esa-sceva/satcom-chunk-collection huggingface dataset:
# Spectrum Slicing for Multiple Access Channels with Heterogeneous Services
## 3 System Model
### Access Model
Finally, based on the allocation in the intermittent and mixed slots, we define the following three subdivisions of OMA and NOMA slicing. We take a slot \(t\) in which the intermittent users can transmit, i.e., any slot in NOMA or one of the intermittent slots in OMA. 1. _TDMA_: The slot is allocated to a single intermittent user, such that \(|\mathcal{A}_{t}\setminus\{u_{B}\}|=1\). 2. _Grouping_: The slot is allocated to \(G\in\{2,\ldots,U_{I}-1\}\) intermittent users s.t. \(|\mathcal{A}_{t}\setminus\{u_{B}\}|=G\) for all \(t\). We consider the case where \((U_{I}\mod G)=0\), i.e., we can divide the intermittent users into groups of equal size. 3. _ALOHA_: All the intermittent users are allowed to transmit in the slot. Hence, \(|\mathcal{A}_{t}\setminus\{u_{B}\}|=\mathcal{U}_{I}\) for all slots, excluding the broadband slots in OMA. The frame structures for the six access schemes resulting from the combining of the slicing and allocation methods described above are illustrated in Figure 2: the circles represent the intermittent users that have access in any given slot, and the color of the square represents the type of access in that slot. We also not that the grouping scheme can be easily extended to cover the two extreme cases in which (i) there is only one group comprising all intermittent users (which is equivalent to ALOHA) and (ii) there is one user per group (which is equivalent to TDMA). Thus, it represents a general scheme which we can apply within OMA or NOMA.
# Efficient and Secure Federated Learning for Financial Applications
###### Abstract  
Conventional machine learning (ML) and deep learning approaches require sharing customers' sensitive information with an external credit bureau to generate a prediction model, thereby increasing the risk of privacy leakage. This poses a significant challenge for financial companies. To address this challenge, federated learning has emerged as a promising approach to protect data privacy. However, the high communication costs associated with federated systems, particularly for large neural networks, can be a bottleneck. To mitigate this issue, it is necessary to limit the number and size of communications for practical training of large neural structures. Gradient sparsification is a technique that has gained increasing attention as a method to reduce communication costs, as it updates only significant gradients and accumulates insignificant gradients locally. However, the secure aggregation framework cannot directly employ gradient sparsification. To overcome this limitation, this article proposes two sparsification methods for reducing the communication costs of federated learning. The first method is a time-varying hierarchical sparsification method for model parameter updates, which addresses the challenge of maintaining model accuracy after a high sparsity ratio. This method can significantly reduce the cost of a single communication. The second method is to apply sparsification to the secure aggregation framework. Specifically, the encryption mask matrix is sparsified to reduce communication costs while protecting privacy. Experiments demonstrate that our method can reduce the upload communication costs to approximately 2.9% to 18.9% of the conventional federated learning algorithm under different non-IID experiment settings when the sparsity rate is 0.01.  
federated learning; gradient sparsification; secure aggregation; optimize communication costs 202313587. [[https://doi.org/10.3390/app1305877](https://doi.org/10.3390/app1305877)]([https://doi.org/10.3390/app1305877](https://doi.org/10.3390/app1305877))
# Investigation of the Dynamic Buckling of Spherical Shell Structures Due to Subsea Collisions
## 3 Dynamic Buckling Criteria
### Relationship between Static Force and Dynamic Force
#### 3.3.5 Parametric Effect of Added Mass  
The simulations have been carried out using the shell radius of 2.0 m, a Poisson ratio of 0.15, the external pressure of 15 MPa, the velocity of 3.0 m/s, and thicknesses of 0.04 m and 0.06 m with different added mass ratios. It should be noticed that the mass ratio can be calculated as Equation (11), where \(M\) is the added mass, and \(M_{0}\) = 35,800 kg. The different mass ratio can simulate the different weights of the submarine.  
\[m=M/M_{0} \tag{11}\]  
Table 10 presents the relationship between forces and mass ratios. It should be noticed that the duration time of contact increases with the incremental increase of the mass ratio. From the regression analysis, it can be obtained that the maximum force and the mean force can be expressed in Equation (12), where the \(k_{M}\), \(C_{M}\), \(km_{M}\), and \(Cm_{M}\) values are the proportional coefficients respective to the mass ratios.  
\[f_{p}=k_{M}\cdot m+C_{M},f_{m}=km_{M}\cdot m+Cm_{M} \tag{12}\]  
Figure 15: Effect of elastic modulus and external hydraulic pressure on forces and impulse. **(a)** Force vs. elastic modulus ratio; **(b)** Force vs. external pressure.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Thickness** & \(k_{e}\) & \(C_{e}\) & \(Km_{e}\) & \(Cm_{e}\) & \(k_{e}/km_{e}\) & \(C_{e}/Cm_{e}\) & **Remark** \\ \hline
0.04 m & 4.2 & 4.0 & 2.4 & 2.4 & 1.75 & 1.67 & Elastic \\
0.06 m & \(-0.4\) & 8.8 & \(-0.26\) & 5.4 & 1.67 & 1.72 & Pressure \\ \hline \hline \end{tabular}
\end{table}
Table 9: The regression analysis into the effect of elastic moduli and external pressure.  
Figure 16a illustrates the relationship between the maximum force and the mean force over a variety of mass ratios, and Figure 16b illustrates the relationship of both forces versus external pressures. It is apparent that both the maximum force and the mean force comply with a linear function, as expressed by Equation (12). In addition, the ratios of \(k_{M}/km_{M}\) and \(C_{M}/Cm_{M}\) are almost the same value of 1.70. It can be observed that in the cases of different added masses, the contact forces have an identical skewness ratio. Furthermore, the ratios of \(k_{M}\) and \(km_{M}\) in different thickness cases of \(t\) = 0.04 meters and \(t\) = 0.06 meters are 0.6 and 0.66 respectively, which are almost identical to the ratios of the corresponding thicknesses. Thus, it can be concluded that Equation (12) is independent of the other parameters.
## 5 Simulation Results and Discussion
### Comparison Results under Varying Temperature  
In this case, using the similar temperature variation of Figure 14, a comparison was made between the proposed AOSM controller and the backstepping controller as displayed in Figure 19. The figure depicts that the proposed controller extracted the maximum power in 0.01 s, which was 0.02 s in the case of the backstepping controller during the changing temperature levels. The solar array system output power comparison of both controllers is shown in Figure 20. The proposed AOSMC transmit maximum power with 97% efficiency to load, and thus outperformed the existing backstepping controller.
# Eigenmode Wavefront Decoupling Algorithm for LC-DM Adaptive Optics Systems
## 3 Materials and Methods
### Selection of the Eigenmode Number  
In general, when the Strehl ratio (SR) reaches 0.8 in an optical system, high-contrast diffraction-limited resolution imaging can be achieved. According to this standard, the SR at the central wavelength of each imaging waveband should reach 0.8 after AO correction. The relationship between the SR and the wavefront residual \(\sigma\) is as follows:  
\[SR=e^{-(k\sigma)^{2}} \tag{20}\]  
\[\sigma=0.075\lambda \tag{21}\]  
For a telescope with an aperture of \(D\) = 2 m, and moderate turbulence of r0 = 10 cm at \(\lambda\) = 550 nm and r0 = 25.5 cm at \(\lambda\) = 1200 nm, when 55 eigenmodes are corrected, the wavefront residual is 0.073 \(\lambda\). For the same parameters of \(D\) = 2 m and r0 = 19.3 cm at \(\lambda\) = 950 nm, 74 eigenmodes must be corrected to make this waveband reach the diffraction limit. In LC-DM cascade AO [13], the LC and DM are responsible for correcting 700-950 and 700-1700 nm wavebands, respectively. When the wavefront residual is <0.075 \(\lambda\), both the bands can achieve diffraction limit resolution. According to the eigenmode number and wavefront residual formula presented in [24], the DM is used to correct only the top 55 eigenmodes to make the infrared waveband reach the diffraction limit, and the LC is employed for secondary correction on the residual wavefront after DM correction. After calculation, the LC must be used to correct the top 69 Zernike modes to attain 0.073 \(\lambda\). Figure 5 presents the simulation of 50 wavefronts for a moderate intensity turbulence with a diameter D of 2 m and an r0 of 10 cm at \(\lambda\) = 550 nm. The proposed wavefront decoupling algorithm is used for simulation. According to the simulation results, the root mean square error (RMS) of residual aberration is approximately 0.09 \(\upmu\)m after the correction of 55 eigenmodes, and 69 Zernike modes are used for residual aberration. After un-decoupling and decoupling correction, the residual errors are approximately 0.05 and 0.03 \(\upmu\)m, respectively. After decoupling, the correction residual error decreases by approximately 0.02 \(\upmu\)m. The decoupled wavefront can significantly improve the LC-DM correction accuracy.
# Assimilation of Earth Observation Data Over Cropland and Grassland Sites into a Simple GPP Model
###### Abstract  
The application of detailed process-oriented simulation models for gross primary production (GPP) estimation is constrained by the scarcity of the data needed for their parametrization. In this manuscript, we present the development and test of the assimilation of Moderate Resolution Imaging Spectroradiometer (MODIS) satellite Normalized Difference Vegetation Index (NDVI) observations into a simple process-based model driven by basic meteorological variables (i.e., global radiation, temperature, precipitation and reference evapotranspiration, all from global circulation models of the European Centre for Medium-Range Weather Forecasts). The model is run at daily time-step using meteorological forcing and provides estimates of GPP and LAI, the latter used to simulate MODIS NDVI though the coupling with the radiative transfer model PROSAIL5B. Modelled GPP is compared with the remote sensing-driven MODIS GPP product (MOD17) and the quality of both estimates are assessed against GPP from European eddy covariance flux sites over crops and grasslands. Model performances in GPP estimation (R\({}^{2}\) = 0.67, RMSE = 2.45 gC m\({}^{-2}\) d\({}^{-1}\), MBE = \(-\)0.16 gC m\({}^{-2}\) d\({}^{-1}\)) were shown to outperform those of MOD17 for the investigated sites (R\({}^{2}\) = 0.53, RMSE = 3.15 gC m\({}^{-2}\) d\({}^{-1}\), MBE = \(-\)1.08 gC m\({}^{-2}\) d\({}^{-1}\)).  
Keywords:gross primary production; crop; grassland; MODIS; data assimilation +
Footnote †: journal:
# Lithographically-Fabricated HA-Incorporated PCL Nanopatterned Patch for Tissue Engineering
## Materials and Methods
### Observation of Cellular Behaviors on EBP-Coated Nanogrooved PCL Patches  
The human-derived dental pulp stem cells (DPSCs) were isolated from the dental tissue of 13 adult patients for treatment purposes (Intellectual Biointerface Engineering Center, Dental Research Institute, College of Dentistry, Seoul National University). The DPSCs were grown in alpha-MEM containing 10% FBS and 1% penicillin-streptomycin (Gibco, Milan, Italy) in the 96-well plate with each sample respectively. After 12 h and 7 days of culture, the DPSCs viability on each sample patches were evaluated using a WST-1 assay kit (Daeillab service, Seoul, Korea). To remove the unattached cells in media, the remained culture media were removed and washed with phosphate-buffered saline (PBS, Gibco, Milan, Italy) before the cell viability measurement. The culture medium and WST-1 solution were mixed at a ratio of 1:10 (WST-1: culture media). The 100 \(\upmu\) of the mixed solution was put into each wellplate. The WST-1 treated samples were incubated at 37 \({}^{\circ}\)C, CO\({}_{2}\) 5%, and 95% RH condition for 1 hour. The supernatant 50 \(\upmu\) of the reaction media was transferred to a new 96-well plate to observe the transmittance at 450 nm. Each group was divided by the absorbance value of F-PCL for normalization.  
After 7 days of culture, cell cytoskeletons were observed under a microscope (Eclipse, Nikon, Japan) through immunofluorescence for \(\beta\)-actin. The morphology of DPSCs on the patches was observed using FE-SEM (Field Emission Scanning Electron Microscope, Supra, Carl Zeiss, Germany), and the specimens were prepared using vapor fixation method [35].  
For osteogenic differentiation studies, the cells were cultured in the osteogenic induction medium (OM) containing 0.1 \(\upmu\)M dexamethasone, 10 mM sodium \(\beta\)-glycerophosphate, and 0.05 mM ascorbic acid-2-phosphate (Sigma-Aldrich, St. Louis, MO, USA). For alkaline phosphatase activity assessment, by using 100 \(\upmu\)L of Triton-X the total protein was extracted from DPSCs cultured on each specimen at 7 days during the period of study. For sedimentation of cell debris, the extracted solution was centrifuged at 10,000 rpm at 4 \({}^{\circ}\)C for 15 mins. After centrifugation, the supernatant was collected and ALP activity was evaluated with an ALP assay kit (SensoLyte pNPP Alkaline Phosphatase assay kit, AnaSpec, Fremont, CA).
# Evolution towards Smart and Software-Defined Internet of Things
## 3 Smart or Cognitive IoT Architecture
### Intelligent WAN Interface  
Non-IP technologies such as RFID, NFC, and Bluetooth have a limited range. Therefore, they can not be used in applications that require a vast region to be monitored by multiple sensor nodes spread over the area. A wireless sensor network (WSN) connects tens to thousands of sensor nodes wirelessly. They collect environmental data and send it to gateway devices that send it to the cloud via the Internet.  
WSN nodes can communicate directly or via multihop. Sensor nodes are limited in power and computational resources, whereas gateway nodes are not. WSNs commonly use star, mesh, and hybrid network topologies for wide area networks (WAN). ML/AI at this interface enables the IoT network to intelligently select WAN protocols and networks as per the requirements of the user.
# Crop Classification for Agricultural Applications in Hyperspectral Remote Sensing Images
## 4 Experimental Results and Analysis
### Discussion
In this work, original HSI data was first converted in discrete cosine transform-based coefficient matrices. The weighted entropy was calculated to quantify each band. Then, top-ranked bands were selected. Finally, SVM was used for classification. Figure 10 shows the crop classification from Indian Pines and Salinas datasets. It can be seen from Figure 10 that the proposed band selection approach is effective in extracting the bands which contain much information about the crops. This is due to the inclusion of the physical properties of light in partitioning the bands and the biological properties of plants in band quantization.
# Positioning Errors Measurement of CNC Machine Tools Based on J-DBB Method
## 3 Positioning Errors Analysis of Machine Tool
### Interference of Detection Devices  
Generally, the errors caused by the interference of detection devices display periodic variations. When the spindle of the machine tool moves in the plane, the errors are decomposed into the positioning errors in the \(x\)-axis direction and that in the \(y\)-axis direction, respectively.  
\[\left\{\begin{array}{l}\delta_{xx}=b_{x}F\sin\theta\sin\left(\frac{2\pi(-x) }{p}+\varphi_{x}\right)\\ \\ \delta_{yy}=b_{y}F\sin\theta\sin\left(\frac{2\pi(-y)}{p}+\varphi_{y}\right) \end{array}\right. \tag{11}\]  
where \(p\) denotes the screw pitch of the ball screw or the grating pitch of the linear grating; \(b_{x}\) and \(b_{y}\) are the ratio of amplitude to velocity; \(\varphi_{x}\) and \(\varphi_{y}\) are the phase angle; \(F\) is the velocity of machine tool spindle, m/s.  
Now, other errors are assumed to be zero, and the error vector \(E\) can be expressed as:  
\[E=\left(-b_{x}F\sin\theta_{3}\sin\left(\frac{2\pi x}{p}-\varphi_{x}\right),b_{ y}F\cos\theta_{3}\sin\left(\frac{2\pi y}{p}-\varphi_{y}\right),0\right) \tag{12}\]  
Substituting Equation (12) into Equation (1), we can obtain the error equation of machine tool caused by the interference of the detection devices:  
\[\Delta R=\frac{F}{2}\sin 2\theta_{3}\left[-b_{x}F\sin\theta_{3}\sin\left(\frac{2 \pi x_{0}\cos\theta_{3}}{p}-\varphi_{x}\right)+b_{y}F\cos\theta_{3}\sin\left( \frac{2\pi x_{0}\sin\theta_{3}}{p}-\varphi_{y}\right)\right] \tag{13}\]
###### Abstract  
Background: Recurrent aphthous stomatitis (RAS) has been recognized as a pathology characterized by ulcerations of the oral mucosa with a wide cross-prevalence in the general population. The aim of the present investigation was to investigate a new gel formulation for the treatment of minor oral ulcers after 2 weeks of treatment. Materials and Methods: A randomized triple-blinded controlled trial was designed for the present purpose. A total of two different study groups was conceptualized: group I (placebo) and group II (active treatment). After the enrolment process, the subjects were included in a domiciliary treatment and evaluated by a blinded operator to clinical scoring and thermography assessment at the baseline, after 1 week, and 2 weeks. Results: A total of 60 patients were observed and treated in the present study. The mean age of the patients was 34.91 \(\pm\) 7.27 years old for a total of 38 females and 22 males. At 1 and 2 weeks, a significant difference in clinical scoring was observed comparing group I and group II (\(p<0.05\)). A significant reduction in pain score was detected in group II patients (\(p<0.05\)) after the topical gel administration. Conclusions: According to the findings of this randomized trial, the gel treatment showed a significant decrease in the signs and symptoms of oral ulcerations and could be considered useful to ameliorate the clinical course of the disease.  
oral ulcers; oral mucosa; recurrent aphthous stomatitis; topical agents Article
## Vehicle Detection
### Experimental Methods and Results: Faster RCNN construction
#### 3.3.2 Detection Performance with Different Scenes  
The standard sample set contains images in different weathers, including 53,535 sunny days, 7125 rainy days, 7888 snowy days, and 181 fog days. This article selects images from some different weather conditions from the standard sample set. The partial data of the standard sample set and the self-made data are combined to form the test set of this paper. In the data set, there are 1272 images on sunny days, 537 images on foggy days, 279 images on snowy days, and 412 images on rainy days. The number of data in different weather conditions is shown in Table 3.  
From the test set, 150 frames of images during the sun, cloudy, foggy and rainy days were selected for testing. The test data is shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Detection Method** &
\begin{tabular}{c} **Candidate Box** \\ **Extraction Method** \\ \end{tabular} & **Feature Extraction Method** & **Classifier** \\ \hline CNN & Sliding window & CNN & SVM \\ RCNN & Selective Search & CNN & SVM \\  & Algorithms & CNN & Softmax \\ Fast RCNN & Region Proposal & CNN & Softmax \\  & Network & CNN & Softmax \\ \hline \hline \end{tabular}
\end{table}
Table 1: Features of the various vehicle detection methods.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detection** & \begin{tabular}{c} **Correct** \\ **Detection** \\ \end{tabular} & \begin{tabular}{c} **False** \\ **Detection** \\ \end{tabular} & \begin{tabular}{c} **Missed** \\ **Detection** \\ \end{tabular} & \
\begin{tabular}{c} **Precision** \\ **Rate/(\%)** \\ \end{tabular} &
\begin{tabular}{c} **Recall** \\ **Rate/(\%)** \\ \end{tabular} \\ \hline CNN & 3136 & 1058 & 806 & 74.8 & 79.6 \\ RCNN & 3581 & 742 & 677 & 82.8 & 84.1 \\ Fast RCNN & 3657 & 606 & 737 & 85.8 & 83.2 \\ Faster RCNN & 4017 & 394 & 589 & 91.1 & 87.2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of vehicle detection methods.  
In different road environments, trained neural networks can better detect vehicles. These include factors like excessive light intensity, complex road background, and excessive distance from preceding vehicle. The vehicle detection results are shown in Figure 4. The correct test results are shown in Figure 4a,b. Figure 4c shows the result of false detection. Figure 4d shows the result of the missed test.
# A Hybrid Genetic Algorithm for Ground Station Scheduling Problems
## 2 Description of Satellite Data Transmission
### Conflict in Ground Resource Utilization  
During the processing of satellite data transmission tasks at ground stations, three primary types of conflict commonly arise:  
* Satellite-to-Ground Visibility Time Conflict (SGVTC);
* Resource Utilization Conflict (RUC);
* Resource Link Conflict (RLC).  
When addressing hardware resource scheduling challenges for satellite data transmission tasks at ground stations, it is crucial to mitigate the occurrence of these three conflicts as much as possible. If avoidance is not feasible, effective strategies must be devised to minimize the number of tasks that need to be aborted or delayed due to these conflicts.
# Accelerometers in Monitoring Systems for Rail Vehicle Applications: A Literature Review
## 1 Introduction
### The Applications of Accelerometers Used in the Industry
#### 1.5.2 Machinery Industry  
In industry, acceleration and vibration data are used to monitor the health of mechanical shafts and equipment bearings such as pumps, turbines, compressors, conveyors, and rollers. To monitor the state of a bearing-shaft system, the authors of [28] demonstrate semi-supervised classification for fault detection using the pattern extraction of multivariate signals. Five accelerometers are used to collect vibration data from the bearing-shaft system to establish the effectiveness of the proposed extraction method.  
In [29], a method based on Symbolic Aggregate Approximation shows promising results regarding the potential use of the methodology to diagnose bearing faults. An Internet-of-things (IoT) system shown in Figure 6 was studied and designed as part of a pump monitoring application presented in [30]. Compressors, like many machines used in factories, generate vibrations and heat while they are running. It is important to keep an eye on their temperatures, because devices called piezoelectric harvesters work best when they are close to room temperature.  
The study [31] herein involves acquiring vibrational data and representing these data graphically as vibration spectra.  
In the paper [32], vibration control is a key challenge in oil and gas facilities using rotating machinery, particularly gas turbines. The rotor can cause instability, requiring measurements in the axial plane of the turbine. The early detection of even minor defects is crucial, as they can lead to significant increases in vibration.  
Figure 5: Schematic model of the harvester used—reprinted from [26].