esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
To meet the urgent need for high-precision ranging and large-capacity transmission in the current BeiDou-3 inter-satellite link system, this paper proposes a novel two-way measurement method based on Co-frequency Co-time Full Duplex (CCFD) system. This approach effectively addresses the limitations of traditional Time-Division Half-Duplex (TDHD) systems, such as complex link establishment processes, constrained ranging accuracy, and limited transmission efficiency. Based on the spatial configuration of the BeiDou-3 satellite navigation constellation, a dynamic link constraint model is constructed, and a comprehensive link budget analysis is conducted for the entire inter-satellite measurement process. The fundamental principle, system model, and key errors of the two-way measurement in CCFD are derived in detail. Theoretical analysis and experimental simulations demonstrate that the proposed CCFD system is feasible and achieves remarkable ranging accuracy improvements. At a carrier-to-noise ratio of 61.6 dBHz, the system attains 1\(\sigma\) ranging accuracy of 1.9 cm, representing a 51.3% enhancement over the 3.9 cm accuracy of the TDHD system. When operating at 69.3 dBHz, the precision further improves to 0.8 cm, outperforming TDHD's 2.2 cm by 66.8%. The introduction of CCFD technology can significantly enhance the performance level of the BeiDou-3 satellite navigation system, demonstrating broad application prospects for the future.  
inter-satellite link; Co-frequency Co-time Full Duplex (CCFD); two-way measurement; BeiDou-3; ranging accuracy  
2025253538  
2  
[PERSON]  
1 Graduate School, Space Engineering University, Beijing 101416, China; [EMAIL_ADDRESS] (H.F.); [EMAIL_ADDRESS] (Z.Y.); [EMAIL_ADDRESS] (Q.C.)  
2
# A Poisson Process-Based Random Access Channel for 5G and Beyond Networks +
## 4 Proposed Poisson Process-Based RACH (2 PRACH) Mechanism  
Since, a very few numbers of preambles for CB-RA are used in each RA slot. The use of uniformly distributed random variable-based RA for preamble collection has a restriction on assembling contending UEs close to an estimated mean value of \(\mu=\frac{(a+b)}{2}\), resulting in higher collisions after a long run or concurrently accessing multiple UEs. Therefore, we recommend that a Poisson process-based RACH (2 PRACH) can be used instead of UD-based RACH. A continues exponential distribution is one of the probability distributions which deals with the time events in a Poisson process. The probability density function (PDF), \(\psi(.)\), of such an exponential distribution with a random variable \(x\) (preamble in a RACH) and constant parameter \(\lambda\) can be defined as,  
\[\psi(x;\lambda)=\begin{cases}\lambda e^{-\lambda x},\forall x\geq 0\\ 0,\forall x<0\end{cases}. \tag{1}\]  
In this equation, \(\lambda>0\) is the constant rate parameter of a Poisson process-based exponential distribution. A Poisson process-based exponentially distributed random variable \(X\) with a constant rate parameter \(\lambda\) allows UEs to stay with the boundaries of mean given by:  
\[E[X]=\frac{1}{\lambda}, \tag{2}\]  
which is evident to make sense that, if a UE try to access the channel at an average rate of \(\lambda=2\) per data frame transmission, then the UE expects to wait \(E[X]=1/2=0.5\) for every next transmission attempt. In addition, the variance of such a UE with random variable \(X\) to access the channel resources is given by,  
\[\mathit{var}[X]=\frac{1}{\lambda^{2}}. \tag{3}\]  
Hence, the standard deviation of the UE remains same as of its mean value. In 2 PRACH mechanism, every UE follow the memory-lessness property of the ED. According to this property, a time domain ED random variable, for example \(T\), follows the relation given by:\[Pr(T>i+j|T>i)=Pr(T>j),\quad\forall i,j\geq 0. \tag{4}\]  
This relationship can be formulated by considering the tail distribution, that is the complementary distribution function, and is given by,  
\[Pr((T>i+j)|(T>i))=\frac{Pr((T>i+j)\cap(T>i))}{Pr(T>i)}\] \[=\frac{Pr(T>i+j)}{Pr(T>i)}\] \[=\frac{e^{-\lambda(i+j)}}{e^{-\lambda(i)}}\] \[=e^{-\lambda j}\] \[=Pr(T>j).\]  
Thus, the time spent by the UE waiting to access the channel relative to its previous or initial time is conditioned on its failure to access the resources at timeslot \(i\), while the remaining channel access time is distributed same as the original unconditional distribution. This can be explained as, if a UE fails to access the channel at timeslot \(i\), the conditional property that channel access will take place at least after \(j\) timeslot and is equal to the unconditional probability of accessing the resources more than \(j\) timeslot after the \(i\) timeslots.
# A Comprehensive Survey on Real-Time Image Super-Resolution for IoT and Delay-Sensitive Applications
## 3 Real-Time Systems and Real-Time SR
### Delay-Sensitive Applications  
Applications that rely on timely data delivery and processing are referred to as delay-sensitive applications. Due to their sensitivity to latency, even minor delays can significantly impact their usability or performance. Although the significance of meeting deadlines may vary, these applications generally require data handling or responses within strict time constraints to ensure proper operation. This typically involves assessing the performance of the operating system, firmware, network, and hardware--both individually and collectively--for the entire system [62]. Low latency is beneficial for latency-sensitive applications, as it signifies a minimal delay between the initiation of an action and its result. High latency is undesirable because it can slow down data transmission and may indicate issues such as packet loss or droppage depending on the type of data. Even if the average latency seems low, a network with excessive jitter is unreliable. Therefore, latency should also be consistent.
## 4 Conclusions  
In this study, the flow characteristics of the Zn/Br redox flow battery (RFB) were investigated, and a new cell frame configuration was proposed to improve the system efficiency. Additionally, the nonuniformity of the electrolyte entering the electrode was numerically defined by performing a flow analysis for the RFB frame geometry. This helps to improve flow nonuniformity, and thus increase the charge/discharge efficiency. The overall system efficiency was also improved by decreasing the power consumption of the pump through a reduction in pressure drop.  
A flow visualization of the conventional RFB indicates that the oily bromide complex causes the flow path clogging problem, and, owing to gravity, the flow direction influences the active surface area of the electrode. Horizontal positioning is required, regardless of the flow direction, so as to obtain the maximum active surface area.  
Based on the analysis of the conventional design problems, the fourth and fifth bifurcations that showed the high symmetry coefficient (\(C_{sym}\)) were integrated, and the downward bifurcations were removed to construct rectangular outlets. This was done to prevent flow path clogging caused by the settlement of the oily bromide complex. Depending on the bifurcations, these modifications reduced the symmetry coefficient (\(C_{sym}\)) and the maximum flow rate deviation (\(D_{m}\)) by up to 98% each. Moreover, the first section exhibited the highest pressure drop because of the chevrons, which exist for the crushing and mixing of the oily bromide complex; they were removed and relocated to reduce the high-pressure loss. This relocation reduces the pressure drop by 15.3% and 11.6% for the inlet and outlet channels, respectively. Consequently, the RFB with the new design can reduce pump loss by 13% compared with the conventional RFB. Through the investigation of the flow characteristics of the flow frame and the modification of configuration, we confirm that the entire RFB's efficiency can be improved by increasing the maximum active surface area and pump efficiency. One area of future work is to predict the overall system performance of the new RFB according to the flow uniformity through charge/discharge experiments for a single cell, as well as stacks.  
Conceptualization, B.-R.K., Y.-H.C. and Y.-H.L.; methodol-ogy, B.-R.K. and D.-Y.S.; software, B.-R.K.; formal analysis, B.-R.K. and D.-Y.S.; valida-tion (experiment), B.-R.K. and Y.-H.L.; writing, B.-R.K., Y.-H.C.; supervision, Y.-H.C.; project administration, D.-Y.S. and Y.-H.C. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the \"Human Resources Program in Energy Technology\" of the Korea Institute of Energy Technology Evaluation and Planning (KETEP), and a granted financial resource from the Ministry of Trade, Industry, and Energy, Republic of Korea (project no. 2017 4010 201410). The authors declare no conflict of interest.
## 3 A Survey on Wavelet Transform-based EEG Denoising Techniques
### Wavelet Denoising with Thresholding  
The most efficient and widely used wavelet denoising is based on thresholding wavelet coefficients. This process follows three important steps: (i) wavelet decomposition: the input signals are decomposed into wavelet coefficients; (ii) thresholding: the wavelet coefficients are modified according to a threshold; and (iii) reconstruction: modified coefficients are used in inverse transform to obtain the noise-free signal. Several researchers have used thresholding wavelet denoising techniques [21; 22; 23; 24]. The universal threshold and statistical threshold functions are efficiently used in wavelet-based EEG denoising.  
[PERSON] et al. [21] proposed automatic detection and elimination of ocular artifacts (OA) from EEG signals using wavelet transform. They used a DWT with a Haar wavelet as the basis function to identify the OA zone. Next, identified OA zones are decomposed into wavelet domain through SWT with Coiflet wavelet with vanishing moment 3 as a basis function. The identified OAs are removed from EEG using a non-linear time-scale adaptive denoising technique, which is based on the wavelet shrinkage strategy. The optimal thresholds are selected based on [PERSON]'s unbiased risk estimate (SURE) and soft-like thresholding function using gradient-based adaptive algorithm. Instead of thresholding all of the wavelet coefficients, [PERSON] et al. [22] performed thresholding on lower-frequency bands as OAs have very low-frequency characteristics. They estimated the threshold by simple statistical analysis of baseline EEG. [PERSON] et al. [23] employed a non-negative garrote shrinkage function during denoising due to its nice tradeoff between soft and hard threshold and modified universal threshold as:  
\[t^{\prime}_{j,l}=K\alpha_{j,l}\sqrt{2\ln N} \tag{2}\]  
where \(N\) is epoch length and \(\alpha_{j,l}\) is the estimated noise variance for the wavelet coefficients at lth level (\(w_{j,l}\)).  
\[\alpha_{j,l}=\frac{median\left(\left|w_{j,l}\right|\right)}{0.6745} \tag{3}\]  
The \(K\) is a new parameter estimated through empirical observations:  
\[K=\left\{\begin{array}{ll}K_{A}&(0<K_{A}<1)\\ K_{D}&(1<K_{D}<3)\end{array}\right. \tag{4}\]  
Recently, [PERSON] et al. [24] proposed an automatic eyeblink artifact correction technique using wavelet transform and metaheuristic algorithms. In their method, the wavelet coefficients are thresholded in a backward manner to modify only the lower frequency bands of the observed EEG signals. Further, to make the system fully automatic, the optimal thresholds are selected through the grey wolf optimizer.
## 1 Introduction
Regarding the first application, several studies are reported in the literature, whereas for aerospace applications only a few works are available [4; 14; 15; 16], maybe due to the hostile space environment that has to be taken into account. To obtain an efficient V-HOE as solar concentrator, the main requirements are: (i) the recording material, (ii) the concentration ratio, (iii) the angular selectivity, (iv) the possibility to implement the passive solar tracking, (v) the efficiency of single and multiplexed elements and (vi) the possibility to split the solar spectrum. The resulting system performance depends on each of these points. However, in our knowledge, the aforementioned focusing points required to achieve an efficient V-HOE as solar concentrator are never considered all together. Thus, we think that future researches should be focused on all of these features. The purpose of the present manuscript is to give an overview of different solar concentrator based on volume holographic devices. Thus, after a brief introduction to the theory behind it and the recording materials commonly used, we analyse the configuration and the performance of several significant results reported in the literature.
# Energy Efficient Management of Pipelines in Buildings Using Linear Wireless Sensor Networks
## 4 Performance Evaluation and Result Analysis
The performance is significantly better than all other three techniques under consideration when the length of the pipeline is 1500 m, which makes it suitable for pipelines inside a large building. When we increase the communication range of the sensor nodes the lifetime of the network increases slightly for all the algorithms. Although, the energy consumed per data packet is proportional to the communication range of sensor nodes, however, due to reduced number of intermediate nodes, the total energy requirement to transfer a data packet is less. Therefore, the normalized lifetime shows improvement upon increasing the communication range of the sensor nodes.
# Infrared Dim and Small Target Detection Based on Local-Global Feature Fusion
## 3 Experiments
### Experiments and Analysis
#### 3.2.3 Analysis  
In Figure 7, it becomes evident that the algorithm we proposed can effectively identify targets within infrared dim and small target datasets, which exhibit varying local signal-to-noise ratios across diverse scenes. However, other algorithms, except for RFBnet, encounter several issues. Although TLLCM detects the target, it produces many false alarms and cannot effectively filter out background noise. Similarly, LIG, IPI, PTSNN, and new top-hat are not immune to sporadic false alarms and struggle to suppress local high-light clutter. Both NARM and NOLC also remove valid signals around the target during the detection process, resulting in missed targets. There are many continuous and uniform cloud layer results in the results of the LIG, and its ability to suppress long strip noise is limited. The new top-hat shows commendable detection performance in simple backgrounds, but its performance in cluttered backgrounds requires improvement. Although the SSD and ISTRU-net algorithms detect most of the targets in the dataset, they fail to detect the target in the data3 sequence, indicating a deficiency in handling interference from high-intensity cloud backgrounds. It is important to note that when the difference between the target and the background is too small, specifically when the LSNR is less than \(-4.4\), the algorithm's detection performance drops sharply, with the \(\mathrm{mAP_{50}}\) falling below 0.38, or the target becoming completely undetectable.  
To further analyze the improvement of the algorithm we proposed compared to YOLOv7, we visualize the curves of losses, \(\mathrm{mAP_{50}}\), and \(\mathrm{mAP_{50.95}}\), as shown in Figure 9.  
It is clear that, compared to the YOLOv7 model, our network model shows faster loss reduction during the initial training stages. The decline slows down around 50 epochs before eventually stabilizing and converging. Throughout the training process, our model minimizes jitter and enhances stability, demonstrating superior performance in detecting dim and small infrared targets.  
Figure 8: ROC curve of data7.
# Suitability of NB-IoT for Indoor Industrial Environment: A Survey and Insights
## 1 Introduction
This is aimed at understanding the relationships and impact of these technologies with NB-IoT, especially in terms of technical features, applications, and challenges in industrial environments. Lastly, to highlight the challenges and future directions of wireless communication of NB-IoT in industries. This paper reviewed research carried out in recent years in the field of Industrial Internet of Things (IIoT) application with some notes on earlier contributions. The process of this review involved a thorough checking of the significant contributions made by journals, articles, conference papers, and report papers. The scholarly databases were systematically queried to extract important information from the literature identified. A total of 368 papers were pulled out of the databases, and 116 papers were selected and comprehensively read, analyzed, and categorized based on their areas of review. Figure 2 depicts the paper extraction procedures. The first process screened out or excluded papers that were in duplicates, non-English written, and had text wrongfully categorized concerning the area of review interest. The review process began with the inclusion of papers on the basis of titles, abstracts, keywords, and conclusions. A total number of 116 papers were finally selected by article type (survey or review, theoretical, practical, etc.). Data were also extracted at this stage to include both theoretical and experimental contents. The third process of the review focused on the challenges that are related to the communication characteristics as supported by the network layers. Figure 2: Data extraction procedure.
## 4 Results and Discussions
### Neurons Number Impact  
Determining the neurons number in the hidden layers also remains integral in the inclusive neural network architecture. An inadequate neurons number in the hidden layers can lead to an underfitting problem. Underfitting arises once there is insufficient neuron number in the hidden layers to satisfactorily learn or detect the signals, especially in a multi-faceted dataset. On the other hand, making use of too many neurons can lead to overfitting problems. Overfitting takes place once the neural network contains too much information processing. It can also result in excessive time increase during neural network training. The amount of training time can increase to the point that it is impossible to adequately train the neural network. It is very clear at this point that some give and take must be grasped between too few and too many neurons number in the hidden layers. Accordingly, by starting with the fastest training algorithm, which is Levenberg-Marquardt (lm), the network was trained and tested with 2, 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50 neurons. This is to ascertain their incremental impact on its performance. Table 3, Figures 5 and 6 display the detailed overall predictive performance of each neurons number using different error statistics. As seen in Table 3, it was expected that a continuing increase in neurons number per layer would result in an upturn in the resolution of the neural network prediction fitting pattern to the measured loss data. At 30 neurons per layer, the neural network model had a good enough fit to the measured loss data with an MAE value of 2, RMSE value of 2.71, STD value of 1.82, R-value of 95 (%) and COE value of 90 (%). Increasing the neuron number to 50 showed no performance improvement, as seen in Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{2}{c}{**Training**} & \multicolumn{2}{c}{**Testing**} & \multicolumn{4}{c}{**Overall Performance**} \\ \hline
**No of** & **MSE** & **R** & **MSE** & **R** & **MAE** & **STD** & **RMSE** & **R** & **COE (\%)** \\ \hline
2 & 12.91 & 0.8874 & 23.22 & 0.7913 & 2.96 & 2.41 & 3.81 & 0.8637 & 75 \\
5 & 12.22 & 0.8643 & 11.93 & 0.9205 & 2.92 & 2.40 & 3.78 & 0.8637 & 75 \\
10 & 7.32 & 0.9320 & 8.01 & 0.8900 & 2.96 & 2.38 & 3.80 & 0.9251 & 86 \\
15 & 7.95 & 0.9229 & 15.72 & 0.8339 & 2.41 & 1.92 & 3.08 & 0.9117 & 83 \\
20 & 8.59 & 0.9247 & 13.79 & 0.9186 & 2.34 & 1.95 & 3.05 & 0.9148 & 84 \\
25 & 7.83 & 0.9296 & 53.08 & 0.4865 & 2.47 & 2.96 & 3.85 & 0.8635 & 75 \\
30 & 5.08 & 0.9591 & 17.06 & 0.8558 & 2.00 & 1.82 & 2.71 & 0.9499 & 90 \\
35 & 4.88 & 0.9531 & 22.45 & 0.8482 & 1.95 & 2.06 & 2.84 & 0.9255 & 86 \\
40 & 7.29 & 0.9360 & 14.31 & 0.7973 & 2.16 & 2.36 & 3.20 & 0.9132 & 84 \\
45 & 7.95 & 0.9315 & 11.06 & 0.8257 & 2.23 & 2.07 & 3.04 & 0.9143 & 84 \\
50 & 3.62 & 0.9673 & 21.07 & 0.7624 & 2.13 & 3.66 & 4.24 & 0.8498 & 72 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Neurons Number impact on MLP Network Predictive Modelling with LM.
## 3 Results
### Repeatability and Robustness of the Results  
Figure 3 shows the repeatability and robustness of the results, in terms of average temperature evolution in charging, discharging, and three fluid heat-exchange modes. At least three tests were performed for each experiment, and the results show a reasonable degree of agreement. For the charging process, an average standard deviation of 0.87 \({}^{\circ}\)C was obtained, with a maximum standard deviation of 1.56 \({}^{\circ}\)C. (The standard deviation was evaluated from the nine thermocouple readings at every time step and the average and maximum values were obtained using the entire process). For the discharging process, the average standard deviation was 0.55 \({}^{\circ}\)C, with a maximum deviation of about 1.02 \({}^{\circ}\)C. Likewise, the repeatability in the three-media HEX operating mode is quite reasonable. The average deviation in the HTF temperature difference between the inlet and outlet (\(\Delta T_{HTF}\)) was 0.2 \({}^{\circ}\)C, with a maximum of 0.67 \({}^{\circ}\)C, and refrigerant temperature difference between the inlet and outlet (\(\Delta T_{ref}\)) was 0.41 \({}^{\circ}\)C, with a maximum deviation of 1.49 \({}^{\circ}\)C.
## Appendix A Online Survey  
The online survey is carried out with a total of 15 experts, denoted as \(E_{i}\), which includes engineering academics (\(A\)) and engineering practitioners (\(P\)) from various countries. The categorical classification of these experts (\(E_{i}\), where \(i=1,\ldots,15\)) is presented in Table 1. Tables 2-3 provide an overview of the responses provided by these experts for the SWOT items as outlined in Table 2-3, respectively.  
\begin{table}
\begin{tabular}{l c c c c c c c c c c c c c c c c c c} \hline \hline
**Hemprete** & & & & & & & & & & & & & & & & & & & & & & & \\
**SWOT Factor** & **SWOT Item** & \(\mathbf{E_{1}}\) & \(\mathbf{E_{2}}\) & \(\mathbf{E_{3}}\) & \(\mathbf{E_{4}}\) & \(\mathbf{E_{5}}\) & \(\mathbf{E_{6}}\) & \(\mathbf{E_{7}}\) & \(\mathbf{E_{8}}\) & \(\mathbf{E_{9}}\) & \(\mathbf{E_{10}}\) & \(\mathbf{E_{11}}\) & \(\mathbf{E_{12}}\) & \(\mathbf{E_{13}}\) & \(\mathbf{E_{14}}\) & \(\mathbf{E_{15}}\) \\ \hline S & \(x_{1}^{(S)}\) & 7 & 4 & 0 & 7 & 8 & 7 & 8 & 8 & 0 & 0 & 8 & 6 & 0 & 7 & 6 \\  & \(x_{2}^{(S)}\) & 7 & 5 & 0 & 8 & 8 & 0 & 8 & 0 & 0 & 6 & 5 & 7 & 6 & 8 & 6 \\  & \(x_{3}^{(S)}\) & 7 & 2 & 0 & 8 & 8 & 7 & 8 & 8 & 0 & 7 & 5 & 7 & 8 & 9 & 7 \\ \hline  & \(x_{1}^{(W)}\) & 6 & 7 & 0 & 7 & 7 & 7 & 3 & 0 & 5 & 5 & 3 & 7 & 8 & 3 & 6 \\  & \(x_{2}^{(W)}\) & 6 & 5 & 0 & 7 & 8 & 0 & 8 & 0 & 7 & 0 & 6 & 0 & 8 & 6 & 6 \\  & \(x_{3}^{(W)}\) & 6 & 3 & 0 & 8 & 1 & 0 & 3 & 0 & 6 & 8 & 3 & 5 & 0 & 5 & 6 \\  & \(x_{4}^{(W)}\) & 6 & 7 & 0 & 9 & 9 & 8 & 4 & 9 & 7 & 8 & 4 & 8 & 8 & 4 & 6 \\ \hline O & \(x_{1}^{(O)}\) & 7 & 5 & 0 & 7 & 7 & 1 & 7 & 0 & 6 & 8 & 3 & 6 & 0 & 9 & 5 \\  & \(x_{2}^{(O)}\) & 8 & 5 & 0 & 8 & 9 & 1 & 8 & 0 & 0 & 6 & 8 & 7 & 0 & 9 & 6 \\  & \(x_{3}^{(O)}\) & 8 & 6 & 0 & 8 & 5 & 7 & 6 & 8 & 6 & 7 & 1 & 6 & 7 & 9 & 6 \\ \hline T & \(x_{1}^{(T)}\) & 6 & 7 & 0 & 9 & 5 & 7 & 5 & 0 & 0 & 7 & 9 & 7 & 0 & 1 & 5 \\  & \(x_{2}^{(T)}\) & 6 & 4 & 0 & 6 & 0 & 7 & 0 & 7 & 8 & 5 & 7 & 0 & 1 & 6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Online survey for hempcrete. Graded from \(x_{min}=1\) (“not relevant”) to \(x_{max}=9\) (“completely relevant”); 0 denotes “unsure”.