esa-sceva/satcom-chunk-collection huggingface dataset:
# Robust Distributed Rendezvous Using Multiple Robots with Variable Range Radars
## 5 Simulation Results  
This section addresses the MATLAB simulation results to demonstrate the effectiveness of the proposed distributed rendezvous scheme with variable range radars. Using computer simulations, we show the superiority of the proposed 3D rendezvous algorithm compared to the original circumcenter algorithms in [13; 14; 15].  
The simulation settings are as follows. In MATLAB simulations, the radar sensing noise (2) uses \(D_{R}=0.01\) km. In addition, we consider the case where \(u_{1}\) is static. Thus all robots need to rendezvous at \(u_{1}\). Since \(u_{1}\) is static, the motion model of \(u_{1}\) is  
\[\mathbf{u}_{1}(k+1)=\mathbf{u}_{1}(k)+\mathbf{n} \tag{10}\]  
instead of (1). In our MATLAB simulations, (1) and (10) use \(\mathbf{n}=[randn*D,randn*D,randn*D]\). Here, \(D>0\) is a constant presenting the process noise strength, and \(randn\) generates a Gaussian noise with a mean of 0 and a variance of 1. We use \(D=0.05\) km.  
At time-step 0, we set the maximum sensing range \(S_{i}(0)\) as \(S_{i}(0)=5\) km for all \(i\in\{1,2,...,N\}\). The upper bound for \(S_{i}(k)\) is set as \(S^{u}=50\) km. In addition, (9) uses \(\beta=5\) km, and \(N^{min}=5\).  
The sampling interval is set as \(dt=1\) second. Furthermore, the maximum speed of each robot is 200 m/s. The rendezvous algorithm is finished when every robot is within \(\delta=3\,\)(km) from each other. In the case where the rendezvous is not achieved, the simulation continues for up to \(T=300\) seconds.  
Initially, robots are randomly deployed in the workspace with size \(20\times 20\times 20\) in km. Since \(S_{i}(0)=5\) km for all \(i\in\{1,2,...,N\}\), the initial connectivity assumption may not hold depending on the initial position of a robot.  
Original circumcenter algorithms in [13; 14; 15] require the initial connectivity assumption for convergence. However, the proposed rendezvous algorithm with an adaptive sensing range does not require this assumption.  
Considering the case where \(N=10\), Figure 2 plots the movement of each robot under our distributed rendezvous algorithm. Each robot's position at every \(dt\) seconds is depicted with a red circle. It takes 95 seconds for all robots to rendezvous at a point.  
Considering the case where \(N=10\), Figure 3 plots the final position of every robot using the proposed rendezvous control. See that all robots are positioned close to each other since multi-robot rendezvous is achieved.  
Figure 2: Considering the case where \(N=10\), a blue asterisk plots the final position of every robot using the proposed rendezvous control. Each robot’s position at every \(dt\) seconds is depicted with a red circle.  
Considering the case where \(N=10\), Figure 4 plots \(S_{i}\) (\(i\in\{1,2,...,N\}\)), the sensing range of each robot, when the rendezvous is done. See that the sensing range of a robot can be distinct from that of another robot.
# Ground-Based Cloud Image Segmentation Method Based on Improved U-Net
## 4 Conclusions  
This paper designs and constructs a deep learning model for cloud image segmentation, which consists of three parts: an encoder, a decoder, and a skip connection layer. The skip connection layer embeds the depthwise separable residual block DS path and the optimized Im-CSAM. Compared to traditional segmentation networks, firstly, the improved U-Net encoder combines the advantages of dilated convolutions and ASPP, preserving the fine features of the input cloud image and reducing the loss of key information in the cloud layers. Secondly, this paper constructs a residual block centered on depthwise separable convolutions and combines depthwise separable convolutions with multi-head attention to optimize channel attention and spatial attention. This is embedded in a cascading manner into the skip connection layers, which helps retain original details while integrating with deep semantic features, reducing feature loss, and significantly enhancing the improved U-Net's ability to extract important information and key features in both spatial and channel dimensions. Finally, in the decoder section, a bicubic interpolation algorithm is used to achieve high-quality segmentation results during the image resolution recovery process. The improved U-Net proposed in this paper achieved optimal segmentation metrics on both the SWINySEG and TCDD datasets. Ablation experiments show that the dilate block, DS path, and Im-CSAM all improve the segmentation performance. In conclusion, the improved U-Net has the best performance and segmentation effect, which is of consequence for cloud amount calculation, weather forecasting, PV power prediction, and other fields. However, the parameter size of the improved model is relatively large, and future work will focus on further optimizing the network structure and reducing the parameter size of cloud image segmentation through lightweight methods.  
Concepts and learning: [PERSON] and K.Z.; software and validation: [PERSON] and K.Z.; formal analysis: H.Q.; investigation and resources: [PERSON], [PERSON] and [PERSON]; data curation and visualization: [PERSON] and K.Z.; writing--original draft: [PERSON] and [PERSON]; editing: [PERSON]; supervision: [PERSON]. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data derived from public domain resources. The data presented in this study are available in [SWINySEG] and [TCDD] at [[[https://vintage.winklerpros.net/swinyseg.html](https://vintage.winklerpros.net/swinyseg.html)]([https://vintage.winklerpros.net/swinyseg.html](https://vintage.winklerpros.net/swinyseg.html)), accessed on 28 June 2024] and [[[https://github.com/shuangliutjun/TJNU-Cloud-Detection-Database](https://github.com/shuangliutjun/TJNU-Cloud-Detection-Database)]([https://github.com/shuangliutjun/TJNU-Cloud-Detection-Database](https://github.com/shuangliutjun/TJNU-Cloud-Detection-Database)), accessed on 19 November 2024].  
The authors declare no conflicts of interest.
## 2 Problem Statement
### Publication Analysis
Additionally, they demonstrate current attack approaches and defense techniques for preserving the FL model. Authors discuss their work within a digital health perspective by describing limitations when adopting FL in healthcare [45]. The authors in [46] conducted a survey on federated learning and the potential threats that might be available in this approach. As with any discovery of research and advancement in technology, there are always vulnerabilities and potential security threats that can lack robustness. The survey paper [46] introduces the risk of privacy and how that could potentially be exploited in an FL setting. Furthermore, the paper presents a brief review on how poisoning attacks can interfere and manipulate the outcome of an FL training model, such as data poisoning and model poisoning. Similarly, inference attacks can also lead to privacy leakage. Furthering this research topic, another survey expands on the threats towards FL as a survey by creating research questions regarding security and privacy and answering them by providing a comprehensive list of threats and attacks [24]. The authors also bring to the surface some unique security threats that exist in the FL environment and what defensive techniques can be implemented towards such vulnerabilities [24]. Defense techniques, such as proactive defenses, are a way to guess the threats and the potential risks associated with them. The authors present SNIPER, knowledge distillation, anomaly detection, and moving target defense solutions. As discussed in recent review papers conducted on FL, many include similar categorizations to the ones above. In another research conducted by authors in [47], FL is discussed in three main aspects, including the design, challenges, and applications. Apart from all of the above details reviewed for FL, the authors review the works carried out using personalization techniques to achieve better models. They describe strategies and techniques to reduce the FL models' communication costs [47]. More surveys are conducted on the topic [44; 48], and even though they introduce the topic of communication and the challenges that may be present, there has not been a dedicated research or survey paper written towards the challenges present in communication and the ways to make it efficient. This paper aims to bridge that gap by solely focusing on communication. A list of recent surveys over the past few years is mentioned in Table 1. There is a common theme with most survey papers: introducing the technology, presenting the applications, and addressing the security and privacy benefits and concerns. In most papers, the topic of communication is introduced too, though it is only a brief surface-level part of the paper. This survey paper aims to be the bridge in that gap and presents a survey paper that focuses solely on the communication component for FL.
## 3 Results and Discussion
Readers--particularly transport engineers, fleet operators, and vehicle retrofitting professionals--can benefit from these findings by applying the outlined models and boundary condition strategies to streamline electric bus conversion projects while avoiding costly recertifications. Regarding future research directions and system-level integration: as a foundation for future research, several directions are recommended: (1) experimental validation of simulation results using physical crash and vibration tests; (2) exploration of scalability to other bus types and bracket geometries; (3) integration of this method into vehicle-level system optimisation, including thermal management, energy distribution, and structural weight reduction for full-system efficiency; and (4) comparing acceleration results with those permissible according to the battery manufacturer's data and considering the possibility of equipping structures with shock absorbers to dissipate impact energy.
## 2 Materials and Methods
### Research Method and Process
#### 2.3.3 Albedo-NDVI Feature Space Analysis  
According to the findings of [PERSON]. [PERSON] et al. [30], there is a significant linear negative correlation between NDVI and albedo in the one-dimensional feature space, and the distribution of different surface cover types in the Albedo-NDVI feature space exhibits a significant divergence pattern, allowing for easy differentiation of various surface cover types [44; 45] (Figure 2).  
Figure 2: Albedo–NDVI feature space. Note: A is arid bare soil (low NDVI and high Albedo), B is water-rich bare soil (low NDVI and low Albedo), C is high vegetation cover with adequate soil moisture content (high NDVI and low Albedo), and D is high vegetation cover area with low soil moisture content and relatively high albedo (low NDVI and high Albedo).  
Surface albedo is a function of vegetation cover and soil water content in the Albedo-NDVI space. The top boundary AD in Figure 2 represents the high albedo line, reflecting the drought condition, and the bottom edge BC is the maximum low albedo line, representing the condition of sufficient surface moisture [30].  
The linear regression equation between NDVI and Albedo was constructed as follows based on their linear negative correlation:  
\[\text{Albedo}=k\times\text{NDVI}+b \tag{5}\]  
where \(k\) is determined by the slope of the fitted curve in the feature space.
## 1 Introduction
Apart from Magnetic Resonance Imaging (MRI) [11; 12] and Ultrasound (US) imaging [13; 14], that are already applied in clinics as supplementary imaging tools, several techniques are presently being investigated for the quantitative imaging of breast cancer [15]. Among them, a hand-held optical probe for TD-DOT could prove useful to improve diagnostic sensitivity, performing non-invasive, cheap and physiological-related examinations to better characterize and classify breast lesions after a positive screening mammography. The purpose of the paper is twofold. First, we present a simulation platform suitable for simulations of large multi-parameter scenarios in DOT, and then we exploit this platform for a systematic study of the performances and feasibility of a TD-DOT hand-held probe to work in reflectance geometry for breast cancer imaging and characterization. More in detail, the key features and the novelty of the platform are: (i) Fully automated scheme which permits to run automatically large multi-parametric scenarios in batch processing; (ii) an objective, quantitative and operator-independent assessment of the outcome of the reconstruction in view of the desired clinical application; (iii) attempt to enforce standardization and performance assessment criteria, following internationally agreed protocols (e.g., BIP [16], MEDPHOT [17], NEUROPT [18]). As compared to existing tomographic packages (e.g., TOAST [19] or NIRFAST [20]) our platform does not offer novelty in the algorithms, but rather aims at studying automatically and with an objective and quantitative outcome the effects of different parameters in a flexible and systematic way. The second aim of the paper is to exploit the simulation platform to investigate TD-DOT in reflectance geometry with a limited set of source-detector couples towards applications for breast diagnostics. Many parameters with a complex interplay were considered simultaneously, at the level of hardware implementation (number and temporal width of the time gates, counts per second), experimental conditions (depth of the absorption inhomogeneity), and reconstruction parameters (regularization, voxel size). Also, an objective quantification of the tomography reconstruction is needed to assess the capability of the system to classify different lesions. The results will display the limits of applicability, the expected performances, and the key bottlenecks of such a compact 3D probe with respect to realistic scenarios. The rest of this paper is organized as follows: In Section 2 we present the architecture of the developed multipurpose simulation platform, followed by a detailed discussion about the specific kernel for time domain breast tomography, with problem specifications. Finally, we describe the systematic set of simulations we performed for investigating a wide combination of variables. In Section 3 we report the main results we obtained along with the accuracy in the estimate of optical parameters.
# Calibration of a Hybrid Machine Tool from the Point of View of Positioning Accuracy
## 3 Test Results
### Measurement Uncertainty of Positioning Accuracy Measurement
This variable speed can affect the resulting compensation factors. During this positioning accuracy check procedure, of the two machine axes that are created with a parallel mechanism (X and Z), the X axis behaved kinematically identically or at least very similarly to the movements that are performed when checking the accuracy of machine tools with linear kinematics, since in this case the two sliders move at the same constant speed as the platform moves. On the other hand, the influence of the movement of the platform along the Z axis at constant speed on the sliders is non-linear, and different speeds and accelerations occur. This is the cause of the appearance of an additional error in the total positioning error along the mentioned axis. As a result, the calibration process had less efficiency (less error reduction). Further tests should include the influence of differences in kinematic and dynamic movement parameters on the mechanical components of mechanisms and include those results in defining compensatory parameters. Additionally, a more profound observation of such influences (observing uneven movements when measuring and measuring two or more axes) should include tests with circular interpolation using the so-called Ballbar device. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Axis** & **Stage of Analyzing** & **E [mm]** & **Error Reduction [\%]** & **A [mm]** & **Error Reduction [\%]** \\ \hline \multirow{3}{*}{X} & Initial state & 0.245 & & 0.265148 & \\  & Unidirectional calibration & 0.1059 & 56.77 & 0.11996 & 54.75 \\  & Bidirectional calibration & 0.06842 & 72.07 & 0.07114 & 73.17 \\ \hline \multirow{3}{*}{Y} & Initial state & 0.87764 & & 0.93336 & \\  & Unidirectional calibration & 0.81262 & 7.40 & 0.85545 & 8.34 \\  & Bidirectional calibration & 0.58622 & 33.20 & 0.63381 & 32.09 \\ \hline \multirow{3}{*}{Z} & Initial state & 0.08956 & & 0.09754 & \\  & Unidirectional calibration & 0.0656 & 26.75 & 0.0681 & 30.18 \\ \cline{1-1}  & Bidirectional calibration & 0.04806 & 46.33 & 0.05266 & 46.01 \\ \hline \end{tabular}
\end{table} 
Table 5: Analysis of the success of the O-X glide mechanism calibration process.
## 4 Object Segmentation into Components and Their Material Estimation
### Object Component Segmentation  
In the actual usage environment, an object was recognized by YOLO v3 [22] from the color image of RealSense worn on the VR headset, and the material was discriminated from the image area of the acquired object, as a result. Therefore, we investigated the accuracy of the material discrimination model, in such a situation. Figure 6 shows an example of the data acquired in the usage environment, and Table 2 lists the accuracy, when the data was recognized, using the trained model. Each item in Table 2 shows the ratio classified into each material, as a result of verifying each object with multiple images.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Material** & **Object 1** & **Object 2** & **Object 3** \\ \hline cloth & & 0.15 & \\ glass & 0.85 & & 0.48 \\ metal & & 0.05 & 0.05 \\ paper & & 0.10 & \\ plastic & 0.15 & 0.36 & 0.48 \\ wood & & 0.31 & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Ratio classified into each material label.  
Figure 5: Confusion matrix of material estimation.  
Figure 6: Examples of images acquired by object recognition in the usage environment.  
The results in Table 2 include the classified materials in the images from Figure 6, but as we can see, the recognition accuracy deteriorated, because multiple materials were included in the image. Another factor is that the image area, recognized by the object recognition of YOLO v3, included the periphery of the object and was wider than the image of the dataset used in the training.  
The image identified, by the object recognition above, was displayed in a bounding box that surrounded the entire object, as shown in Figure 7. We found that the recognition accuracy of the material estimation decreases, in the case of an object consisting of multiple components, which indicates that further subdivision of each component from the recognized object is needed. Processing was, thus, performed in the following three steps, for object component segmentation.  
In Figure 8a, the color information of the object image, acquired from the bounding box detected by object recognition, was reduced, based on [28]. In Figure 8b, we can see that the color information of the detailed part was reduced from the object image. Next, in Figure 8c, the color for each pixel was acquired by HSV from the image after reducing the color information, and clustering was performed using the k-means method. The number of clusters was the one with the highest accuracy, on average, among trials of two-to-five  
Figure 8: Process of object component segmentation.  
Figure 7: Example of bounding box in object recognition.  
classes. We chose three clusters, in consideration of the average number of components contained in each object.
## 4 Discussion
### Implications for Climate Change, Land Degradation and Sustainable Development  
This study, in evaluating ecosystem services at a national scale using remote sensing methods, provides a consistent and standardized methodology that can be used across different spatial scales and in different contexts. Further, repeat surveys can be used to quantify losses or gains in ecosystem service values over time, which provide a baseline for evaluating the impacts of climate change and land degradation on ecosystem values, properties and biodiversity [18; 22; 23]. In this study, evidence for changes in land-cover types over decadal time scales and regional spatial scales reflects the impacts of climate change and not local-scale factors such as land management [71; 77; 80]. Future climate projections can therefore be used to predict future regional ecological patterns at these regional scales (e.g., [70; 78]). We show that changes in ecosystem distributions have implications for land degradation trends (Figure 8) and patterns of increased/decreased ecosystem service values (Table 4, Figure 10). These in turn have implications for strategies toward sustainable development, managing negative impacts of climate change on different ecosystems and their service provision to local communities and wider issues of maintaining biodiversity and reducing soil erosion, amongst others [20; 51; 81]. This is important because some ecosystems can maintain their existing services or even enhance their future service provision under appropriate management strategies [5; 26].
# Online Street View-Based Approach for Sky View Factor Estimation: A Case Study of Nanjing, China
## 3 Results
### Case Analysis  
A total of 1433 sampling points are generated every 200 m along the road network data of the Qinhuai District, which are called the \"actual BSV collection point\", SVF calculations are performed using ArcGIS Pro 3.0. The resulting longitude and latitude coordinates for each point, based on the WGS4 coordinate system, are compiled into a csv file. This file is subsequently imported into BMapSVF, initiating a search for the nearest street view image within a 25 m radius circle centered on each point, totaling 1125 sample points, called the \"desired BSV collection point\". The SVF for these points is then computed, and the distribution of SVFs is visually represented across the Qinhuai District (see Figure 8).  
Comprehensive data on the sampling sites collected in the Qinhuai District were stored in the database using the BMapSVF method (see Figure 9). The analysis reveals that the average and maximum SVF values were 0.51 and 0.98, respectively (see Figure 10). This observation may be attributed to the presence of fewer and sparsely distributed low-rise buildings in the southeast area of the Qinhuai District, whereas the core area exhibits a  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Field** & **Field Type** & **Not Null** & **Description** \\ \hline id & int & yes & key \\ panoid & varchar & yes & the panorama id \\ date & date & yes & acquisition of the BSV panorama \\ lng & double & yes & BD09 longitude \\ lat & double & yes & BD09 latitude \\ description & varchar & no & panorama road name \\ panorama & longblob & yes & BLOB format the BSV panorama \\ panorama\_seg & longblob & yes & semantic segmentation images of panoramas \\ fisheye & longblob & yes & fisheye images of panoramas \\ fisheye\_seg & longblob & yes & fisheye images of panorama\_seg \\ svf & float & yes & SVF is calculated by BMapSVF \\ \hline \hline \end{tabular}
\end{table}
Table 3: Data structure table.  
Figure 7: An example of a BSV panorama and its corresponding metadata collected on Fuhua Rd., Shenzhen City: (**a**) an illustration of the BSV panorama API request URL and the subsequent return of the BSV panorama; and (**b**) an illustration of the BSV panorama API request URL and the resulting metadata for the BSV panorama. This image and metadata were accessed through this URL: [[http://api.map.baidu.com/panorama/](http://api.map.baidu.com/panorama/)]([http://api.map.baidu.com/panorama/](http://api.map.baidu.com/panorama/)) (accessed on 23 September 2023).  
higher concentration of tall buildings, resulting in an average SVF of 0.35 with a standard deviation of 0.25.  
Figure 8: Visualization of the SVF distribution in the Qinhuai District: (**a**) the road network, establishing designated BSV collection points with their actual locations determined using BMapSVF; and (**b**) the representation of the spatial distribution of SVF on BMapSVF using ten colors from the Viridis