esa-sceva/satcom-chunk-collection huggingface dataset:
# Natural Product-Oriented Photo-Induced Deintrogenative Annulations of 1-Alkenylbenzotriazoles
## 3 Materials and Methods
### General Procedure
Methyl 2-(2-(1H-benzo[d][1,2,3]triazol-1-yl)-3,3-dimethyl-6-oxycole-1-en-1-yl) acetate **(15j)**: light yellow oil; Yield: 73%; \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 0.95 (s, 3H), 1.45 (s, 3H), 2.11-2.27 (m, 2H), 2.48 (d, \(J\) = 16.9 Hz, 1H), 2.77-2.86 (m, 2H), 3.03 (d, \(J\) = 16.9 Hz, 1H), 3.50 (s, 3H), 7.37-7.47 (m, 2H), 7.51 (ddd, \(J\) = 8.0, 6.8, 1.0 Hz, 1H), 8.11 (d, \(J\) = 8.4 Hz, 1H). \({}^{3}\)C NMR (100 MHz, CDCl\({}_{3}\)) \(\delta\) 197.3, 170.4, 157. Methyl 2-(6-(1H-benzo[d][1,2,3]triazol-1-yl)-8-oxospiro[4.5]dec-6-en-7-yl)acetate (**15k**): light yellow oil; Yield: 66%; \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 1.19-1.29 (m, 2H), 1.37-1.58 (m, 2H), 1.60-1.70 (m, 2H), 1.96 (ddd, \(J\) = 12.9, 7.7, 4.3 Hz, 1H), 2.20 (t, \(J\) = 6.3 Hz, 2H), 2.38-2.51 (m, 2H), 2.77 (td, \(J\) = 6.9, 5.9, 4.1 Hz, 2H), 3.00 (d, \(J\) = 16.8 Hz, 1H), 3.49 (s, 3H), 7.38-7.48 (m, 2H), 7.49-7.56 (m, 1H), 8.11 (d, \(J\) = 8.3 Hz, 1H). \({}^{13}\)C NMR (100 MHz, CDCl\({}_{3}\)) \(\delta\) 197.6, 170.4, 156.6, 145.1, 134.2, 133.3, 128.7, 124.6, 120.3, 110.3, 52.1, 48.7, 36.8, 34.8, 34.8, 33.9, 31.2, 24.6, 24.5. HRMS m/z calcd for C\({}_{19}\)H\({}_{22}\)N\({}_{3}\)O\({}_{3}\) [M+H]\({}^{+}\): 340.1661; found: 340.1653. Methyl 2-(6-\({}^{\prime}\)oxo-7\({}^{\prime}\)8\({}^{\prime}\)dihydro-6\({}^{\prime}\)H-spiro[cyclopentane-1,9\({}^{\prime}\)-pyrido[1,2-ja]indol]-10\({}^{\prime}\)-yl)acetate (**20k**): light yellow oil; Yield: 18%; \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 1.81-1.96 (m, 6H), 2.02 (t, \(J\) = 6.5 Hz, 2H), 2.15-2.24 (m, 2H), 2.78 (dd, \(J\) = 7.0, 6.0 Hz, 2H), 3.71 (s, 3H), 3.80 (s, 2H), 7.27-7.35 (m, 2H), 7.40-7.47 (m, 1H), 8.43-8.53 (m, 1H). \({}^{13}\)C NMR (100 MHz, CDCl\({}_{3}\)) \(\delta\) 171.6, 169.7, 142.9, 134.4, 130.8, 124.9, 124.0, 118.0, 116.8, 109.4, 52.4, 43.2, 39.3, 34.7, 31.8, 30.7, 25.9. HRMS m/z calcd for C\({}_{19}\)H\({}_{22}\)NO\({}_{3}\) [M+H]\({}^{+}\): 312.16; found: 312.1588. 2,3-Dihydrospiro[carbazole-1,1\({}^{\prime}\)-cyclopentan]-4(9H)-one (**24**): white solid; Yield: 45%; M.p. 229-230 \({}^{\circ}\)C; \({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) \(\delta\) 1.84-1.93 (m, 4H), 1.93-2.08 (m, 4H), 2.14 (dd, \(J\) = 7.0, 5.9 Hz, 2H), 2.65 (dd, \(J\) = 7.0, 5.9 Hz, 2H), 7.20-7.26 (m, 2H), 7.32-7.41 (m, 1H), 8.21-8.29 (m, 1H), 8.89 (s, 1H). \({}^{13}\)C NMR (100 MHz, CDCl\({}_{3}\)) \(\delta\) 194.5, 158.3, 135.8, 125.1, 123.4, 122.7, 121.7, 112.3, 111.1, 43.3, 38.2, 36.6, 36.2, 25.5. HRMS m/z calcd for C\({}_{16}\)H\({}_{18}\)NO [M+H]\({}^{+}\): 240.1388; found: 240.1386.
## 4 Experiment
### Performance Comparisons  
#### 4.3.1 Comparison of Different Input Features  
To verify the effectiveness of STgram as the input for feature extraction, the Mel spectrogram and STgram were selected as the inputs of the feature extraction network for comparison. The neural network was trained using the multi-classification method to distinguish the normal sound data of the five measuring point IDs, namely the front bearing of the main shaft, the rear bearing of the main shaft, the high-speed shaft of the gearbox, and the left and right sides of the generator housing. That is, the output dimension of the loss function layer was 5. The detection results are shown in Table 5 and Figure 12. The detection effect using STgram as the input was better than using the Mel spectrogram alone. The average AUC of the five measuring point positions increased by 4.95%, which indicates that the time-domain features make up for the abnormal information lacking in the Mel spectrogram alone, resulting in a better detection effect.
## 3 In Vivo Applications of MIP-Based DDS
Stimuli-responsiveness is another optional feature that allows on-demand release of the payload, dependent either on the local/endogenous stimuli such as pH, redox gradient, temperature, biomarker concentration, or on external/exogenous stimuli, such as light, ultrasound, electric or magnetic field. Employing the imprinting technology in the fabrication of drug delivery reservoirs has a range of potential benefits, including high drug loading efficacy, better protection for loaded drugs, economical and straightforward synthesis. The imprinted nano-systems are designed to be intravenously administered, while being equipped with active targeting mechanism(s) based on built-in (natural receptor, epitope imprints, aptamers) or external (magnetic field) guiding towards a targeted tissue or organ. In addition to the specific delivery at the action site, payload delivery can also be controlled by a feedback release based on internal or external stimuli. Unfortunately, the injectable administration of DDS involves several immunological risks with serious clinical implications, therefore immunotoxicity must be investigated. The particularities of drug release mechanisms are related to the type of DDS. In the case of drug reservoirs, release can occur by diffusion, mechanical erosion, competitive displacement, or stimuli-triggering. In contrast, drug release from nanoMIPs can only take place by diffusion or as feed-back to stimuli. In all cases of internally administered polymers, different concerns over their safety need to be addressed, especially after long-term administration. These issues include biodegradability, natural clearance, circulating instability and the lack of acute and long-term toxicity. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Fit-For-Purpose** & **Drug Reservoir** & **Targeted Delivery** \\ \hline Drug loading & _Highest attainable_ & _Maximum feasible_ \\ \hline Drug release & _Sustained/controlled release_ & _Immediate, sustained or stimuli responsive_ \\  & simplified posology & \\ \hline \multirow{4}{*}{Size} & _No size restrictions_ & _Ideal size range_ \\  & Often confined at site of & EPR effect (enhanced permeability and \\  & administration & retention effect); avoid various \\  & & biological barriers and premature \\  & & clearance in cancer treatment \\ \hline \multirow{4}{*}{Aim of functionalization} & \multirow{4}{*}{_Lack of acute and long-term toxicity_} & _Build-in (natural receptor, epitope imprints,_ \\  & & _apnames) or external (magnetic field, etc._ \\  & & _guiding towards a targeted tissue/organ,_ \\  & & Avoid first pass metabolization \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\  & & \\ \end{tabular}
\end{table} 
Table 1: Comparison of the characteristics of drug delivery systems based on fit-for-purpose concept. A few studies regarding the safety and efficacy of MIPs in vivo are to be discussed below from the fit-for-purpose perspective, and their most important characteristics are summarized in Table 2.
# Test-Retest Reliability of Kinematic Parameters of Timed Up and Go in People with Type 2 Diabetes
## 2 Materials and Methods
### Procedure  
After reading and signing the written informed consent, participants were measured and weighed. They were also asked about their age and years since diagnosis. Then, the vibration threshold was evaluated and, finally, the TUG was conducted after a light warm-up.  
The foot vibration threshold was assessed using a Biothensiometer Vibratron II (Physitemp Instruments, Inc. Clifton; New Jersey; USA). This device drives vibration to modules A and B placed under the feet of the participant. Each module has a vibrating pole on the top, which vibrates at a frequency of 120 Hz. Therefore, the vibration threshold is determined by modifying the amplitude. These vibration units are related to the amplitude of the movement in microns according to the formula:\(A=x^{2}/2\) (where x is the vibration units (vu) and A is the amplitude in microns (um)). The present study used the protocol the 'force two alternative choices procedure', which is one of the two methods suggested by the manufacturer. Participants were asked to place their first toe on the vibrating pole. The procedure started when the participant felt the vibration in the left or right toe. After that, the amplitude was reduced progressively until the participant was not able to tell which pole was vibrating. When the participant failed to detect vibration, the amplitude of the vibration was increased. The vibration threshold was then calculated using the last five rights and wrongs, but omitting the extreme low and high values. The average of the remaining eight values was then computed to calculate the vibration threshold [17].  
TUG was performed three times, with a 5-min rest in between. The first repetition was for familiarisation, the second was the test measure and the third was the retest measure. All participants performed a light warm-up which included walking and joint mobility for 5 min. In the TUG, two force platforms (Kistler, NY, USA) were placed between the chair and the mark where participants had to turn around. Therefore, participants stepped on the platforms before and after reaching the mark placed at 3 m. The time required to complete the full test was assessed manually with a stopwatch by an expert rater.  
Variables obtained from the force platforms included the duration of (a) the double support phase (both feet on the platforms), (b) left support (only the left foot on the platforms) and (c) right support (only the right foot on the platforms), as well as the left and right peak forces from the forefoot and the heel.  
The results are presented for the whole sample (\(n=56\)), and also according to the degree of neuropathy based on vibration perception, i.e., severe neuropathy (\(n=22\)), moderate neuropathy (\(n=22\)) and normal perception (\(n=12\)). To classify patients into one of the three groups, the cut-off points suggested by normative values of the manual of the measuring device were considered.
## 2 Materials and Methods
### Headspace Solid-Phase Microextraction/Gas Chromatography  
_(HS-SPME/GC-MS) Analysis_  
The volatile compounds in different varieties of foxtail millet were determined by 7610 GC-MS (Thermo Fisher Scientific Inc., Waltham, MA, USA) with a DB-5 ms Capillary GC Column (30 m \(\times\) 0.25 mm \(\times\) 0.25 \(\mu\)m). For this, 1 g foxtail millet flour was placed into a 20 mL headspace vial with 4 mL saturated sodium chloride and 10 \(\mu\)L of 50 mg/L cyclohexanone as the internal standard, and then sealed with a magnetic lid. The vials with samples were thermostatized at 40 \({}^{\circ}\)C for 30 min and then the 50/30 \(\mu\)m DVB/CAR/PDMS SPME fiber (Supelco, Bellefonte, PA, USA) was exposed in the sample vial headspace for 30 min. The fiber was instantly inserted into the injector port at 250 \({}^{\circ}\)C for 5 min.  
The oven program was set as follows: initial temperature was 35 \({}^{\circ}\)C for 2 min; then heated to 280 \({}^{\circ}\)C at 5 \({}^{\circ}\)C/min and held for 3 min. The inlet temperature was set at 280 \({}^{\circ}\)C. The flow rate of helium used as carrier gas was 1 mL/min. The injection mode was splitless. The ion source temperature of mass spectrometry was set at 200 \({}^{\circ}\)C with a mass range of 35-400 \(m\)/z and in the electron ionization (EI) mode at 70 eV. \(n\)-alkanes C\({}_{7}\)-C\({}_{30}\) (Sigma-Aldrich, St. Louis, MO, USA) were used as external references to calculate the retention index (RI) of volatile compounds. Comparing the mass spectra and RI of volatile compounds in the NIST 11 library, volatile compounds were tentatively identified. The levels of volatile compounds were determined by the internal standard method.
# A Robust Mutual Authentication with a Key Agreement Scheme for Session Initiation Protocol
## 7 Comparison
### Performance Discussion  
In this subsection, the estimated time of the proposed scheme was compared with other related protocols [1; 2; 3; 4]. First, a personal smart device (iPhone 6s with ARM(armv8-a) CPU, 2 GB RAM, and iOS 10.1.1 operating system) was used to calculate the execution time of SHA-256, symmetric encryption/decryption operation (AES-GCM), scalar multiplication on ECC (256 bits), and modular multiplication. Our experiment was based on Pairing-Based Cryptography (PBC) library. The PBC library is a free C library that performs the mathematical operations underlying pairing-based cryptosystems.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & **C1** & **C2** & **C3** & **C4** & **C5** & **C6** & **C7** & **C8** & **C9** & **C10** \\ \hline
[1] & N & Y & N & N & Y & Y & Y & Y & Y & Y \\
[2] & N & Y & Y & N & Y & Y & Y & Y & Y & Y \\
[3] & N & Y & Y & Y & N & N & Y & Y & Y & Y \\
[4] & Y & Y & N & N & Y & Y & Y & N & Y & Y \\ Our’s & Y & Y & Y & Y & Y & Y & Y & Y & Y & Y \\ \hline \hline \end{tabular}  
* C1: Provide user anonymity; C2: Withstand replay attacks; C3: Withstand offline password guessing attacks; C4: Withstand user impersonation attacks; C5: Withstand server spoofing attacks; C6: Withstand privilege insider attacks; C7: Withstand stolen verifier attacks; C8: Withstand stolen memory device attacks; C9: Provide known key security; C10: Provide perfect forward secrecy.  
\end{table}
Table 2: Comparison of security features.  
Figure 8: The main process.  
Figure 9: The results.  
Each operation was executed 1000 times and the average running times were computed. The experimental result are shown in Table 3. Because the computation of the XOR operation, point addition operation, and modular inversion operation are very trivial, they can be ignored.  
The computation costs of all the schemes are listed in Table 4, but it is the authentication and key agreement phase which is frequently utilized.
## 2 Distribution Network Flexibility Resources and Flexibility Evaluation Indicators
### Model Solving Methods  
The two-layer planning models proposed in this paper, namely the SOP and ESS models, belong to the category of large-scale mixed-integer nonlinear planning problems. These problems cannot be solved directly by solvers. Particle Swarm Optimization (PSO) is a simple, fast-converging, local-searching algorithm that has the potential to be easily overfitted. Genetic algorithm (GA) has the advantage of diverse populations and suitability for global search. However, the lack of memory in individuals, the blind and directionless nature of genetic operations, and the long convergence times required present significant challenges. In light of the relative merits and demerits of the two algorithms, an enhanced algorithm, GA-PSO, is put forth for consideration [23]. It has the advantages of PSO and GA at the same time and solves the problems of PSO that easily fall into the local optimum and GA convergence time is long.  
Therefore, to decouple the integer variables in the upper layer from the continuous variables in the lower layer, this paper employs GA-PSO and SOCP. The GA-PSO is employed to derive the upper layer SOP and ESS siting and capacity setting scheme. The SCOP is utilized to address the operation optimization problem of the distribution system in the scenario during each iteration of the GA-PSO [24]. The solution flow is depicted in Figure 7.  
The particular solution process is as follows:  
1. The parameters of ADN, PV, wind, and load data are inputted, and typical scenarios are generated according to the C-DCGAN method.
2. The SOP and ESS locations and capacities are encoded, and a population of M chromosomes is initially randomly generated.
3. The average flexibility of ADN containing SOPs and ESSs is calculated using cone planning.
4. The fitness values for each chromosome are calculated.
5. The establishment of a robust gene pool, the updating of the historical optimal values of the chromosomes, and the updating of the optimal values of the population are conducted.
6. The current population is subjected to selection, gene crossover, and mutation operations, resulting in the generation of a new population of M chromosomes.
7. The convergence condition is evaluated. If it is satisfied, the optimal value of the population is output. Otherwise, the process is repeated at step 3.  
This paper proposes a two-layer planning model for SOP and ESS, which considers the spatiotemporal regulation characteristics of both. The model aims to reflect the flexibility of the power side of ADN by DG consumption. It is based on the premise that the penetration of distributed power sources is significant.
# Compact UHF Circularly Polarized Multi-Band Quadrifilar Antenna for CubeSat
## 2 Antenna System Design
### Electromagnetic Modeling of the Proposed Structure
Figure 6: Gain radiation pattern simulation of the antenna without the structure, with RHCP in blue and LHCP in red, plain trace for phi = 0\({}^{\circ}\), and dashed trace for phi = 90\({}^{\circ}\). Figure 7: Gain radiation pattern simulation of the antenna with structure, with RHCP in blue and LHCP in red, plain trace for phi = 0\({}^{\circ}\), and dashed trace for phi = 90\({}^{\circ}\).
# Spin-Orbital Transformation in a Tight Focus of an Optical Vortex with Circular Polarization
## 2 The Denseness of Lengthwise Projections of the SAM and OAM
In reference [15], an equation for the distribution of the OAM in the focus of the field (1) was obtained:  
\[L_{z}=\text{Im}\bigg{(}E_{x}^{*}\frac{\partial E_{x}}{\partial\vartheta}+E_{ y}^{*}\frac{\partial E_{y}}{\partial\vartheta}+E_{z}^{*}\frac{\partial E_{z}}{ \partial\vartheta}\bigg{)}\ =m\gamma_{0,m}^{2}+(m+2)\gamma_{2,m+2}^{2}+2(m+1)\gamma_{1,m+1}^{2}. \tag{6}\]  
Adding Expressions (4) and (6), we obtain the sum of the lengthwise projections of the OAM and SAM:  
\[J_{z}=L_{z}+S_{z}=(m+1)\Big{(}\gamma_{0,m}^{2}+\gamma_{2,m+2}^{2}+2\gamma_{1, m+1}^{2}\Big{)}=(m+1)I. \tag{7}\]  
Figure 1: Optical scheme: laser with a wavelength of 633 nm; P\({}_{1}\), P\({}_{2}\) are polarizers; SLM is liquid crystal light modulator; \(\lambda/4\) is quarter-wave plate; L\({}_{2}\) is spherical lens; D is aperture: O\({}_{1}\) is microobjective with a numerical aperture NA = 0.95; CCD is camera. The phase distribution in the form of a grating with a fork (shown in the inset) is generated in the modulator window. The formula after the quarter-wave plate describes the transverse components of the field (1). Expression (7) shows that the lengthwise component of the sum of orbital and spin angular momenta in the focus of the field (1) is equal to the intensity (3) of light in the focus multiplied by the sum of the topological charge \(m\) and the \"spin\" of the launch field, which equals to 1.
## 4 Numerical Results  
### Verification of Bubble Dynamics Solver  
The bubble dynamics solver is validated by using the measured data, which are obtained from the experimental study by [PERSON] et al. [50] to investigate single bubble behavior in detail. The single bubble was triggered in the undisturbed condition by laser. The subsequent bubble behaviors of expansion and collapse were carefully examined. The same problem is simulated by solving the bubble dynamics equation with the conditions given in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{p_{v}}\) (Pa) & \(\mathbf{\epsilon_{0}}\) (m/s) & \(\mathbf{\rho_{0}}\) (kg/m\({}^{3}\)) & \(\mathbf{\mu}\) (N/m\({}^{2}\)) & \(\mathbf{p_{0}}\) (kPa) & \(\mathbf{\gamma}\) (Pa-m) \\ \hline
2330 & 1482 & 998 & 0.001 & 100 & 0.0725 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation conditions for single bubble simulation.  
Figure 5: Experimental setup: (**a**) photograph of scaled Suboff submarine with two kinds of propellers installed in the test section of the large cavitation tunnel (LCT) in Korea Research Institute of Ships and Ocean Engineering (KRISO); (**b**) schematic diagram for noise measurement.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \(\mathbf{V}\) (m/s) & \(\mathbf{n}\) (rps) & \(\mathbf{P_{T}}\) (kPa) & \(\sigma_{\text{nocenter}}\) & \(\mathbf{\alpha/\alpha_{\text{s}}}\) (\%) \\ \hline
5.0 & 25.0 & 50.24 & 1.77 & 58.6 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Test conditions for noise measurement.  
The bubble simulation is started from the initial conditions, \(R_{b}(t_{0})=R_{max}\) and \(\hat{R}_{b}(t_{0})=\)0 at the time when the bubble radius is maximum, that is \(t_{0}=t_{max}=70.7\) us. The maximum bubble radius, \(R_{max}\), is equal to 747 um, and the time interval is 5 us. The detailed computation conditions are listed in Table 4. Figure 6 shows the simulation result in terms of the time variation of the bubble radius.  
It is seen from the experimental result that a single bubble shrinks and rebound after it expands. This bubble behavior is well followed by the numerical result obtained by solving the Keller-Herring bubble dynamics equation. The measured minimum radius is less than 12 um, and the calculated minimum radius is 8.8 um at the end of the collapse. These results confirm the validity of the bubble dynamics model used in the present study.
# Empirical Study of Fully Homomorphic Encryption Using Microsoft SEAL
## 2 Related Works
### Libraries  
Several key libraries have emerged as foundational tools in the FHE landscape. These libraries and tools represent crucial building blocks for the development and deployment of FHE applications. They encapsulate complex cryptographic operations into more accessible interfaces, enabling the secure processing of encrypted data across diverse domains, from cloud computing to secure multi-party computation and beyond.  
One of the most known ones is the Microsoft Simple Encrypted Arithmetic Library (SEAL) [22]. Developed by Microsoft, SEAL stands out for its focus on providing a set of powerful and efficient primitives for implementing encryption schemes, including FHE. In terms of programming languages, SEAL is written in C++; therefore, it is mainly used with C++ or C++ compatible languages. In addition, it can be wrapped for use with other languages, such as Python. Microsoft SEAL offers a pair of homomorphic encryption schemes, each with distinct characteristics. The Brakerski-Fan-Vercauteren (BFV) and Brakerski-Gentry-Vaikuntanathan (BGV) schemes enable operations like modular arithmetic on encrypted integers. Conversely, the Cheon-Kim-Kim-Song (CKKS) scheme facilitates operations on encrypted real or complex numbers, providing approximate outcomes. For tasks requiring the summation of encrypted real numbers, applying machine learning models to encrypted data, or calculating distances between encrypted locations, CKKS emerges as the superior option. However, for use cases where precise values are critical, the BFV and BGV schemes prove to be more appropriate. SEAL is praised for its comprehensive documentation, ease of use, and active development community, making it a preferred choice for beginners and experienced practitioners alike.  
PySEAL is a Python wrapper for Microsoft SEAL. PySEAL extends the accessibility of FHE to the Python programming community, which is one of the most popular languages in data science and machine learning. This extension enables a broader range of developers and researchers to experiment with and integrate FHE into their Python-based projects, leveraging the rich ecosystem of data analysis and machine learning libraries available in Python. OpenFHE, formerly known as PALISADE, is an open-source project that aims to provide a comprehensive suite of tools for FHE. It supports a variety of encryption schemes, including BGV, CKKS, and BFV, catering to different application needs. OpenFHE is recognized for its flexibility and extensive functionality, offering features that support not just encryption but also key management, serialization, and multi-threaded operations, among others.
## 3 Data Collection, Preprocessing and Methodology  
The procedure used in this study consisted of the following steps:Data collection:
* Flight planning
* Flight itself
* Data preprocessing--mosaics creation
* Data processing--classifications
* Results visualization  
The aim was to compare the used methods, but there was no specific order of the classification methods set.