esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling the Accuracy of Estimating a Neighbor's Evolving Position in VANET
## 3 Model Validation
In Figure 5, we observe that numerical results of our model are close to those of the NS-2 simulation, even with different settings in the size of the beacon and acceleration. Specifically, in all curves, we notice that the numerical results of our model are located within 95% confidence intervals of the simulation results. We found an interesting point: the estimation error increases as the size of a beacon rises. This trend comes from the fact that network congestion rises with the size of a beacon and the estimation error increases with the network congestion. In Figures 4 and 5, we observe that the estimation error is very small, less than 0.1 m. The small estimation errors were due to the following two reasons. First, the estimation from the CSPE estimator can significantly reduce the estimation error, even if an ego vehicle fails to receive beacons from its neighbors. Second, we do not consider GPS error in our model for two reasons. First, we can significantly improve the positioning accuracy using several compensation techniques, such as dead reckoning [20], integration with other in-vehicle sensors (e.g., wheel odometer, INS) [21], and a signal processing algorithm [22]. Second, considering a GPS error in the model significantly increases the model's complexity.
# User-Centric Cell-Free Massive MIMO with Low-Resolution ADCs for Massive Access
## 2 System Model
### Uplink Data Transmission Phase
\[\mathbf{v}_{kl}=\alpha\rho_{k}\Bigg{(}\alpha^{2}\sum_{j=1}^{K}\rho_{k}\Big{(} \widehat{\mathbf{g}}_{j}\widehat{\mathbf{g}}_{jl}^{H}+\mathbf{C}_{jl}\Big{)} +\alpha^{2}\sigma^{2}\mathbf{I}_{N}+\mathbf{R}_{\mathbf{n}_{q}}\Bigg{)}^{-1} \widehat{\mathbf{g}}_{kl} \tag{22}\]  
However, Equation (21) does not consider the low-resolution ADCs in the combining vector, and Equation (22) is not a scalable combining vector. By utilizing both equations, we can derive the LP-MMSE combining factor using low-resolution ADCs as follows:  
\[\mathbf{v}_{kl}=\alpha\rho_{k}\Bigg{(}\alpha^{2}\sum_{j\in\mathcal{A}_{l}} \rho_{k}\Big{(}\widehat{\mathbf{g}}_{j}\widehat{\mathbf{g}}_{jl}^{H}+\mathbf{ C}_{jl}\Big{)}+\alpha^{2}\sigma^{2}\mathbf{I}_{N}+\mathbf{R}_{\mathbf{n}_{q}} \Bigg{)}^{-1}\widehat{\mathbf{g}}_{kl}. \tag{23}\]  
Subsequently, the local estimate \(\widetilde{s}_{kl}\) is conveyed to the CPU via the fronthaul link. The CPU then aggregates \(\widetilde{s}_{kl}\) received from all APs using weights \(w_{kl}\) to obtain \(\widehat{s}_{k}=\sum_{l=1}^{L}w_{kl}^{*}\widetilde{s}_{kl}\), which is eventually used to decode \(s_{k}\). From Equation (20), we finally obtain the estimate of \(s_{k}\), as follows:  
\[\widehat{s}_{k}=\alpha\mathbf{v}_{k}^{H}\mathbf{W}_{k}^{H}\mathbf{D}_{k} \mathbf{g}_{k}s_{k}+\alpha\mathbf{v}_{k}^{H}\mathbf{W}_{k}^{H}\mathbf{D}_{k} \sum_{j=1,j\
eq k}^{K}\mathbf{g}_{j}s_{j}+\alpha\mathbf{v}_{k}^{H}\mathbf{W}_ {k}^{H}\mathbf{D}_{k}\mathbf{n}_{i}+\mathbf{v}_{k}^{H}\mathbf{W}_{k}^{H} \mathbf{D}_{k}\mathbf{n}_{q}, \tag{24}\]  
where \(\mathbf{W}_{k}^{H}=\text{diag}(w_{kl}\mathbf{I}_{N},\dots,w_{k}\mathbf{I}_{N}) \in\mathbb{C}^{(LN\times LN)}\) and \(\mathbf{v}_{k}\in\mathbb{C}^{LN}\). This approach is known as large-scale fading decoding (LSFD) in cellular massive MIMO systems [33; 34].
# Modeling and Performance Analysis of mmWave and WiFi-Based Vehicle Communications
## 2 System Model
### Millimeter Wave Communications  
mmWave communications have gained significant attention in automotive applications due to their wide frequency range extending from 10 GHz to 300 GHz. This huge bandwidth overcomes conventional networks and enhances the capabilities of the 5 th generation (5G) systems. However, despite these advantages, mmWave communication presents limitations due to its transmission characteristics [33] leading to substantial path loss and resulting in limited communication range. Additionally, mmWave signals exhibit poor penetration and diffraction efficiency, potentially impacting overall channel performance. In this case, the path-loss model is given by  
\[PL_{\xi(dB)}=FSPL_{\xi(dB)}+\mathcal{S}_{\xi(dB)}+\mathcal{F}_{\xi(dB)}, \tag{7}\]  
where \(\mathcal{S}_{\xi(dB)}\) presents a random shadowing factor, \(\mathcal{F}_{\xi(dB)}\) indicates a small-scale fading factor, and \(FSPL_{\xi(dB)}\) stands for the free space path loss, which is expressed as follows  
\[FSPL_{\xi(dB)}=20\log_{10}\left(\frac{4\pi f_{\epsilon_{\text{mmWave}}}}{C} \right)+10\alpha_{\xi}\log_{10}(r), \tag{8}\]  
where \(\xi\) is equal to 1 and 2, respectively, for the LoS and NLoS cases, and \(\alpha_{\xi}\) stands for its associated path-loss exponent. By considering that, the expression of the corresponding SNR is given by  
\[SNR_{\text{mmWave}}=\sum_{\xi=1}^{2}\frac{P_{\xi}\mathcal{P}_{T}AS_{\xi} \mathcal{F}_{\xi}r^{-\alpha_{\xi}}}{\mathcal{P}_{N}}, \tag{9}\]  
where \(P_{1}\) and \(P_{2}\) stand for the probability of LoS and NLoS, respectively, and \(A_{M}=[\frac{C}{4\pi c_{\text{mmWave}}}]^{2}\), \(\mathcal{F}_{\xi}\) is the small-scale fading power (in watts), which is supposed to conform a Nakagami-m fading distribution, with various parameters for LoS and NLoS cases; \(\Omega_{\xi}\) and \(m_{\xi}\), where the PDF expression is expressed by  
\[p_{{}_{T_{\xi}}}(f)=\left[\frac{m_{\xi}}{\Omega_{\xi}}\right]^{m_{\xi}}\frac{f^{ m_{\xi-1}}}{\Gamma(m_{\xi})}\exp\left(-\frac{m_{\xi}}{\Omega_{\xi}}f\right), \tag{10}\]  
here, \(\Gamma(x)\) indicates the Gamma function [34]. Moreover, \(\mathcal{S}_{\xi}\) is supposed to be lognormally distributed with mean \(\mu_{\ln(\mathcal{S}_{\xi})}\) and variance \(\sigma_{\ln(\mathcal{S}_{\xi})}^{2}\), where its PDF expression is represented by  
\[p_{{}_{S_{\xi}}}(s)=\frac{1}{s\sqrt{2\pi\sigma_{\ln(\mathcal{S}_{\xi})}^{2}}} \;exp\left(\frac{-\left[\ln(s)-\mu_{\ln(\mathcal{S}_{\xi})}\right]^{2}}{2 \sigma_{\ln(\mathcal{S}_{\xi})}^{2}}\right). \tag{11}\]
# Noise Cancellation Method Based on TVF-EMD with Bayesian Parameter Optimization
## 4 Results and Discussion  
### Data Acquisition  
The CO\({}_{2}\) concentration was analyzed after denoising the CO\({}_{2}\) signals, which were obtained using pump-activated CO\({}_{2}\) concentration detectors installed in offices and university classrooms. The CO\({}_{2}\) signals were sampled at a frequency of 5 s and monitored continuously for 24 h. Figure 3 illustrates the CO\({}_{2}\) signal from an 8 m \(\times\) 8 m \(\times\) 3 m regular-sized office accommodating approximately six to ten people. The detector measured the CO\({}_{2}\) concentration at a height of 2.8 m in the room. The specifications used are listed in Table 1.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Value** \\ \hline Sampling frequency & 5 s \\ Measurement accuracy & 1 ppm \\ Error range & 5\% \\ Time sample length & 24 h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Specifications considered for processing CO\({}_{2}\) signals.  
Figure 3: Office CO\({}_{2}\) concentrations throughout the day.  
Figure 2: Flowchart for the noise-cancellation model.
## 2 Materials and Methods
### Characterization of Plasma Source: Spectroscopic VUV to UV-VIS-NIR Measurement Set-Up  
The spectral emission of the discharge in the ultra-violet, visible and near infrared spectral range (UV-VIS-NIR) was analyzed by using a fiber optic monochromator (Avantes, AvaSpec 3648, 200-1100 nm range, \(\sim\)2 nm resolution, 300 lines/mm grating, blazed at 300 nm, deep-UV-detector coated CCD linear array) with a 0.4 mm diameter orifice and a 0.5 m long optical fiber placed at 5 mm from the plasma. The optical emission spectroscopy (OES) of the discharge in the vacuum ultra-violet (V-UV) range was measured by using a calibrated 0.5 m VUV-monochromator (Acton Research Corp., VM-505, 110-280 nm range, \(\sim\)0.1 nm accuracy, 1200 lines/mm grating, Thorn/EMI 9635 QB detector), described also in [31; 48; 60]. For these measurements, the CAP was placed in front of the MgF\({}_{2}\) entrance window (cut off wavelength of 115 nm) of the monochromator, at a distance d = 4 mm, as depicted in Figure 1.
## 3 Methodology
### The Whole Algorithm
As perturbing keywords in the reviews can effectively change their sentiment polarity, these datasets are widely used in experiments involving adversarial examples. Table 1 provides detailed information about the datasets. ### Evaluation of Attack Effectiveness

We first conducted evaluations using the above datasets on the models under the same query budget of 2000. The evaluation metrics were the attack success rate (Succ, %), perturbation rate (Pert, %), and semantic similarity (Sim, %) as evaluated with Universal Sentence Encoder [25]. Tables 2 and 3 show the results on the English and Chinese datasets, respectively. As shown in Table 2, WordBlitz performs better than HLBB and TextHacker. HLBB exhibits the weakest attack efficiency due to its reliance on a population optimization algorithm, which cannot learn from history; meanwhile, its inability to calculate candidate word importance leads to a higher perturbation rate. TextHacker introduces word importance and updates it from history to generate and optimize the adversarial examples. Therefore, its attack efficiency is higher than that of HLBB. WordBlitz constructs the APS and initializes it based on the adversarial transferability rather than random initialization, enabling it to generate adversarial examples more quickly. Thus, it achieves a higher attack success rate and semantic similarity with lower perturbation across almost all of the datasets and victim models. Taking the results of attacks on BERT as an example, WordBlitz outperforms \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset** & **Language** & **Total Number of Samples** & **Description** \\ \hline IMDB & English & 50,000 & Movie Reviews \\ Yelp & English & 94,000 & Reviews from Yelp \\ MR & English & 10,662 & Movie Reviews \\ Hotel & Chinese & 10,000 & Hotel Reviews \\ Waimai & Chinese & 18,000 & Reviews for Take-out Food \\ Online\_Shopping & Chinese & 60,000 & Reviews for Online Shopping \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The details of datasets. the others in terms of attack success rate by clear margins of 0.3-7.9%, and improves the perturbation rate by 0.1-1.7% and semantic similarity by 2.6-3%. Unlike the English scenario, there are more attack types in the Chinese candidate pool, which leads to a broader search space. This situation challenges the attack efficiency. As shown in Table 3, the gap between the three methods is more significant on the Chinese datasets. WordBlitz and TextHacker, which can perturb vulnerable words based on word importance, are more efficient than HLBB. In particular, WordBlitz initializes the APS based on transferability rather than random initialization, allowing it achieve a 1.6-9.1% higher attack success rate than TextHacker and a 1.8-20.7% higher success rate than HLBB. These results show that WordBlitz performs better on complex languages. ### Stability Evaluation

To consider the influence of random seeds on the three tested methods, we validated their stability by conducting repeated experiments on the Yelp and Hotel datasets with the
# Malware Classification Based on Shallow Neural Network
## 4 Experiments and Evaluations  
### System Implementation and Dataset  
All experiments in this work were performed on a Mac mini PC. Table 1 summarizes the major hardware and software platforms of the environment for the SNNMAC algorithm.  
Due to the sensitivity of malicious code, the data sets used in most related works were not open to the public, and it is difficult to collect a large number of malware samples independently. Additionally, for the sake of the objectivity of the experiment, we used two data sets. The first data set was provided by the Microsoft Malware Classification Challenge (BIG 2015) [33], which contains 10,868 unique malware samples of 9 malware families on the Windows system. Each sample consisted of two files. One was a binary file without the PE header, and the other was the corresponding disassembly file generated by IDA Pro. The contents of the malware family samples are shown in Table 2. The second data set was obtained through the VirusShare website, including 8 families, 10,055 training samples and 1074 test samples. We used the open source tool AvClass to label the family names of malicious code and used an IDAPython script to disassemble the samples in batches and get disassembled files in.asm format. Then, we traversed each line of the.text, CODE and.code sections to fetch instructions. The contents of the malware family samples are shown in Table 3.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Platforms** & **Content** \\ \hline \multirow{3}{*}{Hardware Dependencies} & 2.8 GHz Intel Core i5 \\  & 8 GB 1600 MHz DDR3 \\  & Intel Iris 1536 MB \\ \hline \multirow{6}{*}{Software Dependencies} & macOS Mojave 10.14.4 \\  & Python 3.7.4 \\ \cline{1-1}  & Keras 2.0.9 \\ \cline{1-1}  & Numpy 1.16.2 \\ \cline{1-1}  & Pandas 0.24.2 \\ \cline{1-1}  & Scikit-learn 0.21.2 \\ \cline{1-1}  & Tensorflow 1.13.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The platforms of the environment.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Malware Family Name** & **Sample Size** \\ \hline
1 & Ramnit & 1541 \\
2 & Lolipop & 2478 \\
3 & Kelihos\_ver3 & 2942 \\
4 & Vundo & 475 \\
5 & Simda & 42 \\
6 & Tracur & 751 \\
7 & Kelihos\_ver1 & 398 \\
8 & Obfuscator.ACY & 1228 \\
9 & Gatak & 1013 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The malware sample dataset (1).  
We measured the following performance metrics in evaluation experiments: precision, recall and micro-f1. Micro-f1 is a generalization of the standard f1 value on the multi-classification tasks, and it treats multiple classifications as multiple bi-classifications. The metrics are defined as follows:  
\[precision=\frac{TP}{TP+FP} \tag{7}\]  
\[recall=\frac{TP}{TP+FN} \tag{8}\]  
\[micro-f1=2\times\frac{precision_{sum}\times recall_{sum}}{precision_{sum}+ recall_{sum}} \tag{9}\]
## 5 Case Studies
### Effects Due to Proximity of Two Frequencies of Harmonics and Interharmonics  
In the TF representation, when the distance (i.e., the difference between harmonic and/or interharmonic frequencies) between two TF ridges is close enough, the interaction between two ridges will cause low resolution of the representation and the ridges to look blurry. To show the accurate detection of the proposed method, a test signal of (15) is examined. Figure 9a,b show the simulated waveform and detected TF ridges, respectively. Figure 9c zooms in Figure 9b for the two close frequency components. It shows that the two components at 116 Hz and 120.6 Hz are mixed up. Using the DBSCAN clustering method, the two close frequency components can be more precisely classified, as shown in Figure 9c.  
Table 2 lists the absolute errors between the true values and the calculated ones obtained by the proposed method and fast Fourier transform (FFT). In Table 2,\(f_{1}\)-\(f_{5}\) are the frequencies of the harmonic and interharmonic components of the simulated signal given in (15). The maximum absolute error between the true values and the calculated  
Figure 9: **(a)** The simulated waveform, **(b)** the detected TF ridges, **(c)** zoomed-in view of two TF ridges with close frequencies by the proposed method, **(d)** the detected central frequencies shown in color dots of each rigid of **(b)** using DBSCAN.  
ones obtained by the proposed method is 0.2898 Hz that occurs at estimating the 421.1 Hz interharmonic component. It also shows that the effect of proximity of two frequencies, 116 Hz and 120.6 Hz, can be identified by the proposed method. Overall, it can be seen that the proposed method with DBSCAN leads to more accurate frequency detection than the commonly used FFT.  
\[\begin{array}{rcl}s(t)&=&100\sin(2\pi\cdot 60.3t)+10\sin(2\pi\cdot 116t)\\ &+&30\sin(2\pi\cdot 120.6t)+20\sin(2\pi\cdot 301.5t)+10\sin(2\pi\cdot 421.1t) \end{array} \tag{15}\]
# Distributed Uniform Streaming Framework:
## 5 Experiments
### Use Case 1: Sensor to Cloud Streaming  
The first use case is a configuration of a sensor node that streams events to the cloud, as shown in Figure 4. The producer component generates more excessive data than needed by the consumer. Therefore, a filter is placed between the producer and consumer. The filter performs a decimation of two (i.e., only one message out of two will be passed through). The producer and consumer components are not interchangeable between devices as the data is generated in the sensor node and the events are stored in the cloud node. However, the filter component can be placed on both nodes. The task of the DUST Coordinators is to find the optimal placement of the filter component on one of the nodes within the network. The hardware of the sensor is a Raspberry Pi 3 B+ with only a single core enabled. The cloud server uses an Intel i7-7700 HQ processor.  
For the first experiment, we configured the sensor node to produce 100 messages per second. In Table 2, the results of four different runs are shown to determine the optimal placement of the filter component and observe the behavior of the DUST Coordinators with this configuration. The first two runs examine the performance of the system when the filter component is placed consecutively on the sensor and cloud node without being managed by the DUST Coordinators. The latter experiments indicate that the DUST Coordinators will choose the place the filter component on the sensor node to reduce the network usage. The time to migrate a component between the nodes can vary as the offer to become the organiz  
Figure 4: The streaming configuration from sensor to cloud. The filter can be placed on the sensor or on the cloud depending on the devices and the applications.  
For the second experiment, we increased the number of messages to the point where the sensor is unable to run the producer and filter component. The results in Table 3 show for the first two experiments that the system can deliver more messages to the consumer component when the filter component is placed on the cloud node. When the DUST framework manages the setup, we see that the DUST Coordinators will move the filter to the cloud to increase the throughput of the entire system.
## 5 Experiments
### Trajectory Planning Simulation Experiment  
Figure 10 shows the simulation comparison of different trajectory planning algorithms. Figure 10a is the simulation image of the improved RRT algorithm: blue represents static obstacles of different shapes, and black represents the final trajectory. Figure 10b is the simulation image of the improved A* algorithm: blue represents static obstacles of different shapes, and black represents the final trajectory. The initial positions are (2 m, 2 m), and the target positions are (30 m, 30 m). The improved RRT algorithm takes 0.02 s, and the improved A* algorithm takes 0.05 s.  
Experimental results: from Figure 10, it can be found that the trajectory planned by the improved RRT algorithm in this paper can effectively avoid obstacles and the algorithm is extended, taking into account vehicle kinematics and geometric constraints. The planned trajectory is relatively smooth, which is good for vehicle tracking. According  
Figure 10: Comparison of trajectory planning algorithms: (**a**) the improved RRT algorithm; (**b**) the improved A* algorithm.  
Figure 9: Matlab-Carsim HIL simulation platform.  
to the simulation time, the planning efficiency of the improved RRT algorithm is much higher than that of the improved A* algorithm.
## 4 Future Directions of FES for SCI  
In recent years, the number of elderly SCI patients has increased [6]. In middle-aged to elderly people, they might have several physical problems such as osteoporosis, muscle atrophy and weakness [62; 63]. For treatment of osteoporosis in elderly SCI patients, combination with pharmaceutical treatment and neurorehabilitation has been considered as one of the choices, the meta-analysis by [PERSON] et al. indicates that bisphosphonate administration early following SCI effectively attenuated sublesional bone loss, and FES intervention for chronic SCI patients could significantly increase sublesional bone mineral density near the site of maximal mechanical loading [64]. For prevention and recovery of muscle atrophy in SCI patients, [PERSON] et al. reviewed that atrophic myofib could be rescued by home-based FES using purpose-developed stimulators and electrodes [63]. Future investigations seem required to reach clinically applicable recommendations of neurorehabitations for osteoporosis and muscle atrophy as well as weakness in elderly SCI patients.  
In addition, a drawback of FES for patients with quadriplegic upper limbs due to cervical SCI is that the person cannot operate the device by themselves. Therefore, the therapist is responsible for activating the stimulation in conventional FES therapy. However, recent technology using brain machine interface (BMI), and advances in BMI technology have enabled direct input into the FES device. [PERSON] et al. reported recovery of finger function in combination with a BCI and FES for upper limb paralysis due to C4 level cervical SCI [65]. Several recent studies have reported the reconstruction of reach and grip movements of the upper limbs using the BCI and FES systems in cervical SCI with quadriplegia [66; 67]. Further developments in rehabilitation using BMI technology and FES are expected in the future.
## 5 Current Treatments to Avoid or Reduce PPO Activity in Fresh-Cut Produce
### Electromagnetic Radiations/Ultrasonication
#### 5.7.5 Ultrasonication  
Ultrasound (US) might cause enzyme inactivation by cell lysis due to vibration energy, which produces cavitation bubbles and temporarily generates spots of high pressure and temperature when imploded. Thus, its use might be suitable especially when combined with antioxidant treatments (normally included in washing treatments), just as in the caseof fresh-cut apples. Specifically, PPO activity was reduced by combining US (40 kHz) and ascorbic acid (1%), pointing out the fact that the individual application of these treatments was not effective at inactivating enzyme activity [43]. Other recent study was focused on the suitability of this technique combined with ascorbic acid and citric acid for fresh-cut potatoes, showing around 40 % decrease in PPO activity in treated samples, compared to control samples [38].