esa-sceva/satcom-chunk-collection huggingface dataset:
# A Digitalization Algorithm Based on the Voltage Waveform of the Multifunction Vehicle Bus
## 2 The MVB Voltage Waveform Digitalization Algorithm
### MVB Voltage Waveform Digitalization Algorithm Extraction Algorithm
#### 2.2.4 Data Synchronization  
The experimental part of this study collected MVB communication data from multiple MVB cards' communication processes, using an MVB protocol analyzer to save link-layer data and an oscilloscope to record MVB physical-layer voltage waveforms. The protocol analyzer records data types as digital quantities, so the data storage pressure is small, and the single recording duration can reach tens to hundreds of seconds. However, the physical-layer voltage waveform consists of analog data with a sampling rate of tens of MHz, the data storage pressure is high, and it can only record tens of ms. The data frames recorded by the protocol analyzer all carry timestamp information, while the data collected by the oscilloscope usually do not. In order to provide accurate timestamp information for the physical-layer voltage waveform, this study matches segments of physical-layer data with link-layer data.  
Figure 7: Diagram of the edge distortion and the fitted curve.  
Here are the synchronization procedures:  
1. The first data frame of the physical-layer data is compared with the link-layer data to obtain all indexes of the same data frames.
2. Using ones of these indexes as the synchronization starting point, the data frames of the physical layer are compared one by one with the data-link-layer data (data frames that are abnormal in Section 2.2.3 will be skipped).
3. When all data synchronization is completed, it can be considered that dual-modal data synchronization is completed and that an accurate timestamp for the physical-layer data is obtained. Otherwise, procedure 2 is repeated.
# Effect of Process Parameters on Mode Conversion in Submicron Tapered Silicon Ridge Waveguides
## 2 Structure and Analysis  
In this work, we consider tapered submicron SOI ridge waveguides. A regular lateral adiabatic taper is considered for studying mode conversion. In the present example, the SOI wafer has total silicon (Si) height H = 500 nm, and the refractive indices of the Si and SiO\({}_{2}\) are 3.455 (nSi) and 1.445 (nSiO\({}_{2}\)), respectively. An SOI wafer with 500 nm Si thickness is available with many Multi-Project-Wafer (MPW) offerings [30,31].  
In our analysis, we have considered three different ridge heights, i.e., h = 200 nm (0.4 H), h = 250 nm (0.5 H), and h = 300 nm (0.6 H). For clarity purposes, the etch-depth (h) is given as a ratio to the total silicon height (H). For a deeply etched ridge waveguide, the ridge height (h) is larger than the slab height (H-h), while for low etch-depth, the opposite is true. Figure 1a shows an ideal rectangular ridge waveguide with SiO\({}_{2}\) top cladding. However, the fabrication process is not ideal, and it introduces angled sidewalls [Figure 1b]. Since 8\({}^{0}\) is the most commonly reported sidewall angle [29, 30, 31], we study the effect of this angle on the mode conversion. We also evaluate mode property and light propagation in a ridge waveguide exposed to air with 8\({}^{0}\) sidewalls. By quantifying the effect of etch-depth, angled sidewalls, and asymmetric cladding on mode conversion in tapered ridge waveguides, designs can be made more compact. Finally, we combine all the findings to reduce the length requirement of an adiabatic taper for 100% mode conversion. Additionally, calculations are done to find the best possible TM preserving efficiency that can be achieved in non-adiabatic short tapers.
## 3 Benchmark Results
### Digital Signature Algorithms  
We have evaluated the digital signature algorithms RSA, DSA, ECDSA, BLS and BB using the keys generated in previous Section 3.4. The following results do not include hashing operations, except for the BB case where the hashing is intrinsically embedded in the signature and verification process. Thus, for the RSA case, signing is equivalent to deciphering, and verifying is equivalent to ciphering.
## 3 Span-Based Nested Named Entity Recognition Model
### Methodological Overview and Key Differences  
Our LDE model differs from existing few-shot nested NER approaches in several key aspects:  
* Architecture Simplicity: Unlike multi-stage frameworks such as FIT [7] (focusing-bridging-prompting) and LPNER [8] (span extraction + prototype classification), our approach modifies only the output layer of the biaffine model, replacing the traditional classifier with a label attention mechanism.
* Label Information Integration: While previous label-aware methods [14; 15] combine label information with input features throughout the network, our model incorporates  
Figure 1: Figure of label description embedding model.  
rich semantic descriptions exclusively in the classification layer, enabling more focused utilization of label semantics.
* Semantic Depth: Compared to approaches using single label words [15] or simple prompt templates [8], our model leverages comprehensive natural language descriptions (average 43.13 tokens per label) to capture deeper semantic relationships between entity types.
* Nested NER Focus: Unlike SpanNER [14] and similar approaches designed primarily for flat NER, our span-based architecture inherently handles hierarchical entity relationships while maintaining the benefits of label semantic information.  
This design enables effective few-shot learning through minimal architectural changes while maintaining computational efficiency compared to complex multi-stage approaches.
# A 3D Estimation Method Using an Omnidirectional Camera and a Spherical Mirror
## 7 Conclusions  
The proposed catadioptric imaging system, which utilizes an omnidirectional camera with a fisheye lens and a spherical mirror, offers a variable positional relationship between the camera and the mirror, allowing for observation with a high degree of freedom. This paper also presents a method for estimating the 3D position of the spherical mirror, enabling 3D estimation from single-shot omnidirectional images without the need for prior learning. The proposed method achieves 3D estimation from compact devices and single image capture to combine the advantages of an omnidirectional camera with a wide observation range and a catadioptric imaging system using curved mirrors. This method eliminates the requirement for prior learning. The effectiveness of the proposed method is demonstrated through simulations using a CG model of a room. The simulations suggest the capabilities and characteristics of the 3D estimation method, proposing potential applications. The paper introduced a novel  
Figure 19: Estimation Result Images of Position of Spherical Mirror: (**a**) The image of the mirror image region from the shooting image. (**b**) The image of the segmented mirror region from the image of (**a**). (**c**) The image of the estimated elliptical shape based on (**b**).  
Figure 20: Estimation Result Image on Real-World Simulation: The estimation map using the proposed method, with mirror position as GT. The map is visualized by changing the hue linearly.  
catadioptric imaging system and a 3D estimation method that offers flexibility, compactness, and single-shot capability.  
Conceptualization, Y.H. and I.K.; methodology, Y.H.; software, Y.H.; validation, Y.H. and I.K.; formal analysis, Y.H.; investigation, Y.H.; resources, I.K.; data curation, I.K.; writing--original draft preparation, Y.H.; writing--review and editing, I.K., H.S. and C.X.; visualization, Y.H.; supervision, I.K.; project administration, Y.H.; funding acquisition, I.K. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by JSPS KAKENHI Grant Number 21 KK0070, 22K18303 and 22H01580.  
Not applicable.  
The authors declare no conflict of interest.
# Performance Investigation of Coated Carbide Tools in
## 2 Materials and Methods
### Milling Investigations  
The milling process is a highly dynamic process characterized by intermittent cutting forces, cyclic thermal loads, and high mechanical stresses, leading to various wear  
Figure 4: **(a)** Fatigue endurance determination based on coating failure depth; **(b)** a typical 3D imprint of the impact test and characteristic remaining imprint depths.  
Figure 3: The employed impact tester arrangement for the fatigue endurance investigation.  
mechanisms on coated carbide tools [6; 7; 8]. In particular, abrasive wear takes place in situations where hard particles from the workpiece material rub against the tool surface, causing gradual material removal that induces flank wear and an increase in cutting forces, eventually degrading tool performance. At lower cutting speeds, material adhesion occurs due to interaction between the tool coating and the workpiece that leads to localized coating detachment, exposing the carbide substrate to further wear. On the contrary, high cutting speeds instigate extreme temperatures, prompting chemical interactions between the coating and oxygen (oxidation) or between the coating and workpiece material (diffusion). Finally, mechanical overstressing and surpassing the fatigue strength primarily cause micro-chipping of the coating, particularly in the transitional cutting-edge region between the flank and the tool's rake. Therefore, in the present study, several experiments were conducted with various values on critical machining conditions, such as feed rate, velocity, and radial depth, in order to capture various wear phenomena on the examined coated tools. Explicitly, four levels were considered for the feed rate and the radial depth of cut and three levels for the cutting speed, generating 48 combinations of the investigated parameters. The values for the feed rate were 0.1, 0.2, 0.3, and 0.4 (mm/rev); for the cutting speed, 100, 200, and 300 (m/min); and for the radial depth of cut, 1, 3, 13, and 20 (mm). The milling experiments were performed utilizing a three-axis numerically controlled milling center, and Table 2 exhibits all of the 48 cutting condition combinations along with the achieved tool's life in each set. A flank wear width criterion equal to 0.15 mm was considered in the tests, resulting in an accumulative tool life T\({}_{0.15}\), which was calculated with the aid of the following equation:  
\[T_{0.15}=NC_{0.15}\times\frac{l_{\text{cut}}}{V} \tag{1}\]  
where V is the applied cutting speed, \(l_{\text{cut}}\) composes the undeformed chip length, and NC\({}_{0.15}\) corresponds to the number of cuts (NC) leading to a flank wear width equal to 0.15 mm.
# The Summertime Diurnal Cycle of Precipitation Derived from IMERG
## 2 Materials and Methods  
### Data  
Precipitation estimates from a constellation of space-borne passive microwave radiometers and infrared sensors as well as ground-based rain gauges are incorporated into the IMERG product [11]. The IMERG algorithm is described in detail by [11] and summarised by [15; 20; 21]; the GPM-CO instruments calibrate all microwave precipitation estimates contributing to IMERG, and these estimates then act as calibrators for the infrared estimates. Sampling limitations for passive microwave sensors onboard low-Earth-orbit satellites are addressed by linearly interpolating microwave precipitation estimates using displacement vectors for the motion of features found in infrared estimates, which come from geostationary satellites; this process is known as morphing [22]. Furthermore, precipitation estimates determined from the infrared estimates [23] are incorporated into microwave-sparse regions via a Kalman filter technique to provide near-global gridded precipitation. This study uses the 'final run' product (3B-HHR), which is calibrated to monthly Global Precipitation Climatology Centre (GPCC) gauge estimates.  
The level-3 IMERG product is processed by the NASA Precipitation Processing System, and the data files are freely available from [24]. The version-5 (V05) product, released in November 2017, is used in this work. The level-3 data provide gridded precipitation estimates at 0.1\({}^{\circ}\times 0.1^{\circ}\) and half-hourly spatial-temporal resolution [15]. Furthermore, the product includes additional parameters such as the weighting of infrared input to the final precipitation estimate, the probability of liquid precipitation, a quality index and others [25]. For this research, the final precipitation estimate after gauge calibration (precipitationCal) and the corresponding random error (randomError) are used. Random errors are determined using an approach similar to [26]; more information can be found in [11; 27].
# Dynamic Modeling and Full-Process Simulation of the Core Engine Test Rig Main Test System
## 3 Modularization Modeling Approach
### Gas Steady Apparatus Model
#### 3.2.2 The Throttling Points of the First and Second Orifice Plates  
Mass flow-rate equation of a single throttling orifice:  
\[Q_{\mathrm{m}\mathrm{o}\mathrm{i}\mathrm{o}\mathrm{i}}=\left\{\begin{array}{ c}C_{\mathrm{d}i}A_{\mathrm{o}i}\sqrt{\frac{2\gamma}{\gamma-T}p_{i}\rho_{i}\left[ \left(\frac{P_{i+1}}{P_{i}}\right)^{\frac{2}{\gamma}}-\left(\frac{P_{i+1}}{P_{ i}}\right)^{\frac{2+1}{\gamma}}\right]}}&\frac{P_{i+1}}{P_{i}}>\left(\frac{2}{\gamma+1} \right)^{\frac{2}{\gamma-1}}\\ C_{\mathrm{d}i}A_{\mathrm{o}i}\sqrt{p_{i}\rho_{i}\gamma\left(\frac{2}{\gamma+1 }\right)^{\frac{2+1}{\gamma-1}}}&\frac{P_{i+1}}{P_{i}}\leq\left(\frac{2}{ \gamma+1}\right)^{\frac{2}{\gamma-1}}\end{array}\right. \tag{8}\]  
Figure 4: Finite control volume grids of gas steady apparatus. (**a**) Three-chambers and two-orifices structure. (**b**) Two-chambers and one-orifice structure.  
Total mass flow-rate equation of the \(i\)th layer orifice plate:  
\[Q_{mi}=\sum_{j=1}^{n_{oi}}(Q_{\text{mo}})_{j}=n_{oi}\cdot Q_{\text{moi}},\ i=1,2 \tag{9}\]  
where the flow sectional area of each orifice on the \(i\)th layer orifice plate \(A_{oi}=\pi{d_{oi}}^{2}/4\), and \(n_{oi}\) is the number of throttling orifices on the \(i\)th layer orifice plate; \(C_{di}\) and \(C_{di}\)/ are flow coefficients which represent orifice throttling characteristic, and the flow coefficient curves need to be established for different shapes of throttling orifices by test and appropriately amended for different fluid media according to viscosity and density. In the case of reverse flow, that is, when the equation meets \(0\leq p_{i}<p_{i+1}\), the positions of \(Var_{i}\) and \(Var_{i+1}\) are exchanged and a minus sign is added in front of the mass flow-rate result, where \(Var\in\{\rho,p\}\).  
Local velocity calculation formula at specific cross section: \(u_{\text{A}}=Q_{\text{mi}}/(\rho_{\text{A}}A_{\text{A}})\).
## 4 Results and Discussion
For the best achieved accuracy, with triple-layer LSTM using FastText word embedding, the training time of 1 h 25 min was recorded for the training dataset, with 6.91 us testing time. In the implementation scenario, the input data consist of a single sentence, and therefore the required processing time is around 6.91 us divided by 21,650. There will be no issue with the processing speed when implemented with server computer specifications. We also tested the model using consumer personal computer (PC) with lower computer specifications in order to test its real implementation possibilities. The hardware specifications were: intel I5 with 8 GB memory. The testing time for 21,650 sentences was 16.4 s to achieve the best accuracy with fastText with triple-layer LSTM. Therefore, \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Architectures**} & \multicolumn{2}{c}{**GloVe**} & \multicolumn{2}{c}{**fastText**} \\ \hline  & **Training** & **Validation** & **Testing** & **Training** & **Validation** & **Testing** \\  & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** & **Accuracy** \\ \hline Single-Layer LSTM & 0.904 & 0.854 & 0.867 \(\pm\) 0.005 & 0.928 & 0.876 & 0.881 \(\pm\) 0.006 \\ \hline Double-Layer LSTM & 0.943 & 0.876 & 0.891 \(\pm\) 0.008 & 0.954 & 0.889 & 0.902 \(\pm\) 0.003 \\ \hline Triple-Layer LSTM & 0.955 & 0.881 & 0.903 \(\pm\) 0.008 & 0.967 & 0.896 & 0.909 \(\pm\) 0.007 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results of training, validation, and testing accuracy on 5 fold cross validation. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Architectures**} & \multirow{2}{*}{**Trainable**} & \multicolumn{2}{c}{**Training Time (min)**} & \multicolumn{2}{c}{**Testing Time (us)**} & \multicolumn{2}{c}{**Testing in Consumer PC (s)**} \\ \cline{3-8}  & & **GloVe** & **fastText** & **GloVe** & **fastText** & **GloVe** & **fastText** \\ \hline Single-Layer LSTM & 97,795 & 37 & 37 & 6.68 & 6.20 & 7.31 & 6.28 \\ \hline Double-Layer LSTM & 130,819 & 79 & 78 & 6.91 & 6.90 & 10.8 & 11.9 \\ \hline Triple-Layer LSTM & 163,843 & 117 & 117 & 7.39 & 6.91 & 16.6 & 16.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: LSTM architecture trainable parameters, and testing and training time. every individual sentence required 0.75 milliseconds on average. According to those testing results and testing times, we consider that all of the observed architectures can be feasibly implemented.
# Salivary Extracellular DNA and DNase Activity in Periodontitis
###### Abstract  
Extracellular DNA (ecDNA) is a potential marker and predictor in several inflammatory diseases. Periodontitis, a chronic inflammatory disease, is associated with epithelial cell death and could lead to release of DNA. Our aim was to analyze salivary DNA concentration and deoxyribonuclease (DNase) activity in periodontitis patients. We hypothesized that salivary ecDNA will be higher than in controls and could serve as a marker of periodontitis severity. Samples of saliva were collected from 25 patients with chronic periodontitis and 29 age-matched controls. DNA was quantified fluorometrically in whole saliva, as well as in supernatants after centrifugation (depletion of cells at 1600x \(g\)) and in double-centrifuged supernatants (depletion of cell debris at 1600x \(g\) and 16,000x \(g\)). The subcellular origin of ecDNA was assessed using real-time PCR. In comparison to controls, patients with periodontitis had twofold higher salivary DNA (\(p<0.01\)), higher mitochondrial DNA in centrifuged supernatants (\(p<0.05\)) and lower nuclear ecDNA in double-centrifuged samples (\(p<0.05\)). No correlations were found between salivary DNA and oral health status, but mitochondrial DNA positively correlated with papillary bleeding index in centrifuged samples. Salivary DNase activity was comparable between the groups. In conclusion, we proved that salivary DNA is higher in periodontitis. The source of the higher mitochondrial DNA in cell-free saliva and the causes of lower nuclear ecDNA remain to be elucidated. Further studies should focus on the role of mitochondrial DNA as a potential driver of inflammation in periodontitis.  
cell free DNA; salivary biomarkers; gingivitis; oral diseases; apoptotic bodies; deoxyribonucleases +
Footnote †: journal: Applied Sciences
## 3 Proposed Ranging-Compensated Temperature-Sensing Sensor Design Method
### Circuit Design  
On the basis of the design principle for simplicity, low power consumption, and adaptability to space environment, the design of the ranging compensation temperature-sensing sensor circuit is shown in Figure 5.  
The temperature-sensing circuit converts the temperature into a voltage signal using a thermistor and a pull-up resistor. The thermistor can be distributed in the ISL payload of the APAA, RF channel, and DBD, and selection of the resistance value of the pull-up resistor is made according to the temperature-resistance range of the thermistor. To satisfy multi-module temperature sensing, the ADC uses the multi-channel simultaneous sampling chip AD7865, which is a 14-bit four-channel ADC. The data control and processing terminal uses the FPGA, which can be directly integrated into the ISL payload DBD processing chip. The complete temperature-sensing sensor can be integrated in the ISL DBD printed circuit board except the thermistor, and the occupied area is only 15 mm \(\times\) 12 mm. The FPGA logic and storage resources only introduce six adders and six \(2^{N}\times M\) ROM storage capacity, which is less than the Xilinx XQR4 VSX55 (San Jose, California, USA) slice resource of 1%. In addition, the overall power-consumption increase is less than 90 mW.  
Figure 4: Entire data-processing diagram of the ranging compensation temperature-sensing sensor.  
Figure 5: Design of the ranging compensation temperature-sensing sensor circuit.
## 6 Propagation Time for a Signal, Emitted and Intercepted by Satellites on a Space-Oriented Orbit
### An Earlier Example for an Elliptic Integral, Expressed Also Analytically
The first three iterative solutions are given according to the following formulaes:  
\[E_{(1)}=M+e\sin M, \tag{115}\]  
\[E_{(2)}=M+e\sin E_{(1)}=M+e\sin(M+e\sin M), \tag{116}\]  
\[T_{2}^{(1)}=-\frac{2G_{\oplus}M_{\oplus}}{c^{3}}\cdot\frac{n}{(1-e^{2})^{\frac {3}{2}}}\widetilde{T}_{1},\]0  
Thus, from the formulaes for \(E_{(3)}\), \(E_{(5)}\) and \(E_{(6)}\), the following numerical values can be found:  
\[T_{2}^{(1)}=-\frac{2G_{\oplus}M_{\oplus}}{c^{3}}\cdot\frac{n}{(1-e^{2})^{\frac {3}{2}}}\widetilde{T}_{1},\]1  
\[T_{2}^{(1)}=-\frac{2G_{\oplus}M_{\oplus}}{c^{3}}\cdot\frac{n}{(1-e^{2})^{\frac {3}{2}}}\widetilde{T}_{1},\]2  
\[T_{2}^{(1)}=-\frac{2G_{\oplus}M_{\oplus}}{c^{3}}\cdot\frac{n}{(1-e^{2})^{\frac {3}{2}}}\widetilde{T}_{1},\]3  
\[T_{2}^{(1)}=-\frac{2G_{\oplus}M_{\oplus}}{c^{3}}\cdot\frac{n}{(1-e^{2})^{\frac {3}{2}}}\widetilde{T}_{1},\]4  
The approximation \(E_{(5)}\) up to the ninth digit is identical to \(E_{(4)}\) and \(E_{(6)}\) up to the eleventh digit is identical to \(E_{(5)}\). The approximation \(E_{(6)}\), also, up to the seventh digit is identical to \(E_{(3)}\), given by (118). However, if the approximate value for \(E_{(3)}\) is compared with the value for \(E_{(6)}\), the coincidence only exists up to the fifth digit. It is, therefore, necessary to check what the impact is of the approximations in the eccentric anomaly \(E\) on the approximations of the time of propagation of the signal.