esa-sceva/satcom-chunk-collection huggingface dataset:
# Optimizing Non-Glare Zone Width of Adaptive Driving Beam (ADB) Using Fuzzy Logic Control
###### Abstract  
Adaptive driving beam (ADB) is an advanced vehicle forward-lighting system that automatically adapts its beam patterns to create a non-glare zone around vehicles, providing good long-range visibility for the driver without causing an uncomfortable glare for other road users. The performance of the ADB system is affected by the non-glare zone width. A narrow non-glare zone could create indirect glare in the side rearview mirrors of preceding vehicles during sharp turns while widening it results in poor road illumination. This research studies the trade-off relationship between glare and road illumination when altering the width of the non-glare zone in different driving scenarios. The study is conducted by using virtual driving simulation tools to simulate an ADB vehicle on four S-curve roads with minimum curvatures varying from 25 m to 100 m. Lux data are collected and processed using a fuzzy logic controller to mimic a human test driver to find the best non-glare zone width for balancing the trade-off. The research developed a design methodology allowing for a better understanding of the effect adjusting the width of the ADB non-glare zone has on ADB performance and improved ADB non-glare zone width optimum control system design.  
vehicle forward lighting; adaptive driving beam; non-glare zone; LucidDrive; fuzzy logic control; virtual night drive +
Footnote †: journal: Applied Sciences  
18 August 2021  
13 September 2021  
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**Copyright:** 2021 by the authors. License MDP, Basel, Switzerland. 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/))).
## 4 Evaluation
### Accuracy Evaluation Using Truth Table and Predicted Distances  
In this study, we evaluate the performance of object distance estimation methods by comparing their predicted values with the truth table. The tested methods used the bounding box and the segmentation mask to estimate the object distance. To assess the accuracy of each method, we perform a binary comparison between predicted distances and  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Image Feature** & **Person** & **Bicycle** & **Car** & **Bus** & **Truck** \\ \hline Easy & 2963 & 62 & 12410 & 245 & 768 \\ Medium & 785 & 45 & 7387 & 114 & 600 \\ Hard & 166 & 6 & 4164 & 14 & 271 \\ Unknown & 210 & 15 & 859 & 65 & 285 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of objects classified as Easy, Moderate, Hard, and Unknown in the Truth Table.  
Figure 19: LiDAR point clustering results. **(a)** Shows LiDAR points before noise removal, containing both object-related points and irrelevant data. **(b)** Displays the filtered LiDAR points after clustering, where noise has been removed, leaving only relevant object points.  
the true values from the true table. True Positive (_TP_ ) occurs when the predicted distance matches the true value, while False Positive (_FP_) occurs when the predicted distance does not match the true value. Accuracy is calculated as the ratio of the number of correct predictions (_TP_) to the total number of predictions (_TP_ + _FP_).  
\[\text{Accuracy}=\frac{TP}{TP+FP} \tag{20}\]  
This accuracy metric, shown in Equation (20), is computed independently for both the bounding box and segmentation mask approaches, allowing for consistent evaluation of their performance in estimating object distances.
## 4 ABER for SIMO TOW Systems with Receivers' Diversity
### SIMO TOW System with IM/DD OOK  
The optimum decision metric for a SIMO TOW link with IM/DD OOK is obtained as [47; 50]  
\[P\Big{(}\overrightarrow{y}|on,h_{m}\Big{)} \begin{array}{l}\stackrel{{ \infty}}{{>}}\\ \stackrel{{<}}{{off}}\end{array} P\Big{(}\overrightarrow{y}|off,h_{m }\Big{)}, \tag{22}\]  
where \(\overrightarrow{y}=[y_{1},y_{2},\ldots,y_{M}]\) is the vector signal that describes the corresponding emissions \(m=1,2,\ldots M\), in different receivers or in different time slots the \(M\) copies of the signal. Recall that considering OOK the condition 'on' refers to presence of light at the receiver's side, i.e., to the transmission of bit '1', while the condition 'off' corresponds to light's absence, i.e., to the transmission of bit '0', respectively. Additionally, one of the most significant schemes for the signal reception is the OC [47; 50; 52]. Considering OC diversity with \(M\) branches at the receiver, aperture area of each detector is \(M\) times smaller than the area of detector when there is no diversity implementation. Thus, as a consequence of the background radiation noise, the variance of the noise in each detector at receiver is \(M\) times smaller than the noise variance of the system without diversity implementation, i.e., \(\sigma_{m,n}^{2}=\sigma_{n}^{2}/M=N_{0}/(2M)\). Here, the ABER is given as [50]  
\[P_{e,\alpha\
u,M}^{OOK}=\int_{\rm h}f_{\rm h}(\rm h)\,\,\,Q\Bigg{[}\frac{1}{ \sqrt{2 MN_{0}}}\sqrt{\sum\limits_{m=1}^{M}\left(\eta_{m}h_{m}\right)^{2}} \Bigg{]}d\rm h\,\,\,, \tag{23}\]  
where \(\rm h=[h_{1},h_{2},\ldots,h_{M}]\) is the vector of the TOW channel state for each one the \(M\) copies of the signal. In order to estimate the multiple integral in (23) we initially substitute the approximation of [65] for the Q-function, \(Q(x)\approx\frac{e^{-x^{2}/2}+3e^{-x^{2}/3}}{12}\), into (23). Note that it practically holds that \(0.5\leq\eta_{m}\leq 0.9\). Additionally, the parameter \(h_{m}\) obtains non-negative values, i.e., \(0\leq h_{m}\leq 1\), with \(h=h_{l}\,\,h_{p}\). Specifically considering that for the optical medical window 700 nm \(\leq\lambda_{m}\leq 1300\) nm and that 2 mm \(\leq\delta_{m}\leq 8\) mm for typical TOW links, by using (3), (4), and Table 1 we get that \(0.0003\leq h_{l,m}\leq 0.23\), while also from (9), \(0\leq h_{p,m}\leq\delta_{m}\,A_{0,m}\), i.e., \(0\leq h_{p,m}\leq 1\). Moreover, it practically holds that \(1\leq M\leq 5\) and that \(N_{0}=\left(1.3\,pA/\sqrt{Hz}\right)^{2}\). Under these parameter values, this approximation for the Q-function is valid.
# Regimes in the Response of Photomechanical Materials
## 4 Experimental Results
### Photostress  
We carried out photostress measurements by holding the 5 mm top and bottom ends of the rectangular strips of sample materials fixed and measured the force exerted by the sample on illumination. The nematic director was parallel to the long vertical edge of the samples. The photostress produced by the samples was measured both in air and in water, the latter in order to maintain (nearly) constant temperature. Photomechanical forces were measured by an Entran force sensor attached to the top edge of the samples. Data were collected using NI LabVIEW. A schematic of the setup is shown in Figure 4.  
The light source was a high-powered LED at 365 nm calibrated with a Scientech H410 power meter. The top and bottom edges of the samples were clamped in thin plexiglass sample holders, which were attached to the optical table below (details not shown) and to the Entran force sensor on top via a 100 \(\upmu\)m outside diameter nearly inextensible filament (Fireline jewelry thread). To probe details of the photoresponse, instead of using continuous illumination, samples were photoexcited by 200 ms pulses of UV light, followed by 1 s of darkness, using a shutter. The photostress in air is shown in Figure 5.  
Although the data were collected at different times and on different time scales, the behavior of samples K and E was found to be essentially similar throughout. To avoid unnecessary duplication, we focus on the response of E-type samples below. Samples were suspended underwater in a thin-walled glass container and photostress measurements were taken with pulsed illumination as before. Stress underwater versus time for two different illumination intensities is shown in Figure 6.  
As shown in Figure 6, the response of samples illuminated at different intensities saturates at very nearly the same stress, while the intensities differ by a factor of 2.5. The time to reach the photostationary state, where essentially all the dye molecules are in the _cis_-state, is, in dimensional units from theory, is \(t=\tau/(I/I_{0})=2.5\) s if \(I=40\) mW/cm\({}^{2}\), and \(t=1\) s for \(I=100\) mW/cm\({}^{2}\). The experiment from Figure 6a gives 7 s, and from Figure 6b gives 3 s, which are in good agreement with the theory.  
Figure 4: Schematic of experimental setup for photostress measurement.  
Figure 5: Photostress in air for (**a**) sample K and (**b**) sample E.  
Figure 6: Photostress in water at different intensities at (**a**) 40 mW/cm\({}^{2}\) and (**b**) 100 mW/cm\({}^{2}\).  
Expanding the time scale and showing the leading edge of the light pulses, Figure 7 shows a rapid increase in the photostress with light, then a slow decay to a steady value. The details of the relaxation are not clear; we anticipate a possible connection with the results of [26].
# Voice Activity Detection Using Fuzzy Entropy and Support Vector Machine
## 2 Proposed Methodology
### Framing  
Since the nature of the speech signal is nonstationary, the obtained noise-reduced speech is divided into a sequence of small frames of equivalent size of 20-40 ms long. The frame is to be categorized as speech or noise in most cases, therefore the VAD problem can be treated as a binary classification problem. In this paper, the noise-reduced speech is divided into 32 ms long frames with a frame shift of 10 ms with a sampling rate of 16 kHz and windowed using Hanning window. Therefore, the resultant frame consists of 512 samples and the number of frames vary depending on the length of the speech signal. These values were obtained experimentally.
# Enhanced Multicast Repair Fast Reroute Mechanism for Smart Sensors IoT and Network Infrastructure
## 4 M-REP FRR Mechanism
### Description of the Original M-REP Mechanism  
To describe the M-REP mechanism, the role of routers in IPFRR is modified as follows:  
* S router (source router) is a router that has detected a connection failure with its primary next-hop for a specific destination host. Router S begins to encapsulate the original unicast protected flow (or the protected flow, see Table 2) into packets of a specific multicast (S, G) flow. Here, the S address is the original address of the host, that sends packets. G is a specific, pre-configured multicast group address, that is used by the M-REP IPFRR to encapsulate packets of the protected flow. Router S becomes the root of the tree created by the M-REP mechanism. * D router is a router that performs M-REP IPFRR multicast flow recovery back to the original unicast packets of the protected flow. Router D will further route and forward packets to the destination host as unicast. The destination host, i.e., the target for the original protected flow, must be directly connected or reachable through the D router. * R router is a router with implemented IPFRR M-REP mechanism. The M-REP mechanism is designed to protect only specific important customer data flows, delivered over an Internet Service Provider (ISP) network from the source S to the destination host D (Figure 2a, mark 1, red path). A router that detects a connection failure (link or node) becomes the source router S (Figure 2a, mark 2). At the same time as the failure was detected, routers start the process of network convergence. In this moment, the source router S begins to encapsulate the original unicast packets of the protected flow into a specific M-REP multicast flow identified by (S, G) pair. These multicast packets are sent directly out (utilizing the flooding process of Protocol Independent Multicast-Dense Mode protocol) on all active PIM-DM enabled interfaces of router S (Figure 2b, mark 3, dashed green arrows). This starts the process of creating a multicast distribution tree by the PIM-DM. The result of this flooding represents an alternative route around the detected failure (Figure 2b, mark 4, bold green arrows). Router S continues to perform this process of encapsulation and flooding of the protected unicast flow until the process of network convergence is completed. However, before the network completes the convergence and the new shortest paths are calculated, R routers may receive a multicast packets of M-REP flow even through interfaces do not match their current selection of the correct Reverse Path Forwarding (RPF) interfaces. This statement is conditioned by the fact that the current routing tables have not yet been updated with the new information resulting from a link failure.
## 4 Experimental Results and Discussion
### Electrically Tunable Frequency Difference  
In order to achieve more precise tuning of the beat frequency, a high direct-voltage was applied to the electrode on the LTO crystal. The transverse electric field changed the refractive indexes of b and c axes of LTO, and realized the tuning of the frequency difference. The relationship between the voltage and the frequency difference depends on Equation (5) in Section 3. With no voltage applied, a frequency difference of 0.7 GHz was obtained at 20.8 \({}^{\circ}\)C, as shown in Figure 3b. When the voltage was applied, we could obtain the beat frequency versus the voltage, as shown in Figure 4. The results were detected by the photodiode and measured by a RF spectrum analyzer. Due to the influence of controlling temperature accuracy, the frequency difference had the same fluctuation value as that in Figure 3. Here, we selected the median value of the float as the data point.  
The measurement results showed that the curve of frequency difference vs. voltage changes linearly with a tuning slope of 1.23 MHz/V. Considering that the accuracy of the applied voltage was 1.0 V, it was more sensitive compared with controlling the temperature. On the other hand, the applied voltage not only tunes the frequency difference more accurately and quickly than the temperature, but also facilitates the stabilization of the frequency difference with a phase-locked loop [21]. Compared with the electro-optical coefficient \(\gamma_{13}=8.6\) pm/V, \(\gamma_{33}=30.8\) pm/V of LiNbO\({}_{3}\), the difference between the electro-optic coefficients \(\gamma_{13}=8\) pm/V and \(\gamma_{33}=33\) pm/V of LiTaO\({}_{3}\) is larger. It can be seen from Equation (5) that this is more favorable for obtaining a wide range of tuning of the frequency difference. Theoretically, a similar maximum frequency difference to the temperature tuning can be achieved by increasing the applied voltage. However, due to the low sensitivity, if we want to achieve a frequency difference of up to about 20 GHz, an extremely high voltage of 16 kV is required. Compared with the thermal tuning method, its cost is high and the processing difficulty of insulating the laser cavity is increased. So, large-scale thermal tuning of the frequency difference is needed.
## 4 Simultaneous Utilization of Multiple RANs within a 3 GPP Framework
### Simultaneous Utilization of 3 GPP-Based and Wi-Fi Access Networks over 5 GC or EPC
#### 4.1.1 ATSSS Phase 1
In addition, for the assistance of access network selection, the Performance Measurement Function (PMF) is introduced to support two types of measurements, i.e., Round Trip Time (RTT) measurement per access network from the UE and UPF in the case of the smallest delay steering mode, and access network availability measurement from the UE to report it to the UPF. Moreover, the 5G Quality of Service (QoS) model designed for a single-access PDU session is extended to the MA PDU session. This ensures that the same QoS Flow Identifier (QFI) is employed by the SMF for both 3 GPP and non-3 GPP access networks, thereby facilitating consistent QoS support across both access networks.
## 3 Results and Discussion
### Principal Component Analysis and Hierarchical Cluster Analysis
Regarding the second component, it grouped populations DER071 and DER087 in a separated cluster on the top of the graphic (Figure 2b). Both populations shared a high accumulation of sinigrin and low in esters (Table 2, Table 5). The combined results of the PCA and Hierarchical Cluster Analysis provided information of interest for future breeding programs. The accumulation of GSL-derived compounds is determinant for the bitterness and pungency of the _Brassicaceae_, due to the fact that many of these compounds provide characteristic flavors like hot, pungent, mustard-like or cabbage-like to the plant materials in which they are found [13]. The perception of such pungency can be modified due to the increase/decrease of the levels in which the individual ITCs are found. However, there are other factors that would affect this perception as well. Thus, the presence and relative abundance of other families of volatiles like the green leaf volatiles can reduce the pungent feeling, as the results of [PERSON] et al. [57] suggest. In this sense, the present work identified the population DER045, which combined reduced levels of ITCs and high levels of esters, as a possible candidate in breeding programs aimed at obtaining a variety of mild pungency and a more notable green flavor. Furthermore, population DER068 could be also tested in these terms, as its profile suggests that it could be also mild pungent according to the levels of esters but accumulate at the same time higher levels of ITCs with the potential health benefits associated. Thus, both accessions could be initially selected for further studies including sniffing tests, sensory perception analyses and consumers acceptance. By contrast, several populations that clustered together could be selected for obtaining commercial varieties with enhanced pungent flavor. Since their volatile profiles did not differ greatly, here we suggest that other traits such as the morphology and growth traits could be also considered for the breeding programs [9].
## 2 Experimental Study
### Flexural Behavior  
Flexural tests were conducted to observe the flexural and cracking behaviors of HPFRCC including load-deflection and load-CMOD (Crack Mouth Opening Displacement) relationships. The beam specimens of 100 mm \(\times\) 100 mm \(\times\) 400 mm (width by height by length) were made. After casting, they were stored at room temperature for 1 day prior to demolding, then cured in water until tested at the age of 28 days.  
Figure 4 shows the setup of the flexural test on HPFRCC beam in a four-point bending with a pure span of 300 mm, which is the length between the supports. Relative displacements between the supports and the center point were measured at the center of the beam through the instrument attached on the beam, as shown in Figure 4. The tests were conducted in a hydraulic servo-controlled testing machine with a closed-loop system. The crack-width was measured using a crack mouth opening displacement (CMOD) gauge. When a crack was detected, the actuator displacement was stopped and the initial crack width was measured using a microscope. Then, the crack width was monitored with a CMOD gauge, as shown in Figure 5.  
Figure 3: Stress–strain behavior of HPFRCC in uniaxial tension.  
Figure 2: Specim geometry and test setup for the direct tension test.  
Figure 4: Test setup for four-point flexural test.  
Figure 5: Load–deflection relationship and cracking behavior of a HPFRCC beam. (**a**) initial cracking stage; (**b**) crack gauge attached; (**c**) distributed fine cracks; (**d**) localized crack.  
Figure 5 shows the cracking process of a typical specimen with its load-deflection curve. Initially, with the first crack, the stiffness on the load-deflection curve decreased. In Figure 5a, a few fine cracks, which were hard to observe with a naked eye, are shown. As the deflection increased from Figure 5a to Figure 5b, more cracks gradually formed, but the crack widths still remained fine without localization. In Figure 5b, several cracks were observed, and a CMOD gauge was attached at the widest crack in order to trace the crack width. The initial crack width was measured using a microscope. From Figure 5b to Figure 5c, the number of cracks did not increase, but the deflection increased continuously by evenly distributing additional deformation into every crack. Therefore, the crack widths proportionally increased without localization until the load reached its maximum. Finally, one of the cracks was localized, as shown in Figure 5d. It should be noted that the crack measured by the CMOD gauge was the widest among the observed cracks for this specimen, and the beam failed at the section with the largest crack.
## 2 Materials and Methods
### Motion Compensation  
#### 2.2.1 Defining the Three-Dimensional Coordinate System  
In this study, a new CNS was equipped to measure and provide the running speed and attitude angle data at any time during the navigation monitoring experiments for real-time correction of the errors [29]. After the signal processing module processes these data, the line of sight is corrected in real time. The dual GPS difference is used to determine the attitude angle data of the vehicle-mounted platform, and the average of the motion velocities measured by the dual GPS is used as the motion velocity of the vehicle-mounted platform. When one of the GPS signals fails to be received, it is supplemented by observations from the INS, which are fed into the algorithm as measurements of the motion error. To describe the vehicle-mounted system, we define the right-handed Cartesian XYZ moving coordinate system (referred to as the M-system) of the vehicle-mounted platform and the north-east-down right-handed Cartesian NED fixed global frame of reference (referred to as the F-system) (see Figure 5). In addition, we also define the right-handed Cartesian MNP antenna coordinate system (referred to as the A-system). The origin of coordinates is defined as the antenna center, M-axis is along the optical axis of the radar antenna, and the N-axis is perpendicular to the M-axis. In real operating conditions of CWML, external forces cause a translational motion in the N, E, and D directions (surge, sway, and heave, respectively) and a rotational motion along the N, E, and D axes (roll, pitch, and yaw, respectively) [35]. The changes in running speed and attitude angle of the vehicle platform are represented by the four parameters: real-time operating speed, roll, pitch, and yaw. Moreover, motion compensation (MC) is divided into two parts: attitude compensation [36] and speed compensation [37].  
Figure 5: Schematic diagram of four-beam CWML.