esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Institute** & **Rector Type** & **Objective** & **Ref.** \\ \hline Karlsruhe Institute of Technology, & Semi-batch rector & The visual observation and temperature measurement of hydrothermal flame & [15] \\ Germany & & & \\ \hline Sandia National Laboratories, USA & Semi-batch rector & Threshold concentrations for ignition and their relations with temperature & [48] \\ \hline Ebara Research Company, Japan & Continuous reactors & SCWO strengthened by hydrothermal flame to deal with recalcitrant reactants & [49] \\ \hline McGill University, Canada & Semi-batch rector & The observation, description and environmental evaluation of hydrothermal flame as a tool to destruct complex organic wastes & [50] \\ \cline{2-3}  & Semi-batch rector & The effects of hydrothermal flame on the destruction of naphthalene & [51] \\ \hline MIT, USA & Continuous reactors & Verified the generation and the stability of hydrothermal flame and, its application in thermal spallation drilling & [52] \\ \hline  & Continuous reactors (WCHB-1) & SCWO strengthened by hydrothermal flame to deal with organic components & [53] \\ \hline  & Continuous reactors (WCHB-2) & Evaluation on the newly-designed HFR reactor & [54] \\ \cline{2-3}  & Continuous reactors (Transpiring wall) & The influence of mass flux and temperature of the transpiration on the reactor performance & [11] \\ \cline{2-3}  & Continuous reactors (Transpiring wall) & The performance of the transpiring wall reactor in anti-corrosion and anti-depositional & [12] \\ \cline{2-3} ETH Zurich, Switzerland & & & \\ \hline Continuous reactors (WCHB-3) & Combined experiments with numerical simulation to investigate the ignition process & [43] \\ \hline Continuous reactors (Transpiring wall) & The ignition, extinction and stability of hydrothermal flame & [24] \\ \hline Continuous reactors (WCHB-4) & The forced ignition of methanol-oxygen and its influencing factors & [55] \\ \hline Continuous reactors (WCHB-4) & The forced ignition of turbulent diffusion flame and the drawing of its ignition map & [23] \\ \hline Continuous reactors (WCHB-4) & The new type of ignition device and the performance of different nozzles & [22] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Reactors and main objectives of worldwide research institutes.
## 4 The Proposed Scheme
### Anonymous Authentication Phase
#### 4.2.1 Unlinkable Authentication Scenario  
In this scenario, we refer to [PERSON]'s Boneh (2018) short group signature (SGS) algorithm, which is one of the most famous group signatures. The UE in SGS can randomly generate a temporary identity (TID) that is irrelevant to the real \(ID_{i}\). Owing to the unlinkable anonymity of SGS, the UE can use different TIDs in different sessions, and no other entity knows that these TIDs belong to the same UE. The specific protocol process is shown in Figure 3, and its steps are as follows:  
1. When the UE wants to access the NCC, the message \(M_{i}=(TID_{i}||ID_{LEO}||g^{\tau_{1}}||TS_{1})\) needs to be constructed, where \(r_{1}\in Z_{q}^{*}\) is a random number, \(g^{\tau_{1}}\) is part of the session key, and \(TS_{1}\) is a timestamp that can resist reply attacks. Taking \(M_{i}\), \(gpk\), and \(gsk_{i}\) as input, the UE implements Algorithm 2 and obtains the signature \(\sigma_{i}=\left(T_{1},T_{2},T_{3},c,s_{a},s_{\beta},s_{x},s_{\delta},s_{y}\right)\), where \(\hat{e}(A_{i},\hat{g})\), \(\hat{e}(h,g)\) and \(\hat{e}(h,\omega)\) can be calculated and stored in advance. When the UE needs to reroute its secret keys, it needs to recalculate \(\hat{e}(\hat{A}_{i},g)\) and \(\hat{e}(h,\ \hat{\omega})\). After completing the construction of plaintext and signature, the UE sends \((M_{i},\sigma_{i})\) to an appropriate LEO.
2. When receiving s request from the UE, the LEO first authenticates the timestamp \(TS_{1}\) in the message. The LEO generates the current timestamp \(TS_{1}^{\prime}\) and verifies \(TS_{1}^{\prime}-TS_{1}<\Delta T\), where \(\Delta T\) is adjusted according to different network conditions. If it does not meet the conditions, the LEO returns an error message. Otherwise, the LEO validates the accuracy of the signature using Algorithm 3. If the verification passes, the LEO selects the random number \(r_{2}\in Z_{q}^{*}\) and calculates the session key \(SK=(g^{r_{1}})^{r_{2}}\). If the verification fails, an error message is returned. The LEO signs the message \(M_{i}^{\prime}=(TID_{i}||ID_{LEO}||g^{\tau_{2}}||TS_{2})\) using \(ECSign(M_{i}^{\prime})\) to obtain the signature \(\sigma_{i}^{\prime}\). Then, the LEO sends the message \(\left(M_{i}^{\prime},\sigma_{i}^{\prime}\right)\) to the UE.
3. After receiving the message, the UE first verifies the validity of the timestamp; that is, whether the timestamp satisfies \(TS_{2}^{\prime}-TS_{2}<\Delta T\). If it is valid, the message will be verified by the ECDSA. If verified, the UE calculates the session key \(SK=\left(g^{r_{2}}\right)^{r_{1}}\). The key negotiation between the two sides is complete.  
Figure 3: Authentication phase in the unlinkable authentication scenario.
# Hybrid Arctic Puffin Algorithm for Solving Design Optimization Problems
## 9 Planetary Gear Train Design Optimization Problem
### Parameter Initialization  
The variables are initialized based on the input vector \(x\):  
* \(x\) is rounded to ensure integer values, as gears must have integer numbers of teeth.
* \(Pind\) and \(mind\) are predefined arrays representing possible values for the number of planets and module sizes, respectively.
* \(N1\), \(N2\), \(N3\), \(N4\), \(N5\), and \(N6\) represent the numbers of teeth on different gears.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Optimizer** & **BestF** & **x1** & **x2** \\ \hline AO & 1.343217 & 6.259365 & 5.263324 \\ HHO & 1.341548 & 6.192283 & 5.161075 \\ SA & 1.340366 & 5.926017 & 5.344088 \\ CHOA & 1.353242 & 5.889933 & 5.586923 \\ BWO & 1.353168 & 5.961007 & 5.7868 \\ SCA & 1.37952 & 6.100923 & 4.739083 \\ COA & 1.414806 & 5.411593 & 5.341151 \\ OOA & 1.49929 & 5.033407 & 5.455155 \\ WOA & 1.512381 & 5.003057 & 7.375842 \\ BO & 1.601885 & 9.338224 & 9.939113 \\ GA & 1.524657 & 4.709916 & 5.967707 \\ \hline \hline \end{tabular}
\end{table}
Table 7: _Cont._  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Optimizer** & **Mean** & **Std** & **Min** & **Max** & **Time** & **Rank** \\ \hline APO-JADE & 1.34 & \(5.79\times 10^{-5}\) & 1.339959 & 1.340101 & 0.287393 & 1 \\ FOX & 1.340001 & \(4.61\times 10^{-5}\) & 1.339962 & 1.340079 & 0.316325 & 2 \\ GWO & 1.340052 & \(3.35\times 10^{-5}\) & 1.340021 & 1.340102 & 0.305842 & 3 \\ SMA & 1.340226 & 0.000229 & 1.339982 & 1.340586 & 0.506082 & 4 \\ AVOA & 1.340281 & 0.000278 & 1.339995 & 1.340595 & 0.336832 & 5 \\ HGS & 1.340547 & 0.000346 & 1.340285 & 1.341183 & 0.28181 & 6 \\ WSO & 1.34061 & 0.001461 & 1.339956 & 1.343224 & 0.257556 & 7 \\ DBO & 1.340808 & 0.000389 & 1.340462 & 1.341479 & 0.344311 & 8 \\ MFO & 1.341458 & 0.001184 & 1.340154 & 1.343152 & 0.29497 & 9 \\ COOT & 1.341584 & 0.001556 & 1.340588 & 1.344343 & 0.304864 & 10 \\ MVO & 1.341609 & 0.000717 & 1.340813 & 1.342728 & 0.347133 & 11 \\ AO & 1.346045 & 0.003281 & 1.343217 & 1.351575 & 0.590193 & 12 \\ HHO & 1.348612 & 0.005612 & 1.341548 & 1.356826 & 0.662697 & 13 \\ SA & 1.349451 & 0.010565 & 1.340366 & 1.367014 & 0.5275 & 14 \\ ChOA & 1.366061 & 0.007504 & 1.353242 & 1.372855 & 0.568655 & 15 \\ BWO & 1.369965 & 0.017329 & 1.353168 & 1.397601 & 0.367454 & 16 \\ SCA & 1.399921 & 0.015393 & 1.37952 & 1.419273 & 0.27111 & 17 \\ COA & 1.450365 & 0.057703 & 1.414806 & 1.553001 & 0.690296 & 18 \\ OOA & 1.534791 & 0.022988 & 1.49929 & 1.55805 & 0.568777 & 19 \\ WOA & 1.734372 & 0.180097 & 1.512381 & 1.975023 & 0.291645 & 20 \\ BO & 2.10571 & 0.359286 & 1.601885 & 2.477508 & 2.664379 & 21 \\ GA & 2.128989 & 0.523069 & 1.524657 & 2.923766 & 0.313305 & 22 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Statistical results of Three-Bar Truss Design optimization problem.  
* \(p\) is the number of planets, and \(m1\) and \(m2\) are the module sizes for different gear pairs, selected from \(mind\).
## 1 Introduction
### Beam Oscillation to Overcome the Limiting Process Mechanisms  
Beam oscillation as a dynamic beam-shaping method is one application-oriented approach to overcome the limitations of static beam shaping and improve LBFC. The principle is based on a superimposed modification of the laser beam movement regarding the feed direction. In contrast to static beam shaping, beam oscillation demonstrates a high potential of process improvements regarding faster cutting speed and advanced cut edge quality at the same time [19; 20; 24]. Theoretical considerations of fundamental studies suggest the changed spatial energy deposition (respectively, heat conduction) as the reason, which is caused by the alternating laser beam velocity [2; 39; 40; 41]. Previous experimental investigations of the authors [24] presented a reduced heat-affected zone (HAZ) due to beam oscillation. According to the calculations, the reduced HAZ is based not only on a changed spatial energy deposition but also on the temporal distribution [24]. So far, the explanation for why beam oscillation achieves improved cutting results compared to static beam shaping is based exclusively on theoretical considerations.  
The present paper aims to experimentally investigate the changed process mechanism of LBFC utilizing beam oscillation instead of static beam shaping. Therefore, the temperature distribution in the process zone has been measured for different beam oscillation conditions (amplitude, frequency, and cutting speed) with static beam shaping as a reference. For this purpose, a novel temperature measurement setup has been designed with a high spatial as well as high frequency temporal resolution and calibration for large temperature ranges. Conclusions on local effects, maximal and average melt pool temperature, and size were drawn from the position- and time-dependent temperature distributions. Moreover, microscopic images using focus stacking to expose the topography of the cut front. This has been correlated with the local absorption in order to assess the amount of laser beam energy deposited. The temperature distribution in combination with all other measured parameters (as cutting speed, cut edge quality, cut front topography, etc.) has been used to validate the theoretical approach of the characteristic energy deposition and enhanced heat conduction. Finally, the previously developed explanatory model [24] has been extended and refined to identify the causes for the process improvements due to beam oscillation.
## 4 Hardware Design
If any abnormal issues are received from the end devices, then the gateway triggers the alarm with the assistance of the alarm driver interfacing with the controller board of the gateway and it is shown in the Figure 9a. As discussed earlier, the gateway is embedding with two different communication protocols, and it can be visualized in the PCB layout in the Figure 9b and also in the hardware prototype that is presented in the Figure 9c. Figure 6: (**a**) Monitoring End device. (**b,c**) PCB layout and hardware prototype of the monitoring end device. Figure 8: (**a**) Safety operation controller Safety. (**b,c**) PCB layout and hardware prototype of the safety operation controller. Figure 7: (**a**) Detection end device. (**b,c**) PCB layout and hardware prototype of the detection end device.
## 2 Materials and Methods
### Method  
In this study, firstly, the relationships of parameters with LAI and biomass were examined using linear and nonlinear regression analysis. The parameters of the satellite images included the optical spectral vegetation indices (OSVIs) of HJ-1A/B and the radar polarimetric parameters (RPPs) of RADARSAT-2. Secondly, LAI and biomass were estimated with combined OSVIs and RPPs (the product of OSVIs and RPPs (COSVI-RPPs)). Finally, the COSVI-RPPs were used to estimate the LAI and biomass with multiple stepwise regression (MSR) and partial least squares regression (PLSR) methods. The MSR combines a forward selection and backward elimination. At each step, the best remaining variable was added, provided it passed the significant at 5% criterion test. Then all variables in the regression were checked to see if any could be removed, using the greater than 10% significance criterion. The process continued until no more variables could be added or removed. The remaining regression was used for further analysis [54]. The PLSR is an extension of the multiple linear regression model (e.g., multiple regression or general stepwise regression). This method is particularly useful when one needs to predict a set of dependent variables from a (very) large set of independent variables. In its simplest form, a linear model specifies the (linear) relationship between a dependent (response) variable y and a set of predictor variables [55], the x variables, so that  
\[\mathrm{y=b_{0}+b_{1X1}+b_{2X2}+b_{3X3}+...+b_{pXp}} \tag{1}\]  
In this equation, \(\mathrm{b_{0}}\) is the regression coefficient for the intercept, and the \(\mathrm{b_{i}}\) values are regression coefficients (for variables 1 to p) computed from the data.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Vegetation Index** & **Abbreviation** & **Formula** & **Reference** \\ \hline Ratio vegetation index & RVI1\({}^{\#}\) & \(R_{NII}/R_{R}\) & [49] \\ Normalized Difference Vegetation Index & NDVI & \((R_{NIR}-R_{R})(R_{NIR}+R_{R})\) & [50] \\ Soil adjusted vegetation index & SAVI & \((1+\mathrm{L})(R_{NIR}-R_{\mathrm{R}})/(R_{NIR}+R_{\mathrm{R}}+L)\); \(\mathrm{L=0.5}\) & [51] \\ Optimized soil adjusted vegetation index & OSAVI & \((R_{NIR}-R_{R})/(R_{NIR}+R_{R}+0.16)\) & [52] \\ Enhanced Vegetation Index & EVI & \(2.5(R_{NIR}-R_{R})/(1+R_{NIR}+6R_{R}-7.5\times R_{B})\) & [53] \\ Modified triangular vegetation index 2 & MTVI2 & \(\dfrac{1.5[1.2(R_{\mathrm{max}}-R_{\mathrm{c}})-2.5(R_{\mathrm{*}}-R_{\mathrm{ *}})]}{\sqrt{\left(2\,\mathrm{R_{max}}+1\right)^{2}-(6\,\mathrm{R_{max}}-5 \sqrt{R_{\mathrm{*}}-0.5})}}\) & [18] \\ \hline \hline \end{tabular}  
* **Note:**\# Named by this study; \(\mathrm{R_{i}}\) denotes reflectance at band i (nanometer). \(R_{R}\) represents reflectance of blue band of HJ-CCD, \(R_{G}\) represents reflectance of green band of HJ-CCD, \(R_{R}\) represents reflectance of red band of HJ-CCD, \(R_{NIR}\) represents reflectance of near infrared band of HJ-CCD.  
\end{table}
Table 5: Summary of selected optical vegetation indices used in this study and citations for LAI and biomass.
## 5 Conclusions  
In this study, it was found that the traditional BP performed with the bench in a horizontal position, with a grip width between 150% and 200% of the bi-acromial distance (BAD), results in greater activation of the pectoralis major in most variations compared to the same relative load. However, the declined variant leads to greater activation of the sternal portion. Therefore, the declined variant of the BP can be considered an exercise that allows for a significant increase in intensity, which is a key factor in muscle activation.  
It is important to note that modifying the exercise may have a justified reason, such as targeting other muscles or diversifying training, but it can also decrease the activation of the pectoralis major. These findings highlight the importance of choosing the appropriate variation of the BP to achieve specific training goals while maximizing muscle activation.  
Based on the results of the systematic review and meta-analysis on the activation of the pectoralis major in different variations of the bench press exercise, the following practical recommendations can be made for trainers and researchers:  
1. Grip width: A grip width between 100% BAD and 200% BAD is recommended as it results in similar activation of the pectoralis major with a reduced risk of injury compared to wider gripps. Narrower gripps result in significantly lower activation of the sternal portion of the pectoralis major.
2. Forearm position: The forearm position has little effect on the electrical activation of the clavicular portion of the pectoralis major. However, supination of the forearm increases the activation of the clavicular portion of the pectoralis major.
3. Inclination bench: A greater inclination in the bench press exercise results in greater activation of the clavicular portion of the pectoralis major and less activation of the sternal portion. Therefore, the choice of inclination of the bench press exercise should depend on the training goal and the muscle activation required.
4. Declined bench: The declined bench press exercise results in significantly less activation of the clavicular portion and more activation of the sternal portion of the pectoralis major compared to the horizontal bench press. Therefore, the declined bench press exercise is recommended for targeting the sternal portion of the pectoralis major.
5. Risk of injury: A grip width greater than 200% BAD increases the risk of shoulder abduction, which can result in chronic or acute injury. Similarly, a narrow grip can increase the risk of injury by causing rotation of the elbow in the mid-plane. Therefore, trainers should consider the risk of injury when selecting the grip width for their clients.  
By following these recommendations, trainers can tailor their exercise programs to optimize the activation of the pectoralis major while minimizing the risk of injury. Researchers can also use these recommendations to design studies that compare the activation of the pectoralis major in different variations of the bench press exercise.
## 5 Numerical Analyses and Qualification
### Finite Element Results
For aircraft applications, the endurance profile was based on a duty cycle as per its operative conditions. Because of the usage of the component as an emergency control module, 5000 cycles were considered adequate to represent the functional loading window. The impulse fatigue pressure levels and test cycles were recommended in SAE ARP1383, [73], as follows: for a fixed-wing aircraft category, utility valves were tested for 100,000 cycles, achieving a max pressure equal to the proof limit. The following were the reference test conditions:  
- Hydraulic fluid conforming to MIL-PRF-5606J (type II) for the supply pressure [75];  
- A hydraulic bench, including a pump source, controlled by the relief pressure valve;  
- Test temperature of about +30 \(\pm\) 15 \({}^{\circ}\)C;  
- A contamination level of the hydraulic fluid of class 7 or better, as per NAS 1638;  
Figure 12: Hydraulic manifold test articles: (**a**) AlSi10 Mg [57]; (**b**) Al-HS1 type. \begin{table}
\begin{tabular}{l l l} \hline
**Load Case** & **Description** & **Success/Failure Criteria** \\ \hline \multirow{2}{*}{Proof pressure} & \multirow{2}{*}{Static (2 min)} & No permanent deformation, \\  & & pressure drop, and external \\  & & leakage \\ \hline \multirow{2}{*}{Endurance} & \multirow{2}{*}{Cyclic} & No leakage or evidence of \\  & & excessive wear or \\  & & malfunctioning \\ \hline \multirow{2}{*}{Pulsating cycles} & \multirow{2}{*}{Cyclic} & No failure or permanent \\  & & deformation \\ \hline \multirow{2}{*}{Burst pressure} & \multirow{2}{*}{Static (3 s)} & No rupture, pressure drop, or \\  & & external leakage \\ \hline \end{tabular}
\end{table} 
Table 5: Test matrix and pass/fail criteria. * Hydraulic fluid supplied to the _press_ port, and a pressure value recorded by means of a pressure transducer (Figure 13a). All other ports were closed with actual plugs and hydraulic fittings;
* Load pressure profiles as plotted in Figure 13b. The fulfilment of the structural requirements was proved by a detailed visual inspection addressed to detect any potential cracks or hydraulic oil leakages that occurred. Furthermore, no oil loss and fluid pressure changes were measured in an operational cycle subsequent to the structural test sequences. The test article was then disassembled from any plug and valve in order to submit it to a preliminary nondestructive inspection (NDI) consisting of an immersion cycle of about 30 min in dye penetrating liquids (Figure 14) at the Magnaghi Aeronautica Quality facilities, Naples, Italy. Exposure to UV light then allowed for the verification of the presence of possible surface-breaking defects and wear near the threads (largely stressed by fatigue cycles). Some irregularities detected were mainly associated with the surface porosity and a lack of finishing treatments, which was not foreseen with this prototype, rather than the occurrence of structural flaws. Figure 14: NDI of the hydraulic manifold: **(a)** disassembled test article; **(b)** UV images. Figure 13: Hydraulic manifold test stand: **(a)** hydraulic layout; **(b)** pressure load profiles.
# NTT and Inverse NTT Quantum Circuits in CRYSTALS-Kyber for Post-Quantum Security Evaluation
## 1 Introduction
This paper implements all of these functions used in NTT and InvNTT as part of a quantum circuit that can operate on a quantum computer. Various methods were used to construct an efficient quantum circuit, and details are covered in Section 3. We analyzed the quantum resources used through the implementation of quantum circuits for the internal operations of NTT/InvNTT. CRYSTAL-Kyber [23] is an IND-CCA2-secure KEM, underpinned by the hardness of Module-LWE [24] on a lattice. In the sub-operation of CRYSTALS-Kyber, a polynomial multiplication operation is performed with \(n=256\) and \(q=3329\) parameters for \(Z_{q}[X]/(X^{n}+1)\). Regarding this, NTT is employed to execute modular multiplication efficiently for polynomials \(a\) and \(b\). The ring \(N\) is represented as a product of sub-rings in the NTT operation, and it is divided into polynomials for processing. Each \(a\) and \(b\) polynomial is expressed with 256 coefficients in the sub-ring using NTT transformation. These transformed polynomials are then multiplied point-wise. Unlike the traditional schoolbook multiplication with a complexity of \(O(n^{2})\), NTT-based multiplication has a complexity of \(O(n\log n)\). Therefore, NTT is a good way to reduce the computational complexity of multiplication operations in general computers. To the best of our knowledge, the quantum circuits that have been proposed are the first NTT/InvNTT quantum circuits tailored to the parameters of CRYSTALS-Kyber. Our quantum circuit implemented NTT/InvNTT multiplication for \(Z_{q}[X]/(X^{n}+1)\) to operate at CRYSTALS-Kyber parameters \(n=256\) and \(q=3329\). The NTT operation employs the Montgomery reduction, while the InvNTT operation utilizes both the Montgomery and Barrett reductions. We implemented a negative integer by expressing it using two's complement in qubits, and \(n\times 32\) qubits are used to express \(Z_{q}[X]/(X^{n}+1)\) coefficients. Since we express the intermediate computation process in two's complement, we do not allocate extra qubits to determine negative and positive integers. However, only 1-bit qubits are used for the intermediate conditionals. Finally, we estimate and analyze the quantum resources required for sub-functions to NTT/InvNTT. The structure of this paper is as follows: In Section 2, we present research on quantum computing, the Grover algorithm, and CRYSTALS-Kyber. Section 3 describes the implementation of the proposed NTT and InvNTT quantum circuits. Section 4 estimates and analyzes the resources required for the proposed quantum circuits. Finally, Section 5 concludes the paper.