esa-sceva/satcom-chunk-collection huggingface dataset:
# Advancing OCR Accuracy in Image-to-LaTeX Conversion--A Critical and Creative Exploration
## 2 Background Information
\begin{table}
\begin{tabular}{c c c} \hline \hline
**OCR Approach** & **Performance** & **Challenges and Techniques** \\ \hline \multirow{3}{*}{NLP Integration [7]} & \multirow{3}{*}{Potential} & Complex equations and intricate structural arrangements. \\  & & Visual similarity of mathematical symbols. \\  & & Noise or distortion in input images. \\  & & Leveraging semantic information and linguistic context through NLP techniques. \\ \hline \multirow{3}{*}{Data Augmentation [8]} & \multirow{3}{*}{Improved} & Ensuring adherence to LaTeX syntax rules. \\  & & Reducing generation of syntactically incorrect LaTeX code. \\  & & Incorporating LaTeX syntax constraints during data augmentation. \\ \hline \multirow{3}{*}{Dependency Capture [9]} & \multirow{3}{*}{Enhanced} & Capturing dependencies and relationships between symbols (subscripts, \\  & & superscripts, fraction components). \\  & & Improving accuracy by correcting recognition errors and ensuring the integrity of the converted LaTeX representation. \\ \hline \multirow{3}{*}{Active Learning [10]} & \multirow{3}{*}{Efficient} & Mitigating dependency on large labeled datasets. \\  & & Intelligently selecting informative and challenging examples. \\ \cline{1-1}  & & Involving human annotators in training through active learning strategies. \\ \cline{1-1}  & & (query-by-committee, uncertainty sampling, adaptive sampling). \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of challenges and techniques in OCR approaches to image-to-LaTeX conversion. LaTeX representation by capturing these dependencies and incorporating them into the OCR model. Traditional OCR approaches require large labeled datasets for training, which are expensive, time-consuming, and costly to create [12]. Active learning addresses this challenge by intelligently selecting samples for manual annotation to mitigate the dependency on large labeled datasets. The OCR model can focus on learning from the most informative and challenging examples, leading to more efficient and effective model improvement by actively involving human annotators in training [4]. Active learning strategies like query-by-committee, uncertainty sampling, and adaptive sampling, can be used to select samples that maximize the model's learning potential.
## 3 Results
### Fungal Community Succession During Stacked Fermentation  
Fungal community analysis revealed a total of 11 phyla, 36 orders, 78 families, 160 genera, and 378 species. _Ascomycota_ emerged as the predominant fungal phylum, comprising 96.3% to 99.4% of the relative abundance across samples. The absolute dominant genera included _Saccharomyces_, _Thermomyces_, _Monascus_, and _Ascomycota_, with dynamic changes over the stacked fermentation process (Figure 2B), and are known for the ethanol production and enhancing flavor diversity [38]. _Saccharomyces_ abundance occurred from day 1 to day 4, slowing on day 5. Its abundance rose from 5.5% to 26.9% in workshop L and from 3.2% to 48.9% in workshop H. Moreover, its abundance reaches the highest in workshop H on the 5 th day, while in workshop L, its abundance reaches the maximum on the 4 th day. And its abundance in workshop H is significantly lower than that in workshop L.  
This _thermophilic_ genus, integral to saccharification and flavor development, declined over the fermentation period. In workshop H, its abundance dropped from 61.5% to 18.3%, while in workshop L, it declined from 41.2% to 31.2%, with a slight recovery on day 5. The decreasing trend is more obvious in workshop H, which corresponds to the obvious increasing trend of _Saccharomyces_ in workshop H. Initially scarce, _Monascus_ abundance increased significantly by day 3, peaking on day 4. It rose from 1.4% to 15.5% in workshop H and from 1.6% to 18.7% in workshop L. This genus plays a vital role in saccharification and esterification, producing enzymes like protease and amylase, which are essential for ethanol and ester production [37]. _Ascomycota_ displayed a decreasing trend, particularly in workshop L, where its abundance dropped from 45.8% on day 1 to 8.6% on day 5. In workshop H, a milder decline was observed, suggesting differences in environmental conditions between workshops.  
_Paecilomyces_ and _Pichia_ exhibited minimal abundance during the first two days but increased rapidly by day 3, reaching 3.7% and 4%, respectively. Aspergillus showed an opposing trend, with its abundance declining from \(>\)1% on days 1-2 to \(<\)1% by day 3.  
The dynamic shifts in fungal abundance highlight the significant impact of physicochemical changes during fermentation, particularly on day 3, when fluctuations in temperature, ethanol, and glucose levels had the most pronounced influence on fungal growth.
# Dual-Frequency Signal Enhancement Method of Moving Target Echoes for GNSS-S Radar
## 3 Simulation and Experimental Results
### Experiments
38 are, respectively, processed by the dual-frequency method proposed in this paper, and then the coherent accumulation is carried out for 500 ms to obtain the \"time-distance\" domain accumulation results, as shown in Figure 24. In the figure, the vertical axis is the slow time \(t_{m}\), and the horizontal axis is the bistatic distance R. The two cars move from south to north, and the distance between them and the GNSS-S receiver  
Figure 21: Echo trace diagram of the B1 signals. Figure 20: The original image after correlation and demodulation. changes from close to distant. Therefore, two echo signal trajectories are obtained in Figure 24, which correspond to the echo signals of the two cars, respectively. As the distance increases, the echo signal power gradually becomes weaker. In addition, the echo trajectories obtained by the four BDS satellites in Figure 24 are different, which is due to the difference in bistatic RCS characteristics of the vehicles under different illumination angles of the GNSS satellite. For the satellite echo signals in Figure 24, the SNR curve of the signal is obtained by calculating the SNR row by row, as shown in Figure 25. It can be seen that the SNR of the satellite echo has been greatly improved (the highest SNR reaches 28 dB). As can be seen from Figure 24, the target echo trajectories achieved from different satellites are different, which is due to the different bistatic RCS characteristics of the targets in different directions. Finally, the echo trajectories of the four satellites are incoherently accumulated after geometry calibration to obtain a more continuous target echo trajectory with enhanced energy, as shown in Figure 26. Because the echo signal energy of different satellites is different, it is necessary to normalize the respective signal energies first and then perform the joint accumulation of multisatellite signals. Figure 23: SNR of single-frequency signals in Figures 21 and 22. (**a**) B1 signals; (**b**) B3 signals. Figure 22: Echo trace diagram of the B3 signals. In Figure 26, the horizontal axis represents the bistatic distance of the moving target. As the distance increases, the signal strength gradually weakens, and when the distance exceeds 75 m, the signal disappears. Therefore, it can be inferred that the maximum detectable bistatic distance of the car targets is approximately 75 m. According to the geometric relationship shown in Figure 3, the detectable ground distance for the car targets is approximately 59 m.  
Figure 19:0 Echo trace diagram of dual-frequency differential signals. Figure 19:1 SNR of dual-frequency differential signals in Figure 19:2
## 4 Experiments
### Application and Datasets
#### 4.2.4 Physical Activity Monitoring for Aging People Dataset  
This PAMAP2 dataset consists of recordings from nine users using three different types of sensors (accelerometer, gyroscope, and magnetometer) and a heart rate monitor. The sensors were placed at three different body positions, and data from these were sampled at 100 Hz. The heart rate monitor was set to a 9 Hz sampling frequency. Some of the activities included lying, sitting, standing, walking, running, cycling, and so on. It is a 12-class and balanced dataset.
## 5 Simulation Results
### Aggregate Interference from Starlink to MSS  
In this scenario, we considered the aggregate interference level originating from the Starlink constellation, which comprised 1694 interfering satellites. Starlink satellites were active only when located above landmass areas and remained inactive over oceanic regions. Figure 9 illustrates the results of the aggregate interference from the Starlink system operating in the 1990-1995 MHz frequency band to the typical MSS system.  
The results indicate that in the case of aggregate interference, the MSS end-to-end link experiences a degradation exceeding 10 dB for a significant duration, far surpassing the acceptable threshold for a tolerable Eb/No degradation. This leads to a significant increase in BER levels. The elevated interference BER levels pose a serious threat to the complete  
Figure 8: CDF of single-entry interference from Starlink to the NTN satellite receiver: (**a**) carrier-to-interference reduction; (**b**) throughput loss of the NTN uplink.  
Figure 9: CDF of aggregate interference from Starlink to the MSS satellite receiver: (**a**) carrier-to-interference reduction; (**b**) throughput loss of the NTN uplink.  
outage of MSS uplink operations within the 1990-1995 MHz frequency range. As a result, once the second-generation Starlink system is deployed and serves millions of users, it will render it practically impossible to deploy MSS systemss in specific areas operating within the 1990-1995 MHz frequency band.
# Methods for Assessing Human-Machine Performance under Fuzzy Conditions
## 6 Grey System Theory
### The Assessment Method with GNs  
When using TFNs for the assessment of a system's mean performance, we found that \(X(M)=b\), which means that only the middle component \(b\) is needed for the defuzzification of the mean value \(M(a,b,c)\). This observation gives the hint to search for a \"formal\" assessment method that, analogous to that with TFNs, possibly reduces the required computational burden. This idea led us to utilize GNs [41] instead of TFNs for the system's assessment. The steps of our new method are the following:  
* Attach the numerical scores \(A\) (100-85), \(B\) (84-75), \(C\) (74-60), \(D\) (59-50), and \(F\) (49-0) to the corresponding linguistic grades.
* Assign to each grade a GN as follows: \(A\in[85,100]\), \(B\in[75,84]\), \(C\in[60,74]\), \(D\in[50,59]\), and \(F\in[0,49]\).
* Correspond to each of the system's components one of the above GNs evaluating its performance.
* Using analogous notation with the case of TFNs, calculate the mean value \[M*=\frac{1}{n}\left[n_{A}A+n_{B}B+n_{C}C+n_{D}D+n_{F}F\right]\] (17)
* Since \(n_{A}A\in[85n_{A},100n_{A}]\), \(n_{B}B\in[75n_{B},84n_{B}]\), \(n_{C}C\in[60n_{C},74n_{C}]\), \(n_{D}D\in[50n_{D},59n_{D}]\), and \(n_{F}F\in[0n_{F},49n_{F}]\), one obtains that \(M*\in[m_{1},m_{2}]\), where \[m_{1}=\frac{85n_{A}+75n_{B}+60n_{C}+50n_{D}+0n_{F}}{n}\] \[m_{2}=\frac{100n_{A}+84n_{B}+69n_{C}+59n_{D}+49n_{F}}{n}\]
* Since the distribution of \(M*\) is unknown, take \[W(M*)=\frac{m_{1}+m_{2}}{2}.\] (18)  
The value of \(W(M*)\) provides a crisp representation of the GN \(M*\) evaluating the system's mean performance  
**Remarks 3**: \(\;\)  
* From Equations (15) and (18), one obtains that \(X(M)=W(M*)\). Therefore, one concludes that the assessment methods with the TFNs and the GNs are equivalent to each other, because they provide the same assessment outcomes.  
It is straightforward to check that, if the maximal possible numerical score corresponds to each system's component for each grade, then the mean value of those scores is equal to c or m\({}_{2}\), respectively. In the same way, if the minimal possible score corresponds to each system's component for each grade, then the mean value of all scores is equal to a or m\({}_{1}\), respectively. Consequently, assessment methods with TFNs and GNs give a reliable approximation of the system's mean performance.
# Interpretable Variational Graph Autoencoder with Noninformative Prior
## 5 Experimental Results and Analysis
### Semi-Supervised Node Classification
#### 5.4.1 Experimental Setup  
Following [28], we randomly selected 5, 10, and 20 labels per class to train. For the Cora dataset, for example, there are 7 classes, and we select 5 labels randomly for each class. Hence, there are 35 labels for semi-supervised training in all. We randomly divide the dataset into training, validation, and test sets at each iteration. It can provide more robust performance when the dataset is randomly divided. In the experiment, the encoder of our model (NPIVGAE) contains a two-layer GCN with 32 and 16 hidden units in the first and second layers, respectively, and so does that of NPVGAE. The NPIVGAE and  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{2}{c}{**Cora**} & \multicolumn{2}{c}{**Citeseer**} & \multicolumn{2}{c}{**Pubmed**} \\ \cline{2-7}  & **AUC** & **AP** & **AUC** & **AP** & **AUC** & **AP** \\ \hline GAE & 91.00 \(\pm\) 0.02 & 92.00 \(\pm\) 0.03 & 91.70 \(\pm\) 0.13 & 93.23 \(\pm\) 0.12 & 96.18 \(\pm\) 0.01 & 96.43 \(\pm\) 0.02 \\ VGAE & 91.40 \(\pm\) 0.01 & 92.60 \(\pm\) 0.01 & 91.37 \(\pm\) 0.44 & 92.77 \(\pm\) 0.17 & 96.61 \(\pm\) 0.02 & 96.88 \(\pm\) 0.06 \\ ARGE & 91.98 \(\pm\) 0.01 & 93.20 \(\pm\) 0.01 & 92.73 \(\pm\) 0.01 & 93.00 \(\pm\) 0.01 & 96.80 \(\pm\) 0.00 & 97.10 \(\pm\) 0.00 \\ ARVGE & 92.40 \(\pm\) 0.01 & 92.60 \(\pm\) 0.01 & 92.65 \(\pm\) 0.01 & 93.42 \(\pm\) 0.01 & 96.50 \(\pm\) 0.00 & 96.80 \(\pm\) 0.00 \\ EVGAE & **92.96 \(\pm\) 0.02** & 93.58 \(\pm\) 0.03 & 91.55 \(\pm\) 0.03 & 93.24 \(\pm\) 0.02 & 96.80 \(\pm\) 0.01 & 96.91 \(\pm\) 0.02 \\ \hline NPVGAE & 92.45 \(\pm\) 0.01 & **93.73 \(\pm\) 0.05** & 92.15 \(\pm\) 0.16 & 94.15 \(\pm\) 0.15 & **96.92 \(\pm\) 0.06** & **97.16 \(\pm\) 0.03** \\ NPIVGAE & 91.46 \(\pm\) 1.58 & 91.08 \(\pm\) 1.84 & **93.66 \(\pm\) 0.47** & **94.31 \(\pm\) 0.08** & 96.79 \(\pm\) 0.03 & 97.11 \(\pm\) 0.01 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Experimental results on the link prediction task. (*: outperforms 2 nd-best with \(p\)-value \(<\) 0.01).  
NPVGAE, together with other baselines, are trained 50 iterations, and the learning rate is set to 0.01. We took the average results of 10 runs as a report.
## 3 Theoretical Analysis
### Latency  
To compare the latencies of existing WuRs, we implement the given wake-up protocol to determine the total latency instead of the component's propagation delay time in the WuR circuit. Therefore, the latency emphasizes the important metric PER again. Latency is defined as the time duration between transmission and successful reception of the WuR packet. The optimal preamble time period \(T_{pre}^{opt}\) for our wake-up protocol [24] is designed to achieve the lowest power dissipation for respective existing WuRs. Moreover, the \(T_{pre}^{opt}\) should at least include the eight symbols to represent the wake-up address of 16 bits for addressing 65,536 nodes.  
We assume that latencies are the same when the false and successful wake-up signals occur. The WuR's total latency, \(T_{t}\), is given by [24]:  
\[T_{t}=E[N_{tx}](T_{pre}^{opt}+T_{msg}) \tag{16}\]  
where \(T_{msg}\) is the message time duration. When the transmitter sends a message to the receiver's data radio, the receiver spends \(T_{msg}\) for receiving the message from the transmitter.  
For low-power WuRs with a higher PER, they have larger \(E[N_{tx}]\), as they require more \(Re_{tx}^{wur}\) of wake-up packets. Therefore, they increase the total latency significantly.  
Figure 1: Wake-up protocol.
# Efficient Data Gathering in 3D Linear Underwater Wireless Sensor Networks Using Sink Mobility
## 1 Introduction
The CNs send the received data to the MS at a minimum distance. All of the transmissions made by the nodes are directed to the MS or CNs. In this way, the energy consumption of the network is minimized, such that the NLT is prolonged. The rest of the paper is organized as follows. In Section 2, related work is presented. In Section 3, the motivation and contribution are given. In Section 4, the framework and system description are given. In Section 5, simulation results are presented. In Section 6, scalability analysis is given. In Section 7, the fixed routing and dynamic sink mobility are given. After that, in Section 8, a discussion about the performance trade-offs is given. Finally, Section 9 concludes this paper. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Feature** & **Terrestrial** & **Underwater** \\ \hline Signal & Radio & Acoustic \\ Speed & Light speed (3\(\times 10^{8}\) m/s) & Acoustic signal speed 1500 m/s \\ Anchor & GPS-based & AUV \\ Signal bandwidth & High & Low \\ Location error rate & Low & High \\ Device mobility & Static and mobile & Static and mobile \\ Propagation delay & Low & High \\ Power source & Battery, solar & Battery \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Characteristic differences between terrestrial wireless sensor networks (TWSN) and underwater wireless sensor networks (UWSNs).
# A PEKS-Based NDN Strategy for Name Privacy
## 4 Details of Implementation  
The implementation is written in C++. For NDN operations, we use ndn-cxx [27] library and NFD software [28]. For all cryptographic operations, we use PBC library [29]. In our work, the producer is responsible to generate public/private key pair and public parameters. The consumers who have the producer's public key are able to retrieve the content by using PEKS-based NDN. The NDN nodes need to check incoming interest packets' names by using the PEKS scheme.
## 2 Experimental Data  
### Satellite Atomic Clock and Orbit Information of BDS  
The BDS adopts a hybrid orbit, whose space segment is composed of several Geostationary Orbit (GEO), Inclined GeoSynchronous Orbit (IGSO), and Medium Earth Orbit (MEO) satellites. The main working clock of each satellite is RAFS or PHM; the details are shown in Table 1 (as of 12 March 2022).  
From Table 1, all of BDS-2 satellites were equipped with RAFS, most of which have been in operation for more than 2000 days and are gradually coming to the end of service. Correspondingly, most BDS-3 satellites containing new-type RAFS or PHM operate between 1000 and 2000 days, and are in stable operation. A few BDS-3 satellites have been launched within the last 1000 days and have not entered the stable operation stage. The composition of BDS satellite atomic clocks is so complex that various types need to be taken into account in the subsequent discussion [21].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**System** & **Orbit** & **PRN** & **Clock Types** & **Launch Date** &
\begin{tabular}{c} **Time** \\ **(Day)** \\ \end{tabular} \\ \hline \multirow{8}{*}{BDS-2} & & C01 & RAFS & 17 May 2019 & 1030 \\  & & C02 & RAFS & 25 October 2012 & 3425 \\  & GEO & C03 & RAFS & 12 June 2016 & 2099 \\  & & C04 & RAFS & 31 October 2010 & 4150 \\  & & C05 & RAFS & 24 February 2012 & 3669 \\  & & C06 & RAFS & 31 July 2010 & 4242 \\  & & C07 & RAFS & 17 December 2010 & 4103 \\  & & C08 & RAFS & 9 April 2011 & 3990 \\  & IGSO & C09 & RAFS & 26 July 2011 & 3882 \\  & & C10 & RAFS & 1 December 2011 & 3754 \\  & & C13 & RAFS & 29 March 2016 & 2174 \\  & & C16 & RAFS & 10 July 2018 & 1341 \\  & & C11 & RAFS & 29 April 2012 & 3604 \\  & MEO & C12 & RAFS & 29 April 2012 & 3604 \\  & & C14 & RAFS & 18 September 2012 & 3462 \\ \hline \multirow{8}{*}{MEO} & & C19 & RAFS & 5 November 2017 & 1588 \\  & & C20 & RAFS & 5 November 2017 & 1588 \\  & & C21 & RAFS & 12 February 2018 & 1489 \\  & & C22 & RAFS & 12 February 2018 & 1489 \\  & & C23 & RAFS & 29 July 2018 & 1322 \\  & & C25 & PHM & 24 August 2018 & 1296 \\  & & C26 & PHM & 24 August 2018 & 1296 \\  & & C27 & PHM & 11 January 2018 & 1521 \\  & & C28 & PHM & 11 January 2018 & 1521 \\  & & C29 & PHM & 29 March 2018 & 1444 \\  & & C30 & PHM & 29 March 2018 & 1444 \\  & & C32 & RAFS & 19 September 2018 & 1270 \\  & & C33 & RAFS & 19 September 2018 & 1270 \\  & & C34 & PHM & 15 October 2018 & 1244 \\ BDS-3 & & C35 & PHM & 15 October 2018 & 1244 \\  & & C36 & RAFS & 18 November 2018 & 1210 \\  & & C37 & RAFS & 18 November 2018 & 1210 \\  & & C41 & PHM & 16 December 2019 & 817 \\  & & C42 & PHM & 16 December 2019 & 817 \\  & & C43 & PHM & 23 November 2019 & 840 \\  & & C44 & PHM & 23 November 2019 & 840 \\  & & C45 & RAFS & 22 September 2019 & 902 \\  & & C46 & RAFS & 22 September 2019 & 902 \\  & & C38 & PHM & 20 April 2019 & 1057 \\  & IGSO & C39 & PHM & 24 June 2019 & 992 \\  & & C40 & PHM & 4 November 2019 & 859 \\  & & C59 & PHM & 1 November 2018 & 1227 \\ GEO & C60 & PHM & 9 March 2020 & 733 \\  & & C61 & PHM & 23 June 2020 & 627 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information of BDS on-orbit satellites.
# Compliant Human-Robot Collaboration with Accurate Path-Tracking Ability for a Robot Manipulator
## 2 Problem Formulation and Methodology
### Outer Admittance Control Loop
Fixed time constant gives consistent dynamic behavior of the admittance function and allows the user to quickly get accustomed to the way for operating the robot. Experimental verification of the proposed adaptive admittance law and comparison with other parameter tuning methods were conducted in [23] by implementing a 3D shape tracing task. The admittance laws in comparison included constant parameters and adaptive virtual damping with fixed virtual mass. Objective evaluation of the test results was done by analyzing the path length errors and task execution time. Meanwhile, a subjective evaluation was also conducted by collecting questionnaires that asked each test user to score the performance of each parameter tuning method in terms of vibration of the system, required efforts, and ease of completing the task. The results showed that the proposed adaptive admittance law has the best performance among all compared methods for both objective and subjective evaluation. The outer admittance control loop is summarized as Algorithm 1 below. ```
0:\(f_{ext},f_{v_{t}}s\)
0:\(\hat{r}_{d}\)
1: From path \(r_{p}(s)\), calculate \(t(s),\ n(s),\ b(s)\) using (13)
2: Initialize \(f_{thr},\ f_{v},\ c\) and ratio \(m/c\)
3:for\(t=0\) to \(\infty\)do
4:if (\(s\leq 1\)) then
5: Select mode to choose force input and project it to the tangent direction. 6:if (\(\|f_{ext}\|<f_{th}\)) then
7: Update \(f_{proj}=f_{v}\ t(s)\)\(\triangleright\)\(f_{v}\) is the task-dependent constant virtual force
8:else
9: Update \(f_{proj}=(f_{ext}^{T}t(s))t(s)\)
10:endif
11:else
12: Robot at goal, stop motion. \(f_{proj}=0\)
13:endif
14: Obtain \(t_{d}\) from passing \(f_{proj}\) through the admittance law (15). 15: Normalize \(f_{proj}\) using (17) to obtain \(\hat{f}\)
16: Update \(c\) using (18) and calculate \(m\) to maintain the fixed ratio \(m/c\)
17:endfor
```  
**Algorithm 1** Compliant path tracking using force projection