esa-sceva/satcom-chunk-collection huggingface dataset:
# Social Internet of Vehicles for Smart Cities
## 6 SIoV Privacy Issues
### Privacy in Vehicular Networks  
The main privacy issue in vehicular networks is location privacy [114]. Vehicles in a vehicular network broadcast unencrypted messages that contain a vehicle identifier along with the vehicle's location, speed and heading. In many cases, these data can be linked to the driver's identity. Using these data, a system that is ultimately designed to offer safety and comfort applications to drivers can be abused by third parties, such as employers, insurance companies or criminal organizations to track individuals [115]. This tracking can reveal sensitive locations, such as home or work locations, along with the time and duration of each visit [116], effectively allowing one to infer the detailed behavioural profiles of drivers.  
It is important to note that the location privacy requirements of participants in vehicular networks can conflict with authentication requirements. Because messages in vehicular networks can contain information about safety-critical events, recipients of messages need to make sure that messages have been sent by a trustworthy source before acting on them [117]. If this is realizedby strong authentication of message senders, the vehicular network cannot provide privacy at the same time.  
Therefore, privacy-preserving solutions for vehicular networks often rely on pseudonyms. A pseudonym takes the place of a vehicle identifier, and it should not be possible to link pseudonyms to the real identifier. Because it can be desirable to allow authorities to find the real origin of a message, for example when misbehaviour has been detected, cryptographic schemes have been designed to provide privacy against ordinary participants, but allow authorities to revoke privacy [118]. To protect against tracking attacks, pseudonyms need to be exchanged frequently, and it is important to ensure that successive pseudonyms cannot be linked to each other [119]. Methods to achieve this unlinkability of pseudonyms include mix zones [120] and silent periods [121]. Recently, a new method that guarantees anonymity while at the same time spotting and revoking malicious users of a VANET is introduced [122].
## 1 Introduction
To validate the effectiveness of the proposed method, we use the \(P\&O\) algorithm with fixed step sizes [24], evaluating its performance on architectures with \(N=[2,3,4,5,6,7,8]\). Adopting a variable step was proposed to improve the fixed step \(P\&O\). It was tested in photovoltaic systems to improve the speed and accuracy of maximum power point tracking (MPPT) under irradiance variations [25; 26]. This variable step approach has been extended to two-arm Mach-Zehnder modulators by adjusting the bias voltage [27]. From this standpoint, in our work, we further revisit the adaptive step scheme in a multi-arm Mach-Zehnder interferometer (MZI), using the intensity error to adjust the step size dynamically, optimizing the system stability under conditions with multiple control parameters, and extending \(P\&O\) with adaptive stepping to complex optical systems. Some systems use the PID algorithm for phase stabilization in two-arm configurations [8]. However, this technique is not suitable for multi-arm architectures due to the non-linearities inherent to multi-dimensional systems. The proposed approach allows straightforward operation in these configurations without requiring additional optical control sources or redirecting the optical beam within the multi-arm structure [20; 21]. Furthermore, this approach extends phase control to scalable N-arm architectures, with robustness and effectiveness validated through simulations with up to eight arms. Finally, the improved \(P\&O\) algorithm, incorporating adaptive steps, achieves accurate and efficient compensation of multi-dimensional phase noise while maintaining a simple implementation and demonstrating advantages over previous techniques used in four-arm architectures [22]. This approach is rigorously validated via detailed numerical simulations, managing to measure critical parameters such as optical visibility of 0.99 and crosstalk of less than \(-27\) dB, showing a substantial reduction in steady-state error and a marked improvement in performance across high-dimensional configurations. These advancements enhance system scalability and phase control precision and open new opportunities for applications in telecommunications and quantum information, where stability is crucial in complex, high-interference environments.
## 2 Results and Discussion
### Antioxidant Activity
#### 2.3.4 Lipid Peroxidation Inhibition Ability  
Lipid peroxidation is a complex process that involves the formation and propagation of lipid radicals and lipid hydroperoxides in the presence of oxygen [2]. Each assay, such as DPPH, hydroxyl, superoxide anion, and ABTS cation radical scavenging assays measured an antioxidant property that represents a different mechanism, which could not reflect the multiple mechanisms, by which the sample acted as an antioxidant to inhibit lipid oxidation in a food system and living organisms [51]. Therefore, the activities of RSHP-A, RSHP-B, RSHP-C, and RSHP-D against the peroxidation of linoleic acid were investigated and compared to that of the positive control (GSH) and negative control (without antioxidant) in this section (Figure 9). The inhibiting ability of RSHP-B was higher than those of RSHP-A, RSHP-C, and RSHP-D. However, the inhibiting ability of four isolated APs (RSHP-A\(\sim\)D) was lower than that of the positive control of GSH. The result of lipid peroxidation inhibition assay suggested that RSHP-B might inhibit the lipid peroxidation propagation cycle through reacting with peroxyl radicals. In addition, the activity of RSHP-B on lipid peroxidation inhibition slightly less than GSH can be compensable by increasing the dose.  
Figure 8: Reducing power of the four isolated APs (RSHP-A\(\sim\)D) from protein hydrolysate of red stingray (_D. akajei_) cartilages. All data are presented as mean \(\pm\) SD (\(n=3\)).
###### Abstract
Moreover, the spectral efficiency is greatly improved because the preamble has already carried the TPS, so no extra dedicated resource is required for TPS, which is another contribution of this paper. Therefore, the main contributions of this paper are two-fold as follows:  
* An improved OFDM-based preamble structure that inherits the advantages of both [PERSON]'s and [PERSON]'s methods is proposed for robust PLC transmission. More importantly, a novel scrambling operation is applied in the training sequence of the designed preamble so that the influence of the NBI is eliminated. * In the frequency domain, to achieve diversity gain under the frequency-selective fading channels, two identical training sequences (TS) are distributed alternately in the active sub-carriers. In order to indicate several bits of signaling information for the receiver to acquire the basic transmission parameters quickly, the relative distance between the two TSs could vary. The remainder of this paper is outlined as follows. The OFDM signal model in PLC transmission is briefly introduced and the two most popular conventional preamble designs are reviewed in Section 2. The proposed preamble design and the corresponding preamble detection and synchronization algorithm are presented in Sections 3 and 4, respectively. In Section 5, the performance of the conventional and proposed preambles, as well as the synchronization methods, are tested through computer simulations, which is followed by the conclusions in Section 6.
## 2 Methodology
### Experimental Procedure  
The AMCs in the present study consisted of blended Al-4 Cu elements in addition to fine reinforcement particles of Al\({}_{2}\)O\({}_{3}\). Al powders (average particle size of \(\approx\) 25 \(\upmu\)m, 99.9% purity) were mixed with 4% wt. of Cu powders (average particles size of \(\approx\) 10 \(\upmu\)m, 99.9% purity) and Al\({}_{2}\)O\({}_{3}\) powders (average particles size of 10 \(\upmu\)m, 99.99% purity) in a glove box in a vacuum ambient. The Al\({}_{2}\)O\({}_{3}\) reinforcement was utilized with different concentrations (2.5, 5.0, 7.5, and 10 wt.%) to prepare the studied composites. Then, the powders were blended for a duration of three hours using a tubular blender at a rotational speed of 96 rpm. The blended powders were cold-compacted in a hardened steel (W302) die using a hydraulic press. The compacted Al-4 Cu/Al\({}_{2}\)O\({}_{3}\) composites were in the form of cylindrical discs, with a diameter of 20 mm and varying height-to-diameter (H/D) ratios of 0.75, 1.0, 1.25, 1.5, and 2.0. The compaction processes were performed for 60 min under varying pressures (500, 600, 700, 800, and 900 MPa). Subsequently, the sintering processes were achieved by heating the compacted discs for 2 h at 560 \({}^{\circ}\)C, followed by furnace cooling. Afterward, the aging process was carried out for 2 h at 160 \({}^{\circ}\)C, followed by air cooling. Density measurements were performed by a digital densitometer using the Archimedean principle. However, the theoretical densities were calculated using the mixture rule. Moreover, the discs' cross-section hardness values were measured using a Vickers hardness tester under a 10 N applied load and a dwell time of 15 s. Additionally, compression tests were conducted at room temperature on a 500 KN universal testing machine at a 1 \(\times\) 10\({}^{-3}\) S\({}^{-1}\) strain rate. Accordingly, the limitation of this study ranged between an alumina percentage of 0 and 10 wt.%, the compaction pressure ranged between 500 and 900 MPa, and the H/D ratio ranged between 0.75 and 2.
## 3 Linear Optical Methods for Pulse Characterization
### Evaluation of Interferograms  
One of the simplest methods of interferogram evaluation is based on cosine function fitting. This method requires the intensity distribution of the individual arms. Substituting these values into Equation (16), a normalized intensity  
\[I_{{}_{N}}(y,\omega)=\frac{I(y,\omega)-I_{{}_{S}}(y,\omega)-I_{{}_{R}}(y,\omega )}{2\sqrt{I_{{}_{S}}(y,\omega)\cdot I_{{}_{R}}(y,\omega)}} \tag{43}\]  
can be revealed. Then \(y\) coordinates should be limited to a range where noise is supposed to be negligible, in order to avoid misleading phase information. After normalization, Figure 11 shows the basic steps of the evaluation of interferograms. In step 1, a function of \(f(x)=\cos(a\cdot x+b)\) form is to be fitted with the least-square method on the \(i^{\text{th}}\) column, corresponding to the angular frequency \(\omega_{i}\). This procedure results in parameters \(a_{i}\) and \(b_{i}\). If the regression procedure is applied to only a short range (e.g., one and a half cycles, marked with red curve in the plot for step 1 in Figure 11.) at one time, the residual irregularities of the normalized interferogram have less influence on the extracted phase and the fitting information can be retrieved pixel-by-pixel. This fitting routine needs to be carried out on each column of the interferogram. In step 2, a phase surface can be constructed from the regression results.  
Figure 11: Steps of the phase extraction from the interferograms.  
From the aspect of the spectral phase, the values of \(b_{i}\) parameters play an important role. The cosine fitting causes an ambiguity, namely that beside \(b_{i}\), every \(bi+q\cdot 2\pi\) value can be the solution of the fitting, where \(q\) is an arbitrary integer. Therefore, when connecting these \(b_{i}\) values along the phase jumps of \(q\cdot 2\pi\), the resulting value will only differ from the spectral phase with the whole multiples of \(2\pi\). This constant difference, however, does not affect the values of the phase derivatives, so they can be obtained by fitting a proper (usually third-order) polynomial function.
## 1 Introduction  
Due to their efficiency and ease of handling, the use of implantable alloplastic biomaterials has become an integral part of facial reconstructive and aesthetic surgery. These materials are suited to be used both in pathological conditions (oncological, post-traumatic, or congenital) and cosmetics. Despite the use of autogenous tissues is considered the gold standard, several disadvantages such as long operative and aftercare times, donor site morbidity, and modeling limitations should be taken into account. The ideal facial implant should be capable of being easily placed and permanently maintain its form and position. The preferable implant material can often vary according to the anatomical site and the surgeon's preference [1]. The ideal implant material should be cost-effective, safe, non-antigenic,non-carcinogenic, and resistant to infection. Despite the fact that it should be inert, it should also be easily shaped. Implant materials that allow tissue ingrowth are difficult to shift; however, they are more challenging to remove once they have integrated into the underlying tissue. Two of the most popular implant materials used nowadays are High-Density Porous Polyethylene (Medpor) and Silicone. Silicone rubber is a non-porous material characterized by no fibrovascular ingrowth. The advantages of using silicone, as pointed out by [PERSON], include excellent biocompatibility, modifiability, conformability, and exchangeability [2].  
Although it is true that face implants stability is provided by their sub-periosteal placement and the rigid bone surface of the deep plane, one of the challenges of silicone facial implant surgery is the fixation of the implant itself. The ideal technique should be fast, easy to perform, with a reproducible result among patients and able to minimize possible adverse effects, such as malpositioning or dislocation of the implant itself. Implant migration seems to be correlated with lack of fibrous ingrowth, hematomas, sarcomas, and infections. While many studies found in the literature report the migration of such prosthetics, most of them focus their attention on the causes, not suggesting any countermeasures [3; 4; 5]. Different methods of fixation can influence such an occurrence [6]. Few techniques have been described in the literature in order to provide correct implant placement and avoid its movement until its integration. [PERSON] [7] in 1987 used a screw through the silicone implant to fix it on the underlying bone. [PERSON] [8] in 1994 suggested a 24-h percutaneous fixation using 18-gauge needles inserted in soft tissue and deeply into the implant. [PERSON] [9] commented on the work of [PERSON], describing a new technique with the use of percutaneous fixation through nylon sutures removed after 3 to 7 days. There has not been any update of the matter of subject since.  
In this paper, we present an easy way to place the implant into the harvested pocket and at the same time, fix it percutaneousally.
# Radar Voxel Fusion for 3D Object Detection
## 2 Related Work
### 3D Lidar Object Detection  
The seminal work of [PERSON] et al. [8] introduces a method to directly process sparse, irregular point cloud data with neural networks for semantic segmentation tasks. Their continued work [9] uses a similar backbone to perform 3D object detection from point cloud frustums. Their so-called pointnet backbone has been adapted in numerous works to advance lidar object detection.  
VoxelNet [10] processes lidar points in a voxel grid structure. The network aggregates a feature for each voxel from the associated points. These voxel grid cells are processed in a convolutional fashion to generate object detection results with an anchor-based region proposal network (RPN) [11].  
The network that achieves the highest object detection score [3] on the KITTI 3D benchmark [2] uses both a voxel-based and pointnet-based processing to create their detection results. The processing of the voxel data is performed with submanifold sparse convolutions as introduced in [12; 13]. The advantage of these sparse implementation of convolutions lies in the fact that they do not process empty parts of the grid that contain no information. This is especially advantageous for point cloud processing, as most of the 3D space does not contain any sensor returns. The network that achieves the highest object detection score on the nuScenes data set [1] is a lidar-only approach as well [14]. Similarly, it uses a sparse VoxelNet backbone with a second stage for bounding box refinement.
# Mapping Surface Flow Velocity of Glaciers at Regional Scale Using a Multiple Sensors Approach
## 3 Results
### Uncertainties Analysis
#### 3.3.5 Comparison with Existing Regional Ice Velocity Datasets  
In Figure 8, we compare our Sentinel-2 2017-2018 time-averaged speed map with a map generated from Landsat GoLIVE data available over the Mont Blanc massif [15; 37]. We oversample each GoLIVE ice velocity at a resolution of 50 m and follow the same post-processing methodology described in the Section 2.4 (Figure 8). We also compare the results with a previous Landsat 8 map published by [38] that uses a processing chain similar to ours in terms of the parameters used for correlation [24]. Our map like the one in [38] seems to better capture the main glaciers of the Mont Blanc massif such as the Mer de Glace or Leschaux glaciers, with shear margin transitions that are better defined than GoLIVE. It also appears that many small glaciers are not visible in the GoLIVE map, as for example on the Italian side of the massif with the Rochefort and Planpincieux glaciers.
## 3 LB-MPC in Path Tracking of Mobile Platforms
### Learning and Optimizing Prediction Model
#### 3.1.2 Data-Driven Prediction Models
The learning is used to generate low-uncertainty and non-parametric models on site. According to these models, the linear velocity and angular velocity are predicted in real time. The control framework and experimental terrain of RC-LB-NMPC are presented in Figure 4. The neural networks and model-free reinforcement learning have been used to generate walking gait for motion tasks. The neural networks are used to learn complex state transition dynamics [93]. The information learned regarding the terrain height is helpful for the robot MPC controller to track paths in uneven terrains. \begin{table}
\begin{tabular}{l l} \hline \hline \multicolumn{1}{c}{**Gaussian Model [74]**} & \multicolumn{1}{c}{**Stochastic Model [78]**} \\ \hline Gaussian processes & \multicolumn{1}{c}{A discrete-time, uncertain system} \\ \(r(z)\sim\mathcal{GP}(m(z),k(z,z^{\prime}))\) & \(x_{k+1}=f(x_{k},u_{k},v_{k},\theta)\) \\ \(m(z)\): \(R^{n_{z}}\to R\) Mean function & \(y_{k}=h(x_{k},v_{k})\) \\ \(k(z,z^{\prime})\): \(R^{n_{z}}\times R^{n_{z}}\to R_{0}^{+}\) Symmetric, positive semi definite covariance & \(k\in\mathbb{N}_{0}\) Time index \\ function & \(x_{k}\in R^{n_{z}}\) —System states \\ \(z\), \(z^{\prime}\in R^{n_{z}}\) Independent variables or & \(u_{k}\in R^{n_{z}}\) —System inputs \\ inputs to the GP & \(y_{k}\) System outputs \\ \(r(z)\)— At specific locations & \(\theta\in R^{n_{\theta}}\) System parameters \\ \(z\)— Normal distribution & \(w_{k}\in R^{n_{w}}\) Stochastic process noise \\ \(R\)— The set of real numbers & \(v_{k}\in R^{n_{z}}\) Measurement noise \\ \(R^{n_{z}}\)—The set of n-dimensional real column vectors & \(f(x_{k},u_{k},w_{k},\theta)\) System state equations \\ \(h(x_{k},v_{k})\) Output equations \\ \hline \multicolumn{1}{c}{**Robust Optimization Model [76; 77]**} & \multicolumn{1}{c}{**Immunity Model [79]**} \\ \hline \(\widetilde{u_{f}}(t_{k})=\textit{arg min}_{u_{f}([t_{k}])}f\Big{(}\widetilde{p }_{e}(t_{k}),\,\widetilde{u_{e}}(t_{k})\Big{)}\) & \\ \(u_{f}(t)=M^{-1}\Big{(}\theta_{f}(t)\Big{)}\Big{[}M\Big{(}\widetilde{\theta}_{f} ^{*}(t\mid t_{k})\Big{)}\widetilde{u}_{f}^{*}(t\mid t_{k})+Kp_{fe}(t)\Big{]},t \in[t_{k},t_{k+1})\) & \\ \(\widetilde{u}_{f}^{*}(t_{k})\) Optimal predictive control & \\ sequence & \(u_{f}(t)\) Actual control signal \\ \(l_{f}\Big{(}\widetilde{p}_{e}(t_{k}),\,\widetilde{u_{e}}(t_{k})\Big{)}\)—The cost functions in model predictive control & \\ \(\widetilde{p}_{e}(t_{k})\)—The tracking error of the system & \\ \(\widetilde{u}_{e}(t_{k})\)—The control of input error & \\ \(\theta_{f}(t)\)—The angle of system state & \\ \(p_{fe}(t)\) State of the actual system & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The governing equations for different models.
## 3 Fabrication, Measurements and Discussions
### Measurement and Results  
3.2.1 Insertion Loss and Isolation  
When the RF MEMS switch is in the up-state (namely ON state), the insertion loss displays the signal loss, and when the RF MEMS switch is in the down-state, the isolation shows the signal isolation level (namely OFF state). The S\({}_{21}\) value between the input and output can be used to determine isolation and insertion loss.  
The measurements were conducted in an ultraclean room. The temperature and relative humidity of the measurement environment were 24 \({}^{\circ}\)C and 40%, respectively. An Agilent E3631A provided the DC voltages. The S (S\({}_{11}\) and S\({}_{21}\)) characteristics of the RF  
Figure 13: The top view photograph of the proposed RF MEMS switch.  
MEMS switch were measured using a vector Network Analyzer (R&S ZVA50, Rohde & Schwarz, Munich, Germany). To contact the two ends of the switch, two gold ACP-A-GSG-150 probes (Cascade Microtech, Beaverton, OR, USA) were utilized, and the device was put on a probe table (Cascade Summit 11000B-M, Cascade Microtech, Beaverton, OR, USA). The sweep frequency ranged between 10 MHz and 40 GHz. Figure 14 shows the RF measurement setup of the switch.  
The measured S parameters are shown in Figure 15.  
The measured results show that the insertion loss is better than 0.5 dB at 32 GHz, and the isolation is more than 35 dB at the resonant frequency in the range of 25-35 GHz. Compared with simulated results in Figure 12a,c, the measured results match well with simulated results.  
Figure 14: The RF measurement setup of the switch.
## 4 Verification of PSHRisk-Tool Results
### Analytical Solution
Figure 8: Seismic hazard curve for verification, (**a**) PSHRisk-tool view, (**b**) Comparison. Figure 7: Verification for the probability distribution of magnitude with truncated Gutenberg-Richter relationship, (**a**) PSHRisk-tool view, (**b**) Comparison. As we observe in Figure 9a, the blue bars and green bars for a specific magnitude and distance expressed here almost the same result. Besides this, Figure 9b shows the clear visualization of the coefficient of variation (CoV) between the deaggregation results of the hand-calculation and the PSHRisk-tool. In Figure 9b, the dotted lines show the hand-calculation and solid lines focus on the proposed tool result. After analyzing the comparison, a small CoV has been detected, especially in the pick point of each deaggregation curve, which has a maximum value of 0.033. If the user has the structural seismic response like mean and standard deviation of the fragility parameter, this tool can assess the failure risk of that structure. To verify this part, the fragility parameter has been taken in this study for a building structure from [PERSON] et al. [3] and these are 0.96 (median collapse intensity) and 0.39 (lognormal standard deviation). By using Equation (18), the failure risk assessment has been calculated here by combining with the lambda (\(\lambda\)) value for the area source (Figure 5). The result is shown in Figure 10 indicates the same result with hand calculation, which is acceptable for verification. Figure 9: Verification of hazard deaggregation for PGA \(>\) 0.16 g: (a) 3D plot, (b) Plot for focusing CoV; D = Distance (km).