esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Numerical Procedure  
Transient pressure, airflow, and concentration distribution were solved. The CFD software ANSYS Fluent (version 2019) [39] was adopted to resolve the airflows. The RNG \(k\)-\(\epsilon\) model, together with the standard wall functions [40], was adopted to model the turbulent flow considering its generally good performance [41]. The governing equations were solved by means of the finite volume method. The numerical method used the SIMPLE (semi-implicit method for pressure-linked equations) algorithm for coupling the velocity and continuity equations. The second-order upwind schemes were employed for discretizing the convection and viscous terms of the governing equations. The iterations were terminated when the relative residuals of all of the solution variables were less than \(10^{-6}\).  
The ICEM software (version 2019) was adopted to create the geometry and generate grid cells. Tetrahedral grid cells were generated to be compatible with the dynamic mesh simulation. The grid cell size was set to 0.3 m, and the cell size was refined to 0.1 m near the clearance openings of the protecting doors and room doors, windows, and the vent hole. The grid cell size was shifted at a growth rate of 1.2. The overall grid cell number was 1.015 million. To check the grid-independence, the finer grids with a total grid cell number of 3.09 million were also tested but did not obtain meaningful differences.  
This investigation used the smoothing and dynamic re-meshing scheme in ANSYS Fluent to realize the elevator car movement. The spring-based smoothing method was adopted. The re-meshing sizing options were based on local cells and faces. The time step was set to 0.02 s to prevent negative volumes in the generated grid cells.  
In this study, we utilized the CFD-Post software (version 2019) to extract the data for further analysis. The collected data were processed and visualized. Moreover, we used Python (version 3.6.2) to fit the data and generate graphs.
## 2 Related Work
### Related Work Conclusions and Research Gap  
In this section, a further summary is presented of the prototypes and systems reviewed. From this conclusion, the research gap, which forms the basis of the research work carried out in this manuscript, is highlighted.  
The majority of the work reviewed here is unidirectional, i.e., detection and conversion of hand gesture data into text and or voice. This is a limitation where the ND-M are unable to communicate with the D-M. The other limitation is the input sign language dataset, which in most cases, is one. This means that people who do not know that particular sign language are unable to benefit from the system. Some prototypes designed using gloves and sensors require maintenance and calibration and are difficult to maintain consistency.  
The system proposed in this manuscript provides a full-duplex system where both the D-M and the ND-M can communicate with each other. This system uses multiple sign language datasets, provides training, is easy to use, and has a low cost. Systems based on commercial-off-the-shelf (COTS) devices like LMDs require considerably less effort in  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**References** & **Sign Language Dataset** & **Dataset Size** \\ \hline
[14] & Bangladesh Sign Language & 2660 images \\
[16] & Arabic Sign Language & 50 signs \\
[31] & Word-level American Sign Language & More than 2000 words \\
[32] & Argentinian Sign Language & More than 3200 videos \\
[33] & Turkish Sign Language & 226 signs \\
[34] & Chinese Sign Language & 500 categories \\
[35] & Word-level American Sign Language & More than 10,000 videos \\
[36] & American Sign Language & 10,000 signs and 25,000 videos \\
[37] & Russian Sign Language & 164 lexical units \\
[38] & Arabic Sign Language & Not provided \\ \hline \hline \end{tabular}
\end{table}
Table 2: Summary of sign language datasets reviewed.  
converting the prototype into a product. Affordability is also important, so low initial cost and no operational cost are essential features.
# Review on Cyanobacterial Studies in Portugal: Current Impacts and Research Needs
## 2 Cyanobacterial Studies
### In Vivo Studies
Overall, results support the relative importance of the GST system as a protective mechanism to cope with MC-producing cyanobacterial blooms exposure which seems to be different between the bivalve species [56]. Despite the ability of cyanotoxins uptake and elimination, physiological alterations along with biochemical and genotoxic effects have been shown to be induced in bivalves by toxic cyanobacteria [39; 57]. Studies with fish are in much lesser number when compared with bivalves. In one of the first, [PERSON] [58] showed that several wild fish species, such as carp, barbel and grey mullet, which were collected from Portuguese freshwaters, could accumulate MC. However, the amounts of toxins detected in the edible parts were not very significant in terms of human health. The study of cyanobacterial effects in fish has been essentially related to the neurotoxin anatoxin-a (ATX). Results show that ATX producing cyanobacteria (_Anabaena_ sp.) can promote the acute death of common carp juveniles (\(10^{7}\) cell mL\({}^{-1}\)), as well as induce adverse effects in the early development stages of this fish [59; 60]. Furthermore, it has been shown that sublethal doses of ATX could induce bioaccumulation and alterations in relevant biochemical markers addressing energy metabolism and biotransformation mechanisms in rainbow trout [61; 62]. In the last decade, the effects promoted by cyanobacterial toxins on crop plants attain growing research interest because of contamination via spray irrigation associated with agriculture activities. The effects of exposure of edible plants to MC and CYN (extracts and purified) have been described for rice (_Oryza sativa_) [34; 63], carrots (_Daucus carota_) [64; 65], lettuce (_Lactuca sativa_) and spinach (_Spinaca oleracea_) [66; 67]. Reported results include negative effects on plant growth, oxidative status and nutritional quality. Recently, [PERSON] et al. [68] reported increased sensitivity of spinach to both MC and CYN, individually and in mixture, in comparison with lettuce. Furthermore, plants exposed to CYN/MC mixture showed differential accumulation of both toxins, with CYN being assimilated in a greater amount than MC.
## 2 Materials and Methods
### Simulation Procedure
#### 2.2.3 Simulation of Different Scenarios  
Once the energy model of the surgical suite was calibrated and it behaved as in reality with the minimum discrepancies, a study of its performance was carried out in different scenarios. The objective was to determine an optimal ventilation strategy that allows reducing the annual energy demand while preserving indoor health and comfort conditions.  
Scenario 0, or the current operation mode (COM), was running with outdoor fresh air as supplied as intake air to the surgical suite. A total airflow of 2400 m\({}^{3}\)/h in the case of the ORs (21.5 ACH) and 2680 m\({}^{3}\)/h in the case of auxiliary rooms (6 ACH) was used. The set point temperatures were 22 \({}^{\circ}\)C in the ORs, 23.5 \({}^{\circ}\)C for the ancillary rooms and 24.5 \({}^{\circ}\)C in the corridors.  
Figure 3: Simulation diagram implemented in TRNSYS.  
The thermal zones were simulated using an ideal heating and cooling system, and the hygothermal conditions were kept within values allowed by standards, therefore ensuring the safety of the surgical environment. During occupied periods (working days from 8 am to 3 pm) the recommendations of the Spanish standard were followed [41]. The ORs were maintained at a temperature of 22 \({}^{\circ}\)C and RH between 45% and 55%; the ancillary rooms were preserved at a temperature of 23.5 \({}^{\circ}\)C and RH between 45% and 55%; and the corridors were kept at a temperature of 24.5 \({}^{\circ}\)C and RH between 45% and 55%. During non-occupied periods, the wider range allowed by the French standard was assumed [46]; so the ORs and the ancillary rooms were maintained at a temperature between 15 and 30 \({}^{\circ}\)C, and RH boundless but avoiding condensation. Considering these hygothermal conditions, the following ventilation scenarios were simulated and analyzed (Table 3):  
* Scenario 1. Considering the actual design operation rates. This means 21.5 ACH in the ORs and 6 ACH in the ancillary rooms, with 100% outdoor fresh air intake in both cases.
* Scenario 2. Considering a reduction of the 50% of outdoor fresh air. This means 21.5 ACH in the ORs, 6 ACH in the ancillary rooms, but with an air recirculation rate of 50% in both cases.
* Scenario 3. Considering the American standard requirements for ventilation [42]. This means 20 ACH in the ORs with a total fresh air intake of 4 ACH, and 6 ACH in the ancillary rooms with 2 ACH of fresh air.
# Analysis of the Dynamic Characteristics of Coaxial Counter-Rotating Planetary Transmission System
## 4 Analysis of Dynamic Response Characteristics of the Transmission System
#### 4.1.2 Effect of External Excitation on Dynamic Meshing Forces  
In practical conditions, the transmission system is subjected to complex and varying external loads rather than a single, steady torque. Studying the dynamic response characteristics of the transmission system under varying conditions and load fluctuations helps in comprehending its performance in practical applications when the system is subjected to alternating external loads, as represented by the Fourier series as follows:  
\[T_{out}=596.875+50\cos\left(f_{t1}t+  
In Figures 10 and 11, from the time-domain responses perspective, the overall trends of the dynamic meshing forces considering external loads are consistent with those under rated condition. This similarity is attributed to the variations in TMS and transmission errors. However, there are noticeable differences due to the alternating nature of the external loads. The fluctuation of the meshing forces becomes more prominent, resulting in sharper peaks compared to the meshing forces under the rated condition. The frequent variations in external loads may accelerate fatigue damage on the tooth surfaces. From the frequency-domain responses shown in Figure 12, compared to the frequency-domain response under the rated condition, there are additional frequency components in the spectrum due to the external loads, \(f_{t1}\) and \(f_{t2}\).
# Review of Artificial Intelligence Adversarial Attack and Defense Technologies
## 2 Adversarial Samples and Adversarial Attack Strategies
### Adversarial Capabilities and Goals
#### Black-box Attacks  
The adversaries have no knowledge about target model \(f\) in black-box attacks. They instead analyze the model's vulnerability by using information about the past input/output pairs, e.g., the adversaries attack a model by inputting a series of adversarial samples and observing corresponding outputs. Black-box Attacks can be divided into non-adaptive, adaptive and strict black-box attacks.  
* **Non-Adaptive Black-Box Attack.** The adversaries can only access to the training data distribution \(\mu\) of target model \(f\). Therefore, adversaries choose a training procedure \(train^{{}^{\prime}}\) for model \(f^{{}^{\prime}}\), and train a local model on samples from the data distribution \(\mu\) to approximate the target model. Then, adversaries generate adversarial samples by using white-box attack strategies on model \(f^{{}^{\prime}}\) and apply these samples to model \(f\) to lead misclassification.
* **Adaptive Black-Box Attack.** The adversaries cannot access to any information about the target model but can access the model \(f\) as an oracle. In this case, adversaries query the target model to obtain output label \(y\) by inputting data \(X\), and then adversaries choose a training procedure \(train^{{}^{\prime}}\) and a model \(f^{{}^{\prime}}\) to train a local model on tuples \((X,y)\) obtained from querying the target model. Finally, adversaries apply adversarial samples generated by the white-box attack on local model \(f^{{}^{\prime}}\) to the target model.
* **Strict Black-Box Attack.** The adversaries cannot access to the data distribution \(\theta\) but can collect input-output pairs \((X,y)\) from the target model. It differs from the adaptive black-box attack in that it cannot change the inputs to observe the changes in outputs.  
Compared with the white-box attacks, the black-box attacks do not need to learn the randomness \(r\) or parameters \(\theta\) of the target model. The target for adversaries is using data distribution \(\mu\) to train a local model \(f^{{}^{\prime}}\) in non-adaptive attacks and querying the target model by using carefully selected dataset in adaptive attacks.
## 5 Analysis and Discussion of Calculation Results
### Formation Law of Frozen Soil Curtain
Simultaneously, because the position of the profiled freezing tube is closer to the inner side of the pipe curtain, the curve of Z3 is substantially lower than that of Z4. The average cooling rates of 0#, Z3, and Z4 were \(-\)0.76 \({}^{\circ}\)C/d, \(-\)0.85 \({}^{\circ}\)C/d, and \(-\)0.6 \({}^{\circ}\)C/d, respectively, during 32 to 45 days, indicating that the profiled freezing tube has a strong freezing strengthening effect. The total temperature range of the interface was \(-\)7.3 \({}^{\circ}\)C\(-\)\(-\)19.7 \({}^{\circ}\)C after 90 days, and the temperature on the inner side of the pipe curtain was slightly lower than that on the outer side. The overall distribution was relatively uniform. Figure 7c depicts the temperature-time curve for each point on the axial plane. The difference from the interface temperature distribution with the average cooling rate in 32 d-45 d is that the closer the point is to the 6# hollow pipe, the higher the average cooling rate. The cooling rates of 1#, 2#, 0#, 3#, and 4# are \(-\)1.17 \({}^{\circ}\)C/d, \(-\)0.97 \({}^{\circ}\)C/d, \(-\)0.76 \({}^{\circ}\)C/d, \(-\)0.56 \({}^{\circ}\)C/d, and \(-\)0.34 \({}^{\circ}\)C/d, respectively. The temperature curve variations at each location eventually tend to be horizontal and overlap after 70 days, showing that the temperature differential between adjacent jacked pipes steadily narrows. By 90 days, the temperature at each location is nearly identical, with an overall value of \(-\)19.9 \({}^{\circ}\)C.  
According to the above analysis, the process of forming a frozen soil curtain and the distribution features of the freezing temperature field are directly connected to the layout and opening time sequence of the two types of freezing tubes in hollow and concrete pipes. The results of the calculations demonstrate that the thickness and temperature distribution of the frozen soil curtain can match the design requirements, and that it has high water sealing and safety performance, producing ideal support conditions for future tunnel section excavation.
## 3 Results and Discussion
### Numerical Verification against Monte Carlo Simulations
Since the total thickness of the turbid slab was 100\(\Delta z\), no photon could be transmitted from the exit plane before \(t=100\Delta z/c\) because 100\(\Delta z\) was the shortest path length possible. Therefore, in Figure 4a-d, no photons were seen within the range from 0\({}^{\circ}\) to 90\({}^{\circ}\), and the transmitted photons in these panels were all photons reflected back to the entrance plane. Figure 4e-h demonstrates the transmitted photons during \(t=100\Delta z/c\) and \(t=200\Delta z/c\) (with ballistic photons removed from the results). As can be seen, after \(t=100\Delta z/c\), the fluctuating features associated with the Mie scattering phase function were not pronounced for the backscattered photons due to diffusion effects. Finally, the time-based Markov chain model could generally approximate the angular distribution of the transmitted photons that exited the turbid slab at different times, which could not be achieved by our previous scattering-based Markov chain model. Thus, the performance of the time-based Markov model is verified. The proposed method can approximate the angular distributions received by the sensors at different moments, which are easier to examine experimentally and provide more information for the reconstruction purpose explored in the following section.
## 1 Introduction  
Currently, the measurement of load spectra in mechanical structures is predominantly conducted using in situ strain gauges [1]. Although temperature compensation is addressed through the use of test bridges, various factors--including differences in strain gauge temperatures, creep compensation characteristics, the temperature of the tested object, creep effects, and environmental influences--often lead to drift trends in the time history of measured strain parameters, thereby affecting the accuracy of the test results [2]. Strain signals, which reflect structural changes within a member, are typically weak, with amplitudes on the order of millivolts. During the acquisition and amplification of strain signals, various external disturbances--including temperature, humidity, electromagnetism, and dust--can cause zero-line drift in the measured members under loading conditions. This drift leads to inaccuracies in the calculation of member loading and, consequently, the compiled load spectrum. In this study, a method to solve the drift trend of the strain parameter curve is developed. Additionally, we provide a concrete application example to illustrate the effectiveness of the proposed data processing technique. In recent years, the rapid expansion in the volume of measured data has led to the total number of takeoffs and landings for each flight profile reaching into the hundreds. With nearly one thousand parameters recorded per flight, the resulting dataset encompasses tens of billions of data points [3]. The compilation of aircraft load spectra through flight testing is of great significance for determining and extending the service life of aircraft structures. The large amount of data collected during the actual testing process requires a series of post statistical processing performed by a computer in order to compile a load time history that is in line with the actual usage situation. References [4; 5] provide a detailed discussion on the processing mode of measured data and the principle of compiling median load spectra, and they explain the computer processing methods used. References [6; 7; 8; 9; 10; 11; 12] explain the technologies of computer parallel computing, modern computer optimization models, and massive data processing. Consequently, the time required for data calculation and processing has increased exponentially, significantly impacting the operational efficiency of load spectrum compilation approaches. To address the rapidly increasing demands for data processing and fully exploit the capabilities of existing computational resources, it is imperative to seek effective solutions. Recent advancements in parallel computing technology have significantly enhanced its application across various domains, including vehicle simulation, finite element analysis, oil exploration, and weather forecasting. These fields, which require high-performance scientific computation for large and complex datasets, offer valuable insights and methodologies for addressing the challenges associated with big data processing.
## 2 Materials and Methods
### Determination of Antioxidant Potential by Selected Methods  
CUPRAC, FRAP, ABTS, and DPPH assays were selected for the determination of antioxidant potential of hydrogel beads. For all selected methods for the estimation of antioxidant potential, all samples were analyzed in triplicates, and for the expression of results, a Trolox calibration curve was generated. Results were presented as umol of Trolox equivalents per 100 g of hydrogel beads (umol TE/100 g). For the evaluation of ferric-reducing ability, 0.2 mL of extract and 3 mL of FRAP reagent were inserted and mixed in glass tubes. FRAP reagent was prepared by mixing 25 mL of acetate buffer (300 mM), 2.5 mL of 2,4,6-tripyridyl-s-triazine (10 mM), and 2.5 mL of FeCl\({}_{3}\times\) 6H\({}_{2}\)O (20 mM). Prior to the addition of FRAP reagent to the extract, the reagent was heated at 37 \({}^{\circ}\)C. The obtained mixtures were placed in the dark at room temperature for 30 min, and absorbance was measured at 593 nm [36]. Copper (II)-reducing antioxidant ability or the CUPRAC assay was performed according to the protocol specified by [PERSON] et al. [37]. For evaluation, in a glass tube, 1 mL of copper chloride (0.01 M), 1 mL of necuproine (7.5 mM), and 1 mL of ammonium acetate buffer (at pH 7) were inserted and agitated in the given order. To the obtained mixture, 0.2 mL of extract and 0.9 mL of distilled water were added and mixed. Absorbance was measured at 450 nm after the mixture was incubated for 30 min at room temperature. The DPPH method was performed according to the protocol of [PERSON] et al. [38]. In total, 0.2 mL of extract and 3 mL of DPPH solution (0.5 mM) were inserted and mixed in glass tubes. The obtained mixtures were placed in the dark at room temperature for 15 min, and absorbance was measured at 517 nm. The ABTS method of [PERSON] et al. [39] was also used according to the following procedure: 0.2 mL of extract and 3.2 mL of ABTS reagent were inserted and mixed in glass tubes. After 95 min at room temperature, absorbance was measured at 734 nm. For the development of ABTS radicals, 2,2\({}^{\prime}\)-azino-bis(3-ethylbenzothazoline-6-sulfonic acid) diammonium salt solution (7.4 mM) was mixed with potassium persulfate (2.6 mM), and after 24 h, the mixture was diluted to obtain an absorbance of 1.136.
## 4 Experiments and Results  
In this section, we present our experimental methodology and performance evaluation for the proposed broadcasting schemes. We compare our models against complex fuzzy and reinforcement learning-based strategies, each employing different adaptation mechanisms. We detail our simulation setup, performance metrics, and benchmark algorithms, and then thoroughly analyze the results under various network conditions.
## 1 Introduction
FM has been increasingly attractive to expert and non-expert users because of its operational simplicity, and capability to modulate compounds from a broad volatility range. [PERSON] et al. [22] demonstrated the increased peak capacity of differential flow modulation GC\(\times\)GC. The report using reverse fill/flush (RFF) modulation showed the excellent precision and capacity to handle significant overloading peaks that exhibited poor peak shape and performance in forward fill/flush (FFF) modulation. Moreover, RFF modulation enables cost-effective and robust analysis, bridging the gap between powerful instrumentation and routine analysis. [PERSON] et al. [23] made use of GC\(\times\)GC to trace the terpenic composition of 18 lager beers, revealing the presence of 94 mono and sesquiterpene compounds. The second dimension was important to resolve key compounds such as 1,1,3,5-tetra-methylcyclohexane and \(\beta\)-ocimene. Similarly, GC\(\times\)GC [4] allowed the assignment of 32 VOC related to beer aroma from Portuguese samples. Chromatograms generated by FM-GC\(\times\)GC-MS are structurally complex (i.e., four-way data) and contain a lot of information; thus, it is important to work with an appropriate data processing technique capable of extracting all meaningful and context-oriented information. Multiway principal components analysis (MPCA) is a well-established multivariate statistical method for data processing, which enables pixel-based pattern recognition [24]. In this article, we applied a bottom-up approach involving FM-GC\(\times\)GC-MS and MPCA for assignment of yeast related volatile organic compounds in two beer samples fermented with distinguished yeast strains. Data processing had the aim of highlighting compounds that could be used to differentiate both beer samples.