esa-sceva/satcom-chunk-collection huggingface dataset:
# COMS Visible Channel Calibration Using Moon Observation Data
## 2 Data and Methods
### Methodology
#### 2.2.2 Instrument Irradiance  
COMS MI observes the Moon twice per month using the LA observation mode at an image size of 1200(NS) by 2080(EW) pixels. Moon irradiance observed by instrument was calculated using Equation (7),  
\[\mathrm{I_{Instrument}}=\ \frac{1}{\mathrm{F_{OS}}}\ \sum_{p=1}^{\mathrm{N_{p}}} \mathrm{R_{p}}\times\Omega\ \Big{(}\mathrm{W\!\cdot\!m^{-2}\!\cdot\!\upmu m^{-1}}\Big{)}\, \tag{7}\]  
where \(\mathrm{N_{P}}\) is the total number of pixels in the lunar disk image, \(\mathrm{R_{P}}\) is an individual radiance measurement (i.e., pixel) on the Moon, and \(\Omega\) is the solid angle of a pixel (7.84 \(\times\) 10\({}^{-10}\) steradian), corresponding to one visible detector (28 \(\upmu\)rad). The oversampling rate (\(\mathrm{F_{OS}}\)) of MI along the E-W axis was 28/16. The total number of pixels is dependent on the size of the area of the Moon image of interest, and irradiances are highly dependent on the selection of image size and/or the threshold value of Moon pixels within the Moon image.  
To monitor instrument degradation, calibration coefficients on the ROLO scale were calculated, and the quantity was derived from the equation below,  
\[\mathrm{Slope_{ROLO}}\ =\ \frac{\mathrm{F_{OS}}\cdot\mathrm{I_{ROLO}}}{\Omega_{ \mathrm{Pix}}\cdot(\sum_{i=1}^{\mathrm{N_{Pix}}}(\mathrm{DC_{i}})-\ \mathrm{N_{Pix}}\cdot\mathrm{Off_{DC}})}\left(\mathrm{W}\cdot\mathrm{m}^{-2} \cdot\mathrm{\upmu m}^{-1}\cdot\mathrm{DC}^{-1}\right), \tag{8}\]  
where \(\Omega_{\mathrm{PIX}}\) is the COMS imager pixel solid angle multiplied by its oversampling factor, \(\mathrm{DC_{i}}\) is the integrated digital count (DC) over the Moon pixels, \(\mathrm{N_{PIX}}\) is the number of Moon pixels, and \(\mathrm{Off_{DC}}\) is the average DC of deep space.  
In addition, \(\mathrm{\Delta Irr}\) is a measure (in percentage) of the bias between observations and the ROLO model using the \(\mathrm{I_{Instrument}}\) and the \(\mathrm{I_{ROLO}}\); it provides a measure of the combined instrument-calibration performance,  
\[\mathrm{\Delta Irr}=100\cdot\left(\frac{\mathrm{I_{Instrument}}}{\mathrm{I_{ ROLO}}}\ -1\right). \tag{9}\]  
To retrieve lunar observation irradiance, on-Moon pixels needed to be defined. In this respect, a sensitivity test was conducted to determine the proper digital counter value threshold that discriminates between space and Moon pixels. The procedure, results, and criteria are described in Appendix A.
## 3 Results and Discussions
### Airflow Velocity (V\({}_{a}\)) Study  
In the operating conditions of the available stenter machine, fabric cooling was conducted with an airflow velocity of V\({}_{a,1}\) = 0.5 m/s. It is thought that this velocity value is insufficient for the cooling performance. To prove this, as well as to observe how increasing the airflow velocity affects cooling performance and temperature distribution on the fabric surface, airflow velocities of V\({}_{a,2}\) = 1 m/s and V\({}_{a,3}\) = 2 m/s were determined. The effects of increasing airflow velocity can be seen in Figure 7c. Since all operating parameters identified in the CFD models reflect real operating conditions, based on airflow velocity simulations, the inverter centrifugal fan, located at the end of the stenter machine, should be driven at an airflow velocity of 2 m/s to deliver cooling air. Figure 12 clearly illustrates this. Following the cooling process, the temperature distributions on the fabric are a crucially important factor. From these contours, it can be concluded that an airflow velocity of V\({}_{a,3}\) = 2 m/s enables more homogeneous temperature distributions on the fabric surface.
# Data Aggregation Based on Overlapping Rate of Sensing Area in Wireless Sensor Networks
## 4 Implementation of AggOR Scheme  
The transmission hierarchy diagram of WSN is constructed based on hello messages exchange between sensor nodes. Hello message includes the sender's ID, coordinates, residual energy and the level. At the beginning, all sensor nodes initialize their levels as infinity, and the sink floods hello message including its level 0. Then other nodes update their levels after receiving hello messages. In specific, after receiving a hello message, \(v_{i}\) checks if the difference of its stored level \(L_{i}\) and the level in the hello message is larger than 1. If it is true, \(v_{i}\) updates \(L_{i}\) with the level in hello message plus 1, and then disseminates its hello message with the updated level; otherwise, if the level in the message is \(L_{i}-1\), \(v_{i}\) adds the ID in the message to its candidate parent node set \(CP_{i}\); if the levels are the same, \(v_{i}\) inserts the ID to its neighbor node set \(N_{i}\).  
A topology under construction is illustrated in Figure 5 where the numbers are levels of nodes. Take node \(v_{i}\) as an example; its \(CP_{i}\) and \(N_{i}\) are illustrated.
# Acute and Repeated Toxicological Study of Anti-Inflammatory Herbal Formula, Yeosinsan, in Rats  
[PERSON] 1  
1 Department of Korean Medicine, School of Korean Medicine, Pusan National University, Busandaehak-ro 49, Mulgenum-eup, Yangsan 50612, Korea; [EMAIL_ADDRESS]  
[PERSON] 2  
2 Pathology Research Group, Jeonbuk Branch Institute, Kore Institute of Toxicology, Korea Research Institute of Chemical Technology, Baekhak 1-gil 30, Jeonbuk, Jeongup 56212, Korea; [EMAIL_ADDRESS]  
[PERSON] 3  
3 General Toxicology Research Group, Jeonbuk Branch Institute, Kore Institute of Toxicology, Korea Research Institute of Chemical Technology, Baekhak 1-gil 30, Jeonbuk, Jeongup 56212, Korea; [EMAIL_ADDRESS]  
[PERSON] 7  
4 Department of Korean Medical Gynecology, College of Korean Medicine, Daejeon University, Daehak-ro 62, Dong-gu, Daejeon 34520, Korea; [EMAIL_ADDRESS]  
[PERSON] 8  
5 Department of Korean Medicine Obstetrics and Gynecology, School of Korean Medicine, Pusan National University, Geumo-ro 20, Mulgenum-eup, Yangsan 50612, Korea; [EMAIL_ADDRESS]  
[PERSON] 7  
6 CHO-A Pharm. Co., Ltd., Dansgan-ro 2 5  
recipe, Erbuzhuyu decoction, Dingkun Pill, and Wenshen Yangxue decoction, were previously studied [5; 6; 7; 8; 9]. Several studies have focused on ovarian dysfunction and defective endometrial receptivity as the molecular mechanism of these herbal remedies [10; 11].  
_Paeonia lactiflora_ Pall. (_Paeoniae Radix_) and its major ingredient, paeoniflorin, have anti-inflammatory effects on several diseases, such as rheumatoid arthritis, inflammatory bowel disease, psoriasis, and asthma [12; 13]. _Cyperus rotundus_ L. (_Cyperi Rhiz_) is also used for anti-oxidative and anti-inflammatory purposes [14; 15]. In our previous study, the roots of _P. lactiflora_ and the tubers of _C. rotundus_ were able to treat female infertility by enhancing endometrial receptivity [16; 17]. The possible effect of _P. lactiflora_ on ovarian function was also reported in clinical studies on patients with polycystic ovarian disease [18; 19; 20]. In addition, Yeosinsan, an herbal formula composed of these two herbal medicines, was traditionally used for inflammation-related uterine bleeding and reported as a possible drug for improving female fertility that activates ovarian function and endometrial receptivity [21]. However, in the case of safety experiments, there are no reports on good laboratory practice (GLP)-level safety experiments of these herbal formulas, including BaelanChasgangBang, Zhuyun recipe, Erbuzhuyu decoction, Dingkun Pill, and Wenshen Yangxue decoction. In addition, very limited previous reports were found on the safety of these herbal medicines, including _P. lactiflora_ and _C. rotundus_, or Yeosinsan [14; 22].  
Thus, in this study, we evaluated the possible acute and chronic oral toxicity of Yeosinsan using a GLP rat model according to standard toxicology protocols. The safety data for Yeosinsan might play a crucial role in the approval of an investigational new drug application and in the protection of human subjects in clinical trials.
# Abstract
## 2 Related Work
### Visualization Tools  
Visualization tools refer to pieces of software developed to represent DLTs' data through infographics. In contrast to block explorer, they do not usually provide extended search capability. These DLT's data representations can be sorted into different task domains [16]. Tools focusing on analyzing patterns of individual blockchain components, i.e., transactions, addresses and blocks can be classified under the transaction detail analysis task domain. For example, Blockchain Explorer proposes to visualize weekly or monthly transaction volumes as a tile map [23]. Ethviewer shows the real-time transaction pool in Ethereum using a node-link diagram to represent blocks and transactions [24]. BitExTract is a collection of visual analytic tools that analyses activities among Bitcoin exchanges, including transactional volume, market share, and connectivity between exchanges [25].  
Tools representing information through a network and flows perspective can be classified in the transaction network analysis task domain. This category of representation is usually based on tree or node-link diagrams showing the connectivity among blockchain components. For instance, Daily-Blockchain provides a real-time representation of Bitcoin transactions where the nodes of the network evolve over time [26]. Bitforce5 only shows the most recent transactions [27]. This ensures a constant performance or rendering over time. For more granularity, BlockchainVis can either display the total amount of Bitcoin transactions or a specific address selected by the user [28]. Blockchain.com provides a tree diagram in which users can click through the tree levels to follow the value flow with respect to addresses [29]. Instead of presenting the value flow as a tree structure, BitConeView provides a unique diagram showing the value flow of a seed transaction as it appears in blocks from top to bottom [30]. Unlike the previous static value flow visualizations, BitInfoCharts dynamically represents the flow of transactions over the entire history of a blockchain utilizing a node-link diagram arranged in a linear layout [31].  
Tools representing aggregated statistics of the network can be categorized under the network activity analysis task domain. For instance, Blockchain.com provides a long list of time series charts to display a wide range of Bitcoin network statistics, such as the total hash rate, average block size, total transaction fee, and mining difficulty [29]. BitNodes implements map-based visualizations where a node crawler gathers reachable Bitcoin nodes locations to estimate the global distribution [32].  
The solutions proposed in the literature as well as by the online tools mainly focus on low-level aspects such as block creation, transactions and simple currency exchange taking place in distributed ledgers. These types of representations provide highly technical insights on network status, which do not facilitate intuitive audits of ledgers and might not be accessible for non-expert users. Moreover, due to their very specialised range of analysis, these tools do not offer comprehensive analytics of the data.
## 5 Conclusions  
In this study, we propose a visual odometry method for a low-profile pallet robot using the ground plane images obtained from a fisheye camera. To overcome the inherent radial distortion problem of the fisheye image, we introduce a Ground Plane Image (GPI), which is generated from two faces of the cubemap. Initially, we generate an original GPI and transform the image again to an ortho-rectified GPI to obtain a perfect top-view image from the mobile robot. Consequently, the median of the motion vectors in the ortho-rectified GPI can represent the real and accurate motion at the front head of the robot. The bicycle  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Image Type**} & \multirow{2}{*}{**Odometry**} & \multicolumn{1}{c}{**Test4**} & \multicolumn{1}{c}{**Test5**} & \multicolumn{1}{c}{**Test6**} \\  & & **(12.7826)** & **(14.6013)** & **(17.0111)** & **Average** \\ \hline \multirow{3}{*}{GPI} & T265 & 0.118618 & 0.226798 & 0.151477 & 0.1656 \\  & GPO & 0.073736 & 0.183521 & 0.097809 & 0.1195 \\  & EKF (GPO+T265) & 0.047038 & 0.103502 & 0.068983 & 0.0731 \\ \hline \multirow{3}{*}{Ortho-rectified} & T265 & 0.118618 & 0.226798 & 0.151477 & 0.1656 \\  & GPO & 0.049830 & 0.217686 & 0.085550 & 0.1177 \\ \cline{1-1}  & EKF (GPO+T265) & **0.038309** & **0.067267** & **0.068169** & **0.0579** \\ \hline \hline \end{tabular}
\end{table}
Table 4: Translation error comparison of three odometry methods. (Path length is in parenthesis and unit is in meters).  
Figure 19: Comparison of three odometry methods. From top, trajectories of Test4, Test5, and Test6.  
motion model is employed to estimate the odometry of the robot. The performance of the proposed Ground Plane Odometry (GPO) is compared with a stereo-based VIO method of a T265 camera. The proposed GPO shows better performance than T265 odometry, and the EKF of GPO and T265 achieves the best performance. In addition, it is shown that using the ortho-rectified GPI is better than using the original GPI. The proposed GPO can be used in any low-profile robot equipped with fisheye cameras. In addition, GPO can be used by combining with other robot sensors for better performance. In future studies, GPO will be employed to multi-view fisheye cameras in the pallet robot for more accurate visual odometry performance.  
Conceptualization, S.-Y.P. and S.-H.B.; methodology, software, validation, formal analysis, U.-G.L. and S.-H.B.; sensor calibration U.-G.L.; investigation, S.-Y.P. and S.-H.B.; writing--original draft preparation, U.-G.L. and S.-Y.P.; writing--review and editing, S.-Y.P.; supervision, S.-Y.P.; project administration, S.-Y.P.; funding acquisition, S.-Y.P. All authors have read and agreed to the published version of the manuscript.  
This work was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. 2021R1A6A1A03043144).  
Not applicable.  
The data presented in this study are available upon request from the corresponding author.  
The authors declare no conflict of interest.
# Effect of Structural Materials on Monopropellant Thruster Propulsion Performance in Micro Scale
## 3 Numerical Comparative Performance Analysis
### Gas and Materials Properties
With regard to the HTCC and LTCC, as they have varying properties depending on material combinations and fabrication processes, commercially available materials [37; 38; 39; 40] and properties were considered as an example of such materials. Table 3 summarizes the properties of the solid materials used in the simulation.
## 5 Discussion  
This study identified the dominant factors affecting the crystalline blockages in drainage systems by utilizing the two decision analysis methods. The dominant factors include five categories: hydrology, geology, shotcrete materials, drainage facilities, and the cave environment. Among them, shotcrete materials and hydrology were identified as the most influential factors that could cause crystalline blockages. The hydrology factor is site-dependent, so it is hard to control it through human intervention. This study focused more on understanding the impact of shotcrete materials on crystalline blockages. According to the results of the decision analysis methods, two factors, i.e., the density of shotcrete and the content of calcium in cement, are the most significant. Thus, potential measures that can help avoid crystalline blockages in tunnels include using high-quality cement, aggregates, and admixtures that are specially designed for high-density shotcrete. The use of high-quality materials ensures that the shotcrete has the necessary strength and durability to withstand the effects of calcium and other aggressive substances. Additionally, the density of the shotcrete can be controlled by adjusting the amount of water added to the mix. A lower water-cement ratio results in denser shotcrete that is less permeable to water and more resistant to the effects of calcium.
# Multi-Patch Time Series Transformer for Robust Bearing Fault Detection with Varying Noise
## 2 Related Work
### Time-Frequency-Based Anomaly Classification Research  
[PERSON] et al. developed a novel data augmentation technique leveraging analytical wavelets to enhance time-frequency-based data for bearing fault diagnosis, addressing challenges such as limited availability of fault data and the specialization of existing augmentation methods for image-based studies [4]. Their approach employed analytical wavelets to generate scalograms while preserving the original data's characteristics. This was implemented in the preprocessing stage of a simple CNN model, comprising three convolutional layers and one fully connected layer. By tuning parameters that control the decay rate in the time domain and the bandwidth in the frequency domain (set to three and twenty, respectively), the augmented data provided significant improvements in model performance. When applied to the CWRU dataset, the proposed method achieved an impressive accuracy of 99.49% and demonstrated robust performance (90.14% accuracy) even in low SNR conditions. While this study highlights the effectiveness of wavelet-based data augmentation for time-frequency-specific tasks, it also notes limitations related to the sensitivity of parameter selection in the wavelet design process.  
[PERSON] et al. introduced a Fourier Markov Transition Field (FMTF)-CNN-based method for short-time-series fault diagnosis to overcome the limitations of traditional Markov Transition Field (MTF) techniques in feature extraction and their slow neural network training speeds [5]. The FMTF method transforms time-domain signals into the frequency domain using the Fast Fourier Transform (FFT), followed by the generation of two-dimensional feature images via the MTF technique. This approach enhances the accuracy and efficiency of feature extraction compared to conventional MTF methods. Using the Jiangnan University public bearing dataset, each signal was divided into 256 sample points, and 128 data points containing frequency information within the range of 0-25 kHz were extracted via FFT. The MTF then generated 128 \(\times\) 128 feature images, which were input into a classification model with 16 convolutional filters of size 5 \(\times\) 5. Experimental results showed that FMTF-CNN improved accuracy by 23.61% and reduced iteration steps by 47.77% compared to baseline methods. Within the same training time (100 s), FMTF-CNN achieved an accuracy of 85.65%. However, this study's reliance on a single dataset limits its ability to generalize across diverse industrial environments and noise conditions.
# Privacy-Preserving Electricity Trading for Connected Microgrids
## 3 System and Attacker Models
### Design Objectives  
The presented system was designed in order to preserve the privacy of dwelling consumption patterns while providing an accurate billing. The objectives of the system are enumerated next:  
**Objectives 1**.: _Smart meters can offer and request energy in an anonymous and unlinkable manner._  
**Objectives 2**.: _At the end of a billing period, the substation can determine the sum to be paid or charged to each household. No other private information about their consumption can be inferred._  
Note that Objective 2 implicitly requires the e-coin payment system to be secure. If e-coins could be forged or stolen, the quantities to be charged or paid could not be determined in an accurate way.
# Hollow-Core Optical Fibers for Telecommunications and Data Transmission
## 4 Photonic Bandgap (Photonic Crystal) Fibers
### Commercial Fibers and Their Parameters
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Parameter** & **AccuCore** & **SMF-28e+** \\ \hline Core diameter (nom.) [\(\mu\)m] & 25.0 & 8.2 \\ Core area (nom.) [\(\mu\)m\({}^{2}\)] & 491 & 52.8 \\ Effective area, A\({}_{\text{eff}}\) (nom.) [\(\mu\)m\({}^{2}\)] & 200 & 82 \\ Ratio of A\({}_{\text{eff}}\) to core area & 0.41 & 1.55 \\ Mode field diameter, MFD (nom.) [\(\mu\)m] & 17.9 @ 1550 nm & 10.4 @ 1550 nm \\ Cladding diameter (nom.) [\(\mu\)m] & 82 1 & 125 \\ Jacket diameter (see Section 7.1) [\(\mu\)m] & 220 1 & (125) \\ Effective refractive index, n\({}_{\text{eff}}\) @ 1550 nm & 1.005 & 1.468 \\ Low-loss band [nm] & See Figure 4 & 1260–1675 \\ Attenuation (max.) [dB/km] & 7.0 @ 1550 nm & 0.20 @ 1550 nm \\ Attenuation (typ.) [dB/km] & 4.0 @ 1550 nm & 0.18 @ 1550 nm \\ Chromatic dispersion (typ.) [ps/nm\(\cdot\)km] & \(-70\) @ 1550 nm & 17 @ 1550 nm \\ PMD [ps/\(\sqrt{\text{km}}\)] & 9.15 3 & \(\leq\)0.10 \\ \hline \hline \end{tabular} 1  
\end{table} 
Table 2: Data of single-mode fibers: OFS AccuCore (HC-PBGF) and Corning SMF-28e+ [30].
# A Compact Ultra-Thin 4 \(\times\) 4 Multiple-Input Multiple-Output Antenna
## 3 Working Mechanism
### Parameters Optimization  
This section discusses four key parameters: _L1_, _L2_, _L3_, and _Lcl_. Variable _L1_ mainly influences C\({}_{\text{T-shaped slot}}\) shown in Figure 3c; _L2_ primarily affects the resistance of R1(2); L3 principally affects C1(2) brought by the slot; _Lcl_ affects both C\({}_{\text{T-shaped slot}}\) and Lg1(2). As illustrated in Figure 5a, when the value of _L1_ was 4.5 mm, the proposed AP resonated at approximately 3.4 GHz, and the isolation was more significant than 10 dB at 3.4 GHz. When _L1_ was improved to 5.5 mm, the designed APs system precisely worked at 3.5 GHz, and the isolation was better than 15 dB at 3.5 GHz. As the value of _L1_ was 6.5 mm, the \(-10\) dB impedance matched band moved toward higher frequencies, a poor port impedance matching condition was observed, and the corresponding isolation performance deteriorated to 7 dB. Both parameters _L2_ and _L3_, shown in Figure 5b,c, had the same impact on the port input return loss. With the increase of the value of _L2_ or _L3_, the resonant frequency had a  
Figure 4: Simulated (**a**) surface current field and (**b**) results with the proposed AP without/with the T-shaped slot.  
slight shift to lower frequencies while the isolation was almost unchanged. The optimized value of \(L2\) and \(L3\) was 7.1 mm and 2.5 mm, respectively.  
The impact resulting from the ground clearance is depicted in Figure 6. Figure 6a presents the simulated S-parameters as a function of \(Lcl\), and the normalized impedance varying with the frequency of each situation is also illustrated in Figure 6b. The direction of clockwise indicates a frequency range of 3-4 GHz. When the value of \(Lcl\) was 0.8 mm, the simulated resonant frequency was 3.5 GHz, and the \(-6\) dB bandwidth was 430 MHz (3.3-3.73 GHz). The impedance matching condition is not ideal enough. As shown in Figure 6b, the normalized impedance at 3.5 GHz when \(Lcl\) = 0.8 mm was 1.8393-0.7614 i (m1), which is far away from the center point of the Smith Chart; the mutual magnetic coupling between Ant. 1 and Ant. 2 was only larger than 10 dB within the interested band. When the value of \(Lcl\) was 1.8 mm, the designed AP still resonated at 3.5 GHz, but port impedance matching improved significantly. The \(-10\) dB impedance-matched band is 3.31-3.76 GHz, which can contain the targeted band. The magnetic coupling between two ports in an AP is superior to 15 dB. Furthermore, the normalized impedance at m1, plotted in Figure 6b, was 1.256-0.1135 i (m2), which improved a lot compared with m3. When the value of \(Lcl\) is 2.8 mm; an apparent shift toward the left of the resonant frequency could be observed from Figure 6a, and the normalized port impedance at 3.5 GHz of this time is marked as m3 in Figure 6b, which is 1.1148-0.468 i.  
Figure 5: Simulated S-parameters with the various values of (**a**) \(L1\), (**b**) \(L2\), and (**c**) \(L3\).