esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Homography Estimation and Its Application to Wall Maps of Wall-Climbing Robots
## 4 Homography Estimation for a Non-Center-Aligned Image Pair
### Hierarchical Homography Estimation  
To predict the four-point homography values, the locations of the four corners of image_patch_b were predicted in image_a. To successively reduce the estimation error, we used a hierarchical method, as shown in Figure 7.  
In Stage_1, we took the input image pair of image_a and image_patch_b and used the HomographyFpnNet_A model to directly predict the pixel coordinates of the four corners of image_patch_b in image_a. The training triplet sample of Stage_1 was (image_a, image_patch_b, \(p_{i}\)), \(j\) = 0, 1, 2, 3. In the following Stage_i (\(i\) = 2, 3), we first used the prediction result of the preceding stage (Stage_i - 1) to calculate the homography matrix \(H_{i-1}\) between image_a and image_patch_b, and then used \(H_{i-1}\) to transform the area enclosed by the predicted four corners of Stage_i-1 in image_a (red quadrilaterals in Figure 7) to a new image_patch_tmp of size \(128\times 128\) pixels. At the same time, we used \(H_{i-1}\) to transform the ground truth four-point homography values to image_patch_tmp to provide target regression values for Stage_i. We then took the input image pair of image_patch_tmp and image_patch_b and used the HomographyFpnNet_B model to predict the pixel offsets of the four corners of image_patch_b in image_patch_tmp relative to the four corners of image_patch_tmp. Finally, we used the inverse of \(H_{i-1}\) to transform the predicted four corners of HomographyFpnNet_B back to image_a to get the predicted four corners of image_patch_b in the original image_a for Stage_i. The training triplet sample of stage_i was (image_patch_tmp, image_patch_b, \(H_{i-1}p_{i}\)), \(j\) = 0, 1, 2, 3.  
The model structures of HomographyFpnNet_A and HomographyFpnNet_B are exactly the same as that shown in Figure 3. The difference between HomographyFpnNet_A and HomographyFpnNet_B  
Figure 6: Dataset generation of a non-center-aligned image pair.  
Figure 7: Estimation process of the hierarchical HomographyFpnNet model. The four corners of the green quadrilateral in image_a are the ground truth four-point homography values; the four corners of the red quadrilateral in image_a are the predicted results.  
is that, while the output of HomographyFpnNet_A represents the absolute pixel values of the four corners of image_patch_b in image_a, the output of HomographyFpnNet_B represents the pixel offsets of the four corners of image_patch_b in image_patch_tmp relative to the four corners of image_patch_tmp.  
Warping using the predicted four-point homography values from each stage (red quadrilaterals in Figure 7) resulted in a more visually similar image_patch_tmp to image_patch_b, as shown in Figure 7. We took the predicted four-point homography values of the last stage as our final result.
## 3 Results
### Intelligent Vehicle Platoons Scenario Experiment  
#### 3.2.1 Electric Vehicle Platoon Size  
A hybrid flow of gasoline and electric vehicles, owing to their different energy consumption methods and the adoption of smart driving modes, can have new impacts on road traffic and carbon emission characteristics. When electric vehicles accounted for 40% of the hybrid flow and gasoline vehicles accounted for 60%, the simulation results showing the impact of different electric vehicle fleet sizes on road carbon emissions are depicted in Figure 7.  
In Figure 7, it can be observed that in a mixed flow of conventional fuel vehicles and electric vehicles, when electric vehicles are organized in platoons, road carbon emissions exhibit a decreasing trend with an increase in platoon size. Carbon emissions reached a minimum value at N = 2. Among the different platoon sizes, the outer lane had the highest carbon emissions, followed by the inner lane, whereas the middle lane had the lowest carbon emissions. Carbon emissions showed similar trends in all lanes; however, the inner lane exhibited greater variation.  
The implementation of CACC (Cooperative Adaptive Cruise Control) systems empowers electric vehicle platoons to establish intelligent interconnected vehicle convoys, facilitating efficient and seamless information exchange among convoy vehicles. When the intelligent vehicle convoy is relatively short, enhanced inter-vehicle communication through V2V (Vehicle-to-Vehicle) communication enhances traffic flow stability on the road. However, when the convoy length surpasses a certain threshold, a succession of intelligent vehicle convoys converges into a dynamic \"bottleneck\", impeding traffic flow and resulting in increased congestion and disarray, ultimately leading to a rise in carbon emissions.
# Survey on 5G Physical Layer Security Threats and Countermeasures
## 7 Location-Based Techniques for Enhancing PHY Layer Security  
Among the important threats to the integrity of 5G infrastructure is the proliferation of FBS--rogue devices impersonating legitimate base stations to intercept communications, launch attacks, or deceive users. Detecting and localizing these malicious entities is paramount for safeguarding the confidentiality, integrity, and availability of wireless communications in 5G systems [149; 150]. With the potential for FBSs to exploit vulnerabilities and compromise network security, robust countermeasures are essential to mitigate their impact and ensure the trustworthiness of communication channels [68]. Further, with the recent growth of distributed, cooperative computing methods, such as federated learning [151], the localization of individual nodes could represent an additional layer of security [152]. In general, there exist several methods and approaches to localizing transmitting devices, such as FBS, among others, including the following: signal analysis, anomaly detection, ML and DL algorithms, and network-based approaches.
# Prediction of Blast Crushing Lumpiness Based on CPO-BP Modeling
## 4 Engineering Applications  
### Project  
An iron ore mine in Hunan is located at the junction of Hengyang County, Qidong County and Shaodong County, China, with a mining area of 1.66 square kilometers, which belongs to Qidong Iron Ore Mine's Tianmiaochong--Pair of Jiachong mining section. Resource reserves of the mine reached 320 million tons, the annual design production capacity of 2.8 million tons of iron ore, the actual annual output of 2.33 million tons, 6400 tons of raw ore per day, the production of finished products concentrate stable at 1000-1200 tons. At this stage, open pit mining is adopted, with the thickness of the ore body ranging from 0.53 to 120 m, with an average thickness of 15.7 m, and the ore types are mainly sedimentary metamorphic magnetite, hematite, and mixed ore, and the main minerals are uniformly embedded in the vein matrix with authomorphic-semi-authomorphic structure, and crypto-crystalline particles are distributed in the form of hematite. Mining is faced with the following technical challenges: 1. Demand for blasting block size control: the block size of the ore after open blasting directly affects the subsequent crushing, transportation and beneficiation efficiency; too large block size to increase the cost of secondary crushing, too small rules lead to dust pollution and waste of resources. 2. Complexity of parameter optimization: the traditional blasting test relies on experience to adjust the distance between the holes, the row spacing, the amount of charge, and other parameters; time-consuming, costly, and unstable results. 3. Geological constraints: the ore body shows considerable variation in thickness, hardness, and structural characteristics, which requires dynamic adaptation of the blasting program to ensure mining safety and economy. To overcome the above problems, this study introduces the improved BP neural network model (CPO-BP) of the Crown Porcupine Optimization Algorithm (CPO) based on the previous blasting  
Figure 4: Plot of CPO-BP test value vs. true value residuals.  
test data and accurately predicts the distribution of ore lumps under different blasting parameters by globally optimizing the initial weights of the network and the threshold value. The model can effectively avoid the traditional BP easily falling into the local optimum, poor robustness, and other problems, and, combined with the actual parameters of the mine, achieve intelligent optimization of the blasting program, reduce the number of tests, reduce the production cost, and provide scientific guidance for achieving safe and efficient mining operations.
## 3 Method Proposal to Analyze AST n-Tools Combinations
### Analysis and Discussion
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline
**HIGH** & **Rec** & **F\({}_{1.5}\)S** & **Info** & **MEDIUM** & **F-Mes** & **Rec** & **Prec** & **LOW** & **Prec** & **F\({}_{8}\)S** & **Mark** \\ \hline Arachni + CCE & 1.00 & 1.00 & 1.00 & Arachni + CCE & 1.00 & 1.00 & 1.00 & Arachni + CCE & 1.00 & 1.00 & 1.00 \\ CCE + CLAST & 1.00 & 1.00 & 1.00 & CCE + CLAST & 1.00 & 1.00 & 1.00 & CCE + CLAST & 1.00 & 1.00 & 1.00 \\ ZAP + CCE & 1.00 & 0.98 & 0.95 & ZAP + CCE & 0.98 & 1.00 & 0.95 & Arachni + CLIAST & 1.00 & 0.98 & 0.92 \\ FGRify + CCE & 1.00 & 0.85 & 0.43 & Arachni + CLIAST & 0.96 & 0.92 & 1.00 & ZAP + CCE & 0.95 & 0.96 & 0.95 \\ FSB + CCE & 1.00 & 0.83 & 0.33 & ZAP + CLAST & 0.93 & 0.91 & 0.95 & ZAP + CLAST & 0.95 & 0.94 & 0.86 \\ FSB + CMAST & 1.00 & 0.83 & 0.33 & Fortify + CCE & 0.78 & 1.00 & 0.63 & ZAP + Arachni & 0.86 & 0.63 & 0.43 \\ FSB + Fortify & 1.00 & 0.81 & 0.23 & Fortify + CLIAST & 0.77 & 0.99 & 0.63 & Fortify + CCE & 0.63 & 0.68 & 0.63 \\ FGRify + CMAST & 0.99 & 0.84 & 0.41 & FSB + CLAST & 0.75 & 1.00 & 0.60 & Fortify + Arachni & 0.62 & 0.66 & 0.49 \\ FSB + Arachni & 0.98 & 0.82 & 0.31 & Fortify + Arachni & 0.75 & 0.94 & 0.62 & Fortify + ZAP & 0.62 & 0.66 & 0.47 \\ Fortify + Arachni & 0.94 & 0.81 & 0.36 & Fortify + ZAP & 0.74 & 0.93 & 0.62 & FSR + CCE & 0.60 & 0.65 & 0.60 \\ Fortify + ZAP & 0.93 & 0.80 & 0.35 & FSB + ZAP & 0.74 & 0.98 & 0.59 & FSB + CLAST & 0.60 & 0.65 & 0.60 \\ Arachni + CLIAST & 0.92 & 0.94 & 0.92 & FSB + Arachni & 0.74 & 0.98 & 0.59 & FSB + ZAP & 0.59 & 0.65 & 0.54 \\ ZAP + CLIAST & 0.91 & 0.92 & 0.86 & FSB + Fortify & 0.72 & 1.00 & 0.56 & FSB + Arachni & 0.99 & 0.65 & 0.54 \\ ZAP + Arachni & 0.30 & 0.38 & 0.25 & ZAP + Arachni & 0.44 & 0.30 & 0.86 & FSB + Fortify & 0.56 & 0.62 & 0.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Ranking of 2-tools combinations according to distinct level of web application criticality. vulnerabilities and the false positives can be eliminated in the necessary subsequent audit of the vulnerability reports. The Combinations integrated by SAST+DAST+IAST tools as Fortify + Arachni + CCE or Fortify + ZAP + CCE obtain a very good result in the HIGH, MEDIUM and LOW classifications. In the three 3 classifications the two first 2-tools combinations are the same due to the presence of one or two IAST tools in the combination, but from the third position the three criticality classifications are quite different. In the three 3 classifications the three first 3-tools combinations are the same due to the presence of one or two IAST tools in the combination, but from the fourth position the three criticality classifications are quite different. For high level of criticality, the results reached for the 3-tools combinations are between 0.92 and 1 for recall metric.
## 3 Materials and Methods
### GCP and CP Distribution  
The UAV used in this study was not equipped with precise navigation and orientation instruments, so a sufficient number of GCPs in the study area had to be established and measured before the UAV flight for georeferencing [6]. This step had two main objectives:  
1. The identification and measurement of ground control points (GCPs) for indirect georeferencing.  
2. The identification and measurement of checkpoints (CPs) to determine and evaluate the positional accuracy of the generated dense point cloud, DEMs, and orthophotos [13].  
Figure 4: Samples from four adjacent images; the top two images were taken in one flight line and the bottom two images were taken in an adjacent flight line. (**a**) DJI_0891. (**b**) DJI_0892. (**c**) DJI_0908. (**d**) DJI_0909.  
Field measurements before flights included measuring the coordinates of ground control points and checkpoints using a GNSS/RTK base station and rover [7]. The GCP and CP markers were precisely fabricated from square iron sheets with a side length of 50 cm and divided into four identical parts, which were assigned different colors (black and white) to facilitate their visibility and distinction by drone imagery (Figure 5).  
In this study, 18 points were set and measured (numbered from 1 to 18). Point 1 was used as a reference point or base station, 5 points served as ground control points (GCPs) for indirect georeferencing, and 12 points were used as checkpoints (CPs) to calculate the positional accuracy of the work products. All points were placed in appropriate distribution throughout the study area (Figure 6) and were visible in the images. The GNSS survey of the GCPs and CPs was performed in RTK mode. The planimetric coordinate system was the UTM zone 38N with the geodetic reference system WGS84. The altimetric coordinate system was the EGM96 geodetic model for converting ellipsoidal heights to orthometric heights [14].
## Chloride Ingress Modeling
### Chloride Ingress for Cracked Concrete  
In recent years, much research has been devoted to the development of models for predicting the cracking effects on chloride ingress and corrosion phenomenon (corrosion initiation, propagation,corrosion rate, etc.). [PERSON] et al. [34] have developed empirical models for predicting the rate of corrosion induced by chloride ions in cracked RC structures. These models incorporated the influence of several geometries of concrete cracks and cover depth (with the crack width/concrete cover ratio [35; 36]), as well as the quality of concrete on the corrosion speed. This study was useful to provide recommendations about the best combination of the aforementioned parameters to meet the desired durability performance of the structure. [PERSON] et al. [37] have proposed a numerical method that allowed simulating the chloride ingress into concrete by an unstable diffusion analysis, taking into account damage associated with 3D internal cracks. The crack propagation analysis with a damage model based on fracture mechanics was also taken into account to reproduce the 3D geometry of the internal cracks. Several authors have also developed models of corrosion or chloride transport into cracked concrete (saturated or unsaturated) at the mesoscale, to consider the heterogeneity of concrete in more than two phases [38; 39; 40].  
Several experiments as well as field studies have shown that cracking leads to rapid penetration of chloride ions and early degradation of structures [41; 42; 43]. For example, [PERSON] et al. [35] conducted chloride diffusing tests on cracked RC beams, and concluded that the crack width/concrete cover ratio may be a deterministic parameter for assessing the durability of cracked reinforced concrete. His research has shown that there is a hyperbolic relationship between this ratio and the chloride threshold value in cracked concrete. The introduction of cracking parameters in chlorination models is therefore useful for estimating the time of corrosion initiation taking into account the combined effects of chloride ingress and concrete cracking. To model the diffusion of chlorides in cracked concrete, we propose to use the simplified model described by Equation (4) in which we add an acceleration factor \(\alpha\) which multiplies the diffusion coefficient D:  
\[\mathrm{C(x,t)}=\mathrm{C_{s}}\left[1-\mathrm{erf}\left(\frac{x}{2\sqrt{ \alpha\cdot Dt}}\right)\right] \tag{4}\]  
The acceleration factor is higher than 1 for cracked conditions to consider that major chloride diffusion is expected in a cracked concrete. Since there are significant uncertainties related to this parameter depending on the concrete properties, fabrication of the structural component, loading history, etc. this parameter should be modeled as a random variable to be identified from experimental data.
## 5 Analysis of Factors Influencing Node Ductility and Energy Consumption
### Reinforcing Steel Strength  
For the study on the effect of reinforcement strength, four reinforcement strength grades were established, namely, HPB300, HRB335, HRB400, and HRB500, and the calculation results are shown in Table 6. From the results of the analysis, it becomes apparent that the ultimate bearing capacity of the joints exhibited a positive correlation with the strengthening of the reinforcing steel. The HPB300-grade joint achieved its maximum bearing capacity in the second displacement cycle, while the HRB335 and HRB400 joints exhibited their maximum bearing capacities in the third displacement cycle. In comparison, the ultimate bearing capacity of the HRB500 steel bar joints appeared in the fifth displacement period. The increase in the strength of the steel bar obviously improved the bearing capacity of the structure, and the ductility of the structure obviously increased with the increase in the strength of the steel bar. The energy dissipation coefficient of the structure was greatly different from the displacement point of the ultimate bearing capacity. The HPB300-level joint had a small hysteresis curve area due to the early occurrence of the ultimate bearing capacity, so the energy dissipation coefficient was small. Combined with the remaining data, it can be seen that the energy dissipation coefficient of the structure decreased with increasing steel grade, mainly because the difference in the location of the ultimate load affected the area of the structure's hysteresis curve. However, because of the obvious improvement in ductility in the joint with increasing steel grade, the overall level of energy dissipation in the joint with a higher steel grade increased.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reinforcing Steel** & **Ultimate Bearing** & **Ductility** & **Energy Dissipation** \\
**Strength** & **Capacity (kN)** & & **Coefficient** \\ \hline HPB300 & 134.49 & 6.21 & 0.577 \\ HRB335 & 134.86 & 6.58 & 0.824 \\ HRB400 & 141.77 & 10.79 & 0.645 \\ HRB500 & 146.31 & 13.58 & 0.70 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Influence of reinforcing steel strength on joint.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Concrete Strength** & \
\begin{tabular}{c} **Ultimate Bearing** \\ **Capacity (kN)** \\ \end{tabular} & **Ductility** &
\begin{tabular}{c} **Energy Dissipation** \\ **Coefficient** \\ \end{tabular} \\ \hline C30 & 128.98 & 11.70 & 0.634 \\ C40 & 141.04 & 8.57 & 0.625 \\ C50 & 149.95 & 7.67 & 0.648 \\ C60 & 153.69 & 7.48 & 0.630 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Influence of concrete strength on joint.
# Investigation of Data Quality Assurance across IoT Protocol Stack for V2I Interactions
## 6 Findings and Discussion
### Data Quality Acting as a Complement of Quality of Service  
It is worth noting that this mapping has shown an overlap between DQ and QoS with a shared objective of enhancing the performance of networked applications and services. These two concepts have shared attributes such as accuracy, availability, completeness, reliability, and timeliness [105]. QoS focuses on managing network traffic efficiency, resource allocation, and error handling to satisfy expected performance for various applications and use cases. Most Application layer protocols investigated in this study support QoS with the exception of HTTP REST, XMPP, and WebSocket, while at the Transport layer, TCP and SCTP support QoS, and the Internet layer supports QoS mechanism by default with the help of IPv6. However, while QoS levels supported at these layers may be sufficient for specific IoT applications and use cases, the existing QoS levels and specific configurations need further investigation to satisfy the communication standards required in some V2I use cases.  
The focal point of DQ, on the other hand, is \"content suitability\", which encompasses the assurance that data transmitted via various protocols aligns with the expected standards necessary for reliable decision-making processes and applications. Within the context of data transmission in IP-based communication using the IoT protocol stack, the intersection of QoS and DQ is evident, as mentioned earlier. QoS mechanisms available in various layers of the IoT protocol stack, depending on the type of protocol deployed, play a pivotal role in maintaining and managing the data flow and ensuring end-to-end delivery while supporting various DQ dimensions. For instance, prioritisation mechanisms [106, 107] enable the delivery of time-critical data with high accuracy, meeting accuracy and timeliness dimensions, while retransmission mechanisms ensure reliability. The interfacing of these two concepts will collectively bolster efficiency and reliability in IP-based V2I communications.
# OBU for Accurate Navigation through Sensor Fusion in the Framework of the EMERGE Project
## 2 EMERGE Onboard System Architecture
### EMERGE Navigation System Implementation
#### 2.1.2 Sensor Fusion and Integrity  
The navigation system's core comprises the components in charge of PNT (Positioning, Navigation and Timing) and Integrity estimation. The multi-sensor approach uses an algorithm that integrates all the elements that provide helpful information in the navigation process. Section 3 describes the loosely coupled algorithm that integrates inertial and GNSS data.
# A Filter-Based and Parallel Unknown Tag Identification Protocol in Open RFID Systems
## 4 Filter-Based and Parallel Unknown Tag Identification Protocol
### Collection Phase
#### 4.3.3 Assigning Address Code  
The reader broadcasts the request with parameters \(\langle F_{R},V_{C}\rangle\) to all tags. Upon receiving the request, each singleton tag obtains its address code. Specifically, if the tag \(t_{u_{i}}\) is mapped to the \(j\)th slot and \(F_{R}[j]=1\), the address code of the tag is \(V_{C}[u_{i}]\), where \(\mu_{i}\) is the number of \"1s\" preceding the \(j\)th element in \(F_{R}\). As shown in Figure 4, after receiving \(F_{R}\) and \(V_{C}\), each tag checks its index and obtains its address code. For example, the indexes of \(\{t_{u_{1}},t_{u_{2}},t_{u_{4}},t_{u_{6}},t_{u_{8}}\}\) are \(\{1,2,4,3,5\}\), and the corresponding address codes are \"\(1111\)\", \"\(1010\)\", \"\(1100\)\", \"\(1001\)\", and \"\(1111\)\", respectively. So far, each singleton tag has an address code, which is different and orthogonal to the address codes of other singleton tags in its \(loop\).