esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrating Autonomous Vehicles (AVs) into Urban Traffic: Simulating Driving and Signal Control
###### Abstract  
The integration of autonomous vehicles into urban traffic systems offers a significant opportunity to improve traffic efficiency and safety at signalized intersections. This study provides a comprehensive evaluation of how different autonomous vehicle driving behaviors--cautious, normal, aggressive, and platooning--affect key traffic metrics, including queue lengths, travel times, vehicle delays, emissions, and fuel consumption. A four-leg signalized intersection in Balgat, Ankara, was modeled and validated using field data, with twenty-one scenarios simulated to assess the effects of various autonomous vehicle behaviors at penetration rates from 25% to 100%, alongside human-driven vehicles. The results show that while cautious autonomous vehicles promote smoother traffic flow, they also result in longer delays and higher emissions due to conservative driving patterns, especially at higher penetration levels. In contrast, aggressive and platooning autonomous vehicles significantly improve traffic flow and reduce delays and emissions. Mixed-behavior scenarios reveal that different driving styles can coexist effectively, balancing safety and efficiency. These findings emphasize the need for optimized autonomous vehicle algorithms and signal control strategies to harness the potential benefits of autonomous vehicle integration in urban traffic systems fully, particularly in terms of improving traffic performance and sustainability.  
autonomous vehicles; signalized intersection; PTV VISSIM; microscopic simulation; traffic performance; penetration rate +
Footnote †: journal: Applied  
1
Footnote 1: [[https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci)]([https://www.mdpi.com/journal/applsci](https://www.mdpi.com/journal/applsci))  
2024
## 4 Simulation Results
### Sixteen Quadrature Amplitude Modulation (16 QAM)  
Similar to the last models, the coherent 16 QAM system is simulated for 4 \(\times\) 10\({}^{4}\) bits and SNR values from \(-\)6 to 10 dB. Figure 14 shows the 16 QAM constellation after being separated into sixteen classes using the AdaBoost algorithm of \(T=35\) and \(K=16\).  
Figure 12: Eight Quadrature Amplitude Modulation (8 QAM) modulation technique.  
Figure 13: BER vs. SNR for coherent 8 QAM modulation over the AWGN channel.  
Figure 15 shows the BER vs. SNR curve for the 16 QAM system, both simulated and theoretical. At low SNRs, the simulated curve results are slightly higher than the theoretical ones up to SNR values of 4 dB. After that, the simulated and analytical results seem to match very well, and the two curves seem to coincide with each other up to 16 dB.  
As a comparison with our proposed algorithm, BER curves are added for the performance of a noncoherent scheme using another machine learning (ML) algorithm. In [33], the research aims to handle the joint transmitter and noncoherent receiver optimization for multiuser single-input multiple-output (MU-SIMO) communications through unsupervised deep learning. It is revealed that MU-SIMO can be modeled as a deep neural network with three vital layers, which includes a partially-connected linear layer for joint multiuser waveform design at the transmitter side, and two  
Figure 14: 16 QAM modulation technique.  
Figure 15: BER vs. SNR for coherent 16 QAM modulation over the AWGN channel.  
nonlinear layers for the noncoherent signal recognition. The suggested method shows a remarkable MU-SIMO noncoherent communication performance in Rayleigh fading channels. The results are shown in the following figures. Figure 16 shows the block error rate (BLER) as a function of Eb/No for the single-input single-output (SISO) case. As for Figure 17, it displays the BLER as a function of Eb/No for the MU-SIMO case.  
Figure 16: Block error rate (BLER) as a function of Eb/No for the single-input single-output (SISO) case.  
Figure 17: BLER as a function of Eb/No for the multiuser single-input multiple-output (MU-SIMO) case.
# Technique Evolutions for Microorganism Detection in Complex Samples: A Review
## 2 Current Detection Methods
### Cellular Components Detection and Analytical Methods
#### 2.3.1 Immunological Methods  
Microorganisms can be detected or identified by their specific antigens using antibodies. The antibody-antigen reactions can result in agglutination, colorimetric or fluorimetric changes, allowing both qualitative and quantitative detection. A good example is Enzyme-linked immunosorbent assays (ELISA) [28].
In this paper, we extend our previous work [31] and propose an A-DBP algorithm with long-term memory (LTM) to simultaneously achieve low-complexity and high-robustness nonlinearity compensation, in which the learning rate \(\mu\) is adaptively adjusted based on all of the gradients of previous iterations. The performances of classical gradient-dependent algorithms with such properties, including root mean square propagation (RMSProp), AdaGrad, Adam, and AdaDelta, are numerically compared, showing that RMSProp has the best performance. Afterwards, a proof-of-concept numerical simulation and experiment are carried out based on RMSProp to demonstrate the feasibility of the proposed A-DBP-LTM method. In the simulated transmission of 69 Gbaud DP-16 QAM over 2000 km, our proposed method demonstrates strong robustness to hyperparameters by adapting \(\mu\), and it performs better with same the iterations compared to A-DBP based on a GDA and A-DBP based on a GDAM. Moreover, it outperforms the conventional DBP in the scenarios of biased \(\gamma\), with the same steps per span and reconfigurable systems. It should be noted that the reductions of 31.35%, 58.47%, and 69.62% of complex multiplications (CMs) under the same performance are achieved in A-DBP-LTM based on RMSProp compared to conventional DBP, A-DBP based on a GDA, and A-DBP based on a GDAM, respectively. In order to further verify the effectiveness of this scheme through experiments, we demonstrate a 26-\(\lambda\), 63 Gbaud, DP-16 QAM coherent transmission system over 1200 km. Compared with A-DBP based on a GDA and A-DBP based on a GDAM, high robustness in a few iterations in the scenarios of different launching powers, different channels, or different spans and a 0.33 dB improvement of the optical signal-noise ratio (OSNR) penalty are demonstrated.
## 2 Algorithm Description
### Traditional Single-Satellite Positioning Algorithm
#### 2.2.3 INS+LEO 1 Satellite + Altimeter Integrated Navigation Algorithm  
(1) Algorithm Principle  
According to the principle of the minimum geometric dilution precision (DOP) value, we can select two satellites with the best spatial distribution and visible at any time on the aircraft, and name one of them satellite 1; the other satellite is a virtual satellite named satellite 2. At any time, the aircraft has only the real range value of one satellite, but two satellite range values participate in INS integrated navigation filtering, one of which is the  
Figure 4: Flow chart of the INS+LEO 1 satellite compact integrated navigation algorithm without an altimeter.  
Figure 3: Schematic diagram of the INS+low earth orbit (LEO) 1 satellite compact integrated navigation algorithm without an altimeter.  
range value of satellite 1, and the other is measured by an altimeter fixed to the carrier. The altimeter can measure the altitude of the carrier, and the altitude plus the radius of the earth can obtain the corresponding satellite range value, in order to realize INS+LEO 1 satellite + altimeter integrated navigation and positioning. Figure 5 shows a schematic diagram of the INS+LEO 1 satellite + altimeter tightly integrated navigation algorithm.  
2. Algorithm Flow  
Suppose that the position of satellite 1 in the ECEF coordinate system is \((X_{1},Y_{1},Z_{1})\), the real position of the aircraft in the ECEF coordinate system is \((x_{\mathrm{u}},y_{\mathrm{u}},z_{\mathrm{u}})\), the altitude measured by the altimeter is \(h\), and the radius of the earth is \(R_{e}\). The real range value of satellite 1 is \(\rho_{1}\), and the true range value corresponding to the altimeter is \(\rho_{2}\). Therefore,  
\[\left\{\begin{array}{l}\rho_{1}=\sqrt{\left(X_{1}-x_{u}\right)^{2}+\left(Y_ {1}-y_{u}\right)^{2}+\left(Z_{1}-z_{u}\right)^{2}}\\ \rho_{2}=R_{e}+h\\ \rho_{11}=\sqrt{\left(X_{1}-x_{l}\right)^{2}+\left(Y_{1}-y_{l}\right)^{2}+ \left(Z_{1}-z_{l}\right)^{2}}\\ \rho_{12}=\sqrt{\left(0-x_{l}\right)^{2}+\left(0-y_{l}\right)^{2}+\left(0-z_{ l}\right)^{2}}\end{array}\right.. \tag{15}\]  
After obtaining the corresponding range and range rate measurements, the optimal state estimation can be obtained through UKF, and the state variables can be updated. Looping through the process, the integrated navigation algorithm of INS+LEO 1-satellite + altimeter can be realized.
# Roughness Evaluation of Burnished Topography with a Precise Definition of the S-L Surface
## 5 Conclusions  
According to the whole analysis presented, the following comments can be defined:  
1. Analysis of the surface topography and calculation of the ISO 25178 roughness parameters are dependent on the precision in the definition of the S-L surface. The whole process of S-L surface selection can be roughly divided into proposals of L-surface and S-surface. 2. All of the commonly used (available in commercial software) methods were found to be suitable for the definition of the S-L surface such as least-square fitted cylinder or polynomial plane of nth degrees, regular and robust Gaussian regression filters, regular isotropic spline filter, and fast Fourier transform filter. Nevertheless, the most encouraging issue is to apply them appropriately. Improvements in their application were found with support by autocorrelation function, power spectral density, and texture direction graph. 3. From the analysis of the cylindrical surfaces with additionally burnished dimples, it was found that the order of the surface definition (first the L-surface and then the S-surface) did not affect the precision in the surface roughness parameter calculation. However, for the definition of high-frequency measurement noise, the flat (after an areal form removal process) surface was more relevant for measurement error detection. Therefore, we first suggest selecting the L-surface, and then the S-surface. 4. When considering surfaces containing burnished features, like oil pockets, and dimples, the selection of an L-surface can be a demanding task that the errors in roughness parameters can be enlarged. Usually, this is caused by a distortion of the dimples. It was observed that deep and wide features can radically affect the position of the reference plane (line) for the areal (profile) data. 5. For surfaces containing deep and wide features such as dimples, it was found that digital filtering (e.g., various Gaussian (GRF and RGRF) or spline (SF) filters) can cause serious distortion of the values of the surface roughness parameters. The distortion of valleys was especially visible when considering the profile data, nevertheless, areal surface topography parameters (like \(Sp\), \(Sv\), \(Spk\), \(Svk\) and \(Sk\)) were also falsely estimated. 6. Detection and reduction of high-frequency measurement errors, when defining the S-surface, can be fraught with many errors related to the occurrence of dimples. We suggest selecting areas (or profile parts in 2D considerations) where oil pockets are not located. 7. Reduction in the high-frequency measurement noise was proposed with an application of various functions such as ACF, PSD, and TD. These techniques are essential in the characterisation of the noise surface, represented by the S-surface. This research received no external funding. Not applicable. Data Availability Statement: Data sharing is not applicable to this article. The author declares no conflict of interest. The following abbreviations and surface topography parameters are used in the manuscript:
# Reader-Tag Commands via Modulation Cutoff Intervals in RFID Systems +
## 5 Performance Evaluation
### Counting Protocols  
Similar to anti-collision protocols, the MCI process effect on the performance of counting protocols is evaluated for 40 sets of tag populations, starting from 100 tags up to 4000 tags (with an increment of 100 tags). In our evaluation, all the timing values are based on EPC-standardized specifications [16]. The MCI process significantly enhances the counting speed as it reduces all three types of slots. The different slot times based on the standard timing [16] and based on the MCI process are listed in Table 1.  
The MCI process is applied to the variance and modulation silencing-based estimations (VMSE) algorithm [20]. The performance of the MCI process is compared to the combined simple estimator (CSE) [21], the unified probabilistic estimator (UPE) [22], and the enhanced zero-based (EZB) estimator [22], FPNE estimator [23], and variance and modulation silencing-based estimations (VMSE) [20]. In Figure 13, the average counting times were plotted for counting with a 5% confidence interval (the average over 100 simulation runs) for tags that count from 1000 to 9500 tags.  
The MCI process facilitates significant time saving and, consequently, reader power saving. It is worth mentioning that the reported results for counting with a confidence interval of 5%; however, if more accurate estimation is needed, the number of slots will increase dramatically in CSE [21] and UPE [22]. Note that the MCI process achieves the exact tag count in much less time than other population estimators. Compared to the most effecint algorithm (VMSE with standardized timing), the MCI process achieves more than 55.6% when counting large tag populations. At low and high tag populations, the MCI process is faster than CSE by 15.5 and 12.3 times, respectively.
## 3 Results and Discussion
### Molecular Docking Investigation
Therefore, the attachment of the flavonoid glycoside to the TyrRS enzyme might affect the flexibility of both the HIGH and KFGKS motifs, interfering with their appropriate involvement in the transition states specific to TyrRS-catalyzed reactions. The second molecule target from _S. aureus_ was DNA gyrase, a topoisomerase that is not present in eukaryotes and is therefore an attractive target for antibacterial agents [52]. All complexes involving DNA gyrase as a receptor in the molecular docking test were very stable (\(\Delta\)G\({}_{\rm diss}\) of 13.4-13.6 kcal/mol), where the interfaces with the ligands were stabilized through hydrogen bonds, hydrophobic contacts, ionic bonds and \(\pi\)-stacking interactions (Table 6). All investigated ligands preferentially attached to enzyme clefts located between the dimers of the molecules, in direct contact with the DNA fragment (Figure 3d). In fact, CAT, QUE and RUT shared the same cleft, involving mainly amino acids from chain B and only two amino acids from chain A (Table 6). Among the residues of this ligand-binding site, Asp\({}^{30}\)(B), Ser\({}^{31}\)(B), Gly\({}^{52}\)(B), Lys\({}^{53}\)(B), Arg\({}^{323}\)(A) and Phe\({}^{324}\)(A) were in direct contact with the DNA molecule. On the other hand, the enzyme cleft that accommodates CLA molecules involves mainly amino acids from chain A, and more than half of the residues (Asp\({}^{30}\), Ser\({}^{31}\), Gly\({}^{52}\), His\({}^{282}\) and Gly\({}^{283}\) from chain A and Arg\({}^{323}\) and Phe\({}^{324}\) from chain B) directly interface with the DNA molecule (Figure 3d). Therefore, the possibility of the simultaneous attachment of CLA and any of the CAT, QUE or RUT ligands to DNA gyrase has to be considered, with important consequences for DNA gyrase's activity. The specific DNA topology changes catalyzed by DNA gyrase occur with ATP hydrolysis and are essential for _S. aureus_' survival [28; 52].
## 5 Experimental Study
### Justification for Image Recovery  
The images in the USC-SIPI image dataset are of four different categories: _Misc._, _Aerials_, _Sequences_, and _Textures_. We considered all 39 _Misc._ images, 38 _Aerials_ images, 69 _Sequences_ images, and 64 _Textures_ images of the USC-SIPI image dataset and 10,000 images from the BOWS-2 dataset for experiments. The image recovery statistics are given in Figures 5 and 6. An image is considered to be successfully recovered when there is no loss of information. It is observed from Figures 5 and 6 that most of the images were successfully recovered when the processed block size was set to \(8\times 8\) pixels. It should be noted that the images that were not restored properly with a block size of \(8\times 8\) pixels were completely recovered with a block size of \(16\times 16\) pixels. Some of the sample results acquired in the course of experiments are given in Figure 8.  
The success of the proposed \(MPSA\) scheme lies in the smoothness measure analysis. We believe that the adjacent pixels in a natural image are highly correlated. This property was exploited in the proposed scheme during the image restoration phase. Algorithm 3 analyzes the pixel differences in all possible directions, such as horizontal, vertical, and diagonal. Hence, a correctly recovered block is expected to give a low measure since the adjacent pixel intensities do not vary much. However, pixels in the highly textured area and with the presence of edges can certainly produce a high difference.  
We selected 20 random blocks during the image recovery process of the _airplane_ image to analyze the computed smoothness measure. The block size used for the analysis is of size \(4\times 4\) pixels. Note that during the image restoration and data extraction phase, we try to discover the correct block from four decrypted varieties of the image block. The correctly restored block is identified based on the smoothness measure. The block which will give the lowest smoothness measure is considered the correctly recovered block. Figure 9 shows the smoothness measure obtained from the four decrypted varieties of all 20 image blocks. In Figure 9, the smoothness measure of each correctly restored block is indicated by _Mc_, which is too small in comparison with the measure from corresponding incorrect blocks _Mw1_, _Mw2_, and _Mw3_.  
Figure 7: Histogram analysis on _airplane_ image.  
Figure 8: Sample results acquired in the course of experiments. Original images: (**A-I,B-I,C-I,D-I**); corresponding encrypted images: (**A-II,B-II,C-II,D-II**); corresponding marked images: (**A-III,B-III,C-III,D-III**); corresponding restored images: (**A-IV,B-IV,C-IV,D-IV**).  
Figure 9: The difference in smoothness measure between the correctly restored block and the other three corresponding incorrect blocks during the image restoration process.
In [19], a Monte Carlo simulation of laser-driven PIXE experiments using few MeV laser-accelerated protons is presented, showing the feasibility of measurements on materials of importance for the cultural heritage context. Of particular importance, in [20], the effect of the TNSA fast electrons on the PIXE signal have been analyzed and quantified. Simulations showed that the contribution of such fast electrons is not negligible, and therefore their removal from the beam path is mandatory. In [21], measurements are reported of the characteristics X-ray emission from samples in vacuum after single pulse irradiation with laser-accelerated particles generated using the very powerful TITAN laser of the Jupiter Laser facility at Lawrence Livermore National Laboratory. In [22], a detailed study is reported on the design of a magnetic beamline for laser-accelerated proton energy up to 20 MeV and the final spot of about 10 mm\({}^{2}\), comprised of focusing with magnetic quadrupoles and energy selection with a magnetic chicane. In [23], a detailed experimental investigation on the use of a laser-driven particle source for quantitative PIXE analysis and EDX spectroscopy in vacuum is reported. In general, a key and very appealing characteristic when using few MeV protons for practical applications is the possibility of having access to the particle beam in ambient atmosphere, i.e., with external beam [25]. This allows one to easily irradiate a sample in atmospheric conditions, which is necessary when the sample cannot be placed in vacuum (e.g., biological specimen and samples containing volatile components [26; 27; 28]), or when many samples have to be analyzed in a limited period of time avoiding sample exchange in vacuum (e.g., aerosol samples [29; 30]). Although the proton beam accelerated via TNSA propagates mainly towards the direction normal to the rear side of the laser-plasma target, it has a divergence of typically 10\({}^{\circ}\) to 15\({}^{\circ}\) half-angle [31; 32; 33; 34]. Therefore, for practical use of the laser-accelerated protons, a magnetic transport beamline has to be implemented in order to transfer the protons from the TNSA source to the application site [22; 35; 36; 31]. Moreover, the magnetic beamline (MBL) can remove the unwanted fast electrons created during TNSA. Here we report about the design, implementation, and characterization of a few MeV laser-plasma-accelerated proton beamline in air using a compact and cost-effective proton transport based on permanent quadrupole magnets. The MBL is coupled with a TNSA laser-plasma proton source based on a 14-TW laser, resulting in a collimated few MeV proton beam of about 10 mm in diameter propagating in air over a few cm distance.
# Smartphone-Based Experimental Analysis of Rainfall Effects on LTE Signal Indicators
## 3 Experimental Setup and Data Collection
### Experimental Setup  
**Measurement Locations and Setup.** Three different measurement locations were selected for data collection to investigate the impacts of deployment environments on mobile signal-based rainfall sensing. For each location, the signal data are captured by smartphones. The mobile terminal used in our experiments is the Nokia X20 [30]. To mitigate the effects of phone motion on signal measurements, the smartphone is kept stationary at a fixed location during the data collection process. The same device is used for the entire duration of the experiment to maintain consistency in the collected data.  
**Signal Parameter Collection.** Several mobile applications are available for measuring LTE power metrics. Among them, G-Mon is the most widely used application for capturing signal indicators [16; 23; 24]. G-MoN Pro [31] is used to achieve the extraction and saving of signal measurements such as RSRP, RSSI, RSRQ, SNR, and other LTE metrics from the serving BS since G-Mon is more suitable for the relatively old Android version. While signal parameters from neighboring cells can be viewed on the screen, they cannot be saved to files. The mobile application is configured to store each set of signal metrics along with a timestamp at fixed intervals as Figure 1a, ensuring that the signal data are closely aligned with the weather data.  
Figure 1: Experiment setup: (**a**) Signal data collection and transfer. (**b**) Weather data collection and transfer.  
**Ground Truth.** Ground truth weather data are obtained from publicly accessible weather stations shared on the Ecowitt platform [32]. Thanks to the widespread deployment of Ecowitt Internet of Things (IoT) weather stations, we are able to locate stations near our experimental sites. This allowed us to acquire rainfall data for each mobile terminal used in our study, with a time resolution as fine as 5 min, as provided by Ecowitt's weather data shown in Figure 1b.