esa-sceva/satcom-chunk-collection huggingface dataset:
# An Algorithm to Find the Shortest Path through Obstacles of Arbitrary Shapes and Positions in 2D
## 8 Conclusions  
We have proposed a new algorithm for the construction of the shortest path through a 2D field of obstacles of arbitrary shapes and positions. This algorithm has the important rare advantage of not requiring the preliminary construction of mathematical expressions to represent the obstacles, such as quadtrees or others. It operates directly on a digital map of the terrain without requiring the construction of any mathematical representation of the obstacles. Its implementation requires solely standard digital image processing functions. It can be realized on essentially any computer, because it does not require large amounts of memory or computing power. It can deal with generic terrains, whatever the shapes of the obstacles and their distribution in space. Some examples have been provided of its working with some real-world digital elevation maps.  
It would be worthwhile to make a future study to compare the behavior of the standard Dijkstra algorithm and that of our proposed algorithm, in solving the same problems. Such a study would be along the lines of [PERSON] and [PERSON] who obtained some definite results when comparing the Dijkstra with the standard A* on a set of shortest path problems.  
Given the rapidity of the solution process, this algorithm can be used in real-time, on-board the vehicle, when updates to the mission terrain are received. Thus, future work will consider constructing shortest paths when some of the obstacles are moving. A future non-trivial challenge will be to adapt this algorithm so that it takes into account the existence of a minimum turning radius, which most vehicles have, such as airplanes, ships, and automobiles. Indeed, as constructed by the present algorithm, most shortest paths will have some sharp corners as they graze the boundary of angular obstacles. Further work should follow on this subject.  
No funding was received for this research.  
**Data Availability Statement:** The DEM terrain maps for the tests can be downloaded from the \"Shuttle Radar Topography Mission (SRTM)\" web site at [[https://www.earthdata.nasa.gov/sensors/srtm](https://www.earthdata.nasa.gov/sensors/srtm)]([https://www.earthdata.nasa.gov/sensors/srtm](https://www.earthdata.nasa.gov/sensors/srtm)) (accessed on 5 August 2024).  
**Conflicts of Interest:** The author declares no conflicts of interest.
# DCAGS-IoT: Dynamic Cross-Domain Authentication Scheme Using Group Signature in IoT
## 3 Proposed Dynamic Cross-Domain Authentication Scheme
### System Model  
The cross-domain authentication scenario mainly includes the group manager (GM), tracking manager (TM), a group of member users, and the blockchain. The specific cross-domain authentication system model is shown in Figure 1. The GM is responsible for the management of group members, establishing group resources, and generating the corresponding group public key gpk, which is open to all users in the entire system, and maintains a registration list and revocation list. It stores the identities of registered and revoked group members. Group members are all legal users in a distributed system. The blockchain exists in the system as a storage medium to ensure that the data will not be tampered with. GM acts as a full node in the system. As long as no more than half of the GM in the world is destroyed, the security of the blockchain can be guaranteed. The tracking administrator (TM) can open the signatures of group members, regulate the illegal behavior of users, acts the supervision department in the group, supervise the behavior of members in the group, and complete the behavior responsibility identification and responsibility judgment when the group members are found to have illegal behaviors.
# Probabilistic Analysis of Infinite Slope Stability Considering Variation in Soil Depth
## 2 Materials and Methods
### Estimation of Soil Depth  
Several studies have been conducted on characterizing soil depth, and [PERSON] et al. [15] classified them into the following four categories: (1) developing techniques to observe soil depth directly or indirectly; (2) using interpolation methods to estimate soil depth from limited observations or secondary data; (3) applying statistical methods to predict soil depth using measurable environmental factors; and (4) creating landscape evolution models to predict soil depth by solving the soil mass conservation equation. A detailed explanation of these specific methods and related studies is summarized in [15]. The most accurate measuring method for soil depth is direct field investigation in the field using methods such as excavation pits, auger drilling, and dynamic cone penetration tests. However, these are time-consuming and costly tasks, and performing these methods to determine the soil depth over a wide area in mountainous terrain is not feasible. Moreover, as only point data for a single location can be obtained at a time, it is difficult to estimate the variability of soil depth within a specific slope. Accordingly, indirect methods for measuring soil depth to address these limitations include gravity [23; 24], ground-penetrating radar [25; 26], seismic refraction [27; 28; 29], electrical resistivity [30; 31], and airborne electromagnetic surveying [32; 33]. Although these methods require inversion to estimate the soil depth from the measured values and considerable effort to improve accuracy, their advantage is that soil depth within a slope can be estimated quickly and easily. Among these, the most common geophysical method that provides quantitative information on bedrock depth is the seismic refraction method. They provide quantitative results through P-wave (primary wave) velocities in soils and bedrock, with a relatively straightforward field procedure.
###### Abstract  
This study presents a comprehensive forecasting approach to evaluate the future of electric vehicle (EV) adoption in the United Kingdom through 2035. Using three complementary models--SARIMAX, Prophet with regressors, and XGBoost--the analysis balances statistical robustness, policy sensitivity, and interpretability. Historical data from 2015 to 2024 was used to train the models, incorporating key drivers such as battery prices, GDP growth, public charging infrastructure, and government policy targets. XGBoost demonstrated the highest historical accuracy, making it a strong explanatory tool, particularly for assessing variable importance. However, due to its limitations in extrapolation, it was not used for long-term forecasting. Instead, Prophet and SARIMAX were employed to project EV sales under baseline, optimistic, and pessimistic policy scenarios. The results suggest that the UK could achieve between 2,964,000 and 3,188,000 EV sales by 2035 under baseline assumptions. Scenario analysis revealed high sensitivity to infrastructure growth and policy enforcement, with potential shortfalls of up to 500,000 vehicles in pessimistic scenarios. These findings highlight the importance of sustained government commitment and investment in EV infrastructure and supply chains. By combining machine learning diagnostics with transparent forecasting models, the study offers actionable insights for policymakers, investors, and stakeholders navigating the UK's zero-emission transition.  
e _Article_  
Electric Vehicle Sales Forecast for the UK: Integrating Machine Learning, Time Series Models, and Global Trends.
## 1 Introduction
To provide stability to the algorithm, maximum optical flow value where corresponding points can be located on a physically allowed area is determined, according to relative position difference between sub-images. By using this algorithm, one can extract actual depth information from a light field image that is taken by LFC under the given condition of the LFC. Consequently, the optical flow method suitable for LFC images is explained. A 3D model of the real object can be reconstructed if there are multiple light field images captured from different location of LFCs. Each light field image taken by LFC becomes a perspective light field image from the object, which composes total light field of the object. If the location and orientation of the LFCs for perspectives are given, the 360-degree light field information of the object can be determined. To reconstruct the object by using optical or computational system, the depth and the color information should be extracted from the perspective light field. From the captured light field images, the DOLF can be used to extract depth information and digital refocusing technique can be utilized to calculate clear color images. By binding overall perspective information of depth map and color image, 3D structure of the object can be reconstructed. Therefore, we also propose an optical system that acquires 360-degree light field information of an object by using a light field camera in one-shot. Putting several depth cameras in multiple locations or while using mechanical apparatus to control orientation of subjects is a conventional way to achieve a 360-degree depth profile [25; 26]. However, the systems have limitations on both cost and size, or the output results are a long way from the high-quality images. To overcome these limitations, a compact and inexpensive 360-degree light field capturing system is proposed in this paper. This system can be used as an alternative of 3D scanner. The capturing system allows for capturing not only depth and color information of front view, but also that of back and side views. The proposed system contains two plane mirrors and one LFC. Two mirrors and an object are placed within the viewing angle of the LFC to obtain 4D light field information of the object in a single shot. The locations and orientations of two mirrors are analyzed to optimize 360-degree light field recording and controlled minutely to determine the angle of pickup direction. Experiments and simulation results are presented to support the feasibility of DOLF and the capturing system. Consequently, 360-degree light field information is captured in one-shot and is optically reconstructed as the 3D object. Holographic display is used to reconstruct the wavefront of the captured objects. Experimental results prove the validity of the proposed pickup method with LFC and DOLF.
## 1 Introduction
### Contributions of This Paper  
To further improve the accuracy and efficiency of identity authentication methods for scenarios such as the IoT, an intelligent authentication method with low complexity and high accuracy based on CSI information has been proposed. The key contributions of this paper are threefold:  
* By setting up a real wireless communication environment, we completed the collection of CSI data for Wi-Fi and preliminarily demonstrated the ability of CSI data to support device identity discrimination through data processing and analysis.
* Furthermore, an identity authentication method on the basis of deep Q-network (DQN) has been proposed. By processing CSI and setting parameters including state space as well as action space, the method realizes lightweight mapping of the CSI features and device identity.
* Finally, we verified the performance of the proposed method and the comparative methods based on the collected CSI data. A large number of experiments showed that the proposed method has better authentication accuracy compared with the comparative methods.  
This paper is organized as follows. The system model and CSI data collection process are introduced in Section 2. Section 3 covers the preliminary knowledge related to the proposed method in this paper. Section 4 provides the proposed intelligent identification method using CSI. Section 5 presents the experimental results and performance analysis, and Section 6 provides the conclusions.  
_Notation:_ In this paper, scalars are expressed by a non-boldface type, while matrices, as well as vectors, are expressed by a boldface type. \(E\{\cdot\}\) and \((\cdot)^{\text{T}}\) signify the statistical expectation and matrix transpose, respectively. Finally, \(\omega_{i}\) denotes the \(i\)th entry of the vector \(\mathbf{\omega}\).
# Social-Aware-Based Resource Allocation for NOMA-Enhanced D2D Communications
## 5 Conclusions  
We propose a D2D cluster resource allocation method which combines channel allocation and power control in this paper. First, we present the system model of cellular users and D2D users which includes D2D content requesters and D2D content providers. The utility function of D2D content requesters is defined combined with the social relationship among D2D users in order to form D2D clusters. D2D users in D2D clusters communicate by reusing cellular channels. NOMA is used in D2D clusters for many-to-one communication. In order to ensure the QoS of each user, we also set the SINR threshold. Second, the resource allocation problem is decoupled into two subproblems. Many-to-one matching game is used to allocate channels for D2D clusters. The nonconvex optimization problem is transformed into a convex problem which can be solved by GA through SCA. Hence, we can easily  
Figure 8: Sum rate of D2D clusters for different numbers of cellular channels with different numbers of D2D content requesters, \(K=200\).  
Figure 7: Sum rate of D2D clusters for different numbers of cellular channels with different numbers of D2D content providers, \(N=5\).  
obtain the optimal D2D transmit power. We also discuss the convergence and the computational complexity, respectively. Finally, we set historical channel allocation set and obtain the stable solution through iterating over the two subproblems. The simulation results show the superiority of our algorithm for it can significantly improve the system throughput. Moreover, by considering social relationship between users, we can obtain a more realistic resource allocation scheme.  
[PERSON] conceived the proposal of the paper, analyzed the data, and performed experiments using MATLAB. [PERSON] provided guidance, key suggestions, and finalized the paper. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (61971239 and 61631020) and Postgraduate Research and Practice Innovation Program of Jiangsu Province (KYCX19_0945).  
The authors declare no conflicts of interest.
# Coexistence Scheme for Uncoordinated LTE and WiFi Networks Using Experience Replay Based Q-Learning
## 5 Proposed Coexistence Solutions for Uncoordinated LTE-U and Wi-Fi Networks
### Rule Based Coexistence Scheme  
In this subsection, we describe a rule-based coexistence scheme that is used to determine the number of LTE blank subframes (\(N_{\text{b}}\)) that lead to the highest fairness and spectrum efficiency. This rule based coexistence scheme is formulated for comparison purposes. The main goal of the algorithm is to select a \(N_{\text{b}}\) value based on \(LTE_{\text{State}}\) and \(WiFi_{\text{State}}\). The \(WiFi_{\text{State}}\) represents WiFi state which can be either saturated (\(W_{\text{S}}\)) or unsaturated (\(W_{\text{U}}\)). \(LTE_{\text{State}}\) represents the state of LTE which is determined based on \(LTE_{\text{Tar}}\) and \(LTE_{\text{Ob}}\). The algorithm uses possible states of LTE shown in Table 2. When blank subframes are introduced in the LTE frame, the LTE throughput decreases proportionally [30]. Hence, the 10 states in Table 2 are used to represent all the possible range of throughput levels.  
Algorithm 1 shows the steps of the proposed rule-based coexistence scheme. Initially, 50% of the LTE subframes are set as blank ensuring equal spectrum share for both technologies. Then, the fairness of this action is evaluated. We consider the action as 100% _fair_ if the LTE network achieves sufficient throughput (L\({}_{10}\)) and the WiFi network is unsaturated. We also consider the action as 100% _fair_ if \(LTE_{\text{State}}\
eq L_{10}\) and the WiFi network is saturated when the technologies are set to share the spectrum equally with 50% blank subframes. On the other hand, if the action is not _fair_, the value of \(N_{\text{b}}\) is adjusted based on LTE and WiFi states. If the WiFi network is saturated and the LTE network achieves sufficient throughput (L\({}_{10}\)), the number of blank LTE subframes is increased so that WiFi gets more spectrum thereby leading to better fairness. Contrarily, if the WiFi network is unsaturated and the LTE network does not achieve sufficient throughput, i.e., \(LTE_{\text{State}}\
eq L_{10}\) the number of blank subframes are reduced to achieve better fairness. \(N_{\text{t}}\) represents the number of actions executed until the best action. This process has to be repeated periodically to adapt to the traffic dynamics of the co-located networks.
# Generation of a Virtual Cohort of Patients for in Silico Trials of Acute Ischemic Stroke Treatments
## 2 Materials and Methods  
### Collection of Patient-Specific Data  
The clinical data of 100 patients affected by an AIS due to an occlusion in the M1 segment of the MCA were retrospectively collected from the MR CLEAN Registry, a randomized clinical trial for stroke treatments involving several intervention centers in the Netherlands [21]. Images of the patients' cerebral vasculature were acquired with non-contrast computed tomography (NCCT) and computed tomography angiography (CTA) before treatment. The image segmentation was performed with StrokeViewer (NICO.LAB, Amsterdam, The Netherlands), and the iCAFE V1.3.0 software [22] (\(\copyright\) 2016-2018 University of Washington. Used with permission) was used to extract the centerlines and diameters of the cerebral vessels (a diameter associated with each point of the centerline) and to label the different segments. Thanks to the labeling of the vascular tracts, only the segments of interest for this work were collected, namely the ICA, ACA, M1, and M2. For each patient, the vessels on the contralateral side with respect to the occlusion were selected to gain a complete vascular representation (since in the occluded side the imaging is available only up to the proximal end of the occlusion).  
For the purpose of creating a training dataset for the generation of virtual patients, exclusion criteria were imposed to select patients with a complete set of vessels of interest. Therefore, vascular segmentations not including the ACA or either of the M2 branches were excluded, resulting in a cohort of 88 patients in the training dataset.
# Request Expectation Index Based Cache Replacement Algorithm for Streaming Content Delivery over ICN
## 2 Scenario Description and Assumptions
### Definitions and Assumptions  
In our setting, we make the following assumptions, and our notation is given in Table 1.  
* The name of the streaming chunk in ICN is abstracted as two components: the streaming file name \(i\) and the chunk sequence number \(j\), which can be used to distinguish different chunks of the file (e.g., /3/9).
* The chunk sequence number \(j\) is ordered based on the streaming sequence (e.g., the sequence number of the first chunk is 1, the second is 2, etc.).
* Accordingly, a streaming file \(f_{i}\) consists of a set of chunks \(\{C_{i,1},C_{i,2},...,C_{i,j}...\}\), which are ordered by the sequence number \(j\), and each chunk \(C_{i,j}\) is assumed to have the same chunk size.
* We assume that the streaming chunks are requested in sequence starting with the first chunk.
* The request duration of \(f_{i}\) (the period between the start and end of one continuous user request for file \(f_{i}\)) follows the distribution \(Q(x)\). For example, in the case of video, viewers may quit the session before the end of the video.  
Figure 2: In-network caching for a streaming content delivery scenario. ICN: Information-Centric Networking; CS: Cache Space.
# Z-OFDM: A New High-Performance Solution for Underwater Acoustic Communication
## 3 Communication System Model
### Performance Analysis
#### 3.2.2 Signal Interference plus Noise Ratio  
This section analyzes the Signal Interference plus Noise Ratio (SINR) of the Z-OFDM communication system [47; 48; 49; 50].  
According to the literature, the SINR of the Z-OFDM system can be defined as [51]  
\[SINR_{i}=\frac{E[R(\tau)]}{Var[I_{Q}]} \tag{21}\]  
where \(I_{Q}\) denoted the total interference.  
This section \(I_{Q}\) was only considered as AWGN to simplify the research, \(R_{i}(\tau)\) which was given in  
\[R_{i}(\tau)=\int_{\tau}^{T_{c}}P_{Zj}(t)P_{Zj}(t-\tau) \tag{22}\]  
where \(P_{Zj}\) is the average power of the Z-OFDM signal, which is given in  
\[\begin{split} P_{Zj}&=\frac{1}{T_{c}}\int_{\tau}^ {T_{c}}y_{z}^{2}(t)dt\\ &=\frac{D}{T_{c}}\int_{\tau}^{T_{c}}|X(\frac{2\pi f}{D})|^{2}d \frac{f}{D}\\ &=P_{i}D\end{split} \tag{23}\]  
Combining Equations (21), (22), and (23), they can be written as Equation (23) in another way [52].  
\[\begin{split} SINR_{Zj}&=\frac{E[R_{Zj}(\tau)]}{ Var[I_{Q}]}\\ &=\frac{E[R_{i}(\tau)D^{2}]}{Var[I_{Q}]}\end{split} \tag{24}\]  
The comparison between the Z-OFDM system and the FFT-based OFDM system over different values of factor \(D\) is shown in Figure 4. The SINR of the Z-OFDM system increased with the increase in factor \(D\) and is higher than the SINR of the FFT-based OFDM system.  
Figure 4: SINR of Z-OFDM system over AWGN channel.
# Effects of Diet on Mercury Bioaccumulation in Farmed Gilhead Seabream (_Sparus aurata_)
## 1 Introduction
Despite these promising findings, to date no studies have evaluated the efficacy of incorporating _S. cerevisiae_ and tannin-rich plant extracts into fish diet as a strategy to reduce mercury bioaccumulation in vivo, beyond controlled aqueous or synthetic media. In this study, mercury levels were investigated in muscle, gills, gut, liver and kidney of farmed gilthead seabream fed with experimental diets enriched by the yeast _S. cerevisiae_ and tannin-rich plant extracts. This represents the first in vivo study to evaluate the effects of these nutraceutical substances on mercury bioaccumulation in the different fish tissues and organs. The findings have the potential to improve sustainable aquaculture practices aimed at preserving fish health and consequently minimizing health risk for consumers.
# Self-Supervised Learning across the Spectrum
## 6 Experiments and Results
### Evaluation Metric  
We chose to use mIoU as the main evaluation metric for evaluating our model's segmentation performance. Many prior works on satellite image segmentation [65; 66] use this metric as opposed to overall segmentation accuracy since it is more robust to datasets with high class imbalance. For example, in a dataset that consists mostly of background class labels, a model's accuracy can be high by simply overpredicting the background class. However, mIoU is a much more strict metric that gives equal importance to all the predicted classes. Formally, mIoU can be defined by the following equation:  
\[\text{mIoU}=\frac{1}{N}\sum_{i=1}^{N}\frac{\text{TP}_{i}}{\text{TP}_{i}+\text {FP}_{i}+\text{FN}_{i}} \tag{4}\]  
where TP, FP, and FN denote the number of true positive, false positive, and false negative pixels, respectively. N is the total number of classes.