esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Research Background
### AR Application Cases for Construction Site Management
However, many of the commercial systems have been developed for general use purposes and are not tailored for specific work types or field-related needs. Furthermore, they do not provide empirical evidence of their practical applicability and utility on-site. This study focused on field testing AROS via inspection experts to determine their robustness for conducting safety and quality tasks for infrastructure projects. Infrastructure projects such as bridges provide unique challenges, as their virtual models are large in scale and traverse over long distances. As discussed in Section 5, such challenges require formalizing approaches to ensure stable and accurate placement projections of the 3D models and related project data.
## 4 Computational Complexity of 3D Sound Field Reconstruction
### Comparative Analysis  
To facilitate the comparison between a single CBF reconstruction point and all the increased computations in the combined method, complex calculations are converted into multiplication and division types through numerical calculation principles.  
In computer, the Taylor series expansion method is used to calculate the approximate value of exponential and trigonometric functions. Exponent expansion to \(n\) items (\(n>2\)) requires \((n-1)^{2}+1\) MD operations. The expansion of the trigonometric function sine and cosine to \(n\) terms requires approximately \(2n^{2}\) times of MD. The tangent is composed of the ratio of sine and cosine, and it requires approximately \(4n^{2}+1\) times of MD [25].  
Root calculations use the Newton-Raphson method to continuously iterate to solve: In this type of calculation, in addition to a division operation to find the initial value, approximately \(3n+1\) times of MD are required for \(n\) iterations [25].  
Generally, the accuracy of the numerical solution is 10 decimals: the index needs to expand about 15 items, which converts into 197 times the MD; the trigonometric function needs to expand about 8 items, which is converted into 128 times the MD; tangent is converted into 257 times the MD; when the Newton-Raphson method is used for calculation, when the solution accuracy is 10, about 5 to 6 iterations, and 6 iterations are approximately equivalent to 19 MD.  
A reconstruction point in beamforming requires approximately times of multiplication/division operations. The overall increase of the algorithm in this paper is about T\({}_{add}\) times the MD, then:  
\[\frac{\text{T}_{add}}{\text{T}_{1}}=\frac{62\text{R}+19\text{R}+1282}{197\text {M}+\text{M}(4+14\text{L})+19\text{M}}=\frac{81\text{R}+1282}{\text{M}(220+14 \text{L})} \tag{10}\]  
The difference in computation amount mainly depends on the number of reconstruction points R, the number of sound data points L and the number of array channels M of the measured target area.  
For example, the number of points in the beamforming scan reconstruction area is 100 \(\times\) 100, M = 16, L = 25,600 Hz/30 _fps_\(\approx\) 853. If the number of reconstruction points after extracting the target is half of the scanning area (R = 5000), the increased calculation amount is only equivalent to 2.1 points. The total calculation amount is reduced by nearly 1/2.
# Improved Hybrid A* Algorithm Based on Lemming Optimization for Path Planning of Autonomous Vehicles
## 3 Improved Hybrid A* Algorithm
### Distance Heuristic Function  
In the hybrid A* algorithm, distance heuristics are used to estimate the cost from the current node to the target node, helping the path to converge towards the target. These heuristics typically combine multiple estimation methods, taking into account the vehicle's kinematic constraints and environmental information to generate the shortest and most feasible path. Common distance heuristics include the following.  
(1) Manhattan distance:  
\[H(\mathrm{n})=\left|x_{n}-x_{goal}\right|+\left|y_{n}-y_{goal}\right| \tag{15}\]  
The Manhattan distance refers to the sum of the horizontal and vertical distances between the current node and the target node.  
Figure 6: Schematic of the 2D predator avoidance model.  
(2) Euclidean distance:  
\[H(n)=\sqrt{\left(x_{n}-x_{goal}\right)^{2}+\left(y_{n}-y_{goal}\right)^{2}} \tag{16}\]  
The Euclidean distance represents the straight-line distance between the current node \((x_{n},y_{n})\) and the target node \((x_{goal},y_{goal})\).  
The search performance of the hybrid A* algorithm is influenced by the heuristic \(H(n)\). When \(H(n)\) is closer to the true cost, the total computational cost decreases, and the search efficiency improves. However, in practice, it is difficult to accurately estimate the value of \(H(n)\). The Manhattan distance, which is the sum of the horizontal and vertical distances, is often much larger than the actual distance, while the Euclidean distance, representing the straight-line distance between two points, does not account for the realistic constraints of vehicle movement. Therefore, a middle value between the Manhattan distance and the Euclidean distance can be adopted as the heuristic estimate \(H(n)\). In Figure 7, the diagonal segment represents the Euclidean distance, while the dashed segment denotes an intermediate value between the Manhattan distance and the Euclidean distance. The improved \(H(n)\) estimate is illustrated in Figure 7.  
(1) When \(d_{1}=d_{2}\)  
\[H^{*}(n)=\sqrt{{d_{1}}^{2}+{d_{2}}^{2}}=\sqrt{2}d_{2} \tag{17}\]  
(2) When \(d_{1}>d_{2}\)  
\[H^{*}(n)=\sqrt{{2d_{2}}^{2}}+d_{1}-d_{2}=\left(\sqrt{2}-1\right)d_{2}+d_{1} \tag{18}\]  
(3) When \(d_{1}<d_{2}\)  
\[H^{*}(n)=\left(\sqrt{2}-1\right)d_{1}+d_{2} \tag{19}\]  
In summary, the expression for \(H^{*}(n)\) is as shown in Equation (20):  
\[H^{*}(n)=\begin{cases}\left(\sqrt{2}-1\right)d_{2}+d_{1}\;d_{1}\geq d_{2}\\ \left(\sqrt{2}-1\right)d_{1}+d_{2}\;d_{1}<d_{2}\end{cases} \tag{20}\]  
where the calculation formulas for \(d_{1}\) and \(d_{2}\) are as shown below in Equation (21); \(d_{1}\) and \(d_{2}\) represent distances in a two-dimensional plane. \(N(x_{n},y_{n})\) denotes the coordinates of the current node, and \(G(x_{goal},y_{goal})\) represents the coordinates of the target point.  
\[\begin{cases}d_{1}=\left|x_{goal}-x_{n}\right|\\ d_{2}=\left|y_{goal}-y_{n}\right|\end{cases} \tag{21}\]  
Figure 7: Schematic diagram of the improved distance heuristic function.
# Smart System for the Optimization of Logistics Performance of the Pruning Biomass Value Chain
## 4 Implementation and Management of the SLS
### Data Acquisition and Management  
The platform enables users to have an effective data acquisition, monitoring, and utilization system. [PERSON] et al. [22] discussed the importance of data-driven supply chains and indicated that coordination and supply chain responsiveness increases the financial performance of firms. The developed platform receives data from two sources:  
1. [label=()]
2. Actors (farmers, traders, etc.) can enter data (regarding biomass quality parameters and biomass supply related activities) directly into the platform.
3. The Smart box measures some parameters such as relative humidity, temperature, GPS coordinates during biomass transport and transmits these data directly to the platform via GPRS/GSM devices. The platform can receive data from different Smart box tools and  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Status** & **Definition** \\ \hline Pending & The pruning has been entered into the system; the provider can still edit or delete that pruning. \\ \hline \multirow{2}{*}{Ready} & The pruning has been flagged as ready; all consumers can access the system to view its quality parameters and order it. \\ \hline \multirow{2}{*}{Ordered} & The pruning has been ordered by a buyer (consumer or trader) and assigned to a specific transporter. That transporter can access the system to view its source and destination locations and add it to a shipment. \\ \hline \multirow{2}{*}{Routed} & The pruning has been selected by a transporter to be included in one of the shipment routes. \\ \cline{1-1}  & The transporter plans its best delivery route \\ \hline \hline \end{tabular}
\end{table}
Table 2: Pruning record status.  
store the data along with the identification number (Cargolog serial number) of the respective Smart box.  
The collected data will be stored in the server of the platform and monitored by the administrator of the system. The platform has a spreadsheet-based database on the server. This data can be used for different purposes, such as analysis of biomass quality against required quality standards, performance analyses of biomass distribution and traceability systems, and identifying improvement strategies.  
The data gathered and stored in the data base can be categorized as:  
1. information that should be kept for documentation, but non-traceable; and
2. information that should be included in the labelling/traceability system and follows the product.  
The information that documented in the database and needed to be public could be printed as a barcode or QR code. The farmers/producers will then deliver the barcode/QR code with the pruning to the transport company. The information from the barcode or QR code can also be used for traceability purposes. The information from the barcode/QR code will be first transferred to the sensor unit and then transferred to the web-based platform. Tables 3 and 4 present data source, destination and transfer intervals.
## 3 Experiments and Analysis
### Results and Analysis
#### 3.3.3 Fruit Counting Accuracy
Otherwise, the original hypothesis is accepted, i.e., there is no significant difference between the algorithm counting value and the manual value. We calculated a \(p\)-value of 0.0133 for the data and in the chi-square test, the significance level for this statistic was chosen to be 0.01. Since the \(p\)-value was greater than the significance level, the original hypothesis was accepted that there is no significant difference between the algorithm counting value and the manual value. The result also provides support for the accuracy of the dynamic tracking and counting for obscured citrus in the Smart Orchard Based on Machine Vision. Figure 5: Comparison of manual counting and algorithmic counting in citrus video.
# A Low Overhead Mapping Scheme for Exploration and Representation in the Unknown Area
## 2 A New Mapping Scheme of Rmap
### Data Structure of Rmap  
This study's basic idea is to use the rectangles that bind pixels instead of the pixel units of the grid map, as shown in Figure 4. These rectangles of the _Rmap_ for performing SLAM allow the map to be maintained and used with much less memory compared to the grid map. Therefore, these rectangles are managed as nodes and links of the graph. Thus, the _Rmap_ can be expressed as follows:  
\[Rmap=\{\textit{VNode, ANode}\} \tag{2}\]  
The data structure of the _Rmap_ consists of the _VNode_ and the _ANode_, as shown in Expression (2). The _VNode_ is vertex node and contains information of the rectangles in the _Rmap_. The _ANode_ means the adjacent node that contains information of the adjacent nodes of the _VNode_. The _VNode_ includes the identification information (_ID_) of the nodes, the coordinate information (_CoordinateR_) of the nodes, the next node information (_pNext_), and the list information (_pANode_) of the adjacent nodes, as shown in Expression (3).  
\[\textit{VNode}=\{\textit{ID, CoordinateR, pNext, pAnode}\} \tag{3}\]  
The _ANode_ as shown in Expression (4) includes the identification information (_ID_) of the adjacent nodes--the adjacent node intermediate contact information (ACP: adjacent center point), the distance value (_nDistance_) of the adjacent nodes, the list information (_pNext_) of the adjacent nodes.  
\[\textit{ANode}=\{\textit{ID, ACP, nDistance, pNext}\} \tag{4}\]  
The _Rmap_ has a graph structure of the _VNode_ and the _ANode_, as shown in (a) of Figure 5, and the _VNode_ of area 1 is connected with the _ANode_ of 2 and 4. The (b) of Figure 5 visualizes the data structure of (a) and shows area 1 connected with area 2 and area 4, and also indicates the _ACP_ in the adjacent  
Figure 4: An example of the Rmap representation that corresponds to the grid map.  
parts of each area. Finally, (c) is a graph representation of the data structure (a). The map data can be constructed through this logic, which enables efficient memory usage.
# Factorial Design Analysis for Localization Algorithms
## 2 Related Work
### Classification of the Localization Techniques
Table 1 shows a classification of the localization techniques according to whether they are distance-based or not, the performance metrics used, the number of hops between the NOI and the RNs, and the simulation environments for which they were designed. Ref. [19] presents the performance evaluation in terms of accuracy and precision measured by the MSE and the CDF, respectively, of range-free localization techniques such as Centroid Localization (CL) [22], Weighted CL (WCL) [22], Relative Weighted Localization (RWL) [22], Relative Exponential Weighted Localization (REWL) [22] and of range-based techniques such as Hyperbolic positioning algorithm, Weighted Hyperbolic positioning algorithm, circular algorithm and WLS multilateration [37], considering a one-hop network and both indoor and outdoor environments. For its part, in [32] performance is also analyzed in terms of accuracy and precision as measured by the MSE and the CDF, respectively, of the localization techniques DV-Hop, IDV-Hop, WDV-Hop, Weighted Hyperbolic positioning algorithm and PSO algorithm [34; 35] considering a multi-hop network in an outdoor environment. The performance of the LSDV-Hop algorithm is evaluated in terms of the ALE considering a multi-hop network in outdoor environments [33]. Finally, in [36] the Vertex Projection, Vertex Projection with Correcting Factor and maximum-likelihood algorithms are evaluated for normalized Root Mean Squared Error (RMSE) considering a one-hop and multi-hop network in outdoor environments. Since the localization techniques described in Table 1 are evaluated in different scenarios and with different performance parameters, they cannot be compared directly. In this paper, four localization techniques were selected, and evaluated in a single localization scenario [19]. In the proposed evaluation scenario, Received Signal Strength Indicator (RSSI) is used to estimate the distance between the NOI and the RNs [19; 26].
## 4 Results  
### Evaluation for Components Clustering  
As described in Section 3.2.2, based on the time series processing framework of decomposition and integration, a time series extraction method based on KNN was proposed. Take traffic data collected by a base station in the area as an example, as shown in Figure 6. After EMD decomposition, traffic data was decomposed into 10 IMFs as shown in Figure 7.  
Figure 6: Sixty-two days of traffic of one base station.  
As can be seen from Figure 9, trend component of the traffic was relatively stable, and there was no obvious increase or decrease trend for this base station. Periodic component had typical tidal characteristics. Randomness of essential component was strong, which is one of the key factors affecting the quality of network.
## 3 Channel Modeling of the Ground Electrode Current Field in a Tunnel Environment
### Simulation Analysis of the Relationship between the Receiving Voltage and Collapse Distance
In order to verify the correctness of the path loss proposed in this article, field tests were conducted on both sides of a mountain in Changing District, Beijing. The transmitting end and receiving end of the test are 230 m apart, the ground electrode is 4 m apart, and the ground electrode is driven into the ground at a depth of 1.5 m. The test structure is shown in Figure 6, and the test environment is shown in Figure 7. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Category**} & \multirow{2}{*}{**Name**} & \multicolumn{2}{c}{**Approximate Value**} & \multicolumn{2}{c}{**Variation Range of Resistivity under Different Conditions \(\Omega\cdot\)m**} \\ \cline{3-6}  & & **of Resistance** & **Wet (General Areas,** & **When It is Drier** & **When the** \\  & & \(\Omega\cdot\)**m** & **Rainy Areas)** & **(Less Rain Area,** & **Groundwater** \\  & & & **Rainy Areas)** & **Desert Area)** & **is Saline** \\ \hline sand & sand, grit & 1000 & 250–100 & 1000–2500 & 3–30 \\ \hline rock & gravel, crushed stone & 5000 & — & — & — \\ \hline \multirow{3}{*}{concrete} & in the water & 40–55 & — & — & — \\  & in wet soil & 100–200 & — & — & — \\  & in dry soil & 500–1300 & — & — & — \\ \hline mine & metal ore & 0.01–1 & — & — & — \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._  
Figure 5: The relationship between the penetration depth, resistivity, and operating frequency. Transmit a single frequency sine wave signal of 1-10 Hz at the transmitting end, and after receiving the voltage at the receiving end, calculate the path loss at different frequencies according to the path loss formula \(PL=-20\log_{10}(1\,\mathrm{V_{receive}}\,^{1}/\,\mathrm{V_{transmit}}\,^{1})\) and compare it with the theoretical path loss value obtained from Formula (3), as shown in Figure 8.  
Figure 8: Comparison of theoretical calculation value and actual measurement value of path loss at 1-10 Hz frequency. Figure 6: Test structure. Figure 7: Test site. As can be seen from Figure 8, when the transmitter frequency is 1-10 Hz, the theoretically calculated path loss and the actually measured path loss are both above 10 dB, and both increase as the frequency increases. The difference between the actual measurement value and the theoretical calculation value is that in the actual measurement value, the path loss increases with the increase in frequency by a greater amplitude, reaching 7 dB, while the theoretical calculation result is within 1 dB, indicating that in the actual channel, the path loss decreases more severely with frequency, so in order to achieve better reception effects, a lower frequency should be used as the operating frequency of the system.
## 5 Cyber-Physical Inverter-Based Microgrid Implementation and Experimental Results
### Assessing the MG's Stability with the Secondary Control  
This section assesses the cyber-physical MG's operation with the centralized secondary controller that eliminates system frequency deviations. Beginning from this part forward, the developed ns3-based communication model establishes the communication links between the central controller and the physical system in OPAL-RT, encompassing the communication network features and the cyber vulnerability affecting the system operation. While operating solely with the primary control layer from t = 0 s, the activation of the communication model initiates to link the primary and secondary control layers at t = 36.5 s, assuming ideal communication channels (delay = 2 ms) between LAN-1 and LAN-2. The categorization of this experimental study includes the following four distinct phases:  
* Phase 1 (0-36.5 s): The primary control only (based on droop control).
* Phase 2 (36.5-71 s): The activation of the ns3-based communication model and the secondary controller are working.
* Phase 3 (71-100 s): A step-up change in the total connected load with a value of 5.8 kW.
* Phase 4 (100-120 s): A step-down change with a value of 5.8 kW.  
Figure 11 demonstrates the information flow, including bidirectional data transmission from the centralized controller node at LAN-2 (IP: 10.1.2.21) to the local controller nodes at LAN-1 (IP addresses: 10.1.1.21 and 10.1.1.22). As shown in Figure 12, during Phase 1, the droop control, a well-known method to control parallel inverters within the primary control layer, allows frequency synchronization at 59.8 Hz with deviations from the reference value. After activating the centralized secondary control in Phase 2 at t = 36.5 s, the frequency swiftly returns to the reference value of 60 Hz. Within Phase 3 and Phase 4, regardless of the additional load connection or disconnection from the MG, the steady-state frequency remains constant at 60 Hz. Accordingly, this implies that the designed centralized secondary control, based on the conventional PI controller, can eliminate the frequency deviations induced while operating with the primary control layer only.  
Figure 11: Wireshark capture of the data flows through the proposed ns3-based communication model (at the centralized controller node).
## 2 Materials and Methods
### SLR Validation Method
\(\Delta cLRA\) is an LRA-special range correction between the position of LRAs center and reference point of LRAs. \(\Delta cECC\) is the correction of station eccentricity according to SLRF2014. In addition to the basic signal propagation model for SLR range measurement given by Equations (3)-(5), \(\Delta\rho_{rel}\) and \(\Delta\rho_{trop}\) in Equation (2) can be corrected through modeling. The relativistic correction on the SLR measurement is about 13 mm for MEO satellites at orbital altitudes of 22,000 km [25]. Tropospheric range delays which are in the order of meters must also be modeled in SLR range models, which can be modeled with an accuracy of a millimeter level [28; 29].
# Blockchain for Future Wireless Networks: A Decade Survey
## 4 Blockchain: A Solution for Security and Privacy in WNs
### Blockchain Layer  
This is the middle layer in the proposed architecture, which establishes secure communication between the WNs layer and the application. First, data generated at the application layer are captured using the blockchain layer over the blocks. Once it is captured, one cannot alter it due to the immutability feature of blockchain. Then, the data are transferred from the source to the destination node securely using the WN layer. In WNs, blockchain technology offers numerous security services like access control, data integrity, and authentication, which are as follows [10]:  
* Access Control: It is a physical layer security that restricts unauthorized users from accessing authorized services running on WN. The conventional access control is centralized and utilizes the standard encryption techniques that lack in providing trust in the WN application. Such a centralized system has a risk of single-point failures and privacy leakages from the key generator schemes. Therefore, as an alternative to centralized access control, trusted blockchain-based access control can help in resolving the above-mentioned issues in WN. To do that, access control permission, i.e., read and write permissions, are only granted to an authorized user, device, and machine. In addition, blockchain uses a smart contract (a set of codes to establish contracts within two parties) to secure the system against any malevolent threat [183].
* Data integrity: Data integrity is another such issue where the attackers tamper with the data of the smart application. As a consequence, the falsified data can mislead the behavior of the smart application. Therefore, storing the data inside the blockchain can ensure that the data are not manipulated. Furthermore, it performs data integrity verification of both the communicating parties by auditing all the transactions that occurred between them [184].
* Authentication: blockchain incorporates authentication capabilities to increase the robustness of the network, which detects and prevents malicious activity in the network resources. Smart contracts perform request authentication to avoid unauthorized access from malicious users [184]. Moreover, it offers a secure and authenticated environment to create virtual WNs (VWNs). Using this network, wireless resource owners can rent their resources, such as infrastructure and a slice of the RF spectrum, to the mobile virtual network operator [10].