esa-sceva/satcom-chunk-collection huggingface dataset:
# Application of Machine Learning Techniques to
## 4 Results and Discussion
### Climate Zones
#### 4.1.2 Agglomerative Hierarchical Clustering (AHC)  
We then identified all the potentially homogeneous regions inside the study area. The extent of the homogeneity in these homogeneous regions was identified using different cluster validity indices. The first 20 PCs were employed in the AHC analysis. These PCs were obtained in the previous step using the Python scikit module. The validity of different site/station clusters was evaluated by performing the silhouette score, CH and S Dbw tests. Figure 6 summarizes the cluster validation results. The machine learning algorithms for the cluster validity were run repetitively using different input values for the maximum Euclidean distance, followed by a comparison of the resulting clusters to obtain their validity. According to the results, the optimized partitioning of the data based on the CH Test was investigated while the lowest scores were estimated corresponding to clusters 13, 15, 14 and 14 for the warm-wet, cold-dry, cold-wet and warm-dry seasons, respectively, as shown in Figure 6a. The partitioning based on the S Dbw test suggests that optimized clustering can be performed when the data is partitioned into clusters 13, 13, 14 and 13 for the warm-wet, cold-dry, cold-wet and warm-dry seasons, respectively, as shown in Figure 6b. These clusters were selected based on the lowest S Dbw scores. The partitioning of data into clusters 11, 15, 13 and 13 corresponds to the highest silhouette test scores for the warm-wet, cold-dry, cold-wet and warm-dry seasons, respectively, as shown in Figure 6c.  
Based on Figure 6, all validity measures agree with the same number of clusters. Therefore, based on the recommendations of previous studies [70], we partitioned the stations for optimized clustering as clusters 13, 13, 14 and 13 for the warm-wet, cold-dry, cold-wet and warm-dry seasons, respectively. The members/stations in different clusters were obtained via the truncation of the dendrogram at the Euclidean distances that correspond to the optimized clusters. The dendrograms were obtained from  
Figure 6: The cluster validity indices/scores for cold-dry (cd), cold-wet (cw), warm-wet (ww) and warm dry (wd) seasons (**a**) Calinski–Harabasz score, the number of clusters valid are marked corresponding to the minimum score in each season (**b**) S Dbw score, the number of clusters valid is marked corresponding to minimum score in each season and (**c**) Silhouette Score the number of clusters valid is marked corresponding to maximum score in each season.  
the AHC of the selected PCs. The maximum Euclidean distances were identified as corresponding to the proposed cluster numbers, where Figure 7 shows the cut-off/truncation bars for each season. Based on this truncation, we identified the groups of stations/sites in each cluster. Figure 7 shows the dendrograms that present the cluster trees for all four seasons.
## 2 Materials and Methods
### Methods
#### 2.2.4 Air and Water Surface Temperatures  
On-site observations of the dam structure temperatures are not always available, and structure temperatures are closely related to air temperature [11]. Indeed, any change in the dam structure's temperature is mainly affected by changes in external temperatures. External temperatures include air and water body temperatures. However, the distribution of the water temperature as a function of the depth is not constant; indeed, lakes are characterised by thermal stratification, as can be described by the thermal convection-diffusion equation [75]. Within this simplified approach, we monitor only the water surface temperature of the reservoir. Water surface temperature, \(T_{\rm W}\) (K), is here estimated by radiometrically processing a Landsat 8 TIRS time series. In particular, a time series of \(T_{\rm W}\) for the central pixel of the reservoir was retrieved by applying the Statistical Mono-Window (SMW) algorithm developed by the Climate Monitoring Satellite Application Facility (CM-SAF) [76]; in particular, the Google Earth Engine Open Source Code developed by [PERSON] et al. [77] was employed. Water surface temperature follows a sinusoidal behaviour, \(T_{\rm W}=\overline{T}+\Delta T\times\sin(\omega t+\varphi)\), which is a function of the yearly average of \(T_{\rm W}\), \(\overline{T}\) (K); the yearly amplitude of \(T_{\rm W}\), \(\Delta T\)(K); the phase difference \(\geq\)\(\phi\) (\(-\)); the time \(t\), here assumed as the DOY; and the yearly angular velocity of revolution of the earth (1.7214 \(\times\) 10\({}^{-5}\) rad DOY\({}^{-1}\)). Thus, remotely sensed values were interpolated to facilitate the interpretation. Analogously, a sinusoidal curve was fitted to the measured air temperature \(T_{\rm A}\) (K) as well to make the interpretation easier.
# Exploration of Swarm Dynamics Emerging from Asymmetry
## 4 Measures to Quantify the Swarm Dynamics
### Fitness Function Candidates
#### 4.3.1 Fitness Function A  
One straightforward way to characterize the swarm structure can be the geometrical asymmetry of a swarm. We have defined a fitness function which represents the positional deviation of the centroid of each type from the global centroid. This measure is computed from the snapshots of the swarm.  
\begin{table}
\begin{tabular}{c l|c|c|c} \hline
**FF** & **Meaning** & **Explicity of Aymmetry** & **Asymmetry Type** & **Dyanicity** \\ \hline A & Typewise centroid deviation & & Positional & Static \\ \hline B & Variation of pairwise distance & & & \\ \hline C & In-/out-degree difference & & & \\ \hline D & Derivative of in-/out-degree difference & Explicit & & \\ \hline E & Sum of derivatives of in-/out-degrees & & & \\ \hline F & Fluctuation of in-/out-degree difference & & & \\ \hline G & Max. Lyapunov exponent \(\lambda_{\text{max}}\) & Implicit & & \\ \hline \end{tabular}
\end{table}
Table 2: Fitness functions (FF) proposed in this paper.  
In this sense, it is a metric of the static structure of swarm. In a mathematical form, this fitness function could be written as:  
\[\phi = \frac{1}{M}\sum_{t=1}^{T}\sum_{j=1}^{M}\left\|\vec{x}_{jC}-\vec{x}_ {GC}\right\| \tag{6}\] \[\vec{x}_{jC} = \left(x_{jC},\,y_{jC}\right)\] (7) \[\vec{x}_{GC} = \left(x_{GC},\,y_{GC}\right), \tag{8}\]  
where \(M\) is the number of types, \(\vec{x}_{jC}\) is the centroid coordinate of \(j\)-th type, and \(\vec{x}_{GC}\) is the centroid coordinate of all agents. \(T\) denotes the whole duration of the simulation. The centroid coordinate of \(j\)-th type equals to the \((x,\,y)\) positions averaged over the set of agents belonging to that type \(j\). Similarly, the global centroid coordinate equals to the \((x,\,y)\) positions averaged over all the agents. When the emerged swarm has a unique shape like ones shown in Figure 2, there are cases where the type-wise centroid does not overlap the global centroid while achieving balances between the different types. There is a possibility that maximizing this function leads to an interesting pattern.
# An Instrument Error Correlation Model for Global Navigation Satellite System Reflectometry
## Appendix A
The relevant partial derivatives \(\frac{\partial F}{\partial N}\) from Equation (A10) become the following:  
\[\left|\frac{\partial F}{\partial P\!\!\!^{g}}\right|=\frac{4\pi L^{atm}L^{Z}G ^{Z}}{P^{Z}\xi^{E}G^{R}R^{tot}\bar{A}}\] (A13a) \[\left|\frac{\partial F}{\partial P\!\!\!^{z}}\right|=\frac{P\!\!\!^{g}4\pi L ^{atm}L^{Z}G^{Z}}{(P^{Z})^{2}\xi^{E}G^{R}R^{tot}\bar{A}}\] (A13b) \[\left|\frac{\partial F}{\partial G^{z}}\right|=\frac{P\!\!\!^{g}4\pi L ^{atm}L^{Z}}{P^{Z}\xi^{E}G^{R}R^{tot}\bar{A}}\] (A13c) \[\left|\frac{\partial F}{\partial G^{R}}\right|=\frac{P\!\!\!^{g}4\pi L ^{atm}L^{Z}G^{Z}}{P^{Z}\xi^{E}(G^{R})^{2}R^{tot}\bar{A}}\] (A13d) \[\left|\frac{\partial F}{\partial\xi^{E}}\right|=\frac{P\!\!\!^{g}4\pi L ^{atm}L^{Z}G^{Z}}{P^{Z}(\xi^{E})^{2}G^{R}R^{tot}\bar{A}}\] (A13e)  
To estimate \(E_{n}\), as shown in Table 1, these partial derivatives are evaluated at the 1-sigma value for a reference 10 m/s wind speed using Equation (A6). The total error model becomes the following:  
\[R_{mod}(i,j)=\frac{1}{\mathcal{N}}\Big{[}K_{ps}(i,j)+K_{P}^{2}(i,j)+K_{G^{Z}} (i,j)+K_{G^{R}}(i,j)+K_{\xi^{E}}(i,j)\Big{]},\] (A14)  
where \(K_{ps}\) is described in Appendix B, \(K_{P}^{z}\) is described in Appendix C, \(K_{G^{Z}}\) and \(K_{G^{R}}\) are described in Appendix D, and \(K_{\xi^{E}}\) is described in Appendix E. \(\mathcal{N}\) is a normalization constant that forces \(R_{mod}\) to behave like a correlation such that \(-1\leq R_{mod}\leq 1\) and can be thought of as an estimate for the rolled-up variance. Because this model has tunable parameters, it is not necessarily representative of the true variance of \(\sigma^{o}\) but rather of the modeled variance from our bottoms-up model construction.
## 3 Results and Discussion
### ANN Model Results
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Dataset** & **MSE Weight** & \begin{tabular}{c} **MSE** \\ **Energy** \\ \end{tabular} & \begin{tabular}{c} **MAE** \\ **Weight** \\ \end{tabular} & \begin{tabular}{c} **MAE** \\ **Energy** \\ \end{tabular} & \begin{tabular}{c} **R\({}^{2}\) Weight** \\ **Energy** \\ \end{tabular} & \
\begin{tabular}{c} **R\({}^{2}\) Energy** \\ **Consumption** \\ \end{tabular} &
\begin{tabular}{c} **R\({}^{2}\) Total** \\ \end{tabular} \\ \hline
**Train** & \(8.61\times 10^{-6}\) & 0.2516 & 0.0023 & 0.3682 & 99.16\% & 95.99\% & 97.58\% \\
**Validation** & \(8.22\times 10^{-6}\) & 0.4354 & 0.0024 & 0.5371 & 98.95\% & 88.57\% & 93.76\% \\
**Test** & \(1.62\times 10^{-5}\) & 0.7285 & 0.0032 & 0.5235 & 98.49\% & 84.12\% & 91.31\% \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Result of ANN model. The model exhibited high accuracy and predictive power across the training, validation, and test sets, particularly for the weight response variable. While there was a noticeable decline in performance for energy consumption on the validation and test sets, the overall predictive capability remained strong. These results suggest that the model is well suited for predicting the weight but may require further refinement to improve the accuracy for energy consumption.
# KCFS-YOLOv5: A High-Precision Detection Method for Object Detection in Aerial Remote Sensing Images
## 3 Improvements of the KCFS-YOLOv5  
Aiming at the above problems, we put forward several measures to improve YOLOv5.
* We use the K-means++ algorithm [41] to optimize the initial clustering points of the anchor box, which makes the initial clustering points of anchor box distributed on the whole feature map as much as possible. In this method, the object on the feature map can be detected as much as possible, and the probability of missing detection can be reduced.
* We embed the Coordinate Attention (CA) module [42] into the backbone network of YOLOv5 to improve the feature extraction ability of objects in aerial remote sensing images, especially tiny objects.
* We embedded the Bidirectional Feature Pyramid Network (BiFPN) [43] in the neck network of YOLOv5 to optimize the network framework to improve the fusion ability of the neck network for different dimensional features.
* Based on the characteristics of aerial remote sensing images which contain many tiny objects, we added a new tiny object detection head based on the conventional YOLOv5 to preserve the texture features of tiny objects and improve the detection accuracy of tiny objects.
* We replaced the GIoU function with the GIoU [44] function, which can reduce the deviation between the predicted box and the ground truth box and improve the detection performance.
# Entangled Frequency-Tunable Microwave Photons in a Superconducting Circuit
## 4 Numerical Results
### Creation of the Photon Pairs
\[g_{ab}^{(2)}(t,\tau) =\frac{\langle:\hat{D}_{a}(t)\hat{D}_{a}(t+\tau)\hat{D}_{b}(t+ \tau)\hat{D}_{b}(t):\rangle}{\langle\hat{D}_{a}(t)\rangle\langle\hat{D}_{b}(t )\rangle} \tag{12}\] \[\propto\frac{\langle\hat{a}^{\dagger}(t)\hat{a}(t+\tau)\hat{b}^{ \dagger}(t+\tau)b(t)\rangle}{\langle\hat{a}^{\dagger}(t)\hat{a}(t)\rangle \langle\hat{b}^{\dagger}(t)\hat{b}(t)\rangle},\]  
where the dots \(::\) denote the normal ordering. As shown in Figure 5, we reconstruct the second-order coherence function with zero delay \(g_{ab}^{(2)}(t,0)\) at dc working points \(\{-0.5,-0.25,0.25,0.5\}\Phi_{0}\). The \(g_{ab}^{(2)}(t,0)\) starts from a high value, which means the output microwave photons between the two modes are emitted in pairs out of the emitter at all dc working points. The \(g_{ab}^{(2)}(t,0)\) decreases significantly as time goes on due to the losses of both resonators, which destroy the correlation between both modes, and also due to the increase in photon number as shown in the Figure 4b. Figure 4: (**a**) Photon number of modes \(a\) and \(b\) as functions of the dc flux bias \(\Phi_{\text{dc}}\) in the time domain. (**b**) Detailed photon creation along the horizontal white dashed line in (**a**) with \(\Phi_{\text{dc}}=0\) where the red line (right axis) is the second-order coherence functions \(g_{ab}^{(2)}(t,0)\) between the two modes with zero delay.
# A Novel Aerodynamic Modeling Method Based on Data for Tiltrotor evtol
## 5 Conclusions  
A neural network structure that conforms to the aerodynamic characteristics and mechanism of the tiltrotor evtol was constructed using an MLP neural network. To create a dataset for neural network training and validation, wind tunnel test data from the XV15 tiltrotor with similar aerodynamic characteristics and structure were selected. The dataset was constructed by analyzing the sample data structure and constructing a sample vector space with constraints. Finally, the optimal hyperparameter combination for the neural network was selected by designing evaluation indicators. Predictions were then made based on the test set under different tilt angle conditions. The simulation results show the following:  
1. Neural-network-based data-driven methods can accurately predict the aerodynamic characteristics of tiltrotor evtol aircraft during the transition phase, with an error rate of less than 2% compared with wind tunnel test data.
2. Traditional physical modeling methods exhibit larger errors in representing the complex aerodynamic characteristics of tiltrotor aircraft due to the limitations of mathematical formulae, typically up to nearly 10%.
3. The MLP neural network structure constructed based on the aerodynamic characteristic mechanism of the tiltrotor evtol is effective.  
In summary, this study shows that data-driven methods using neural networks can predict the aerodynamic characteristics of tiltrotor evtol aircraft during the transition phase. Due to the high fidelity fitting and relatively simple modeling process of MLP for nonlinear and strongly coupled aerodynamic characteristics, this method can be extended and applied to the full degree of freedom and full flight envelope aerodynamic modeling of tiltrotor evtol aircraft.  
Writing--original draft preparation, H.W.; writing--review and editing, P.L. and D.W. All authors have read and agreed to the published version of the manuscript. This research was funded by the China Scholarship Council, grant number 202308320187.  
The data presented in this study are available in [NASA] at [[[https://ntrs.nasa.gov/citations/19730022217](https://ntrs.nasa.gov/citations/19730022217)]([https://ntrs.nasa.gov/citations/19730022217](https://ntrs.nasa.gov/citations/19730022217))], reference number [20].  
The authors declare no conflict of interest.
# Classifying Inundation in a Tropical Wetlands Complex with GNSS-R
## 4 Classification Methodology
### Performance Validation and Results
The accuracy results of the GNSS-R inundation classification obtained from the MDTR algorithm are assessed in Table 4.  
FV had a classification accuracy of 69%, demonstrating the ability of the forward scattered L-band signal to penetrate through the vegetation canopy and detect inundated vegetation. OW had an accuracy of 86%, despite the narrowness of the river in the study area relative to the footprint size of the CYGNSS reflection. The roughness of the surface had an impact on the total power received (a rougher and larger scattering area yields a lower SNR and the signal becomes incoherent), and hence the contribution of the river was attenuated in cases where it covered only a small part of the scattering area and the specular point fell far from it. Finally, NF classification accuracy was 99.7%, however, a high percentage of OW and FV pixels were misclassified as NF, which can also be observed in the location of the misclassifications shown in Figure 10. This is likely explained by the presence of areas of OW or FV that were small relative to the size of the CYGNSS footprint, resulting in an incoherent signal (larger footprint) and reducing the SNR to that of NF. This effect was more noticeable for FV, because the vegetation contributed to the loss of coherency. Table 4 also shows that the classification with the MDTR algorithm resulted in overall better results than a single DT (Table 3F, highlighted in bold black). A comparison between the two approaches indicates that the MDTR algorithm improved classification accuracy of OW by 20%, FV by 9%, and NF by 5% when compared to a single DT. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**PALSAR-2 Class** & **GNSS-R Class** & **OW** & **FV** & **NF** \\ \hline
**OW** & **86.53\%** & 1.22\% & 12.25\% \\
**FV** & 0.23\% & **69.14\%** & 30.63\% \\
**NF** & 0.02\% & 0.22\% & **99.76\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy results of the GNSS-R inundation classification obtained with the MDTR algorithm. The confusion matrix displays the accuracy results of the classification algorithm built using corrected peak SNR + TES + LES + \(\Delta\) GLO 1 + SRTM90 DEM + GW biomass density as inputs. Data used to assess the classification results correspond to that of the reference validation dataset (cycles 74 and 91). Figure 9 illustrates the classification results of the GNSS-R derived inundation classifications validated using data from cycle 74 (left column) and cycle 91 (right column). Figure 10 shows the location of the misclassifications for both cycles 074 and 091. Figure 10: Location of the misclassifications observed for (**a**) cycle 074 and (**b**) cycle 091.
# Gaze-Guided Control of an Autonomous Mobile Robot Using Type-2 Fuzzy Logic
## 1 Introduction
In this platform, two high-resolution cameras are used. The first camera is mounted on the robot moving platform for transmitting a live video to the user screen. The second camera is used to identify the user's eye movement and translate this gaze direction to the host system to calculate the robot's steering control by using soft computing technique. The goal is to process to robot's movements where the user is looking at the display screen. To make the robot move around in the vibrant environment under the visibility of an overhead camera, it is focused on the remote settings with a 2D gaze guided driving system such as forward, backward, left and right. This paper is organized as follows. Section 2 represents the procedure of the gaze guided system including a vision system of gaze tracking, the control system of the type-2 fuzzy mechanism and its application and experimental platform. The experimental design and results of the proposed methods are given in Section 3. Section 4 includes the discussion. Finally, Section 5 concludes the paper and recommendation for future works.
# Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
## 5 Future Perspective
The figure can be read from left to right, where (**A**) represents the user control center, (**B**) represent servers, cloud and the related storage accessories, (**C**) represents the real players, e.g., satellites, remotely piloted acirrafts (RPAs), autonomous robots, metrological stations, etc., for data acquisition, (**D**) represent data processing using machine learning and related platforms that ultimately produce infographics and other useful information in (**E**).
## 2 Methods
### Adaptive Data Block Partitioning Strategy  
To reduce the data volume processed per PSInSAR computation and enable adaptive partitioning, we propose a dynamic partitioning strategy based on a regular grid, utilizing a sliding window mechanism and edge sub-block merging to ensure seamless coverage of the entire area. The method is implemented as follows: first, an initial grid size \(B_{h}\times B_{w}\) and overlap size \(O\) are predefined. The sliding step sizes are then computed as \(S_{h}=B_{h}-O\) and \(S_{w}=B_{w}-O\). Based on the original data matrix size \(H\times W\), initial sub-blocks are generated with row and column sizes defined as  
\[B^{\prime}_{h0}=\min(B_{h},H-i:S_{h}),\ B^{\prime}_{w0}=\min(B_{w},W-j\cdot S_ {w}) \tag{1}\]where \(i\) and \(j\) are the sub-block indices, with \(i\in[0,\lceil H/S_{h}\rceil)\) and \(j\in[0,\lceil W/S_{w}\rceil)\).  
For edge sub-blocks (with row index \(i_{\max}\) or column index \(j_{\max}\)) that may be smaller than the predefined size, a dynamic edge adjustment mechanism is introduced. If \(B^{\prime}_{h0}<B_{h}\) (i.e., the remaining height in the original data is less than the initial grid size), the last row sub-block \((i_{\max},j)\) is merged with its adjacent upper sub-block \((i_{\max}-1,j)\), adjusting its height using  
\[B^{\prime}_{h}=H-(i_{\max}-1)\cdot S_{h} \tag{2}\]  
The adjusted sub-block size satisfies \(B^{\prime}_{h}\in[B_{h},\,2B_{h})\), with a similar adjustment applied in the column direction. This adaptive boundary extension strategy ensures complete coverage of the monitoring area, avoiding fragmented sub-blocks caused by non-integer multiples of the grid size in traditional partitioning methods. It also maintains strict consistency in overlapping regions between adjacent sub-blocks. For example, given an original data size of 8300 \(\times\) 6700 pixels, with grid size \(B_{h}=B_{w}=2000\) and overlap size \(O=500\), the proposed partitioning method generates 20 sub-blocks. The last row sub-block has a height of 2300 pixels, and the last-column sub-block has a width of 2200 pixels, while all other sub-blocks remain at the standard size (Figure 2). This strategy effectively balances the data completeness and computational resource allocation efficiency while significantly simplifying the sub-block adjacency relationship retrieval in subsequent merging stages.  
Figure 1: Flowchart of the proposed method.