esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Intelligent Congestion Control Model Based on DRL in Heterogeneous V2X
### Basic Model  
The network performance parameters obtained by a user in V2X are only related to the current network state and the current selected action, and have nothing to do with the previous network state, which conforms to Markov characteristics. Therefore, this section uses [PERSON]'s random memoryless property to establish a congestion control model based on DRL to describe the changing process of congestion control in the Internet of Vehicles. The above congestion control process is defined as the [PERSON] decision process \(\{S,A,R,P,\gamma\}\). The specific definition is as follows:  
State Space \(S\): The set of network states \(s_{t}\) at the current time \(t\), and \(s_{t}\in S\); the initial state is \(s_{0}\).  
Action space \(A\): The set of actions \(a_{t}\) that can be performed at any moment \(t\), and \(a_{t}\in A\).  
Transition probability \(P\): The probability \(P(s_{t+1}|s_{t},a_{t})\) that the network state changes from \(s_{t}\) to the next state \(s_{t+1}\) when a node takes some action \(a_{t}\).  
Reward \(R\): When a node takes an action \(a_{t}\), the network state changes from \(s_{t}\) to the following state \(s_{t+1}\) and the reward \(r_{t}\) is obtained, which is represented by \(R(s_{t},a_{t})=E[R_{t+1}|s_{t},a_{t}]\).  
Discount factor \(\gamma\): The discount factor determines the importance of the reward at the next moment and the reward at the current moment, and \(\gamma\in[0,1)\). It shows that the reinforcement learning algorithm prioritizes the immediate reward value, while the future reward value will decay in a certain proportion. When \(\gamma\) is close to 1, it means that the rewards obtained at the current moment have more weight; on the contrary, when \(\gamma\) is close to 0, it means that the rewards obtained at future moments are more important.  
The RDL algorithm starts from the initial state \(s_{0}\), selects action \(a_{t}\) according to the strategy function \(\pi(a_{t}|s_{t})\) at the current observed state \(s_{t}\), reaches the new state \(s_{t+1}\) according to the state transition probability \(P\), and obtains reward \(r_{t+1}\) from the environment. Moreover, the goal of reinforcement learning is to optimize the strategy function in order to maximize the expected value of reward, which is defined as Formula (1).  
\[\hat{R}=r_{0}+\gamma r_{1}+\cdots+\gamma^{t}r_{t}=\sum_{t=0}^{T}\gamma^{t}r_{t} \tag{1}\]  
In this paper, DRL is used as a common method to solve the Markov decision process mentioned above. By interacting with the environment in a trial-and-error way, we try to maximize the cumulative reward function to accelerate the design of the neural network, and finally obtain the optimal strategy.
## 3 Simulation Setup and Results  
We perform numerical simulations to complement the experiments. We use the MATLAB-based Robochameleon framework [51]. It is a coding framework and component library for simulation and experimental analysis of optical communication systems. We focus on transmitter imperfections and CD tolerance. The simulation setup is shown in Figure 5 and its main configuration parameters are specified in Table 1. The transmitter includes a digital to analog converter (DAC), an intensity modulator (IM), and a continuous wave (CW) laser. The link is based on lossless single-mode fiber. In the receiver, we have a variable optical attenuator, a photodetector with a transimpedance amplifier (PD-TIA), and an ADC. Then we perform a typical DSP to recover transmitted bits. We choose a simple DFE with 6-FFT and 3-FBT to perform channel equalization since the modulator is driven in the linear region and we have a linear dispersive fiber link. We use hard-decision demodulators for the 100 Gbaud OOK, PAM4, and PAM8 signals after the equalization. We set a CD coefficient to 16 ps/nm/km. The PD has a bandwidth of 112 GHz and the Gaussian receiver filter has bandwidth of 75 GHz, as can be seen from Table 1. We use root-raised cosine pulse shaping with 0.75 roll-off to have similar signals as in the experiment.  
We study the required ROP for a certain BER (see Figure 6a-c) and the power penalty (see Figure 6d-f) for the different ENOB and ER at the transmitter for 100 Gbaud OOK, PAM4, and PAM8 signals for optical b2b. The power penalty was calculated by using separate references for each modulation format. For the OOK and PAM4 signals, we sweep the ENOB from 2 to 6, while for the PAM8 from 3 to 7. This is because PAM8 has more amplitude levels to be represented. For all three modulation formats, we choose the extinction ratio to be from 3 to 10 dB. Values are color-coded with the respect to the minimum and the maximum ROP and penalty values, respectively. One can observe from the curves that the extinction ratio of the signal at the transmitter has a crucial impact on the required ROP for a certain BER and the power penalty. In all numerical simulations, we are using the 7% HD-FEC limit with a BER of 5E-3. One can see that the required ROP  
Figure 5: Simulation setup. DAC: digital to analog converter, CW laser: continuous-wave laser, IM: intensity modulator, SMF: single-mode fiber, VOA: variable optical attenuator, PD-TIA: photodetector with a transimpedance amplifier, ADC: analog to digital converter, DSP: digital signal processing, LPF: low-pass filter, DFE: decision feedback equalizer, FFT: feed-forward taps, FBT: feedback taps, BER: bit error rate.  
is modulation format-dependent. For instance, the required ROP is around minus 4.5 dBm for OOK, minus 1 dBm for PAM4, and 2 dBm for PAM8 when the ER is set to 5 dB and the ENOB to 5. We observe around 4 dB power penalty for OOK and PAM4 signals for the same ER and ENOB. The penalty is slightly higher for PAM8. For further simulations, we set ER = 5 dB and ENOB = 5, because it is the closest value to the experiment.
After this first introductory section, the primary characteristics of the study area are presented in Section 2; satellite data and in-situ data are commented on in Section 3; Section 4 discusses the methodology for parameters and estimation; results are analyzed and discussed in Section 5; finally, conclusions and future work are presented in Section 6. Main key points of this investigation are the following: (i) assess the capability of Sentinel-3A imagery to estimate depth values in deep waters with Sentinel-3 optical images from both instruments SLSTR and OLCI, validated by bathymetry data from in situ campaigns; (ii) introduce an unsupervised method for Sentinel-3 SLSTR images to estimate water surface area of AHDL; (iii) propose the estimation of water volume variations in AHDL by applying [PERSON]'s algorithm and using level measurements by the Sentinel-3 radar altimetry instrument.
# VANET Clustering Based Routing Protocol Suitable for Deserts
###### Abstract  
In recent years, there has emerged applications of vehicular _ad hoc_ networks (VANETs) towards security, safety, rescue, exploration, military and communication redundancy systems in non-populated areas, besides its ordinary use in urban environments as an essential part of intelligent transportation systems (ITS). This paper proposes a novel algorithm for the process of organizing a cluster structure and cluster head election (CHE) suitable for VANETs. Moreover, it presents a robust clustering-based routing protocol, which is appropriate for deserts and can achieve high communication efficiency, ensuring reliable information delivery and optimal exploitation of the equipment on each vehicle. A comprehensive simulation is conducted to evaluate the performance of the proposed CHE and routing algorithms.  
ad hoc networks; VANET; clustering; routing; CBRP Article
# Role of Protein Glycoslation in Host-Pathogen Interaction
## 4 Glycosylated Proteins of Pathogens Act as Weapons to Attack the Host
### Secreted Glycoprotein  
Increasing secreted proteins, or called effector, contribute to the virulence of pathogens [25; 106]. Some effectors must undergo glycosylation to activate their function. A typical example is LysM Protein1 (Slp1) secreted by the rice blast fungus _Magnaporthe oryzae_. Slp1 is a critical virulence factor for _M. oryzae_ that can bind with the fungal pathogen-associated molecular pattern (PAMP) chitin to escape the PAMP perception by the rice (_Oryza sativa_) chitin elicitor binding protein (CEBiP), such that rice blast fungus can successfully evade host innate immune [21]. The activity and stability of the chitin-binding of Slp1 depend on the existence of \(N\)-glycosylation. Specifically, Slp1 is unable to bind chitin and degrades significantly faster without glycosylation. Nonglycosylated Slp1 results in higher free chitin build-up that is more recognizable by CEBiP and, in turn, triggering plant innate immunity responses [22]. Other effectors like BAS4 (from _M. oryzae_) [22], CBH1 (from _Trichoderma reesei_) [23], and PCIPGII (from _Plytophthora capsaicin_) [24] also confirm \(N\)-glycosylation as an essential factor for pathogen virulence.  
The glycans are covalently linked to effectors within the fungus cells. However, effectors from plant-parasitic nematodes can recruit host post-translational machinery rather than their own to fulfill glycosylation. As an example, the effector MgGPP secreted by the rice root-knot nematode _Meloidogygraminicola_ promotes parasitism by suppressing plant defenses, but only validate after \(N\)-glycosylation. Interestingly, this \(N\)-glycosylation occurs in the ER of host cells instead of _M. graminicola_ cells [25]. Similarly, the effector GrCLE in the cyst nematode _Globodera rostochiensis_ is also glycosylated using the host cellular machinery, following secretion into plant cells. The arabinylation of the GrCLE is crucial for successful parasitism by binding a CLAVATA2-like receptor (StCLV2) from potatoes to regulate plant development [26].  
Regardless the limited evidence available, plant-parasitic nematodes are unlikely to \(N\)-glycosylate effectors by their own post-translational machinery for three possible reasons: (I) no glycosyltransferase is expressed in esophageal gland cells (the key effector's generator in plant-parasitic nematodes); (II) there is no appropriate glycan donor in esophageal gland cells; (III) the consensus domains in \(N\)-glycosylation proteins from plant-parasitic nematodes are unique. Therefore, the effector is likely to be \(N\)-glycosylated in host cells rather than ER-Golgi apparatus in esophageal glands. This special mechanism in plant-parasitic nematodes may have evolutionary merit in evading host immunity.
# Dual PID Adaptive Variable Impedance Constant Force Control for Grinding Robot
## 1 Introduction
Examples include fuzzy logic control [45], robust force control [46], optimal control [47], etc. Therefore, the key to establish a force tracking controller is to compensate for environmental disturbance, which is oriented to practical applications, and the control method should not be too complicated. Force overshoot usually occurs when the robot end effector touches the workpiece. Although this problem only occurs in a short time, it will lead to poor system stability and low surface quality. Therefore, it is extremely important to ensure the stability of the system [15]. The nonlinear feedback control method is used to plan the trajectory and contact force simultaneously to reduce the force overshoot [48]. [PERSON] et al. [49] adopted an impedance shaping method to reduce the influence of force overshoot on system stability and achieve stable contact state transition. These methods usually require accurate estimates of the environmental position and stiffness, and this approach is difficult. In this work, a dual PID adaptive impedance control method is proposed to achieve smooth, stable, and high-precision robot end contact force control. PD is used to compensate for the force error, and PID is used to update the damping parameters to compensate for the environmental uncertainty. A nonlinear tracking differentiator is used to reduce the impact of the transitional contact state. The interference term of the end 6-Dof force sensor is compensated using the least square method to ensure the accuracy of force control. The stability, convergence, and effectiveness of the force control algorithm are verified with theoretical analysis, simulations, and experiments. Furthermore, the thin-walled workpiece is used in a robot grinding experiment to verify the accuracy of force control. The remainder of this paper is organized as follows: In Section 2, a nonlinear tracking differentiator is used to stabilize the contact transition state. In Section 3, a position-based dual PID adaptive impedance control method is established to compensate for the uncertainty of environment. In Section 4, the interference term of the 6-Dof force sensor is compensated for. The proposed method is verified in MATLAB/Simulink in Section 5. In Section 6, the effectiveness of the method is verified via force control and grinding experiments.
# Exploring Multi-Channel GPS Receivers for Detecting Spoofing Attacks on UAVs Using Machine Learning
## 3 Methodology
### Preprocessing  
#### 3.2.1 Extract Missions  
In our study, we focus on the three-dimensional version of the dataset, which includes thirteen features recorded across eight channels. We closely explore attack simulations as well as the target variable to assess class balance and verify the integrity of the recorded signals. Additionally, we examine the RX feature, which indicates the time in seconds since the GPS receiver has acquired each signal. This analysis allowed us to extract three distinct time ranges corresponding to separate acquisition of GPS signals.  
* [491,568-492,039.42 s], which covers 471.42 s (7 min, 51 s, and 420 ms).
* [173,640-174,233.86 s], which covers 593.86 s (9 min, 53 s, and 860 ms).
* [262,704.02-264,109.68 s], which covers 1405.66 s (23 min, 25 s, and 660 ms).  
Finally, for further improvement, we separate these three missions into different files; the details are demonstrated in Table 3.
## 5 Approximation Algorithms for \(\mathsf{SVP}^{(p)}\) and \(\mathsf{CVP}^{(p)}\)
### Algorithm for Approximate \(\mathsf{CVP}^{(p)}\)
```
Input: (i) A basis \(\mathbf{B}=[\mathbf{b}_{1},\ldots\mathbf{b}_{n}]\) of a lattice \(L\), (ii) Target vector \(\mathbf{t}\), (iii) Approximation factor \(\tau\), (iv) \(0<\gamma<1\), (v) \(\xi\) such that \(\frac{1}{2}\max(1,\tau/2)<\xi<\frac{(1-\gamma)\tau}{2-\gamma}-c^{\prime}\) where \(c^{\prime}\) is a small constant, (vi)\(\alpha>0\), (vii) \(N\in\mathbb{N}\) Output: A \(2\tau-\)approximate closest vector to \(\mathbf{t}\) in \(L\)
1\(d\leftarrow(1+a)\) ;
2\(T\leftarrow\mathcal{O}_{j}\) ;
3 while\(d\in n\cdot\max_{i}\|\mathbf{b}_{i}\|_{p}\)do
4\(S,S^{\prime}\leftarrow\mathcal{O}\) ;
5\(\mathbf{B}^{\prime}\rightarrow[(\mathbf{b}_{1},0),\ldots,(\mathbf{b}_{n},0),( \mathbf{t},\tau d/2)]\) ;
6\(L^{\prime}\rightarrow\mathcal{L}(\mathbf{B}^{\prime})\) ;
7\(M\rightarrow\operatorname{span}(\{(\mathbf{v},0):\mathbf{v}\in L\})\) ;
8 for\(i=1\) to \(N\)do
9\(\mathbf{e}_{i}\leftarrow\)uniform \(B_{n}^{(p)}(\mathbf{0},\zeta\lambda)\) ;
10\(\mathbf{y}_{i}\leftarrow\mathbf{e}_{i}\mod\mathcal{P}(\mathbf{B})\) ;
11\(S\gets S\cup\{(\mathbf{e}_{i},\mathbf{y}_{i})\}\) ;
12
13 end for
14\(R\gets n\max_{i}\|\mathbf{b}_{i}\|_{p}\) ;
15 while\(R>\frac{\zeta d}{\Gamma}\)do
16\(S\leftarrow\) sieve\((S,\gamma,R,\xi)\) using Algorithm 2 ;
17\(R\leftarrow\gamma R+\zeta d\) ;
18
19 end for
20\(S^{\prime}\leftarrow\{\mathbf{y}-\mathbf{e}:(\mathbf{e},\mathbf{y})\in S\}\) ;
21 Compute \(\mathbf{w}\in S^{\prime}\) such that \(\|\mathbf{w}\|_{[n]}\|_{p}=\min\{\|\mathbf{v}^{\prime}|_{[n]}\|_{p}:\mathbf{v}^ {\prime}\in S^{\prime}\text{ and }(\mathbf{v}^{\prime})_{n+1}\
eq 0\}\) ;
22\(T\to T\cup\{\mathbf{w}\}\) ;
23\(d\to d(1+\alpha)\) ;
24
25 end for
26
27 Let \(\mathbf{v}_{0}\) be any vector in \(T\) such that \(\|\mathbf{v}_{0}\|_{[n]}\|_{p}=\min\{\|\mathbf{w}|_{[n]}\|_{p}:\mathbf{w}\in T\}\) ;
28\(\mathbf{v}_{0}^{\prime}\leftarrow\mathbf{v}_{0}|_{[n]}\) ;
29 if\((\mathbf{v}_{0})_{n+1}=-\tau d/2\)then
30 return\(\mathbf{v}_{0}^{\prime}+\mathbf{t}\) ;
31 else
32 return\(\mathbf{v}_{0}^{\prime}-\mathbf{t}\) ;
33
34 end if
```  
**Algorithm 3**Approximate algorithm for \(\mathsf{CVP}^{(p)}\)  
**Theorem 6**: _Let \(\gamma\in(0,1)\), and for any \(\tau>1\) let \(\xi>\max(1/2,\tau/4)\). Given a full-rank lattice \(\mathcal{L}\subset\mathbb{Q}^{n}\), there is a randomized algorithm that, for \(\tau=\frac{\xi(2-\gamma)}{1-\gamma}+o(1)\), approximates \(\mathsf{CVP}^{(p)}\) with a success probability of at least \(1/2\) and a space and time complexity of \(2^{(c_{s}+c_{c})n+o(n)}\), where \(c_{c}=\log\left(2+\frac{2}{\gamma}\right)\) and \(c_{s}=-\log\left(0.5-\frac{1}{4\xi}\right)\)._  
Again, using Mixed Sieve in Algorithm 1, we obtain space and time complexities of \(2^{(c_{s}+c^{(p)})n+o(n)}\) and \(2^{(c_{s}+c^{(p)})+c_{p})n+o(n)}\), respectively, where  
\(c^{(p)}=\log\left(2+\frac{2}{A\gamma}\right)+c_{p}\) and \(c_{p}=\log(1+A)\), respectively (in the Euclidean norm, the parameters are as described in Theorem 4).
## 2 Method
### Feature Extraction of Differential Entropy Measures  
Given the non-Gaussian distribution of EEG signals, utilizing \(DE\) emerges as a potent technique for extracting features from such signals. This method aptly captures the complexity and uncertainty inherent in EEG signals during feature extraction. Lower entropy values signify a higher degree of signal orderliness and predictability, while higher entropy values indicate greater signal complexity and unpredictability.  
As a method for feature extraction, entropy measurement offers a means to characterize the diversity and intricacy of information embedded within EEG signals, facilitating the extraction of pertinent features crucial for subsequent analysis and classification. Specifically, the employment of \(DE\) enables us to quantify the complexity and stochasticity inherent in EEG signals. Through the computation of \(DE\), we gain insights into the temporal or spatial uncertainty present in the signals, alongside a quantitative assessment of the informational content they carry. Moreover, by examining the dynamic attributes of the signal, we can derive valuable features conducive to further analysis and classification endeavors.  
As illustrated in Figure 3, the initial EEG signal collected comprises data from 62 channels, which underwent preprocessing. Initially, the EEG data were downsampled to 200 Hz and then subjected to a bandpass filter ranging from 0 to 75 Hz to eliminate any signal noise. We identified and rectified artifacts in the EEG signals stemming from electrode displacement and external interference.  
After preprocessing the EEG signals from 62 channels, we utilized a 256-point Short-Time Fourier Transform technique along with a 1 s non-overlapping Hanning window to extract five distinct frequency bands from the following preprocessed EEG signals: delta (1-4 Hz), theta (4-8 Hz), alpha (8-14 Hz), beta (14-31 Hz), and gamma (31-50 Hz). From each frequency band, the \(DE\) feature is computed, culminating in a total of 310 \(DE\) features (62 channels \(\times\) 5 frequency bands). The calculation formula for \(DE\) is outlined as follows:  
\[DE=-\int_{\infty}^{\infty}P(x)\ln(P(x))dx \tag{1}\]  
We assume that the EEG signals follow a Gaussian distribution: \(x\sim N(\mu,\sigma^{2})\). Consequently, the calculation of the \(DE\) features can be simplified:  
\[DE=-\int_{\infty}^{\infty}\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\frac{(x-\mu)^{2} }{2\sigma^{2}}\log(\frac{1}{\sqrt{2\pi\sigma^{2}}}\exp\frac{(x-\mu)^{2}}{2 \sigma^{2}})dx=\frac{1}{2}\log(2\pi e\sigma^{2}) \tag{2}\]  
Figure 2: Five emotion-related stimulation videos: fear, happy, disgust, sad, and neutral.
# MCST Scheme for UAV Systems over LoRa Networks
## 5 MCST Scheme over LoRa Network
Therefore, we proposed a modified MCST over the LoRa network, namely mMCST/LoRa, following the LoRaWAN standard of bi-directional transmission as illustrated in Figure 7. In the proposed mMCST/LoRa, the control frames of the handshaking mechanism are reduced by considering the downlink transmission of the STS control frame from the relay node, the uplink transmission of the STS control frame from the destination node, and the ACK control frame to indicate completion of DATA transmission as the transmission in the receive window of class A and class B LoRaWAN MAC. This way, the control frame transmission time and latency can be reduced, which could lead to the optimal BRF throughput and energy consumption. Figure 8 describes the frame formats of the control, i.e., RTS, CTS, STS, ACK, and DATA messages as the MAC payload frame of LoRaWAN format for MCST scheme with minor modification of LoRa specification [27]. Figure 6: The message handshaking of MCST/LoRa scheme.
# FlowHaptics: Mid-Air Haptic Representation of Liquid Flow
## 5 Experiment 2: Different Velocity Profiles
### Results and Discussions  
We present our results in terms of the box plots shown in Figure 11. We used a one-way repeated-measures analysis of variance (ANOVA) to analyze the data, followed by a paired t-test with Bonferroni correction to find any significant differences.  
In the case of 1F, presented in Figure 11a, the paired t-test showed a significant difference between the constant velocity profile and the _FlowEst2_ profile in the 1F/3T condition with the rate (\(t=2.39\), \(p<0.05\)). In the case of 3F, presented in Figure 11b, we did not find strong support between each condition as no significant difference was observed. However, by breaking down the analysis, we looked at the highest score among each velocity profile, presented in Figure 12, and we found that the participant preferred the _FlowEst1_ and _FlowEst2_ rendering techniques over the constant velocity profile by over 50% in all conditions. _FlowEst1_ and _FlowEst2_ with the dynamic change of the flow velocity were considered realistic for some participants, but were considered too complex for some participants. As shown in _1 FIT_, _3 FIT_, and 3F3T, some participants preferred the _Constant_ condition over _FlowEst1_ and _FlowEst2_ because of its simplicity. Especially in 3F3T, _FlowEst1_ was considered to be the most difficult to interpret for a number of participants; therefore, it was significantly lower than in the other three conditions. As a result, in 3F3T, subjects who preferred a realistic rendering would choose _FlowEst2_ over _FlowEst1_, and subjects who preferred a simple rendering would choose the _Constant_ condition. We also did not find a statistical difference between the other conditions, but we did receive positive feedback from the participants. Example comments from the participants were \"Though there is no wetness, I feel flow similar to when water comes out of the tap\" and \"Compared to three-finger, I felt more realistic flow sensation during an experiment with one finger\", while another participant reported, \"The presented flow at the fastest speed was very strong that I feel the heaviness of water\".