esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Acquisition
### Field Work Framework for UAV Operations  
To obtain a high-quality 3D model of the slope, this study employed a UAV-based multi-angle nap-of-the-object photogrammetry technique for image acquisition. Building upon conventional photogrammetry, this method integrates the topographic variability of the slope and the spatial distribution of discontinuities, enabling the capture of high-resolution images of complex rock masses.  
The process began with oblique terrain-following photogrammetry to acquire the initial topographic data of the slope. The area was then subdivided into several terrain sub-units characterized by relatively gentle relief (Figure 3a). For each sub-unit, multipleclose-range flight paths were planned, with priority given to capturing high-resolution images perpendicular to the slope surface (Figure 3b). On the basis of field investigations and preliminary UAV survey results, dominant discontinuity sets were identified. Additional flight paths were subsequently designed for each major set, with the camera orientation adjusted to align as closely as possible with the planes of the discontinuities (Figure 3c). Furthermore, supplementary flights were conducted in shaded or recessed areas to eliminate blind spots and ensure comprehensive high-resolution coverage of all discontinuities (Figure 3d).  
According to this strategy, the DJI M300 from DJI company in China, equipped with a Zenmuse P1 camera, is employed to capture images of the slope, with the relevant  
Figure 3: UAV-based multi-angle nap-of-the-object photogrammetric method scheme.  
Figure 2: Overview of the study slope: (**a**) location, (**b**) appearance, and (**c**) dominant discontinuity.  
parameters detailed in Table 2. The initial topographic survey was conducted at a flight altitude of 255 m, with 80% forward overlap and 60% side overlap. A total of 1245 images were collected, covering an area of approximately 0.3 km\({}^{2}\). The images were imported into DJI Terra (Mapping Version) software to automatically generate a preliminary digital elevation model (DEM). On the basis of the DEM, the slope was divided into two terrain sub-units, and multi-angle close-range flight paths were planned for each. During this phase, the UAV operated at a speed of 1.5 m/s, with 85% forward overlap and 70% side overlap. A total of 625 high-resolution images were acquired.
# The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
## 3 Improved Genetic Algorithm
### Population Initialization
#### 3.1.1 Dichotomy Method  
The core idea of the dichotomy method is to recursively find the midpoint between two grid cells. It is primarily implemented using two approaches: the row-based method and the column-based method.  
Figure 1: Grid matrix diagram.  
The processing flow of the base-row system and base-column system adjusts the judgment sequence according to the base dichotomy type: the base-row system checks if the vertical coordinate difference \(\mid\!Y-y\!\mid\) is greater than one, while the base-column system checks if the horizontal coordinate difference \(\mid\!X-x\!\mid\) is greater than one. When the difference exceeds the threshold, a bisection operation is performed: the coordinate difference is divided by two and randomly rounded (either rounded up or down), with the same calculation rule applied to both horizontal and vertical coordinates. After generating the midpoint, if an obstacle is encountered, the movement direction is chosen based on the base direction type: the base-row system uses horizontal (left/right) displacement, while the base-column system uses vertical (up/down) displacement, continuing to move within the current row or column to find a feasible grid. If no valid path is found after the displacement, the algorithm is terminated, indicating that no path exists between the start and end points in the current map. The base-row system and base-column system each ensure that there is one grid per row or column. By evenly splitting the population into two base direction modes and introducing the random rounding of the half-coordinate difference and obstacle-avoiding random-direction selection mechanisms, the initial population diversity is significantly enhanced, thereby optimizing the global performance of the algorithm.  
After applying the dichotomy method to generate intermediate points between the start and end points, the path is divided into two segments. For each segment, the search for intermediate points continues, further subdividing the path until adjacent rows or columns can no longer be divided. The final path generated will consist of multiple nodes that are evenly distributed.
## 3 Results and Discussion
### X-Ray Diffraction (XRD)  
XRD was performed on the raw biomass and that obtained under the optimal conditions of the three treatments with the different organosolvents in order to determine if there were any changes in the crystallinity following the processes (Figure 2).  
All samples showed the typical reflections corresponding to the (002) lattice planes of the cellulose I structure. The crystallinity index, \(CI\), of the original biomass was 46.0%,  
Figure 1: Composition of biomass before and after the different organosolv pretreatment at optimum conditions.  
increasing slightly (49.6%) when GVL was used and significantly when ethanol (69.5%) or acetone (68.4%) was employed, thus providing a major advantage for the production of cellulose-based nanomaterials. This increase in crystallinity values may be due to a deterioration of the amorphous cellulose, which would imply that the crystalline structure of the resulting biomass is more significant in percentage. However, due to the complex nature of the amorphous and crystalline regions of the cellulose in this kind of biomass, it is difficult to interpret the effect of the pretreatments that were carried out.  
The crystallite size, \(cs\), in the original biomass, determined by reflection (002), was 4.60 nm, which is a common value for cellulose from woody materials such as olive-tree prunings [66]. However, the values of this parameter were slightly lower when GVL (4.27 nm), ethanol (4.03 nm), or acetone (3.87 nm) were used. This reduction in crystallite size after the pretreatments may confirm what has already been indicated in reference to the degradation of the amorphous phase during the processes.
## 1 Introduction
This complete method solves intrusion detection system constraints and lays the groundwork for secure IoT network management, assuring high scalability, flexibility, and operational integrity against growing cyber threats. The following are the main contributions of this work:  
1. The ResVGG-SwinNet model has been designed, which combines ResNet, VGGNet, and Swin-Transformer. Hybrid federated learning guarantees privacy while attaining outstanding accuracy in DDoS detection. The Dual Adaptive Selector (DAS) for relevance-based feature selection and redundancy reduction has been designed, thereby greatly improving computational efficiency and model accuracy. * Novel Preprocessing Methods: To properly handle data heterogeneity and class imbalance, Adaptive Feature Normalising (AFN), Contextual Traffic Augmentation (CTA), and Multi-scale Temporal Balancing (MTB) are used. * Multi-label Sensitivity Index (MSI) and Optimisation Efficiency Score (OES) have been designed for a more complex and all-encompassing multi-label DDoS detection capability assessment. * A federated-learning-based intrusion detection system has been designed, ensuring strong DDoS detection in over-dispersed IoT networks while respecting user data privacy and adjusting to changing network traffic circumstances. Structure of the article: Section 2 evaluates relevant literature to identify gaps in IoT-based intrusion detection and federated learning for DDoS mitigation. Section 3 discusses the proposed system's hybrid ResVGG-SwinNet architecture, preprocessing, feature optimisation, and federated-learning framework. Section 4 shows simulation data, performance metrics, comparison analysis, and statistical assessments to prove the model works. Section 5 closes the study and suggests further research to improve the framework's scalability and usability in varied IoT scenarios.
## 3 Results and Discussion  
Figures 3-6 show the results of the InSAR coherence and change detection analysis for the two events that occurred in January 2018 and December 2014, which are analysed and discussed in detail  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**InSAR Pair (DD/MM/YYYY)** & \(B_{t}\) **[days]** & \(B_{p}\) **[m]** & **Figures** \\ \hline
10/07/2014–12/04/2015 & 275 & 14 & Figure 6b \\
13/12/2017–30/01/2018 & 48 & 19 & Figures 3e, 4e and 6c \\
10/01/2018–30/01/2018 & 20 & 165 & Figures 3d and 4d \\
18/01/2018–26/01/2018 & 8 & 4 & Figures 3b and 4b \\
26/01/2018–30/01/2018 & 4 & 472 & Figures 3c and 4c \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dates and perpendicular (\(B_{p}\)) and temporal (\(B_{t}\)) baselines of the COSMO-SkyMed InSAR pairs used for the analysis of the December 2014 and January 2018 events that occurred at the Nasca Lines.  
in Sections 3.1 and 3.2, respectively. A nonchronological order for the presentation of the two events of site disturbance was intentionally adopted to: (i) first demonstrate what can be achieved for a recent event, only a few days or weeks after its occurrence; and (ii) then export the same approach to a much older event. This is achieved by going back through the time series, to showcase that the same stack of SAR data can also be exploited at any time to reconstruct historical events, as well as to verify present site condition and confirm the absence or presence of further landscape disturbance.  
InSAR coherence maps in Figure 3b-e, Figures 4b-e and 6b,c use a colour scale, whereby areas with high coherence are represented in yellow, whilst low-coherence areas are represented in dark violet. This colour scale enhances very well the identification of sectors where coherence drops due to temporal and/or spatial decorrelation. The interpretation of the causes for coherence loss is then carried out by accounting for the characteristics of the InSAR pairs (e.g., \(B_{t}\) and \(B_{p}\)), other ancillary information about the two events, and evidence from the analysis of VHR and HR satellite data.  
The Sentinel-2B multispectral imagery in Figure 5b,c is represented in a true colour red-green-blue (RGB) full-resolution combination of its 10-m resolution bands B4 (red), B3 (green), and B2 (blue), to allow comparison with VHR data from Google Earth and recognition of surface reflectance changes in the visible region of the spectrum.
# A GNSS-Cellular Network Hybridization Strategy for Robust Positioning
## 2 Materials and Methods
### Measurement-Level Hybridization Method
#### 2.2.2 Pseudoranges Between the BS and the UE
Since the BS positions are assumed to be known with a high accuracy (maximum error \(\leq\) 0.05 m; see Table 1), the uncertainty in both values, \(\sigma_{0}^{2}\) and the accuracy of the pseudodistance \(BS_{p}-rj\), is attributed entirely to the UE's positioning error. Therefore, the constant value \(\sigma_{0}^{2}\) in Equation (31) is computed as the average of all the reported accuracy values observed throughout the experimental campaign. In our setup, the number of BSs was fixed to three per epoch, due to the static location of the UE and the known, unchanging positions of the BSs throughout the test. In contrast, the number of GNSS satellites varied dynamically depending on the constellation geometry and visibility. The Xiaomi Mi 8 smartphone, equipped with the Broadcom BCM47755 chip, supports dual-frequency and multi-constellation GNSS reception. Under favorable conditions, it can typically track between 15 and 20 satellites per epoch across GPS, Galileo, and other constellations. This information regarding the number of BSs and GNSS satellites per epoch is relevant to both hybridization approaches described in Sections 2.2.1 and 2.2.2, as it affected the structure and complexity of the observations processed in each case.
## 3 The Structure of the Decision Support Platform
### The Consumption and Cost Analysis Module  
Decision-making implies the need to know the activities within the technological flow of an SME. The role of this module is to translate the user's queries into an optimized framework so that the Decision-Maker obtains the information about the energy consumptions over the requested period (year, month, week, and day), the energy costs, and evolution of the economic factors.  
The analysis can be done on years, months, or days, choosing an analysed period. A period between 2 months (e.g., January and February) or a single month, for one or more years, can be used in the monthly analysis. The days (all, working, or weekend) from the same month, but different years or different months from the same year, can be selected to be used in the daily analysis. The data are uploaded from the energy consumption file.  
The module presents the results through the graphical representations of the variables (energy consumption, energy cost, or economic indicators) or the statistical parameters calculated over the analysed period.  
The statistical parameters refer to:  
* _The mean_ represents a synthesis expression in a single representative level of everything essential, typical, and objective in its occurrence, manifestation, and development of the analysed variable. \[X_{man}=\frac{\sum\limits_{i=1}^{n}x_{i}}{n}\] (1) where: \(X_{man}\)--mean of the analysed variable from the selected period, uploaded from the matrices \(DW\), \(DC\), \(DE\), and \(DI\); \(x_{i}\)--value \(i\) of the analysed variable, uploaded from the matrices \(DW\), \(DC\), \(DE\), and \(DI\); \(n\)--the number of records (rows) associated with the selected period.  
* _The standard deviation_ represents the most common parameter used to analyse the variation of a data set. The data with high homogeneity have a small value of the standard deviation. \[\sigma=\sqrt{\frac{\sum\limits_{i=1}^{n}{(x_{i}-X_{mant})^{2}}}{n}}\] (2)
* _The median_ is the value that divides the data set of the analysed variable (sorted in ascending or descending order) into two equal parts. If the series has an odd number of terms, then: \[X_{median}=x_{\frac{n+1}{2}}\] (3) If the series has an even number of terms, then: \[X_{median}=\frac{x_{\frac{n}{2}}+x_{\frac{n}{2}+1}}{2}\] (4)
* _Quintiles_ are position indicators that divide the data set into more parts with equal values. In the module, the quintiles are calculated as: \[x_{\frac{1}{4}}=Q_{1};\ x_{\frac{2}{4}}=Q_{2}=X_{median};\ x_{\frac{3}{4}}=Q_{3}; \ x_{\frac{4}{4}}=Q_{4}\] (5) where \(Q_{1}\), \(Q_{2}\), \(Q_{3}\), and \(Q_{4}\) are quintiles and divide the data set into four equal parts \(n\)/4.  
The Decision-Maker will analyse the characteristics of the first quintile with the lowest energy consumption and the fourth quintile with the highest energy consumption.
# Latent Structure Matching for Knowledge Transfer in Reinforcement Learning
## 2 Related Work
### Low Rank Embedding  
The algorithm we use to derive latent structures of value function space falls in the category of low rank embedding (LRE) [29]. Low rank embedding is a two-part algorithm for solving embedding in low-dimensional space by using the low rank matrix approximation method. Now given a data set \(X\), at the beginning, LRE minimizes the following loss function so as to calculate the reconstruction coefficient matrix \(\hat{X}\):  
\[\frac{1}{2}\|X-X\hat{X}\|_{F}^{2}+\lambda\|\hat{X}\|_{*}, \tag{1}\]  
where \(\lambda>0\), \(\|X\|_{F}=\sqrt{\sum_{i}\sum_{j}|X_{i,j}|^{2}}\) is the Frobenius norm. As for singular values \(\sigma_{i}\), \(\|X\|_{*}=\sum_{i}\sigma_{i}(X)\) is the spectral norm. LRE uses the alternating direction method of multipliers (ADMM) algorithm [30] to minimize (1) to calculate the reconstruction coefficient matrix \(\hat{X}\), and then minimizes the reconstruction error of \(X\) in low-dimensional space to obtain the embedding \(F\). The reconstruction error function is as follows:  
\[\frac{1}{2}\|F-F\hat{X}\|_{F}^{2}s.t.F^{T}F=\mathbb{I}, \tag{2}\]  
where \(\mathbb{I}\) is the identity matrix, and the constraint \(F^{T}F=\mathbb{I}\) can remove arbitrary scaling factors in the low-dimensional embedding and can ensure that the minimization problem of (2) is a well-posed problem. In [31], it is shown that (2) can be minimized to obtain the embedding \(F\) by calculating the \(d\) smallest non-zero eigenvectors of the Gram matrix \((\mathbb{I}-\hat{X})^{T}(\mathbb{I}-\hat{X})\).  
To estimate efficiently the similarity between source and target tasks, we modify low rank embedding, and utilize the modified algorithm in RL (please see Section 3.1). To the best of our knowledge, our approach is a novel attempt to use LRE in knowledge transfer for RL.
## 3 Simulation
### Simulation
#### 3.2.2 Obstacle Environment Scenarios
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Environment Coordinate (m)** & \multicolumn{5}{c}{**Dynamic Obstacle Number**} \\ \hline  & 1 & 2 & 3 & 4 & 5 \\ Env 1 & \(x\) & \(-9.099\) & \(2.012\) & \(-13.483\) & \(0.199\) & \(-10.104\) \\ Env 1 & \(y\) & \(-11.562\) & \(10.575\) & \(-11.825\) & \(-5.842\) & \(-0.648\) \\ Env 2 & \(x\) & \(-4.105\) & \(-5.356\) & \(1.036\) & \(-11.294\) & \(2.471\) \\ Env 2 & \(y\) & \(-1.913\) & \(5.383\) & \(5.106\) & \(-3.613\) & \(-7.456\) \\ Env 3 & \(x\) & \(9.870\) & \(1.162\) & \(5.208\) & \(-4.766\) & \(1.636\) \\ \(y\) & \(10.960\) & \(6.508\) & \(-10.065\) & \(-6.276\) & \(11.270\) \\ Env 4 & \(x\) & \(-10.723\) & \(13.674\) & \(14.438\) & \(11.899\) & \(2.262\) \\ \(y\) & \(-8.629\) & \(-2.413\) & \(1.724\) & \(-11.664\) & \(-12.032\) \\ Env 5 & \(x\) & \(6.089\) & \(-5.218\) & \(-13.275\) & \(9.756\) & \(1.148\) \\ \(y\) & \(-6.932\) & \(3.602\) & \(-8.044\) & \(5.578\) & \(14.735\) \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Dynamic obstacle points information. As shown in Table 13, in the obstacle environment, the overall average failure rate of algorithm 4 is 13%, and the overall average failure rate of Algorithm 5 is 2%, which has a relatively obvious improvement in obstacle avoidance ability.
## 4 Experiments  
### Data and Similarity Measures  
The algorithm in this paper was implemented on a Windows 10 system, using the programming language Python version 3.6, and the neural network framework was built using the Pytorch framework. The hardware configuration was Intel(r) Xeon(r) CPU E5-2680 v4 @ 2.40 GHz, 128G RAM, and NVIDIA TITAN X (Pascal). The DIV2K dataset, which includes 800 training images and 2K resolution, was selected as the training data. Two test datasets, Set5 and Set14, were used for the image reconstruction test. The convolution layer parameter settings are shown in Figure 2. The generator was trained using the loss function presented in Equation (15) with \(\lambda=0.001\) and \(\mu=-0.001\). The evaluation index used PSNR and structural similarity, SSIM, to evaluate the super-resolution image. PSNR evaluates the image quality based on the error between images. The smaller the mean squared error between corresponding pixels, the larger the PSNR and the better the image quality. SSIM has three aspects: brightness, contrast, and structure. The value of the image quality has a range of \([0,\ 1]\). The larger the value, the better the image quality; the relevant formula is as shown in Equation (18). MSE is the mean square error between the original image and the processed image. \(n\) is the \(n_{th}\) pixel value of the original image. \(\mu_{x}\) and \(\mu_{y}\) represent the mean values of images \(x\) and \(y\), respectively, and \(\sigma_{x}\) and \(\sigma_{y}\) represent the standard deviations of images. \(\sigma_{xy}\) represents the covariance of the images \(x\) and \(y\). \(c_{1}\) and \(c_{2}\) are specified numbers:\[PSNR=10\log_{10}(\frac{(2^{n}-1)^{2}}{MSE}), \tag{18}\] \[SSIM(x,y)=\frac{(2\mu\mu y+\tau_{1})(\alpha y+\epsilon_{2})}{(\mu_{ x}^{2}+\mu_{y}^{2}+\epsilon_{1})(\sigma_{x}^{2}+\sigma_{y}^{2}+\epsilon_{2})}.\]
## 4 Collision Free Path Optimization
### The Secondary Optimization of ACO (SO-ACO)
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{\(N\)} & \multirow{2}{*}{\(M\)} & \multirow{2}{*}{**Method**} & \multicolumn{6}{c}{**Path Length L (mm)**} & \multicolumn{6}{c}{**Time t (s)**} \\ \cline{3-10}  & & & **Mean** & **SD** & **Min** & **Max** & **Mean** & **SD** & **Min** & **Max** \\ \hline \multirow{2}{*}{5} & \multirow{2}{*}{5} & ACO & 681.000 & 97.002 & 560.000 & 910.000 & 1.094 & 0.105 & 0.909 & 1.294 \\  & & SO-ACO & 206.326 & 8.760 & 202.507 & 242.524 & 1.242 & 0.102 & 1.066 & 1.403 \\ \hline \multirow{2}{*}{10} & \multirow{2}{*}{10} & ACO & 579.500 & 55.864 & 490.000 & 670.000 & 4.234 & 0.286 & 3.739 & 4.950 \\  & & SO-ACO & 203.085 & 0.617 & 202.390 & 204.582 & 4.369 & 0.303 & 3.844 & 5.098 \\ \hline \multirow{2}{*}{20} & \multirow{2}{*}{20} & ACO & 460.500 & 33.162 & 400.000 & 530.000 & 13.391 & 0.440 & 12.672 & 14.584 \\  & & SO-ACO & 203.219 & 0.931 & 202.390 & 206.187 & 13.511 & 0.436 & 12.783 & 14.676 \\ \hline \multirow{2}{*}{30} & \multirow{2}{*}{30} & ACO & 361.000 & 12.937 & 340.000 & 390.000 & 25.038 & 0.901 & 23.776 & 28.028 \\  & & SO & 203.039 & 0.770 & 202.507 & 205.451 & 25.152 & 0.893 & 23.901 & 28.122 \\ \hline \multirow{2}{*}{40} & \multirow{2}{*}{40} & ACO & 330.500 & 2.236 & 330.000 & 340.000 & 43.199 & 2.118 & 38.954 & 47.122 \\  & & SO-ACO & 202.930 & 0.357 & 202.370 & 203.426 & 43.332 & 2.125 & 39.063 & 47.263 \\ \hline \multirow{2}{*}{50} & \multirow{2}{*}{50} & ACO & 330.000 & 0.000 & 330.000 & 330.000 & 56.213 & 3.892 & 50.827 & 68.322 \\  & & SO-ACO & 203.120 & 1.069 & 202.390 & 206.187 & 56.340 & 3.901 & 50.930 & 68.468 \\ \hline \multirow{2}{*}{60} & \multirow{2}{*}{60} & ACO & 330.000 & 0.000 & 330.000 & 330.000 & 76.361 & 6.807 & 65.979 & 89.289 \\  & & SO-ACO & 202.800 & 0.477 & 202.390 & 204.443 & 76.492 & 6.814 & 66.093 & 89.455 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the results optimized based on two algorithms. Figure 9: Welding robot path planning based on the Secondary Optimization of ACO (SO-ACO). (a) View A; (b) View B.  
the SO-ACO can improve the optimization effect of the algorithm, and it can be applied for actual optimization processes to achieve a satisfactory result with a small iteration \(N\) and population size \(M\).