esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Measurement Results
### Jitter Analysis  
Jitter is a critical parameter characterizing high-speed data communication systems, as it can cause errors in transmitting bits, leading to incorrect estimation at the receiving end and increasing BER. Jitter can be caused by a system, data transmission, or random noise. Figure 15a shows the real-time eye diagram of the system output OUT at the Rx end captured at a 0.5 Gbps input digital signal. The Eye width indicates a clear opening of 1.9 ns mean-value. The measured total jitter (TJ) value at both edges of the eye is 100 ps, composed of the early/late transition deviations from the ideal time location. The BER bathtub curve of the eye diagram is shown in Figure 15b. Each symmetrical Gaussian tail is made of two segments: the measured section (in blue) and the extrapolated section (in gray). The digital isolator system achieved the lowest BER of \(10^{-15}\), which is computed by intersecting the effective eye width with the bathtub curve. The accumulated jitter is the main cause of bit errors that occurs below the threshold line. TJ of the system is the difference between the unit interval (UI) and the eye width, which is 188.18 ps.  
Typically, TJ is composed of deterministic jitter (DJ) and random jitter (RJ). DJ is further categorized into data-dependent jitter (DDJ) and periodic jitter (PJ). RJ is related to thermal noise, shot noise, and pink noise. Figure 16a shows DDJ versus bit, where the first two bits of the generated stream are zoomed. The negative jitter value at the falling edge of the  
Figure 14: (**a**) Measured system output signal in the presence of CMT pulse at the rising edge surge (500 V/us); (**b**) system output at the falling CMT surge (125 V/us).  
Figure 13: (**a**) Measured continuous oscilloscope waveforms of the overall digital isolation system input-output functionality at 250 MHz and \(-27\)\({}^{\circ}\)C; (**b**) Measured waveforms at 250 MHz and 68\({}^{\circ}\)C.  
first bit (\(-10.9\) ps) indicates that the transition arrived earlier than it should have, while for the second bit, the transition arrived later, resulting in increased transmission errors in the system. Figure 16b illustrates the extracted composite jitter histogram, showing the distribution of three types of jitters present in the system: TJ, RJ/PJ, and DDJ. The amount of RJ/PJ is evaluated by subtracting DDJ from TJ, which is 166.38 ps or 83.19 ps at each peak. By computing the peak frequencies of inter-symbol-interference (ISI) and duty cycle distortion (DCD) using a digital signal analyzer time interval error (TIE) algorithm, DDJ can be eliminated.
# Multi-Scale Safety Helmet Detection Based on SAS-YOLOv3-Tiny
## 4 Experiments and Results Analysis
### Dataset and Evaluation Criteria  
#### 4.1.1 Dataset  
Dataset is crucial for deep learning-based object detection algorithms. In our work, a set of safety helmet datasets was made, which contained 7656 images and was obtained by searching on the Internet, taking photos with cameras and web crawlers, and the format was produced in VOC format. Labeling software (labelImg) was used to label the collected images. There were four categories of objects: helmet (wear a safety helmet for two-wheelers), cap (wear a non-protective hat), Nowwear (wear nothing) and safety-cap (wear an industrial helmet). Additionally, the annotated image coordinate information was saved as an XML file. Next, the training set, the validation set and the test set were randomly divided, and the 8:1:1 ratio was adopted in our study, so there were 6063 training samples, 827 validation samples and 766 test samples. Specifically, the training set was used to train parameters of neural network. The validation set was used to test the effect of the current model after each epoch. The test set was used to test the model's final generalization performance because it did not participate in the training process at all.
# Research on an Ultraviolet Spectral Denoising Algorithm Based on the Improved SVD Method
## 3 Theoretical Method  
In this paper, we present an innovative method for the optimal order selection in SVD denoising. The process begins by reconstructing each singular value associated with the noise-containing signal into a one-dimensional signal. This signal is then transformed into the frequency domain. Next, we employ a FFT on each individual signal to identify the peaks as distinctive signal characteristics. Subsequently, we differentiate the FFT peaks by delaying the first-order values and arrange them in descending order according to the singular values. The singular value corresponding to the first FFT peak within the differential spectrum, surpassing a predetermined threshold, is identified as the optimal choice for denoising.
# A Robust Method Based on Deep Learning for Compressive Spectrum Sensing
## 5 Experiment Results
### BEISTA-Net  
#### 5.1.1 Reconstruction Accuracy  
As shown in Figure 6a, we compare the r-MSE of BEISTA-Net with that of ISTADR-Net [18], R-ADMM [13], and ISTA-Net under various SNR conditions in the TVWS dataset. From this figure, it is evident that as SNR increases, the r-MSE of all four algorithms decreases. When \(\text{SNR}=10\) dB, the r-MSE of BEISTA-Net is even lower than 0.2. BEISTA-Net and ISTA-Net significantly outperform the other two algorithms across all SNR conditions. Furthermore, BEISTA-Net demonstrates a substantial improvement in performance over ISTA-Net. These findings suggest that the BE-Block effectively extracts and enhances the BSF of wideband spectrum signals, improving reconstruction accuracy.  
As shown in Figure 6b, we compare the r-MSE of BEISTA-Net, ISTADR-Net [18], R-ADMM [13], and ISTA-Net under different sparsity level in TVWS dataset. As the sparsity level increases, the r-MSE of all algorithms also increases. The results demonstrate that under different sparsity levels, both BEISTA-Net and ISTA-Net significantly outperform the other two algorithms. Furthermore, BEISTA-Net exhibits a substantial improvement in performance over ISTA-Net. As the sparsity level increases, the performance of BEISTA-Net gradually approaches that of the other three algorithms. This is because the performance enhancement of BEISTA-Net relies on the sparsity of the signal. Generally, the sparser the signal, the greater the performance improvement achieved by BEISTA-Net.  
As shown in Figure 7, we compare the performance of BEISTA-Net with that of the other three algorithms under different SNR conditions and sparsity levels in the NR dataset. From these figures, it can be observed that the proposed BEISTA-Net outperforms the  
Figure 6: TVWS dataset. (**a**) r-MSE versus SNR (dB) of ISTADR-Net, R-ADMM, ISTA-Net, and BEISTA-Net when \(P=8\), \(L=16\), and \(\mu_{TVWS}\in(0,0.5]\). (**b**) r-MSE versus sparsity level of ISTADR-Net, R-ADMM, ISTA-Net, and BEISTA-Net when \(P=8\), \(L=16\), and \(\text{SNR}\in[-10,10]\) dB.  
comparison algorithms across various SNRs and sparsity levels in the NR dataset. This indicates that the proposed BEISTA-Net demonstrates strong robustness.
# Mitigating Distractor Challenges in Video Object Segmentation through Shape and Motion Cues
## 3 Methods
### Object Motion Prediction Module
#### 3.3.2 Motion Prediction Using Memory  
We infer the object frame \(\mathbf{m}_{t}^{k}\) through the utilization of a network based on a recurrent neural network (RNN). Specifically, taking \(\mathbf{m}_{t-nt:t-1}^{k}\) as input, we extract features \(\mathbf{f}_{t-nt:t-1}^{k}\) using the mask encoder \(E_{m}\) and Conv-LSTM. Subsequently, concatenating these extracted features with motion signatures \(\mathbf{\hat{z}}_{(\cdot)}^{k}\) retrieved from the memory bank, we then input them into the mask decoder \(D_{m}\) to infer the \(\mathbf{m}_{t}^{k}\). Our training process is divided into two stages. During the training process, we have access to the ground truth labels \(\mathbf{m}_{t-nt:t}^{k}\) for the object mask, and we only update the memory bank (\(\mathbf{M}\)) in the first step. Other training details are consistent with [20].
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 5 Experimental Evaluation
### Results
#### 5.3.3 Maximum Packet Loss  
This test aims at verifying the maximum packet loss of the 5 GT System. During the experiment, the 8 CTDs sent NB-IoT data during an interval of 120 s, while an attenuation  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**CTD \#ID** & **RSSI [dBm]** & **RSRP [dBm]** & **Latency Min/Max [s]** \\ \hline
7898 & \(-\)68.7 & \(-\)74.7 & 1/4 \\ \hline
6940 & \(-\)68.7 & \(-\)74.7 & 1/4 \\ \hline
2259 & \(-\)78.9 & \(-\)84.9 & 1/5 \\ \hline
4805 & \(-\)78.9 & \(-\)84.9 & 1/5 \\ \hline
5333 & \(-\)82.1 & \(-\)89.4 & 1/6 \\ \hline
7464 & \(-\)82.1 & \(-\)89.4 & 1/6 \\ \hline
6599 & \(-\)99.2 & \(-\)105.3 & 2/10 \\ \hline
3714 & \(-\)68.7 & \(-\)74.7 & 1/4 \\ \hline  & & Average Min Latency & 1.1 \\ \hline  & & Average Max Latency & 5.5 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Minimum and maximum latency.  
Figure 3: Example of a CTD message.  
was introduced by shielding the CTD antennas to provide statistically significant results. Table 5 presents the results obtained during the experiments. More specifically, the number of transmitted and received messages is detailed for each CTD. The results show that the average packet loss is equal to 0.9%, which is reasonable. It is worth pointing out that both in Tables 4 and 5, measured RSSI and RSRP values are in line with latency and packet loss measurements. More specifically, when RSRP values become lower than \(-90\) dBm values, performance is worse and both maximum latency and number of not received messages increase.
# Enhancing Telexistence Control Through Assistive Manipulation and Haptic Feedback
## 5 Evaluation
### Method  
The present study employed a within-subject design to examine the effects of different experimental conditions on body ownership. Participants completed all randomized conditions to control for potential learning effects. The experiment began with an introduction to the system, its components, and its control method. Next, each user had a familiarization session that lasted 10 min in total and included two trails to move and place the cube in a specific location using our system (NN and HN).  
Similarly to the procedure of previous works [7], each condition consisted of three trials in which participants attempted to move a cube from its initial position to a designated destination. The settings for each trial remained consistent within a given condition. The conditions were randomized to avoid potential learning effects. After completing the three trials of each condition, participants completed the Alpha IVBO questionnaire [36], designed to measure body ownership. The questionnaire has three main components: acceptance, control, and change. Each question was rated on a 7-point Likert scale, with 1 indicating \"Strongly Disagree\" and 7 indicating \"Strongly Agree\".  
After completing all the conditions, participants participated in a semi-structured interview to gather their impressions and opinions about the system. The inclusion criteria for participants included being between 18 and 50, being able to read and understand the language used in this study, and having no known physical or cognitive impairments that would prevent them from completing the task. Exclusion criteria included cybersickness from VR or prior experience with the system.  
In conclusion, this study used a within-subject design to examine the effects of different experimental conditions on body ownership, using the Alpha IVBO questionnaire as a measure. This study aimed to control potential learning effects by randomizing the order of conditions and to gather participants' impressions and opinions about the system by conducting a semi-structured interview.
# Accurate Performance Analysis of Coded Large-Scale Multiuser MIMO Systems with MMSE Receivers
## 4 Performance Analysis of Coded Multiuser MIMO System with an MMSE Detector
### Asymptotical Diversity Order  
Now, we are interested in further analyzing the derived BER expression to find the diversity of the coded large-scale multiuser MIMO systems with MMSE receivers. In the high-SNR region, the asymptotical diversity order is defined as:  
\[\begin{split} D&=-\lim_{\text{SNR}\to\infty}\frac {\log_{10}P_{\text{b}}}{\log_{10}\text{SNR}}\\ &\simeq-\lim_{\text{SNR}\to\infty}\frac{\log_{10}f(d_{free},\mu, \mathcal{A})}{\log_{10}\text{SNR}}.\end{split} \tag{33}\]  
Next, we analyze the various parameters that make up \(f(d_{free},\mu,\mathcal{A})\). Since \(\text{SNR}=\frac{E_{\text{s}}\text{tr}(\mathbb{P})}{KN_{0}}\), \(\rho=\frac{KN_{0}}{E_{s}}\), \(\delta_{1}=\zeta_{k}\mathcal{G}\) and \(\delta_{2}=\zeta_{k}/\rho\), both \(1/\rho\) and \(\delta_{2}\) are proportional to SNR. When SNR approaches infinity, \(\delta_{1}\) approaches a constant. Therefore, in the high SNR region, the parameter \(A\) is proportional to \(\text{SNR}^{-(R-K+1)}\), and the parameter \(B_{m,l,\mathcal{E}}\) can be regarded as a constant. In addition, when the distance between the constellation points is fixed, the SNR tends to infinity, which is equivalent to the noise power tending to zero. Then, the denominator part of Equation (27) can be treated as a constant. The essence of the integration is a summation, without changing the exponential portion of the parameters associated with SNR. From the above analysis, we can see that, in the high SNR region, \(f(d_{free},\mu,\mathcal{A})\) is proportional to \(\text{SNR}^{-d_{free}(R-K+1)}\), and then combined with Equation (33), the asymptotical diversity order \(D=d_{free}(R-K+1)\) is obtained.
## 2 Proposed Enhanced PNCC Algorithm
### Noise Suppression
#### 2.2.3 Mean Power Normalization  
The human auditory processing system contains an automatic gain control that decreases the effect of amplitude variation with the perceived acoustic wave. In the PNCC system, using the power function nonlinearity that is explicated in the next stage drives the produced power values from the processing to 145, which is sensitive to the absolute power variation, although the impact of variation is usually small. The mean power normalization is applied to minimize the potential effect of amplitude scaling. The input power is normalized by dividing the perceived power by a running average of the total power. Initially, the estimated mean power \(\mu[m]\) is calculated from the following equation:  
\[\mu[m]=\lambda_{\mu}\;\mu[m-1]+\frac{\left(1-\lambda_{\mu}\right)}{L}\sum_{l= 0}^{L-1}\widetilde{Q}[m,l] \tag{5}\]where \(\lambda_{\mu}\) is a forgetting factor whose value is 0.999. Then the normalized power is computed from the evaluated running power \(\upmu[m]\) as per the following equation:  
\[U[m,l]=k\frac{\overline{Q}[m,l]}{\mu[m]} \tag{6}\]  
where \(k\) an arbitrary constant [15; 17].  
Figure 3: _Cont._
# Authentication-Based Secure Data Dissemination Protocol and Framework for 5G-Enabled VANET
## 5 Performance Evaluation
### RSU's Overhead  
In our proposed scheme, the message authentication task is assigned to the RSU. It is assumed that each vehicle sends only one message in the 300 ms as specified by DSRC. It can be seen in Figure 8 that the overhead of our scheme is better than other schemes when the system does not have any invalid signature and when all message authentication overheads are assumed to be within one RSU's domain.  
Figure 8: RSU’s Overhead.  
Figure 7: Execution time.
# EV Charging Behavior Analysis Using Hybrid Intelligence for 5G Smart Grid
## 4 Simulation
### k-NN Classification for EV Driver Behavior
The AUC can be obtained by calculating the total area under the ROC curve. For example, supposing that the ROC curve is formed by sequentially connecting a series of points of coordinates \(\{(x_{1},y_{1}),(x_{2},y_{2}),(x_{3},y_{3}),\ldots,(x_{m},y_{m})\}\), the AUC can be estimated as:  
Figure 8:2  
AUC-ROC curve is a performance measurement for a classification problem at various threshold settings. ROC is a probability curve and AUC represents degree or measure of separability. It tells how much a model is capable of distinguishing between classes. The higher the AUC, the better the model is at predicting 0 as 0 and 1 as 1. The higher the AUC, the better the model is at distinguishing between the drivers with regular behavior and irregular behavior. As shown in Figure 13, the ROC curve is plotted with TPR against the FPR where TPR is on the \(y\)-axis and FPR is on the \(x\)-axis. Area A, B and C are the AUC of three different classification results with different parameters. From Figure 9, it can be seen that the AUC-A is larger than AUC-B, and AUC-B is larger than AUC-C. After comparing these three curves, we could know that the AUC-ROC curve corresponding to AUC-A has the best performance. Figure 8:3 The confusion matrix of the classification result. The ROC curve of the classification model we built is shown in Figure 8:4 The grey dotted line is the set of points where FPR equals PTR. We found that the curve was close to the upper border with AUC = 0.994, indicating that the classification effect of our model was very good. Therefore, using this model, we could expect to classify the behavior of new users of electric vehicles to determine whether they could be efficiently uniformly scheduled. Power companies could potentially use these classification results to develop appropriate scheduling strategies to centrally coordinate the charging behavior of these electric vehicles in order to achieve peak-shaving and valley filling.
## 1 Introduction
### Literature Review  
On-road projections offer a wide possibility for designing new ways of communication for road users and drivers. In the past, projection systems attracted the attention of many researchers, particularly in the field of human factors for transportation. Studies show that the on-road projection of two straight parallel guided lines helps new drivers to maintain the vehicle in the center of the lane [30]. Similarly, the visibility of the projection symbols, such as the luminance contrast of the on-road projection, was investigated. Twelve subjects were involved in the nighttime experiment keeping the distance and size of the projection symbol fixed and concluded that positive contrast symbols are easily visible to the subject drivers [31]. In another study, The impact of road projection symbols on road users, i.e., pedestrians, cyclists, and drivers, was considered in the backing up situation in parking lots. A total of thirty-two subjects were involved in this experiment [32]. Results showed that on-road projection in the backing up situation increases the on-road awareness, understanding, and perception of the whole situation. In another study related to the rear-end collision cases. A delay of 0.1-0.2 s in the brake reaction time was observed in response to the on-road assistant projection system [33]. Nevertheless, the feeling of safety is still under discussion in the research community [34; 35]. In the field study of overtaking scenarios with an on-road projection, eye-tracking data of thirty-nine subjects were collected to observe the distraction potential offered by the projection symbol. Few glances away from the front road ahead were observed. However, no significant difference was found [24]. Thus, the study focused on the distraction potential imposed by the on-road projection symbol and completely ignored the impact on user satisfaction, feeling of safety, workload, and acceptability towards a system in driver-driver interaction. Some other researchers focus on the optimal position of the projection symbol. The user readability rating of the oncoming vehicle showed that a 15 m distance from the front of the projection vehicle to the center of the projection symbol with a 50 K luminance value is optimal [33]. Similarly, particular to overtaking maneuver, eye glances data right before overtaking on the highway shows that people significantly distribute a higher percentage of eye glances towards their shoulder to check for a blind spot before maneuvering [36].
# Free Space Optic Receiver with Strongly Overlapped Photodetectors' Field of View
## 2 Free Space Optic (FSO) Receiver Design
### FSO Receiver Bandwidth  
The bandwidth is determined by the desired communication speed and the coding used. Thus far, no reference about the chosen encoding scheme has been given in this study. Due to multipath light signal propagation, simple encoding schemes are more reliable than complex schemes. Data are transferred by on/off keying related to light intensity modulation. Even though many good encoding schemes are available, the well-known phase encoding (PE) scheme, sometimes called \"Manchester\" encoding, was selected for the following reasons: balance of low and high symbols removing the DC part, resynchronization capability at the receiving end, and high noise immunity. Many other integrated solutions that facilitate PE implementation on the communication chain are available on the market, including devices such as HD-6409 by Intersil (Intersil, Milpitas, CA, USA, 2015) or DP8391 (alternatively DP83910A) by National Semiconductor (National Semiconductor, Santa Clara, CA, USA, 1995). With respect to bit error rate (\(BER\)) testing, the device used must maintain data transfer even with error symbols passing. Please note that this is not a usual process. For example, this is not the case with the HD-6409 device. The DP83910A device was ultimately selected as the best candidate. It possesses an analogue phase-locked loop (PLL) that allows the highest frequency range of the transferred data. It was built for a 10 Mbit\(\cdot\)s\({}^{-1}\) transfer speed but maintains functionality even for a 2 Mbit\(\cdot\)s\({}^{-1}\) transfer speed, which is an advantage because it broadens the testing capability and includes slower equipment.  
By knowing the speed of the transmitter, PE codec hardware and the spectral density of the PE scheme [30], calculating the demanded receiver bandwidth, or more precisely, the receiver's low and high cut-off frequencies, is not complicated. Even though both transmitter and PE codec hardware can transfer at speeds as high as 10 Mbit\(\cdot\)s\({}^{-1}\), some space remained, and the maximum lowered to a transfer speed of 2 Mbit\(\cdot\)s\({}^{-1}\). Based on this, the receiver's low and high cut-off frequencies were 100 kHz and 1.5 MHz, respectively. Please note that the high cut-off frequency follows the rule of optimal filtering. This means that the PE spectral density high frequency decline starting at \(\frac{3}{4}f_{bit}\) may be approximated by the first order low pass filter roll-off, expressed as the number \(f_{h}=\frac{3}{4}f_{bit}=\frac{3}{4}\)\(2\times 10^{6}=1.5\) MHz. The low cut-off frequency of \(f_{l}=100\) kHz was determined from \(BER\) measurements on the 2 Mbit-s\({}^{-1}\) data stream.