esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Wireless Soil Moisture Sensing Applications
### Applications in Hydrological Research
[11] used soil moisture sensor response times from a WSN to investigate controls on preferential flow at the catchment scale. They found that the spatial occurrence of preferential flow showed no obvious relationship with spatial attributes, such as topographical and soil physicochemical parameters, but was governed mainly by small-scale soil and biological features and local processes. [PERSON] et al. [76] investigated the influence of throughfall variability on soil moisture variability in a forest stand using data from a dense soil moisture WSN and found that soil hydraulic properties were the dominant drivers for spatial soil moisture patterns. Recently, WSNs have contributed to achieving advances in real-time hydrological monitoring [92]. For instance, WSNs have been used to develop real-time flood monitoring systems that combine soil moisture sensors with hydrometeorological sensors to provide important information for the efficient management of flood events [93; 94]. For example, a ZigBee-based WSN was used for an early flood monitoring and warning system to provide flood warning. The WSN system informs the public of the water level even without network connection in their mobile application and helps them to reach a safe place in a timely manner [95]. Furthermore, real-time soil moisture WSNs have been used to develop local early warning systems for drought that combine soil moisture data from a WSN with hydrologic model data and locally observed precipitation [96]. Finally, [PERSON] et al. [97] used a combination of dendrochronological data and soil moisture information from a WSN to identify climate-growth signals of forest trees at the catchment scale. They found that water availability was the dominant growth factor and that the drier sites at the hillslopes showed stronger climate-growth reactions compared with sites located in the riparian zone with less variable water availability. This promising result indicates that the detailed data from soil moisture WSNs are also useful for obtaining a deeper understanding of species-specific growth limitations.
## 4 Theory  
Theoretical analysis is based on the sine-Gordon equation [50; 54]:  
\[\phi_{tt}+\alpha\phi_{t}-\phi_{xx}=\beta\phi_{xxt}+\eta-\sin\phi, \tag{1}\]  
where indices \(t\) and \(x\) denote temporal and spatial derivatives, \(\phi\) is the phase order parameter. Space and time are normalized to the Josephson penetration length \(\lambda_{I}\) and to the inverse plasma frequency \(\omega_{p}^{-1}\), respectively, \(\alpha=\omega_{p}/\omega_{c}\) is the damping parameter, \(\omega_{p}=\sqrt{2 el_{c}/\hbar C}\), \(\omega_{c}=2 el_{c}R_{N}/\hbar\), \(\beta\) is the surface loss parameter, \(\eta\) is the bias current normalized to the critical current. The boundary conditions that simulate simple RC-loads, see Refs. [34; 38; 40; 41], have the form:  
\[\phi(0,t)_{x}+r_{L}c_{L}\phi(0,t)_{xt}-c_{L}\phi(0,t)_{tt}+ \tag{2}\] \[\beta r_{L}c_{L}\phi(0,t)_{xt}+\beta\phi(0,t)_{xt}=\Gamma,\] \[\phi(L,t)_{x}+r_{R}c_{R}\phi(L,t)_{xt}+c_{R}\phi(L,t)_{tt}+\] (3) \[\beta r_{R}c_{R}\phi(L,t)_{xt}+\beta\phi(L,t)_{xt}=\Gamma.\]  
Here \(\Gamma=H_{e}/\left(\lambda_{I}J_{c}\right)\) is the normalized magnetic field. The dimensionless capacitances and resistances, \(c_{L,R}\) and \(r_{L,R}\), are the FFO RC-load placed at the left (output) and at the right (input) ends, respectively. The power \(P\) of radiation, emitted from the junction output edge is normalized to the Josephson power \(P_{I}=V_{I}^{2}/Z_{0}\), where \(Z_{0}\) is the characteristic impedance of the junction.  
The computer simulations of the sine-Gordon equation are performed for the following parameters: damping \(\alpha=1.5\), surface loss \(\beta=0.1\), and dimensionless junction length \(l=70\).  
Figure 8 shows the theoretical IV curves for various values of applied magnetic field \(\Gamma\). The color indicates the level of radiation power \(P\). In the present case, the antenna bandwidth is not considered, assuming that the junction is perfectly matched to a load in the whole frequency range. We observe the regime of a continuous flow of fluxons, which for larger magnetic fields smoothly transforms into the travelling wave regime. The inset of Figure 8 shows the radiated power level for several values of the magnetic field. Here we marked the voltage position of the maximum power as \(V_{mp}\) and the maximum power as \(P_{mp}\).  
Figure 7: The maximum response depending on YBCO voltage for bicrystal junction with the length \(L=50\) μm for phase rotation regime (blue solid curve) and traveling wave regime (red dashed curve).  
Figure 9 shows the dependence of the power level versus normalized voltage \(P_{mp}(V_{mp})\) for zero external magnetic field (blue solid curve) and nonzero external magnetic field, leading to a traveling wave regime (red dashed curve). Both theoretical curves show the behavior, qualitatively similar to the experiment (see Figure 7), they both have maxima, but the maximum, corresponding to YBCO FFO, is wider and reaches larger values. Therefore, such dependences demonstrate the advantage of a long YBCO JJ, operating in the traveling wave regime, compared with short and long junctions operating in the phase rotation regime, as THz broadband sources.
## 3 Experimental Results
### Results of BTA Classification System  
The BTA classification system was evaluated using 4 different classification methods: Random Forest, J48 DT, Ada Boost, and SVM. The findings revealed that J48 DT yielded the top accuracy rate with 97.7308%. Table 11 shows the success rates for all classifiers. While Table 12 presents the confusion matrix generated for the J48 DT classifier, Table 13 demonstrates the evaluation metrics for J48 DT.
# Telecommunication Systems for Small Satellites Operating at High Frequencies: A Review
## 6 Conclusions  
In this paper, a review of the telecommunication systems for three different CubeSat missions is presented. For each of the missions, the main telecommunication requirements and constraints are listed, and the impact on the telecommunication design is discussed. The main telecommunication components are described and lessons learned from performing operations are shown.  
The development of telecommunication systems for these three missions and their operational success show that it is possible to use CubeSats to perform certain science investigations in Low Earth Orbit and in Deep Space, and that it is possible to relay sufficient data even with these small platforms. Future telecommunication developments in the CubeSat and small satellite arena focus on increasing bandwidth capabilities by using higher frequencies, optical telecommunication and by using multiple platforms or swarms.  
**Funding:** This research received no external funding.  
**Acknowledgments:** Part of this work was performed at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement by the United States Government or the Jet Propulsion Laboratory, California Institute of Technology.  
**Conflicts of Interest:** The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Research on the Applicability of Transformer Model in Remote-Sensing Image Segmentation
## 2 Methods
### SETRnet (Segmentation Transformer)  
The segmentation transformer SETRnet is the first representative model of the vision-transformer-based semantic segmentation proposed by [PERSON] et al., in 2021 [33]. The structure of SETRnet is shown in Figure 3.  
SETRnet abandons the stacked convolutional feature-extraction method in the encoded layer and instead uses the transformer-only feature-extraction method. In the model, the images are first sliced, and then all the two-dimensional image slices are considered as a one-dimensional sequence and fed into the network as a whole. The input one-dimensional series will become a one-dimensional feature-embedding series. In each layer, the input of attention consists of a query, key, and value triad computed by \(Z^{l-1}\in\mathbb{R}^{L\times C}\) (where \(L\) is  
Figure 3: SETRnet structure.  
the sequence length and \(C\) is the hidden channel size). The computed query, key, and value triad can be expressed as follows:  
\[\textit{query}=Z^{l-1}w_{Q},\textit{ key}=Z^{l-1}w_{K},\textit{ value}=Z^{l-1}w_{V} \tag{4}\]  
where \(W_{Q}\), \(W_{K}\), \(W_{V}\in\mathbb{R}^{C\times d}\) are the learnable parameters of the 3 linear projection layers and d is the dimensionality of (query, key, value). Then the attention function of SETRnet can be expressed as follows:  
\[SA\Big{(}Z^{l-1}\Big{)}=Z^{l-1}+\textit{softmax}\Bigg{(}\frac{Z^{l-1}W_{Q}(ZW_{ K})^{\top}}{\sqrt{d}}\Bigg{)}\Big{(}Z^{l-1}W_{V}\Big{)} \tag{5}\]  
The output of SETRnet's multihead self-attention (\(MSA\)) module is converted by an multilayer perceptron (\(MLP\)) module with residual jumps; the structure is shown in Figure 4.  
\[Z^{l}=MSA\Big{(}Z^{l-1}\Big{)}+MLP\Big{(}MSA\Big{(}Z^{l-1}\Big{)}\Big{)}\in \mathbb{R}^{L\times C} \tag{6}\]  
SETRnet's decoder is designed with three methods, namely plain upsampling (naive), progressive upsampling (PUP), and multilevel feature fusion (MLA). SETRnet constructs a new semantic segmentation model from a new perspective. Compared with the traditional semantic segmentation model, SETRnet models the global context in each layer of the encoder by using the transformer as the encoder, which effectively obtains the global context information and removes the semantic segmentation network's dependence on convolution.  
Figure 4: SETRnet’s transformer layer structure.
## Appendix B Image Counts and Corresponding Parameter Values by Feature Classification (Gabor Filter) \begin{tabular}{c c c c c c c c c c c c c} \hline \hline
**Features** & \multicolumn{4}{c}{**Gabor-Mean**} & \multicolumn{4}{c}{**Gabor-Variance**} & \multicolumn{4}{c}{**Gabor-Energy**} \\ \hline  & \multicolumn{4}{c}{**Class I**} & \multicolumn{2}{c}{**Class II**} & \multicolumn{2}{c}{**Class I**} & \multicolumn{2}{c}{**Class II**} & \multicolumn{2}{c}{**Class I**} & \multicolumn{2}{c}{**Class II**} \\ \hline \(\lambda\) & \(\theta\) & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** & **Mean** & **SD** \\ \hline \(3\) & \(0\) & \(60.846\) & \(1.669\) & \(80.74\) & \(0.877\) & \(48.917\) & \(1.16\) & \(63.373\) & \(1.441\) & \(188.951\) & \(2.346\) & \(225.676\) & \(1.584\) \\ \(3\) & \(\pi/8\) & \(71.264\) & \(2.009\) & \(104.904\) & \(1.247\) & \(44.264\) & \(1.193\) & \(57.174\) & \(1.165\) & \(209.145\) & \(2.047\) & \(239.915\) & \(1.021\) \\ \(3\) & \(\pi/4\) & \(63.754\) & \(1.818\) & \(93.328\) & \(1.096\) & \(42.98\) & \(1.36\) & \(53.967\) & \(1.324\) & \(203.857\) & \(2.338\) & \(236.162\) & \(1.275\) \\ \(3\) & \(\pi/8\) & \(71.366\) & \(1.996\) & \(104.918\) & \(1.270\) & \(44.871\) & \(1.208\) & \(57.028\) & \(1.101\) & \(209.131\) & \(2.063\) & \(239.566\) & \(0.999\) \\ \(3\) & \(\pi/2\) & \(60.207\) & \(1.643\) & \(79.887\) & \(0.926\) & \(47.051\) & \(1.207\) & \(61.67\) & \(1.397\) & \(189.794\) & \(2.349\) & \(226.706\) & \(1.584\) \\ \(3\) & \(\pi/8\) & \(71.118\) & \(1.984\) & \(104.798\) & \(1.266\) & \(44.118\) & \(1.227\) & \(56.482\) & \(1.106\) & \(209.672\) & \(1.020\) & \(240.196\) & \(0.987\) \\ \(3\) & \(3\pi/4\) & \(63.725\) & \(1.801\) & \(93.16\) & \(1.101\) & \(42.672\) & \(1.359\) & \(53.745\) & \(1.305\) & \(204.042\) & \(2.36\) & \(236.513\) & \(1.256\) \\ \(3\) & \(\pi/8\) & \(71.324\) & \(1.979\) & \(104.947\) & \(1.26\) & \(44.414\) & \(1.2\) & \(57.431\) & \(1.094\) & \(209.228\) & \(2.049\) & \(239.01\) & \(0.997\) \\ \(6\) & \(0\) & \(7.041\) & \(0.527\) & \(2.328\) & \(0.199\) & \(19.378\) & \(0.975\) & \(36.913\) & \(1.481\) & \(8.703\) & \(0.602\) & \(2.509\) & \(0.203\) \\ \hline \hline \end{tabular}
# Performance of Retro-Reflective Building Envelope Materials with Fixed Glass Beads
## 4 Results and Discussion
### Discussion  
From the above results, we can see that the glass beads with a refractive index of 1.9 are more effective than 1.5 in improving the retro-reflectivity of a material surface. The commercial prism sheet  
Figure 8: Angular retro-reflectivity of RR sample 7 (a) silver layer and refractive index of 1.5 and RR sample 8 (b) silver layer and refractive index of 1.9.  
Figure 10: Angular retro-reflectivity of RR sample (a) capsule sheet and RR sample (b) prism sheet.  
Figure 9: Angular retro-reflectivity of RR sample 9 (a) transparent layer and refractive index of 1.5 and RR sample 10 (b) transparent layer and refractive index of 1.9.  
and capsule sheet have larger retro-reflectivity than the developed RR samples at low incident angles (from 0\({}^{\circ}\) to 60\({}^{\circ}\)). However, their price is relatively expensive, and the retro-reflectivity is lower than some of the developed samples when the incident angle is above 60\({}^{\circ}\) (for the capsule RRM) or 75\({}^{\circ}\) (for the prism RRM). The angular retro-reflectivity of sample 2 (white layer and glass bead with a refractive index of 1.9), sample 4 (yellow layer and glass bead with a refractive index of 1.9) and sample 6 (gray layer and glass bead with a refractive index of 1.9) are larger than the other developed glass bead RR samples but still lower than the prism and capsule sheet for the 0\({}^{\circ}\) to 60\({}^{\circ}\) incidence angles. Moreover, their change in angular retro-reflectivity is relatively constant over the range of angles, and the price of glass beads is relatively cheap compared to the prism and capsule sheet. Further, glass beads can be applied to wet paint without specialist knowledge or equipment. Therefore, it is considered that a white, yellow, or gray layer and glass bead with a refractive index of 1.9 could possibly be used to create the RR materials for building surfaces and could be better than the other color layers and glass beads with a refractive index of 1.5 through this experiment.  
These findings are in accordance with our previous research [13]. Thus, it is considered that the glass bead RR material with a refractive index of 1.9 is more effective than that with a refractive index of 1.5 in terms of reflecting incident light back towards the source when applied to building facades as a RR material at lower incident angles. This would make them better at retro-reflecting sunlight throughout the day.
## 4 Column Generation for \(Mtvp\)-\(Vw\)-\(Tw\)
### Master Problem for \(Mtvp\)-\(Vw\)-\(Tw\)  
A tour is the set of all the routes a vehicle performs during a day. So, the decision variable \(y_{w}\), which refers to a column corresponding to a tour. \(\Omega\) is the set of all feasible tours, \(d_{w}\) is the total distance of tour \(w\in\Omega\), \(|W|\) is the total number of wagons, \(|V|\) is the number of the vehicles and \(n_{w}\) is the number of wagons used for a vehicle that is used in tour \(w\) and \(n_{w}\in\{1,2,3\}\). \(a_{iw}\) is one if customer \(i\) is in tour \(w\), 0 otherwise. We use the following decision variable:  
\[y_{w}=\begin{cases}1,&\text{if tour $w$ is chosen}\\ 0,&\text{otherwise}\end{cases} \tag{17}\]The master problem is:  
\[\min\sum_{w\in\Omega}d_{w}y_{w}\] (18) s.t. \[\sum_{w\in\Omega}a_{iw}y_{w}\geq 1\quad\forall i\in C \tag{19}\] \[\sum_{w\in\Omega}y_{w}\leq|V|\] (20) \[\sum_{w\in\Omega}n_{w}y_{w}\leq|W|\] (21) \[y_{w}\in\{0,1\}\qquad\forall w\in\Omega \tag{22}\]  
The objective function (18) is to minimize the total distances of all tours. Constraints (19) ensure that each customer is visited at least once. The constraint (20) states that the number of all tours must be less than the number of vehicles. Constraint (21) states that the number of wagons used in all the vehicles must be less than the total number of wagons.  
The solution is a subset of \(\Omega\). As the number of columns is exponential in the number of customers, we solve the restricted master problem (\(RMP\)) with a limited number of columns for the initial solution. The columns are progressively added into \(RMP\). The LP-relaxation of \(RMP\) (\(RLMP\)) is solved with an \(LP\) solver to obtain the dual variables associated with the optimal solution of the \(RLMP\). These dual values are sent to the subproblem to determine new tours with a negatively reduced cost, and these new tours are added to the master problem. The process will continue until there are no more tours with a negatively reduced cost. This guarantees an optimal solution to the \(RLMP\).
## 4 Discussion  
Despite the high spatial resolution of the PlanetScope images, it was very difficult to distinguish red tide pixels from non-red tide pixels due to low spectral resolutions and SNR. To overcome the limitations of PlanetScope, we used the U-Net model to better exploit the spatial and spectral features of the images. In this study, the U-Net model provided more reasonable red tide information from high spatial resolution imagery in coastal areas than the traditional threshold-based method. However, several factors affect the performance of predicting red tide maps using RTI and U-Net models.
# Development of a Path Tracker Based on a 4 WS Vehicle for Low-Speed Automated Driving Systems
## 3 Path-Tracker Design
### Stanley Method  
The Stanley method is a path-tracking control algorithm used by Stanford University's autonomous vehicle, Stanley, during the 2005 DARPA Grand Challenge. As shown in Figure 2, this algorithm is geometry-based and uses the lateral distance error between the center of the vehicle's front axle and the nearest path point as a nonlinear feedback function to reduce the lateral deviation. Additionally, the steering angle, \(\delta_{f}\), is designed to converge to the heading angle, \(\psi_{p}\), of the reference path. The Stanley method can be expressed by Equations (6) and (7) [11].  
\[\psi_{e}=\psi-\psi_{p} \tag{6}\]  
\[\delta_{f}=\psi_{e}(t)+\tan^{-1}\frac{k_{e}e(t)}{\mathbf{V}_{x}(t)} \tag{7}\]  
In Equation (6), \  
more aggressively towards the reference path, enhancing trajectory tracking performance. Figure 3 illustrates the overall architecture of the Stanley method.  
In Section 4, the [PERSON] method is used as a comparison benchmark to verify the performance of the proposed path tracker against conventional 2 WS and conventional 4 WS. The conventional 2 WS operates in the same mechanism described in this chapter. In contrast, the conventional 4 WS applies the same front steering angle as in this chapter, but the rear steering angle operates in a counter-phase steering mode, fixed at 30% of the front steering angle.
## 4 Case Study
Before the landslide body is formed, there is no significant difference or change in the resistivity values of the soil layers above and below the sliding surface position, and there is no typical resistivity stratification at the sliding surface position of the landslide body. After the landslide body is formed, the resistivity values at the sliding surface position show noticeable changes at different periods. Above the landslide body, within the depth range from the surface to the sliding surface, the resistivity values of the soil layers exhibit significant differences, while the resistivity values of the soil layers below the sliding surface show smaller differences. Outside the landslide body, only the resistivity values of the surface soil layers show significant changes, whereas the resistivity values of the soil layers at depths of meters and meters do not exhibit significant differences. To verify the results of DC method exploration, several boreholes were arranged at the locations of the aforementioned survey lines (Table 1). The drilling results showed that the fully weathered bedrock surface is buried at a depth of 0-6 m, with significant undulations and a slope of 25-30\({}^{\circ}\), which is generally consistent with the findings of the high-density resistivity method. This indicates that the high-density resistivity method is effective in the exploration of this landslide.
###### Abstract
Therefore, there is a pressing need for a method that can measure the reliability of prediction results, aiding decision makers in better understanding the model's predictions and making more accurate and reliable decisions. To effectively solve these challenges, we propose a multi-task evidential sequence learning model for reliable AD clinical diagnosis and cognitive score prediction. Specifically, we introduce recurrent neural networks to the multi-task learning framework, utilizing longitudinal time series data from individuals at risk of AD to simultaneously predict their future clinical diagnosis and ADAS-Cog13 cognitive scores. We then introduce evidential learning to provide uncertainty estimates for the model's predictions, thereby quantifying the confidence level of the model's outcomes. Furthermore, we integrate the Dynamic Weight Average (DWA) scheme to automatically allocate learning resources for different tasks in the training process, aiding in the improvement of model performance. Experiments conducted on the TADPOLE dataset [8] substantiate that our proposed model not only achieves satisfactory performance in disease progression prediction but also provides effective uncertainty estimation. Furthermore, the core idea inherent in our model is not only applicable to AD progression prediction, but it also offers the potential for extension to various deep learning applications in the medical domain. By effectively modeling the uncertainty of model results, the credibility of most existing deep learning models in the medical field can be enhanced, thereby contributing to the advancement of trustworthy and reliable methodologies in healthcare settings. In conclusion, this paper makes the following contributions:  
* We propose a multi-task evidential sequence learning model for the trustworthy prediction of disease progression, which performs AD clinical diagnosis and cognitive score predictions while providing reliable uncertainty quantification of each prediction. * To achieve balanced learning of various tasks, we introduce the Dynamic Weight Average (DWA) weighting scheme into our model to automatically adjust the weights of each task, resulting in improved prediction accuracy. * We conduct extensive experiments on the TADPOLE dataset which validate the superior effectiveness and accuracy of our model thanks to the promising uncertainty estimation and DWA. The structure of this paper is as follows: the related works are discussed in Section 2, while Section 3 expounds upon the formulation of the problem. Section 4 describes the implementation of our proposed model. Section 5 is dedicated to the verification and evaluation of the model's performance, and Section 6 provides a more comprehensive analysis of the model. Section 7 summarizes this paper.