esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Remote Sensing ET Model (PT-JPL-Thermal)
#### 3.3.2. Soil Evaporation  
Potential soil evaporation \(ETp_{s}\) was downscaled to actual \(ET_{s}\) by applying a soil moisture constraint (\(f_{sm}\)), which is outlined in [PERSON] et al. [42]. The soil thermal inertia (_TI_) can be indicative of soil moisture content variations [56]. A simple approximation to this concept is the apparent thermal inertia (_ATI_) derived from multi-spectral remote sensing images [57; 58]. _ATI_ results from combining the RS surface albedo \(\alpha\), maximum daily land surface temperature oscillations (\(LST_{Day}-LST_{Night}\)), and a solar flux correction factor. The formulation of \(f_{sm}\) based on _ATI_ in this paper relies on the minimum and maximum seasonal _ATI_, respectively. Assuming that maximum _ATI_ values occur at saturated soil moisture content conditions and that minimum _ATI_ corresponds to the residual soil moisture content in the soil, \(f_{sm}\) would estimate the difference between actual soil moisture content and field capacity [58]. As the _ATI_ requires both day and night _LST_, which increased significantly the number of gaps when using daily data, we assumed that 8-day data can capture well the general soil moisture dynamics. The daily maximum \(LST_{Day}\) (daily minimum \(LST_{Night}\)) temperature was estimated for each pixel and 8-day period as the maximum (minimum) value between the day (night) observations from Terra and Aqua MOD11A2 and MYD11A2.
## 4 Experiment and Analysis
### Dataset  
The effectiveness of the proposed model is assessed using CMU-MOSI and CMU-MOSEI datasets, recognized benchmarks in the domain of multimodal sentiment analysis. These two public datasets are also more conducive to comparative analysis with models proposed by other researchers in order to better evaluate the performance of the proposed models. Table 1 presents important statistics about these datasets. Here, we provide a succinct overview of the aforementioned datasets.  
**CMU-MOSI**: The dataset is a multimodal corpus derived from 93 opinion videos. The videos are segmented into 2199 segments, and each example is endowed with a sentiment intensity rating that ranges from 3 (indicating extremely negative emotions) to 3 (representing extremely positive emotions).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Dataset** & **Train** & **Valid** & **Test** & **All** \\ \hline CMU-MOSI & 1284 & 229 & 686 & 2199 \\ CMU-MOSEI & 16,326 & 1871 & 4659 & 22,856 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Statistics of CMU-MOSI and CMU-MOSEI.  
**CMU-MOSEI**: This dataset is based on the above dataset with improvements, which offers a larger and more diverse collection, consisting of 23,453 annotated video clips. It involves 1000 distinct speakers discussing a wide range of 250 topics. Like its predecessor, the dataset assigns sentiment scores within the range of [ 3, +3] to each video clip, enabling in-depth sentiment analysis.
# Residual Vibration Reduction in Flexible Systems Based on Trapezoidal Velocity Profiles
## 3 Optimization of Parameters for Vibration Suppression
### Analysis of Vibration Suppression Condition
If condition C1 is satisfied, the vibrations caused by the jerks at the start and end times of the acceleration/deceleration phase cancel each other out, i.e., the first impulse response is compensated by the second one, and the third impulse response is compensated by the fourth one, leading to zero vibration, both in the cruise phase and after motion completion (see Figure 3). If condition C2 is satisfied, the vibrations caused by the jerks during the acceleration phase are canceled out by those during the deceleration phase, i.e., the first impulse response is compensated by the third one, and the second impulse response is compensated by the fourth one, leading to zero vibration only after motion completion (see Figure 4).
## 4 Protein Quality Evaluation Methods
### In Vitro Models
#### 4.3.3 Semi-Dynamic Methods
\begin{table}
\begin{tabular}{c c c c c} \hline
**Food** & **Process** & **Total Protein (\%)** & **Protein Digestibility (\%)** & **References** \\ \hline Bean & Raw & 23–25. & 92.8 & [50; 97] \\ Bean & Raw & 23–27 & 84.0 & [50; 98] \\ Bean & Autoclaved & 23–26 & 92.3 & [50; 97] \\ Bean & Autoclaved & 23–25 & 84.2 & [50; 98] \\ Bean & Microwaved & 21–23 & 85.8 & [50; 99] \\ Cowpea & Raw & 22–32 & 71.3 & [53; 100; 101] \\ Cowpea & Raw & 22–32 & 82.3 & [53; 101] \\ Cowpea & soaked & 22–32 & 75.3 & [53; 100; 101] \\ Cowpea & soaked & 22–32 & 87.5 & [53; 101] \\ Cowpea & Boiled & 22–32 & 98.1 & [53; 101] \\ Cowpea & Germinated & 22–32 & 79.5 & [53; 100; 101] \\ Cowpea & Roasted & 22–32 & 77.6 & [53; 101] \\ Cowpea & Autoclaved & 22–32 & 90.3 & [53; 101] \\ Cowpea & Microwaved & 22–32 & 92.8 & [53; 101] \\ Cowpea & Fermented & 22–32 & 85.1 & [53; 101] \\ Cowpea & Micronized & 22–32 & 80.0 & [53; 101] \\ Finger millet & Raw & 6–13 & 72.3 & [102; 103] \\ Finger millet & Milled & 6–13 & 44.9 & [103; 104] \\ Foxtail millet & Raw & 11–19 & 77.1 & [61; 102] \\ Kidney bean & Raw & 22–28 & 78.0 & [101; 105] \\ Kidney bean & soaked & 22–28 & 83.2 & [101; 105] \\ Kidney bean & Boiled & 22–28 & 94.3 & [101; 105] \\ Kidney bean & Roasted & 22–28 & 73.0 & [101; 105] \\ Kidney bean & Autoclaved & 22–28 & 86.1 & [101; 105] \\ Kidney bean & Microwaved & 22–28 & 88.6 & [101; 105] \\ Kidney bean & Fermented & 22–28 & 80.9 & [101; 105] \\ Kidney bean & Micronized & 22–28 & 75.5 & [101; 105] \\ Pea & Raw & 19–23 & 80.1 & [50; 101] \\ Pea & Soaked & 19–23 & 85.5 & [50; 101] \\ Pea & Boiled & 19–23 & 95.8 & [50; 101] \\ Pea & Roasted & 19–23 & 75.0 & [50; 101] \\ Pea & Autoclaved & 19–23 & 88.3 & [50; 101] \\ Pea & Microwaved & 19–23 & 90.9 & [50; 101] \\ Pea & Fermented & 19–23 & 82.9 & [50; 101] \\ Pea & Micronized & 19–23 & 77.9 & [50; 101] \\ Soybean & Cooked & 40– 41 & 90.9 & [75; 106] \\ Soybean & Roasted & 35–40 & 87.9 & [75; 106] \\ Soybean & Texturized & 40–49 & 88.8 & [75; 106; 107] \\ \hline \end{tabular}
\end{table} 
Table 2: Protein digestibility of different plant protein sources measured using in vitro models.
## 8 Fixed and Adaptive Wave Domain Equalization
### Room Geometry-Aware Methods  
The knowledge of the room geometry can be used to compute the resulting sound field in the room, for instance by the mirror image method. The control capabilities of a sound field synthesis system can then be used to cancel out the assumed contributions from the room. Methods which explicitly exploit knowledge on the room geometry can be seen as a specialization of the methods discussed so far, since they are based on a wave-theoretic view of the problem. A method for the equalization of early reflections for wave field synthesis (WFS) has been published in [209]. Here the mirror image sources are canceled out by anti-phase virtual point sources placed at the pre-computed positions of the image sources. A similar approach is presented in [210] for higher-order Ambisonics. An approach to room equalization for a linear loudspeaker array producing beams for a virtual surround system is discussed in [211]. The equalization of room reflections is achieved by accounting for the reflection of the beams in the room. The equalization filters are computed by solving the underlying least-squares problem in closed-form. In [212], a method is presented which is based on numerically simulating the impulse responses between the loudspeakers and control points. Only the early reflections are considered. The simulated impulse responses are fed into a MIMO solver for derivation of the equalization filters.
# A Comprehensive Survey on Real-Time Applications of WSN
## 3 Applications of WSN
### WSN and Military  
The United States military used WSN for the first time in a military application in 1960, called the Sound Surveillance System (SOSUS). This is used for the detection and tracking of submarines. Nowadays, WSN is used in many ways like tracking or the moving of a solder or vehicles during the surveillance mission. This is also used for collecting data or necessary information from the target area and sending it to the base station. However, the military needs to improve the quality of WSN security by the routing algorithm [80]. Table 12, represent the examples of real-time applications for Military.  
[PERSON] et al., 2005 investigated the deployment of a dense wireless network sensor, which is coined as \"Tripwire\". The reason behind this project is to limit the intruder from crossing the border, including any kind of sabotages, and from smuggling important plant species which might be an indigenous species and will be a national treasure for the country. Overall, for security reasons, the sensors need to be deployed in the border region between the countries.  
[PERSON] et al., 2006 investigated the underwater sensor network for monitoring fish detection, collision avoidance, underwater vehicles, and long-term ecological inspection. The authors studied how to enhance the shelf life of the sensor and increment of the bandwidth and obtained a comprehensive result.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**References** & **Application** & **Motes** & **City Deployed in** \\ \hline [PERSON] et al. (2014) [78] & UNS—Ubiquitous Navigation System & RF based, Google Maps & Lille, France \\ \hline [PERSON] et al. (2016) [79] & FWI—Fire Weather Index & Internet of Things, GPS Sensors & Oman \\ \hline \hline \end{tabular}
\end{table}
Table 11: Examples of prototyped applications for automated vehicles.
# Machine Learning Reconstruction of Wyrtki Jet Seasonal Variability in the Equatorial Indian Ocean
## Appendix A
### Correlation Analysis of Marine Parameters with U  
Based on the spatial correlation analysis between various marine parameters and U during 1993-2022, the primary dynamical factors influencing the interannual variability of U were revealed. To further explore the transient response characteristics of environmental factors on the equatorial zonal velocity at the seasonal scale, Figure 13 focuses on the year 2022 and provides in-depth insights by analyzing the correlation coefficients between each parameter and the WJ. Figure 13 displays the correlation coefficients between U from the OSCAR dataset and various parameters in 2022, including SLA, MLD, Tx, Ty, CurlZ, SSS, SST, and D20. The results indicate that Ty and Tx exhibited significant positive correlations with U, with correlation coefficients of 0.11 and 0.07, respectively. This suggests that wind stress is a key dynamic factor influencing zonal velocity in the equatorial region. The increase in wind stress, closely related to monsoon activity and seasonal changes in atmospheric circulation, can directly accelerate the zonal movement ofsurface seawater. Additionally, SSS and MLD showed relatively strong positive correlations with U, with correlation coefficients of 0.04 and 0.03. This implies that SSS and MLD may indirectly regulate surface current intensity by affecting oceanic density stratification and vertical mixing processes. In contrast, SST and D20 had weak correlations with U, with correlation coefficients of 0.022 and 0.025, indicating limited direct thermal control over zonal velocity in 2022. Notably, SLA and Curlz showed negative correlations with U, with correlation coefficients of \(-0.08\) and \(-0.15\). The negative correlation of SLA may be linked to geostrophic flow effects, in which elevated sea levels correspond to weakened eastward flow. The negative correlation of Curlz was associated with wind-driven Ekman upwelling; an increase in wind stress curl strengthens upwelling, which suppresses the acceleration of zonal velocity.
## 2 Principles and Methods  
### Signal Preprocessing and Feature Extraction  
#### 2.1.1 Preprocessing  
Most sound signals are non-stationary, but they typically exhibit short-term stationarity characteristics. Therefore, by applying frame-based windowing operations, these signals can be segmented into short segments and processed as stationary signals.  
Assuming \(y(n)\) represents the discrete sequence of sound signals received by the microphone, \(w(n)\) represents the window function, \(N\) represents the total number of frames after segmentation, \(L\) represents the frame length, and \(inc\) represents the frame shift, then the \(i\)th frame signal \(y_{i}(n)\) can be represented as:  
\[y_{i}(n)=\omega(n)*y((i-1)*inc+n),1\leq i\leq N \tag{1}\]  
The short-time energy of the \(i\)th frame signal is denoted by \(e_{i}\), and the expression is as follows:  
\[e_{i}=\sum_{n=0}^{L-1}y_{i}^{2}(n)\ 1\leq i\leq N \tag{2}\]  
In general, the frame length \(L\) is typically set to 10 to 30 milliseconds. The ratio of frame shift to frame length is typically taken between 0 and 1/2.
## 3 Data and Preprocessing  
### Dataset  
We used the publicly available Saarbrucken Voice Database [20]. It is a collection of voice recordings where one subject's samples consist of recordings of the vowels _/u_/, _/i_/, and _/u_/ produced at a normal, high, low, and low-high-low pitch. The length of the recordings with sustained vowels is from 1-2 s. We exported 194 samples from this database, of which 94 samples belong to patients with dysphonia (41 men, 53 women) and 100 samples belong to healthy ones. The age of all subjects is over 18 years.
# A Medical Image Encryption Scheme for Secure Fingerprint-Based Authenticated Transmission
## 3 Materials and Methods
### Key Security Analysis  
The security of the proposed scheme is based on visual security, and a random permutation key \(k\). Without \(k\) is extremely complex to know the mapped vector \(c_{m}\) embedded in reference image sub-band to reconstruct the singular value vector and fingerprint feature vector. The space of possible values of \(c_{m}\) is equal to the size of sub-band. Furthermore, to correctly reconstruct the singular value vector and fingerprint feature vector from \(c_{m}\) through LSTSQ algorithm, the order of the vector values is essential. Therefore, the values of \(c_{m}\) extracted must be in the same order as the original vectors to ensure a correct reconstruction. Figure 8 shows the reconstructed medical images using the random shuffle of mapped vector \(s_{m}\) values to simulate the scenario where an attacker tries to reconstruct a medical image without knowing \(k\).  
\begin{table}
\begin{tabular}{c c c c c c} \hline  & [48] & [50] & [51] & [52] & [54] & **Proposed Scheme** \\ \hline Barbara & N/A & N/A & N/A & N/A & N/A & 32.291 \\ Lena & **55.512** & N/A & N/A & N/A & N/A & 38.786 \\ airplane & **56.582** & N/A & N/A & N/A & N/A & N/A \\ pepper & **55.5071** & 40.9310 & 32.3513 & 35.1347 & 34.51 & 39.718 \\ baboon & **55.1570** & 40.9187 & 37.1058 & 36.4906 & N/A & 32.998 \\ girl & **57.3175** & N/A & N/A & N/A & N/A & 40.316 \\ \hline \end{tabular}
\end{table}
Table 4: Comparison of the PSNR values of the visually meaningful encrypted images (the best results are shown in bold).  
To correctly reconstruct a medical image without knowing \(k\) it is necessary to make a number of possible attempts equal to the number of ordered sequences of \(N\) distinct elements and extracted from the set of \(M\) elements. Therefore, the number of possible attacks attempts with a medical image size of 512 \(\times\) 512 and a reference image size of 1024 \(\times\) 1024 is given in Equation (19).  
\[D_{M,N}\,=\,\frac{M!}{(M-N)!}\ ;\ \frac{(512\times 512)!}{((512\times 512)-512)!} \ ;\ \frac{262144!}{(262144-512)!} \tag{19}\]  
Without \(k\) is computationally costly correctly reconstructed medical image for an attacker, as shown from Equation (19).  
Figure 8: Reconstructed medical images with the random shuffle of mapped vector \(s_{m}\).  
The security of \(k\) is given by the hybrid asymmetric cryptography scheme based on ECC and AES. The secret key used in the proposed scheme to encrypt and decrypt \(k\) consists of 256 bits, and thus the key space is \(2^{256}\). It is a key space sufficiently large to resist brute-force attack. Without the secret key, it is not possible to retrieve \(k\) and thus is not possible reconstructed medical image.
## 1 Introduction  
It is well known that the galactic cosmic-ray spectrum extends over 14 orders of magnitudes in energy and about 12 in intensity, and the detection methods can be divided into two classes. First, there is the \"direct detection\" of the primary cosmic rays in space or at high altitude, which includes experiments on stratospheric balloons, satellites, etc. Second, there is the \"indirect detection\" of secondary particles, namely, the extensive air showers produced by a primary cosmic-ray particle impinging the atmosphere. The first method is more adapted to studying the low-energy portion of the spectrum, while the second one is more suited to investigating the region at high or even ultra-high energies. Moreover, while low-energy particles have been more easily studied in the past, their variability in time (mostly linked to solar activity) is continuously challenging scientists, who are trying to model such variation to asses potential risks for human health and activities on the ground and in space. On the other hand, high-energy particles (linked to a galactic or extra-galactic origin) are more difficult to measure, due to the large sensitive areas required to obtain some statistical significance, but they are somewhat more dangerous and show a lower degree of variation.  
During the last few decades, new experiments with advanced techniques have been looking to unveiling the properties of cosmic radiation, both at low and high energies. In this Special Issue, both direct and indirect measurements are presented, coming from experiments in data collection or already completed data collection. Emphasis is placed on low-energy electrons and protons detected in flight, and during geomagnetic storms. As for indirect detection, the muon flux determination and modulation at ground are studied in great detail. Some of the most interesting results are presented, and a couple of new techniques in cosmic-ray detection reported.
# Mini-Review: Modeling and Performance Analysis of Nanocarbon Interconnects
## 4 All-Carbon 3-D Interconnects  
In general, CNTs grow vertically, whereas graphene is formed horizontally. So, it is natural to develop the 3-D interconnects by combining vertical CNT vias and horizontal GNR interconnects, as shown in Figure 14. [PERSON], et al. [91] firstly conceived, designed and realized the experiment to grow an MWCNT via on multilayer graphene. The critical issue to fabricate such \"all-carbon\" 3-D interconnects is to achieve a low electrical contact between the CNT via and the GNR interconnect. Further, [PERSON], et al. [92] introduced a process to selectively grow CNTs on monolayer graphene. They demonstrated that the growth of CNTs would not damage the integrity of graphene and characterized the contact resistance between CNTs and graphene. [PERSON], et al. [93] studied the CNT-graphene interface using transmission electron microscope and found that C-C bonding exists between CNT and graphene. Recently, [PERSON], et al. [94] comprehensively investigated the fabrication, integration, and reliability of such \"all-carbon\" 3-D interconnects. Besides, it is worth noting that another \"all-carbon\" 3-D interconnect scheme, i.e., a dense vertical and horizontal graphene structure, has been demonstrated in [91].  
Using the finite-element method (FEM), the electrothermal characteristics of all-carbon 3-D interconnects have been studied in [95]. Figure 15a shows the simulation model, which is formed by one horizontal interconnect and two vertical vias. The 3-D interconnect structure is embedded into an interlayer dielectric, whose thermal conductivity is about 0.12 W/m-K. The bottom temperature is assumed as 363 K, and the other boundaries are set as adiabatic. The out-plane electrical conductivity of the MLGNR is 1 S/m. The geometrical parameters are adopted at the 22 nm technology node from the ITRS projection [2]. With a current of 0.4 mA injected, the temperature profiles are plotted in Figure 15b-d. In the simulation, a 1 nm-thick thin plate was used to capture the influences of contact resistance. Due to the impact of quantum contact resistance, all-carbon 3-D interconnect is more resistive than Cu counterpart, thereby increasing the temperature rise. On the contrary, the CNT vias help heat dissipation from hotspots to the bottom layer. Therefore, the maximum temperature is  
Figure 14: Schematic of all-carbon 3-D interconnect.  
Figure 13: (**a**) Schematic of the back propagation neural network. (**b**) Optimal repeater size of MWCNT interconnects obtained using the PSO algorithm and trained neural network [90].  
slightly increased with the implementation of all-carbon 3-D interconnects. The results also imply that CNT vias are more suitable to be placed near bottom layer.
## 2 Data Collection and Pre-Processing
### Basic Statistics and Data Pre-Processing
#### 2.3.2 Data Pre-Processing  
In terms of data mining, data pre-processing is an essential step for improving the performance of analytical results [15; 27]. In particular, the AIS data to be used in this study had to be pre-processed into data suitable for analysis because they contain standard and reception errors due to non-input information from ships [8; 28]. In addition, because of the characteristics of the AIS data interval mentioned above, it was necessary to utilize a pre-processing technique for unit scaling. Therefore, in this study, the data cleaning and scaling methods were applied among the pre-processing methods.  
Data cleaning consists of processing missing values and noisy data to make them suitable for analysis [27]. The AIS error data, which can be called noisy data, were pre-processed through the list-wise delete method [29]. In addition, the entire dataset was divided into arrival and departure categories for analysis. The time when the pilot boarded and disembarked was the basis for the ship's arrival and departure, respectively. Especially outside the port, the ships sailed without regularity such as drifting and anchoring. Therefore, to analyze the pattern of maneuvering in the port, the dataset was pre-processed based on the location of the pilot station, latitude 34.93 N. The result is shown in Figure 5.  
Data scaling is the process by which data units are standardized [20]. When analyzing data, they should be standardized so that no error is transmitted to the analysis result values due to the differences in units [27]. The data in this study all featured different arrival and departure times, and there was a difference in the intervals due to the ship's speed; therefore, the unit was unified for the time series analysis through scaling. In this study, among the data scaling methods, min-max normalization was used. When the time according to position i of each ship was S(\(t_{i}\)), the time correspon  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Status**} & **Todo** & \multicolumn{4}{c}{**Pier**} \\ \cline{3-6}  & **Passing** & **Pier 2 No.4** & **Pier 2 No.5** & **Pier 3 No.1** & **Total** \\ \hline Arrival & Left & 5 & 17 & 1 & 23 \\  & Right & 23 & 4 & - & 27 \\  & Left & 18 & 12 & 1 & 31 \\  & Right & 10 & 9 & - & 19 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Number of times and on which side ships passed Todo.  
Figure 4: Visualization of ship trajectory using AIS data: (**a**) raw data; (**b**) data to use for analysis.  
the section was defined as S(\(t_{min}\)), and the final point was defined as S(\(t_{max}\)). In this case, the equation used in this study for the data scaling was as follows:  
\[MintMax\;time\;normalization=\frac{\text{S}(t_{i})-\text{S}(t_{min})}{\text{S}(t_{ max})-\text{S}(t_{min})} \tag{1}\]