esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 The Proposed Framework for Testing
### Simulator Architecture
#### 3.1.3 Scenario Based Approach for Test Cases Generator  
A scenario is an explanation of an actual or imaginable act and sequence of events resulting in a particular act [41]. Scenarios specify user requirements. In addition, they are also used to discover and explain system behavior. With reference to scenarios, a use case can be viewed as a general form of explanation for a set of tangible scenarios. Specifically, we adopt a scenario-based approach to describe test cases in our framework. The use case model that we used for the scenario-based approach is shown in Figure 5. \begin{table}
\begin{tabular}{c c} \hline \hline
**Sr.** & **Identified Synchronization Bugs** \\ \hline
1 & Data received at Rx (receiver) with delay. \\
2 & Data transmission from Tx (transmitter) with delay. \\
3 & No data transmitted from Tx. \\
4 & No data received at Rx. \\
5 & Data overriding during communication \\
6 & No synchronization \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Synchronization bugs in inter-processor communication. Figure 5: Use case model. Use cases can be interconnected with other use cases. Figure 5 also shows the use case attributes. Because use cases act as the standard type explanation for the related scenarios, every scenario inherits the attributes described for its use case, for example trigger, goal, precondition, post condition, etc. To observe the bugs shown in Table 1, various use cases were developed. Use cases illustrated the simulation scenarios where the communication results were examined. Use cases were defined on a deeper level by specifying scenarios. We generated test data based on the possible scenarios of all physical interfaces. The data received at Rx with the delay scenario written as a use case is shown in Table 2. It shows the template for \"data received at Rx with start delay\" scenario. The template provides comprehensive steps of events to identify bugs through simulation [42; 43; 44]. Different test cases were generated using case scenarios of all interfaces, where the communications at different links occur. One-to-one correspondence was used between the use case and the test case. A test case is a collection of several actual tests, while a use case is a collection of several scenarios. Essentially, each test was a refined corresponding scenario. The scenarios related to the use cases were much more general and were most often described informally. A test case may be initiated by a certain event or action, known as a trigger. While it is the same trigger as described in the subsequent use case, it is represented in a formalized way in a test case. Conversely, both test pre-condition(s) and test post-condition(s) were formulated and the improved form of the respective conditions was specified as in respective use case. The test case model that was used for testing in the proposed framework is shown in Figure 6.
## 1 Introduction
Compared with the indirect measurement method, the direct measurement method can obtain real-time laser wavelength. An interferometer comparator can be used for laser wavelength measurements by analyzing the relationship between the phase [15; 16] or the period [17; 18] of interference fringes and the laser wavelength. The interference fringes are obtained when an optical path difference between a reference path and a measurement path is generated in the interferometer. The common measurement systems are Michelson interferometers [19; 20] and Mach-Zehnder interferometers [21] with mirrors or cube corners. However, in the interferometer comparator, a reference laser is necessary, and the measurement accuracy depends significantly on the laser coherence. Optical beating methods with a certain optical path different can be employed to make a highly accurate measurement of the laser wavelength [22; 23]. Optical beating methods are based on frequency comparisons with a laser stabilized by means of the saturated absorption method [22] or with a comb-generator [23]. Wavelength can also be measured based on specific wavelength-dependent material properties, such as specialties of optical fiber [24], polarization of light [25], and diffraction gratings [16; 26]. The nature of simple structure and portability of this kind of wavelength measurement method predestinates it to be applied in different kinds of commercial systems, especially gratings, in which laser wavelength can be calculated by detecting the variation of diffraction angle with a charge-coupled device (CCD) [16]. However, CCD cannot provide high-precision wavelength measurements due to its low resolution and accuracy. In this study, a laser-diode-based miniature Michelson interferometer (MMI) with self-wavelength correction based on the grating diffraction principle and the autocollimation principle is proposed. Combining the MMI with a dual-angle sensor based on the autocollimation principle, a compact three-degree-of-freedom laser measurement (3 DOFLM) system is developed, which is employed as the displacement feedback sensor, as well as pitch and yaw angular sensors, of a nanopositioningstage. The optical layout of the 3 DOFLM system is designed in such a way that the displacement and two angular motions of the nanopositioning stage can be detected simultaneously. The laser wavelength can be corrected in real time during the displacement feedback. A prototype system was constructed. The principle of the 3 DOFLM system design and its performance tests are presented in the following sections.
# A Decision Tree for Rockburst Conditions Prediction
###### Abstract  
This paper presents an alternative approach to predict rockburst using Machine Learning (ML) algorithms. The study used the Decision Tree (DT) algorithm and implemented two approaches: (1) using DT model for each rock type (DT-RT), and (2) developing a single DT model (Unique-DT) for all rock types. A dataset containing 210 records was collected. Training and testing were performed on this dataset with 5 input variables, which are: Rock Type, Depth, Brittle Index (BI), Stress Index (SI), and Elastic Energy Index (EEI). Other ML algorithms, such as Random Forest (RF), Support Vector Machine (SVM), Artificial Neural Network (ANN), K-Nearest Neighbor (KNN), and Gradient-Boosting (AdaboostM1), were implemented as a form of comparison to the DT models developed. The evaluation metrics and relative importance were utilized to examine some characteristics of the DT methods. The Unique-DT model showed a promising result of the two DT models, giving an average of (F1 = 0.65) in rockburst condition prediction. Although RF and AdaboostM1 (F1 = 0.66) performed slightly better, Unique-DT is recommended for predicting rockburst conditions because it is easier, more effective, and more accurate.  
rockburst; rockburst condition; decision tree; machine learning algorithms; predictions; metrics 2023
## 5 Performance Evaluation
### Performance Matrices
#### 5.2.1 Quality-of-Experience  
Quality-of-Experience (QoE) is a measure of the overall level of user satisfaction, which can be increased by enhancing the gap between maximum allowable time to get a response (i.e., QoS) and the response time for a request [18]. We measured the average QoE for all requests, \(R\), arriving during the simulation period as follows:  
\[\overline{\kappa}=\frac{1}{|R|}\sum_{r\in R}\frac{q_{r}-(\varpi_{r}+\zeta_{r,b })}{q_{r}}, \tag{17}\]  
where \(\varpi_{r}\) is the waiting time in the queue and \(\zeta_{r,b}\) represents the required time to process a request, \(r\), on a BBU, \(b\). The greater the value of \(\overline{\kappa}\), the better is the capability of the system to maximize user QoE.
# Simplified Design of Magnetic Gear by Considering the Maximum Transmission Torque Line
## 5 Conclusions  
A simplified method for designing a magnetic gear through the estimation on the total volume for the desired transmission torque, using the MTTL approach, was presented. The MTTL was developed from various analyses of magnetic gears with randomly different parameters, where the line is formed by maximum value of torque at every total volume of PMs. The equation of relationship between total volume of PMs and parameters of MG was derived to determine the parameters of MG from the estimated total volume of PMs, and the required radius to achieve the required transmission torque was derived. The MTTL was validated with five models that represent different requirements in applications. The result shows up to 8.5% of error in targeting the desired transmission torque from the models that referred to the MTTL. The proposed MTTL is relevant to be the reference in designing any surface-type PM coaxial magnetic gears that give an early approach in the direction of magnetic gear design. Thus, it helps the magnetic gear designer to design the magnetic gear that fulfills the required transmission torque instead of resulting in an unpredictable performance.  
Conceptualization, N.M. and L.M.S.; methodology, N.M. and L.M.S.; software, N.M. and L.M.S.; resources, N.M. and L.M.S; data curation, N.M. and L.M.S.; writing--original draft preparation, N.M. and L.M.S.; writing--review and editing, I.A., C.A.V., and H.T. All authors have read and agreed to the published version of the manuscript.  
This research is funded by the Ministry of Education, Malaysia for financial support under the \"Translational Research\" Grant (TR@M).  
The authors would like to express their gratitude to Universiti Putra Malaysia for the facilities provided during the research work.  
The authors declare no conflict of interest.  
FEMAFinite element method  
DCDirect current  
\(\ell\)Axial length of magnetic gear  
gAir gap length  
GrGear ratio  
MGMagnetic gear  
MTTLMaximum Transmission Torque Line  
\(n_{\text{hs}}\)Speed of high-speed rotor  
NhsNumber of permanent magnets at high-speed rotor  
Figure 6: The simulated result of the models on the Maximum Transmission Torque Line (MTTL).
## 2 Materials and Methods
When it reaches the pressure needed for a supercritical state (up to the critical point, see Figure 2b), the carbon dioxide is heated by a heat exchanger (5). At the supercritical state, the CO\({}_{2}\) is directed towards the autoclave (6), which contains the components (7). Then, thermocouples are used to control the temperature (8). The carbon dioxide is recovered \begin{table}
\begin{tabular}{c c c c} \hline
**Polymer** & **Weight Fraction, \%** & **Density, g/cm\({}^{3}\)** & **Melting Point, K** \\ \hline PP & 2.68 & 0.90 & 418 \\ PEG & 4.95 & 1.21 & 335 \\ SA & 0.35 & 0.94 & 341 \\ \hline \end{tabular}
\end{table} 
Table 2: Characteristics of polymer binders used in the present study. Figure 1: Weight evolution for the injected specimens obtained by an injection-molding process (15-mm length and 10-mm diameter). (9), cooled, and directed towards a recycling line (3). At the end of the process, the components are weighed again to estimate the quantity of PEG removed. Three successive tests were performed for each debinding temperature. A diagram of the equipment setup is shown in Figure 2a. [PERSON] et al. [6] identified the optimal supercritical debinding kinetics as 40 MPa and 423.15 K to remove 100% of the PEG, with a molar weight of 20,000 g/mol, from the injected components in four hours. [PERSON] et al. [33] reported that the density of the PEG/CO\({}_{2}\) binary solution increased with an increasing pressure, and the molecular weight of the polymer had a slight effect on the density of the binary system. In this study, the cylindrical components were debound at an optimal pressure of 40 MPa over four hours at three different temperatures: 383.15 K, 403.15 K, and 423.15 K. It is supposed that the mass loss is totally due to the PEG extraction. To quantify the extraction kinetic, three tests at two different time steps (1 h and 2 h) under optimal conditions (423.15 K and 40 MPa) were conducted.
## 2 Materials and Methods
### Extraction of Phenolic Compounds and Determination of Individual Compound Using HPLC-DAD-MSn  
Individual phenolic compounds were extracted from 3 g of fresh fruits (several homogenized fruits in 5 repetitions, \(n=5\)) with 6 mL 80% MeOH with 3% formic acid in ultrasonic bath at 0 \({}^{\circ}\)C for half an hour. The extract was centrifugated and filtered through 0.2 \(\upmu\)m polyamide filter (Macherey-Nagel) and transferred into vials. Phenolic compounds were analyzed on a HPLC system (Dionex UltiMate 3000, Thermo Scientific, San Jose, CA, USA) coupled with a diode array detector at 280 nm, 350 nm and 530 nm as previously described by [PERSON] et al. (2017). The identification of individual phenolics was done on HPLC system tandem with mass spectrometer (LTQ XL(tm) Linear Ion Trap, Thermo Scientific, San Jose, CA, USA) with an electrospray ionization (ESI) operating in positive (anthocyanins) and negative (other phenolic group) ion modes. Full scan data dependent MSn scanning from \(m/z\) 115 to 1500 was performed for the analyses as described by [PERSON] et al. (2017).  
The content of phenolic compounds was calculated from peak areas of the sample and corresponding external standards and expressed in mg per g fresh weight (mg/g FW).  
For phenolics of which the external standards were not available, contents are presented as equivalents of related substances (phenolic acids derivatives as their aglycone equivalents, procyanidin dimers as procyanidin B1, quercetin-3-O-glucuronide as quercetin-3-O-galactoside, kaempfer-3-O-hexoside, kaempfer-3-O-acetylgllucoside, and kaempfer-3-O-glucuronide as kaempfer-3-O-glucose equivalents, pelargonidin-malonyl-glucose and pelargonidin-3-O-rutinoside as pelargonidin-3-O-glucose).
## 2 Materials and Methods
### GC-MS Analysis  
The analysis of volatile compounds obtained by SPME and SDE was performed on a GC-MS using an Agilent 5975C mass selective detector coupled with an Agilent 7890A GC (Agilent, Santa Clara, CA, USA), equipped with HP-Wax capillary column (60 m \(\times\) 0.25 mm inner diameter, 0.25 \(\upmu\)m film thickness). Helium was used as a carrier gas and the flow rate was 1 mL/min. For the SDE, 1 \(\upmu\)L extract was injected in splitless mode at 250 \({}^{\circ}\)C; for SPME analysis, desorption was also in splitless mode at 250 \({}^{\circ}\)C for 5 min. GC oven temperature was programmed at 40 \({}^{\circ}\)C (held for 6 min), increased to 100 \({}^{\circ}\)C at 3 \({}^{\circ}\)C/min, and finally increased to 230 \({}^{\circ}\)C (SPME held for 10 min, SDE held for 20 min) at 5 \({}^{\circ}\)C/min. The MS conditions were as follows: The transfer line temperature was 280 \({}^{\circ}\)C; the ion source temperature was 230 \({}^{\circ}\)C; and ionization energy 70 eV, and mass range, 20 to 350 a.m.u.  
All experiments were carried out in triplicate and the results were reported as mean values.
# Enhancing BER Performance Limit of BCH and RS Codes Using Multipath Diversity +
## 3 Forward Error Correction
### Reed-Solomon Codes  
The RS codes belong to the family of BCH codes, but the symbols in this code are non-binary (multiple bits per symbol (integer)). RS codes are very efficient for dealing with bursts of errors [13] because, even if all of the bits of one symbol are in error, this counts as only one symbol error in terms of the correction capacity of the RS code. For integers \(m\) and \(t\), the RS possible codes are:  
\[\begin{array}{ll}\text{Block length}:&N=2^{m_{2}}-1\\ \text{Parity check bits}:&N-K\leq m_{2}\times 2t\\ \text{Minimum distance}:&d=m_{2}\times 2t+1\end{array}\]  
where \(m_{2}\) represents the number of bits per symbol, \(N\) represents the number of symbols in each codeword, and \(t\) is the number of symbol errors that can be corrected in a codeword, where:  
\[t=\left\{\begin{array}{cc}(N-K)/2&if\ (N-K)\ even\\ (N-K-1)/2&if\ (N-K)\ odd\end{array}\right. \tag{2}\]  
Figure 1: BCH and RS code codeword block.  
The codewords are formatted by first multiplying the data by \(x^{N-K}\), then dividing the result by the generator polynomial, \(g(x)\), to produce a quotient \(q(x)\) and a remainder \(r(x)\). The transmitted codeword \(C(x)\) will be presented as:  
\[C(x)=m_{i}(x)\times x^{N-K}+r(x), \tag{3}\]  
The general decoding process steps of RS code are [14]:  
* From the received codeword, calculate the syndrome. The syndrome is the error pattern which can be derived directly from the received data [15]);
* Find the error locator and error value polynomial by using \(2t\) nonlinear equations derived from the syndrome; and
* Correct the corrupted symbols by using the error location and error value polynomial.  
The major advantage of the RS code is the capability of correcting both burst errors and erasures. RS codes have a special attraction because their efficiency grows with the code length [16].
# On Localized Countermeasure Against Reactive Jamming Attacks in Smart Grid Wireless Mesh Networks
## 4 Overview and Fundamental Results
### Overview of Identification Procedure  
The overview of the trigger identification procedure is depicted as follows: The sets of nodes are locally divided into hexagonal groups, and each hexagonal group is colored into _disjoint interference-free groups_, where the transmission of nodes within a group will not activate the same jammer whose adversarial signals will disrupt the communications with other groups. The diameter of each hexagon is small enough so that all nodes in the same hexagon can communicate with each other directly and hence the latency of forwarding messages is avoided. These groups are called _testing groups_ in the remainder of this paper. The set of nodes within testing groups with the same color are then scheduled to fulfill a sequential GT procedure simultaneously in order to identify all trigger nodes over each testing group. Notice that all nodes in a network do not need to exchange additional messages in order to partition themselves into disjoint groups (to be explained in detail later) and run a sequential GT procedure since the basic premise of the Smart Grid wireless mesh networks in this paper are synchronized loosely in the order of seconds.  
The trigger identification algorithm within each pool uses Sequential Group Testing (SGT) to detect only the Smart Grid wireless devices on the convex hull of the group of trigger devices which is much less than the size of triggers.  
In principle, the description of the identification procedure is: (1) Partition the set of nodes into hexagonal _testing groups_; (2) Assign colors to hexagons in order to maximize the number of disjoint interference-free _testing groups_ and schedule them according to the colors; (3) Perform sequential group testing within each hexagonal _testing group_ during the assigned time slot for each group, in order to discover all trigger nodes.  
_How to divide the set of nodes into interference-free testing groups_ and _how to discover all trigger nodes within minimum latency_ play fatal roles in our localized approach against reactive jamming attack and will be described with the theoretical analysis in the following sections.
## 4 A Typical Application: Wuhan Huoshenshan Hospital
### Differences between the Simulation Results in the Transition and Final Design Phases  
It should be noted that the surrounding buildings considered in the final design phase may have an impact on the simulation results of the maximum instantaneous concentrations near the fresh-air intakes. Hence, the simulation cases with wind speeds of 1.9 m/s in the transition and final design phases, which have been introduced in Sections 4.3.1 and 4.4.1, are used here to compare the maximum instantaneous concentrations of exhausted harmful air near the fresh-air intakes between the final design phase (surrounding buildings are considered) versus the transition design phase (the neighborhood is not included).  
Figure 22 shows the differences between the simulation results of the two design phases. In the figure, the height of the column indicates the ratio of the simulated maximum instantaneous concentration near the fresh-air intakes in the final design phase to that in the transition design phase. It can be seen that for the east, southwest, and west wind directions, the relative changes of the simulated maximum instantaneous concentrations are within 5%. Hence, it is considered that for these three wind directions, the surrounding buildings considered in the final design phase do not have an obvious impact on the simulation results of the maximum instantaneous concentrations near the fresh-air intakes, given the random nature of the calculation in CFD. The relative changes of the simulated maximum instantaneous concentrations for the south and northwest wind directions are in the range of 5% to 10%, while those for the north, northeast, and southeast wind directions are in the range of 10% to 20%. It means that for these five wind directions, the impact of the surrounding buildings on the simulation results should not be neglected during the transition design phase. For all wind directions, the maximum relative change of the simulated maximum instantaneous concentration is 17.5%. And in the final design phase, the maximum instantaneous concentration of \(66.112\times 10^{-6}\) occurs in Case-1.9-NW, which is consistent with the conclusion drawn from the simulations in the transition design phase. Furthermore, it needs to be noted that although the simulations in the transition design phase did not consider the influence of surrounding buildings, it is still very necessary to perform such simulations to help engineers look into the pros and cons of different design schemes as quickly as possible.