esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
In general, HPP preserves the color of many types of foods, but some pressure-resistant enzymes and bacteria can lead to degradation of the color during storage and result in browning [16]. By comparison, it has also been shown that pressure applied at low and moderate temperatures can preserve pigments such as carotenoids and chlorophyll [17]. Pressures up to 600 MPa have been shown to inactivate vegetative pathogens, spoilage bacteria, yeast and molds [18]. On the other hand, spores can survive pressure treatments up to above 1000 MPa, and some enzymes are also pressure-resistant [19]. Often, combinations of HPP and heat are required to obtain inactivation of spores and enzymes. Pressure-resistant spore formers and enzymes are, however, less temperature-resistant when subjected to high pressure. It is generally recognized that high pressure yields increased food safety, but this is dependent on the food being investigated [20]. The food must contain certain amounts of water to achieve an effective reduction in microbial counts. Bacterial inactivation will also depend on the type of microbe, food composition, pH, and water activity of the food [20]. [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] found that raw cauliflower was associated with recalls due to the potential for _L. monocytogenes_ contamination [21]. This bacterium is ubiquitous in soil and therefore a potential risk, but several processing steps can reduce the presence of this microbe. [PERSON], [PERSON], and [PERSON] found that HPP of cauliflower (300/350/400 MPa, 30 min, 5 \({}^{\circ}\)C) gave no growth of total aerobic bacteria. HPP is a technology that can eliminate or inactivate vegetative microbes, but it needs to be tested on each specific product to evaluate its safety [22]. There are few studies on the effect of HPP on the quality of vegetables given processing in larger pieces. For this reason, we decided to undertake a study on the effect of HPP on the quality characteristics of cauliflower _Brassica oleracea_ var. _botrytis_.
# Evolution of Residual Stress Based on Curvature Coupling in Multi-Roll Levelling
## 4 Evolution of the Residual Stress in Multi-Roll Levelling
### Evolution of the Residual Stress in the First Bending
\(Y_{2}\) represents the residual stress after the first bending, including three triangular regions, where \(OO_{1}P_{1}\) and \(P_{1}A_{c}^{N+1}A_{c}^{(N+1)^{\prime}}\) are residual stresses caused by roll levelling and \(OO_{1}B\) is the internal stress left by the initial residual stress. During the bending process, \(OO_{1}B\) is always in the elastic region and remains unchanged in the initial state due to no plastic deformation, while \(O_{1}BCD\) and \(DEA_{c}^{0}\) in the plastic region all disappear and are replaced by the residual stress caused by roll levelling. According to the two cases of the first bending described above, the following results are obtained. In the plastic region, there are only rolling residual stresses in both cases and the stress distributions  
Figure 3: Evolution of residual stress in the first bending. (**a**) A sheet without initial residual stress and (**b**) a sheet with initial residual stress. are the same. In the elastic region, the distributions of the rolling residual stress are the same in both cases, but if there is an initial residual stress, the part in the elastic region of it cannot be eliminated (the residual stress corresponding to the region \(OO_{1}B\) in Figure 3b), which is also the only trace of initial residual stress left after levelling. Therefore, the characteristics of the first bending in multi-roll levelling are as follows. First, the roll levelling will cause some residual stresses. Second, the initial residual stress in the elastic region remains unchanged, while the initial residual stress in the plastic region is completely eliminated. Thus, if the plastic deformation region of the first bending is large enough, most of the initial residual stress will be eliminated. However, large plastic deformation causes large rolling residual stress and eventually the total residual stress cannot be effectively reduced.
# Seismic Performance of Reinforced Concrete Short Columns Subjected to Freeze-Thaw Cycles
## 3 Test Results and Analysis
### Energy Dissipation and Damping Ratio  
Dissipated energy, an important indicator for seismic performance, is calculated by integrating the areas enclosed by the hysteretic loops. Curves showing the energy dissipation of every loop and the cumulative one for columns subjected to different numbers of FTCs are provided in Figure 13(a),b.  
It was first observed that the difference between pristine column (ZD-0) and freeze-thawed column (ZD-1) was insignificant, and that the difference became prominent with a further increase of FTCs. When the imposed drift was larger than 2%, the rate of cumulative energy increasing of ZD-2 became slower than the other two since the loop energy dissipation began to drop at this drift level, as shown in Figure 13(a). The reason for this is that the freeze-thaw attack changed the cracks evolution of the column, as highlighted earlier, which in turn led to less ductility and less area bounded by hysteretic loops. Secondly, the cumulative energy was 80.7%, 81.5% and 51.6% of the total energy at the ultimate limit state (\(\Delta_{\text{u}}\)) for the three columns (ZD-0, ZD-1, and ZD-2) respectively.  
In addition, equivalent viscous damping ratio \(\xi\) was also calculated using Equation (4) as the index to represent the dynamic response of the test columns.  
\[\xi=\frac{1}{2\pi}\cdot\frac{S_{\text{loop}}}{S_{1}+S_{2}} \tag{4}\]  
where, \(S_{\text{loop}}\) is the area of the hysteresis loop, \(S_{1}\) and \(S_{2}\) are the corresponding elastic energy in two directions, respectively. The values of the equivalent viscous damping ratio at the yield load \(\xi_{y}\), at the  
Figure 14: Energy dissipation versus lateral displacement.  
Figure 13: Contribution of shear displacement components to total lateral displacement.  
peak load \(\xi_{p}\), and the ultimate load \(\xi_{u}\) are listed in Table 3. For all the tested specimens, the damping ratio increased from yielding to failure stage, which owns the progressively accumulative inelastic damage. The comparisons among them show that \(\xi_{p}\) and \(\xi_{u}\) decrease with the increasing freeze-thaw applications, while \(\xi_{y}\) was similar.
# Secure Video Communication Using Multi-Equation Multi-Key Hybrid Cryptography
###### Abstract
The primary objective of this research work is to develop a robust cryptography technique that incorporates dynamic key generation, the utilisation of multiple keys, and the application of asymmetric cryptography methodology. The security of our earlier work [13] is improved by this effort, as Figure 2 illustrates. The previous work employed a hybrid multi-key cryptography technique. In the previous work, the Elliptic Curve Cryptography (ECC) equation was used to generate distinct keys for each video chunk, which were subsequently utilised for encryption. In this study, we aim to optimise the encryption complexity by leveraging a series of mathematical equations. In addition, user attributes are considered alongside the MAC address. The salient characteristics of the proposed methodology are outlined below:  
* Hybrid multi-key: The proposed technique encrypts the video segments using RSA and AES. In this case, the video data and its accompanying metadata are encrypted using different passwords. * Dynamic key generation: A dynamic and automatic key generation technique based on equations has been implemented. The algorithm can produce distinct keys for each video chunk. * Equation set: A set of equations is used in dynamic key generation. In [13], we have used only one equation for every chunk of video data. Here, we are increasing the complexity of the cryptography technique using the equation set. Here, each video chunk will use a different equation from the set. The proposed model has been developed and evaluated in a real-world environment. Subsequently, a comprehensive analysis of its performance has been carried out, focusing on the delay parameters across a wide range of circumstances. Given the uniqueness and novelty of the concept, we have opted to utilise our previously validated research as a basis for conducting a comparative study. The research discusses the impact of randomness on computational complexities and provides an understanding of its influence on visual communication. The primary goal is to achieve real-time streaming while upholding a robust security framework. The Android platform has been carefully considered for both the implementation and streaming of the video sequence. The rest of the paper is organised as follows: Section 2 discusses the related works. Section 3 presents the proposed mechanism. the evaluation and numerical results of the algorithm are detailed in Section 4. Finally, the conclusions drawn are described in Section 5.
###### Abstract
The application of EEG and its reliability has been already well explored by the authors in a variety of laboratory and realistic settings by evaluating human-robot interaction in training assessments, driving, and air traffic control [36; 37; 38; 39; 40]. Although commercial and cheap EEG devices are available on the market, their correct usage requires specialized personnel to be able to check the correct position of the sensors (electrodes located exactly over the corresponding brain area) and quality of the EEG signals to achieve the results [41; 42]. Therefore, the possibility of monitoring vigilance degradations through the analysis of head and body movements could be a valid alternative, especially due to the simple and cheap integration of IMUs (accelerometers, gyroscopes) into electronic devices such as smartwatches, virtual reality, and biosignal recording systems (e.g., EEG). This aspect also allows for tracking movements in environments in which global positioning system (GPS) tracking is not possible or difficult [43; 44]. IMUs have been extensively used in clinical applications to monitor patients' rehabilitation both in conditions in which robotic exoskeletons are involved and during free movement rehabilitation [45], stroke rehabilitation [46], and posture evaluation and rehabilitation progression in children with cerebral palsy [47; 48]. Also, in operational environments, the recognition of human activity (HA) by the means of wearable sensors has gained high importance to safely assess the position in time and space of the operators and improve their performance, especially where human-robot interaction is involved [49]. For example, [PERSON] et al. [50] used inertial sensors to spot the visual focus of attention of a driver, while [PERSON] et al. [51] embedded an inertial sensor in a custom-made glove to assess driver stress based on driving behavior. Previous studies have highlighted how it is possible to identify different human activities [52], discriminate stress conditions [51] or variations in vigilance and drowsiness [53] based on drivers' steering behavior, or identify different positions of the head with the possibility of linking them to different attentional states [54] with the use of inertial sensors. However, to the best of our knowledge, the micromovements of the head and variations in body posture with a decrease in vigilance have not yet been addressed. The present study, therefore, aims to understand the relationship between the micromovements of the head and changes in body posture with vigilance decrease by analyzing data from inertial sensors. In particular, given the potential of IMU devices, the present study aims to develop and validate a vigilance index based on the user's head micromovements and body posture. In fact, data collected through IMUs are easily available and do not require professional personnel for setting up the sensors on the user's body, as the neurophysiological measures do.
## 3 Methodology
### Datasets
#### 3.2.1 Noise Addition  
The addition of noise is a fundamental data augmentation technique used in audio signal processing, where simulated background noise is added to the original speech signal. This technique is essential for enhancing the model's resilience by exposing it to variations in acoustic environments, enabling the system to generalize effectively in real-world scenarios where ambient noise is unavoidable. The pseudocode for noise addition is given in Algorithm 1. This algorithm uses a normal distribution to generate realistic background noise, enhancing the model's ability to generalize in noisy environments.  
```
Input: audio_data: Numpy array representing the original audio time series data. Output: augmented_audio: Numpy array representing the augmented audio data.
1: noise_ratio \(\leftarrow\) 0.05 //Calculate Noise Standard Deviation
2: noise_std \(\leftarrow\) noise_ratio \(\times\) np.max(np.abs(audio_data)) //Generate Random Noise from Normal Distribution
3: noise \(\leftarrow\) np.random.normal(0, noise_std, len(audio_data)) //Add Noise to Audio Data:
4: augmented_audio \(\leftarrow\) np.clip(audio_data + noise, \(-\)1.0, 1.0)
5: return augmented_audio
```  
**Algorithm 1** Data augmentation: Noise addition  
Noise addition was employed to improve the model's ability to generalize across different acoustic environments, rather than introducing potential biases. Previous studies have suggested that models may learn to associate specific phrases with background noise, which can affect recognition performance [9, 19]. To mitigate this, we introduced noise with a controlled signal-to-noise ratio (SNR), ensuring that the primary speech features  
Figure 3: The flow of data preparation for the SER.  
remained distinguishable [21]. This approach enabled the model to recognize emotions across both clean and noisy conditions, improving its robustness in real-world scenarios.
## 2 Materials and Methods
### Experimental Set-Up  
#### 2.2.1 Beam Features of the ElectronFLASH Accelerator  
The system used for the characterization of the SiC detectors is the ElectronFLASH (EF) LINAC produced by SIT-Sordina company in Italy [27]. The LINAC is operated in electron mode only, with nominal energies ranging between 5 and 12 MeV, and a dose- rate ranging from 0.01 to 4000 Gy/s and higher. Pulse duration can be easily varied according to the user requirement between 1 and 4 us. The first unit of EF was installed at Orsay Research Center of Institute Curie in 2020; a second one was installed at the University of Antwerp, Belgium; and a third one was recently installed at Centro Pisano Flash Radio Therapy (CPFR) in Pisa, thanks to funding from the Fondazione Pisa [28]. The second machine, which can produce and accelerate electron beams at 7 and 9 MeV, is the one used for performing the SiC detector characterization at UHDRs discussed in this work. The accelerating waveguide concept of the EF is based on the design of IORT-dedicated LINACS previously experienced by SIT staff in [29]. In particular, the wave guide is designed by adopting the radial focusing technique: the electron beam trajectories along the LINAC are directly guided by the electric field of the cavity without any external magnetic solenoid. Moreover, a different approach has been employed for the beam collimation if compared to the standard medical LINAC. Specifically, no thick scattering foil is used: the beam is defocused by means of two quadrupoles, where the gradient can be changed according to the desired field. Such approach shows several advantages for a LINAC operating in FLASH conditions:  
* The absence of the thick scattering foils minimizes the radiation leakage.
* Large clinical fields can be easily obtained by properly setting the quadrupoles current and magnetic field.
* The EF system has been designed to work both in FLASH and conventional conditions. The beam current within the single pulse can be varied by choosing between the \"FLASH\" mode and the \"CONVENTIONAL\" mode.  
Figure 3: (**a**) Distribution of the energy deposited in the detector active layer for the five geometrical configurations under study with 9 MeV electron incident beam. Gaussian fit performed on the different distributions are also shown. (**b**) Angular distribution obtained after 10 μm thick (purple line) and 500 μm thick (black line) SiC detector traversed by a 9 MeV electron beam having an initial angular distribution shown in the figure with the blue line.
# Hybrid Sensing Platform for IoT-Based Precision Agriculture
## 3 Materials and Methods  
In this section, the materials and methods that are used to implement, deploy, and test the HSP are discussed in detail. From a material perspective, the HSP is composed of three main components, including an aerial node mounted on a UAV, ground-based nodes deployed on the ground to collect data on plant and environmental conditions, and a cloud node to collect the ground-based and aerial data. From a methods perspective, the HSP employs statistical and supervised machine learning models for data analysis to improve the data quality, for a more efficient and effective farm management process. Figure 1 shows the system architecture of the HSP.  
Figure 1: Architecture of HSP for IoT-based precision agriculture.
## 3 Results and Discussion  
### Spectral Features and Pre-Processing  
The mean spectra of high-WHC and low-WHC samples are shown in Figure 2a, respectively. Samples with high WHC and low WHC had similar spectral trends. Nevertheless, obvious differences of the amplitudes variations can be identified, the spectrum reflectance values of samples with low  
Figure 1: Main steps involved in the measurement and classification of WHC of fresh chicken breast.  
WHC tended to be higher than those with high WHC. Chicken breast fillets with high values of \(L^{*}\) were more reflective to light and showed higher reflectance values. This result can be explained by the conclusion reported in previous articles that the value of meat lightness (\(L^{*}\)) was negatively correlated with WHC, suggesting that the paler the color, the lower the WHC [9; 46].  
Before model development, PCA was applied to the pre-processed spectra to obtain an overview of spectral distribution of different samples and to identify spectral outlier data. The first five principal components (PCs) explained over 98% of the variation among samples. Two confidence ellipses were calculated on the score plot of the first two PCs (accounted for 75.2% of the total variation), as shown in Figure 2b. The confidence level of these two confidence ellipses was set to 95%, which meant that we are 95% confident that data located inside the ellipse belonged to the corresponding group [30]. Therefore, six samples located outside confidence ellipses in the figure were removed. Finally, the number of samples retained for the high-WHC group was 51, and that for the low-WHC group was 58. Furthermore, it can be found that there was an intersection of spectra for high-WHC and low-WHC fillets; nevertheless, the spectra of high-WHC samples tended to locate in the negative side of PC\({}_{1}\), while spectra of low-WHC samples tended to locate in the positive side of PC\({}_{1}\). Figure 2 indicates that samples with different physicochemical properties (e.g., WHC) had distinguishable spectral features.
# An Optimal Sizing Design Approach of Hybrid Energy Sources for Various Electric Vehicles
## 5 Conclusions  
The conclusions of this study are summarized as follows:  
1. Performance indices and vehicle requirement: the average specific price, specific energy at a constant volume, specific energy at a constant mass, and specific power at a constant mass for three energy sources, SCs, LTO batteries, and Li3 batteries, were studied. The vehicle requirements including the maximum output power, vehicle acceleration, climbability, and maximum speed as the design constraints were formulated.
2. The GSA of the HEES: the global search method with four steps including parameter input, definition of cost function and constraints, global search, and optimal results was developed.
3. Numerical analysis of five types of EVs: by (1) and (2), five types of EVs including the electric sedan, electric forklift, long-distance electric bus, short-distance electric bus, and electric sports car were analyzed for optimal sizing of the HEES and the optimal position of the DC/DC converter with the lowest cost function. For the electric sedan, the optimal sizing was LTO+Li3 with a ratio of 9.9:90.1 and the DC/DC position was at Li3 side; for the electric forklift, the optimal sizing was SC+Li3 with a ratio of 4.5:95.5 and the DC/DC position was at SC side; for the long-distance electric bus, the optimal sizing was pure Li3 with no DC/DC converter; for the short-distance electric bus, the optimal sizing was LTO+Li3 with a ratio of 44.4:55.6 and the DC/DC position was at Li3 side; and for the electric sports car, the optimal sizing was SC+Li3 with a ratio of 0.58:99.42 and the DC/DC position was at Li3 side.  
Utilizing these results, the integrated system and control designs of the HEES using the GSA, more applications for green energy sources, and different types of EVs will be studied in the future.  
Conceptualization and methodology, S.-Y.C., Y.-H.H., K.-K.J., G.-H.Y., and P.-L.S.; data search, C.-Y.C., Y.-H.H., C.-Y.C., and P.-L.S.; theories and equation derivation, S.-Y.C., Y.-H.H., and C.-Y.C.; software design and simulation, Y.-H.H. and C.-Y.C.; writing, Y.-H.H. and C.-Y.C. All authors have read and agreed to the published version of the manuscript[PERSON]  
This research was funded by Ministry of Science and Technology, grant number: MOST 110-2221-E-003-010-MY3, MOST 108-2628-E-003-001-MY3, MOST 110-2223-E-003-001-MY3 and MOST 111-2622-8-005-03-TE1; also was founded by National Chung-Shan Institute of Science and Technology, grant number: NCSIST-013-301(110)[PERSON]  
Not applicable.  
The authors declare no conflict of interest.
# A Survey of Machine Learning-Based System Performance Optimization Techniques
## 2 Background
### Machine Learning Based Index Structures
#### 3.1.2 Learned Hash-Map  
A Hash-Map uses hash-functions to map the position in the array. The goal of the learned Hash-Map is to reduce the hash conflict [7]. To address the problem, they replace the hash-function with a machine learning-based model. They used a regression-based model. The model is also modeled as CDF for the key distribution, and it also uses RMI like the range index. In Figure 3, when the trained model receives a key as an input, it predicts the position of the key in the array, similar to a hash function. When the learned hash-function was compared with the existing hash-function, the collision rate of the learned hash-function for the given dataset was reduced by up to 77%.
## 2 Methodology
### Hybrid Feature Selection Based on Both Machine Learning and Statistical Methods  
By combining the above machine learning methods and statistical methods, we propose a hybrid feature selection approach. In more detail, for machine learning methods, we summed the frequencies of SVMRFE, RFFS-GI and RFFS-OOB, then we selected the features with total frequency greater than a given value \(b\) to obtain the significant feature subset. At the same time, we selected features with the absolute values of the Kendall correlation coefficient greater than a given value \(c\) and those with the \(p\)-value of two-sample \(t\)-test, as well as that of the permutation test less than 0.05 as the significant feature subset, respectively. Finally, we integrated the significant feature subset from both the machine learning and statistical method as the optimal feature subset. The above process is a hybrid feature selection procedure, and the flowchart is shown in the Figure 1. The experiment results will show that the proposed hybrid feature section method is an effective attempt to combine machine learning and the statistical methods.