esa-sceva/satcom-chunk-collection huggingface dataset:
# Control for Autonomous Intersection Management Based on Adaptive Control Barrier Function
## 1 Introduction
### Connected Vehicles (Centralized Control)  
Several centralized methods have been proposed in [12; 13; 14; 5; 17] to eliminate parking delays. In these methods, traffic management services collect information on all vehicles entering the intersection and compute optimized paths for all vehicles in a centralized manner.  
Vehicle-to-anything (V2X) communication technology [12; 13] is commonly used as a centralized control technique for intersection management problems. Its main goal is to reduce traffic congestion and prevent accidents, especially in areas prone to accidents like road intersections. A control approach that considers spacing data for both immediate predecessor and successor was proposed under V2X communications environment [17]. However, there are two disadvantages of V2X communication technology in intersection management. First, it is not suitable for large-scale multi-agent systems due to its centralized features, which may cause severe network delays when many vehicles enter an intersection simultaneously. Second, this technology requires high infrastructure requirements such as 5G networks covering the entire intersection and each vehicle having a vehicle-mounted terminal to communicate with the base station.  
Figure 1: Sketch of autonomous intersection management (AIM) (**a**) without and (**b**) with guaranteed collision avoidance.
# Profitability Assessment of Precision Agriculture Applications--A Step Forward in Farm Management
## 4 Conclusions  
In this research, an innovative module for assessing the profitability of precision agriculture applications was developed, namely ProFit. In terms of architecture, ProFit is embedded within a cloud-based farm management information system, namely ifarma, while taking advantage of pre-existing functionalities, such as a precision agriculture database provided by other ifarma modules, like PreFer.  
ProFit offers an easy-to-use interface, which encourages farmers to enter economic records quickly and reliably while using an empirical method to share apportionable expenditures between fields (when and where site-specific maps are not available). At the same time, it uses the map view environment of PreFer to display the profitability maps.  
ProFit moves farm management one step forward by operationally bridging the gap between precision agriculture applications and profitability assessment. Future work will focus on widening the range of precision agriculture practices (i.e., beyond fertilization) within ProFit, such as soil tillage, seeding, irrigation, weed management, and crop protection.  
Conceptualization, C.K., M.C. and S.M.; methodology, C.K., M.C. and V.V.; software, K.S. and V.V.; validation, M.C., A.M. and K.S.; formal analysis, C.K. and M.C.; investigation, M.C. and O.T.; resources, M.C. and O.T.; data curation, C.K., A.M. and K.S.; writing--original draft preparation, C.K., M.C. and O.T.; writing--review and editing, M.C. and O.T.; visualization, A.M. and K.S.; supervision, S.M. and V.V.; project administration, V.V.; funding acquisition, S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Figure 6: An example of a yield (**a**) vs. profit (**b**) map of the same fields and year displayed in the ifarma/ProFit environment; different spatial patterns in some of the fields are apparent.  
**Data Availability Statement:** Data will be made available upon request.  
**Acknowledgments:** Special thanks to [PERSON], [PERSON], and [PERSON], who are rice farmers in Greece, for their ideas and the permission to use their data for the demonstration needs of this work.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 3 GNSS-Free IoT Localization Methods
### Learning-Based Localization Methods  
As mentioned, each category of Signal-Based localization methods has pros and cons that make them suitable for a special system. However, all these categories are implemented for a definite environment. Therefore, they become unreliable in the presence of environment error sources that are not included in the underlying models. To overcome this issue, Machine Learning (ML)-based localization methods are becoming increasingly popular [47]. ML methods are used in combination with Signal-Based localization methods. In this paper, Fingerprinting and ML methods for Path-Loss modeling have been investigated.  
* Path-Loss Modeling: When a signal travels from the transmitter to receiver over a distance \(d\), path loss or propagation loss leads to received signal power decrements. By correct determination of system path-loss model, location estimation accuracy improved. The path-loss model of the signal can change based on the environment. For path-loss model determination, the ML method needs collected data of experiment environment under different situations by Equation (3). \[PL=P_{T_{x}}+G_{T_{x}}+G_{R_{x}}+10.log_{10}(1+1/SNR)-RSSI\] (3)where \(P_{T_{x}}\) is the transmission power, \(G_{T_{x}}\) and \(G_{R_{x}}\) are the gains of the transmitting and receiving antenna, respectively. By large enough number of \(SNR\) and \(RSSI\) measurements as input of ML, correct determination of system path-loss model as ML output can be achieved. Free space, indoor and urban path-loss models are more usual defined path-loss models [48]. The authors in [49] studies the propagation of LoRaWAN signals in forest, urban, and suburban vehicular environments.
* Fingerprinting/Scene Analysis: The purpose of the fingerprinting method is to find the location of an end node by comparing its signal characterizes received from multiple GWs with a stored signal characterizes. For example, RSSI fingerprinting-based localization method has two phases: offline and online phase. In the offline phase, the characteristics of signals for specific locations (x,y) are collected and stored according to Equation (4). \[F_{t}(x,y)=[RSSI_{1,t},RSSI_{2,t},\ldots,RSSI_{N,t}]\] (4) where \(N\) is the number of gateways and \(t\) is the time that signal is collected by gateways. In the online phase, the distance matching of received signal characterizes with stored data is used for location estimation. Distance matching indicates how close a fingerprint at one reference location to the received fingerprint at an unknown location [50]. The fingerprint process is shown in Figure 4.
* Proximity Analysis: In this category, service provider do not need precise location information of end user. Distance between two end user is the purpose of Proximity Analysis. This analysis is useful in smart factories [51].
# Performance of Cooperative Relay NOMA with Large Antenna Transmitters
## 4 Numerical Results and Analyses
### Achievable Rate Performance  
For achievable rate analysis, (28) and (29) were used to determine the achievable rates of AF and DF. In Figure 6, the UE\({}_{1}\) achievable rate obtained using DF coding scheme is higher than that of the AF coding scheme across the entire SNR, irrespective of the TX type used in the proposed 3D GBSM channel model. It can also be seen from the comparison of the results of DF and AF for CA channel model that the capacities of UE\({}_{1}\) and UE\({}_{2}\) have similarities with higher capacity values of AF coding scheme at higher SNR values.
# Cloud-Based Internet-of-Things System for Long-Term Bridge Bearing Monitoring Using Computer Vision
## 3 Experimental Validation
### Lab-Scale Test  
#### 3.1.1 Displacement Measurement Accuracy Validation  
The FHD (1080p) USB camera equipped with Sony's IMX-477 CMOS sensor was utilized to validate the accurate measurement of 3D displacement using ChArUco markers. For this purpose, the linear stage shown in Figure 6 was employed, which allows for precise  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Service** & **Method** & **URI** & **Parameter** \\ \hline Save Tags & Post & /tags & Region, sensor ID \\ Save Fields & Post & /field & Time, batter voltage \\ Save Image & Post & /image & Image \\ Process Data & Get & /process & Displacement \\ Report Status & Post & /status & Status (normal, low) \\ \hline \hline \end{tabular}
\end{table}
Table 2: RESTFUL web services currently employed on the cloud server.  
Figure 5: Data mapping between the server object and database.  
adjustments with an accuracy of 0.1 mm per axis. The detailed specifications of the camera and linear stage are presented in Table 3.  
In the lab-scale experiment, the 40 cm distance between the camera and the ChArUco marker was arbitrarily chosen to ensure a sufficient field of view and resolution under well-controlled lighting conditions. However, for field installations, LED markers were used to ensure visibility in low-light conditions. Regarding the camera angle, the system measures three-axis displacements based on marker detection, making it less sensitive to angle variations. Nevertheless, the camera was positioned perpendicularly to further estimation was evaluated by comparing the actual displacement with the estimated values. As shown in Figure 7, the experimental index represents incremental adjustments from 0 mm to 7 mm and the corresponding measured 3D displacements. The displacements measured along each axis were independent and consistently exhibited an error of less than 3%. As shown in Table 4, the average root mean square error (RMSE) was approximately 0.02 mm when compared with the actual displacements.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Parts** & **Model** & **Parameter** & **Value** \\ \hline \multirow{2}{*}{Camera} & \multirow{2}{*}{IMX-477} & Resolution & 1920–1080 (2k) \\  & & Effective pixels & 2.07 MP \\ \hline \multirow{3}{*}{Linear stage} & \multirow{3}{*}{LD60-LM} & X range & 13 mm \\  & & Y range & 13 mm \\ \cline{1-1}  & & Z range & 10 mm \\ \cline{1-1}  & & Min scale & 0.1 mm \\ \hline \end{tabular}
\end{table}
Table 3: Specification of experimental equipment.  
Figure 6: Lab-scale experimental setup.  
Figure 7: Results of the 3D displacement measurement: (**a**) \(x\)-axis test results; (**b**) \(y\)-axis test results; (**c**) \(z\)-axis test results.
# On the Use of Structured Prior Models for Bayesian Compressive Sensing of Modulated Signals
## 3 Experimental Results
### Simulated Example
#### 3.1.1 Test Signal  
The test signal \(s\) is of length \(N\) = 50,000 and is simulated by multiplying two periodic signals \(s_{c}\) and \(s_{m}\) (\(s=s_{c}\times s_{m}\)). More specifically, we have used \(B=3\) carrier harmonics with normalized fundamental frequency \(f_{c}=0.07\) (the sampling frequency is \(f_{s}=5000\) Hz) and 6 modulations with normalized fundamental frequency \(f_{m}=0.005\). We have set \(Q=1749\), so that each active block has a frequency bandwidth of size \(f_{c}\). It is worth to note that the test signal is expressed in the unit of the actual experimental signal (e.g., [m/s2]), say [U].  
The models that are followed in this paper can be applied to real applications in different fields such as telecommunication, electricity, mechanics and optics where modulated signals are frequently encountered. However, there is no versatile structured model. For each application, the prior model should be well adapted to the intrinsic structure shape of the considered spectrum (e.g., amplitude or phase modulations) and take possible symmetry mismatch into account. For instance, in our experiments, we are particularly interested in the modulated signals encountered in mechanical systems, such as Gearboxes. Modulations appear in the spectrum mainly due to amplitude and phase modulations (generated by transmission errors), leading to a quasi-symmetrical spectrum, as detailed in Section 2.1.1. Furthermore, the energy of such a structure becomes stronger when a damage appears in the gear pair. When the active blocs (composed each of a meshing frequency harmonic and its modulations) are considered, one can notice that there is no necessary rule concerning the energy of each bloc. Moreover, the transfer function between the source (e.g., the gear pair) and the accelerometer (i.e., transducer measuring transverse vibrations) alters the real signature that is produced by the source. All of these factors make the vibration signal structure unpredictable and the reconstruction task quite challenging, especially for small modulations in low signal-to-noise ratio (SNR) and high compression rate conditions. Accordingly, the adapted constraints to test signal should be imposed to approach the reality of vibration signals. In that respect, the test signal is set so as to have quasi-symmetrical spectrum around the carrier frequencies (symmetrical support, but with slightly different amplitude). Furthermore, the active blocks in the spectrum of the test signal have different energy levels.
# Exploring the Role of Distraction in Weak Flow-Performance Link Based on VR Searching Tasks
## 6 Empirical Study 2: Effect of Reducing Distractors on the Weak Flow-Performance Link
### Results  
We firstly conducted two independent-sample \(t\) tests to investigate the difference in visual comfort and familiarity with VR between the two versions. Results showed that there was no significant difference (_ps_ > 0.05). Thus, these extra variables were controlled without affecting the subsequent analysis.  
Additional independent-sample \(t\) tests were performed to investigate the difference in flow and performance between the two conditions. Results showed that there was a significant difference in flow [\(t\) (37) = 2.906, \(p\) = 0.005], and participants in version 0 had higher flow than those in version 1 (Figure 8a). The difference in task performance was also significant [\(t\) (37) = 5.302, \(p\) < 0.001]. Contrary to the results of flow, participants in version 1 achieved better task performance than those in version 0 (Figure 8b).  
Correlation analysis and regression analysis were conducted un-der each condition. Results showed that the correlation between flow and performance was not significant in high distraction conditions, and flow did not significantly predict performance (\(\beta\) = 0.110, \(p\) = 0.509). In comparison, data from low distraction condition showed that the correlation between flow and performance was significant, and flow significantly and positively predicted task performance (\(\beta\) = 0.351, \(p\) = 0.031).  
Figure 7: Differences between version 0 (**a**) and version 1 (**b**) on distractors. (**a**): Many kinds of marine creatures navigating in the scene as distractors. (**b**): Very few distractors appear.  
The results showed that reducing conspicuous distractors not only strengthened the flow-performance link but also improved the task performance. However, this approach impaired the quality of flow experience to some extent. It meant that although distractions reduced task performance, they helped achieve better experience during the task. Anyway, these results supported H2, suggesting that guideline 1 was an effective way to alleviate the weak link. Next, experiment 3 tested the effectiveness of guideline 2.
## 6 Simulation and Analysis
### Validation of the Analytical Model
(**a**) Mean file round-trip delay against \(T_{w}\); (**b**) energy consumption against \(T_{w}\). Figure 3: \(M=10\), \(T_{w}=20\). (**a**) Mean file round-trip delay against \(T_{s}\); (**b**) energy consumption against \(T_{s}\). since the number of packet forwarding did not increase, the energy consumption did not increase. This additional waiting time did not cause lots of packets forwarding, so a higher \(T_{w}\) did not lead to more energy consumption. In addition, a good match between analysis and simulation curves can be observed in Figure 4a,b, which can validate the accuracy of our model. Meanwhile, the fit between the analytical model and simulations improved when the delay decreased, as a longer round-trip delay accumulated bigger inaccuracies.
# LEO Constellation-Augmented Multi-GNSS for 3D Water Vapor Tomography
## 2 Materials and Methods
### Tomographic Theory and Approach
In Figure 5, the abscissa is the satellite elevation angle, and the ordinate is the residual value of SWV. Each point represents an observation ray. Figure 4: Plot of maximal difference in SWV calculated by GPS and water vapor radiometer with elevation angle. Figure 5: Observing the scattered point distribution of the SWV residual value of the ray. Once the residual values were obtained, SWV observations with an error term could be described by the following equation:  
\[y_{simu}=A\cdot x_{ERA5}+\epsilon \tag{9}\]  
where \(\epsilon\) is the residual term of SWV. After obtaining observed data \(y_{simu}\), water vapor density in the tomographic region could be solved by using Equation (3). Since matrix \(A\) was singular, the simultaneous iterations reconstruction technique (SIRT) was used to solve the inverse of Equation (3) [40], as follows:  
\[x_{j}^{k+1}=x_{j}^{k}+\sum_{i}r_{0}a_{i,j}\frac{y_{i,simu}-\left\langle A^{i}, x^{k}\right\rangle}{\left\langle A^{i},A^{i}\right\rangle} \tag{10}\]  
where \(k\) denotes the \(k\)th iteration; \(x_{j}^{k}\) denotes the result of the \(j\)th voxel in the \(k\)th iteration; \(r_{0}\) is the relaxation parameter; \(a_{i,j}\) is the value of the element for the \(i\)th row and \(j\)th column of matrix \(A\); \(A^{i}\) denotes the \(i\)th row of the matrix; \(x^{k=0}\) is the initial density value for water vapor density, which is obtained by averaging the sounding data of the last three days. Relaxation parameter \(r_{0}\) gives the correction weight for each voxel with respect to the initial voxel value [41]. The number of iterations determines whether the algebraic reconstruction algorithm can achieve a stable result in the end. We set \(r_{0}\) to 1.2094, and the maximal number of iterations to 150. The data-processing flow is shown in Figure 6. The GNSS and LEO satellites in the line-of-sight direction, and ground-base stations can build a design matrix for tomographic analysis. ERA5 data are mainly used to provide water vapor density values. GPS and radiometer comparison data provide SWV residuals. Radiosonde data are used to provide initial water vapor density. Combining the above data, water vapor density valuation in voxels can be calculated by using SIRT. Lastly, the ray distribution, and tomographic time resolution, accuracy, and spatial resolution are analyzed in the paper. Figure 6: Data-processing flow chart. Then, the ratio of errors in water vapor density before and after tomography was selected as an accuracy index for assessment, described as follows:  
\[\eta=\left[1-\frac{\text{RMS}\Big{(}x_{ERA5}-x^{cat}\Big{)}}{\text{RMS}\big{(}x _{ERA5}-x^{k=0}\big{)}}\right]\cdot 100\% \tag{11}\]  
where \(\eta\) is the improvement in water vapor density before and after tomography; \(x_{ERA5}\) is water vapor density that was considered to be a true value; \(x^{cat}\) is the voxel density of water vapor obtained from tomography; and \(x^{k=0}\) is the initial density value of water vapor density.
## 4 Review of [PERSON] et al.'s Protocol
### Masquerade Attack  
We assume that a malicious adversary \(U_{ma}\) can obtain the SG device of legal user \(SD_{i}\) and intercept information transmitted in open channel, and then may attempt to masquerade \(SD_{i}\). According to Section 1.1, \(U_{ma}\) can extract secret information \(\{RID_{i},TC_{i},h(),E_{p}(a,b),G\}\) using power analysis attack. Finally, \(U_{ma}\) performs the masquerade attack as below:  
**Step 1:**: \(U_{ma}\) generates a random number \(u_{ma}\in Z_{p}^{*}\) and calculates \(U_{ima}=u_{ma}.G\), \(C_{ma}=h(TC_{i}||T_{1})\oplus h(RID_{i}||U_{ima}||T_{1})\). After that, \(U_{ma}\) sends message \(\{U_{ima},C_{ma},T_{1}\}\) to \(UC_{j}\) over insecure channel.
**Step 2:**: After receiving the message from \(U_{ma}\), \(UC_{j}\) checks \(|T_{1}-T_{1}^{*}|\leq\Delta T\). If the condition is valid, \(UC_{j}\) calculates \(D_{j}=C_{ma}\oplus h(RID_{i}||U_{ima}||T_{1})\) and generates a timestamp \(T_{2}\). \(UC_{j}\) then selects a random number \(v\in Z_{p}^{*}\) and computes \(V_{j}=v.G\), \(W_{ma}=v.U_{ima}=(u_{ma}v).G\), \(SK_{ma}=h(W_{ma}||D_{j}||h(RID_{j}||TC_{j}||T_{2})\), \(SK_{ma}=h(SK_{ma}||RID_{i}||T_{2})\), and \(Z_{ma}=h(RID_{j}||TC_{j}||T_{2})\oplus h(RID_{i}||U_{ima}||V_{j}||T_{2})\). After that, \(UC_{j}\) sends the message \(\{V_{j},Z_{ma},SKV_{ma},T_{2}\}\) to \(U_{ma}\).
**Step 3:**: After receiving the message from \(UC_{j}\), \(U_{ma}\) checks condition \(|T_{2}-T_{2}^{*}|\leq\Delta T\). If the condition is valid, \(U_{ma}\) computes \(E_{i}=Z_{j}\oplus h(RID_{i}||U_{ma}||V_{j}||T_{2})=h(RID_{j}||TC_{j}||T_{2})\), \(W_{i}^{\prime}=u_{ma}.V_{j}=(u_{ma}v).G\), and \(SK_{ma}^{\prime}=h(W_{ma}^{\prime}||h(TC_{i}||T_{1})||E_{i})\). Then, \(U_{ma}\) generates a timestamp \(T_{3 ma}\) and computes \(SKV_{ma}^{\prime}=h(SK_{ma}^{\prime}||RID_{i}||T_{2})\). After that, \(U_{ma}\) sends message \(\{SKV_{ma}^{*},T_{3}\}\) to \(UC_{j}\) over insecure channel.
**Step 4:**: After receiving the message from \(U_{ma}\), \(UC_{j}\) checks condition \(|T_{3 ma}-T_{3 ma}^{*}|\leq\Delta T\). If the condition is valid, \(UC_{j}\) calculates \(SKV_{ma}^{*^{\prime}}=h(SK_{ma}||RID_{i}||V_{j}||T_{3 ma})\) and checks if \(SKV_{ma}^{*^{\prime}}=SKV_{ma}^{*}\) holds. If the condition is valid, \(U_{ma}\) and \(UC_{j}\) store session key \(SK_{ma}(=SK_{ma}^{\prime})\).  
Therefore, \(U_{ma}\) can successfully generate a session key between \(U_{ma}\) and \(UC_{j}\) and send a legitimate authentication request message. Consequently, we show that [PERSON] et al.'s protocol does not withstand masquerade attack.
# Kalman Filter Using a Third-Order Tensorial Decomposition of the Impulse Response
## 3 Proposed Kalman Filter Based on TOT Decomposition
Using constant values for these parameters (e.g., a priori set) leads to an inherent compromise between the main performance criteria. In order to improve this aspect, the uncertainties can be evaluated within the algorithm, in a time-dependent manner, by using a certain measure that captures the variation in the system from one iteration to another. Both approaches will be analyzed in the next section, in the context of the experimental results. The computational complexity order of the conventional Kalman filter (KF) is \(\mathcal{O}_{\text{KF}}=L^{2}\), since it is proportional to the square of the filter (full) length. On the other hand, the proposed KF-TOT combines the solutions of three shorter Kalman filters, of lengths \(L_{2}^{2}\), \(PL_{12}L_{2}\), and \(PL_{11}L_{2}\), with \(L=L_{11}L_{12}L_{2}\). Thus, its complexity order is \(\mathcal{O}_{\text{KF-TOT}}=L_{2}^{2}+(PL_{12}L_{2})^{2}+(PL_{11}L_{2})^{2}\). These complexity orders are provided in Table 1, related to the example from Section 2.3, where \(L=512\), so that the TOT factorization uses \(L_{11}=L_{12}=16\) and \(L_{2}=2\). For the decomposition parameter of the KF-TOT, we consider \(P=1,2\), and \(3\) (as also supported in the next section). As we can notice in Table 1, the complexity order of the KF-TOT is much lower as compared to its conventional counterpart.