esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Formalin-Induced Paw Licking Test
#### 3.8.3 Histopathology Studies  
In order to ascertain that the formulated ETB-NEG is non-toxic and non-irritant for topical delivery application, a histopathology studies test was conducted. This study is important to confirm its utility for clinical applications. The irritation potential of ETB-NEG was tested in comparison to different samples and controls. The results showed that no severe irritation symptoms such as erythema (redness) and edema (swelling) occurred during 24 h, except for in the reference positive control group II (formalin solution). The anatomy of the ETB-NEG treated rat skin was compared with the untreated groups I, positive control, and blank NEG as shown in Figure 6A-D. The untreated control group I showed an intact and normal structure of skin with two layers; the epidermis and dermis. The epidermal-dermal junction shows many dermal papillae (Figure 6A). The formalin  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Group**} & \multicolumn{2}{c}{**Acute Phase**} & \multicolumn{2}{c}{**Late Phase**} \\ \cline{2-4}  & **Licking Time (s)** & **\% Inhibition** & **Licking Time (s)** & **\% Inhibition** \\ \hline Control & 89.5 \(\pm\) 2.6 \({}^{\#,\#,5}\) & 0.00 \(\pm\) 00 \({}^{\#,\#}\) & 76.5 \(\pm\) 5.4 \({}^{\#,5}\) & 0.00 \(\pm\) 0.0 \({}^{\#,\#}\) \\ \hline ETB gel & 71.8 \(\pm\) 3.1 \({}^{\#,\#,5}\) & 18.01 \(\pm\) 4.7 \({}^{\#,\#,5}\) & 49.8 \(\pm\) 4.8 \({}^{\#,\#,5}\) & 30.51 \(\pm\) 9.8 \({}^{\#,\#,5}\) \\ \hline ETB-NEG & 54.5 \(\pm\) 3.7 \({}^{\#,\#,5}\) & 37.37 \(\pm\) 5.9 \({}^{\#,\#,5}\) & 33.6 \(\pm\) 3.4 \({}^{\#,\#,5}\) & 51.95 \(\pm\) 4.4 \({}^{\#,\#,5}\) \\ \hline Blank NEG & 80.8 \(\pm\) 1.6 \({}^{\#,\#}\) & 7.97 \(\pm\) 2.6 \({}^{\#,\#}\) & 63.3 \(\pm\) 4.0 \({}^{\#,\#,5}\) & 12.65 \(\pm\) 7.0 \({}^{\#,\#}\) \\ \hline \end{tabular} (Statistical inferences: \({}^{\#}\), \(p\) < 0.05, compared to control; \({}^{\dagger}\), \(p\) < 0.05, compared to ETB; \({}^{\#}\), \(p\) < 0.05, compared to blank NEG).  
\end{table}
Table 6: Effects of ETB gel, ETB-NEG, and blank NEG in the formalin-induced paw locking test.  
treated rat skin showed the thickened epidermis (Ep), invaginated keratin layer into the epidermis, and intercellular edema (Figure 6B). The images of the blank NEG group and ETB-NEG groups showed moderately thickened epidermis, and no abnormal changes in the treated rat skin as compared to the control group (Figure 6C,D). Thus, the study results confirmed that the ETB-NEG formulation was non-irritant and safe for topical application. The non-irritancy of NEGs is proved also in previous studies [26].
## 2 Materials and Methods
### Biomechanical Property Studies
#### 2.2.2 Permeability  
Permeability (\(K\)) is expressed by Darcy's Law, meaning the ability of a fluid per unit area to pass through a porous medium per unit time, and is derived from Darcy's Law with the expression (3).  
\[K=-Q\frac{\Delta L}{\Delta P}, \tag{3}\]  
where \(K\) is the permeability in m\({}^{2}\); \(Q\) is the volume velocity through the porous medium in m\({}^{3}\)/s; \(\Delta L\) is the length of the path of the fluid through the porous medium in m; and \(\Delta P\) is the pressure difference of the fluid on the flow path \(\Delta L\) in Pa.  
[PERSON]'s law with the fluid velocity and fluid viscosity of the expression for (4):  
\[K=-\eta\frac{v}{\Delta P}, \tag{4}\]  
where \(\eta\) is the fluid viscosity in Pa-s and \(v\) is the velocity at the inlet of the porous medium in m/s.  
The use of permeability as a design parameter for bone tissue repair scaffolds lies in the fact that it encompasses the influence of multiple factors on the mass transport properties of the scaffold, such as scaffold geometry structure, pore size, and porosity. Permeability is critical for cell growth, and scaffolds with high permeability produce favorable conditions for cell growth [23]. However, too high permeability can also negatively affect the scaffold performance, e.g., leading to lower overall mechanical properties, reduced cell-scaffold interactions, and unfavorable cell attachment to the scaffold [24]. In different articles, the permeability of scaffolds with different structures ranges from 0.018 to 50 (\(\times 10^{-9}\) m\({}^{2}\)), and permeability increases with increasing porosity and scaffold cell size. Permeability may also be related to specific surface area. Research by [PERSON] et al. demonstrated that the fluid permeability of TPMS-structured scaffolds is inversely proportional to the specific surface area of these structures [35]. The existing literature reports that the threshold permeability required for vascularization and mineralization within porous implants is approximately 3 \(\times\) 10\({}^{-11}\) m\({}^{2}\)[36]. Human trabecular bone exhibits permeability ranging from 0.02 to 20 \(\times\) 10\({}^{-9}\) m\({}^{2}\), with an average value of 5.13 \(\times\) 10\({}^{-9}\) m\({}^{2}\)[37; 38]. Consequently, the designed permeability of bone tissue repair scaffolds should be no less than 5.13 \(\times\) 10\({}^{-9}\) m\({}^{2}\).  
It is worth explaining that the nutrients required and metabolites discharged by cells per unit of time are limited, so there is a threshold for the increase in the permeability of bone tissue repair scaffolds, and after exceeding the threshold, the increase in permeability will no longer promote the growth and differentiation of cells. For scaffolds of the same structure, the permeability is determined by the pore diameter and pore spacing. When the pore diameter increases to a certain value, the supply of nutrients reaches saturation, and then, according to the \"curvature-driven mechanism\", scaffolds with smaller pore diameters provide higher mean curvature, thus promoting cell proliferation.
## 4 Discussion  
UAV multispectral technology plays a crucial role in efficiently gathering extensive data on jujube fruit. Multispectral data of jujube fruit show a strong correlation with quality parameters like moisture content and soluble solids content. Previous research on fruit quality often utilized a single relative azimuth angle to collect multispectral data, resulting in low-dimensional input variables for prediction models [24]. For instance, [PERSON] et al. [37] studied the ripeness of Moriche palm fruit using UAV-based multispectral images, tracking changes in reflectance at various wavelengths across the phenological cycle. Similarly, [PERSON] et al. [38] estimated the yield of a single apple tree using UAV multispectral technology, all while focusing on spectral data acquired at a single relative azimuth angle. In contrast, this study collected multispectral data from multiple relative azimuth angles, increasing the dimensionality of input variables and ultimately enhancing the accuracy of the prediction models' results.  
This study employed a UAV multispectral camera to capture multispectral data of jujube fruit at different relative azimuth angles. The purpose was to predict the MC and SSC. The results indicated that the characteristic azimuth angles for predicting MC and SSC were distinct, with 90\({}^{\circ}\) being optimal for the former and 180\({}^{\circ}\) for the latter. Subsequently, the multispectral data based on the characteristic azimuth angle and the multispectral data based on 8 relative azimuth angles were separately used as inputs to detect the MC and SSC of the jujube fruit. A comparison of the two methods showed that the latter method yielded significantly better results. The latter method consistently achieved an R\({}_{\text{c}}\) greater than 0.85 and an R\({}_{\text{p}}\) greater than 0.70. In comparison to previous studies that relied on a single relative orientation angle for the quantitative detection of agricultural products or crops, this method demonstrated a substantial improvement in detection effectiveness. Therefore, it is expected that this method will find valuable applications in the field of quantitative detection of crops or agricultural products in orchard scenes. The optimal relative azimuth angles obtained may vary due to factors such as the placement of jujube fruits and the solar elevation angle. This study demonstrates that data collected under different relative azimuth angles can have varying impacts on the models' accuracy contribution.  
This study also demonstrated that while multispectral data at a single relative azimuth angle did not significantly enhance the accuracy of predicting MC and SSC, utilizing multispectral data at multiple relative azimuth angles simultaneously improved the models. The combined contributions of these angles led to better results. Consequently, in future research, it may be possible to substitute costly hyperspectral cameras with more affordable multispectral cameras for more accurate quantitative detection of crop or agricultural product quality in orchard scenes.
# Benchmark Sea Trials on a 6-Meter Boat Powered by Kite
## 5 Results
### Average Data
#### 5.3.2 Polar Diagrams of the Kiteboat  
The aim of this section is to draw a polar diagram of the kiteboat, associating the true wind angle of the runs with the boat speed. In order to obtain reliable data, the other parameters had to remain as constant as possible. Cases were selected from the experience matrix with this requirement. This meant setting the configuration for: the kite, the tethers length, the daggerboard, the location of the attachment point and the propulsion. The choice of the retained cases among the given parameters was achieved by maximizing the number of runs. Hence, 54 cases were selected fitting these criteria:  
* Kite Cabrinha\({}^{\text{\textregistered}}\) 12 m\({}^{2}\)
* 50 m tethers
* Black daggerboard
* Kite attachment point at 2.68 m
* No Motor  
All selected runs are shown in Figure 14, with the average true wind speed of the run given thanks to the colorbar. In order to have a more consistent plot, by symmetry consideration, true wind angles for all runs are concentrated between 0 and 180\({}^{\circ}\). The port side runs (with negative true wind angle) are, however, denoted with another symbol than the starboard runs. No symmetry issue between port side and starboard runs can be noticed.  
Figure 13: Evolution of the roll (a) and pitch (b) angles for all the runs achieved with the kite only.  
Other choices can be made on the parameter set. Thus, in Figure 15a, the true wind speed is limited to remain within the range from 4.5 to 6 m/s. The impact of the daggerboard choice is highlighted by plotting the runs done with the two types of daggerboard with different markers. Other parameters remain identical to the previous plot. On the one hand, it is immediately visible that the black daggerboards allow runs closer to the wind than the standard daggerboards. On the other hand, when no significant lift is produced, the black daggerboards generate larger drag than the standard set due to their larger wetted surface. The diagram highlights the significant impact of this increase in drag on boat speed during downwind runs.  
Figure 14: Polar of the kiteboat in the most used configuration (Kite Cabrinha® 12 m\({}^{2}\), Tether length 50 m, Black daggerboard, Kite attachment point at 2.68 m, No motor). Port side and starboard runs were set together, but marker type (square or triangle) allows the identification.  
Figure 15: **(a)** Impact of the daggerboard choice on speed performances of the kiteboat according to the true wind angle (true wind speed from 4.5 to 6 m/s). **(b)** Overview of the running resistance curve of the kiteboat built from all runs completed during the trials. The colors are based on the kite used during each race. The black line denotes the best quadratic fitting applied on the entire cloud of points.
# Noise-Free Security Assessment of Eviction Set Construction Algorithms with Randomized Caches
## 7 Random Eviction Last-Level Cache
### Results and Discussion  
To evaluate the resilience of the random eviction strategy against the Prime-Prune-Probe algorithm, we evaluated the success rate of this algorithm as a function of the two parameters \(f\) and \(n\). For this purpose, we modelled a two-level cache memory hierarchy. The size, associativity number and replacement policy of each cache level were the same as in the previous analysis (see Section 5). To provide security against the group testing algorithm, we used a L1 cache with a dynamically random addressing policy and a remapping frequency of 260 K memory accesses. For the last cache level, we enabled the random eviction policy.  
To evaluate the success rate for each parameter of the random eviction strategy, we ran the Prime-Prune-Probe algorithm 100 times. According to the analysis results in Section 6, the size of the candidate set was fixed at 20 K. Thus, the Prime-Prune-Probe algorithm had a probability of 100% of being successful. Figure 12 shows the success rate of this algorithm for different eviction frequencies and different numbers of lines to be evicted. As shown in Figure 12, the success rate was about 0% when the frequency of eviction was low. When the eviction frequency increased, the success rate started to increase. However, a small eviction frequency increased the miss rate, which drastically degraded the performance. Therefore, the eviction frequency and the number of evicted lines should be carefully chosen so as not to compromise security and performance. For example, suppose we evict 16 cache lines per 20 K memory accesses; in that case, the success rate of the Prime-Prune-Probe algorithm would be reduced from 100% to 4%.  
The reason for the decrease in the success rate when using the Prime-Prune-Probe algorithm was that the pruning filter became more aggressive. During the pruning phase, the attacker re-accessed the candidate set in multiple iterations (three iterations at most for a cache with no countermeasures [53]) to eliminate the addresses that evicted others. In this way, the attacker could determine which cache sets were accessed by the victim. However, when the random eviction strategy was enabled, the pruning filter ran many times due to the unexpected evictions that were not caused by cache collisions. In this case, the pruning filter became aggressive and removed congruent addresses from the candidate set. Even when the attacker succeeded in constructing eviction sets and observing that the cache line had been evicted, it could not know whether the line was randomly evicted by the random eviction policy or by the victim's accesses. This prevented the attacker from creating minimal eviction sets.
## 2 Materials and Methods
### Models
#### 2.3.5 Artificial Neural Network  
The bilayer artificial neural network (NN) was applied for gap-filling of the time series. NN models have been utilized by many previous studies of meteorological variable gap-filling [13; 24; 60]. The structure of NN is composed of an input layer, two hidden layers, and an output layer. Each hidden layer has ten nodes (neurons). Additional experiments were performed with a variation of the number of hidden layers and number of neurons. The NN was trained through Bayesian regularization backward propagation. Another validation subset was selected from the initial training dataset to prevent the model overfitting. The validation subset size consisted of 15% from the training dataset.
## 3 Results
### Performance Analysis on Reference Evaptranspiration  
This section is focused on the performances of ET\({}_{0}\) averaged over the entire irrigation season of years 2008-2018. The following subsection will present monthly based results.  
Figure 5 shows the average BIAS and RMSE of ET\({}_{0}\) in the period 2008-2018 for the whole region. The spread of the box plots refers to the variability of the statistical indicators among the 18 AWS sites. ET\({}_{0}\) was computed by, respectively using weather data from kriging interpolation, UERRA MESCAN-SURFEX (UMS) and ERA5-Land (E5L). Figure 5 reveals that ET\({}_{0}\) computed with weather data estimated by kriging is generally more accurate and precise than reanalysis data. However, the differences in ET\({}_{0}\) estimate performances among datasets are negligible: the differences in RMSE between kriging estimator and E5L are on average less than 3% of the mean daily ET\({}_{0}\) and the differences in RMSE between kriging estimator and UMS are on average less than 8% of the mean daily ET\({}_{0}\). The differences among databases are even smaller in terms of BIAS.  
The BIAS of the daily ET\({}_{0}\) computed with kriging interpolated observations varies from \(-0.49\) mm day\({}^{-1}\) to \(0.59\) mm day\({}^{-1}\) with a mean value of \(0.04\) mm day\({}^{-1}\); the RMSE from \(0.32\) mm day\({}^{-1}\) to \(0.86\) mm day\({}^{-1}\) with a mean value of \(0.56\) mm day\({}^{-1}\). The BIAS of the daily ET\({}_{0}\) computed with UMS weather data varies from \(-0.31\) mm day\({}^{-1}\) to \(0.51\) mm day\({}^{-1}\) with a mean value of \(0.08\) mm day\({}^{-1}\); the RMSE from \(0.70\) mm day\({}^{-1}\) to \(1.23\) mm day\({}^{-1}\) with a mean value of \(0.87\) mm day\({}^{-1}\). The BIAS of the daily ET\({}_{0}\) computed with E5L weather data varies from \(-0.53\) mm day\({}^{-1}\) to \(0.55\) mm day\({}^{-1}\) with a mean of \(0.21\) mm day\({}^{-1}\); the RMSE ranges from \(0.44\) mm day\({}^{-1}\) to \(1.04\) mm day\({}^{-1}\) with a mean of \(0.67\) mm day\({}^{-1}\).
## 3 Results
### Heat Transport Simulations
#### 3.2.1 Scenario Descriptions  
The reference scenario (scenario 1) reproduces as far as possible the actual situation as known from the different available data for the three ATES systems. The ATES in Building 1 started operation on 1 March 2014, the ATES in Building 2 started on 1 August 2017, and finally, the ATES in Building 3 started on 1 April 2020.  
The operational characteristics of this last ATES are assumed as foreseen in the first feasibility study [34], leading to a mean power that is unbalanced between the heating and cooling seasons (Table 1). Here, however, the injection temperatures are assumed constant at 7 \({}^{\circ}\)C and 18 \({}^{\circ}\)C for the heating and cooling operations, respectively [34]. Each doublet is assumed to contribute equally (1/5) to the total needed flow rate.  
Other different scenarios can be considered for the heat transport simulations. A second scenario is considered with a 50% increase in the cooling needs in Building 3. The third scenario corresponds to a constant \(\Delta T\) equal to 6 \({}^{\circ}\)C for the ATES in Building 3. Scenarios 4 and 5 consider increased values of thermal conductivity (up to 3 W/m \({}^{\circ}\)K) and hydraulic conductivity (from 1 \(\times\) 10\({}^{-7}\) to 4 \(\times\) 10\({}^{-7}\) m/s), respectively, in the main aquitured between the shallow and the deep aquifers. However, the results for those last scenarios will not be commented on here as no significant changes in the simulated heat plumes and heat interactions were simulated.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & \multicolumn{3}{c}{**Power (4W)**} \\ \cline{2-5}  & \multicolumn{1}{c}{**Heating**} & **Cooling** & \(\Delta T\) (\({}^{\circ}\)C) & **Number of Doublets** \\  & \multicolumn{1}{c}{**(1 October to 31 March)**} & \multicolumn{1}{c}{**(1 April to 30 September)**} & \\ \hline Building 1 & 44 & 86 & 6 & 4 \\ Building 2 & 82 & 79 & 6 & 4 \\ Building 3 & 286 & 285 & \(T_{\text{inf}}\) cst (7 and 18) & 5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mean power extraction by the different ATES systems in Buildings 1, 2 and 3 and the associated assumed \(\Delta T\).
# Joint Channel Estimation Algorithm Based on DFT and DWT
###### Abstract  
Channel estimation is an important component of orthogonal frequency division multiplexing (OFDM) systems. The existence of virtual subcarriers leads to energy spreading in the time-domain when using Inverse Fast Fourier Transform (IFFT), resulting in poor noise reduction by the conventional Discrete Fourier Transform (DFT)-based channel estimation algorithm. To tackle this problem, this paper first proposes a segmental threshold-assisted DFT-based channel estimation algorithm. The key idea is that, by utilizing the distribution characteristics of the channel and the noise components of the channel impulse response in the time-domain, different thresholds for channel estimation under different SNR conditions are set. Compared with the traditional single-threshold DFT-based algorithm, the performance of the proposed algorithm is improved. However, it still has an estimation performance floor under high SNR. Motivated by the fact that the discrete wavelet transform (DWT)-based channel estimation algorithm can achieve better estimation performance under high SNR, we propose a joint channel estimation algorithm based on DFT and DWT, which can achieve dynamic optimal selection of the two estimation methods without any prior information. Simulation results of the Wi-Fi 6 system show that the mean square error (MSE) simulation performance of the joint channel estimation algorithm is close to its theoretical approximation. It achieves the optimal estimation of MSE and BER performance across the entire SNR range compared with the separated DFT-based or DWT-based channel estimation algorithms.  
channel estimation; discrete Fourier transform; discrete wavelet transform; mean square error; Wi-Fi 6
## 3 AR Geography Applications  
The educational AR apps were specially designed, including game-based elements. They were also based on the Greek curriculum for teaching Geography in the fifth and sixth grades. Three AR apps were developed in total: one for the fifth grade and two for the sixth grade. The reason why there are two apps for the sixth grade is that both of them refer to the planet Earth, but the first AR app (see Section 3.2) focuses on the population, density, etc., while the second (see Section 3.3) on the solar system, atmosphere, etc.  
All three AR apps have the same architecture and can be downloaded from the links provided in the section \"Supplementary Material\". More analytically, the pupil has the opportunity to play the app for a better understanding of the educational material with the help and guidance of his/her instructors (teachers). By selecting an app, the instructions are presented briefly, explaining what the pupil must do to complete the game. Then, the camera of the device is activated, and s/he has to place in front of the camera a specific image from the school textbook (Figure 1). After tracking the appropriate image target (image or QR Code), the real image of the textbook is augmented with 3D virtual objects, and the user's AR experience and learning begin by playing the game. Each game differs from the other, offering different functions and capabilities.  
Several technologies were combined in order for the games to successfully function. All three AR apps were developed in the Unity Game Engine [30] using the C# programming language [31]. The Vuforia Engine [32], which is the most used platform for AR development, was also used to track and recognize images in real-time as well as to superimpose 3D virtual objects onto real ones. Some of the assets were imported from the Unity Asset Store [33], while others were created using Blender [34].