esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Challenges for the Users  
To produce training data for the OCR, one has to find and transcribe text lines (considering a line-based approach) manually, which is a highly non-trivial task when dealing with very old fonts and historical languages. However, the combination of all steps for automatic transcription with manual support can be supported by components of open-source tools such as OCRopus, Tesseract, or Calamari. While these tools are highly functional and very powerful, their usage can be quite complicated, as they:  
* in most cases lack a comfortable GUI, which leaves the users with the often unfamiliar command line usage
* usually rely on different input/output formats, which requires the users to invest additional effort in order to put together an end-to-end OCR workflow
* sometimes require complicated and error prone installation and configuration procedures where, e.g., the users have to deal with missing dependencies
* have a steep learning curve (at least for non-technical users)  
These aspects are particularly problematic for inexperienced users with limited technical background like humanities scholars to produce machine actionable text and GT from scans of historical printings. Therefore, providing adequate interfaces with an entire workflow available for automatic transcription, as well as for manual correction by non-technical users is of key importance, since most tools usually do not cover the entire workflow described above, at least not in a satisfactory manner, but rather excel on smaller sub-tasks.
# Post-Quantum Migration of the Tor Application
## 2 Literature Review
### Reflection  
The attraction towards hybrid models is justified by the potential fallback to classical encryption in case a given quantum-robust scheme fails. Even if they have been cautiously examined, new PQC schemes could potentially be broken by an adversary. As an analogy, SIKE reached the fourth round of NIST competition before being proven insecure [52]. To avoid solely relying on relatively young solutions, hybrids appear to outweigh the faster, fully PQC solutions (KEMTLS). As for the ease of instantiation, the concatenation method of algorithms allows for the simpler implementation of these hybrids.  
The circuit layer comes up as the most urgent layer to migrate to PQC. Implementing a transitional hybrid scheme would require a KEM and a DSA mechanism. The second migration would concern the link protocol. In comparison, TLS has benefitted from a greater number of deployments and more extensive research. While OS reuses these fundamental blocks, it incorporates additional mechanisms such as introduction, rendezvous points, and hidden service descriptor encryption, which have separate cryptographic processes. Public key cryptography is used in both cases: for key exchange during the introduction and rendezvous points; and authentication for both the points and descriptors.  
Regarding Tor's components that should be tested with quantum-robust KEMs, four stand out:  
* The circuit-extension handshake;
* Onion skin;
* Link layer TLS;
* OS Introduction, rendezvous points.  
As for the ones that can be evaluated using DSA, the following stand out:  
* Consensus document;Relay identity authentication;
* Link TLS handshake authentication;
* Encrypted descriptor.  
Section 2.1.2 highlighted the fact that Tor already faces cryptography challenges that will not be solved by the integration of quantum-resistant schemes. The latter implies a potential redesign of certain underlying protocols beforehand, as proposed by [PERSON] and [PERSON] [15]. Overall, the review emphasised the various cryptographic layers and examined their interactions. It appears technically feasible to transition to PQC standards incrementally, layer by layer. Regarding the necessity of migrating the encapsulated TLS layer to PQC when its outer layer is already quantum-resistant, the recommendation is affirmative, as it is preferable to ensure comprehensive quantum security.  
On the other hand, [PERSON] et al. [53] proposed an integration of QKD for symmetric key exchange without relying on trusted nodes. The design presents a quantum relay to facilitate the key exchange between client and nodes, yet this approach would require a specific quantum communication infrastructure, such as quantum repeaters, which are still in development and not widely available. It can be observed that the diversity of KEM standards is currently limited compared to that of DSA (ML-DSA, SLH-DSA, XMSS, and LMS).
# Unmanned Surface Vehicle Using a Leader-Follower Swarm Control Algorithm
## 3 Control Algorithm of the USV  
### CTE Compensation Algorithm  
CTE represents the vertical distance error between the target path of the platform and the actual movement path caused by various disturbances such as waves and wind. Figure 3 shows a schematic of CTE.  
This CTE can be calculated using [PERSON]'s formula, which uses \(S\), half the value of the sum of the three paths shown in Figure 3: the straight path \(D_{K}\), the path \(D_{USV}\) that the USV had previously targeted, and the waypoint path \(D_{WP}\) that the USV had to travel to reach the target point. The CTE value calculated through [PERSON]'s formula is expressed as Equation (9).  
\[D_{CTE}=\frac{2\sqrt{S(S-D_{USV})(S-D_{K})(S-D_{WP})}}{D_{WP}} \tag{9}\]  
Figure 3: Schematic of cross-track error.  
When CTE occurs during operation, the result value of image sonar sensors such as side scan sonar (SSS) and multi-beam is abnormal, or it consumes more energy to reach the target point. Thus, it has a negative effect on energy efficiency.  
In this study, the automatic navigation function of the USV was improved by including a compensation algorithm that compensated for CTE in the operation-related control function of the USV. Figure 4 shows a schematic representation of the CTE compensation algorithm.  
The CTE compensation algorithm is used to improve the forward control performance of the USV. To reduce the CTE occurring during forward movement, the coordinates \(\big{[}X_{P},\ Y_{P}+sgn\big{(}e_{\psi}\big{)}Y_{CTE}\big{]}\) are newly created from the center point \([X_{P},\ Y_{P}]\) of the USV to the Y axis moved by \(Y_{CTE}\).  
After that, an imaginary circle \(\rho_{CTE}\) with the newly created point as the center point is drawn, and a new target direction angle \(\psi_{CTE}\) is obtained that targets the bow direction contact point \(\big{[}X_{P}+X_{CTE},\ Y_{P}+sgn\big{(}e_{\psi}\big{)}Y_{CTE}\big{]}\) between the circle and the target path.  
Through the difference between \(e_{\psi}\), which is the target direction angle of the existing USV, and the new target direction angle \(\psi_{CTE}\), the error value \(E_{CTE}\) is used as a new error value for the control. Equation (10) are mathematical expressions of the control algorithm to which CTE compensation is applied [17].  
\[\begin{split} E_{\psi}=e_{\psi}-\psi_{CTE}\\ T_{CTE}=K_{P\psi}E_{\psi}+K_{I\psi}\int E_{\psi}+K_{D\psi}\dot{E}_{ \psi}\end{split} \tag{10}\]  
Here, \(T_{CTE}\) represents the output value of the controller to which CTE compensation is applied. The controller outputs the driving force of the port-side and starboard-side thrusters according to the sign of the \(e_{\psi}\) value of the USV. Figure 5 shows a block diagram of the controller to which the CTE compensation algorithm is applied.  
Figure 4: Schematic of the cross-track-error compensation algorithm.
## 2 Modeling Approach
### Turbulence Model  
The turbulence model that is chosen has a significant impact on the outcomes of simulation, since it defines the liquid's distribution of velocity. By solving two independent transport equations, two-equation turbulence models can determine both a turbulent length and time scale. Since the ejector contains turbulent flows with high Reynolds number, it is better to use standard \(k-\varepsilon\) and realizable \(k-\varepsilon\) models for the existing ejector. Because of the rotational effects, the \(k-\varepsilon\) realizable model is expected to perform better, but the standard \(k-\varepsilon\) model is chosen because it is simpler and requires less computational time than the \(k-\varepsilon\) realizable model. The standard \(k-\varepsilon\) model [41] is a model based on model transport equations for the turbulence kinetic energy (k) and its dissipation rate (\(\varepsilon\)). The exact equation and physical reasoning are used to derive the model transport equation for k and \(\varepsilon\), respectively. Transport equations for the standard \(k-\varepsilon\) model, are given by:  
\[\frac{\partial}{\partial t}(\rho k)+\frac{\partial}{\partial x_{i}}(\rho Ku_{i} )=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+\frac{\mu_{t}}{\sigma_{ k}}\bigg{)}\frac{\partial k}{\partial x_{j}}\Bigg{]}+G_{k}+G_{b}-\rho \varepsilon-Y_{M}+S_{K} \tag{26}\]  
\[\frac{\partial}{\partial t}(\rho\varepsilon)+\frac{\partial}{\partial x_{i}}( \rho\varepsilon v_{i})=\frac{\partial}{\partial x_{j}}\Bigg{[}\bigg{(}\mu+ \frac{\mu_{t}}{\sigma_{\varepsilon}}\bigg{)}\frac{\partial\varepsilon}{ \partial x_{j}}\Bigg{]}+C_{1\varepsilon}G_{k}\frac{\varepsilon}{k}-C_{2 \varepsilon}\rho\frac{\varepsilon^{2}}{k}+S_{\varepsilon} \tag{27}\]\[G_{k}=\mu_{t}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u_{j}}{ \partial x_{i}}\right)\frac{\partial u_{i}}{\partial x_{j}} \tag{28}\]  
\[G_{b}=\beta g_{i}\frac{\mu_{t}}{P_{tt}}\frac{\partial T}{\partial x_{i}} \tag{29}\]  
\[\mu_{t}=\rho_{L}C_{\mu}\frac{k^{2}}{\varepsilon} \tag{30}\]  
\[S=\sqrt{2S_{\rm ij}S_{\rm ij}} \tag{31}\]  
\[S_{ij}=\frac{1}{2}\left(\frac{\partial u_{i}}{\partial x_{j}}+\frac{\partial u _{j}}{\partial x_{i}}\right) \tag{32}\]  
where \(G_{\rm k}\) denotes the kinetic energy generated by turbulence as a result of the average velocity gradients. The buoyancy-induced generation of turbulence kinetic energy is denoted by \(G_{\rm b}\). In compressible turbulence, \(Y_{\rm M}\) is the effect of the fluctuating dilation. \(S_{\rm K}\) and \(S_{\varepsilon}\) are source terms. \(C_{1\varepsilon}=1.42\), \(C_{2\varepsilon}=1.92\), \(C_{\mu}=0.09\), and \(\beta=0.012\) are constants. \(\sigma_{k}=1\) and \(\sigma_{\varepsilon}=1.3\) are the turbulent Prandtl numbers for k and \(\varepsilon\). \(S\) is the tensor of deformation and \(S_{\rm ij}\) is the mainstream time strain rate. The initial values of turbulent kinetic energy (k) and turbulent dissipation rate (\(\varepsilon\)) for air and water are considered \(1\) m\({}^{2}\)/s\({}^{2}\) and \(0.22\) m\({}^{2}\)/s\({}^{3}\), respectively.
## 3 PIESAT-01 Multiple Satellites Interferometric Imaging  
The four-satellites interferometric imaging mode of the PIESAT-01 constellation is characterized by multi-satellite strip mode, with a resolution of 3 m and a swath width of 20-30 km (range direction), making it suitable for rapid and large-scale surveying tasks execution. The PIESAT-01 constellation consists of one main satellite equipped with both transmitting and receiving antennas, while the three auxiliary satellites are configured with only receiving antennas for interferometric strip imaging. The imaging process can be conceptualized as a monostatic SAR imaging of the main satellite and a bistatic SAR imaging involving three sets of main and auxiliary satellites. The imaging process is illustrated in Figure 2.  
The chirp scaling (CS) algorithm [26] was used for the main satellite monostatic SAR imaging, which initially corrects the linear range migration along the range dimension, followed by range cell migration correction (RCMC) and range-matched filtering to achieve range compression [27]. Subsequently, matched filtering is performed to accomplish azimuth compression, resulting in the acquisition of 2D-focused strip SAR images. To enhance the accuracy of InSAR interferometric height measurement in bistatic SAR imaging of the main and auxiliary satellites, pre-filtering is incorporated into the CS algorithm to remove non-overlapping range spectral beams. Additionally, synchronous phase extraction technology and high-coherence imaging technology are employed to ensure the accuracy of phase information.
# Mechanical Performance Analysis and Experimental Study of Four-Star-Type Crank-Linkage Mechanism
## 4 Experimental Study of Four-Star-Type Crank Linkage
### Analysis of Experimental Data
#### 4.2.1 Effect of Clearance Size on the Dynamic Response of the Mechanism  
In the dynamic response analysis of the mechanism, the existence of clearance is usually ignored, and the clearance of the kinematic pair is idealized. In engineering practice, the clearance in the kinematic pair is formed by the machining of the parts, the assembly of the kinematic pairs, and the wear during operation, which cannot be avoided. The collision force caused by the clearance not only consumes the energy of the mechanical system but also causes the vibration, noise, and dynamic output of the system. In high-speed, heavy-duty machinery, the clearance will make the elements of the kinematic pair violent during contact collision. The supporting reaction force of the kinematic pairs will rise to more than ten times the normal value, which makes the deformation of the components increase, the wear intensify, and the vibration and noise become greater, seriously affecting the efficiency and service life of the mechanism.  
A file was used to polish the inner ring of the fisheye bearing. Then, the surface was polished with sandpaper to create a wear clearance. Due to the irregularity of the wear process, only a qualitative analysis of the test results was made. The maximum wear was about 0.1 mm. The working conditions were divided into large wear, medium wear, and no wear. The motor speed was 240 r/min. The slider 1 was used as the object of study. The acceleration-velocity phase diagrams for different  
Figure 30: Time-domain diagram of slider acceleration.  
Figure 29: Schematic diagram of the data acquisition system. (**a**) Data acquisition site. (**b**) Schematic diagram of vibration measurement point arrangement. (**c**) The 56-channel LMS acquisition card. (**d**) Arrangement of slider acceleration sensors.  
clearance sizes are shown in Figure 31. In general, the phase trajectories are all an ellipse. With the increase of wear, the acceleration amplitude increases, while the area of the center of the ellipse decreases, and the shape becomes more regular.
## 2 Materials and Methods
### Data Source
#### 2.2.3 Meteorological Data  
We used the China meteorological forcing dataset ([[https://poles.tpdc.ac.cn/zh-hans/data/7a35329c-c53f-4267-aa07-e0037d913a21/](https://poles.tpdc.ac.cn/zh-hans/data/7a35329c-c53f-4267-aa07-e0037d913a21/)]([https://poles.tpdc.ac.cn/zh-hans/data/7a35329c-c53f-4267-aa07-e0037d913a21/](https://poles.tpdc.ac.cn/zh-hans/data/7a35329c-c53f-4267-aa07-e0037d913a21/)), accessed on 29 January 2023), which was developed by the Institute of Tibetan Plateau Research, Chinese Academy of Sciences [39], to analyze climate change and its impact on glacier mass balance in this region. This dataset is based on the internationally available Princeton reanalysis data, GLDAS (Global Land Data Assimilation System) data, GEWEX-SRB (Global Energy and Water Cycle Experiment--Surface Radiation Budget) radiation data, and TRMM (Tropical Rainfall Measuring Mission) precipitation data, and integrates the conventional meteorological observation data of the China Meteorological Administration (CMA), which contains seven elements such as near-surface air temperature and surface precipitation rate [39]. Two meteorological elements with a spatial resolution of 0.1\({}^{\circ}\times\)0.1\({}^{\circ}\), near-surface air temperature, and surface precipitation rate, were utilized to analyze the climate change in the region from 2000 to 2014. In the results of near-surface air temperature, there were large anomalies from 2002 to 2004, and an anomaly removal process was carried out.
# An FPGA-Oriented Baseband Modulator Architecture for 4G/5G Communication Scenarios
## 3 Baseband Modulator Architecture  
This section presents our proposed baseband modulator architecture. First, the modulators for each selected 5G waveform are described; then, the top-level reconfigurable architecture is presented. Our baseband modulator was implemented on a cost-optimized Zynq xc7z020 device.
# Comparison of Water Pretreatment Devices for the Measurement of Polar Odorous Compounds
## 2 Materials and Methods
### Experiment Procedure  
#### 2.4.1 Analytical Methods  
In order to evaluate the reliability of the analytical instrument, a calibration curve was created and its linearity was confirmed. To verify the reliability of the measured data, the method detection limit (MDL) was used. Furthermore, the limit of quantification (LOQ) and precision were evaluated. In this study, a mixed standard gas (5 Odor Mix, Rigas Co., Ltd., Republic of Korea), including 10.3 ppm of MEK and 10.4 ppm of i-BuAl, MIBK, BuAc, and styrene, was used.  
The validity of the calibration curve was verified at 10-100 ppb, which was the valid concentration range according to the test method. Five concentrations (i.e., 10, 20, 40, 80, 100 ppb) were selected in the quantitative range. Samples were prepared in Tedlar bags (SKC, USA) with 10 L of volume. Mixed standard gas (5 Odor Mix, Rigas Co., Ltd., Republic of Korea) was injected into each of the 5 Tedlar bags (10, 20, 40, 80, and 100 mL) and filled with N\({}_{2}\) (99.999%, Rigas Co., Ltd., Republic of Korea) to prepare a sample for the calibration curve. Then, the prepared samples were introduced to adsorption tubes for 5 min at a flow rate of 100 mL/min using a suction pump equipped with a flow meter.  
To verify the reliability of the measured data, a sample with a 0.5 ppb of concentration was prepared for detectable concentration by using a gastight gas syringe (SGE, Australia). It was introduced into  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Compound**} & \multicolumn{1}{c}{**CAS No.**} & \multicolumn{1}{c}{**Molecular Weight**} & \multicolumn{1}{c}{**Solubility \({}^{*}\)**} & \multicolumn{1}{c}{**Vapor Pressure**} \\ \hline
**Full Name** & **Short Name** & **(g/mol)** & **(g/L)** & **(mmHg, 20 \({}^{\circ}\)C)** \\ \hline Methyl ethyl ketone & MEK & 78-93-3 & 72.11 & 275 & 78 \\ Isobutyl alcohol & i-BuAl & 78-83-1 & 74.12 & 95 & 9 \\ Methyl isobutyl ketone & MIBK & 108-10-1 & 100.16 & 19.1 & 16 \\ Butyl acetate & BuAc & 123-86-4 & 116.16 & 6.8 & 9 \\ Styrene & S & 100-42-5 & 104.15 & 3 & 5 \\ \hline \hline \end{tabular} \({}^{*}\) Solubility in water at 20 \({}^{\circ}\)C.  
\end{table}
Table 1: Target odorous compounds.  
the adsorption tubes for 5 min at a flow rate of 100 mL/min, the sample analysis was repeated 7 times. Detection limits were calculated by multiplying the standard deviation obtained from these analyzed results with 3.14. The LOQ was calculated by multiplying the standard deviation with 10.  
In addition, the precision was determined using 80 ppb of standard gases. The standard gas, which had the concentration within the calibration-curve range, was injected 3 times to evaluate the standard deviation (SD). Then, 80 mL of mixed standard gas (5 Odor Mix, Rigas Co., Ltd., Republic of Korea) was injected into the 10 L Tedlar bag and filled with N\({}_{2}\) (99.999%, Rigas Co., Ltd., Republic of Korea) using a mass flow controller (Line Tech, Republic of Korea) calibrated with a soap bubble meter to prepare a sample for precision evaluation.
# Efficient Secure Mechanisms for In-Vehicle Ethernet in Autonomous Vehicles
## 4 Simulation Experiment and Result Analysis
### Performance Analysis of Data Encryption and Authentication Algorithms
This improvement not only speeds up the authentication process but also conserves computational resources, making the IMD5 algorithm more efficient for real-time applications. In summary, the IMD5 algorithm showcases notable enhancements in security and performance compared to the traditional MD5 algorithm. The detailed analysis of the avalanche effect experiment results, the comparison of bad point rates, and the assessment of the authentication rate improvements provide a comprehensive understanding of the IMD5 algorithm's superiority in maintaining data integrity and security within cryptographic applications. 1. AES-ECC algorithm simulation result:  
The efficiency of the hybrid AES-ECC encryption algorithm was evaluated and compared with the traditional AES-128 and ECC encryption algorithms. This comparison aimed to assess the performance of the hybrid algorithm in terms of encryption time across varying data sizes. Four distinct groups of data were selected, with each group representing a different data size. To obtain reliable average encryption times, each group of data was encrypted 5000 times using each of the three algorithms. The hybrid AES-ECC algorithm demonstrates encryption times that are intermediate between the AES-128 and ECC algorithms, as shown in Figure 19. In Table 2, the ECC  
Figure 18: Experimental results of the avalanche effect of the IMD5 algorithm. algorithm's encryption time increases significantly with larger data sizes, which is expected due to its computational complexity. In contrast, the hybrid AES-ECC algorithm maintains a relatively stable encryption time, even as the data size grows. This stability is attributed to the algorithm's design, which leverages a fixed-length key for the AES component, thus avoiding the escalating encryption times associated with the ECC algorithm. The hybrid AES-ECC algorithm's consistent performance indicates an improvement in efficiency over the ECC algorithm, particularly for larger data sizes. This enhancement in efficiency is achieved without compromising security, as the algorithm combines the strengths of both AES and ECC encryption methods. The hybrid approach ensures that the security level is maintained, as it still requires the generation and management of ECC keys, which are known for their strength in resisting attacks. In conclusion, the hybrid AES-ECC encryption algorithm presents a viable solution for applications requiring high-security standards and efficient encryption processes, especially in the context of in-vehicle Ethernet communication where data integrity and speed are paramount. The simulation results support the design objectives of the algorithm, showcasing its potential for real-world implementation in secure communication systems.
## 6 Discussion
#### 6.1.4 Conclusions  
Figure 9 illustrates the relationships of the compression ratio with reconstruction error and iteration number for the reconstruction of three different gas signals (namely the gas mutation signal, the gas steady signal and the complex gas signal) using the TVS-MH algorithm.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \(M\) & **43** & **86** & **129** & **172** & **215** & **258** & **301** & **354** & **397** & **430** \\ \hline Iteration number & 157 & 127 & 121 & 107 & 97 & 87 & 79 & 80 & 78 & 70 \\ Error (\%) & 21.02 & 14.27 & 7.87 & 5.37 & 3.06 & 2.50 & 1.71 & 1.09 & 1.04 & 0.89 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The relations of \(M\) with the reconstruction error and the computation complexity during the reconstruction of the complex gas signal.  
Figure 8: Compression perception and the reconstruction error of the complex gas signal (TVS-MH).  
When the TVS-MH algorithm is used, the reconstruction for the gas steady signal exhibits the smallest iteration number and error, while the reconstruction for complex gas signal has the greatest iteration number and error.  
According to the above-described simulation results, the TVS-MH algorithm exhibits favorable applicability for different signals; under certain compression ratios, the reconstructions of the gas mutation signal, the gas steady signal and the complex gas signal can all satisfy the expectations, and the reconstruction accuracy can meet the requirements on the allowable error of a mine gas sensor; additionally, the computations are not too complex. All of these demonstrate that TVS-MH is applicable to compressing sampling of the mine gas signal.