esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Establishing the Simulation Environment
### RSU Placement Using Voronoi Diagrams  
Voronoi diagrams can be used as a tool for strategically deploying RSUs in connected vehicular networks, as discussed in [28]. To address the challenge of optimizing RSU placement, we employ Voronoi diagrams in a rural area. This geometric method divides the network area into convex polygons, each representing the coverage area of an individual RSU. Voronoi diagrams ensure that any point within a polygon is closer to its respective RSU than to any other, thereby maximizing network coverage efficiency.  
Considering the possibility of 100 potential locations for 10 RSUs, we encounter \(1.73\times 10^{13}\) configurations. Figure 10 visually illustrates this Voronoi-based approach, estimating the distribution of 10 RSUs within a 10 km square rural area, specifically, the Peak District. In the figure, red dots denote RSU locations, and blue-bordered Voronoi cells delineate their unique coverage areas, each with a radius of approximately 900-1000 m. This strategic placement ensures efficient wireless communication coverage, facilitating seamless vehicle connectivity throughout the Peak District.  
Figure 10 provides an estimated optimization for deploying RSUs in rural regions, where connectivity is typically sparse and the undulating terrain presents significant barriers to signal transmission. In the context of the Peak District, with its rugged landscapes and limited existing infrastructure, the diagram anticipates the optimal locations for RSUs to maximize coverage and minimize the impact of natural obstructions like hills and valleys. This approach is crucial for improving vehicle communication in such rural areas. The estimation acknowledges the unique challenges of RSU deployment in these environments, offering a strategic method to overcome nature's impediments to connectivity.
## 5 Conclusions  
Estimation of the resilience of the ecosystem after a significant atmospheric event helps us better understand other changes in the ecosystem. It was expected that atmospheric events would have a lesser impact on ecosystems in areas that are less prone to human activity. In this study, the resilience of the ecosystem had a negative impact in human-active areas and, in contrast, a lesser impact in areas with little or null anthropogenic activity. Segregation of the different land usages will better represent these areas. Nevertheless, NDVI was used as a measure of greenness (representation of plant population). The results observed by the estimations of ET, NDVI, and GPP showed that affected coastal urban areas, after Hurricanes Irma and Maria, took longer to recover and stabilize than less altered areas. This is normal, as humans alter indigenous vegetated areas and substitute them with roads and plants that present reduced ecological resilience in this type of environment.  
One of the main limitations of this work was that MOD16A2, used for ET estimation, had many null data values that were amended using the GOES-PRWEB datasets. Daily gap-filled ET products were not available for Puerto Rico. When comparing the data from GOES-PR and MODIS, GOES-PRWEB underestimated actual ET values. Therefore, values of ET estimated from GOES-PRWEB are not to be used for correlation matrices.  
In conclusion, we observed that MODIS products can be used for national/large-scale regions rather than small, insular territories where cloud presence can cause under- or overestimation of parameters due to the geographical location of the study area. Additionally, multiday products derived from Landsat can help mitigate the noise caused by cloud coverage.  
Conceptualization, S.F.A.-G.; methodology, Y.N.B.-R. and S.F.A.-G.; formal analysis, Y.N.B.-R. and S.F.A.-G.; investigation, Y.N.B.-R.; resources, S.F.A.-G.; data curation, Y.N.B.-R. and S.F.A.-G.; writing--original draft preparation, Y.N.B.-R.; writing--review and editing, Y.N.B.-R. and S.F.A.-G.; supervision, S.F.A.-G.; project administration, S.F.A.-G.; funding acquisition, S.F.A.-G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the Agriculture and Food Sciences Facilities and Equipment (AGFEI) program, grant number 2022-70008-38366, from the USDA-National Institute of Food and Agriculture (NIFA). The authors also acknowledge partial support from the College of Agricultural Sciences of the UPRM, a graduate assistantship for Y.N.B.-R., and new faculty seed funds for S.F.A.-G.  
The data presented in this study is available upon request to the corresponding author.  
The authors would like to acknowledge [PERSON], who kindly provided us with raw data from PRWEB servers.  
The authors declare no conflicts of interest.
## 3 Methods
### Estimation of the Initial 3D Box
#### 3.1.5 Estimation of Angle and Dimension  
As shown in Figure 5, in addition to regress the center position of the 3D box, we also need to regress the dimension and angle. The dimension regression branch estimates the dimension offsets \((\delta_{l},\delta_{w},\delta_{h})\) of the 3D box. Knowing the average value \((\overline{l},\overline{w},\overline{h})\) of the dimension of the 3D box in the dataset, the dimension of the 3D box can be calculated by the following formulas:  
\[\begin{bmatrix}l\\ w\\ h\end{bmatrix}=\begin{bmatrix}\overline{l}\cdot e^{\delta_{1}}\\ \overline{w}\cdot e^{\delta_{w}}\\ \overline{h}\cdot e^{\delta_{h}}\end{bmatrix} \tag{7}\]  
Figure 5: Detection head branches of the keypoint estimation network.  
The angle detection branch estimates the heading angle of the object, which is the global angle \(\theta\). As shown in Figure 6, when the car drives in a straight line, the global angle \(\theta\) of the car remains unchanged, the imaging angle of the car on the image plane follows the angle \(\theta_{ray}\) in a constant change, and the local angle \(\theta_{l}\) changes accordingly. In view of the high correlation between the imaging angle of the object on the image plane and the local angle of the object, we first estimate the local angle \(\theta_{l}\) and then utilize the following formula to obtain the global angle \(\theta\).  
\[\theta=\theta_{l}-\theta_{ray}=\theta_{l}-\arctan\left(\frac{z}{x}\right) \tag{8}\]  
Referring to the multi-bin regression method of Deep3 Dbox [16], we estimate the local angle \(\theta_{l}\) of the object by means of a multi-interval classification and residual regression.
# A Novel Dynamic Transmission Power of Cluster Heads Based Clustering Scheme
## 6 Conclusions  
This paper has optimized the clustering algorithm of the FANET to adapt to the complex combat environment and make the network reliable. Based on the traditional K-means algorithm, this paper has presented the clustering and CH election method. The variable transmission power mechanism of CH has been also proposed. The simulation results show that, compared with DSWCA and GWO, the proposed DTPCH-C has enhanced the lifetime of the FANET, reduced the energy consumption and improved the packet delivery ratio. In the subsequent study, we will continue to further study the improvement of the FANET's lifetime. We will consider the optimization of energy consumption for CMs to make the lifetime of the FANET longer.  
Conceptualization, M.N. and T.L.; methodology, M.N.; software, M.N.; validation, M.N. and T.L.; formal analysis, M.N.; investigation, T.L.; writing--original draft preparation, M.N.; writing--review and editing, P.H., J.Z., Y.L. and T.Z. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Program (No. 2019-JCJQ-ZD-108-01), the National Natural Science Foundation of China under Grant 62271068, and the Beijing Natural Science Foundation under Grant L222046.  
The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Methods** & **Cluster Formation** & **CH Selection** & \begin{tabular}{c} **Dynamic** \\ **Transmission** \\ **Power of CH** \\ \end{tabular} & \
\begin{tabular}{c} **Cluster** \\ **Maintenance** \\ \end{tabular} &
\begin{tabular}{c} **Overall** \\ **Complexity** \\ \end{tabular} \\ \hline DTPCH-C & \(O(KNI)\) & \(O(N)\) & \(O(KN)\) & \(O(N)\) & \(O(KNI)\) \\ GWO [14] & \(O(KNI)\) & - & - & \(O(N)\) & \(O(KNI)\) \\ DSWCA [20] & \(O(KN1)\) & \(O(N)\) & - & \(O(N)\) & \(O(KNI)\) \\ \hline \hline \end{tabular}
\end{table}
Table 6: Time complexity analysis.  
Figure 11: Packet delivery ratio of different clustering schemes with varying numbers of UAV nodes.
# Device Free Detection in Impulse Radio Ultrawide Bandwidth Systems
## 1 Introduction
All of these approaches used experimental demonstrations and no analytical framework was developed for a UWB system. An analytical framework was developed for UWB radar in [46]. However, this approach utilized the frequency domain technique to measure the vital signs such as breathing rate and heartbeat frequency. Cramer-Rao lower bounds (CRLBs) for estimation of signal parameters for single- and multi-path channel conditions were studied in [47]. In this paper, we develop a novel device-free indoor detection method that can be applied to IR-UWB networks. UWB technology is capable of localizing within 10-30 cm depending on different environments, where the indoor industrial environment is the most challenging due to a large number of reflections. By developing this UWB analytical framework, the device detection can be achieved with high accuracy followed by distance prediction, especially when operating in an LoS indoor environment. The development of such a framework at this moment is critical and will further lead to an accurate indoor localization system that is not present at the moment. This new method can be easily extended to existing device-free and device-based techniques and is easily applicable to any pulse-based UWB system using arbitrary multiple access or a modulation scheme. The main contributions of this paper are summarised as follows:  
* The proposed detection method utilizes time domain concepts, which are different from the frequency-based approaches discussed in [39; 40; 42; 43; 46]. * In the context of using an UWB system as an indoor radar, the Neyman-Pearson (NP) criteria is applied. Detection probability and miss probability for a slow time hopping pulse position modulation (STH-PPM) system is developed. * Characteristic function (CF) is utilized to measure the moments of the presence of the user. With the help of CF, higher orders of the moments can be calculated if required. * No training is required to measure or calibrate the system operating parameters as needed in [24; 25]. The rest of the paper is organized as follows. Section 2 describes the system model of the STH-PPM UWB system. The channel model, transmitted, and received signal is also discussed in this section. Section 3 help design the basic hypotheses testing for the proposed STH-PPM UWB system. In Section 4, the presence of the user is modelled and CF is utilized to measure the mean and variance of the user. The optimal threshold is also calculated in this section. In Section 5, the corresponding \(P_{FA}\) and \(P_{D}\) are derived. Performance analysis is carried out in Section 6. Finally, the paper is concluded in Section 7.
# Local Community Detection Based on Small Cliques
## 2 Density-Based Local Community Detection Algorithms
### Two-Phase L  
The originally proposed algorithm for L-based community detection [17] is slightly more complicated than GCE L. It considers three cases for the updated measures \(L^{\prime}_{in}\) and \(L^{\prime}_{ex}\) that may lead to a higher value \(L^{\prime}\):  
1. \(L^{\prime}_{in}>L_{in}\) and \(L^{\prime}_{ex}<L_{ex}\)
2. \(L^{\prime}_{in}<L_{in}\) and \(L^{\prime}_{ex}<L_{ex}\)
3. \(L^{\prime}_{in}>L_{in}\) and \(L^{\prime}_{ex}>L_{ex}\)  
In a first discovery phase, they iteratively add the node that leads to the maximum \(L^{\prime}\), but only if it is in case 1 or 3, otherwise they do not consider that node again. In a second examination phase, they re-consider every node and keep it only when it is in the first case. It may happen that the original seed node is removed from the community in the second phase. Then they say that there is no local community for that seed node.  
Implementation Details  
For the first discovery phase, we can use the same data structures as described for GCE L. In the second examination phase, we can simply iterate over the neighborhood of every node again to determine the change in \(L^{\prime}_{in}\) and \(L^{\prime}_{ex}\) its removal would cause. This means in total we get the same running time as GCE L, though the running time depends on the community size after the first phase, not on the final size.
## 3 Methods
### Machine Learning Methods  
#### 3.1.1 Support Vector Regression (SVR)  
SVR is based on the Support Vector Machine (SVM) algorithm proposed by [21] and is commonly applied to regression problems. Unlike other regression models that attempt to minimize the error on all the training sets, SVR relies only on a subset of the training data. The cost function in SVR disregards samples whose predictions are close to their targets.  
Given a training dataset \(\{(x_{i},y_{i}),i=1,\cdots,N\}\), where \(x_{i}\in R^{M}\) and \(y_{i}\in R\), we construct a linear regression function \(f(x_{i})\):  
\[f(x_{i})=w^{T}x_{i}+b \tag{1}\]  
where \(w\) is weight vector, and \(b\) is constant term.  
The goal is to find a function \(f(x_{i})\) that has at most \(\epsilon\) deviation with the target value \(y_{i}\) for all the training data and is as flat as possible. For flatness, it is required to minimize the Euclidean norm \(\|w\|^{2}\). Formally, this can be represented by a convex optimization problem:  
\[\begin{split}\underset{s.t.}{min}\frac{1}{2}\|w\|^{2}\\ s.t.\ |y_{i}-f(x_{i})|\leq\epsilon,\ \ \ \ \ \ \forall i \end{split} \tag{2}\]  
If not all training data satisfy (2), \(f\) may not be generated. For the slack variables \(\xi_{i}\) and \(\xi_{i}^{*}\), some errors are allowed [22]. SVR solves non-linear regression problems by mapping feature space to a high-dimensional feature space through kernel functions such as a polynomial function or radial basis function (RBF). It uses a kernel trick without converting the data into higher dimensions. In this study, SVR uses the RBF kernel function.
## 1 Introduction  
Urban air mobility (UAM) is an operating system of an air vehicle that carries people or materials and operates at low altitudes in and out of the city [1]. In the low-altitude environment where UAM is operated, there are various obstacles (buildings, tower cranes, etc.), noise-sensitive areas, prohibited or restricted areas, etc. So, it is necessary to establish new airspaces and procedures to ensure safety. Therefore, UAM corridors are being designed based on the performance of the UAM vehicles so that they can be operated safely and efficiently at low altitudes. Communication, navigation, surveillance and information (CNSi) research and the establishment of a concept of operations are also in progress.  
It is necessary to establish a low-altitude holding area in order for UAM vehicles to perform holding procedures at a narrow-low altitude or to return to an alternative vertiport other than the destination vertiport due to air traffic management or for the avoidance of bad weather conditions. However, the holding areas for existing aircraft (general and commercial aviation aircraft) do not have a defined radii or lengths, and it is impossible to apply them in the same way in low-altitude environments because legs are set at the time depending on the situation. In the low-altitude environment where there are many obstacles and the area is narrow, the holding area should be clearly defined, including its length and radius. In addition, the performance of the aircraft must be guaranteed so that procedures can be performed in the holding area at low altitude. In this study, a conceptual design of low-altitude holding areas for UAM operations has been created based on Seoul and the suburbs of Seoul, Korea. Additionally, configurations and the performance of the aircraft capable of performing procedures in the designed holding area have been analyzed.  
Figure 1 shows the concept of a holding area for conventional aircraft. The standard turning direction is right, and, in principle, the aircraft starts turning immediately after passing the holding fix.
# Anomaly Detection of Remote Sensing Images Based on the Channel Attention Mechanism and LRX
## 2 Related Work
### Channel Attention Mechanism  
The attention mechanism, inspired by the human eye's mechanism of observing external objects, only concentrates on some crucial local information of an image. In other words, it creates a mask to identify the important parts of an image, aiming to understand the weight distribution of features. The attention mechanism has already been applied in various fields such as natural language processing, machine translation, and classification.  
Attention mechanisms can be roughly divided into three categories: spatial attention mechanism, channel attention mechanism, and spatial-channel attention mechanism. The spatial attention mechanism adjusts and calculates the weights by locating the target region in the image and performing affine transformations. The channel attention mechanism, on the other hand, adjusts weights based on the influence of different bands of the feature map on the output data [27]. The spatial-channel attention mechanism is a combination of the two above-mentioned mechanisms.  
Hyperspectral images are characterized by low spatial resolution and high spectral resolution, and the uniqueness of their spectral characteristics provides the possibility to detect and identify targets. In order to better focus on the spectral features, this paper adopts the channel attention mechanism, which takes the spectral bands as input. Since the feature map of each channel contains different information, the response values of different land covers in different bands are different. Therefore, the channel attention mechanism can identify the different channel information of land covers and focus on more effective bands.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 5 Limitations of Current AGB Maps and Future Improvements
### Accuracy Assessment  
Uncertainties in AGB maps arise from uncertainties in sampling methods, modelling algorithms, datasets used, the choice of allometric equations, and the mismatch of field data and remotely sensed data [9]. Most AGB mapping studies have not fully quantified these uncertainties. The methods used for quantifying mapping uncertainties include the Monte Carlo error propagation approach [66; 83; 124], bootstrap techniques [97], confidence intervals [78; 125], and model-based estimators [86; 87]. However, these published studies have only partially quantified uncertainties in the estimation, more effective and efficient methods to express and quantify uncertainty in all its forms are needed [126; 127]. Moreover, the uncertainty caused by temporal mismatches in datasets has often been ignored. Since studies use field data to calibrate remotely sensed datasets, temporal mismatches between measured field data and predictors could lead to uncertainties in estimated biomass. Comprehensively addressing more sources of uncertainty would help to better understand the accuracy of generated biomass maps and further promote the application of biomass maps in related fields.
## 4 Case Study: Conceptual UAM Flight Routes and Establishment of Holding Areas
### UAM Flight Routes Conceptual Design based on Seoul-GyungIn Area  
To establish a holding area, UAM conceptual flight routes in the Seoul and GyungIn area have been established based on the UAM flight operations system, K-UAM roadmap published by the Korean government, and aeronautical information publications (AIP) [18; 19]. The Seoul and GyungIn area comprises the cities with the highest population densities in Korea, and since it includes Incheon International Airport and Gimpo  
Figure 11: VFR routes in Seoul and GyungIn area [18].  
Figure 10: Airspace of Seoul and GyungIn area [17].  
International Airport, it is highly possible that the first UAM operational services will be realized there. For this reason, UAM flight routes have been designed in the Seoul and GyungIn area. In Seoul, the VFR corridors of the Han River have been applied, and in the case of Incheon and western Gyeonggi-do, VFR flight routes connecting each visual reference point have been applied [16]. However, unlike conventional general aviation aircraft, UAM vehicles operate at low altitudes. Therefore, flight routes have been designed along rivers and major roads to avoid noise-sensitive areas and obstacles [1]. Figure 12 shows the UAM flight routes designed for the Seoul and GyungIn area.  
The UAM flight routes are managed by the monitoring and support of stakeholders such as Provider Services for UAM (PSU). For UAM vehicles to operate safely in low-altitude environments, an Urban Air Traffic Management (UATM) system should be established, and information should be exchanged through integration with current Air Traffic Management (ATM) providers [5]. In particular, in the Gimpo International Airport controlled area, it is possible to pass through visual reference points, but in order to pass through the center of the airport where current aircraft depart and arrive, it is necessary to open and close the UAM corridor. These procedures are carried out in collaboration between the PSU and air traffic controller (ATC).  
In addition, if one UAM flight route is closed due to local severe weather or traffic jams, the Gimpo Airport UAM corridor and the Gwanaksan (Mt. Gwanak) UAM corridor have been established so that they could operate in other flight routes. These multiple flight routes are highly efficient because they allow continuous operation without the interruption of flights, even in local severe weather. In addition, since visual reference points and infrastructures are utilized, it is possible to quickly establish flight routes without many additional considerations [1].
# Metrics Related to Confusion Matrix as Tools for Conformity Assessment Decisions
###### Abstract  
Conformity assessment refers to activities undertaken to check whether some product, service or process meets certain criteria and specifications given by internationally accepted standards. The decision on whether a property of interest is aligned with the set standards is made based on measurement. However, uncertainty associated with the measurement results may lead to incorrect decisions. Measurement results may be falsely rejected as non-conforming, although they meet specifications. This is referred to as the producer's risk. If the measurement result that does not meet the required specifications is accepted as conforming, this is referred to as the consumer's risk. This paper covers calculations of global consumer's and producer's risk using the Bayesian approach and deals with the application of metrics related to confusion matrices in conformity assessments. These techniques have been used to assess the conformity of the bearing ring diameter with the given specifications. Based on the behavior of these metrics, the optimal length of the guard band was determined with the aim of minimizing the global consumer's and producer's risk.  
consumer's risk; producer's risk; guard band; tolerance interval; acceptance interval; confusion matrix