esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Experiment Evaluation
### Comparison with the State-of-the-Art  
[PERSON] and [PERSON] [22] conducted an evaluation study for impostor detection using the Balabit dataset that contains only 10 users [23]. They extracted 39 features and used a random forest classifier with an average accuracy of 81.17%. [PERSON] and [PERSON] [38] investigated the performance of a continuous authentication system using a Gaussian naive Bayes classifier. They conducted their experiments using the dataset of [PERSON] and [PERSON] [40], consisting of eight features for each user data, and obtained an accuracy of 93.563%. Compared to these two works, we have presented an empirical evaluation of online CA and AD using 87 features extracted from the raw mouse dataset. For each of the CA phase and the AD phase, three scenarios were evaluated: Scenario A (single mouse movement action), Scenario B (single point-and-click action), and Scenario C (set of mouse movement and point-and-click actions). In both phases, KNN, DT, RF, and CNN classifiers were used separately to identify users by their mouse behavior; the evaluation assessed the ability of each classifier to distinguish between genuine users and imposters.
# NFT Technology for Enhanced Global Digital Registers: A Novel Approach to Tokenization
## 8 Experimental Evaluation
### Testing Scripts and Procedures
#### 8.3.2 Explanation of Measured Metrics  
1. Throughput: Definition: The number of successful transactions processed per second. Measurement method: The script executes a predefined number of registerItem transactions in rapid succession and calculates the average number of transactions completed per second. Significance: Throughput is a critical metric for assessing the system's capacity to handle high-volume registration scenarios, such as bulk data imports or peak usage periods;
2. Latency: Definition: The time taken for a single transaction to be processed and confirmed. Measurement method: The script measures the time elapsed between initiating a registerItem transaction and receiving confirmation of its successful execution. Significance: Latency impacts the user experience and is crucial for applications requiring near real-time updates to the registry;
3. Gas Consumption: Definition: The amount of computational work required to execute a transaction, measured in gas units. Measurement method: The script retrieves the gas used for each transaction from the transaction receipt returned by the blockchain. Significance: Gas consumption directly translates to transaction costs on the Ethereum network, making it a key factor in assessing the economic viability of the system;
4. Function Execution Time: Definition: The time taken to execute a specific smart contract function, excluding network latency. Measurement method: The script measures the time elapsed for non-transactional function calls, such as getItemDetails. Significance: This metric helps identify potential bottlenecks within the smart contract logic and assess the efficiency of data retrieval operations;
5. Batch Operation Performance: Definition: The efficiency of processing multiple items in a single transaction compared to individual processing. Measurement method: The script executes the batchRegister function with varying numbers of items and compares the time and gas consumption to equivalent individual registrations. Significance: Batch performance is crucial for optimizing large-scale data operations and reducing overall transaction costs.
## 3 Results
### Main Findings
#### 3.4.2 Parameters Tested  
In all the included papers, the authors measured a total of ten spatiotemporal parameters: stride length (SL), contact time (CT), swing time (ST), duty factor (DF), aerial time (AT), step frequency (SF), step length (StL), relative step length (RStL), step angle (SA), and flight time (FT). The number of parameters was closely related to the main focus of each paper. The most spatiotemporal parameters at different inclinations were measured by [PERSON] et al. [31], who analyzed a total of six parameters: CT, AT, DF, SF, StL, and RStL. Five parameters were analyzed by [PERSON] et al. [30] (SF, SL, CT, ST, DF) and [PERSON] et al. [40] (CT, FT, SA, SF, StL). The great majority of authors analyzed two to four spatiotemporal parameters [32; 33; 34; 35; 36; 37; 39; 41; 43]. [PERSON] et al. [29], [PERSON] et al. [38], and [PERSON] et al. [42] examined one parameter each. The former performed the analysis for SL and the latter for SF. At the same time, these were the most frequently analyzed parameters among all authors.  
Of all the criteria analyzed, the greatest variation occurs for the slope used. The researchers, most often, adopted a slope between \(-11\%\) and \(11\%\). Only three authors used an inclination beyond the above-described scale. [PERSON] et al. [31], [PERSON] et al. [30], and [PERSON] et al. [37] conducted studies at the following gradients: \(-17.6\%\), \(17.6\%\), \(-15.8\%\), and \(15.8\%\). However, there was a lack of consistency in all the research protocols evaluated as to the choice of slope values. The vast majority of authors used the capabilities of the mechanical treadmill and available equipment to analyze uphill and downhill running at the same incline [29; 33; 34; 35; 36; 38; 40]. Some researchers focused their attention solely on uphill running [34; 35; 40]. Only [PERSON] et al. [37] analyzed the uphill run without analyzing the uphill run on the same gradients. The most problematic aspect of selecting individual studies was the lack of consistency in the exact value of gradients in the selected protocols. In the 15 included articles, the total number was 23 gradients. The smallest difference in the applied inclinations for two different authors was 0.2%. The only common inclination for all authors except [PERSON] et al. [29] and [PERSON] et al. [43] was a reference to a 0% inclination value.
# Smart Classification Paradigms for Protein Samples from 1-D Electrophoresis Gel +
## 6 Analysis of Results
### Analysis of Results for the Four Paradigms
To perform the selection of the wavelet transform, Haar, biorthogonal, and Daubachies were tested, achieving similar results. However, only the Daubechies results are shown, because the visual coefficients of greater importance in the protein signal content highlight the molecular weight score. Then, the decomposition levels are analyzed, where the wavelet transform Daubechies showed with a higher illumination, the said protein score. On the other hand, the parametric statistics and the histograms obtained established an analogy between the detection of molecular weights and the electrophoresis samples. With the variant Daubechies to a level of decomposition 3, the approximation coefficients of each sample were obtained. From Wavelet Daubechies Transform were obtained parametric statistics such as the mean, the median, the minimum value, maximum, and standard deviation. Of these five statistics, which are considered as characteristics of the image, only in the case of the minimum value, a correspondence of molecular weight is found. The other characteristics did not show much correspondence for the correct detection of molecular weights, because there were no predefined ranges, the readings were spliced or there was definitely no adequate correspondence. Within the 30 experiments that are observed in Table 3, in 93% of the cases, at least one image corresponds effectively to the molecular weight that the users want to find or compute, that is, the system recovers statistics and at least one sample of molecular weight similar to the query sample. The recall value is 0. The back propagation neural network uses supervised learning. The accuracy of classification was of 96.7%. The 29 electrophoresis samples were classified in the corresponding group, only one was out of the classification (see Figure 11). The groups that had all the cases classified in the correct group were those of molecular weight 15, 20, and 37 kD. In the self-organization neural network, the same data set was inserted as in the previous methods, for the input nodes of the network. Ten missing samples were wrongly classified among the other groups: two in the 15 kD group, three in the 20 kD group, and five in the 37 kD group. Then, most of the samples were classified in the groups close to the 25 kD. In the decision tree, J48, the WEKA software, and the matrix of the 30 samples are used. The five parametric statistics were used (minimum, maximum, mean, median, and standard deviation). By using the five statistics, the correct classification was exceeded at 83.3%. Only three samples of 25 kD molecular weight were not classified in the correct group.
# Biosynthesis of Natural Rubber: Current State and Perspectives
## 2 Biosynthesis of Natural Rubber and the Involved Genes/Proteins
### Genes/Proteins Involved in Natural Rubber Biosynthesis
#### cis-Prenyltransferase
The deduced amino acid sequences of TbCPTs showed \(\sim\)98% identity with each other and were conserved at a lower level with _H. brasiliensis_ CPTs (\(\sim\)53% identity with HRT1, \(\sim\)52% with HRT2) [62]. They were functionally analyzed by complementary expression in yeast, and were demonstrated to catalyze the synthesis of _cis_-polyprenol dedol-PP [33]. The TbCPTs RNAi lines exhibited a significant reduction in rubber biosynthesis, fewer and smaller rubber particles and a complete loss of in vitro long-chain prenyltransferase activity of whole latex protein, indicating that rubber biosynthesis in _T. brevicornicultatum_ latex is fully dependent on CPT activity [63]. Four deduced CPT-like protein sequences similar to an _H. brasiliensis_ CPT (AAR88763.1) were identified in expressed sequence tag (EST) collection of cold-acclimated _P. argentatum_, but so far no independent enzymatic activity assay of PaCPT has been performed [64].
## 3 Results
### Processing the Signals and Modal Parameters
Figure 5: The identified type of the natural modes for all prestress levels: (**a**) the rotational mode; (**b**) the flexural mode. At the 13 th pre-stress level, the modes are clearer and sharper, and the dominant frequency is induced around the value of 10 [Hz]. The diagram of the PSD acceleration presents an increase of the first mode, which is visible with the increase of the prestress level, where a change from approximately 6 to 10 Hz has occurred. Figure 6: The acceleration in the frequency domain read from the 4 th node at directions x (upper), y (central) and z (lower) for striking direction at azimuth: (**a**) 120 degrees and (**b**) 330 degrees. Figure 7: The acceleration in time domain, the acceleration in frequency domain, the power spectral density (PSD) of the acceleration in frequency domain of the signal 4x induced by the azimuth 240 degree impulse strike for: (**a**) the prestress level 4; (**b**) the prestress level 13. After the preparation of signals, the post-processing procedure was performed in order to extract natural modes and frequencies. The pLSCF method was used with the following parameters: frequency 1%, damping 5%, vector 1%, transfer norm 50%, the minimum modal phase collinearity 95% and minimum two identified natural modes in one setup. The explanation of the identification parameters and procedures can be found in e.g., [34]. Although choosing the frequency and modes from the stabilization diagram is somehow arbitrary, as shown in an exemplary way in Figure 8, this issue is especially important for tensegrity structures, where the resonance spectrum often includes very closely situated frequencies. This is the case for frequencies around 16 Hz in Figure 8, which is a result of a high degree of a structure's symmetry. Therefore, third flexural mode (Table 4) had very closely related double eigenmodes. These double natural modes are both presenting a flexural mode for different directions of vibrations. Hence, interferring natural frequencies can be an obstacle in recognizing the correct resonance and is susceptible to human error. The natural frequencies and modes are better or worse seen in the stabilization diagrams, which are subjected to the direction of the measured and induced signal (Figure 8). To find a certain type of mode, it is good to induce only this mode. Figure 8b presents finding only two modes, both of the rotational type, after inducing the impulse for the tangential direction. To find all modes of the structure, it may be good to induce generally more of them by applying a randomly directed signal, such as presented in Figure 8a in the direction of 120 degrees for node P4, wherein one can see at least five modes of the rotational and flexural type.
# An Estimation of Speech Privacy Class Based on ISO Parameter
## 4 Results and Discussion
### Correlation between the Articulation Index (AI) and SPI Parameters  
The relationship between the Articulation Index _AI_, calculated using Equation (10), and the proposed parameter _SPI_ is illustrated in Figure 7. All 43 measurement results are represented by black circles. Based on the values of the two parameters, the least squares method was used to fit the data using two regression curves. The first approach uses linear regression to obtain a relationship similar to the one between _SPC_ and _SPI_. The second approach uses polynomial regression to fit the data using a second-degree polynomial function.  
The best linear fit for the data presented in the diagram is \(SPI=-53\)_AI_ + 56 dB. The linear model is represented by a black line in Figure 7. To assess the similarity of the obtained data, Pearson's coefficient of correlation (\(r\)) is calculated, and its value is \(r=0.85\). Comparing these two correlation coefficient values, it can be concluded that there is a very good correlation between _AI_ and _SPI_. The second-degree polynomial model is described by the equation \(SPI=77.5\)_AI_\({}^{2}-105.6\)_AI_ + 90 dB and represented by the red line in Figure 7. The Pearson correlation coefficient between this model and the analyzed data is \(r=0.90\). Based on the values of the Pearson coefficients, it is concluded that the polynomial model better approximates the relationship between _AI_ and _PI_ parameters. The literature provides data on the relationship between _AI_ and _SPC_, indicating that the relationship for enclosed spaces is not linear but polynomial [52]. Since this study has demonstrated that the relationship between _SPC_ and _SPI_ is linear, it can be argued that the obtained dependency is coherent with the literature results. This analysis shows that the proposed parameter _SPI_ is well correlated with the Articulation Index _AI_ (and consequently, the Privacy Index). This implies that _SPI_ can be used to describe the state of privacy in partitioned spaces.
## 2 Materials and Methods
### Canopy Spectra  
In this study, continuous canopy spectral observations under the two distinct irrigation regimes were conducted at high temporal resolution via an upgraded AutoSIF observation system (Bergsun Inc., Beijing, China) [19]. The system comprises a control computer, two spectrometers, and an optical multiplexer (MPM-2000, Ocean Optics Inc., Dunedin, FL, USA). The MPM-2000 device features 6 channels for connecting spectrometers and provides channel switching in less than 150 milliseconds. Among these channels, two channels are connected to vertically oriented optical fibers with cosine correctors for measuring the downward irradiance, another two are dedicated to measuring the dark current, and the remaining channels are used for measuring the upward radiance from the different canopies.  
The two spectrometers used for spectral data collection are as follows: The first spectrometer is a QE65 Pro spectrometer (Ocean Optics Inc., Dunedin, FL, USA), which exhibits a spectral range of 640-800 nm and a spectral resolution of approximately 0.7 nm. It features a sampling interval of approximately 0.35 nm and a signal-to-noise ratio of 1000:1, and it was used to extract the far-red SIF. The second spectrometer used is an HR2000 spectrometer (Ocean Optics Inc., Dunedin, FL, USA), with a spectral range of 300 to 1200 nm, a spectral resolution of approximately 3 nm, a sampling interval of 1.5 nm, and a signal-to-noise ratio of 250:1. This spectrometer was employed to calculate vegetation index values.  
A vertically oriented bare optical fiber affixed to an adjustable bracket above each plot was utilized for measuring the upward radiance from the canopy. The frame height was adjusted to maintain the optical fibers at approximately 2 m above the canopies throughout the observation period, thus ensuring a circular field of view with a diameter of 1 m (Figure 1a). The measurement sequence alternates between canopy upward radiance, downwelling irradiance, and dark current measurements, and the specific measurement method is described by [PERSON] et al. [28]. To avoid unreliable data caused by excessively high solar zenith angles, observations were conducted daily between 08:00 and 16:00.
## 3 Experimental Procedure
### Equipment for Verifying Radio Wave Absorption Effect
#### 3.3.3 Outline of Antenna for 28 GHz Band  
Figure 7 shows the receiving horn antenna and RF Detector, and the transmitting horn antenna and Multiplier used for the 28 GHz band measurement. The receiving horn antenna and the RF Detector were connected using a waveguide/coaxial connector converter. The transmitting horn antenna and Multiplier were connected using an isolator in order to reduce the influence of reflected waves. A laser marker was attached to each antenna so that the angle of incidence could be visually confirmed.  
Figure 8 shows the outline of the installation of the antenna and the sample stand.  
The transmitting and receiving horn antennas were installed in a 6-axis precision adjustment mechanism to adjust the height, angle, tilt, etc. These adjustments were made using a laser marking device and a digital level (See Figure 8). The laser marking device used here has a gyro mechanism and automatically draws horizontal and vertical laser beams on the front and both sides. This was used to adjust the installation position and angle of each antenna and the sample stand.  
Figure 7: (**a**) Receiving antenna and RF Detector; (**b**) Multiplier and transmitting antenna.  
Note: The laser marking device and digital level used for adjustment were removed during measurement, but the laser markers installed on each antenna were operated during measurement to monitor the angle of incidence.  
3.3.4 Outline of the Verification System for the Amount of Radio Wave Absorption for the 75 GHz Band  
The 75 GHz band verification system used the same equipment as the 28 GHz band, except for the horn antenna, Multiplier, and reception down converter. Figure 9 shows an outline of the installation of a horn antenna for the 75 GHz band, a Multiplier, and a down converter for reception.  
The transmission system generated an RF signal from 74.1 GHz to 75.9 GHz by multiplying the RF signal from 12.35 GHz to 12.65 GHz oscillated from the Sweep Generator by 6 with the Multiplier. The receiving system used a down converter in which a frequency 73 GHz lower than the received frequency is output as an intermediate frequency (IF). By supplying this IF output to the RF Detector, the transmission loss was measured. The down converter used here is a super heterodyne method because it has high sensitivity and is less affected by unnecessary waves.
## 4 Numerical Results
### Simulation Scenarios  
An IRS-aided SISO VLC system with \(K\) IRS units is considered in a room of size 6 m \(\times\) 6 m \(\times\) 3 m. The coordinate origin is located in one corner of the ceiling in the _xoy_ plane as shown in Figure 1. The LED is located at \((3,3,0)\) and the PD at \((3,1.5,2)\). The IRS units of size \(0.01\times 0.01\) are evenly deployed on the wall in the \(xoz\) plane from point \((2.965,0,1.215)\) within a rectangle area. The LoS and IRS-reflected NLoS channel gains are calculated according to (2) and (4). The rest of the simulation parameters are listed in Table 1.  
The jointly optimized PAM constellation and reflectances are employed in the tunable IRS-aided VLC links. The evenly distributed PAM constellation points with the simple scaling-based dimming scheme are adopted for the mirror-based IRS-aided VLC links with all reflectances set to \(\gamma\) to make the results comparable [15]. Moreover, the gray coding scheme is applied to label the constellation points, and maximum likelihood (ML) detection is used in the demodulation:  
\[\hat{s}=\arg\min_{s_{m}}\|Y-(h_{L}+\mathbf{r}_{m}^{T}\mathbf{h}_{NL})As_{m}\|. \tag{19}\]
## 5 Experimentation, Results, and Analysis
### Significance of Dynamic Context Modeling  
Dynamic context modeling is one of the cornerstone features that sets DCAT apart from traditional image captioning models. Unlike static models that operate on pre-determined features and attention mechanisms, DCAT's dynamic context modeling allows the system to adapt to the intricacies of the image in real time. This is of critical importance in addressing several challenges in image captioning, which we discuss below.
# Near Real-Time Irrigation Detection at Plot Scale Using Sentinel-1 Data
## 4 Results
### Classifying Irrigated and Non-Irrigated Plots over Catalonia
For the intersection scenario, the obtained results remained nearly the same for the four accuracy metrics. Moreover, in the combined classification scenario, the overall accuracy increased by 4.0% (Figure 10a) where the F-measure of the irrigated class increased by 4.5% (Figure 10b). The F-measure of the non-irrigated class (Figure 10c) also increased for the four different scenarios. Finally, the weighted F-measure also increased by 2.0%, 4.0% and 3.4% for the morning, evening and the combined classification scenarios, respectively (Figure 10d). Therefore, a priori information about the irrigation period can help reduce the uncertainty in the proposed model for better irrigation detection. This priori information helps limiting the size of the studied temporal series and thus reduces the chance of detecting additional false irrigation events. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Scenario** & \begin{tabular}{c} **Condition to be** \\ **Irrigated** \\ \end{tabular} & **Class** & **F-Measure** & \begin{tabular}{c} **Weighted** \\ **F-Measure** \\ \end{tabular} & \begin{tabular}{c} **Overall** \\ **Accuracy** \\ \end{tabular} \\ \hline SAR Morning & Two points and more & \begin{tabular}{c} Non-irrigated \\ Irrigated \\ \end{tabular} & 0.90 & 86.1\% & 85.7\% \\ SAR Evening & Two points and more & \begin{tabular}{c} Non-irrigated \\ Irrigated \\ \end{tabular} & 0.88 & 83.4\% & 82.5\% \\ Intersection Morning & \
\begin{tabular}{c} One point and more \\ Irrigated \\ \end{tabular} & 0.90 & 86.0\% & 85.9\% \\ Combined Morning &
\begin{tabular}{c} Three points and more \\ Irrigated \\ \end{tabular} & 0.89 & 84.7\% & 85.4\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The values of the overall accuracy and F-measure obtained for classification of irrigated and non-irrigated plots using the detected irrigation points for four different scenarios. Figure 10: _Cont._