esa-sceva/satcom-chunk-collection huggingface dataset:
# Graph Convolutional Networks for Privacy Metrics in Online Social Networks
## 5 Experimental Evaluation
### Experiment 1  
Currently, in the study of privacy metrics on social networks, researchers are committed to numerically quantifying multiple relevant factors of privacy leakage and ultimately combining these quantitative values to comprehensively calculate the privacy score. However, the procedures are cumbersome, and the selection of factors is subjective and limited. Meanwhile, the deep connections between these factors and features cannot be used.  
We are the first to propose a privacy metric method based on a deep neural network. To verify its accuracy, we select 50 unlabelled samples in each category of model output results for manual labelling and compare them with the model output results to calculate accuracy. It is worth noting that there is considerable subjectivity in manual labelling. It is unfair to use our label to verify other researchers' methods, so we only verify the accuracy of models under different parameters in our methods. Due to space limitations, we choose the combination of parameters in Figures 3 and 4 for verification.  
Figure 4: \(\lambda\) and \(C\) in Dataset 2.  
Figure 3: \(\lambda\) and \(C\) in Dataset 1.  
From Table 4, it can be seen that the performance of the model can be greatly improved with the optimisation of parameters, whether in a small dataset or a large dataset. In addition, we can see that the model has the best measurement effect when the privacy score has the highest or lowest value. Since these two types of users are mostly public figure accounts and abandoned accounts, they are very different from normal users.
# Registration of Urban Aerial Image and LiDAR Based on Line Vectors
## 2 Methods
### The Coordinate Transformation of Line Vectors  
As shown in Figure 3, \(hg\) is an image feature line passing through point \(h\) and point \(g\). Imaging ray \(Sh\) from the photographing center \(S\) runs through point \(h\) and \(Sg\) runs through point \(g\). As a corresponding control line in LiDAR data, \(GH\) runs through point \(G\) and \(H\). In regard to lines \(Sh\), \(Sg\), and \(GH\), assume their directional vectors of are \(\mathbf{o}_{h}\), \(\mathbf{o}_{g}\), and \(\mathbf{O}\), moment vectors are \(\mathbf{p}_{h}\), \(\mathbf{p}_{g}\), and \(\mathbf{P}\), and all three lines are coplanar at photographing, then, based on Equation (4), the LiDAR data coordinate system and the image coordinate system have the following relation:  
\[\left\{\begin{array}{l}F_{1}=\mathbf{o}_{h}\cdot\mathbf{P}+\mathbf{O}\cdot\mathbf{p}_{h}=0 \\ F_{2}=\mathbf{o}_{g}\cdot\mathbf{P}+\mathbf{O}\cdot\mathbf{p}_{g}=0\end{array}\right. \tag{5}\]  
In the aforesaid equation, the coordinate transformation relationship between the LiDAR data coordinate system and the image coordinate system is characterized by representing coordinates of \(g\) and \(h\) in the image coordinate system, as well as coordinates of \(G\) and \(H\) in the LiDAR coordinate system. Any two LiDAR points can determine control lines and two image points can determine corresponding image lines, so \(g\) and \(h\), as well as \(G\) and \(H\), are not required to be corresponding points, which is the advantage of registration using line characteristics.  
Figure 2: Line vector coordinate of lines.  
Directional vectors and moment vectors of imaging rays cannot make Equation (5) workable because there are certain deviations of the original position and direction of the photographing center. Such a photographing center should be moved to restore the correct position and direction of \(Sh\) and \(Sg\). That is to say, \(Sh\) and \(Sg\) should be carried out of the coordinate transformation to meet Equation (5). The coordinate transformation of the line vector is generally achieved by a dual quaternion. Line \(\mathbf{L}^{\prime}\) can be obtained from \(\mathbf{L}\) by the dual quaternion, \(\mathbf{q}\):  
\[\mathbf{L}^{\prime}=\mathbf{q}^{-1}\mathbf{L}\mathbf{q} \tag{6}\]  
where \(\mathbf{q}=q_{1}+q_{2}\mathbf{i}+q_{3}\mathbf{j}+q_{4}\mathbf{k}+\epsilon(q_{01}+q_{02}\mathbf{i} +q_{03}\mathbf{j}+q_{04}\mathbf{k})\), \(\mathbf{i}^{2}=\mathbf{j}^{2}=\mathbf{k}^{2}=\mathbf{i}\mathbf{j}\mathbf{k}=-\mathbf{1}\), \(\epsilon^{2}=\mathbf{0}\), and \(\mathbf{q}^{-1}\) is the conjugation of \(\mathbf{q}\).
## 4 Concisely Indexed Multi-Keyword Rank Search
### Document Index and Query Vector Condensing  
To do keyword search in the original space, one can construct a query vector and do the inner product of the query vector and the document indexes, as in Equation (2). The inner product can be an indicator for the similarity of the query associated to a particular document. In fact, the result of the inner product of the projected query vector and the document indexes in the new space is equal to that in the original space, as shown in the following equation:  
\[\left(U^{T}\mathbf{q}\right)\cdot\left(U^{T}\mathbf{a}_{j}\right)=\mathbf{q} ^{T}UU^{T}\mathbf{a}_{j}=\mathbf{q}\cdot\mathbf{a}_{j}, \tag{15}\]  
where \(U\) is the basis of the new space, \(\mathbf{q}\) is the query vector, and \(\mathbf{a}_{j}\) is the index of the \(j\)th document. The benefit of the query in the new space is that the dimensions of the indexes can be significantly reduced with relatively small information loss. Therefore, the search efficiency can be increased, and the space can be saved. It is noted that by Equation (13), the projection of \(\mathbf{a}_{j}\) can be derived as follows:  
\[U^{T}\mathbf{a}_{j}=U^{T}\left(\mathbf{x}_{j}+\bar{\mathbf{a}}\right)=\left( \Sigma W^{T}\right)_{j}+U^{T}\bar{\mathbf{a}}. \tag{16}\]  
Thus, the projection of \(\mathbf{a}_{j}\) can be obtained by shifting \(\left(\Sigma W^{T}\right)_{j}\) with \(U^{T}\bar{\mathbf{a}}\), as in Equation (16). Without directly doing the projection \(U^{T}A\), the computation time can be saved, since \(\Sigma W^{T}\) can be obtained by the SVD of \(X\) without further computation.  
To achieve the search by condensed indexes, the data owner will generate the condensed document index for each document by projecting \(\mathbf{a}_{j}\) to the low dimensional space spanned by the first \(r\) principal components. Let \(\mathbf{h}_{j}\) be the condensed index of document \(f_{j}\). The condensed index is given by  
\[\mathbf{h}_{j}=U_{r}^{T}\mathbf{a}_{j}=\left(\Sigma_{r}W_{r}^{T}\right)_{j}+U _{r}^{T}\bar{\mathbf{a}}. \tag{17}\]  
Analogously, the query vector has to be mapped to the low dimensional space. Let \(\mathbf{g}\) be the condensed query vector, which is given by  
\[\mathbf{g}=U_{r}^{T}\mathbf{q}. \tag{18}\]
# Analysis of Conflict Distribution Characteristics between Motor Vehicles and E-Bikes at Roundabouts
###### Abstract  
Roundabouts are a common traffic infrastructure, which are supposed to facilitate safe and smooth traffic flow. Electric bikes as a common traffic tool in a lot of cities in China play an important role in relieving traffic congestion due to the rapid increase of motor vehicles on roads. However, compared with cyclists, e-bikers are more vulnerable because of their higher speed when colliding with motor vehicles. In this research, the spatial-temporal distribution characteristics of conflicts between motor vehicles and electric bikes (e-bikes) at signalized roundabouts are explored. First, the time distance method was used to identify conflicts, and the time to collision (TTC) was selected as the discrimination index we proposed for two representative conflict types. On conflict heat maps and conflict distribution during one signal cycle, we found a series of spatial-temporal conflict distribution regularities. Spatially, the proposed two representative types of conflicts were mainly distributed at exit areas and near the outermost circulatory lanes. Conflict Type 1 was mainly distributed around the outermost motor vehicle lanes, and Type 2 was mainly distributed inside the outermost vehicle lanes and behind the second stop line. In time span, both types of conflicts showed rapid increase before reaching a peak at 30 percentile green time and then decreasing gradually after that peak. Type 1 presented a sharp increasing range during 0-10 percentile green time, while the sharp increasing range for Type 2 presented during the 10-20 percentile. The conclusions developed by this article could provide a theoretical basis for improving traffic safety at roundabouts.  
e-bikes; roundabout; conflicts distribution characteristics; traffic safety Article
# Energy and Environment-Aware Path Planning in Wireless Sensor Networks with Mobile Sink
## 4 Formulation for Optimal Solution
### Optimal Routing Problem  
The developed routing algorithm aimed at minimizing packet loss ratio and end-to-end delay of the routing path simultaneously. In case urgent data messages occur in some areas that the mobile sink has already visited or has not visited yet, it should be delivered promptly, and thus the routing procedure is invoked. Hence, this paper's third problem is finding the optimal route for urgent data transmission. A set of constraints is defined for such optimization problems, including energy consumption balance, environmental impact minimization, real-time data delivery, and reliability of data transmission. It is a known fact that in WSNs the communication energy is related to the transmission distance. The quickest way considerably decreases energy use. However, the shortest path approach did not prolong the network's lifetime. The network's energy usage must be balanced for energy-efficient routing. Relying entirely on sensor nodes' residual energy is not the ideal way to establish network energy balance [14]. The routing protocol must refrain from using low-energy, high-traffic nodes as next-hops in order to achieve a better energy balance. By using the recommended new function, the energy load function, it is feasible to include the remaining energy and traffic load of sensor nodes and let them have a significant impact on choosing the next hop [14]. The total number of messages sent by each cluster head should match that of the node. This should contain the number of messages from each cluster head member and other cluster heads to relay. Equation (56) shows cluster head \(y\)'s traffic load at time \(t\). \[NL_{y}(t)=\left\{ \begin{matrix}TM_{y}(t)&if\ ED_{(y,MS)}(t)\leq ED_{(x,MS)}(t)\\ 0&otherwise\end{matrix} \right. \tag{56}\]  
\[ECL_{y}(t)=\
icefrac{{1}}{{\text{exp}}}\left(\frac{IE_{y}-\left(RE_{y}(t)- \left(NL_{x}(t)*HE_{xy}\right)\right)}{IE_{y}}\right) \tag{57}\]  
Equation (57) suggested that the new energy consumption load function expresses cluster head \(y\)'s energy use after sending all messages. Any tiny change in the exponential function input causes a huge output change. By exponential function, a slight change in nodes' energy leads to the selection of the most energy-efficient relay node [32]. The cluster head that can transmit data packets quicker than other candidates and fulfil the requisite latency should be the next hop. Therefore, it is appropriate to employ relaying delay to support routing decisions.
## 5 Conclusions  
The proposed intelligent system using neuro-fuzzy helps the fresher doctors for the detection of renal cancer. It is helpful for those physicians who do not have much experience to detect renal cancer and to evaluate its stage. Hence, this system has the capability to act as a training and supporting tool for doctors. It can be used in those areas where the resources are not equivalent to requirements. The various parameters are calculated to find the performance of the proposed systems. These parameters are precision, classification accuracy, sensitivity and specificity. According to the comparison study on the performance of both models, the medical intelligent system using the neuro-fuzzy technique is more suitable, accurate and precise. A computer system with MATLAB software is the only requirement to implement this proposed work.  
The developed systems for the diagnosis of renal cancer is more accurate than other used methods as the fuzzy logic can deal with the imprecise data, as all technologies used in related work deal with a crisp dataset. The system can also work with the dataset that contains missing values of test cases. The fuzzy logic has the limitation that it cannot train the system from the examples and is not capable of adapting itself according to the environment or by considering the new unseen data. This limitation of fuzzy logic has been overcome by merging it with the neural network and making a hybrid system known as Adaptive Neuro-Fuzzy Inference System (ANFIS).  
For future work in the detection of renal cancer, researchers can take more performance parameters for the evaluation. The input variables or risk factors can also be enhanced for accurate classification and to obtain a correct result for the diagnosis of renal cancer.  
Investigation, J.S.; Methodology, N.J., J.S., B.K., H.S., D.P., S.J., C.S. and G.P.J.; Project administration, B.K., H.S., D.P., S.J., C.S. and G.P.J.; Resources, B.K., H.S., D.P., C.S. and G.P.J.; Software, N.J.; Validation, B.K., H.S., S.J., C.S. and G.P.J.; Visualization, N.J.; Writing--original draft, H.S., D.P., S.J., C.S. and G.P.J.; Writing--review and editing, B.K., H.S., D.P., S.J., C.S. and G.P.J. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Institute of Information & Communications Technology Planning & Evaluation (IITP) grant funded by the Korean government (MSIT) (No.2017-0-01714).  
The authors declare no conflict of interest.  
Figure 15: Bar chart for comparison of FIS and ANFIS.
## 3 Synthesis of Optical Coherence Function
### Principle of Synthesis of Optical Coherence Function  
References [15; 16] describes the proposal of the SOCF technology as a way to realize distributed sensing. Figure 3 shows basic setup of the SOCF [16]. The light source is a laser diode (LD), which modulates the lightwave frequency directly through simply changing the injection current. Namely, the lasering frequency of LDs is easily changed. This setup is a heterodyne interferometer with an intermediate frequency \(f\) setting by an acoustic optical modulator (AOM). The interference component is selectively acquired by a band-pass filter (BPF) of the center frequency \(f\) and a square low detector.  
Figure 4: Schematic drawings of schemes for the distributed sensing of reflections in an optical fiber with three kinds of synthesis of optical coherence function, which were demonstrated in the early experimental trial [16]. Distributed sensing with (**a**) changing the synthesized optical coherence lengths; (**b**) synthesizing sinusoidal-shaped optical coherence functions; and (**c**) synthesizing a delta function-like optical coherence function.  
Figure 5: Experimental results obtained by the three kinds of synthesis of optical coherence function (SOCF)-based distributed sensing schemes, as shown in Figure 4[16]. Results shown in Figure 5a–c correspond to the three SOCF schemes shown in Figure 4a–c, respectively.
## 1 Introduction
Along with accelerating convergence time, the results also show that the new method can improve the accuracy of the PPP solution. Accounting for the serious ill-conditioned problem in medium-long baseline RTK and the special satellite structure for the BDS system, introducing external troposphere information for BDS medium-long baseline RTK is expected to be valuable, and merits an investigation. Therefore, the main purpose of this study is to assess the performance of BDS medium-long baseline RTK using a priori empirical troposphere information. First, the principle of the RZTD-constrained medium-long baseline RTK model is introduced. Then, the factors affecting the precision of BDS RTK positioning are analyzed and compared with GPS performance. Finally, the effects of the RZTD-constrained GPS, BDS, and GPS/BDS-combined RTK are evaluated using 15 baselines ranging from 38 km to 167 km.
# Spoofing Detection Using GNSS/INS/Odometer Coupling for Vehicular Navigation
## 6 Conclusions  
A GNSS authenticity verification approach based on integration of an IMU and a vehicle odometer outputs was proposed and tested. Contrary to conventional GNSS/INS coupling where the INS corrections are updated at each mechanization interval (mechanization and error correction rates are the same), the authenticity verification loop error correction rate in the present case is much lower than that of the mechanization process. The IMU/odometer navigation solutions can then be used to detect spoofing attacks at each correction interval. This approach is effective in detecting spoofing attacks, especially in covered spoofing scenarios when the authentic signals are blocked. For nominal operation scenarios and typical GPS and IMU sensors, a 20 s observation interval provided an acceptable detection performance in terms of probability of detection and mean time to detect the attack in the scenarios evaluated. Detection performance is a function of the relative authentic-spoofing trajectories; when the vehicle is static or travels in a straight direction with a constant speed, no feature can however be used to detect an attack. Some specific motion features such as continuous user velocity and heading changes (e.g., stops at traffic control lights and turns) provide additional features, resulting in better detection performance. Actual measurements in sub-urban and dense urban environments using different IMUs provided promising results to detect spoofing attacks in practical vehicular scenarios. GPS measurement quality affected by poor satellite geometry, signal attenuation and multipath occurring in urban canyons, degrades spoofing detection performance. In an actual implementation, these effects might be first detected using other metrics (e.g., C/N\({}_{0}\)) and then can be used to adjust the detection threshold. For the given data and spoofing scenario described in the papers, a mean spoofing detection time of 4 s was achieved.  
Authors namely, [PERSON] and [PERSON], have cooperated in technical parts, data collection, analyses and writing of the paper.  
Fund for this research was provided by iCORE/Alberta Innovates Technology Futures.
# Automated Aviation Wind Nowcasting: Exploring Feature-Based Machine Learning Methods
## 3 Materials and Methods
### Methods
#### 3.2.1 Data Validation and Preprocessing  
The raw data, composed of direct wind readings from each sensor, underwent a preprocessing phase via a Python algorithm to validate each three-second interval record. This step ensures valid readings for both wind speed and direction upstream, preventing issues during the rest of the process. Instances of missing or corrupted data were identified and discarded, resulting in a usable dataset consisting of 99.93% of the original raw data. Aligning with the WMO recommendation for local reports in aeronautical purposes, the raw data were subsequently reformatted into two-minute steps [2].  
Figure 4: Schematic pipeline diagram illustrating the research methodology.  
Figure 3: Violin plots representing the full range of wind speeds recorded by the official wind sensors at Madeira International Airport over 2018 and 2019.
# Quality of Experience in 6G Networks: Outlook and Challenges
## 5 Quality of Experience in 6G Networks  
In general, 6G services are essentially cyber-physical, immersive, and personalized. These characteristics come in stark contrast to possibilities in either 4G or 5G networks.  
5G networks are capable of a high level of synchrony, one where geographically separated musicians can seamlessly jam together [64]. As previously noted, more mission-critical applications of low-latency are already supported, including communications in vehicular networks. Similarly, delay and synchrony requirements in IIoT and remote surgery applications allow for little to no tolerance [65].  
6G networks also realize services where the user context is densely, reliably, and seamlessly connected in a real, virtual, or mixed environment. These networks will continue to allow for immediate or near-immediate interaction and will substantially surpass distinguishable performance. Specifically, 6G may offer a network performance beyond what can be appreciated given the limitations of human senses. Put differently, 5G and its evolution have made substantial gains in addressing QoS concerns [66].  
QoE cannot be disassociated from QoS measures, and the state of the network does impact the user experience. However, what needs to be overcome is viewing the network as the main contributing factor in evaluating QoE. Innovations such as [67] improve the modeling of the QoE-QoS interactions but continue to limit QoE consideration to what can be measured and managed through the network. Studies such as the one offered in [68], while certainly more comprehensive, remain speculative and static.  
The objective, therefore, is realizing a more prompt and comprehensive captures of QoE. Concurrently, it is necessary that QoE captures can be easily related to their impact factor(s) for the network to respond effectively. In what follows, we elaborate on potential solutions for both these aspects.
## 3 Results and Discussion  
### Iceberg Drift  
Since their deployment, Cryologger ITBs have achieved up to 1530 days of continuous operation. Collectively, these ITBs have transmitted more than 125,000 GNSS positions and travelled a combined distance of over 16,500 km. The total approximate cumulative drift distances for each beacon are shown in Table 3. Results have shown that the drift patterns and speeds differed considerably between iceberg targets, with a maximum measured drift speed of 6.0 km/h and a median drift speed for all beacons (excluding those deployed on glaciers) of 0.3 km/h.
# Investigation of Segmented Halbach Array Design for Passive Magnetorheological Brake System
###### Abstract  
The Halbach array is a significant advancement in designing permanent magnets with a specialized arrangement due to their unique capacity to generate strong unidirectional magnetic fields, which have attracted substantial attention in various applications. This arrangement allows for the construction of more efficient and compact magnetic systems, contributing to innovation across multiple fields. This study introduces a segmented Halbach array design using NdFeB permanent magnets in a cylindrical configuration, optimized to maximize flux density and minimize flux leakage. Critical design parameters, including the magnet dimensions and the gap width between the magnets, are systematically analyzed to identify the optimal arrangement. A corresponding simulation design was performed by COMSOL Multiphysics software, which can model complex magnetic fields and flux patterns. The proposed Halbach array design offers significant advantages over conventional magnet arrangements, including a 50% increase in flux density, a 75% reduction in magnet material usage, and improved magnetic field uniformity. Using the segmented Halbach arrangement in the development of passive magnetorheological braking systems can double the braking torque, leading to higher torque output in more compact designs. The outcomes of this research contribute to the progress of energy efficiency, providing efficient and reliable braking performance.  
segmented Halbach array; magnetic flux density; permanent-magnet magnetorheological brake; braking torque enhancement Article