esa-sceva/satcom-chunk-collection huggingface dataset:
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Training Methods
#### 4.5.1 Different Masking Techniques  
To handle irregular masks and fully exploit mask information, the authors of some studies have modified convolutional methods. [PERSON] et al. [58] employed partial convolutions,  
Figure 10: Forward and reverse diffusion models.  
where the convolution is limited to valid pixels, to reduce artifacts caused by distribution discrepancies between masked and unmasked regions. However, this approach can yield corrupted structures when missing areas become extensive and contiguous. Other advancements include gated convolutions [59], lightweight gating [84], and regional convolutions [118]. The generating of masks used to train the inpainting network has also been considered, including random holes [56] and object-shaped masks [83; 84]. According to multiple research findings [49; 50; 51], the specific method used for generating training masks is less critical if the contours of these masks offer sufficient diversity. [PERSON] et al. [119] proposed an encoder-decoder architecture similar to U-net. All convolutional layers are of the gated convolution type, accepting free-form masks, sketches, and colors as inputs. In addition to irregular masks, there is a need to include regular, specialized, and shapes of any other style. To address this limitation, [PERSON] et al. [60] introduced a method for determining the robustness of arbitrary inpainting models using diver masks. They proposed masking convolutions and renormalization to utilize valid pixels more effectively in handling irregular masks. [PERSON] et al. [120] used seven types of mask-generating strategies to randomly produce samples. These generated masks include not only narrow masks but also larger masks, achieving superior results compared to previous strategies. Masks are typically intricate, showing various shapes and sizes at different positions in an image. A single model cannot entirely capture the vast gap between different masks. To tackle this issue, [PERSON] et al. [121] learned to decompose a complex mask region into several basic mask types, using specific type generators to restore the damaged image in a patch-wise manner. The proposed multirelational interaction network (MRIN) model consists of a mask-robust agent and an adaptive patch generation network. The mask-robust agent, which includes a mask selector and a patch locator, creates mask attention maps to select a patch in each layer. The mask-robust agent is trained in a reinforcement learning manner, constructing a sequence of mask-robust processes to learn the optimal inpainting patch route. Then, based on the predicted mask attention map, the adaptive patch generation network restores the selected patches, allowing it to sequentially repair each patch according to its mask type.
# Optical Flow-Based Extraction of Breathing Signal from Cone Beam CT Projections
## 2 Literature Review
### Image-Based Methods  
#### 2.6.1 Fiducial Marker Tracking  
Fiducials are in the form of gold seed markers placed surgically near the tumor site, and are about 2 to 4 mm in diameter. Fiducials help in the better localization of the radiation beam to the target area, and protect the healthy tissues from harmful radiation exposure. One of the essential requirements for accurate monitoring of tumor motion through fiducial tracking is that the fiducial does not migrate from its implanted location over time. [PERSON] et al. [14] investigated fixation rate and displacement of a 1.5-millimeter marker implanted in lung cancer patients. About 75% of the fiducials were identified for the entire treatment process. It was observed that the markers are better attached in bronchial lumens having narrower diameters. The internal marker and tumor attachment suffered significant variation after a period of two weeks. Implantation of metal markers poses certain risks for the patients, such as causing the lung to collapse, known as pneumothorax, spitting of blood (hemoptysis) or haemorrhage [4]. [PERSON] et al. [15] studied the safety aspect of a fiducial marker used during radiotherapy. Out of the 44 markers implanted in lungs of different patients, 20 were observed to have shown pneumothorax. Even though internal markers are effective in localizing the tumor site during radiotherapy, the associated health risks raise the need to shift towards markerless estimation of respiratory motion.
# Review of GaN Thin Film and Nanorod Growth Using Magnetron Sputter Epitaxy
###### Abstract  
Magnetron sputter epitaxy (MSE) offers several advantages compared to alternative GaN epitaxy growth methods, including mature sputtering technology, the possibility for very large area deposition, and low-temperature growth of high-quality electronic-grade GaN. In this article, we review the basics of reactive sputtering for MSE growth of GaN using a liquid Ga target. Various target biasing schemes are discussed, including direct current (DC), radio frequency (RF), pulsed DC, and high-power impulse magnetron sputtering (HiPIMS). Examples are given for MSE-grown GaN thin films with material quality comparable to those grown using alternative methods such as molecular-beam epitaxy (MBE), metal-organic chemical vapor deposition (MOCVD), and hydride vapor phase epitaxy (HVPE). In addition, successful GaN doping and the fabrication of practical devices have been demonstrated. Beyond the planar thin film form, MSE-grown GaN nanorods have also been demonstrated through self-assembled and selective area growth (SAG) method. With better understanding in process physics and improvements in material quality, MSE is expected to become an important technology for the growth of GaN.  
gallium nitride; magnetron sputter epitaxy; sputtering +
Footnote †: journal: Applied sciences
## 4 Discussion
### Data Privacy Attacks and Protections
#### 4.4.2 Data Privacy Protections for Federated Learning
Homomorphic encryption was categorized into fully homomorphic encryption (FHE) and additively homomorphic encryption (AHE) [65]. An FHE scheme is an encryption method that allows analytical functions to be run directly on the encrypted data while producing the same encrypted output as if the functions were executed in plaintext. In other words, if we perform an add or multiply operation on the ciphertext, the decryption result is the same as the actual result obtained by performing the same operation on the plaintext. In comparison, the AHE is an encryption method that allows only one type of operation to be run directly on the encrypted data and produces the same encrypted output as if the functions were executed in plaintext. In other words, the AHE scheme is intended for use with specific applications that require simple addition or multiplication operations. Formally, an encryption method is called homomorphic over an operation \"+\" if it supports Equation (10):  
\[E(w_{1})+E(w_{1})=E(w_{1}+w_{2})\ \forall w_{1},w_{2}\in W \tag{10}\]  
where \(E(\cdot)\) is the encryption method and \(W\) is the machine learning model parameters. For instance, in the AHE scheme, for parameters \(w_{1}\) and \(w_{2}\), one can obtain \(E(w_{1}+w_{2})\) by using \(E(w_{1})\) and \(E(w_{2})\) without knowing \(w_{1}\) and \(w_{2}\) explicitly. Most of the FL studies for healthcare applications leverage the AHE rather than the FHE since FHE is computationally more expensive than AHE. For example, [PERSON] et al. [20] and [PERSON] et al. [21] incorporated the AHE in their local model parameters sharing and gradient aggregation between healthcare nodes and the aggregate server. [PERSON] et al. [51] adopted two AHE schemes for a lightweight privacy module to prevent the patient EMRs' privacy leakage in the medical edge devices.
# Study on Corrosion Monitoring of Reinforced Concrete Based on Longitudinal Guided Ultrasonic Waves
## 3 Finite Element Simulation of Corroded Reinforcement
### Finite Element Model
#### 3.1.2 Effects of Reinforcement Corrugation  
Taking the excitation signal with a frequency of 100 kHz as an example, the time domain signal waveforms at the center node of the rod end face are extracted, as shown in Figure 4, and it is found that the signal waveforms received at the end of the smooth and threaded reinforcement are nearly similar. The corrugation on the threaded reinforcement does not have any significant effect on the GUW propagation property. The wavelength  
Figure 3: Finite element model of reinforcement.  
(45.1 mm) of the excitation signal is much larger than the corrugation spacing (10 mm) on the threaded reinforcement. [PERSON] et al. [30] performed a modal analysis of GUWs for threaded reinforcement and found that when the ratio of the wavelength to the feature size of the corrugation is sufficiently large, the modal features are almost unaffected by the corrugation, and the threaded reinforcement can be equivalently treated as smooth reinforcement. This is consistent with the simplified treatment of [PERSON] et al. [29], who simulated the propagation of GUWs in reinforcement to study debonding. Therefore, the longitudinal GUWs do not interact with corrugation and generate other wave modes or reflection. In view of this, the smooth reinforcement was used for the corrosion study in RC due to ease of finite element modeling, and geometrical and practical considerations.  
Simulation Method for Random Corrosion Pits on Reinforcement Surfaces  
Based on the real morphology, the simulation of corrosion pits is somewhat simplified; that is, the shape of the pits is modeled as a sphere, the position of the pits is uniformly distributed along the surface of the reinforcement, and the depth of the pits obeys a Gaussian distribution. A mathematical model for the probability distribution of the etch pit depth distribution during corrosion was investigated in Ref. [31]. In this paper, the corrosion pit depth is adopted as a Gaussian distribution with average value \(\mu=0.396\) mm and variance \(\sigma^{2}=0.305^{2}\), through drawing on the results of this reference. The relationship (Equation (4)) between the mass loss rate \(\eta\) as a description of the corrosion degree and the pit number \(n\) is as described in Ref. [32]. The mass loss rate of the reinforcement under different conditions is calculated from [PERSON]'s law to determine the number of pits and the average radius of the pits in the finite element model. The code for the random distribution of the pits is written in Python (3.11.15) and imported into ABAQUS (2022) software to produce a geometric reinforcement model with different corrosion degrees, as shown in Figure 5.  
\[\eta=1.625\times\frac{1-e^{-n/224.017}}{D} \tag{4}\]
# Dual-Fuel Combustion for Future Clean and Efficient Compression Ignition Engines
## 3 Paths for Increasing RCCI Efficiency  
During RCCI combustion, the fuel energy is typically apportioned in the ranges shown in Figure 2, where the exact values obtained depend on the specific operating conditions and engine characteristics. The present work investigates the effectiveness of different ways to modify the energy outgoing flow path for minimizing the energy losses, thus providing the guidelines to maximize the efficiency of this combustion concept. The different paths are studied in a progressive way, so that the best solution coming from a previous path was maintained for the following steps.  
The first path studied to increase the RCCI efficiency is the reduction of the losses associated to incomplete combustion. As seen in Figure 2, this source of energy loss is not very relevant if compared to the others, as it only represents the 5% of the input energy in the worst case. However, the incomplete combustion results in very high levels of HC and CO emissions, which can compromise the effectiveness of the aftertreatment systems and other subsystems. As the literature demonstrates, higher combustion losses occur at low loads, where lower in-cylinder pressure and temperature occur [22]. The solution explored in this work to minimize combustion losses is the optimization of the engine settings, because it is the most straightforward solution to manage the in-cylinder reactivity.  
The second way proposed for increasing the gross indicated efficiency is the reduction of the heat transfer losses. In this sense, it is expected that only a portion of the recovered energy will be extracted as additional gross work, while the rest will be merely rejected as thermal exhaust loss due to the higher exhaust temperature. In any case, rising the exhaust temperature is more desirable than rejecting the heat to the coolant, because it contributes to increasing the conversion efficiency of the aftertreatment systems.  
The third path to maximize RCCI efficiency relies on the fuel properties modification to look for a proper in-cylinder fuel reactivity that enhances the combustion propagation. In this sense, several studies confirm that, in order to achieve high efficiency while reducing NOx and soot emissions, the higher portion of the energy should come from the low reactivity fuel [22; 26]. This fact suggests that the low reactivity fuel characteristics and its amount in the blend must have a key role on the in-cylinder reactivity, so this will be the fuel source varied during the investigation.
# The First Result of Relative Positioning and Velocity Estimation Based on CAPS
## 2 CAPS Structure and Mathematical Model of Observations
### Raw Observation Model
This is similar to traditional GNSSs insofar as the carrier-phase observation can be used as a high-precision pseudo-range measurement when the ambiguity is correctly fixed. To provide a more intuitive understanding of the sources of error in CAPS, a brief discussion of the errors in Equations (4) and (5) was given as follows:  
\(t_{u}\)--is related to the receiver clock, and can be absorbed by the parameter of the clock bias during positioning. \(t\)--contains two parts: the model error of \(T\) which is usually smaller than 1 ns [10] and the ephemeris error which is about 2-3 m [3]. This ephemeris error reflected in the measurement is the projection on the vector from the satellite to the user. \(\tau_{iono}\)--is the difference between the ionospheric delay from the satellite to the master control station and the ionospheric delay from the satellite to the user receiver. In addition, for the same propagation path, the ionospheric delay of C1 frequency is about 1/6 of the ionospheric delay of L1 frequency (inversely proportional to the square of the frequency [13]). Therefore, the ionospheric delay error of CAPS is much smaller than that of traditional GNSSs. Figure 4: The calculation of the virtual satellite clock (\(S(x,y,z)\) is the true position of the satellite, and \(S^{\prime}(x^{\prime},y^{\prime},z^{\prime})\) is the calculated position of the satellite with the ephemeris message). \(\tau_{trop}\)--is the difference between the tropospheric delay from the satellite to the master control station and the tropospheric delay from the satellite to the user receiver. The value is related to the location of the user receiver and to the atmospheric conditions. \(\tau_{mul,\rho}\), \(\tau_{mul,\phi}\)--are related to the surroundings of the receiver antenna. For the carrier-phase measurement, the effect of the multipath is much smaller for the C-band than that of the L band signal. The doppler measurement is the difference of the observed frequency of a signal from the CAPS satellite and its corresponding nominal frequency, and this difference is generally the doppler shift caused by the relative motion of the receiver and satellite. The observation equation can be expressed as Equation (6):  
\[f_{d}^{i}=\mathbf{D}\cdot(\mathbf{v}^{i}-\mathbf{v}_{u})+cb+n_{d} \tag{6}\]  
where \(f_{d}^{i}\) is the doppler observation; \(\mathbf{D}\) is the direction vector; \(\mathbf{v}\) is the velocity of the \(i\)th satellite; \(\mathbf{v}_{u}\) is the velocity of the user; \(b\) is the frequency shift of the receiver clock; and \(n_{d}\) is the noise of the doppler measurement. This is a linear equation and the velocity of the user can be directly calculated by the least square when tracking more than four satellites with a good Dilution of Precision (DOP) value.
# Bonding Failures of Lingual Orthodontic Brackets:
## 4 Discussion
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Debonded** & **Not Debonded** & **Total** \\ \hline Anterior & 8 (1.3\%) & 253 (41.6\%) & 261 (42.9\%) \\ Posterior & 38 (6.2\%) & 309 (50.8\%) & 347 (57.1\%) \\ Total & 46 (7.6\%) & 562 (92.4\%) & 608 (100\%) \\ \hline \hline \end{tabular} Values are reported as frequency (percentage); Chi-squared test = 13.25, \(p<0.001\).  
\end{table} 
Table 4: Chi-squared test for debonding frequency between anterior and posterior brackets. the use of different bonding materials and procedures. [PERSON] et al. observed in an in vitro study that the extended resin base of the KommonBase system provided greater values of debonding force and shear bond strength compared to the Incognito system [31]. In another in vitro study, the KommonBase system showed significantly higher shear bond strength than Incognito(tm) brackets [22]. However, the results of the present study suggest that in vivo the KommonBase system is not offering a significant advantage over the Incognito system in terms of bracket failure rate, yet not bringing a disadvantage either. Therefore, the choice between the two systems should not rely on bond strength, the main advantage of the KommonBase system being the possibility to use any standard commercial lingual bracket, combined with an extended anatomic bonding base. Another interesting advantage of the KommonBase system is the possibility to bond each tooth individually if needed, due to the self-positioning base [11; 12; 13]. This can be an advantage in case of major crowding, or when some extractions are planned. However, data are needed about the accuracy of the bonding and re-bonding procedure, and this might be the topic of future studies. Concerning the limitations of the present study, the limited sample size and the retrospective nature of this study did not allow to control some confounding factors, like the dietary habits of the patients. In addition, the two bracket systems were bonded using different materials: although this could be considered as a source of bias, the purpose of this study was not to compare two bonding materials, but the overall clinical performance of a lingual bracket system.
# Quality Monitoring and Analysis of Xinjiang 'Korla'
## 2 Materials and Methods  
### Cold Chain Example  
As Korla is the most important fragrant pear's region of origin in China, and Beijing is one of the main markets for fragrant pear in China, the cold chain from Korla to Beijing was sampled as the experiment chain. Field observation and an interview were conducted to clarify the 'Korla' fragrant pear business flow, and it is shown in Figure 1.  
* Step 1: Harvest of 'Korla' fragrant pears. The test harvest began on 28 August, and the official harvest of fragrant pears began on 2 September, during which the average ambient temperature was 25 \({}^{\circ}\)C in Korla. Farmers pick ripe fragrant pears of which total soluble solids are above 11.0%, then grade them by weight and surface damage, and finally pack them.
* Step 2: Packed fragrant pears are transported through ordinary transport to the cold store for precooling in less than 24 hours. Fragrant pears are deposited in the normal atmosphere without refrigeration because of short-distance transport.
* Step 3: Fragrant pears are precooled in the cold store. To reduce the field heat brought by the harvested pears, enhance the storage quality, and reduce quality deterioration during storage, fragrant pears are usually precooled at 4 \({}^{\circ}\)C for about 48 hours.
* Step 4: Fragrant pears are stored in the cold store with suitable temperature (0 \({}^{\circ}\)C) and relative humidity (90%) until being transported to the market. This stage lasts about three days.
* Step 5: Transport of fragrant pears. In this process, fragrant pears are transported using refrigerated trucks at 4 \({}^{\circ}\)C from Korla to Beijing. This stage lasts about five days.
* Step 6: Sale in the market. After being transported to the market, fragrant pears will go through temporary storage for display and sale, they are neatly placed in the freezer at 10 \({}^{\circ}\)C for customers to choose.
* Step 7: Store fragrant pears at customers' home. If fragrant pears cannot be eaten up in a short time, consumers usually put them in the refrigerator at 5 \({}^{\circ}\)C or preserve them at room temperature.
## 4 Experimental Testing and Result Analysis
### Experimental Evaluation Metrics  
The evaluation metrics for YOLOv8-pose include overall and key-point recall (Recall), precision (Precision), mean Average Precision (mAP), and mAP\({}_{50-95}\)[16]. Recall represents the proportion of the number of correctly detected targets to the total number of actual targets, and precision represents the proportion of correctly detected targets out of all detected targets. mAP requires the calculation of the average precision for each category, that is, the area under the PR curve, and mAP is the average of APs across all categories. mAP\({}_{50-95}\) calculates the average precision at ten different IoU thresholds, from IoU = 0.50 to IoU = 0.95, with a step size of 0.05. In addition, the miss rate (MR) is also considered, which is the proportion of the number of missed targets to the total number of targets.  
The formulas for calculating each metric are as follows:  
* Recall: \[\text{Recall}=\frac{TP}{TP+FN}\] (10)
* Precision: \[\text{Precision}=\frac{TP}{TP+FP}\] (11)
* mAP, mean Average Precision: \[\text{mAP}=\int_{0}^{1}p(r)dr\] (12)
* mAP\({}_{50-95}\): \[\text{mAP}_{50-95}=\frac{1}{10}\text{$\sum_{k=1}^{10}$}AP_{IoU_{k}}\] (13)  
where:  
* TP represents the number of correctly identified targets;
* FP represents the number of incorrectly identified non-targets;
* FN represents the number of actual targets that are incorrectly identified as non-targets [16].
# Physico-Chemical Characterization of Sesame/Rapeseed Oil Mixtures
###### Abstract  
The production of edible plant seed oil used in the food industry is increasing globally. More than 75% of lipids in the human diet come from edible vegetable oils. Among them, sesame oil has the highest resistance to oxidation, valuable physiological properties, and a unique flavor and aroma. However, sesame oil is more expensive than rapeseed oil, and often both oils are mixed to reduce costs. In this study, we performed a physical and physico-chemical analysis of sesame oil and sesame/rapeseed oil mixtures (5/95, 10/90, 30/70, 50/50, 70/30 and 90/10 \(w\)/\(w\)). The investigated oils were characterized based on their fatty acid composition, peroxide value, iodine value, phase transitions, refractive indices, color and UV-Vis adsorption. The fatty acid composition of mixtures made from sesame and rapeseed oils depended on the ratio of the two oils. Increasing the content of sesame oil in the mixtures resulted in a decrease in the levels of oleic and linolenic acids, while the levels of linoleic acid increased. A very good linear correlation was observed between the temperatures and enthalpies of crystallization of the mixtures, which could be used to establish the composition of a mixture between sesame and rapeseed oil. Information about these parameters could increase the possibilities for the commercial use of the investigated oils.  
sesame oil; rapeseed oil; oil mixtures; DSC; UV-Vis; refractive index Article