esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions and Outlook  
Extracting buildings and detecting their changes using VHR RS images are crucial tasks that have attracted increasing attention. The powerful SOTA DL models have achieved promising results for these challenging tasks. However, most studies treat these two tasks separately with different focuses and dedicated datasets, which leads to the redundant research effort and annotation burden. In contrast, we proposed an MTL approach for BE and CD from RS images. Specifically, we integrated a SOTA Swin transformer and light heads into the MTL network to use the available training samples for representative feature learning. In addition, we adaptively learned the task weights to balance different losses and obtain efficient and effective optimization. Extensive experimental results demonstrate the superior performance of our proposed solution than the SOTA BE and CD models using different public datasets. Moreover, we achieved fourth place in the 2021 Gaofen Challenge. We also demonstrated our approach's potential in test settings with a domain shift. Moreover, our approach can be easily adapted to a wide variety of application scenarios, such as urban monitoring from time series and general CD tasks from RS images. More related RS applications will be explored in the future to validate the proposed MTL idea.  
Figure 6: Comparative CD results on the LEVIR dataset of two models trained with the Challenge dataset. (**a**) \(t_{1}\) Image. (**b**) \(t_{2}\) Image. (**c**) CD GT. (**d**) CD results of ChangeStar. (**e**) Our CD results. Red indicates false positives, and green indicates false negatives.  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Model** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline ChangeStar & 56.16 & 69.11 & 74.98 & 71.92 \\ Ours & 58.65 & 63.49 & 88.50 & 73.94 \\ \hline \end{tabular}
\end{table}
Table 5: Comparative CD results on the LEVIR dataset using models trained on the Challenge dataset. The bold values represent the better results.  
**Author Contributions**: Conceptualization, A.Y. and C.Q.; methodology, A.Y.: software, A.Y. and D.H.: validation, D.H., A.Y. and C.Q.: formal analysis, D.H., A.Y. and C.Q.: investigation, A.Y.: data curation, A.Y., X.C.: writing--original draft preparation, D.H., A.Y. and C.Q.: writing--review and editing, D.H., A.Y., C.Q., X.C. and B.L.: visualization, D.H. and Y.Q.: supervision, A.Y. and B.L.: project administration, A.Y.: funding acquisition, A.Y. All authors have read and agreed to the published version of the manuscript.  
**Funding**: This research was funded by the National Natural Science Foundation of China OF FUNDER grant No. 42101458, No. 4180138, No. 42201513.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: The data presented in this study are available on request from the corresponding author.  
**Conflicts of Interest**: The authors declare no conflict of interest.
## 2 Methodology
### Case Study
#### 2.1.4 Ground Truth  
Assessing the accuracy of the machine learning algorithms requires information on the occupancy of the floor. For the supervised method, this so-called ground truth is not only crucial for the model assessment but also training of the model. Cameras installed on the four entrances to the floor were installed to measure ground truth in this study. These cameras were equipped with people counting software which reported the number of people entering and exiting the floor.  
Commercial people counters, such as the ones used in this study, are not accurate enough to give absolute truth [27]. In some instances, it happens that counters undercount, especially when several persons pass under the camera at the same time. When calculating the occupancy from the people counts, this error was visible at the end of the day when it seemed that some persons had not left the floor. The error at late night hours was not crucial, since this study focused on the regular working hours. Additionally, occupancy calculation was reset to zero each day at midnight. An alternative would be manual people counting, which would be very difficult to perform hourly throughout every hour of seven months on four entrances. Therefore for this study, where the development of the method of high accuracy was not the goal, it was decided to accept this error.  
Figure 1: The pilot floor of this study consists of open layout offices and meeting rooms.
# Weakly Coupled Distributed Calculation of Lyapunov Exponents for Non-Linear Dynamical Systems
## 2 Lyapunov Exponents in Non-Linear Dynamical Systems  
There are several different definitions of Lyapunov exponents which can be found in the literature. For instance, there are at least two widely used definitions of Lyapunov exponents for linearised systems [7]. For the sake of simplicity, and in order to focus this paper into the development of the parallel scheme, we work with a simple definition of Lyapunov exponents for the fully non-linear dynamical system herein treated (see Section 2.1).  
Let \(\dot{r}=f(r,t)\) be a first-order differential equation. In order to solve it numerically, it must be discretised, for example, by an Euler method for the sake of brevity. Then, the value of \(r\) in the \(n\)-th time step would be  
\[r_{n}=r_{n-1}+\Delta tf(r_{n-1},t_{n-1})\]  
Now let \(t_{0}\) and \(t_{1}\) be two initial conditions such that \(|t_{1}-t_{0}|=\delta<<1\), and let the values of \(r\) in the (\(n+1\))-th time step with the simulation started at \(t_{0}\) and \(t_{1}\) be \(r_{n}^{(0)}\) and \(r_{n}^{(1)}\), respectively. Then, the separation of both solutions with adjacent initial conditions is defined to be  
\[\left|r_{n}^{(0)}-r_{n}^{(1)}\right|=\delta e^{n\lambda}\]  
where \(\lambda\) is the Lyapunov exponent. From this definition, we can observe that if \(\lambda\) is negative, the two trajectories shall converge (at time step \(n\)), but if it is positive, the nearby orbits diverge (at least exponentially), with chaotic dynamics arising. The Lyapunov exponent thus represents the average exponential growth per unit time between the two nearby states. It can be shown that, with some algebra, \(\lambda\) is:  
\[\lambda=\lim_{n\to\infty}\frac{1}{n}\sum_{i=0}^{n-1}\ln\left|\frac{r_{n}^{(0) }-r_{n}^{(1)}}{\delta}\right|\]  
where the limit and the sum take into account the separation of adjacent trajectories (in initial conditions) for the whole dynamics of the system [8]. Again, in the last equation it can be seen that if the Lyapunov exponent is positive, the separation of the trajectories grows at least exponentially, indicating the presence of chaotic behaviour. The set of all possible values of \(\lambda\), when sweeping the interval of possible values for the initial conditions, constitute the Lyapunov spectrum of the system. It must be noted that, for a given system, there are as many Lyapunov exponents as there are variables.
# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 3 LocFiND--Localization Using Fingerprinting and Novelty Detection
### Using Prediction Probability  
In the first technique, called Prediction Probability, we evaluate the confidence of the estimated position by using the classification probability, available in the scikit-learn KNN implementation. In this implementation, the predict_proba function returns the probabilities that a sample has for belonging to each one of the different classes (trained positions, labels). The most probable class is the result of the classification. For probability estimation, predictors usually implement methods that quantify the confidence of the predictions. In KNN, each class probabilities are the normalized weighted average of indicators for the k-nearest classes, weighted by the inverse distance [44].  
Prediction Probability approach uses the model generated by KNN using the fingerprint database from the training phase. The model evaluates each new, online sample received and returns the probabilities that it belongs to each one of the available classes. In  
Figure 1: LocFiND architecture: the training phase generates the fingerprint database, which is used in the online phase to estimate the position of the mobile devices.  
this approach, we choose the class with the highest probability and, if this probability is greater than a threshold \(\alpha\), the classification is accepted and the new estimated position is sent to the application. Otherwise, the result is rejected. Figure 2 shows the complete diagram for this confidence evaluation.
## 4 Experiment and Results
### Ablation Studies  
Two groups of ablation studies were set up in this paper. The first group removed the text boundary and only performed the original text and non-text segmentation tasks. The second group replaced the feature extraction backbone network (HRNet) with ResNet101. Table 1 shows the original method in this paper and the results of two groups of ablation experiments.  
This paper took the calligraphy character region boundary as an essential attribute of segmenting the calligraphy character region so that it will compare their detection effect. The detection precision with boundaries and without boundaries were close, but the recall rate of the former was 95.60%, and the F1 Score of the former was 95.61%, which were significantly higher than the 92.13% recall rate and the 93.83% F1 Score of the latter. Table 1 shows that the recognition effect of increasing the boundary is better than the effect without the boundary. Figure 12 shows a generated calligraphy picture and its calligraphy character detection feature diagram with boundaries and without boundaries, in which we can find calligraphy character detection with boundaries that can distinguish each character in dense calligraphy.  
Furthermore, the prediction rate of the original method was slightly higher than that of the second ablation experiment by 0.7%, the recall rate of the former was 2.58% higher, and the F1 Score was 1.64% higher. In addition to the prediction rate, the other two have considerable advantages.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **Precision** & **Recall** & **F1 Score** \\ \hline The original method in this paper & 0.9563 & 0.9560 & 0.9561 \\ \hline Method without & & & \\ boundaries & 0.9561 & 0.9213 & 0.9383 \\ \hline Method of replacing HRNet with ResNet101 & 0.9493 & 0.9302 & 0.9397 \\ \hline \end{tabular}
\end{table}
Table 1: Results comparison of ablation studies.  
Figure 11: Detection results.
## 4 Fuzzy Neural Network for Predicting Rains and Temperatures
### Fuzzy Neural Networks  
Fuzzy neural networks are hybrid models designed using two intelligent techniques generally applied in problem-solving, highlighting the training methodologies of neural networks and the ability to extract knowledge and interpretability of problems arising from fuzzy systems [51].  
Fuzzy neural network models produce architectures similar to the Multilayer Perceptron [52] networks, but with the experience to acquire knowledge from the database submitted to the model. FNNs are structured in layers, where each has specific employment. Usually, the first layers are responsible for the fuzzification process, converting inputs into fuzzy sets. Inner layers are capable of performing and/or fuzzy aggregating rules. Finally, the last layers of the model can transform fuzzy numbers into traditional numerical values [53]. The main highlight of these hybrid models, making them different from other artificial intelligence models, is their expertise in building fuzzy rules that can lead to expert problem-solving systems [54].  
These hybrid models have an extensive area of expertise in several economic and social areas. Since the 1990s, heterogeneous models have been employed for frame control [55], pattern recognition applications [56], and in power electronics and motion control [57]. Already in the 2000s, a FNN was used to solve problems such as delta wing control [58], motion control systems [59], and financial reporting fraud risks [60]. In addition, in health problem solving, [PERSON] et al. [61] used a fuzzy neural network for real-time premature ventricular contraction detection and [PERSON] et al. [62] employed the ability of intelligent hybrid models to detect features of cardiac arrhythmia. Finally, in the last decade, scientific work using such techniques has addressed detection of celestial materials (called Pulsars) [63]; prediction of autism in children [64], adolescents [65], and adults [66] through database obtained from mobile devices; breast cancer [67]; absenteeism at work [68]; control for active power filter [69; 70]; data knowledge [71]; and speech recognition [72]. The diversity of these models also covers the field of predicting software building efforts [73], cybersecurity [74], help in cryotherapy and immunotherapy treatments [75; 76], and different classification and regression problems [77; 78; 79; 80; 81; 82; 83], where the models differ according to the training techniques, fuzzification or defuzzification process, architecture, number of layers, elements present in the model structure, etc.  
Figure 16: Fuzzy Neural network [13].
# Bar-Wave Calibration of Acoustic Emission Sensors
## 3 Results and Discussion
### AE Sensor Response
#### 3.2.7 PAC WD Sensor  
This sensor has a 12.7 mm diameter case, but is based on a multiple-element design. Results of bar-wave calibration tests are given in Figure 15 as solid curves, while those for normally incident waves as dashed curves. Color code is similar to that in Figure 11. A large dip is seen at 350 \(\pm\) 30 kHz, which is similar to the first dip of V103. In this case, element size is difficult to estimate: taking it as 10 mm, this corresponds to 2.8 mm/us at 350 kHz. A few bar-wave modes exist at this position in Figure 5c. As in other cases presented, this sensor again showed vastly different responses to bar waves and to normally incident waves. While the latter is relatively smooth from 200 to 1200 kHz, the same cannot be said for the bar-wave response.
## 3 Analysis of Main Rotor Rotating Effects
### Configurations and Grid  
The numerical investigation of the main rotor blade rotating effects is initiated with the calculation of the rotor-off configuration as shown in Figure 6, with a fuselage of 11 m used as the reference length for the aerodynamic coefficient. The length of the landing skids is 28.6% of the fuselage length with circular cross-sections. The horizontal tail is located at the position of 76% of the total fuselage length, while the vertical tail is installed at 87.3% of the total length. The H-topology is used with the O-topology around the configuration in order to describe the viscosity effects of the boundary layer. The height of the first layer near the wall is installed as a \(10^{-5}\) reference length so as to obtain a small y+ \(\approx 1\). The complete grid consists of 19,082,201 nodes. Figures 7 and 8 show the grid topology and surface grid of the configuration. The rotor-off configuration is simulated under the forward flight condition as listed in Table 2.  
\begin{table}
\begin{tabular}{c c} \hline Freestream Mach number & 0.16 \\ Reynolds number & \(4.09\times 10^{7}\) \\ Angle of attack & \(-8^{\circ}\) \\ Main rotor advanced ratio (rotor-on configuration) & 0.2 \\ Main rotor tip Mach number (rotor-on configuration) & 0.8 \\ \hline \end{tabular}
\end{table}
Table 2: Simulation conditions.  
Figure 4: The isosurface of the Q-criterion of the blade wake.  
Figure 5: Vorticity distribution on the symmetrical plane of the blade wake.  
The model of the rotor-on configuration with the four-bladed main rotor with a radius of 5.5 m and a chord length of 0.42 m is given in Figure 9. It is necessary to simplify the rotor-on configuration for the purpose of facilitating the numerical simulation and reducing the calculation time; therefore, the rotor hub, hub fairing and other components are removed to simplify the computational model. This simplification will cause a significant reduction in the difficulty of grid generation. It should be noted that the main rotor rotates in a counterclockwise direction as seen from above. The computational grid for the current model can be considered as the combination of the main rotor domains and fuselage domains with the application of dynamic patched-grid technology. To improve the resolution in the wake area, the grid beneath and behind the main rotor is refined. The  
Figure 8: Surface grid of the rotor-off configuration.  
Figure 6: Rotor-off configuration.  
Figure 7: Grid topology of the rotor-off configuration.
## 3 Methods
### Data Partitioning and Parallelization  
#### 3.3.1 Polygon Number Division and Parallelization  
The traditional polygon number division method is a direct and simple method of dividing data in accordance with the order of polygon storage. This method is easy to implement; it does not cause damage to the input data when dividing, and the number of polygons in each group after division is equal. Moreover, this method has clear termination conditions for the loop traversal clipping process of the target layer and can be easily parallelized based on OpenMP. Figure 4 shows a schematic diagram of the multicore parallel computing method based on the number of polygons. The main process is to sequentially extract each polygon element from the vector data, ensure that the number of polygon elements in each group of data is equal, distribute the divided data among different CPU threads, and perform the polygon overlay analysis operation in parallel. The complexity of each group of data grouped by this method varies greatly, and the balance is poor when multiple cores execute the polygon overlay analysis algorithm.
# Landsat Surface Product Validation Instrumentation:
## 4 Thermal Sensor Accuracy and Precision  
### UAS Sensors  
Because of the difficulty involved in developing thermal targets that are stable over time, the results in this section will focus primarily on the accuracy and consistency across sensors during simultaneous measurements. Both UAS-borne and ground instruments will be compared. Primarily, due to the high degree of accuracy of the \(\mu\)FTIR and the TidbiTs, these will be used as reference sensors. Of particular interest will be the accuracy of the UAS FLIR sensor, as we aim to determine whether thermal imagery captured from a UAS platform can be used to validate higher-level products from satellite-borne systems.
## Materials and Methods
### Restricted Maximum Likelihood (REML) Method in Single-Locus Screening Stage
#### 2.2.3 Optimal Solution via Efficient REML  
In order to get the estimation \(\hat{\omega}_{g}\), optimize the following restricted log-likelihood function with respect to \(\omega_{g}\),  
\[\begin{split} L_{r}(\omega_{g})=&-\frac{1}{2}\log \lvert\mathbf{V}\rvert-\frac{1}{2}\log\lvert\mathbf{F_{Q}^{T}}\mathbf{V}^{-1}\mathbf{F_{Q}} \rvert-\frac{n-c}{2}\big{(}\mathbf{y_{Q}}-\mathbf{F_{Q}}\hat{\mathbf{b}}\big{)}^{T}\mathbf{V} ^{-1}\big{(}\mathbf{y_{Q}}-\mathbf{F_{Q}}\hat{\mathbf{b}}\big{)}\\ =&-\frac{1}{2}\log\lvert\mathbf{V}\rvert-\frac{1}{2}\log \lvert\mathbf{F_{Q}^{T}}\mathbf{V}^{-1}\mathbf{F_{Q}}\rvert-\frac{n-c}{2}\mathbf{y_{Q}^{T}}\bm {y_{Q}}\end{split} \tag{5}\]  
where  
\[\hat{\mathbf{b}}=\big{(}\mathbf{F_{Q}^{T}}\mathbf{V}^{-1}\mathbf{F_{Q}}\big{)}^{-1}\mathbf{F_{Q}^ {T}}\mathbf{V}^{-1}\mathbf{y_{Q}} \tag{6}\]  
\[\mathbf{S}=\mathbf{V}^{-1}-\mathbf{V}^{-1}\mathbf{F_{Q}}\big{(}\mathbf{F_{Q}^{T}}\mathbf{V}^{-1}\mathbf{F_{ Q}}\big{)}^{-1}\mathbf{F_{Q}^{T}}\mathbf{V}^{-1} \tag{7}\]  
Limited-memory BFGS (L-BFGS) [35; 39] is an optimized algorithm of quasi-Newton methods for efficient solution. The libLBFGS is a user-friendly C library implementation of the L-BFGS method written by [PERSON] [40]. This library requires that the objective function \(L_{r}(\omega_{g})\) and its gradient \(\partial L_{r}(\omega_{g})/\partial\omega_{g}\) are computable. However, the gradient function cannot be obtained directly by closed form. Fortunately, the well-known C++ Boost library [41] provides a finite difference method for solving the gradient, which is located in a boost/math/differentiation/finite_difference.hpp file. After \(\hat{\omega}_{g}\) is estimated, \(\hat{\beta}\) and \(\hat{\alpha}_{n}^{2}\) can be easily obtained for restricted log-likelihood functions, which are as follows, respectively,  
\[\hat{\beta}=\omega_{g}\mathbf{X_{Q}^{T}}\mathbf{S}\mathbf{y_{Q}} \tag{8}\]  
\[\hat{\alpha}_{n}^{2}=\frac{1}{n-c}\mathbf{y_{Q}}\mathbf{S}\mathbf{y_{Q}} \tag{9}\]  
so that the variance of \(\hat{\beta}\) denotes  
\[\text{var}(\hat{\beta})=\omega_{g}\hat{\omega}_{n}^{2}-\omega_{g}\mathbf{X_{Q}^{T} }\mathbf{V}^{-1}\mathbf{X_{Q}}\omega_{g}\hat{\omega}_{n}^{2} \tag{10}\]  
The Wald test is used to conduct a hypothesis test on each marker effect \(\beta\)--that is, \(H_{0}:\beta=0\), \(H_{1}:\beta\
eq 0\). The Wald test is  
\[W=\frac{\hat{\beta}^{2}}{\text{var}(\hat{\beta})} \tag{11}\]\(W\) follows the chi-square distribution with 1 degree of freedom, and the \(p\)-value corresponding to \(W\) can be computed by the C++ Boost library, which is located in a boost/math/distributions/chi_squared.hpp file. In the single-marker screening stage, a relatively loose and flexible \(P\) cutoff is adopted.
## 3 Results
### Estimation of \(f^{esc}\) Using \(fapar\)\(R_{canopy}\) and \(fapar\)\(R_{leaf}\)  
SIF escape probabilities were also estimated using two types of fAPAR (i.e., \(fapar\)\(R_{canopy}\) = \(fapar\)\(R_{leaf+novody}\) and \(fapar\)\(R_{leaf}\)) whose simulation methods were described in Section 2.2.3. Figure 8  
Figure 6: Comparison of SIF escape probability estimated by SIT and using leaf area index (LAI) and clumping index (CI) as input for the estimation of canopy interceptance (i.e., \(f_{SIT}^{esc}\)) with reference values that were simulated by DART (i.e., \(f_{DART}^{esc}\)). ‘Real’ LAI\({}^{*}\)CI was used to plot (**a**). Additional –50% and +50% errors of LAI\({}^{*}\)CI were introduced to plot (**b**,c**). Relative error was calculated as \((f_{SIT}^{esc}-f_{DART}^{esc})/f_{DART}^{esc}\).  
Figure 7: Comparison of the total emitted SIF (\(SIF_{tot}^{SIT}\)) using different SIF escape probabilities with DART reference values (\(SIF_{tot}^{DART}\)). (**a**) Plot using \(f_{SIT}^{esc}\) estimated with ‘real’ LAI\({}^{*}\)CI; (**b**,c**) plots using \(f_{SIT}^{esc}\) estimated with additional errors of LAI\({}^{*}\)CI: –50% error of LAI\({}^{*}\)CI for (**b**); +50% error of LAI\({}^{*}\)CI for (**c**).  
shows that the values of SIF escape probability estimated using fAPAR\({}_{empty}\) as input were smaller than that using fAPAR\({}_{leaf}\) as input in all simulations. The estimation accuracy of SIF escape probability was improved only for the case with high FVC (FVC equal to 0.8), and the estimation accuracy was decreased for other simulation cases.