esa-sceva/satcom-chunk-collection huggingface dataset:
# Goal Estimation of Mandatory Lane Changes Based on Interaction between Drivers
###### Abstract  
In this paper, we propose a novel method to estimate a goal of surround vehicles to perform a lane change at a merging section. Recently, autonomous driving and advance driver-assistance systems are attracting great attention as a solution to substitute human drivers and to decrease accident rates. For example, a warning system to alert a lane change performed by surrounding vehicles to the front space of the host vehicle can be considered. If it is possible to forecast the intention of the interrupting vehicle in advance, the host driver can easily respond to the lane change with sufficient reaction time. This paper assumes a mandatory situation where two lanes are merged. The proposed method assesses the interaction between the lane-changing vehicle and the host vehicle on the mainstream lane. Then, the lane-change goal is estimated based on the interaction under the assumption that the lane-changing driver decides to minimize the collision risk. The proposed method applies the dynamic potential field method, which changes the distribution according to the relative speed and distance between two subject vehicles, to assess the interaction. The performance of goal estimation is evaluated using real traffic data, and it is demonstrated that the estimation can be successfully performed by the proposed method.  
Keywords:goal estimation; lane change; trajectory prediction; autonomous driving +
Footnote †: journal: Applied Sciences
## 1 Introduction
However, the provision of a tether limits the drone's mobility and altitude, while also introducing vibrational modes associated with tension in the tether. However, advances in retroreflected links have led to achievements in drone-to-ground communications. In one case a 560 m, 500 Mb/s drone-to-ground link was demonstrated by using a modulating retroreflector [21]. In this paper, we present a 1.3 km round-trip retroreflected drone link, using a hardware transceiver based on CCSDS 141.0-B-1. Using a drone in this scenario is presented as a highly accessible, high-uptime testbed for FSO communications and flight operations. PPM symbols were transmitted and received over a 1.3 km folded link formed between an optical terminal and an optical payload, including a corner-cube retroreflector (CCR), mounted to a multi-rotor drone. Angle-of-arrival variations are exhibited by CCR-folded links despite atmospheric reciprocity [22], so the optical terminal must simultaneously demonstrate correction of the 'downlink' beam angle while pre-compensating for the 'uplink' beam pointing. The 4-PPM symbols with 8 ns slot widths were transmitted, received and demodulated for a line rate of 50 Mb/s, using commercial-off-the-shelf (COTS) components.
# Coordination in Emergency Response System Design:
## 2 Literature Review
They proposed a bi-level programming model, which is applied to a real-life case from Daojiao city, China. The same case study is used in [31], which proposed a two-stage robust optimization approach for designing an emergency response system. The location and capacity of the emergency response facilities are determined in the first stage, whereas the second stage aims at minimizing the time-varying rescue risk by identifying the optimal resource allocation. Ref. [30] presented a bi-objective formulation where the configurational decisions pertaining to a closed-loop supply chain are optimized simultaneously with the locations of the hazmat emergency response teams. They developed a two-phase methodology to characterize all the solutions on the cost-risk Pareto frontier. Ref. [32] focused on the uncertainties concerning demand, service, scope, and safety parameters in determining the optimal sites for rail emergency response facilities. The authors introduced a robust control safety parameter to represent the risk preferences of the decision maker. In this paper, we consider the need for multiple types of resources in responding to an emergency. In the context of hazmat transport, we explicitly model the time between the arrival of two different types of emergency response teams at the accident site. This has clear implications concerning the duration of the response to an emergency incident, which has been studied extensively in the empirical literature on transportation using hazard-based duration models. Typically, these models establish a survival function through the link between the hazard function and a duration model, e.g., Weibull, exponential, Log-logistic.
# Electronically Controllable Phase Shifter with Progressive Impedance Transformation at K Band
###### Abstract  
This communication presents the design of a two-port electronically tunable phase shifter at K band. The phase shifter consists of a 3 dB hybrid coupler loaded with reflective phase-controllable circuits. The reflective circuits are formed by varactors and non-sequential impedance transformers which increase the operational bandwidth and the provided phase shift. The final phase shifter design is formed by two loaded-coupler stages of phase shifting to guarantee a complete phase turn. An 18 GHz phase shifter design with dynamic range of 600 degrees of phase shift is depicted in this document. The prototype is manufactured and validated through measurements showing good agreement with the simulation results.  
microwave tunable phase shifters; 3 dB/90\({}^{\circ}\) coupler; K band +
Footnote †: journal: Applied Sciences
# Machine Learning- and Deep Learning-Based Multi-Model System for Hate Speech Detection on Facebook
## 4 Outcomes and Discussion
### Error Analysis
#### 4.8.2 Confusion Matrix for CNN with FastText Embeddings  
Likewise, we analyzed the classification errors produced by the CNN in Figure 3 with an F-score equal to 95.1%. This resulted in another 1063 false positives and 1191 false negatives, which indicates slightly higher rates of misclassifications compared to LSTM, as seen in the confusion matrix. The performance deterioration is anticipated considering that CNN depends on local augmentation rather than including sequential dependencies, which are sometimes vital for comprehending sophisticated Roman Urdu statements. CNN was good at picking up on overt hate, but bad at things like context-dependent and longer comments that require a holistic understanding of relationships between distant words. Although CNN proved to be a great alternative given its quick implementation and general performance, the results prove that sequence-aware architectures such as LSTM would play a role in challenging NLP tasks like offensive language detection.  
The reason for CNN being less accurate than LSTM is due to the architectural difference. CNN is good at capturing local patterns (e.g., offensive word combination or phrase) but is not capable of modeling long-term dependencies and the temporal flow of the text. In contrast, LSTM is explicitly designed to remember context over greater temporal spans through memory cells and gating. LSTM can make sense of context/person-dependent or nuanced hate speech, which CNN might overlook, especially in the code-mixed or non-grammatical nature of Roman Urdu inputs.  
Figure 2: Confusion matrix for LSTM.
## 6 Conclusions  
PMVs with an active inward tilting mechanism with three wheels, double front wheels + single rear wheel and front steering + rear traction, are studied regarding front inner wheel lifting phenomena, capability of obstacle avoidance and energy balance of active tilting mechanism.  
From the following results, it was shown that PMVs with an active inward tilting mechanism have sufficient social acceptability.  
* Front inner wheel lifting phenomenon is only that vehicle roll is unavailable to follow target roll angle _TRA_ due to roll moment of inertia of sprung mass _Ixx*_. Smaller _Ixx*_ and larger _Kqp_ improve front inner wheel lifting phenomena.
* It is possible to suppress unstable roll phenomena without intentional steering input related to front inner wheel lifting phenomena by decreasing the I gain of roll tracking control (PID).
* PMVs with an active tilting mechanism have two advantages of vehicle dynamics. One is lateral transfer of vertical loads on front both wheels, in the same way as general cars, and the other is inward tilting on turning, the same as motorcycles.
* PMVs with an active tilting mechanism have an obstacle avoidance ability equal to or higher than that of passenger cars, because they have a much smaller roll moment of inertia than passenger cars and responsiveness equivalent to passenger cars.
* Energy consumption for an active tilting mechanism is much smaller than energy saving due to avoiding cornering drag by using the camber angle. This shows there is no need to worry about energy consumption with active tilting mechanisms.
* From a general energy efficiency point of view, smaller and lighter PMVs are significantly more efficient than general cars, because rolling resistance caused by vehicle mass and the tire rolling resistance coefficient is the major resistance in these vehicles.  
Conceptualization, T.H.; formal analysis, T.H.; methodology, T.H. and T.K.; supervision, I.K.; validation, T.H.; writing--original draft, T.H.; writing--review and editing, T.H.  
This research received no external funding.  
The authors declare no conflict of interest.  
Figure 38: Comparison of energy consumption of general vehicles on rolling resistance.
# A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters
## 2 Data and Methods
### Inherent Optical Properties  
#### 2.2.1 Atmospheric Aerosol Model  
The inherent aerosol optical properties (volume fraction of fine-mode aerosol particles and aerosol volume fraction) used as input in AccuRT [32; 33] forward RT algorithm were obtained from AERONET [34; 35] and were derived based on a bi-modal log-normal volume size distribution given by [36]  
\[v(r)=\frac{dV(r)}{dr}=\frac{1}{r}\frac{dV(\ln r)}{d\ln r}=\sum_{i=1}^{2}\frac{V_ {i}}{\sqrt{2\pi\sigma_{i}}}\frac{1}{r}\exp\left[-\left(\frac{\ln r-\ln r_{ni}} {\sqrt{2}\sigma_{i}}\right)^{2}\right], \tag{1}\]  
where \(r\) gives varying radii the particles can have in the distribution, the subscript \(i\) represents the mode (fine or coarse), \(V_{i}\) is the total volume of particles associated with mode \(i\), \(r_{ni}\) is the mode radius or volume geometric mean radius, and \(\sigma_{i}\) is the geometric standard deviation. In terms of the number density, Equation (1) becomes  
\[n(r)=\frac{dN(r)}{dr}=\frac{1}{r}\frac{dN(\ln r)}{d\ln r}=\sum_{i=1}^{2}\frac{ N_{i}}{\sqrt{2\pi\sigma_{i}}}\frac{1}{r}\exp\left[-\left(\frac{\ln r-\ln r_{ni}} {\sqrt{2}\sigma_{i}}\right)^{2}\right], \tag{2}\]  
where \(N_{i}\) is the number of particles and \(r_{ni}\) is the volume geometric mean (or mode) radius.  
According to [PERSON] et al. [36], \(N_{i}\) and \(r_{ni}\) in Equation (2) are, respectively related to \(V_{i}\) and \(r_{vi}\) in Equation (1) as follows:  
\[\ln r_{ni}=\ln r_{vi}-3\sigma_{i}^{2}, \tag{3a}\] \[N_{i}=\frac{V_{i}}{\frac{4}{3}\pi r_{ni}^{3}}\exp(-4.5\sigma_{i}^{2}). \tag{3b}\]  
Since  
\[\int_{0}^{\infty}\frac{dr}{\sqrt{2\pi\sigma}}\frac{1}{r}\exp\left[-\left(\frac {\ln r-\ln r_{ni}}{\sqrt{2}\sigma_{i}}\right)^{2}\right]=1, \tag{4}\]  
integration over all sizes for both modes in Equations (1) and (2), yields  
\[\int_{0}^{\infty}v(r)dr=V_{1}+V_{2}=V_{T}, \tag{5a}\] \[\int_{0}^{\infty}n(r)dr=N_{1}+N_{2}=N_{T}. \tag{5b}\]  
\begin{table}
\begin{tabular}{l c c c c c} \hline \hline
**Primary Use** & **Band** & **Bandwidth** & **Center** & **Spectral** & **Required** \\  & & **(nm)** & **Wavelength (nm)** & **Radiance 1** & **SNR** \\ \hline Land, cloud, & 3 & 459–479 & 465.6 & 35.3 & 243 \\ aerosol properties & 4 & 545–565 & 553.6 & 29.0 & 228 \\ \hline  & 8 & 405–420 & 411.3 & 44.9 & 880 \\  & 9 & 438–448 & 442.0 & 41.9 & 838 \\  & 10 & 483–493 & 486.9 & 32.1 & 802 \\ Ocean color, & 11 & 526–536 & 529.6 & 27.9 & 754 \\ phytoplankton, & 12 & 546–556 & 546.8 & 21.0 & 750 \\ biogeochemistry & 13 & 662–672 & 665.5 & 9.5 & 910 \\  & 14 & 673–683 & 676.8 & 8.7 & 1087 \\  & 15 & 743–753 & 746.4 & 10.2 & 586 \\  & 16 & 862–877 & 866.2 & 6.2 & 516 \\ \hline \hline \end{tabular} \({}^{1}\)(W m\({}^{-2}\) sr\({}^{-1}\) \(\mu\)m\({}^{-1}\)).  
\end{table}
Table 1: MODIS channel specifications.  
If we let subscript \(i=f\) denote the fine mode, and the subscript \(i=c\) to denote the coarse mode, then Equation (5a) becomes \(V_{T}=V_{f}+V_{c}\). Hence the volume fraction of fine-mode particles is given by \(f=V_{f}/V_{T}\).
## 4 Energy Balanced Strategies for Sparsely Deployed UWA-SNs
### Apply the linear network to two-dimensional underwater sensor networks  
We have designed two energy balanced strategies for underwater acoustic moored monitoring system that can be applied to two-dimensional UWA-SNs, which is used for ocean bottom monitoring, such as environment monitoring or monitoring of underwater plates in tectonics [31].  
Just as Figure 6 shows, the network is composed of underwater sensor nodes, underwater sink nodes and surface sink node.  
Figure 6: Underwater acoustic sensor networks.  
Figure 7: Two-dimensional underwater sensor network deployment.  
**Underwater sensor nodes:** The underwater sensor nodes are deployed on the sea floor anchored to the ocean bottom [32]. The sensors are equipped with floating buoys to push the nodes upwards, thus they are relatively stationary nodes [3]. Using acoustic links, they relay data to underwater sink directly or via multi-hop path.  
**Underwater sink nodes:** Underwater sink nodes take charge of collecting data of underwater sensors deployed on the ocean bottom and then send to the surface sink node. They may be equipped with vertical and horizontal acoustic transducers. The horizontal transceiver is used to collect the sensors' data and the vertical transceiver provides transmitting link between underwater sink and the surface sink node.  
**Surface sink node:** Surface sink node is attached on a floating buoy with satellite, radio frequency (RF) or cell phone technology to transmit data to shore in real time.  
We design two different deployments of sparsely deployed two-dimensional sensor networks as shown in Figure 7(a) and 7(b). In Figure 7(a), the sensors are deployed in a circle. There is only one underwater sink in the circle. In Figure 7(b), the sensors are deployed in a grid. There are many underwater sinks in the grid, and each sink takes charge of a chain where the underwater sink is in the center of it. Using TDMA or CDMA [29; 30], the sensors can send their data to the sink along a chain in which they reside without interference. The underwater sinks directly send collected data to the surface sink using vertical acoustic links.
## 3 4-SSB System Design
### The Concept of Application of 4-SSB into OFDM M-QAM 4-SSB Uncoded System Using the Shadow Equalizer
#### 4.3.4 The Constraint of the Shadow of M-QAM  
Figure 5 represents the shadow constraint in 4-QAM.  
The shadow area has distance d with the original MF algorithm. However, the main difference between the development of decision feedback and the SAC is the feedback representation. Specifically, when the feedback is zero, the error propagation will be increased. SAC avoids null feedback by applying the shadow distance to determine the probability of soft decision as the following equation:  
\[\widetilde{d}_{n}[i]=\ w_{n}^{H}d_{n}[i]. \tag{38}\]  
The system avoids the null feedback by generating MF to minimize the error burst.  
In general, QAM constellation can be determined since \(\widetilde{d}_{n}\) refers to reliability rated from shadow distance d [13]:  
\[\left\{\begin{array}{l}\left[\mathcal{R}\big{[}\widetilde{d}_{n}\big{]} \mathcal{R}[a_{K,n}]\right]>d/2\\ \left[\mathcal{S}\big{[}\widetilde{d}_{n}\big{]}\mathcal{S}[a_{K,n}]\right]>d /2\end{array}\right.. \tag{39}\]  
The high SNR means low complexity as we aim to get the best case for estimating symbol. The remaining part of the equation is \([a_{K,n}]\) which is the constellation point of M-QAM with the nearest soft decision of \(\tilde{d}_{n}=\ w_{n}^{H}d_{n}[i]\) in the \(n\)th layer:  
\[a_{K,n}=\ arg\min_{a_{k}\in A}\|w_{n}^{H}\tilde{d}_{n}[i]-\ a_{k}\|^{2},\ k=1, \ldots,C. \tag{40}\]  
Figure 5: 4-QAM Shadow Area Constellation.
# EUV-Induced Plasma: A Peculiar Phenomenon of a Modern Lithographic Technology
## 2 EUV-Induced Plasma
### Key Parameters of EUV-Induced Plasma  
As can be observed in Figure 1, plasma is created everywhere that the pulsed EUV light travels through and interacts with the background gas. The EUV photon flux decreases, while the beam travels through the tool, due to the absorption of these photons. The absorbed time-dependent intensity \(I_{abs}(t)\) of the EUV light is governed by:  
\[I_{abs}(t)\ =\ I_{0}(t)\Big{[}1-exp\Big{[}-n_{gas}\sigma_{pi}l_{abs}\Big{]} \Big{]}\approx I_{0}(t)n_{gas}\sigma_{pi}l_{abs} \tag{3}\]  
Here, \(I_{0}(t)\) is the initial intensity of the EUV pulse, \(n_{gas}\) is the initial neutral gas density, \(\sigma_{pi}\) is the total photoionization cross section, and \(l_{abs}\) is the length of the light path under investigation. Of course, \(\sigma_{pi}\) depends on the type of gas and on the wavelength of the light used. In the current generation EUV scanners, a budget of 4% of transmission is reserved for losses due to absorption by gas molecules (from the intermediate focus (IF)--i.e., the interface between EUV source and scanner--to the wafer) [3]. This loss is directly related to the creation of plasma species through direct photoionization events creating fast electrons (i.e., ~77 eV in H\({}_{2}\)). Secondary electron emission from EUV-irradiated surfaces is another possible source of energetic electrons. On their turn, these fast electrons may produce additional plasma species through electron-impact ionization events, and further lose energy through other inelastic processes (e.g., various excitations of H\({}_{2}\) molecules).  
Where ion fluxes, ion energy distributions functions (IEDFs), and radicals describe to a large extent the interaction of plasma with plasma-facing surfaces (subject of Section 3), two quantities that  
Figure 1: Photograph of the inner side of an experiment chamber in which a low pressure (argon) gas is irradiated with a pulsed beam of Extreme Ultraviolet (EUV) photons. The blueish glow at the position where the EUV beam travels indicates the interaction between the EUV photons and the gas.  
are predominantly important in describing the \"ecosystem\" in the bulk of the pulsed plasma are the density and the temperature of the free electrons. Note that, although negative H\({}^{-}\) ions may be formed as well in hydrogen plasmas, it was derived by [PERSON] et al. [12]--while using cross sections for dissociative electron attachment of highly vibrationally excited H\({}_{2}\)* (e.g., \(\upsilon\) = 4) [13] and for direct excitation to such states [14]--that the densities of H\({}^{-}\) ions are negligible in the current case.
## 2 Methods and Principle
### Kalman Filter  
A Kalman filter is a dynamic data processing method. It is a filter method that linearly optimally estimates the state of the system to eliminate noise. When the noise of the system is normally distributed, a Kalman filter can calculate the minimum variance of the system. When the noise of the system is not normally distributed, a Kalman filter can calculate the linear minimum variance of the system.  
Kalman filtering is a recursive filtering method based on time series. The principle is to introduce new measurement data of the system as an information supplement in each step of the filter process while continuously returning to the system filter process. In the filter iteration process, the estimated value of the system state can be corrected in time, the state estimation error can be reduced, and the optimal estimation can be achieved.  
The Kalman filter has been applied in practical engineering tasks such as target tracking, comprehensive industrial monitoring, fault-tolerant control, etc. The multi-sensor distributed fusion estimation based on the Kalman filter has certain research and application to network problems, such as transmission delay, packet loss, limited bandwidth and sensor power [22; 23]. In the actual project, the capacity of the system hardware to store data is reduced by applying Kalman filter, and the time for the system to access data is reduced, thereby simplifying the calculation process and operation time of the system.  
The state equation and observation equation of the Kalman filter are as follows:  
\[X(k+1)=\Phi X(k)+\Gamma w(k) \tag{12}\]  
\[Y(k)=HX(k)+v(k) \tag{13}\]  
In formulas (12) and (13): \(X(k)\) is the state of the system at time k. \(Y(k)\) is the observation signal of the state. \(w(k)\) is the input white noise with the variance matrix \(Q\). \(v(k)\) is the observation noise with the variance matrix \(R\). The two are uncorrelated. \(\Phi\) is the state transition matrix and \(H\) is the observation matrix, with suitable dimensions.  
The recursive Kalman filter is:  
State one-step prediction \(\hat{X}(k+1\ket{k})\):  
\[\hat{X}(k+1\ket{k})=\Phi\hat{X}(k) \tag{14}\]  
One-step prediction error variance matrix \(P(k+1\ket{k})\):  
\[P(k+1\ket{k})=\Phi P(k\ket{k}\Phi^{T}+\Gamma Q\Gamma^{T} \tag{15}\]  
Kalman gain \(K(k+1)\):  
\[K(k+1)=P(k+1\ket{k}H^{T}\Big{[}HP(k+1\ket{k}H^{T}+R\Big{]}^{-1} \tag{16}\]  
State estimation \(\hat{X}(k+1)\):  
\[\hat{X}(k+1)=\hat{X}(k+1\ket{k})+K(k+1)\big{[}Y(k+1)-H\hat{X}(k+1\ket{k})\big{]} ^{-1} \tag{17}\]  
Filtering error variance matrix \(P(k+1\ket{k+1})\):  
\[P(k+1\ket{k+1})=[I_{n}-K(k+1)H]P(k+1\ket{k}) \tag{18}\]  
In formula (18), \(I_{n}\) is the unit matrix. As long as the initial values \(\hat{X}(0)\) and \(\hat{P}(0)\) are given, the estimated state \(\hat{X}(k)\) at time k can be calculated recursively according to the observed signal \(Y(k)\).
## 4 Simulation and Discussion
### The Performance Comparison of the Gated Mode and the Free-Running Mode  
In order to further illustrate the results of Figure 6, the BER performance of gated mode and free-running mode is compared, and the results are shown in Figure 7. The gated mode is operated in the optimal gate-ON time, and the free-running mode does not have an additional external gate signal, the SPAD can be exposed in the whole symbol cycle. When the signal flux is low, the performance of a free-running SPAD is equivalent to the time-gated SPAD. With the increase of signal photon flux, the BER of the free-running SPAD receiver decreases and reaches the lower limit. The lowest BER of free-running AQ-SPAD is determined by the background light. For time-gated SPAD, the performance is equivalent to free-running mode at low signal light flux. As the signal increases, the BER of free-running SPAD achieves the lower limit. The BER of the time-gated SPAD decreases monotonously, the background light has a small impact, and the time-gated SPAD can achieve a lower BER.  
Figure 6: The optimal gate-ON time under different signal intensity. (**a**) \(N_{g}=16\). (**b**) \(N_{g}=64\).