esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### The Triple Structure Siamese Network for Velocity Spectra Lateral Target Tracking
#### 2.2.2 Weight Coefficients  
The weight coefficient is determined by the similarity between the current target image and the previously saved image. Here, we use a perceptual Hash algorithm to judge the similarity [33]. We know that the high-frequency information in an image can provide details, and the low-frequency information can describe the frame. Hash algorithm is a method to detect the similarity between images with the low-frequency information of images. First, the image is down-sampled to 8 \(\times\) 8 to remove high-frequency information and discard the difference caused by different sizes. Next, the image is converted into a gray-scale image, and its gray-scale average value is calculated. Further, the gray value of each pixel in the image is compared with the average gray value. If the gray value is greater than or equal to the average value, it is recorded as 1, and if the gray value is less than the average value, it is recorded as 0. Finally, these 64 values are combined to form the Hash fingerprint of this image. This method is fast and efficient and is not affected by image size or scale. The main process of determining the weight coefficient with Hash algorithm is as follows:  
Step 1: calculate the Hash fingerprint of the initial target image and each predicted image.  
Step 2: calculate the number \(Num\) of different points of the Hash fingerprint between the target image and the predicted image.  
Step 3: determine the weight coefficients \(a_{1}\) and \(a_{2}\) according to the value of \(Num\). There are several situations. If \(Num\) < 5, the current target is considered to be similar to the initial target image, replace the initial target with the current target, and set \(a_{1}\) = 0, \(a_{2}\) = 1. If 5 < \(Num\) < 10, perform image fusion and set \(a_{1}\) = 0.5, \(a_{2}\) = 0.5. If \(Num\) > 10, there is a large difference between the current target and the initial target image, the current image is discarded and set \(a_{1}\) = 1, \(a_{2}\) = 0, that is, the initial target image will not be updated. The reasonable threshold (5 and 10 or other number) can be determined by manual test.
## Nomenclature  
\begin{tabular}{l l} \((r,\varphi,z)\) & cylindrical coordinate system \\ \(b\) & positive constant \\ \(T_{s}\) & Stretchable rotating disk surface temperature \\ \(T_{ref}\) & Constant reference temperature \\ \(B(t)\) & applied along z-direction \\ \(C_{ref}\) & reference concentration \\ u, v and w & velocity components \\ \(k^{*}\) & mean absorption coefficient \\ \(\left(\rho C_{p}\right)_{nf}\) & heat capacity \\ \(\rho_{nf}\) & Density \\ \(\theta_{w}\) & temperature ratio parameter \\ \(Rd\) & radiation parameter \\ \(\beta\) & similarity variable at free space \\ \(c\) & stretching parameter \\ \(\Omega\) & rotating rate of disk \\ \(T_{0}\) & Temperature at the origin \\ \(C_{0}\) & concentration at the origin \\ \(C_{s}\) & concentration at the disk surface \\ \(\sigma_{nf}\) & electrical conductivity \\ \(\sigma^{*}\) & Stephan-Boltzmann constant \\ \(\
u_{nf}\) & kinematic viscosity \\ \(\alpha_{nf}\) & thermal diffusivity \\ \(S\) & measure of unsteadiness \\ Pr & Prandtl number \\ \(\omega\) & rotation parameter \\ \(M\) & magnetic parameter \\ \end{tabular}
# Automatic Estimation of Dynamic Lever Arms for a Position and Orientation System
## 3 Proposed SINS/GPS Integrated Navigation Model
### Error Distribution Analysis  
The purpose of error distribution analysis for stochastic systems is to evaluate the final estimation accuracy of the concerned states, and to distinguish which error sources primarily contribute to the concerned estimation uncertainties. Monte-Carlo simulations are appropriate to determine error distributions, but suffer from a large computational burden. Based on the law of large numbers [30], Monte-Carlo simulations necessitate a large number of single-factor simulation tests and very long times to establish the error distributions if many error sources are involved. Considering linear error propagation, covariance simulation programs [31; 32] are commonly used to provide numerical time histories depicting the accuracy of a given system configuration in terms of the covariance of its associated linearized error state vector, avoiding large numbers of simulations. According to the established SINS/GPS Kalman filter, we designed a covariance analysis method to get the error distribution budgets, especially focusing on which error sources contribute to the estimation uncertainty of lever arms and the final position errors. Three system models are involved in the covariance analysis method: the real world model, the full-order design filter, and the reduced-order filter model [33]. The real world model, also known as true model, describes the behavior of the actual system, to the best of one's knowledge and ability. The full-order filter is dedicated to yielding the estimate for all state errors of the real world. The reduced-order filter model is designed for filtering calculation in actual system, in which some weakly observable states have been excluded from the full-order filter to ease computational burden. In this paper, the real world model is defined in Appendix A, the corresponding full-order filter is depicted in Appendix B, and the reduced-order filter model is designed in Section 3.1. The state vector of the reduced-order filter is composed of the first 21 elements of the full-order filter state. In addition, the full-order filter model contains three types of measurement noise, the GPS positioning noise, the ISP encoder noise and the time-asynchrony noise, and only the first type is selected as the measurement noise of the reduced-order filter model. For the reduced-order filter model, after the state equation and measurement equation are established and discretized, we update the standard linear Kalman model in two calculation loops [34]: the state filtering loop and covariance calculation loop.
## 3 Results and Discussion  
### Build Orientation Effect  
The effect of the build orientation on optimal part geometries and objective functions is presented for three case studies: cantilever, bridge shape, and L-beam. The topology optimization with AM constraints such as overhang is performed to minimize compliance under six different build orientations. The optimization results are shown in Table 2 and Figures 5-7. In the case of the cantilever example, the minimum compliance was achieved when the build orientation was along the \(-X\) direction. Similarly, for the bridge-shaped design and the L-beam, the \(+Y\) direction yielded the best results with the smallest values for the design objective. Interestingly, the impact of build orientation on the optimal geometry is relatively smaller for the bridge shape compared to the cantilever and L-shaped beams. This result shows that the bridge geometry is less sensitive to variations in build orientation due to the overall symmetric geometry and load distribution characteristics of the bridge example. However, for the cantilever, when the build orientation is in the \(\pm Z\) directions, the optimizer attempted to reduce the support structures (i.e., surface angles greater than 45\({}^{\circ}\)) but resulted in non-symmetric geometries and uneven surfaces, as shown in Figure 5. These converged results are not ideal and would require significant post-processing time and cost to correct. On the other hand, when the build orientation is in the \(\pm X\) and \(\pm Y\) directions for the cantilever example, the optimizer converges to overly simplified shapes to reduce support structures. This is because the optimization process with these build orientations prioritizes minimizing support structures over exploring intricate designs to reduce part volume. Additionally, in the case of the L-shaped beam, when the build orientation was along the \(-X\) and \(-Y\) directions, the optimized design was redundant, and convergence could not be achieved, while non-symmetric geometries were observed when the build orientation was in the \(\pm Z\) directions. It is likely that the results are due to the inherent geometry of the L-shape, which does not align well with these orientations.  
These results show that selecting the appropriate build orientation is critical and depends on the specific part geometries. Improper build orientation can lead to suboptimal designs, non-symmetric geometries, and difficulties in achieving convergence during topology optimization. It is essential to carefully consider the build orientation to ensure that topology optimization is effectively applied in AM processes.
# PrECast: An Efficient Crypto-Free Solution for Broadcast-Based Attacks in IPv4 Networks
## 4 The PrECast Solution Architecture
### Modification to Switching Devices  
IPv4 broadcasting is restricted to an IP subnet and a single Ethernet broadcast domain. The Ethernet broadcast domain consists of Ethernet switches, repeaters and hubs. Since repeaters and hubs are obsolete, today's network uses only switches to create a local-area segment. In this subsection, we present what essential software modification needs to be done at switches that will support the PrECast protocol. This modification must be done by the switch manufacturer.  
Since Ethernet is the most popular and the de-facto Layer-2 protocol, we demonstrate our solution through Ethernet segments.  
By design, Ethernet switches do not care about the content of any higher layer protocols encapsulated inside an Ethernet frame. As a first step, a manufacturer modifies this feature, so that switches open an Ethernet frame and record the source and the destination IP address present in an IP packet. Today's corporate network uses Layer-3 switches, mainly for QoS support, manageability, access control, and so on. Thus, these switches need no modification and support Layer-3 features natively.  
Switches use a special type of memory called Content Addressable Memory (CAM) to store port numbers and MAC address relations in order to switch a frame effectively [21]. PrECast adds one more field to include the IP addresses of the hosts associated with the MAC addresses. We call this table a Modified CAM (MCAM) table. The structure of the traditional CAM table and the MCAM table is presented in Figure 2. The first column represents the port ID or the switch ID. If a host is directly connected to the switch, this field represents the port number to which the host is connected. If the MAC address corresponds to a remote host, then this field represents the ID of the switch to which the host is connected. The MCAM table can either be kept at the DRAM of a switch or the CAM architecture can be modified to include a 32-bit IP address along with other standard information.  
If a switch cannot be modified, it can still be a part of the PrECast infrastructure. However, the IPv4 broadcast-related issues still exist and are confined to hosts that are connected with a switch that does not support the PrECast protocol.
# A Novel Geo-Localization Method for UAV and Satellite Images Using Cross-View Consistent Attention
## 4 Experiments  
### Implementation Details  
#### 4.1.1 Dataset and Evaluation Metrics  
The benchmark University-1652 [13] is widely used to test the method performance of drone and satellite image retrieval for geo-localization. University-1652 consists of UAV and satellite images with different buildings from 72 universities worldwide. The training set and the test set contain 701 image pairs of different buildings. Each image pair has 1 satellite image and 54 corresponding UAV images. When measuring the accuracy of drone-to-satellite image retrieving tasks, the reference database comprises 701 satellite images, while the query database comprises 37,854 UAV images. The reference and query databases are swapped while measuring the accuracy of satellite-to-drone image retrieval.  
The evaluation metrics of our experiments are the same as in previous works [10; 38]. The top-\(k\) recall rate, denoted as **R@k**, is employed, which measures the rank position of positive samples among all reference samples. R@k can be expressed as the following formula:  
\[\text{R@k}=\frac{\text{T}_{\text{p}}\text{@k}}{\text{T}_{\text{p}}\text{@k}+ \text{F}_{\text{n}}\text{@k}} \tag{9}\]  
where T\({}_{\text{p}}\)@k represents the true positive samples in first k terms and F\({}_{\text{n}}\)@k represents the false negative samples in first k terms. The recall rates of k = 1, 5, and 10 are calculated in the experiment. Moreover, **AP** is used as an evaluation metric for prediction accuracy. AP can be expressed as the following formula:  
\[\text{AP}=\frac{1}{\text{m}}\sum_{\text{k=1}}^{\text{N}}\text{P(k)}\cdot \text{rel(k)} \tag{10}\]  
where N represents the related N images, m represents the total number of all query images, rel(k) indicates whether the kth image is related. If the correlation is positive, the kth will be 1, otherwise it will be 0. The value of AP demonstrates the number of related image in the query database and the rank of the related image.
# The Effect of BDS-3 Time Group Delay and Differential Code Bias Corrections on Positioning
## 2 Theory of BDS-3 TGD/DCB Correction Models
### General Observation Model  
The pseudorange \(P\) single-frequency observations equations of BDS-3 B1I, B3I, B1C and B2a can generally be written as follows:  
\[\left\{\begin{array}{c}P_{B1I}=\rho+c\cdot dt_{r}-c\cdot dt^{5}+d_{trop}+\ I_{B1I}+B_{B1I}\\ P_{B3I}=\rho+c\cdot dt_{r}-c\cdot dt^{5}+d_{trop}+\gamma\cdot I_{B1I}+B_{B3I}\\ P_{B1C}=\rho+c\cdot dt_{r}-c\cdot dt^{5}+d_{trop}+\ I_{B1C}+B_{B1C}\\ P_{B2a}=\rho+c\cdot dt_{r}-c\cdot dt^{5}+d_{trop}+\beta\cdot I_{B2a}+B_{B2a} \end{array}\right. \tag{1}\]  
where the subscripts \(r\) denotes the receiver; the superscript \(S\) denotes the BDS-3 satellite; \(dt^{5}\) and \(dt_{r}\) stands for the clock offset of satellite and receiver, respectively; \(\rho\) stands for the geometric distance, \(c\) is the speed of light. \(d_{trop}\) represents slant troposphere delay; \(\gamma\) and \(\beta\) represent the frequency-dependent multiplier factors (\(\gamma=\frac{f_{B1I}}{f_{B3I}}\),\(\beta=\frac{f_{B1C}}{f_{B2a}}\)); \(I_{B1I}\) and \(I_{B1C}\) indicate the ionospheric delay on the frequency \(f_{B1I}\) and \(f_{B1C}\), respectively. For users, the code bias of the receiver will not be degrading accuracy of positioning. Therefore, \(B_{j}\) the (\(j=B1I\), \(B3I\), \(B1C\), \(B2a\)) represents the code bias only for the satellite part in this contribution. \(B_{B1I}\), \(B_{B3I}\), \(B_{B1C}\) and \(B_{B2a}\) are the code biases on B1I, B3I, B1C and B2a frequency, respectively.  
The dual-frequency observation equation of pseudorange P for BDS-3 B1I, B3I, B1C and B2a will be described below:  
\[\left\{\begin{array}{c}P_{B1I/B3I}=\rho+c\cdot dt_{r}-c\cdot dt^{5}+d_{trop} +B_{B1I/B3I}\\ P_{B1C/B2a}=\rho+c\cdot dt_{r}-c\cdot dt^{5}+d_{trop}+B_{B1C/B2a}\end{array}\right. \tag{2}\]  
where \(PC_{i/j}\) (\(i,j=B1I\), \(B3I\), \(B1C\), \(B2a\)) represents the ionosphere-free pseudorange observable. \(B_{i/j}\) (\(i,j=B1I\), \(B3I\), \(B1C\), \(B2a\)) denotes the code bias after ionosphere-free linear combination (LC).
# Target Identification via Multi-View Multi-Task Joint Sparse Representation
## 6 Experiment and Result Analysis
### Experiment and Analysis of Custom Datasets  
To further verify the application performance of the proposed algorithm in the actual scene, we adopt the targets from the user-defined sequence frames and compare the performance of different algorithms. The custom dataset comes from the field shooting in a port. Six sequence frames are selected from the ship video Ship-1, ship video Ship-2, ship video Ship-3, and ship video Ship-4, ship video Ship-5, and ship video Ship-6. Among them, Ship-1, Ship-2, Ship-3, and Ship-4 are dangerous watercourse scenes taken in different places during the day, Ship-5 is the scene taken at night, and Ship-6 is the video sequence obtained through infrared rays. As with the public dataset, all pictures are resized to 320 \(\times\) 240 (pixels). In this experiment, the source code provided by L1T and MTT and the proposed MTMV source code are tested in MTLAB. Meanwhile, the above STSV (single-view single-task), STMV (multi-view single-task), and MTSV (single-view multi-task) algorithms are compared in the experiment. The identification results of these seven algorithms are shown in Figure 14.  
Figure 13: The errors of the proposed MVMT algorithm and SVST, MVST, SVMT algorithms (pixel).
## 4 Discussion
### Limitations of This Review  
The review process has several limitations. Restricting the included studies to those published in English introduced a language bias, potentially excluding relevant research from non-English sources. Additionally, while the selected databases offer broad coverage, studies in less commonly indexed journals or newer repositories might have been overlooked. The exclusion of meta-analyses, review articles, and studies with insufficient methodological details limited the scope of evidence and broader context. Reviewer bias may have influenced study selection, as resolving disagreements involved subjective judgment. Heterogeneity in study designs, populations, and outcome measures complicated comparisons and reduced generalizability. The reliance on preclinical models further limits the applicability of the findings to clinical settings. Furthermore, the search period cut-off might have excluded recent advancements in GTR technologies. Lastly, the focus on healing outcomes and membrane efficacy may have overlooked critical factors such as patient variability, long-term impacts, and economic considerations. These limitations underscore the need for more comprehensive and inclusive future research in this field.
# Enhanced QoS Routing Protocol for an Unmanned Ground Vehicle, Based on the ACO Approach
## 2 Method
### V2X-Based Optimization  
The V2X routing protocol for the VANETs was intensively researched [16; 17; 18]. The V2X routing protocol predicts a series of valid intersections to transfer the data packets from the firefighting UGVs to each other and the fixed infrastructures in the urban situations [19; 20; 21]. These links are calculated using the geographic locations, the speed, acceleration, and the vector direction of the UGV. The V2X, moreover, disregards the real-time traffic data (such as vehicle density) inside the road segments. The ACO is suggested to enhance the behavior of the UGVs in the environments of the V2X depends on the vehicle motion specifications and the intersections among vehicles and the units in the side road [22], in this study, the ACO algorithm scheme's algorithmic information is adjusted to accomplish a greater rate of closure and stable VANET [23]. An interested low latency protocol is called \"DUBHE\" and is used to transport information of the firefighting UGVs to the infrastructure. The protocol consists from the delay, path, and broadcast parameters [24]. The IGRP is a V2X routing system, based on the efficient road intersection selections, in terms of the network connectivity, latency, error rate, and capacity [25]. The IGRP, moreover, requires a full route and is incapable of dealing with fast topology changes in the systems of the VANET. In the VANET, an adaptation technique, based on the throughput of the ACO route is suggested to achieve an improved efficiency. To begin, we develop an ACO-based technique for calculating the best tracking route. Furthermore, Instead than following the whole path provided in the packet headers, the data packets are transmitted per intersection, as is the case with other source-driven routing protocols, to boost the routing stability, respond to topology changes, and minimize the network cost. Furthermore, we developed the theoretical criteria to predict the QoS of road segments, in terms of the connection likelihood, latency, and delivering ratio.
# A Review on Millimeter-Wave Hybrid Beamforming for Wireless Intelligent Transport Systems
## 3 An In-Depth Analysis of the mmWave Hybrid Beamforming Landscape
### Band  
The hybrid beamforming techniques are attractive in narrowband and wideband applications.
# Practical Grammar Compression Based on Maximal Repeats +
## 2 Preliminaries
### Basic Notations and Terms  
Let \(\Sigma\) be an _alphabet_, that is, an ordered finite set of symbols. An element \(T=t_{1}\cdots t_{n}\) of \(\Sigma^{*}\) is called a _string_ or a _text_, where \(|T|=n\) denotes its length. Let \(\epsilon\) be an empty string of length \(0\), that is, \(|\epsilon|=0\). Let \(\Sigma^{+}=\Sigma^{*}\backslash\{\epsilon\}\) and \(T=t_{1}\cdots t_{n}\in\Sigma^{n}\) be any text of length \(n\). If \(T=usw\) with \(u,s,w\in\Sigma^{*}\), then \(s\) is called a _substring_ of \(T\). Let \(T[i..j]=t_{i}\cdots t_{j}\) for any \(1\leq i\leq j\leq n\) denote a substring of \(T\) beginning at \(i\) and ending at \(j\) in \(T\), and let \(T[i]=t_{i}\) denote the \(i\)th symbol of \(T\). For a finite set \(S\) of texts, text \(T\) is said to be a _superstring_ of \(S\) if \(T\) contains all texts of \(S\) as substrings. Let \(\#\text{occ}(s)\) denote the _frequency_ of \(s\), i.e., the number of occurrences of \(s\) in a text as a substring. If there exists an isomorphism from an alphabet \(\Sigma\) to another alphabet \(\hat{\Sigma}\), texts \(\Sigma^{*}\) and \(\hat{\Sigma}^{*}\) are said to be _isomorphic_ for \(\Sigma\) and \(\hat{\Sigma}\).
# VNF-Enabled 5G Network Orchestration Framework for Slice Creation, Isolation and Management
## 4 Proposed Strategy  
As seen in Figure 1, the planned work is divided into three phases, each with its own set of responsibilities. Phase I focuses on the slice creation of physical infrastructure as well as NSRs. Phase II is responsible for Slice Isolation which ensures that each NSR achieves the optimal allocation of resources in PI. Phase III combines the findings of Phases I and II and incorporates slice management through resource transfer. The following subsections describe the procedures involved in each phase.
## 2 Materials and Methods
### Quality Assurance  
The analysis methods followed were validated by measuring several quality parameters, including linearity, limit of detection (LOD), limit of quantification (LOQ), recovery, accuracy and precision [25; 26]. LOD and LOQ were calculated with ten times standard deviation in seven replicates of standard solutions. Linearity was evaluated from the calibration curve of heavy metals and As species standards with at least six different concentrations, including the blank. The recovery, precision (%C.V), and accuracy (%bias) of the analytical method for seaweed were tested by analyzing the CRM 7405a (Hijiki powder; National Institute of Advanced Industrial Science and Technology, Japan) and SRM 3232 (Kelp power; National Institute of Standard and Technology, USA) for the determination of Cd, Pb, As, and As(V). For fish and shellfish, the CRM DORM-4 (Fish protein; National Research Council, Canada) and SRM 1566b (Oyster issue; National Institute of Standard and Technology, USA) were also analyzed for quality assurance of Cd, Pb, and As determination.