esa-sceva/satcom-chunk-collection huggingface dataset:
# Dual Threshold Self-Corrected Minimum Sum Algorithm for 5G LDPC Decoders
## 5 Design Architecture and Implementation  
The Vivado High-Level Synthesis (HLS) platform launched by Xilinx has implemented a rapid design with IP as the core, greatly improved productivity, and has been widely used in many designs [34; 35]. Vivado HLS is a high-level programming language that helps to achieve Register Transfer Level (RTL) hardware functions, improving the level of abstraction of the system design.  
An architecture of the LDPC decoder is designed in Figure 5, and mainly includes three modules: initialization (readData), information update (updateInfo), and decision (Check). The information update module consists of CNP (rowUpdate) and VNP (colUpdate). In the Vivado HLS platform, the LDPC decoder with a code length of 1584 bits and a code rate of R = 2/3 is simulated and synthesized, using the chip \"xc7k420 ffg901-2\" from Xilinx's device library.  
Figure 5: Design of low-density parity-check (LDPC) decoders based on Vivado High-Level Synthesis (HLS).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Multiplication** & **Division** & **Addition** \\ \hline BP [10] & \(11 NW-6(N+K)\) & \(N(W+1)\) & \(N(3W+1)\) \\ MS [11] & 0 & 0 & \(N(4W-1)+K(\log W-2)\) \\ NMS [17] & 0 & \(NW\) & \(N(4W-1)+K(\log W-2)\) \\ SCMS [31] & 0 & 0 & \(N(4W-1)+K(\log W-2)\) \\ DT-SCMS & \(2 NW\) & \(NW\) & \(N(6W-1)+K(\log W-2)\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Computational complexity of decoding algorithms.  
In Figure 5, _llr_ indicates the channel input information, and _blockdout_ indicates the output of decoding results. In Vivado HLS, each module is implemented by C ++ functions. After compilation, it can be simulated and synthesized, and verified by the test platform. The initialization module mainly completes reading the initial value from the input channel information (_llr_) and storing it in the variable node message (\(L\big{(}q_{ij}\big{)}\)) and the judgment information (\(L(q_{i})\)). The CNP module is also called \"the row update module\", which mainly calculates the check node message (\(L\big{(}r_{ji}\big{)}\)). The VNP module is also called \"the column update module\". It first calculates the judgment message (\(L(q_{i})\)), and then calculates the variable node message (\(L\big{(}q_{ij}\big{)}\)). The performance evaluation of the decoder after synthesis in Vivado HLS is shown in Figure 6.  
It can be seen that a total of 42,336 clock cycles are required to complete one decoding process (22 iterations), of which approximately 1586 cycles are required to read the channel soft information, and 1802 cycles are required for decoding iterations. As described in Section 4.1, compared with the SCMS algorithm, the proposed DT-SCMS algorithm can greatly reduce the number of decoding iterations. Therefore, the total latency of decoding can be correspondingly reduced when the average number of iterations required for the decoding is reduced.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 1 Introduction
### Related Research
#### 1.2.2 Related Work Concerned with Smart Transportation  
With the development of smart cities, the integration of edge computing technologies plays an important role in transforming urban infrastructure. The adoption of edge computing not only facilitates real-time data processing at the network's edge, enhancing the efficiency and responsiveness of smart transportation applications, but also opens up new avenues for addressing complex challenges inherent in urban environments. This complexity is underscored by both the comprehensive survey by [PERSON], [PERSON] et al. [10] on task offloading in edge and cloud computing and the detailed examination of resource scheduling strategies in edge computing by [PERSON] et al. [11]. These works illustrate the diverse approaches and theoretical foundations developed to optimize task offloading processes and resource scheduling across different edge computing scenarios. The approach also aligns with the broader objectives of improving traffic flow, enhancing vehicular communication, and ensuring safety, thus contributing to the overall efficiency and sustainability of urban living.  
The next part is within the smart transportation of edge computing realm. The first related work discussed a decision support method of event-driven architecture for a traffic management system [12]. This paper illustrates how event-driven architecture (EDA) and complex event processing are used for real-time processing and analysis of extensive data streams generated by sensors and vehicles. The core objective is real-time monitoring and control of traffic flow, exemplified in a smart traffic management system prototype in Bilbao, Spain. While the paper effectively demonstrates the use of event-driven architecture for traffic management, our research focuses on leveraging edge computing technologies. This approach significantly reduces communication latency and real-time responses, which is a crucial aspect in smart transportation systems. [PERSON] et al. proposed a novel design and implementation of a smart traffic signal control (STSC) system that enhances vehicular communication and traffic management [13]. We were inspired by the design of the smart traffic signal control system; however, we used a different solution that combines Elixir and event-driven architecture to handle the vehicular communication in real time. The work by [PERSON] [14] focused on edge computing for real-time near-crash detection in smart transportation. It used IoT devices like Nvidia Jetson TX2 for processing video streams to identify near-crash events.  
The research highlighted above offers diverse solutions and implementations for smart transportation systems. Differently, our study introduces a more novel approach by leveraging an edge-based framework using Elixir, combined with event-driven architecture and MQTT, to efficiently handle the real-time processing of huge volumes of data in intelligent transportation systems.
## 4 Results  
### Speech Analysis  
Figure 6 shows the data of turn duration, reaction latency, speaking time, pause time, and pause count of every participant. The black bars mean the means of each measurement. We analyzed the data using the Mann-Whitney test because we could not assume that some of the data followed a normal distribution. We summarized the descriptive statistics values and the results of the analysis (statistics U, p-value, and effect size Cohen's d) in Table 1.  
The Mann-Whitney test showed that turn duration, speaking time, and pause count had significant differences between the human interlocutor group and the robot interlocutor group (alpha level was 0.05). Turn duration and speaking time were longer in the human interlocutor group than the robot interlocutor group, and pause count was more in the human interlocutor group than the robot interlocutor group. On the other hand, reaction latency and pause time have no significant difference between both groups.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline  & **Group** & **N** & **Mean** & **SD** & **U** & **_p_ & **Cohen’s d** \\ \hline Turn duration & Human & 18 & 35,111 & 14,049 & 81 & 0.010 & * & 0.906 \\  & Robot & 18 & 24,022 & 10,108 & & & & \\ Reaction latency & Human & 18 & 3243 & 2591 & 162 & 1.000 & & 0.299 \\  & Robot & 18 & 2607 & 1524 & & & & \\ Speaking time & Human & 18 & 22,389 & 10,650 & 89 & 0.016 & * & 0.830 \\  & Robot & 18 & 14,692 & 7667 & & & & \\ Pause time & Human & 18 & 10,599 & 6119 & 106 & 0.064 & & 0.596 \\  & Robot & 18 & 7322 & 4797 & & & & \\ Pause count & Human & 18 & 8.65 & 4.61 & 97 & 0.041 & * & 0.834 \\  & Robot & 18 & 5.52 & 2.62 & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The summary of the descriptive statistics values and the analysis results of participants’ speeches. The symbol * means that the p-value is lower than 0.05.
## 4 Discussion  
The original SIFT method uses the gradient calculation method of the difference operator to process the SAR and optical images. Due to the influence of the non-linear radiation difference of the heterogeneous image, the gradient magnitude and direction calculated in the SAR image by this calculation method are quite different from the calculation result in the optical image. At the same time, due to the SAR due to the influence of image noise, the feature points detected by this method from heterogeneous images are often poor in stability, and the ratio of control points with the same name is low, which is easy to cause mismatches. These all cause the phenomenon that the correct rate of feature points matching by this method is not high.  
In this chapter, the SIFT registration method based on uniform extraction of feature points and gradient consistency has successfully dealt with the shortcomings of the SIFT algorithm. Using the combination of phase consistency intensity screening and uniform distribution feature point detection, it improves the robustness of feature points, while taking into account the uniformity of the feature points in the coordinate space. In addition, the extended phase consistency method is used to calculate the gradient amplitude of the image; thus, the accuracy of the main direction calculation of the original SIFT method is improved. In the descriptor construction process, the descriptor constructed by dividing the sector area under polar coordinates has better reliability. In this way, the feature point matching method is optimized from the feature point extraction to the feature point description link, so the highest matching success rate and registration accuracy can be obtained.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Image No.** & **Method** & **CMR/\%** & **RMSE/Pixel** \\ \hline \multirow{3}{*}{\(1\)} & SIFT & 64.79 & 1.1344 \\  & SAR-SIFT & 79.65 & 0.9276 \\  & Proposed & 88.21 & 0.5128 \\ \hline \multirow{3}{*}{\(2\)} & SIFT & 67.58 & 1.3153 \\  & SAR-SIFT & 71.29 & 1.1452 \\  & Proposed & 81.89 & 0.8127 \\ \hline \multirow{3}{*}{\(3\)} & SIFT & 62.46 & 0.9008 \\  & SAR-SIFT & 75.34 & 0.7097 \\ \cline{1-1}  & Proposed & 89.73 & 0.4982 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Quantitative comparison of the proposed method with other SIFT-based algorithms.  
Figure 4: (**a**) Optical image; (**b**) SAR image; matches found in pair 3 using (**c**) SIFT, (**d**) SAR-SIFT, and (**e**) the proposed method. The reference image is shown on the left and the sensed image on the right.
## 6 Conclusions  
In this paper, we developed a spectrum-saving transmission method in multi-antenna satellite communication systems where beam steering is used at the satellite and UTs. By realizing the different transmission capabilities at the HS and UTs, the spectral resource of the HS was shared with UTs. To successfully demodulate the information of UTs, the SIC and parallel filtering are taken into consideration at the HS. As a further advance, we derived the ergodic sum rate for performance analysis and analyzed the impact of the accessed UTs in terms of the system availability. Simulation results verified that the spectrum-saving method is capable of providing better performances with less spectrum expense, while the BER would not be obviously degraded.  
Conceptualization, T.L., X.H. and X.Y.; methodology, T.L. and X.Y.; validation, T.L.; writing--original draft preparation, T.L.; writing--review and editing, T.L., X.Y. and X.H. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China under Grant No. 62201533.  
Not applicable.  
The authors declare no conflict of interest.
# High-Precision Light Spot Position Detection in Low SNR Condition Based on Quadrant Detector
## 6 Conclusions  
In light spot position detection, we use a QD as the detector. However, in the traditional direct detection method, after signals are run through the amplifier, some DC offsets are brought into the QD output electric signals. In order to solve the problem, we considered to use a modulation method and replaced the low pass filters with band pass filters. For white noise, we used a Kalman filter to eliminate it. Then, we made an experimental verification, and the results showed that after using the improved detection method, the linearity of QD was improved and the root-mean-square errors decreased \(51.5\%\) compared to the direct detection method. In addition, the QD output SNR was about \(-10\) dB, which means the improved detection method can work in low SNR condition. What is more, Kalman filtering does not require a large amount of data, which means it works efficiently.  
Conceptualization, J.Y. and Q.L.; methodology, J.Y. and Q.L.; software, J.Y., Q.L., H.L., and Q.W.; validation, G.Z. and Y.H.; formal analysis, J.Y., Q.L., H.L., Q.W., D.H., and S.X.; investigation, J.Y., Q.L., H.L., Q.W., D.H., and Y.X.; resources, Y.X. and S.X.; data curation, J.Y., Q.L., and H.L.; writing-original draft preparation, J.Y. and Q.L.; writing-review and editing, J.Y.; visualization, J.Y., Q.L., Q.W., D.H., and S.X.; supervision, G.Z. and Y.H.; project administration, G.Z. and Y.H.  
This research received no external funding.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Points** & **Proposed Method** & **Direct Method** \\ \hline
10 & 0.0062 mm & \\
40 & 0.0053 mm & 0.0101 mm \\
80 & 0.0049 mm & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of the root-mean-square (RMS) errors in different points of iteration.  
Figure 8: **(a)** The calculated x coordinate curves using the two methods (80 points Kalman filtering); **(b)** the absolute errors using the two methods (80 points Kalman filtering).  
**Acknowledgments:** Thanks to my tutor and all the colleagues in the Key Laboratory of Optical Engineering, Chinese Academy of Sciences.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Physical-Layer Watermarking Scheme Based on 5G NR
## 2 Watermark System Model on 5G NR
### Improved Watermarking Scheme
#### 2.2.2 Improved Watermark Embedding Scheme  
In Section 2.1, the process of embedding the watermark signal into the phase of the DMRS was described in detail. However, the DMRS is of great significance and is the basis for demodulating the user's signal. An important principle of embedding watermark information into the DMRS is to minimize the influence of the watermark on channel estimation and then reduce the interference with users' signals.  
For the receiver, after embedding the watermark, the DMRS is expressed in (3). To calculate this phase offset, the signal can be quadrupled first, and the result is as follows:  
\[\widetilde{y}^{4}(n)=e^{j(4\widetilde{q})}e^{j(\varphi(n)\pi+4 aw(n))}=-e^{j(4 \widetilde{q}+4 aw(n))} \tag{9}\]  
Figure 7: Watermark generation schemes. Scheme I is the basic watermark generation scheme, Scheme II adds channel coding on this basis, and Scheme III uses the pseudo-user-bit instead of the user symbol as the input of the hash algorithm.  
When using DMRS to estimate the channel, the phase can be calculated as follows:  
\[Arg\left(-\sum_{n=1}^{24}e^{i(4\widehat{\varphi}+4\pi w(n))}\right)=Arg\left(-e^{ \mathrm{i}i\widehat{\varphi}}\sum_{n=1}^{24}e^{\mathrm{i}\mathbf{k}aw(n)}\right) \tag{10}\]  
where \(n\) is the number of subcarriers. To accurately calculate the phase offset caused by signal transmission in the channel, the embedded watermark bits must meet the following conditions:  
\[\sum_{n=1}^{24}e^{\mathrm{i}\mathbf{k}aw(n)}=1 \tag{11}\]  
Since \(w(n)=\pm 1\), (11) can be equal to  
\[\sum_{n=1}^{24}w(n)=0 \tag{12}\]  
We call this important condition the symmetry of the watermark. In this paper, to realize the symmetry of the watermark, the channel coding in the watermark generation scheme is improved. Firstly, every four watermark bits are divided into a group, and each group of watermark bits is coded by (7, 4), a cyclic code. Finally, according to the value of bit \(1\) in the 7-bit code, bit \(0\) or bit \(1\) is duplicated in the 8 th bit to realize the symmetry of the watermark. It is worth noting that there are all 0s and all 1s in the traditional cyclic coding, at which time the symmetry of the watermark cannot be realized. Therefore, the information codes with all 0s and 1s can be regarded as adjacent information codes 0001 and 1110, and then encoded. The mapping relationship of the improved (7, 4) cyclic codes is summarized in Table 1. The number of watermark bits after coding is twice that before coding. There are two DMRS symbols in each sub-frame, and each symbol occupies 24 subcarriers. If channel coding is not performed, the number of watermark source bits that can be transmitted in each sub-frame is 48, and if channel coding is performed, the number of watermark source bits in each sub-frame is 24.
# Methods for Studying Bacterial-Fungal Interactions in the Microenvironments of Soil
## 3 Get Your Hands Dirty: Methods for Studying BFI  
Advances in molecular methods and bioinformatics have provided enormous benefits in enlightening BFI from many different perspectives. The availability of genomic methods, such as DNA sequencing, especially with high-throughput sequencing methods, enabled deeper insights into the soil microbial diversity, and contributed enormously to a more comprehensive database build-up, and thus to better network analysis inferences. They surely provide the theoretical background of what to expect from a specific soil sample and undoubtedly increased the knowledge on unculturable microorganisms (some of the relevant literature on the topic include work by [68; 69; 70; 71; 72; 73]). However, virtually all -omics approaches entirely ignore the geometry of the pore space in soils or the characteristics of microenvironments (e.g., [26; 74; 75; 76]). Even highly confident co-occurrence network analyses cannot distinguish between true ecological interactions and other non-random processes (e.g., cross-feeding versus niche overlap) [69]. Although benchmarking studies are few and urgently needed, their relatively high false-positive interaction rate can probably be decreased by omitting interactions that could physically not have occurred. Thus, our experimental approach should be adapted to make it a better fit for exploring the complexity of BFI in soil (Figure 2).  
As microorganisms interact at micro-scale, micro-scale data from microbiological, physical (e.g., porosity, water, air) and (bio)chemical (soil organic matter, micro-elements, pH) methods, including their new technological advances, should be combined to study BFI. As such, some of the methodologies discussed in the following chapters have also been applied to the investigation of abiotic and biotic aspects of the soil matrix [3], but here they will only be presented in vision of bacterial and fungal interactions. An overview of the abbreviations used to shorten the techniques' names is reported in Table 1.  
Figure 2: Overview of methods and technologies discussed in the review. They are divided by the applicability and information they can provide in regards to studies researching bacterial—fungal interactions. Often, by combining more than one together (e.g., microfluidics and FISH-SIP-nanoSIMS), information at different scales and levels are obtained.  
Although not discussed, the combination of -omics techniques (e.g., metagenomics, transcriptomics, proteomics) alongside other standard protocols remains a very valuable strategy, provided that high-quality raw material is available. Furthermore, the reader should bear in mind other important methodological factors. It is essential to consider the heterogeneity of the soil for sampling strategies [26], the consistency of the experiment for reproducible results, and the applicability of the techniques for eventual high-throughput applications [77].
# The Effects of Acidic, Alkaline, and Neutral Anolytes on Electrochemical Seawater Deoxygenation
## 3 Results and Discussion
### Acidic Anolyte  
The results using 0.1 M HNO\({}_{3}\) solution as an anolyte in the cell are shown in Table 3. The pH of the anolyte was stable at pH 1 during both the stabilization (2 h) and the test period. The pH of the catholyte changed from 7 to 5 during the stabilization period, and remained at pH 5 during the test period. The oxygen concentration in the catholyte decreased from 8.18 to 2.99 ppm and from 8.18 to 1.6 ppm using 1.9 V or 2.3 V applied voltage, respectively, as shown in Table 3. The conductivity of the anolyte was quite stable at about 42.8-43.2 mS-cm\({}^{-1}\) and for the catholyte at about 46.7-47.1 mS-cm\({}^{-1}\).  
During a test at a constant voltage of 2.2 V resulting in a constant current of 20 mA, and an oxygen concentration lowered to 1.9 ppm, the use of acidic anolyte was not found to be suitable compared to the alkaline anolyte. In a similar experiment done in Cell B with 2.2 V applied voltage, potentials of 1.3 V and \(-\)0.5 V were obtained at the anode and at the cathode, respectively. The hydrogen peroxide concentration was measured to be about 0.5 ppm after 150 min of applied voltage. However, the theoretical current calculation showed that there was <5% current output for hydrogen evolution at 2.2 V, whereas the higher negative potential at the cathode is expected to evolve more hydrogen gas at the cathode. The higher negative potential at the cathode and detection of hydrogen peroxide indicate the possibility for more side reactions taking place in the deoxygenation cell. The lower potential at  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Time** & **Voltage** & **Current** & **Oxygen Conc.** & **Analytic Conductivity** & **Analytic Conductivity** & **Analytic Conductivity** & **Conductivity** \\ (min) & **(V)** & **(mA)** & **(ppm)** & **(ms-cm\({}^{-1}\))** & **Analytic pH** & **(ms-cm\({}^{-1}\))** & **Catholyte pH** \\ \hline
**0** & - & - & 8.44 & 14.6 & 13 & 46.8 & 9 \\
**30** & 1.9 & 14 & 1.31 & 14.6 & 13 & 46.8 & 9 \\
**60** & 2.0 & 17 & 1.20 & 14.6 & 13 & 46.8 & 9 \\
**90** & 2.1 & 21 & 1.06 & 14.5 & 13 & 46.9 & 10 \\
**120** & 2.2 & 27 & 0.81 & 14.2 & 13 & 46.9 & 10 \\
**150** & 2.3 & 34 & 0.55 & 14.1 & 13 & 46.9 & 10 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Effect of voltage on oxygen removal, pH, and conductivity in 0.1 M NaOH anolyte.  
the anode in the acidic electrolyte shows that there was less oxidation of water resulting in the lower current output.
# Analysis and Correction of Antenna Pattern Effects in AMAO Spaceborne SAR Images
## 2 Antenna Pattern Characteristics
### Geometric Properties
From the point of view of radiometric effect analysis, the azimuth variation of the slant-range distance can be approximated by a linear model, written as  
\[R(\theta,t)=R_{c}(\theta)-Vt\sin\phi_{c}. \tag{5}\]