bat use and to test the feasibility of a predictive curtailment algorithms in the near-offshore environment. Our study sought to address these data gaps with a large sample of acoustic bat occurrence data off the Eastern Shore of Virginia (ESVA). From 2012 to 2019, the Virginia Department of Wildlife Resources deployed acoustic monitors at four barrier island sites and one offshore site. We used this large acoustic dataset to develop a model to describe migratory tree bat nightly occurrence relationships to season, atmospheric conditions, and site-specific characteristics. Our modeling served two purposes--description and prediction [49]. We describe the parameters that reveal the effects of various potential drivers of nightly occurrence of tree bats. Then, we use the model as a predictive tool of bat occurrence and hence potential risk for regional wind turbine collisions once deployed. We hypothesized that tree bat occurrence in mid-Atlantic coastal environments is closely related to season due to the seasonal fluctuations in which tree bats use coastal landscapes and oceanic space. We predicted strong positive effects in spring and fall, moderate effects in summer, and negative effects in winter. We also developed competing hypotheses that the seasonal effect is explained by either (1) unique sites, or (2) the availability of local day-roosting habitat and potentially important foraging habitat (e.g., trees/forests, fresh water). We predicted that if the seasonal pattern is best explained by site specifics that unique sites would have noticeably different occurrence relationships to season. If the seasonal pattern is best explained by roosting habitat, sites with limited roosting habitat would have similar occurrence relationships to season (e.g., peaks only during migration). Lastly, we hypothesized that tree bat occurrence is closely related to multiple atmospheric conditions. We predicted that occurrence would be negatively related to wind speed, positively related to nightly temperature.
## 2 Network Model and Underwater Acoustic Propagation
### Transmission power  
The source level SL is related to the transmitted signal intensity at 1 m from the source which can be expressed as:  
\[SL=10\log\frac{I_{T}}{1\mu Pa} \tag{6}\]  
where \(I_{T}\) is in \(\mu Pa\). From Equation (6) we obtain \(I_{T}\) in _Watts/m\({}^{2}\)_:  
\[I_{T}=10^{SL/10}\times 0.67\times 10^{-18}. \tag{7}\]For shallow water, to achieve an intensity \(I_{T}\) at a distance of 1 m from the source, the source transmitter power \(P_{T}(d)\) should be:  
\[P_{T}(d)=2\pi\times 1m\times H\times I_{T} \tag{8}\]  
in _Watts/m\({}^{2}\)_, where \(H\) is the sea depth in \(m\).  
For deep sea, the source transmitter power \(P_{T}(d)\) should be:  
\[P_{T}(d)=4\pi\times(1m)^{2}\times I_{T} \tag{9}\]  
in _Watts/m\({}^{2}\)_.  
Now we can calculate the required transmit power for signal transmissions at a given distance \(d\) and frequency \(f\).  
Through Equation (1), we represent source level SL as:  
\[SL=SNR+TL+NL-DI \tag{10}\]  
where TL can be calculated via Equation (2) and (3) in shallow water or in deep sea. Then through Equation (7), we obtain source intensity \(I_{T}\). Finally, using Equations (8) and (9), we obtain the required transmit power \(P_{T}\) in shallow water or deep sea.  
For shallow water,  
\[P_{T}(d)=P_{T-shallow}(d)=2\pi H\times 10^{SL/10}\times 0.67\times 10^{-18} \tag{11}\]  
For deep sea,  
\[P_{T}(d)=P_{T-deep}(d)=4\pi\times 10^{SL/10}\times 0.67\times 10^{-18} \tag{12}\]
# Deep Directional Network for Object Tracking
###### Abstract  
Existing object trackers are mostly based on correlation filtering and neural network frameworks. Correlation filtering is fast but has poor accuracy. Although a neural network can achieve high precision, a large amount of computation increases the tracking time. To address this problem, we utilize a convolutional neural network (CNN) to learn object direction. We propose a target direction classification network based on CNNs that has a directional shortcut to the tracking target, unlike the particle filter that randomly finds the target. Our network uses an end-to-end approach to determine scale variation that has good robustness to scale variation sequences. In the pretraining stage, the Visual Object Tracking Challenges (VOT) dataset is used to train the network for learning positive and negative sample classification and direction classification. In the online tracking stage, the sliding window operation is performed by using the obtained directional information to determine the exact position of the object. The network only calculates a single sample, which guarantees a low computational burden. The positive and negative sample redetection strategies can successfully ensure that the samples are not lost. The one-pass evaluation (OPE) evaluation results of the object tracking benchmark (OTB) demonstrate that the algorithm is very robust and is also faster than several deep trackers.  
Keywords:object tracking; deep directional network; scale variation; sliding window; single sample +
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