esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experiments
### Algorithm Validation
However, with the increase of data size, the accuracy rate has declined and has become unstable. The prediction accuracy fluctuates greatly when the number of samples is small. The accuracy rate reaches the lowest point when the number of samples is 600, with the increase in the number of samples, the accuracy rate increases, the accuracy based on M-SVM reaches the maximum when the number of samples is 1200, and then the accuracy rate begins to decline. The GA-SVM model reaches the maximum when the number of samples is 1400, and then the accuracy rate begins to decline. The AC-SVM model gets the best classification accuracy when the number of samples is 800, but then it has poor performance. The accuracy based on the PSO optimization method achieves the maximum value when the number of samples is 1600, and the accuracy is basically better than the GA method. We just used the SVM method and the training set established a model without optimization of the parameter selection. The accuracy of the test data based on this SVM model is shown in Figure 4. With reference to SVM, the GA-SVM and PSO-SVM models are more accurate when the number of samples is large. Figure 4: (a) the accuracy curve obtained by running the test data in the trained model; (b) the time required to build the model as the number of sample changes. Figure 3: (a) the best classification accuracy curve under the final coefficient of variation (CV) condition is the training set based on different parameters’ optimization algorithms; (b) the curve of accuracy rate changes with the data scale by using a model of training data to predict the training set. The model without optimization parameters takes less time to establish and can be neglected. The time curves of M-SVM and AC-SVM are linear, the slopes are small and the growth is slow. As the number of samples increases, the curve of the PSO-SVM method changes faster, and the time is positively correlated with the sample size. The curve of GA-SVM is on the rise, and the pre-growth rate is the fastest among the four methods. With the increase in the number of samples, the time required to establish the model becomes extremely unstable. Overall, the GA-SVM method takes the longest time to calculate.
# Potential of Photovoltaic Generation in the Putumayo Department of Colombia
## 3 Results  
It was found that for the department of Puttumayo and its borders, IDEAM has ten meteorological stations in operation which provide among their different variables, monthly average temperature, and total monthly solar brightness data. Figure 3 shows the location of each meteorological station in the different regions of the department. Table 1 shows the coordinates of each station: the altitude, the average interannual temperature, and the year of the beginning and end of the data series. As can be observed there are data series with 27 or more years, in this work the data from 1985 to 2012 were selected to make the comparison between the different stations in the same time range. It is important to note that the calibration of sensors that measure direct and global radiation, whether pyrheliometers, pyranometers, or actinographs, uses an Absolute Cavity Pyrheliometer of first order precision, certified by the World Reference Center for Solar Radiation (WRR) in Davos, Switzerland, and following ISO standards. Calibration consists of determining the calibration factor K, which adjusts the radiation values to those of the standard reference.
# Prediction and Analysis of Airport Surface Taxi Time: Classification, Features, and Methodology
## 2 Classification of Research Perspectives
### Type of Surface Taxi Time  
As shown in Figure 2, a time mapping diagram for the taxiing phase is shown. Taxi time is defined as the time between the actual off-block time (AOBT)/actual landing time (ALDT) and the actual takeoff time (ATOT)/actual in-block time (AIBT).  
From the perspective of taxi time type, surface taxi time prediction studies can be categorized into two main types: reference time analysis and actual taxi time prediction. Reference time is typically utilized as a reference for calculating additional time in taxi-phase, and the difference between the actual taxi time and the reference time is used to assess airport congestion and aircraft taxiing efficiency.  
Figure 1: Research framework of surface taxi time prediction.  
There are three primary methods for estimating reference time: the FAA APO method, the regression modeling method [12; 13; 14; 15], and the percentile method [16; 17; 18]. The difference between the actual taxi time and the reference time of an aircraft is then the additional time in taxi-phase.  
Furthermore, some scholars have focused on predicting taxi times for arrival and departure aircraft, accounting for additional time in taxi-phase by calculating probability distributions of additional time in taxi-phase and takeoff time [19; 20; 21; 22; 23]. These predictions contribute to a more comprehensive understanding of taxi time variations and enable more effective management of additional time in taxi-phase at airports.
## 1 Introduction  
The age of alluvial deposits on the Atlantic coast of Galicia (NW Spain) (Figure 1) is poorly known, despite the fact that they represent the existence of older continental processes where today marine processes are developing due to sea-level rise during the transgressive episodes of the Late Pleistocene and Holocene. The only available ages correspond to fluvial terraces of the lower course of the Mino River [1] and braided-type deposits of the lower Ulla River [2], both on the south coast, of the Late Pleistocene age, as some of the few examples dated with suitable techniques on the Atlantic coast of northwest Spain and northern Portugal related to coastal fluvial sediments and glacio-eustatic oscillations. It should be mentioned that any regional correlation could include a high contribution of uncertainty associated with netectonic processes since this area has very particular characteristics as it is an extensive plate border with lithospheric uplift [1]--different from the Cantabrian Coast (N Spain) where the European plate subducts under the Iberian plate, showing much higher rates of lithospheric uplift that have been described [3]. For its part, the Atlantic coast of the southern Iberian Peninsula also presents its tectonic peculiarities due to the collision of the African and Iberian plates.  
Regarding the alluvial deposits of the Mero River Basin, some of them are located on the current sea shore, in the innermost of the Ria de Coruna (Figure 1), a wide estuary formed by the flooding of the valley lowlands during the Holocene transgression [4]. Despite this, their chronology is still unknown. Tentative ages were ambiguously assigned from the Plio-Quaternary boundary [5, 6] to the recent Quaternary [7]. Subsequent geomorphological studies on these sediments [4] proposed more recent relative ages by correlating their formation with sea-level oscillations during the Late Pleistocene. In this sense, the relative Holocene age assigned by other authors [8] was completely inconsistent. However,the impossibility of applying suitable dating techniques to these azoic sediments at that time prevented a more reliable age from being assigned.  
Thus, the aim of this work was to obtain, for the first time, a depositional age for eight selected alluvial deposits of the Mero River (Figure 1) by optically stimulated luminescence (OSL) on quartz grains to situate them in a defined palaeo-climatic context.  
Figure 1: Atlantic coast of Galicia (NW Spain) and the alluvial deposits studied in the Mero Basin and Ria de Coruna. S: Metasediments (green area); G: granitoids (blue area). (1, 2... 8): Mero samples, UTM (WGS84) coordinates and relative height (m).
## 3 Proposed Chaos-Based Stream Ciphers Based on Secure PRNGs of Chaotic Sequences
### Performance Analysis of Stream Ciphers Based on Proposed PRNGs-CS
#### 3.4.1 Cryptanalytic Analysis  
To evaluate the security of the proposed chaos-based stream cipher against the most common attacks, we perform key sensitivity and statistical analysis on various encrypted images below.  
**Sensitivity analysis**  
A robust cryptosystem must also be sensitive to secret keys. In other words, a small change to the secret key will generate a completely different ciphered image. The sensitivity is typically determined by two criteria, NPCR (Number of Pixel Change Rate) and UACI (Unified Average Changing Intensity) [39]. We also apply the Hamming Distance (HD), which is measured in bits. In our view, the HD parameter is more accurate than NPCR and UACI criteria. The formulas for these parameters are given below, with \(C_{1}\), and \(C_{2}\) being the two encrypted images of the same original image P.  
\[NPCR=\frac{1}{M\times N\times P}\sum_{i=1}^{M}\sum_{j=1}^{N}\sum_{p=1}^{P}D(i, j,p)\times 100\% \tag{41}\]  
\[D(i,j,p)=\left\{\begin{array}{ll}1&if\ C_{1}(i,j,p)\
eq C_{2}(i,j,p)\\ 0&if\ C_{1}(i,j,p)=C_{2}(i,j,p)\end{array}\right. \tag{42}\]  
where \(M\), \(N\), and \(P\) are the width, height, and plane sizes (for a gray image, \(P=1\); for an RGB color image, \(P=3\)) of \(C_{1}\) and \(C_{2}\).  
The NPCR calculates the proportion of distinct pixel numbers in two encrypted images.  
\[UACI=\frac{1}{M\times N\times P\times 255}\sum_{i=1}^{M}\sum_{j=1}^{N}\sum_{p=1 }^{P}|C_{1}(i,j,p)-C_{2}(i,j,p)|\times 100\% \tag{43}\]  
which computes the average intensity of differences between \(C_{1}\) and \(C_{2}\).  
For a random image, the expected values of NPCR, UACI, and HD are 99.6094%, 33.4635%, and 50%, respectively. We have tested five color images with the same size \(512\times 512\times 3\) and different features as shown in Figure 12.  
Table 8 displays the NPCR, UACI, and HD values obtained for the original images of Lena, Peppers, Baboon, Barbara, and Boats. These findings show that the generated NPCR, UACI, and HD values are extremely near the optimal values. These results exhibit that the proposed chaos-based stream ciphers are highly sensitive to slight cha  
Figure 12: Five test images with same size \(512\times 512\times 3\). (**a**) Lena. (**b**) Peppers. (**c**) Baboon. (**d**) Barbara. (**e**) Boats.
# A Comprehensive Review of In-Body Biomedical Antennas: Design, Challenges and Applications
## 5 In-Body Antenna Applications  
This section explains the range of applications of IBBDs (implantable, ingestible and injectable devices) implemented with different types of in-body antennas.
# Study on the Earth Pressure during Sinking Stage of Super Large Caisson Foundation
## 2 Overview of Changtai Yangtze River Bridge
### Sensor Arrangement  
The earth pressure at the foot blade can directly reflect the stress characteristics at the bottom of the caisson foundation, which is of great significance to analyze, predict and guide the sinking construction of the caisson foundation. For the earth pressure, a total of 119 earth pressure cells were arranged at the bottom of the foot blade, of which 36 sensors were located in the outer bulkhead, 18 sensors in the outer partition wall, 28 sensors in the inner bulkhead and 37 sensors in the inner partition wall. The arrangement of sensors was shown in Figure 5, in which solid dot represent earth pressure sensors. The earth pressure cells were installed on the tread of the foot blade, which was typically shown as Figure 6. Therefore, the total vertical stress can be obtained in real time during soil excavation process.  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \begin{tabular}{c} **Serial** \\ **Number** \\ \end{tabular} & \begin{tabular}{c} **Statum** \\ **Altitude/m** \\ \end{tabular} & \begin{tabular}{c} **Top** \\ **Altitude/m** \\ \end{tabular} & \begin{tabular}{c} **Bottom** \\ **Altitude/m** \\ \end{tabular} & \begin{tabular}{c} **Thickness** \\ **/m** \\ \end{tabular} & \begin{tabular}{c} **Weight** \\ **/(RxN/m\({}^{3}\))** \\ \end{tabular} & \begin{tabular}{c} **Deformation** \\ **Modulus** \\ **/MPa** \\ \end{tabular} & \begin{tabular}{c} **Poisson/s** \\ **Ratio** \\ \end{tabular} & \
\begin{tabular}{c} **Cohesion** \\ **/kPa** \\ \end{tabular} &
\begin{tabular}{c} **Internal Friction** \\ **Angle** \\ **/(\({}^{5}\))** \\ \end{tabular} \\ \hline
1 & Sility clay & \(-18.1\) & \(-22.7\) & \(4.7\) & \(19.4\) & \(8.4\) & \(0.35\) & \(28.3\) & \(11.1\) \\
2 & Loose silt & \(-22.7\) & \(-28.2\) & \(5.5\) & \(20.6\) & \(18.5\) & \(0.27\) & \(5.6\) & \(31.4\) \\
3 & Hard plastic & \(-28.2\) & \(-31.3\) & \(3.1\) & \(20.0\) & \(4.7\) & \(0.32\) & \(41.3\) & \(20.9\) \\
4 & Sility clay & \(-31.3\) & \(-33.2\) & \(1.9\) & \(20.6\) & \(8.1\) & \(0.28\) & \(6.5\) & \(35.4\) \\
5 & \({}^{\text{Sof plastic}}\) & \(-33.2\) & \(-39.7\) & \(6.5\) & \(19.0\) & \(3.8\) & \(0.34\) & \(37.9\) & \(18.1\) \\
6 & Medium dense silt & \(-39.7\) & \(-48.2\) & \(8.5\) & \(20.6\) & \(9.3\) & \(0.26\) & \(5.6\) & \(31.4\) \\
7 & Dense fine sand & \(-48.2\) & \(-53.9\) & \(5.7\) & \(19.8\) & \(15.9\) & \(0.25\) & \(4.7\) & \(35.2\) \\
8 & Dense medium sand & \(-53.9\) & \(-56.3\) & \(2.4\) & \(20.0\) & \(15.9\) & \(0.25\) & \(3.9\) & \(36.5\) \\
9 & Sility clay & \(-56.3\) & \(-64.9\) & \(8.6\) & \(19.0\) & \(10.2\) & \(0.34\) & \(29.1\) & \(10.5\) \\
10 & Dense fine sand & \(-64.9\) & \(-69.7\) & \(4.8\) & \(19.8\) & \(31.8\) & \(0.24\) & \(2.0\) & \(35.3\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Stratum and soil parameters.  
Figure 4: Schematic diagram of underwater slope.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 5 Radar Experiments
### Radar Experiments on a Practical Wind Turbine
Conversely, lower wind rotor speeds result in larger tip clearance to the tower. Figure 17: Cont. Through careful analysis and comparison of the data, it is evident that with increased wind speed and rotor speed, the blades bend inward and the clearance value decreases. The curve of the clearance value and the wind speed clearly shows the inverse trend. Nevertheless, there are certain abrupt points in the measured clearance value, and these can be attributed to inconsistencies in the degree of deformation of the three blades and sudden changes in instantaneous wind speed. Notably, the results of the mmWave circularly polarized radar test, conducted under various weather conditions including sunny, cloudy, overcast, light rain, and thunderstorm, demonstrate its accurate detection of target objects and provision of reliable data. These findings confirm the high interference resistance and adaptability to different weather conditions of the proposed mmWave circularly polarized radar. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Manual Measurement** & **Radar Measurement** \\ \hline Minimum rotor speed & 12 m & 11.534 m \\ (6 RPM) & 5.5 m & 5.85 m \\ \hline Maximum rotor speed & & \\ (12 RPM) & & \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison between the manual and radar measurements at minimum and maximum rotor speed. Figure 17: Test results under different weather conditions. Table 5 compares the proposed mmWave radar with other works, highlighting a longer detection range and the all-weather operation. In contrast to the laser method in [1], the radar in this paper is installed top of the tower, with a longer detection distance and a wider coverage area. In the literature [2], the ground camera sensor is placed 30-40 m away from the wind turbine, which cannot effectively measure the blade-tip clearance under different yaw azimuth. In reference [3], mmWave radars are positioned in the middle of the tower, with a detection range of 5 to 20 m. Furthermore, the installation of up to eight mmWave radars is expensive. In particular, this paper for the first time presents the experiments in various weather conditions including sunny, cloudy, overcast, light rain, and thunderstorm, which are not mentioned in the literature [1; 2; 3].
## 4 Discussion  
In this investigation, the validated CFD model was used to study the airflow and pollutant transport in five cases. This study adopted a simplified manikin geometry model to simulate a passenger. The body profiles of the manikins would affect the airflow pattern and the transport of pollutant in the vicinity of the body [34,35]. However, a high-resolution streamlined body geometry would demand tremendous computing resources. Most investigations [11,14,22,23] adopted simplified manikins for simplicity. In addition, previous investigation [36] found that there was no big difference in the macroscopic airflow patterns between a real-shaped manikin and a simplified manikin. Therefore, this study  
Figure 12: Airflow and normalized pollutant concentration distribution when misaligning the seat rows in Case 5: (**a**) air flow distribution in the cross-section across the heads of passengers in 4A to 4C, (**b**) airflow distribution in the cross-section across the heads of passengers in 4D to 4F, (**c**) comparison of the average pollutant concentration in respiratory zone of passengers in the half cabin opposite to the pollutant source between Cases 1 and 5, (**d**) comparison of the average pollutant concentrations in respiratory zone of the passengers in each row for the pollutant source at 4B, (**e**) airflow distribution in the longitudinal section from seats 3C to 5C in Cases 1 and 5.  
used a rectangular block to represent a passenger. Furthermore, this investigation used the same manikin model for all of the passengers, which was apart from reality. The diversity of passenger geometry, such as the geometry of adults, children of different ages, and even adults with different weights, could be accounted for in future exploration. Note that the passengers in different body geometries could cause different heights in their breathing zones, for which the pollutant concentrations might vary greatly.  
In addition, numerous combinations of seat inclination existed in reality, with an inclined seat at a random position. The remaining seat inclination situations could still be further investigated in the future. Moreover, during the flight multiple passengers are breathing at the same time in the cabin. The passengers with pollutant release may be in random seats. Due to the limitation of computing resources, this investigation considered only three typical situations of seat inclination and focused on the pollutant release from the exhalation of only one passenger.  
In this study, the modeled pollutant concentrations in the half cabin opposite to the pollutant source were adopted to evaluate the cross-contamination. The changes in modeled pollutant concentrations in each row with different seat layouts were used to study the longitudinal transport of pollutants. The results implied that it is crucial to maintain the symmetric seat layout to minimize the cross-transport of pollutants. The asymmetric seat layout either by inclining random seats or misaligning the seat rows would strengthen the cross-contamination in an aircraft cabin.
## 3 End Effector-Based Devices for Gait and Balance Rehabilitation
### Future Research Directions
However, both are based on a unique Stewart platform, and thus the option of guiding each foot independently during gait rehabilitation is not possible. In this context, it could be interesting as a future research line to design end effector-based devices with one footplate for each foot that can provide gait and direct balance rehabilitation independently and simultaneously. This type of device would reduce setup time, since both gait and direct balance functions can be tested with the same machine. In addition to providing more complete gait rehabilitation, the advantage of using two independent footplates for direct balance rehabilitation lies in the possibility of correcting possible asymmetric posture problems and delivering more realistic perturbations to challenge the balance. According to [167], the vast majority of the rehabilitation end effectors that are driven by PKMs are used for ankle motion. Despite ankle rehabilitation being their predominant application, the use of PKMs is also widespread in functional (i.e., balance and gait) rehabilitation. In fact, in this review, 25 (including CAREN) end effectors driven by PKMs were identified. Certain features of PKMs make them attractive for balance and gait rehabilitation end effectors; however, a significant number of prototypes are driven by 6-DOF or redundantly actuated PKMs [136; 151]. In order to take advantage of their features in a cost-effective way, we suggest the use of lower-mobility PKMs and the development of novel lower-mobility PKMs that could generate training movements for balance and gait rehabilitation end effectors. Recently, the authors in [168] proposed a synthesis method to design lower-mobility PKMs with potential for balance rehabilitation. Rehabilitation devices driven by these manipulators could be an interesting option. Most of the selected studies focused on rehabilitation rather than on the assessment of gait and balance function. However, when developing new rehabilitation devices, assessment should be taken into account, in particular the type and location of the assessment tools. In the case of end effectors, sensors can be easily embedded in the footplates and baropodometric meshes or force plates in combination with a motion capture system that can be used to assess the recovery level of the patient in terms of gait and balance function. Instead of using different devices for assessment and rehabilitation, a single device for both rehabilitation and assessment is recommended. This would provide objective data about the state and recovery level of the patient and ease the task of the therapists. In addition, assessment tools are necessary for the proper selection of the rehabilitation mode (active, passive, or hybrid) and therefore to tailor the rehabilitation process according to each patient's need.
## 2 Some Mathematics on Spatial Resolution
### Lateral Spatial Resolution of a Magnetic Field  
To acquire information about the lateral resolution one must use a different method. The lateral spatial resolution relates to the spatial symmetry mentioned above (Section 2.2). The divergence of B is zero, \(\
abla\bar{B}=0\), so B is constant in any volume element \(\Delta\)V = \(\Delta x\times\Delta y\times\Delta z\), (\(\Delta y\cong\Delta s\) in Equation (7)), one can further assume that the spatial resolution is \(\Delta x=\Delta z\), i.e., if the horizontal and vertical divergence (\(\phi_{hor},\phi_{vert}\)) are equal, otherwise, \(\Delta z\
eq\Delta x\) because the vertical divergence is \(\phi_{vert}\
eq\phi_{hor}\). The lateral spatial resolution is directly measured from moving a defined fringe pattern across the detector array, i.e., one measures the convolution of the pattern with the detector array. If \(p=p(x,z)\) is the fringe pattern function and \(D=D(x,z)\) the detector function, and one moves the pattern across the detector \(D\), one measures the \(I=I(x,z)\),  
\[I(x,z)=\int\limits_{-\infty}^{\infty}D(x,z^{\prime})\cdot p(x,z-z^{\prime}) \cdot dz^{\prime} \tag{9}\]  
where \(x\) means one detector row. The convolution is described experimentally below (Section 3.3). Other considerations given Section 2.2 can be adopted for lateral magnetic resolution, too.