esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Model-in-the-Loop Testing
### Close Rendezvous and Capture  
The profile for the close-range rendezvous is depicted in Figure 19. It represents the high-fidelity simulation of the GNC performance in the execution of the approach profile previously depicted in Figure 10. A station keeping is started at FKP (top left of Figure 19), where an initial dispersion around this point is stabilized by the Control and Navigation. The servicer performs a forced motion to the IPP outside the V-b  
Figure 17: Client Phasing trajectories in LVLH.  
Figure 18: Far Rendezvous trajectories in LVLH. All the trajectories are contained within the AZ (1 km radius from the target) and never cross the KOZ (a 6 m radius from the target, not clearly visible in the plot).  
angle allows for a 6 DOF relative Navigation initialization. After this point, the conditions formulated in Section 3.3 are assessed and, if satisfied, the GO for capture is commanded. The servicer returns to the V-bar and approaches the target along this direction, entering the approach corridor.  
The results demonstrate that the Navigation and Control systems can provide robust solutions to meet mission requirements since, for all cases in the MC campaign, the servicer successfully controlled its position and attitude while navigating towards the client within the KOZ. These results confirm the servicer's capability to perform 6 DOF control and to use forced motion to safely approach the client, as intended by the design described in Section 4.2.  
The navigation assessment is complemented by test campaigns that made use of synthetic image generation with the scattering of relevant optical parameters, and an optical test bench at GRAALS with a target mockup and a camera model, demonstrating that the navigation coped with the effects of external sources such as Earth in Field of View (FoV) and with illumination conditions, as mentioned in Section 4.1, and that it effectively computed the relative estimates.  
The Abort corridor, presented in Section 3.4, is well defined and the trajectory is contained within this region with a considerable margin, as shown in Figure 20. In addition, the Navigation system operates correctly for all the phases, and in the presence of external sources and for different operating conditions, in accordance with the design described in Section 4.1.
## 4 Location Estimation-Based Congestion-Aware Routing Protocol (LECAR)--Detailed Proposal
### Defining Message Format and Interval
#### 4.2.5 Update Mechanism  
Upon reception of each hello message, the one- and two-hop neighbor tables are updated. The flowchart in Figure 9 summarizes the update mechanism. In addition, the global pheromone map and location information tables are updated each time the UAV reaches a waypoint or receives the global pheromone map and location information from a neighboring UAV. The UAV updates its global pheromone map and location tables using the entries with the latest \(T_{pheromone\_update}\) and \(T_{loc\_update}\), respectively.  
Figure 8: Global pheromone map and location information message format.  
Figure 6: Hello message format.  
Figure 7: Message format for one-hop neighbor details in the hello message.
## 4 Test Results
### Principal Components Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multicolumn{1}{c}{\multirow{2}{*}{**Variable**}} & \multicolumn{6}{c}{**Factors-Variables Correlation (Factor loadings)**} & \multicolumn{6}{c}{**Eigenvector (Based on Factor loadings)**} & \multirow{2}{*}{**Mean**} & \multicolumn{1}{c}{**Standard**} \\ \cline{3-11}  & & **Z1** & **Z2** & **Z3** & **Z4** & **Z1** & **Z2** & **Z3** & **Z4** & **Deviation** \\ \hline \multirow{11}{*}{Phase angle, \(\delta\), (\%)} & 10 Hz & & **0.43117** & -0.194805 & -0.384104 & -0.305796 & 0.149790 & -0.166651 & -0.247479 & -0.014028 & 5.94 & 1.502 \\  & 1 Hz & \(-\)7 °C & **0.903497** & -0.016120 & -0.282438 & 0.056170 & 1.93354 & -0.158429 & -0.082130 & 0.001265 & 6.74 & 1.429 \\  & 0.1 Hz & & **0.783171** & -0.189013 & -0.260476 & 0.203446 & 0.148202 & -0.152221 & -0.059817 & 0.111420 & 7.66 & 2.093 \\ \cline{1-1} \cline{2-11}  & 10 Hz & & **0.792772** & -0.220053 & -0.290767 & 0.284398 & 0.180983 & -0.168883 & -0.017755 & -0.039694 & 7.42 & 1.708 \\  & 1 Hz & 5 °C & **0.917516** & -0.247012 & -0.211193 & 0.023746 & 0.159792 & -0.244269 & -0.020888 & -0.0162434 & 8.75 & 1.786 \\  & 0.1 Hz & **0.924085** & -0.272070 & -0.186273 & 0.213562 & -0.287374 & -0.015123 & -0.00824 & 9.89 & 2.066 \\ \cline{1-1} \cline{2-11}  & 10 Hz & & **0.678478** & -0.458771 & -0.413935 & -0.017960 & 1.09687 & -0.292696 & 0.051975 & 0.071940 & 9.94 & 2.250 \\ \cline{1-1}  & 1 Hz & **0.875979** & -0.436316 & -0.123562 & 0.001620 & 0.105857 & -0.312653 & 0.038885 & 0.027579 & 10.33 & 2.361 \\  & 0.1 Hz & **0.671367** & -0.419046 & -0.089993 & -0.142656 & 0.106238 & -0.303822 & 0.068367 & 0.045908 & 10.05 & 2.521 \\ \cline{1-1} \cline{2-11}  & 10 Hz & & **0.819764** & -0.464915 & -0.026712 & -0.050765 & 0.161115 & -0.079935 & -0.197457 & 2.22125 & 5.13 & 1.323 \\ \cline{1-1}  & 1 Hz & 25 °C & **0.723780** & -0.672517 & -0.030673 & -0.020785 & 0.202564 & -0.089729 & -0.140378 & 0.018546 & 5.58 & 1.290 \\ \cline{1-1}  & 0.1 Hz & & **0.614327** & -0.766381 & -0.022571 & -0.010607 & 0.204014 & -0.098831 & -0.128314 & 0.000892 & 6.36 & 1.475 \\ \cline{1-1} \cline{2-11}  & 10 Hz & & **0.40629** & **-0.085795** & 0.0787194 & 0.02109 & -0.193199 & -0.166612 & -0.069795 & 0.034163 & 11.012.43 & 4352.296 \\ \cline{1-1}  & 1 Hz & 40 °C & 0.478259 & -0.860698 & 0.058054 & 0.053310 & -0.1986866 & -0.148324 & -0.078607 & 0.036105 & 9921.76 & 4004.010 \\ \cline{1-1}  & 0.1 Hz & & 0.481209 & **-0.834901** & 0.025330 & 0.058778 & -0.203667 & -0.129212 & -0.080790 & 0.031033 & 8793.24 & 3645.950 \\ \hline \hline \end{tabular}  
All variables highly correlated with given factor were bolded.  
\end{table} 
Table 3: Conf.
# Experimental Study on Dynamic Mechanical Properties of Sandstone Under Different Damage Degrees
## 6 Triaxial Dynamic Compression Experiment and Result Analysis  
After completing the above experiment, a triaxial dynamic compression experiment was conducted on the remaining three groups, totaling 36 rock specimens. Each group of specimens was labeled starting from \"01\". During the experiment, the confining pressure levels were set to 4 MPa, 8 MPa, 12 MPa, and 16 MPa, respectively. Subsequently, the triaxial dynamic compression experiment was performed on the rock specimens at different confining pressure levels using impact pressure values of 1.2 MPa, 1.4 MPa, and 1.6 MPa. After the experiment, the data were processed, and the results are shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen** & **Confining Pressure (MPa)** & **Impact Pressure (MPa)** & **Strain Rate (s\({}^{-1}\))** & **Compressive Strength (MPa)** & **Cumulative Specific Energy Absorption (MPa)** \\ \hline HS-01 & 4 & 1.6 & 391.8 & 214 & 8.25 \\ HS-02 & 8 & 1.6 & 389 & 233.4 & 7.93 \\ HS-03 & 12 & 1.6 & 335.6 & 246.9 & 7.71 \\ HS-04 & 16 & 1.6 & 330.5 & 269.4 & 7.54 \\ LS-01 & 4 & 1.6 & 384.6 & 215.9 & 8.1 \\ LS-02 & 8 & 1.6 & 370.2 & 235.6 & 7.81 \\ LS-03 & 12 & 1.6 & 357.8 & 253 & 7.64 \\ LS-04 & 16 & 1.6 & 310 & 275.9 & 7.54 \\ NS-01 & 4 & 1.6 & 434.4 & 234.1 & 8.07 \\ NS-02 & 8 & 1.6 & 323.5 & 250.8 & 7.64 \\ NS-03 & 12 & 1.6 & 308.4 & 264.6 & 7.57 \\ NS-04 & 16 & 1.6 & 274.1 & 279.3 & 7.15 \\ HS-05 & 4 & 1.4 & 361.2 & 210.7 & 7.76 \\ HS-06 & 8 & 1.4 & 345.9 & 226.2 & 7.23 \\ HS-07 & 12 & 1.4 & 327.7 & 242.8 & 6.84 \\ HS-08 & 16 & 1.4 & 286.6 & 261.8 & 6.66 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Parameters and results of the triaxial dynamic impact experiment on rock specimens.  
Figure 15: Failure modes of sandstone after the uniaxial dynamic compression experiment; (a) Failure modes of rock specimens drilled with high-impact load. (b) Failure modes of rock specimens drilled with non-impact load.
## 3 Results
### Stroke Size
#### 3.5.2 Simple vs. Complex Shape Conditions  
As the shape complexity increases we observed that stroke size decreases for habitual motor tasks, both for the PD (\(p<0.0001\)) and the H group (\(p<0.01\)). Conversely, stroke size does not change significantly when performing novel tasks, both for PD patients (\(p>0.9\)) and H subjects (\(p>0.4\)).
# A Review of Recent Literature on Audio-Based Pseudo-Haptics
## 3 Discussion
### Applications to Medical Training
In cardiovascular interventions, such as coronary artery bypass grafting and percutaneous coronary interventions, pseudo-haptic feedback can enhance trainees' ability to navigate complex vascular anatomy and perform intricate procedures with precision. By simulating the tactile sensations of catheter manipulation and vessel engagement, pseudo-haptics can improve trainees' spatial orientation and procedural dexterity, leading to safer and more effective interventions. Overall, the integration of pseudo-haptics in surgical training may represent a paradigm shift in medical education, offering trainees a novel and economic approach to acquiring surgical skills in a safe, standardized, and immersive environment. Using pseudo-haptics to simulate the tactile sensations and auditory feedback encountered during real surgical procedures, pseudo-haptics can accelerate the learning curve, improve procedural proficiency, and ultimately enhance patient outcomes across a wide range of surgical specialties.
# GNSS Accuracy Under White Gaussian Noise Jamming +
###### Abstract  
The jamming of Global Navigation Satellite Systems (GNSSs) is now a major threat for GNSS-based Position, Navigation, and Timing (PNT) users. A jammed receiver will lose its fix at a certain distance and will not be able to provide PNT information. At greater distances, there will be a fix, so this PNT information can be obtained; however, the information will be less accurate, as the carrier-to-noise (\(C/N_{0}\)) ratios of the received signals will be suppressed by the jammer. In this paper, the pseudo-range accuracy of a GNSS receiver under jamming conditions is investigated in order to provide more insight into the effects of a jammer on the accuracy of a GNSS receiver. The theory available in the literature will be reviewed, after which this theory will be evaluated by comparing the theoretical results with actual measurements using a high-end GNSS signal simulator and a software-defined GNSS receiver.  
GNSS; GPS; PNT; jamming; interference; RFI; accuracy; positioning; navigation +
Footnote †: This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 The Proposed Detector for Uplink GF-NOMA Networks: FROMPUS
Therefore, the idea of the initial support set is introduced as:  
\[\mathbf{\zeta}_{ini}=\mathbf{\zeta}_{corr}\;\cup\;\;\mathbf{\zeta}_{err}, \tag{6}\]  
where \(\mathbf{\zeta}_{corr}\) is the correct support set estimate and \(\mathbf{\zeta}_{err}\) is the erroneous estimated support set. Insofar as the support set has been determined, the related values of the signal can be easily estimated using the least square (LS)-based estimator. Hence, once the perfect initial support set \(\mathbf{\zeta}_{ini}\), as expressed in Equation (6), has been found, the performance of greedy algorithms at detecting users' transmitted signals in the networks can be improved. It should be noted that greedy algorithm approaches can be serial (one-by-one selection of support set) or parallel (simultaneous selection of multiple support set elements), and reversible (during which previously selected elements of the support set can be eliminated later if determined to be undependable) or irreversible (where elements added to the support set remain permanent) [37]. The proposed FROMPUS-based MUD falls into the group of serial pursuit greedy algorithms and uses reversible support set reconstruction mechanisms. By following a similar concept to that in [37], in the development of the proposed FROMPUS-based MUD, the \"serial-include\" technique of incorporating probable candidate elements into the support set is called \"Forward-Include\". The union of the outputs of the modified OMP and modified SP algorithms is employed in the \"Forward-Include\" node of the proposed FROMPUS-based MUD. Following this is the reliability testing procedure, in which the most significant \(ith\) element of the support set is selected from (\(i+1\)) elements. This process is referred to as \"Reverse-Select\". The final stage is the detection of the sparse active user signals with the aid of the least square algorithm, using the refined support set from the \"Reverse-Select\" node. Each of these nodes is detailed below.
## 2 Performance Assessment
### Signal-to-Noise Ratio Simulations
#### 2.2.5 Signal-to-Noise Ratio Simulations at 1.5 \(\upmu\)m  
InGaAs or Ge photodiodes are commonly used at 1.5 \(\upmu\)m. Though InGaAs photodiodes are more expensive, they have higher bandwidth and less noise than the Germanium type. In this section, an APD photodetector module and a PIN module are simulated, both based on InGaAs diodes.  
Standard values of intrinsic responsivity \(R_{io}=0.93\) A/W, gain \(M=10\) and excess noise factor \(F=5.5\) are assumed for the APD module. The same intrinsic responsivity is assumed for the PIN module, but in this case there is no gain (\(M=1\)) and so \(F=1\). It is assumed that these photodetector modules incorporate a TIA with the same noise and bandwidth characteristics as those considered in the simulations at 905 nm (\(B_{N}=200\) MHz, \(J_{s}=6.5\) pA/\(\sqrt{\mathrm{Hz}}\) ). Applying Equation (10), a _NEP\({}_{m}\)_ of  
Figure 3: Signal-to-noise ratio _vs._ system constant due to a spray drift cloud located at 500 m for variants 7 to 12 (Table 3).  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Variant** **Number**} & \multirow{2}{*}{**Photodetector**} & \multicolumn{2}{c}{**R\({}_{io}\)**} & \multirow{2}{*}{**M**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**NEP\({}_{m}\)** [\(\mathrm{pW}/\mathrm{Hz}\) ]**} & \multicolumn{2}{c}{\(K_{b}\)**} & \multirow{2}{*}{**Required \(K_{s}\)**} \\  & & & & & & & & **[m\({}^{2}\)-nm\(\cdot\)sr]** & **[W\(\cdot\)m\({}^{3}\)]** \\ \hline
**7** & Silicon APD & 0.34 & 100 & 4 & 0.19 & 0.5 & \(6.17\times 10^{-9}\) & 28.95 \\
**8** & Silicon APD & 0.34 & 100 & 4 & 0.19 & 0.25 & \(6.17\times 10^{-9}\) & 57.88 \\
**9** & Silicon APD & 0.34 & 100 & 4 & 0.19 & 0.1 & \(6.17\times 10^{-9}\) & 144.7 \\
**10** & Silicon PIN & 0.34 & 1 & 1 & 19.12 & 0.5 & \(6.17\times 10^{-9}\) & 1513 \\
**11** & Silicon PIN & 0.34 & 1 & 1 & 19.12 & 0.25 & \(6.17\times 10^{-9}\) & 3027 \\
**12** & Silicon PIN & 0.34 & 1 & 1 & 19.12 & 0.1 & \(6.17\times 10^{-9}\) & 7568 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Required system constant, \(K_{s}\), for various combinations of design parameters at 1064 nm.  
0.7 and 7 pW/-\(\sqrt{\text{Hz}}\) is obtained for the APD and PIN modules, respectively. Three optical transmissivity values are also considered: \(\xi_{0}\) = 0.5, 0.25 and 0.1.  
It can be seen in the simulations of Figure 4 that with an APD module an SNR of 5 is achieved for values of \(K_{s}\) (between 58.29 and 291.5 W\(\cdot\)m\({}^{3}\) in Table 4) one order of magnitude lower than when using a PIN module (\(K_{s}\) between 530.7 and 2654 W\(\cdot\)m\({}^{3}\) in Table 4).  
For a wavelength of 1.5 \(\upmu\)m and using InGaAs APD modules, system constants between 2 and 3.6 times higher are required when compared with the simulations at 905 nm. Assuming values of \(D_{r}\) = 50-100 mm, it is concluded that the required pulse energies range between 50 and 1000 \(\upmu\)J.
# Extensions and Enhancements to \"the Secure Remote Update Protocol\"
## 7 Potential Attack Mechanisms  
In the event that an attacker is able to gain physical access to the device, it can be assumed that, unless the device has been built to utilize encrypted storage and a trusted boot process [27], the attacker would gain access to any data stored on the device. In the context of a SRUP device, most significantly, this equates to the server's public key and the device's private key. Since the server's public key is by definition publicly available (on demand via the registration process, if nowhere else), we may largely disregard this as being of use to an attacker. Having access to the device's private key, however, means by the definition used in Section 4.2 that the attacker may take the identity of the device. As such, any messages that the system would permit the device to send could be spoofed and be sent by any other device that the attacker may devise (and assign the original device's identity to).  
Although a system based on SRUP makes no attempt to provide a mechanism to monitor or detect such identity theft, the centralized nature of the C2 server paradigm (with no peer-to-peer messaging) means that the potential damage that can be caused by that identity theft may be contained to the server. Other devices on the SRUP network cannot be directly affected. Given that by definition devices are always subordinate to the server, there is limited scope for damaging the system via spoofing device messages, beyond that which may be caused by directly interfering with the device's sensors. In the event that such an attack were detected, the device can be de-registered to revoke its ability to communicate on the system.  
Although such a device may be able to re-register and gain a new identity; in a human-moderated system, it would not be able to join the server without the assistance of a compromised (but trusted by the system) human (a situation that almost no security paradigm would be able to protect against). Even in an automated or unmoderated system, being able to join (or retain) membership of a control group is, as previously discussed, of very limited value to an attacker.  
The greatest threat to any centralized C2 system is the compromise of the centre; however, the asymmetric cryptographic approach adopted by SRUP means that compromise of a subordinate node has no impact on the security of the central server.  
The greatest threat to the system is if an attacker were to compromise a beacon node; as in this eventuality, the attacker would be able to spoof any observation check conducted using that beacon. Although protection against physical tampering is beyond the scope of SRUP, it should be noted that protecting an observer node from physical compromise is critical to the overall security of any system utilizing such devices.
# Hybrid Early Warning System for Rock-Fall Risks Reduction
## 4 Methodology
### Hybrid Risk Reduction Model  
The proposed model combines the output of detection and prediction models to issue an early warning. The model decreases the vulnerability of vehicles to risk by alerting vehicles before approaching the threat zone at the time of the rock-fall incident. The rock-fall risk reduction was assessed based on the possibility that vehicles will not reach the hazard zone after receiving the early warning signal at the time of the incident [50]. In this study, the risk reduction value was determined by a probability equation that combines the probability of vehicle response, the system reliability, and the average number of vehicles. The following Equation (5) indicates that:  
\[\mathbf{P}_{(Reduction)}=\mathbf{N}_{v}\times\mathbf{R}_{(h)}\times\mathbf{P}_{(response)} \tag{5}\]  
where \(\mathbf{P}_{(Reduction)}\) is the risk reduction probability, \(\mathbf{P}_{(response)}\) is the probability that a given vehicle does not reach the affected road segment after receiving the warning signal, \(\mathbf{N}_{v}\) is the average number of vehicles, and \(\mathbf{R}_{(h)}\) is the hybrid model reliability. The overall reliability of the two detection and prediction models connected in parallel is calculated according to Equation (6) [51]:  
\[\mathbf{R}_{(h)}=1-(1-\mathbf{R}_{(d)})\times(1-\mathbf{R}_{(p)}) \tag{6}\]  
\(\mathbf{R}_{(d)}\) is the reliability of the detection model, and \(\mathbf{R}_{(p)}\) is the reliability of the prediction model. The \(\mathbf{P}_{(response)}\) can be calculated according to Equation (7):  
\[\mathbf{P}_{(response)}=1-\left(\frac{Total\ Stopping\ Distance}{Safe\ Distance\ to\ Stop}\right) \tag{7}\]  
The total stopping distance was measured by combining reaction time, brake contact distance, and physical force distance. The safe distance to stop is the reaction interval that passes until the brakes are applied. It was evaluated by multiplying the vehicle velocity by the driver reaction time.  
The driver reaction time is the reaction interval that passes until the brakes are applied. It takes a random value between 0.4 and 2 s [52]. When the physical force distance travels a distance after the brakes are engaged, it can be determined by multiplying vehicle speed by brake reaction time.
# A Lightweight High Definition Mapping Method Based on Multi-Source Data Fusion Perception
## 4 Fusion Mapping on Cloud Server  
In this section, we will discuss the steps and improved algorithm of fusion mapping on-cloud. The following are the details of the method:  
1. First, all local semantic maps (LSMs) generated by the vehicle are transferred from the local coordinate system to the global coordinate system through global pose graph optimization, which ensures that all LSMs are aligned to the same global reference frame.
2. Next, semantic alignment or trajectory alignment is performed to improve the position accuracy of semantic elements in the global coordinate system by optimizing the poses of multiple trajectories. This step is crucial for determining the quality of the resulting map.
3. Finally, semantic elements are aggregated to instantiate aligned semantic elements and generate vector semantic maps.  
The structure of on-cloud fusion mapping is illustrated in Figure 5.