esa-sceva/satcom-chunk-collection huggingface dataset:
# Microencapsulation of an Extract of _Sechium edule_ (Jacq.) Sw., with Antineoplastic Activity
## 3 Results
### Effect of the Microspheres on HeLa and Lymphocytes  
Both the loaded and the empty microparticles showed inhibition in the proliferation of the cancerous HeLa cell line, with a dose-response relationship without significant effect on the proliferation of human lymphocytes (normal cells), which suggests that they have a certain specificity of action (Figure 3). The empty microspheres exerted a percentage of inhibition of 26 to 87%, while the loaded ones showed inhibition values of 33 to 91% due to the release of the Perla negra extract (Figure 3), without presenting a significant difference, attributed to the lower initial concentration of extract. The statistical mean inhibitory concentration (IC\({}_{50}\)) of the loaded microspheres was 8 ug mL\({}^{-1}\) obtained through linear regression. The analysis (ANOVA) for comparison of means with [PERSON]'s test, applying a (\(p\leq 0.05\)) compared with the control (0 mg mL\({}^{-1}\)), showed a significant difference in the HeLa cell line with the different treatments, while there was no significant difference in lymphocytes with no treatment; therefore, there is no significance (*) in any brown bar (Figure 3).  
Table 1 shows the metabolites that make up the encapsulated extract of cv. Perla negra, corroborating its content in the microspheres. The foregoing suggests its biological activity in therapeutic use.  
\begin{table}
\begin{tabular}{l l l l} \hline
**Metabolite** & **Concentration** & **Flavonoid** & **Concentration** \\ (**Triterpenoid**) & (**\(\mu\)g mg\({}^{-1}\))** & & **(\(\mu\)g mg\({}^{-1}\))** \\ \hline Cucurbitacin B & 137.00 & Rutin & 0.34 \\ \hline Cucurbitacin D & 935.94 & Myricetin & 1.85 \\ \hline Cucurbitacin E & 98.21 & Quercetin & 0.25 \\ \hline Cucurbitacin I & 40.37 & Naringenin & 2.26 \\ \hline  & & Phloretin & 4.72 \\ \hline  & & Galangin & 0.43 \\ \hline  & & Apigenin & 0.18 \\ \hline \end{tabular}
\end{table}
Table 1: Composition of the microencapsulated extract of cv. Perla Negra (_Sechium edule_ (Jacq.) Sw.).  
Figure 2: Release curves of the methanol extract of cv. Perla negra (_Sechium edule_ (Jacq.) Sw.) contained in the microspheres. The bars indicate \(\pm\) standard deviation of \(n=6\). (MEC = loaded microspheres; PBS = phosphate buffer saline).
## Materials and Methods
### Field Verification Test Section
#### 2.3.2 Test Site and Layout  
The experiment was conducted in Qulai Town (114 deg55 degE, 27 deg05 degN) of Ji'an City, Jiangxi Province, China. The test site was a wide field of vision without shelters, and the main spraying area was grassland with a grass height of 5-10 cm. As shown in Figure 5, according to the wind direction and the maximum effective spray width, three parallel 40-m long sampling lines were at a distance of 40 m apart and perpendicular to the wind direction in the spraying area. Each sampling line was marked in the order of \(-20\) to 20 m from west to east (W-E), and the droplet collection card was placed at an interval of 1 m. The sampling point 0 m was on the helicopter route, which is north to south (N-S) parallel to the wind direction. One-way spraying operation (1#-3#) was carried out three times in a sequence of nozzle CP02, CP03, and CP04. The flight height and velocity of all three tests were set as 11 m and 16.7 m/s (60 km/h) according to actual operating experience.  
The experiment used the mass fraction of 5% urea aqueous solution 400 L instead of liquid pesticide for spraying. The droplet collection card, 80 mm \(\times\) 30 mm, was made of water-sensitive paper (WSP) (Chongqing Liuliushanxia Plant Protection Technology Co., Ltd, Chongqing, China), and placed at a height of 30 cm from the ground. Simultaneously, a Kestrel 5500 Link micro meteorological station (Nielsen-Kellerman Co., Boothwyn, PA, USA) was located at a height of 2 m above ground and 40 m far away from the route. The weather data, such as wind velocity, wind direction, temperature, and humidity in the natural environment, were recorded every 2 seconds during the test.  
After each spray application test, the WSPs were immediately gathered and put into marked envelopes and placed in a cool place to be brought back to the laboratory later. Then WSPs were analyzed by using image processing software DepostiScan (USDA. Wooster, MA, USA) to obtain droplet data of each sampling point.
# A Microservice-Based Smart Agriculture System to Detect Animal Intrusion at the Edge
## 6 Evaluation
### Imaging Quality and Animal Identification  
To fully evaluate the performance of the system, we must also consider the ability to identify the pictured animals. If the animal image is not clear in the picture, it will be difficult to identify. This depends on two factors: the number of pixels that the animal occupies in the image and the pixel requirements of animal recognition algorithms listed in Table 5 [28]. We calculate the number of pixels occupied by different animals at different distances between the camera and the animal based on the camera parameters (as shown in Table 1) and present the results in Table 6.  
If the animal is a cow, by comparing these two tables, we can confirm that these widely used models listed can successfully identify animals when the distance between the animal and the camera is 40 m, and we can still use the very effective GoogLeNet and SqueezeNet1_1 when the distance is 80 m. Therefore, to ensure both a large camera coverage to improve the quality of animal imaging and the tolerance of prediction errors, we need to control the camera placement and maximum rotation angle. Specifically, we must ensure that the maximum distance between the camera and the intersection of the coverage boundary and the sensing boundary (i.e., \(D\) in Figure 10) does not exceed 80 m, with 40 m being the optimal distance. This allows us to adopt MobileNet, which can achieve the best performance shown in Table 3. Applying the same strategy to other animals, we need to make similar adjustments to achieve optimal overall performance. This adjustment should be tailored based on the types of animals most commonly found around the field. For instance, if elephants or bisons are the animals causing crop damage, then placing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Distance between Camera**} & \multicolumn{3}{c}{**Accuracy (\%)**} \\ \cline{2-4}  & **Layout A** & **Layout B** & **Layout C** \\ \hline
5 & 66.67\% & 94.44\% & 38.89\% \\ \hline
10 & 100\% & 94.44\% & 94.44\% \\ \hline
15 & 100\% & 94.44\% & 100\% \\ \hline \hline \end{tabular}
\end{table}
Table 4: Camera placement and prediction accuracy.  
Figure 10: Camera placement.  
cameras a little further from the boundary might be effective. On the other hand, if the animals are deer or goats, we need to reduce the distance between the cameras and the boundary, which will require deploying more cameras.
## 2 Materials and Methods
### Data Preparation  
Hyperspectral reflectance measurements that are collected using the EcoSpec system must be preprocessed prior to analysis. For this paper, we prepared the data using parsimonious processing methods to remove outliers and mitigate sun-angle effects to demonstrate the usefulness of measurements collected using the system.  
We first identified outliers in hyperspectral reflectance measurements by applying two criteria that define the allowable range for instantaneous data values (i.e., values greater than 0 and smaller than 0.8) and the allowable change in reflectance values within a limited time period (e.g., absolute change greater than 30% within 30 min). We removed data points that were identified as outliers from the dataset for subsequent processing and analyses. We developed a prototype methodology for minimizing the sun-angle effects on hyperspectral reflectance collected from land surface, known as a bidirectional reflectance distribution function (BRDF). We modeled an overall temporal trend and pattern in reflectance values on each date. Sunny dates had stronger, more symmetric pattern than cloudy dates. We removed the modeled trend and pattern from the reflectance values prior to analyses. To preserve the integrity of the measurements, an independent model was developed for each of the 2150 spectral channels.  
After the measurements were preprocessed as described above, we calculated the average hyperspectral reflectance values over 30 min to match the temporal frequency to that of the meteorological data collected using an eddy covariance method. Using the half-hourly hyperspectral data, we computed the Normalized Difference Vegetation Index (NDVI) [21; 22], Normalized Difference Water Index (NDWI) [23], the Photochemical Reflectance Index (PRI) [24], and Moisture Stress Index (MSI) [25]. We compared the index values with selected flux tower variables during the season to demonstrate the usefulness of the hyperspectral reflectance measurements collected using the EcoSpec system.
## 3 PR Calculation in a GNSS Receiver
### Simulation Experiments  
In this section we investigate which is the effect of the correlation distortions on the PR measurements. In fact it is known that the PRs are measured in scheduled time epochs with a typical rate of 1 Hz (or few Hz), while a DLL computes correlations with a much greater rate (e.g., 50 Hz). Therefore, we expect that a distortion in the correlation may have a different impact if it occurs during the local code update performed by the DLL, or at the epochs of PR measurements. To analyse these different effects we have performed some simulation tests. The simulation scenarios were created by using the signal generator described in [20]. The analyzed datasets represent a static position with four satellites in view all affected by one-reflected ray of MP. The received signal is GPS L1 C/A, it lasts 60 s with different values of Carrier-to-Noise ratio (\(C/N_{0}\)), but constant for the whole duration.  
The dimension of the time windows of the MP events are different, so as to test the impact of the duration on the accuracy reached by the receiver on the PVT stage. The MP model used in the simulation is  
\[s(t)=A_{0}D(t-\tau_{0})C(t-\tau_{0})\cos(2\pi(f_{IF} +f_{d}(t))t+\phi(t))\\ +\sum_{i=1}^{N}A_{i}D(t-\tau_{i})C(t-\tau_{i})\cos(2\pi(f_{IF}+f_{ d_{i}}(t))t+\phi_{i}(t))\]  
where \(A_{0}\) is the signal amplitude of the Line-of-Sight (LOS) and \(A_{i}\) is the amplitude of the reflected rays. The Doppler frequency, \(f_{d}(t)\), is variable in time but, to simplify the simulations, all the Doppler frequencies of the MPs are equal to the Doppler of the LOS (\(f_{d_{i}}(t)=f_{d}(t)\)). The values of the initial phase and delay of the reflected ray are randomly chosen. All the satellites in view are affected by a simulated single ray MP during the time windows indicated in Table 1. The Multipath-to-Signal Ratio (MSR) is the parameter used to assign the MP amplitude. It is defined as the ratio between the power of the MP signal and the power of signal itself. MSR is constant and equal to \(-6\) dB (about half amplitude of the signal) for all the time windows.
## 3 OOF2: 2D Real Microstructure RVE Generation  
An RVE of a typical microstructure with precipitates was created by employing a commercially available meshing program called OOF2, developed by NIST. The OOF2 interface can be accessed online [40], so there is no need for installation. The RVE was generated by utilizing data from an electron backscattered diffraction (EBSD) experiment conducted on a standard aluminum alloy sheet. The experiment was performed using SEM to capture images of the specimen. OOF2 distinguishes the pixel colors of each phase, merges them into a single group, and automatically generates the ABAQUS input file. Figure 7 displays the binary representation of the actual image of the typical aluminum alloy (AA7075-O aluminum alloy at room temperature [41]) at the microscale. This representation was achieved by applying thresholding using ImageJ and the RVE generated in ABAQUS. The length and width of the RVE are in the micrometer scale.  
To provide a clear explanation of how an RVE can be simulated, a flow chart is presented in Figure 8. This figure demonstrates the sequential process of generating the RVE in OOF2 [29].  
Figure 7: Typical results achieved by utilizing OOF2 for creating an RVE in ABAQUS: (**a**) an actual microstructure image [41]; (**b**) the generated RVE within ABAQUS.
## 3 Results and Discussion
### Fourier-Transform Infrared Spectra  
The IR spectra for the proteins included the characteristic bands of amides I, II, A, and B (Figure 3). The bands of amide A (\(\sim\)3300 cm\({}^{-1}\)) and amide B (\(\sim\)3000 cm\({}^{-1}\)) are associated with vibrations of NH stretching, the presence of hydrogen bonds, and the asymmetric stretching of CH\({}_{2}\)[26]. The amide I band (\(\sim\)1600 cm\({}^{-1}\)) represents the stretching vibrations of the carbonyl bond (C=O) from the amide (peptide bond), the amide II band (\(\sim\)1500 cm\({}^{-1}\)) is attributed to the bending vibrations of the N-H bond, and the amide III (\(\sim\)1200-1300 cm\({}^{-1}\)) complex bands result from a mixture of several coordinate displacements [24]. These bands are essential to determine secondary structures because they are in the backbone of polypeptide chains, and their hydrogen bridge interactions give place to different conformations (\(\alpha\)-helix, \(\beta\)-sheet, and random coil).  
The FTIR spectra of the UPG and the CG are depicted in Figure 3. Similar patterns in FTIR spectra, including amides A (3269-3272 cm\({}^{-1}\)), I(1629-1632 cm\({}^{-1}\)), II (1516-1532 cm\({}^{-1}\)), and III (1231-1234 cm\({}^{-1}\)) were observed. As the intensity of the ultrasonic treatment increased, slight displacements (\(\sim\)3 cm\({}^{-1}\)) were shown towards higher wavenumbers in the case of amides A, I, and III; however, the amide II peak for the UPG (1532 cm\({}^{-1}\)) showed a significant displacement (16 cm\({}^{-1}\)) with  
Figure 3: FTIR spectra of tilapia (_Oreochromis niloticus_) skin gelatins that were treated with high intensity ultrasound pulses.  
cm\({}^{-1}\)). These higher wavelength shifts with increasing ultrasound intensity have been related to the loss of ordered structures (unfolding) by an increase in the interruption of intermolecular interactions, which has been associated with more severe extraction conditions for gelatin [27].  
As mentioned before, the amide I and II peaks are the leading indicators of changes in the secondary structure, and it is assumed that the displacement of these bands is a product of the changes and disorder in the conformations of the secondary structures when increasing the intensity of the ultrasonic pulses [10; 25; 27]. These results may be related to the results in the previous section, where it was shown that the different treatments caused significant changes in the proportions of the \(\alpha\)-helix and the \(\beta\)-sheet in the UPG.
# Performance Evaluation of MPTCP on Simultaneous Use of 5G and 4G Networks
## 3 Experimental Setup
### Simulation Environment  
As stated earlier, we want to observe the performance of MPTCP schedulers and CCAs in an environment where the UE is connected to both the 5G and 4G networks. Similarly, we want to see how the MPTCP scheduler and CCAs behave when the network condition (i.e., the signal strength) changes in real-time.  
To simulate such a situation, we consider a scenario where the 5G and 4G base stations are located at the same tower in a non-standalone setup. As shown in Figure 2, the 4G/5G base station is located at the north-mid point of the simulation scenario. Here, the 5G base station offers the 5G mmWave [49] data connection, while the 4G base station offers the 4G Long-Term Evaluation (LTE) [50] connection. To keep the experiments close to practical experiences, we set the uplink and downlink peak rates for 5G as 200 Mbps and 100 Mbps, respectively. Similarly, we considered the uplink and downlink peak rate for 4G as 100 Mbps and 50 Mbps, respectively. The UE moves from the south-west corner of the simulation scenario towards the south-east corner at a 60 km/h speed. There is a house located at the south-mid point of the simulation scenario. At some point in the simulation experiment, while the UE is moving from the south-west corner to the south-east corner, the house comes as an obstacle in the line-of-sight between the base station and UE. As a result, the 5G signal drops when the line-of-sight communication is lost due to the house because of the susceptibility of the 5G signal to obstacles.  
We mainly run two types of tests to observe the total amount of received data during the simulation experiment, throughput, and FCT. For observing the total received data and throughput, iperf data transfer takes place for 20 s. To observe the FCT, we repeatedly send a fixed 10-megabyte data for the 20 s simulation time using iperf and take the average FCT. For further investigation and to keep track on the signal strength received by the UE, we record the SINR received by the UE during the simulation time. Furthermore, with the aim of finding a relation among SINR, throughput, and packet losses, we record the number of sent and received protocol data units (PDUs) by the server (_serTx_) and the UE (_ueRx_) during each second of the simulation experiment, respectively. It should be noted that for MPTCP, PDU refers to MPTCP segments. Then, we calculate the difference (_APPDU_) bysubtracting _serTx_ from _ueRx_. From \(\Delta PDU\), we can infer the received and lost number of MPTCP segments, which can also be regarded as the data packets for the transport layer. A negative and positive value of \(\Delta PDU\) represents the number of lost and recovered PDUs, respectively. For each of the test cases, we run at least 30 experiments and present the average results.
## 2 IMS Bearing Dataset and Signal Analysis Techniques
### Signal Analysis Techniques
As previously anticipated, four powerful and complex cyclostationary techniques have been employed for the analysis of the dataset: (i) Spectral Correlation \(S_{2x}(\alpha,f)\), defined in the spectral frequency (\(f\))-cyclic frequency (\(\alpha\)) domain as the bidimensional Fourier transform of the Instantaneous Autocorrelation Function \(R_{2x}(t,\tau)\); (ii) Cyclic Spectral Coherence \(\text{CSCoh}_{2x}(\alpha,f)\), which is a normalized version of the previous technique; (iii) Improved Envelope Spectrum \(\text{IES}_{\text{CSCoh}}(\alpha)\), obtained as the integral of \(\text{CSCoh}_{2x}\) computed on a limited spectral frequency band \([F_{1},\,F_{2}]\), providing information similar to that provided by the traditional envelope analysis but from a different and unconventional perspective; and (iv) Wigner-Ville Distribution \(\text{WVD}_{2x}(t,f,\phi)\), belonging to the class of the time-frequency domain techniques [38]:  
\[S_{2x}(\alpha,f)=FT_{t\to\alpha}FT_{\tau\to f}\{R_{2x}(t,\tau)\} \tag{15}\]  
\[\text{CSCoh}_{2x}(\alpha,f)=\frac{S_{2x}(\alpha,f)}{\sqrt{S_{2x}(0,f)S_{2x}(0,f-\alpha)}} \tag{16}\]  
\[\text{IES}_{\text{CSCoh}}(\alpha)=\frac{1}{F_{2}-F_{1}}\int_{F_{1}}^{F_{2}}| \text{CSCoh}_{2x}(\alpha,f)|df \tag{17}\]  
\[\text{WVD}_{2x}(t,f,\phi)=FT_{\tau\to f}\{Rs_{2x}(t,\tau)\} \tag{18}\]  
where:  
\[Rs_{2x}(t,\tau)=\int_{-\infty}^{+\infty}x(u+\tau/2)x^{*}(u-\tau/2)\phi(t-u, \tau)du \tag{19}\]  
As for fault prognosis, the suitability of the statistical parameters (excluding [PERSON]'s ones) as metrics exploitable for predicting future signal trends, namely the so-called \"health indicators\" (or \"health indexes\"--HI), was assessed with the computation of the prognostic values of Correlation (Corr), Monotonicity (Mon) and Robustness (Rob), defined as [20]:  
\[\text{Detect}=\text{Act}_{(dB)}-\text{Mob}_{(dB)}+\text{Com}_{(dB)} \tag{11}\]0\[\text{Detect}=\text{Act}_{(dB)}-\text{Mob}_{(dB)}+\text{Com}_{(dB)} \tag{11}\]1  
\[\text{Detect}=\text{Act}_{(dB)}-\text{Mob}_{(dB)}+\text{Com}_{(dB)} \tag{11}\]2  
in which \(X\) is the evaluated quantity (as a statistical parameter), \(T\) is the time domain, Cov is the covariance, \(r\) represents the rank of a vector, composed by the positions of its elements according to a given sorting, and \(\widetilde{}\) is the symbol of the smooth value, obtained with the use of a moving-average filter applied on all the \(k-1\) elements before the \(k\)-th one. Specifically, Correlation (aka Pearson's Correlation) measures the linearity between \(X\) and \(T\) and is expressed by a number included in the interval \([-1,1]\) (where 1 states perfect linearity), Monotonicity (aka Spearman Correlation) is defined on the same range and provides information about the continuity of the increasing or decreasing of the quantity over time, and Robustness is related to the amount of noise that affects the signal, and therefore its value is desirably as low as possible so not to compromise the prognostic ability of the parameter.
# Multi-Focus Microscopy Image Fusion Based on Swin Transformer Architecture
## 2 Related Work  
### Traditional MFIF Method  
Traditional MFIF methods are typically categorized into two main groups: transform domain methods and spatial domain methods [33]. Transform domain methods mainly operate the decomposition coefficient after image transformation, encompassing three key fusion stages: image transformation, coefficient after image transformation, and inverse transformation reconstruction [34]. According to the application of the image transform, transform domain methods can be further classified into multi-scale decomposition (MSD)-based methods (e.g., Laplacian pyramid [14; 35], discrete wavelet transform [36; 37], nonsubsampled contourlet transform [38; 39; 40], neighbor distance filtering [41; 42], empirical mode decomposition [43]), sparse representation (SR)-based methods (orthogonal matching pursuit [44; 45]), gradient domain (GD)-based methods (structure tensor [46; 47]), methods based on other transform (independent component analysis [48], cartoon-texture decomposition [49]) and methods based on the combination of different transforms (curvelet transform and wallet transform [50]). In the spatial domain methods, as the name suggests, source images are fused in the spatial domain based on the spatial features of images,which can be divided into block-based methods [51; 52; 53], region-based methods [8; 54] and pixel-based methods [55; 56].
## 2 Theory and Method
### Construction of Low-Rank Tensor Completion Model
#### 2.2.2 Construction of Low-Rank Tensor Completion Model
The decomposed low-rank tensor completion reduces the time complexity of the low-rank tensor completion algorithm. The nonlinear set CG algorithm based on Riemannian manifold reduces the decomposition of large-scale singular values in low-rank tensor completion. The tensor completion algorithm based on the Douglas-Rachford separation technology further considers the existence of the noise of the source data [25], which greatly enhances the robustness of the model. In addition, low-rank tensor completion also has a series of characteristics such as fast convergence speed and high computational accuracy.