esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Performance Evaluation of Multicast R-GOOSE over Public WAN
### Results and Discussion
At the communication network level, redundant communication channels can be designed to maximize network availability. This can be achieved via the use of edge routers that support two (primary and backup) SIM cards connecting to two different mobile operators. At the protection application level, non-communication-dependent backup solutions (based on only local measurements) can also be designed for the power system, for example, time-based Logic Selectivity [26] as the backup solution for GOOSE-based Logic Selectivity and the Passive LOM method [53] as the backup for Communication-based LOM. The protection IEDs can be configured to switch dynamically from the primary solution (communication-based protection logic) to the backup solution if there is a communication failure or bad quality. Although backup solutions are only based on local measurements and do not have the full performance of communication-based solutions (which are based on both local and remote data), they can manage risks in a timely manner; they can recover their protection capability; and they can prevent physical consequences in case of loss-of-communication. This will enhance the safety of electrical power systems. In practice, DSO or TSO can utilize the proposed architecture solutions to realize multicast communication between the substations in communication-based protection applications. DSO/TSO saves money because they use cellular Internet instead of using their own communication network which requires both implementation and maintenance costs.
## 2 Materials and Methods
### Particle-Size Physical Fractionation and Determination of Organic Carbon in Fractions  
Fraction-size separation was obtained by ultrasonic dispersion, according to [PERSON] and [PERSON] [22], and wet sieving, according to [PERSON] et al. [23] (Figure 1).  
About 150 mL of milli-Q\({}^{\circledR}\) ultrapure water were added to 30 g of air-dried soil and the suspension was gently sonicated by placing the probe tip 15 mm below the water surface and using a probe-type sonicator Sigma Aldrich, 500-Watt model (60 J mL\({}^{-1}\)). This weak sonication was used for preserving micro-aggregates from disruption [24]. The first fraction (macro-aggregates fraction, A: 2000-250 \(\upmu\)m) was separated from the suspension by wet sieving (250 \(\upmu\)m), and the filtered remnant was sonicated a second time at 440 J mL\({}^{-1}\) and separated by wet sieving using sieves with different meshes (53 \(\upmu\)m and 20 \(\upmu\)m). After this step, the obtained fractions were: fraction B (microaggregates fraction, 250-53 \(\upmu\)m), fraction C (coarse silt-sized fraction 53-20 \(\upmu\)m), and fraction D (free fine silt plus clay fraction, \(<\)20 \(\upmu\)m). All fractions were dried at 35 \({}^{\circ}\)C before elemental analysis. The water-soluble organic fraction was isolated and discharged from each D fraction, according to [PERSON] [25], to avoid any interference. Briefly, an aliquot of each air-dried D fraction was suspended in water (1:10, w/v), and mechanically shaken for 15 min. The suspensions were then centrifuged at 6000 rpm for 15 min and the supernatant was removed.  
The OC content of all soil fractions was determined in triplicate using a Flash 2000 CHNS-O Elemental Analyser (Thermo Scientific) calibrated by an organic analytical standard consisting of a low organic content soil with 1.55% (w/w) of carbon. About 6-7 mg of each soil fraction were dried at 40 \({}^{\circ}\)C and pre-treated with hydrochloric acid (HCl 1%) to dissolve carbonates. The OC stocks were calculated by multiplying the C concentrations and the corresponding particle-size weights.
# Metabolite Profiling of _Christia__vevertilionis_ Leaf Metabolome via Molecular Network Approach
## 2 Materials and Methods
### Fractionation and MS-Targeted Isolation of Compounds **28** and **47**  
The crude extract (23.86 g) was reconstituted in a methanol:water (1:2) solvent system and subjected to liquid-liquid solvent partitioning using solvents of increasing polarity (n-hexane, chloroform, and ethyl acetate). Owing to the typical solubility of the chemical class of the targeted compounds in ethyl acetate [18; 19], the ethyl acetate soluble fraction obtained was evaporated to dryness via rotary evaporator, with the temperature maintained at 40 \({}^{\circ}\)C, to yield 0.36 g of dried fraction, which was further subjected to isolation using semipreparative high performance liquid chromatography (semiprep HPLC). Separation of the chemical constituents was achieved using an XBridge(tm) C\({}_{18}\) column (5 \(\upmu\)m, 150 \(\times\) 10 mm i.d.; Waters Corporation, USA), operated on a JASCO liquid chromatography system equipped with JASCO PU-2086 Plus Intelligent preparative pumps and a JASCO UV-2077 Plus 4-wavelength Intelligent UV/VIS DAD detector, using a water:acetonitrile (solvent A:solvent B) solvent system as the mobile phase, with a flow rate of 3 mL/min. The optimized mobile phase composition for the separation was set according to the following gradient sequence of percentage solvent B increments: 10-15% for 0-25 min, 15-25% for 25-30 min, 25-30% for 30-40 min, 30-100% for 40-50 min, maintained at 100% until 52 min. The chromatogram was monitored at 254 nm, and the targeted compounds were manually collected as they eluted from the column at their respective retention times. The structures of the isolated compounds were further analyzed by 1D (\({}^{1}\)H with water presaturation at 700 MHz) and 2D NMR experiments (HSQC, HMBC, and TOCSY) recorded on a Bruker Ascend 700 MHz spectrometer (Bruker Bio spin, Germany) equipped with a TCI cryoprobe. The 2D NMR spectra were recorded using regular pulse sequences. For data analysis, the FID files were processed with MestReNova version 6.0.2-5475 (Mestrelab Research, Santiago de Compostela, Spain), while chemical shifts were reported in parts per million (\(\delta\)), relative to the signal of the internal standard, tetramethylsilane (TMS). All experiments were performed in deuterated methanol (CD\({}_{3}\)OD). The \({}^{13}\)C chemical shiftdata of the isolated compounds were gleaned from HSQC (1\({}^{1}\)_J_ correlations) and HMBC (2\({}^{-3}\)_J_ correlations).
## 2 Materials and Methods
### Kinetic Analysis  
The fundamental rate equation for solid-state thermal decomposition processes typically assumes that the conversion rate is directly proportional to the reactant concentration and is temperature dependent. In non-isothermal experiments, the sample's mass is tracked continuously as the reaction progresses under a constant heating rate, \(\beta\) (K/min). The kinetics of thermal degradation can be characterized by the rate of change of conversion, d\(\alpha\)/dt. Under a linear heating rate, the kinetic expression is defined by two separate, independent functions--the temperature-dependent rate constant, k(T), and the fractional conversion function, f(\(\alpha\))--as shown in (1).  
\[\frac{d\alpha}{dt}=\beta\frac{d\alpha}{dT}=k(T)f(\alpha) \tag{1}\]  
The dependency of the rate constant, k, on temperature is described by Arrhenius equation, where \(E_{a}\) is the activation energy, \(A\) is the pre-exponential factor and \(R\) is the gas constant, as follows:  
\[k(T)=\text{A}\exp\left(-\frac{E_{a}}{RT}\right) \tag{2}\]  
Integrating the temperature dependency of the reaction based on the Arrhenius law and subsequently adapting it for linear heating, (1) is transformed into (3), as follows:  
\[\beta\frac{d\alpha}{dT}=A\exp\left(-\frac{E_{a}}{RT}\right)f(\alpha) \tag{3}\]  
The conversion degree of the reaction is the extent of conversion and is represented by the following equation:  
\[\alpha=\frac{w_{o}-w_{t}}{w_{o}-w_{f}} \tag{4}\]  
where \(w_{o}\)\(w_{t}\) and \(w_{f}\) represent the sample mass presented at initial, arbitrary, and final times respectively.  
Equation (3) can also be integrated into  
\[\int\limits_{0}^{\alpha}\frac{d\alpha}{f(\alpha)}=g(\alpha)=\frac{A}{\beta} \int\limits_{T_{0}}^{T}\exp\left(-\frac{E_{a}}{RT}\right)dT\equiv\frac{AE_{a}} {\beta R}p(u) \tag{5}\]  
where g(\(\alpha\)) and p(u) are known as the integrated form of fractional conversion function f(\(\alpha\)) and the temperature integral, respectively. Then, various mathematical approximations can be employed to obtain the solution of p(u) through different approaches. In this study, four distinct models--[PERSON], Flynn-Wall-Ozawa (FWO), Kissinger-Akahira-Sunose (KAS) and Starink --with iso-conversional approaches were utilized to compute the kinetic parameters.  
Equations (6)-(9), below, depict the linearized representations of the kinetic models utilized in the iso-conversional kinetic analysis.  
\[\ln\left(\beta\frac{d\alpha}{dT}\right)=\ln A+\ln f(\alpha)-\frac{E_{a}}{RT} \ \ \text{(for Friedman)} \tag{6}\]\[\ln\beta=\ln\frac{AE_{a}}{Rg(\alpha)}-5.331-1.052\frac{E_{a}}{RT}\ \ \text{(for FWO)} \tag{7}\]  
\[\ln\left(\frac{\beta}{T^{2}}\right)=\ln\left(\frac{AR}{E_{a}g(\alpha)}\right)- \frac{E_{a}}{RT}\ \ \text{(for KAS)} \tag{8}\]  
\[\ln\left(\frac{\beta}{T^{1B}}\right)=C_{s}-1.0037\frac{E_{a}}{RT}\ \ \text{(for [PERSON])} \tag{9}\]  
The requirement for achieving accurate and satisfactory values of activation energy is contingent upon the highest regression coefficient (R\({}^{2}\)) for the fitted regression line of the models.
# Development and Testing of a Two-UAV Communication Relay System
## 2 Related Works
They tested the performance of their system and showed that 12 Mbps communication can be achieved at around 300 m. [PERSON] et al. [27] also did a follow up work on a two-UAV network using 802.11a. In this work, they showed that high throughout can be achieved using two-hop communications. [PERSON] et al. [28] described the field experiment with a fixed wing UAV acted as a wireless relay for Underwater Vehicle. They only tested the download data rates. Notably, although the Wi-Fi network is convenient to build multiple access points, its communication range is highly restricted and a large number of nodes are needed for long range applications. One of the advantages of a Wi-Fi network is its high throughput. However, for UAV telemetry communication, the distance is a more important requirement than the transmission speed. For experimental works without the use of Wi-Fi network, [PERSON] et al. [29] analyzed the use of small UAVs for 3G cellular network relay. They tested the upload and download throughputs and the ping time in both urban and rural environments. [PERSON] et al. [30] developed the UAVs and the communication relay system for power line inspection. Their system consisted of multi-platform UAVs and multi-model communication system for image/video transmission. However, they did not test the communication range or quality of their system. The only one work used 433/915 MHz as the communication frequency was [PERSON] et al. [31] who measured the low-altitude air-to-ground channel at 915 MHz. They mainly focused on studying the spatial diversity to and from the airborne node and also measured time delay and received power. For all above-mentioned works, The UAVs in their systems did not exchange messages with the GCS by the relayed communication, which means the UAVs were used as carriers to realize communication relay for other systems rather than acting as the active parts in the relay systems. Therefore, the applications would be constrained if UAVs lost connection with the GCS. The highlightsof our work are the building of an interface and protocol with the flight controller and direct monitoring and control of the UAVs with relayed communication. This study provides a viable and economic way, with detailed hardware and software methods described, to construct a prototype of the UAV communication relay system.
## 2 Methods and Materials  
This study employs various research methodologies such as literature review, inductive summarization, and comparative analysis. The primary literature source is the Engineering Village database, in combination with professional databases such as Web of Science and CNKI. It collects and organizes related literature worldwide over the last 20 years, refining and analyzing the results of literature research in relevant fields from the perspective of HCI design. The subject search terms were set as (surgical robot OR robotic surgery) AND interaction. Searches were conducted in the Engineering Village database, the Web of Science database, and the CNKI database with a publication time limit set to the last 20 years. The search yielded a total of 3619 papers, with 1496 papers from the Engineering Village database, 1980 papers from the Web of Science database, and 143 papers from the CNKI database. The results were statistically analyzed as illustrated in Figure 1: an annual count of publications over the past 20 years is displayed in Figure 1a, revealing a year-by-year increasing trend in related research. Papers were also classified based on their research theme, as shown in Figure 1b. The HCI design research of surgical robots mainly focuses on force feedback, virtual reality, manipulators, and haptic interfaces. The study summarizes six different types of robot workflows for MIS and usage scenarios, four directional requirements for HCI design of minimally invasive surgery robots, and three types of interaction information in related fields. It also comparatively analyzes 23 representative surgical robot products on the market, summarizing four commonly used HCI methods for surgical robots and their advantages and disadvantages. Starting from the perspective of HCI design, this paper analyzes and organizes the related achievements based on the above three methods to form a review report. It aims to fill the absence and shortcomings of review results in this field, providing inspiration and guidance for subsequent scholars to deepen design research.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Type** & **Application Scope** \\ \hline \multirow{2}{*}{Laparoscopic surgery robot} & General surgery, urology, gynecology, and \\  & thoracic surgery \\ \hline \multirow{2}{*}{Vascular intervention surgery robot} & Cardiovascular, cerebrovascular, and \\  & peripheral vascular surgery \\ \hline \multirow{2}{*}{Percutaneous puncture surgery robot} & Hepatocellular carcinoma, lung cancer, prostate \\  & cancer, and other organ biopsies and treatment \\ \hline \multirow{2}{*}{Neurosurgical robot} & Brain, spinal cord, and neurological surgery \\ \hline \multirow{2}{*}{Natural orifice surgery robot} & Bronchoscopy, gastroscopy, colonoscopy, and \\  & other endoscopic examinations and treatment \\ \hline \multirow{2}{*}{Orthopedic surgery robot} & Trauma and spine, joint replacement, and other \\  & orthopedic surgery \\ \hline \hline \end{tabular}
\end{table}
Table 1: Classification of minimally invasive surgery robots.
## 2 Materials and Methods
### Method Validation  
#### 2.6.1 Calibration and Detection Limits  
Calibration curves were created for the quantification of volatile compounds using the optimized HS-SPME-GC\(\times\)GC-TOFMS conditions. Individual standard stock solutions were mixed in different categories and then diluted with the solution mentioned in Section 2.2.2 to a serial concentration to set up the calibration curve. The sample preparation method and IS addition amount used for calibration were the same as those used for the analysis of Baijiu samples. The linear ranges of the method were analyzed by creating calibration curves using different concentration levels of a model synthetic solution. The linearity of each compound was determined via evaluation of the regression curves (ratio between the area of the chromatographic peak of the standard and the area of the IS against the concentration ratio) and was expressed using the coefficient of determination (\(R^{2}\)). The limits of detection (LOD) and quantitation (LOQ) were determined from the calibration curves' data. The LOD was defined as the lowest concentration of the calibration curve based on a signal-to-noise ratio of 3 and the LOQ on a signal-to-noise ratio of 10. All analyses were performed in triplicate.
# Navigation Map Construction Based on Semantic Segmentation and Multi-Submap Integration
## 4 Methods
### Multi-Submap Fusion Mapping Method for Outdoor Environment
#### 4.2.1 Map Update Strategy for a Single Submap
6:Create two variables, \(fXInMapSpace\) and \(fZInMapSpace\), to store the values representing the X and Z coordinates in the map coordinate system from \(\mathbf{T}_{cm}\). 7:Convert the map coordinates to grid indices \(KFRowCol\). 8:Compute the range offset \(nDeltaIdx\) based on \(CellNearKFRadius\) and \(sfCellLength\). 9:Determine the grid boundaries \(nRowEnd\) and \(nColEnd\). 10:Iterate through each grid cell within the grid range \((nRow,nCol)\). 11:Transform the center of the grid cell to the camera coordinate system. 12:Calculate the depth value and image coordinates \((u,v)\). 13:If \((u,v)\) is within the image range: If the mask value is valid, increase the corresponding \(mvCells\) value. If the mask value is invalid, decrease the corresponding \(mvCells\) value. 14:End the iteration and return the values of \(mvCells\). ```  
**Algorithm 2** Submap Update Using KeyFrame Observations  
When new ground map points are introduced, they alter the parameters of the fitted ground plane, denoted as \((\mathbf{n},d)\), which subsequently impacts the grid map. To maintain map accuracy, the grid map must be reconstructed whenever \((\mathbf{n},d)\) changes significantly. We assume that the grid map was constructed based on the value of \((\mathbf{n}_{s},d_{s})\) during the last reconstruction. As the camera moves and more ground points are observed, the difference between the updated \((\mathbf{n},d)\) (calculated using Algorithm 2) and the original \((\mathbf{n}_{s},d_{s})\) increases. Figure 3: The projection process among different coordinate systems. When this difference exceeds a specified threshold, the error in the grid map constructed from \((\mathbf{n}_{s},d_{s})\) becomes unacceptable, thereby necessitating reconstruction. In this study, two thresholds, \(t_{1}\) and \(t_{2}\), are defined to determine when grid map reconstruction is required. The conditions for reconstruction are as follows:  
\[\mathbf{n}^{\mathsf{T}}\mathbf{n}_{s}<t_{1} \tag{20}\]  
\[\|d-d_{s}\|>t_{2} \tag{21}\]  
Equations (20) and (21) represent the directional difference of the normal vectors and the distance difference between two planes, respectively. If either of these conditions is satisfied, \((\mathbf{n},d)\) is assigned to \((\mathbf{n}_{s},d_{s})\), and the grid map is reconstructed based on the updated \((\mathbf{n}_{s},d_{s})\).
## 3 2D-DOA Total Variation Regularization
### Total Variation Regularization
#### 3.1.2 Total Variation Matrix  
The total variation matrix \(D_{tv}\) is built from one-dimensional first-order total variational operator \(D_{1d}\). It is defined as a \((M-1)\times M\) dimensional banded matrix. The sum of each row of the matrix is zero, and any element \(d_{i,j}\) in row \(i^{th}\) is assigned the value \(-1\), \(d_{i,j+1}\) is assigned the value \(1\), and the rest is zero.  
\[D_{1d}=\begin{bmatrix}-1&1&0&0&\cdots&0&0\\ 0&-1&1&0&\cdots&0&0\\ 0&0&0&-1&\cdots&0&0\\ \vdots&\vdots&\vdots&\vdots&\cdots&\vdots&\vdots\\ 0&0&0&0&-1&1\end{bmatrix}\]  
The matrix \(D_{tv}\) is generated by removing the first and last rows of the autocorrelation matrix of \(D_{1d}\). The total variation matrix \(D_{tv}\) can be expressed as:  
\[D_{\text{tv}}=\begin{bmatrix}-1&2&1&\cdots&0&0\\ 0&-1&2&\cdots&0&0\\ 0&0&-1&\cdots&0&0\\ \vdots&\vdots&\vdots&\cdots&\vdots&\vdots\\ 0&0&0&\cdots&-1&2\end{bmatrix} \tag{18}\]  
Let \(S_{Z}=\begin{bmatrix}S&S^{T}\end{bmatrix}\), then for the source matrix \(S\), there is:  
\[D_{\text{tv}}S_{Z}=D_{tv}\bigl{[}S&S^{T}\bigr{]} \tag{19}\]
# Precision Calibration and Linearity Assessment of Thin Film Force-Sensing Resistors
## 4 Discussion  
The results of this study indicate that both the Flexiforce A201-1 and Interlink FSR-402 sensors exhibit excellent linearity and repeatability, making them reliable for accurate force measurements. The linear relationship between applied load and output voltage was consistent across a range of loads for both sensors. This consistency suggests that with proper calibration, these sensors can be effectively used in various applications requiring precise force measurement.  
The slightly higher conductivity observed in the Interlink FSR-402 sensor compared with the Flexiforce A201-1 sensor is likely due to differences in their material composition and design. This difference highlights the importance of selecting the appropriate sensor based on the specific requirements of the application, including sensitivity, precision, and cost considerations.  
Figure 8: Conductivity as a function of load. (**a**) Conductivity as a function of resistor (Flexiforce A201\(-\)1), (**b**) conductivity as a function of the straight line calculated in Table 3 (Flexiforce A201\(-\)1), (**c**) conductivity as a function of resistor (Interlink FSR\(-\)402), and (**d**) conductivity as a function of the straight line calculated in Table 4 (Interlink FSR\(-\)402).  
The use of a voltage divider circuit and multiple fixed resistances in this study provided a robust framework for analyzing the output characteristics of the FSR sensors. The derived calibration equations allowed for the accurate translation of sensor output into applied loads, significantly enhancing the precision of force measurements. This methodological approach proved effective in ensuring the reliability of the sensors.  
However, this study also revealed some challenges and areas for further research. For instance, while both sensors demonstrated high linearity and repeatability under static loading conditions, their performance under dynamic loading conditions remains to be investigated. Future research should focus on optimizing calibration techniques and exploring new materials and sensor designs to improve sensor performance. Additionally, investigating the sensors' responses to different types of forces, such as shear and torsional forces, could further expand their applicability.  
Although the linearity of sensors is usually provided by the non-linear error percentage to a full-scale voltage output, we tried to utilize the conductivity line in order to check the reliability of the two sensors in the case of using different resistances. Thus, instead of using the data of non-linear errors, we tried to demonstrate good reliability with different resistances by showing how the straight lines of conductivity in Figure 8 are in a close neighborhood.