esa-sceva/satcom-chunk-collection huggingface dataset:
# Novel Generalized Three-Step Phase-Shifting Interferometry with a Slight-Tilt Reference
## 3 Computer Simulation and Optical Experiments
### Optical Experiment  
Optical experiments have also been carried out with the optical setup shown in Figure 5 with a 532 nm laser, and the target object is a USAF resolution target. The CCD installed has a recording chip with a resolution of 1392 by 1040 pixels, and the pixel size is \(6.45\times 6.45\)\(\upmu\)m.  
Figure 4: Phase distribution across the center of the zones of the object wave, (**a**) original phase distribution and (**b**) reconstructed phase distribution.  
Figure 5: Three-step generalized phase-shifting interferometry (TGPSI) recording setup with a slight-tilt reference, where the dotted lines illustrate the tilted reference actually used. A piezoelectric transducer (PZT) is used as a phase shifter.  
Figure 3: Simulation results: (**a**) intensity of the original object wave; (**b**) intensity of reconstructed object wave; (**c**) original phase distribution; (**d**) reconstructed phase distribution.  
In our experiment, the phase shift is generated by a phase shifter. The results are illustrated in Figure 6. The three interferograms with unknown phase shifts are shown in Figure 6a-c. Figure 6d is the spectrum of Figure 6a, where the center part is enlarged and shown in the circle. We have used the spectrum of \(I_{1}\) instead of \(I_{0}\) to locate the coordinates \((-u_{p},-v_{p})\) and \((u_{p},v_{p})\) and they are found to be \((-34,0)\) and \((34,0)\). The reason is that there are two bright spots in the spectrum already. The enlarged portion of the spectrum clearly illustrates the two bright spots. Subsequently, the reference slight tilt angle can be calculated by Equation (23) and the result is about \(\theta_{x}\) = 0.1 degree in the experiment. Phase shifts are calculated by using the brightest point at these locations on the three Fourier spectra of holograms \(I_{1}\), \(I_{2}\) and \(I_{3}\). The phase shifts calculated using the proposed method are 0.5211 and 1.8018 rad. Figure 6e is the intensity distribution of the reconstructed image, and the reconstructing distance is 10.9 cm. Both the accuracy of the phase shift extraction and the quality of the reconstructed image are excellent.
# D-Limonene: Promising and Sustainable Natural Bioactive Compound
## 4 Antimicrobial Mechanism of D-Limonene
### Antibacterial and Antifungal Mechanism of D-Limonene
#### 4.1.3 Disturbance of Energy Metabolism  
Moreover, modulations of energy metabolism pathways are also ways for D-limonene to inhibit microorganism growth. According to transcriptomics studies, D-limonene at a MIC of 20 uL/mL interfered with the transcription of key enzyme genes of the glycolysis pathway, tricarboxylic acid cycle, and oxidative phosphorylation pathway in _C. tropicalis_[118] and, in another study, the ATP concentration of _L. monocytogenes_ decreased significantly after exposure to D-limonene, the activity of the respiratory chain  
Figure 2: The antimicrobial mechanisms of D-limonene.  
I-V complex decreased, and the protein units were downregulated, which indicated that D-limonene could inhibit the synthesis of the respiratory chain complex proteins in _L. monocytogenes_, interfering with its respiratory function and energy metabolism, which led to the death of the cells [79]. D-limonene can also interfere with the synthesis of cellular ATP by inhibiting the respiratory complex and the ATPase in _C. albicans_, thereby affecting the respiratory intensity and energy metabolism [114].
## 3 Results and Discussion  
### Microarray  
An exploratory protein microarray study, which is a high-throughput ELISA based antibody array that gives qualitative/semi-quantitative protein expression profiling, was conducted. The tear fluid and blood plasma samples were prepared and probed with an Explorer Antibody Array and a Cancer Biomarker Array. We chose the explorer array and cancer array to aid in understanding the overall protein expression levels and one that is associated with a systemic disease. As depicted in Figure 1, the Explorer array identified 121 proteins to have a positive signal in which 71 proteins were commonly expressed across both the tears and plasma samples. They also showed 30 proteins to be uniquely expressed in tears and 20 proteins in plasma. In the cancer biomarker array, 62 proteins had a positive signal with 49 proteins commonly identified in both samples with an additional seven proteins expressed specifically in tears and six proteins unique to plasma. Proteins detected in both tears and plasma samples from the microarray panels are listed in Table 1.  
Appendix A, Table 1 lists the proteins identified unique to the two sample types. Figure 1: Microarray data analyzed for common protein expression in tears and plasma: (**a**) Explorer biomarker array, and (**b**) Cancer biomarker array. The proteins found unique to tears were further analyzed using the Panther Database and resulted in 28 pathway hits. These results revealed that the expressed tear proteins
# Application of Augmented Reality in Waterway Traffic Management Using Sparse Spatiotemporal Data
## 4 Methodology
### Ship Target Detection Under Low Visibility Conditions
#### 4.2.1 Ship Detection Under Foggy Conditions  
Under foggy conditions, reduced maritime visibility significantly degrades the accuracy of the target detection algorithm. To mitigate this issue, we employ image enhancement techniques based on the dark channel prior and guided filtering to enhance vessel image clarity.  
The dark channel prior is an image restoration method based on the atmospheric scattering model, designed to enhance image quality. For any given input image, \(J\), its dark channel is defined as follows:  
\[J^{dark}(x)=\min_{y\in\Omega(x)}\ [\min_{ce\{r,g,b\}}J^{c}(y)] \tag{10}\]  
\[J^{dark}\rightarrow\ 0 \tag{11}\]  
Here, \(J\) represents each channel of the color image, and \(\Omega(x)\) denotes a window centered at pixel x. In computer vision and computer graphics, the atmospheric scattering model is widely used to describe foggy images, necessitating pixel-wise image modeling:  
\[I(x)=J(x)\ t(x)+A\ (1\ -t(x)) \tag{12}\]  
Here, \(I(x)\) represents the observed foggy image, \(J(x)\) is the original, undistorted image, \(t(x)\) is the transmission, which indicates the proportion of light passing through the fog, and \(A\) denotes the atmospheric light, representing light pollution in a foggy environment. The brightest part of the foggy image is typically the atmospheric light. Next, we aim to estimate \(J(x)\) based on the known value of \(I(x)\). The formula is then transformed to derive Equation (13):  
\[\frac{I^{c}(x)}{A^{c}}=\frac{J^{c}(x)}{A^{c}}t(x)+1\ -t(x) \tag{13}\]  
Suppose that the transmission, \(t(x)\), of each window is constant, denoted as \(\widetilde{t}(x)\), and the value of \(A\) is given. By applying two minimum operations on both sides of the equation, we obtain Equation (14):  
\[\min_{y\in\Omega(x)}\left(\min_{c}\frac{I^{c}(y)}{A^{c}}\right)=\widetilde{t} (x)_{y\in\Omega(x)}^{\min}\left(\min_{c}\widetilde{t}(y)\right)+1\ -\widetilde{t}(x) \tag{14}\]  
Based on the dark channel prior theory, the following can be derived:  
\[\min_{y\in\Omega(x)}\left(\min_{c}\frac{I^{c}(y)}{A^{c}}\right)=0 \tag{15}\]  
Substituting Equation (14) into Equation (13) yields the atmospheric transmission \(\widetilde{t}(x)\).  
\[\widetilde{t}(x)=1\ -\min_{y\in\Omega(x)}\left(\min_{c}\frac{I^{c}(y)}{A^{c}}\right) \tag{16}\]  
With the image's depth under consideration, a parameter, \(\lambda\), is introduced to correct for this. The estimation of transmission is as follows:  
\[\widetilde{t}(x)=1\ -\lambda_{y\in\Omega(x)}^{\min}\left(\min_{c}\frac{I^{c}(y)} {A^{c}}\right) \tag{17}\]If the transmission value is too small, it can lead to an overestimation of J. To address this, a threshold, \(T_{0}\), is set to ignore the minimum transmission values. When \(t(x)<T_{0}\), we set \(t(x)=T_{0}\). The final image restoration dehazing formula is then:  
\[J(x)=\frac{I(x)-A}{max(t(x),t_{0})}+A \tag{18}\]
## 5 Conclusions  
This paper presents a DL-based convolutional neural network (DCNN) prediction model for the recognition of wafer map failure types based on the real-time wafer map image dataset. The performance of the proposed prediction model was compared with three state-of-the-art ML-based prediction models--LR, RF, GBDT--and several well-known DL models--VGGNet, ResNet, and EfficientNet. Consequently, our findings indicated that the proposed prediction model achieved the best performance among the selected popular ML- and DL-based models. In addition, the proposed prediction model outperformed the other models with fewer or much deeper layers. However, the 'Local' failure pattern type was recognized less accurately than other failure pattern types. Therefore, we will consider generating a more robust model using more image datasets and removing the noise in the dataset for experiments in the future. Moreover, the rule-based fuzzy classifier [50; 51] has a unique advantage with respect to interpreting the classification, which can be considered in future work, because the ML- and DL-based techniques have a weakness with respect to interpretability.
# Pattern Recognition in Dried Milk Droplets Using Lacunarity and Deep Learning
## 3 Results  
### Pattern in Dried Droplets of Milk with NaCl  
Figure 2 displays the dried deposits of whole milk (a) and lactose-free milk (b) at varying concentrations of NaCl (\(\phi_{N}\) = 0-4 wt%). At \(\phi_{N}\) = 0%, both types of milk exhibit a well-defined coffee ring with a homogeneous central region. As the NaCl concentration increases to \(\phi_{N}=2\) wt%, a noticeable thickening of the coffee ring occurs, accompanied by the formation of amorphous crystalline structures in the central area of the deposits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\phi\) (wt%) & **Water (\%)** & **Number of Images** & **Number of ROIs** \\ \hline \multirow{4}{*}{0} & 0 & 120 & 7268 \\ \cline{2-4}  & 20 & 120 & 5209 \\ \cline{2-4}  & 40 & 120 & 6341 \\ \cline{2-4}  & 60 & 120 & 8003 \\ \cline{2-4}  & 80 & 120 & 5209 \\ \hline \multirow{4}{*}{2} & 0 & 136 & 14,393 \\ \cline{2-4}  & 20 & 128 & 8535 \\ \cline{2-4}  & 40 & 136 & 8058 \\ \cline{2-4}  & 60 & 108 & 5782 \\ \cline{2-4}  & 80 & 128 & 8269 \\ \hline \multirow{4}{*}{4} & 0 & 148 & 18,028 \\ \cline{2-4}  & 20 & 148 & 16,038 \\ \cline{2-4}  & 40 & 112 & 16,388 \\ \cline{2-4}  & 60 & 136 & 19,510 \\ \cline{2-4}  & 80 & 108 & 15,406 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Number of images acquired and ROIs selected for whole milk samples at different \(\phi\) values and water concentrations.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \(\phi\) (wt%) & **Water (\%)** & **Number of Images** & **Number of ROIs** \\ \hline \multirow{4}{*}{0} & 0 & 124 & 10,025 \\ \cline{2-4}  & 20 & 100 & 6416 \\ \cline{2-4}  & 40 & 124 & 7137 \\ \cline{2-4}  & 60 & 84 & 4955 \\ \cline{2-4}  & 80 & 100 & 5403 \\ \hline \multirow{4}{*}{2} & 0 & 132 & 9825 \\ \cline{2-4}  & 20 & 80 & 6839 \\ \cline{2-4}  & 40 & 132 & 9924 \\ \cline{2-4}  & 60 & 120 & 7912 \\ \cline{2-4}  & 80 & 120 & 13,992 \\ \hline \multirow{4}{*}{4} & 0 & 140 & 16,647 \\ \cline{2-4}  & 20 & 84 & 10,238 \\ \cline{2-4}  & 40 & 100 & 10,194 \\ \cline{2-4}  & 60 & 140 & 18,054 \\ \cline{2-4}  & 80 & 120 & 16,021 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Number of images acquired and ROIs selected for lactose-free milk samples at different \(\phi\) values and water concentrations.  
In lactose-free milk, an intermediate homogeneous region is observed between the coffee ring and the amorphous crystalline structures, which is less pronounced in whole milk. At \(\phi_{N}=4\%\), the deposits show well-defined coffee rings and prominently structured cross-shaped crystalline formations that cover the entire deposit surface. Notably, the crystalline structure density is more pronounced in the whole milk deposits compared to the lactose-free samples.
###### Abstract  
Although approximately one-tenth of the world's population lives near volcanoes, most of the 1500 active volcanoes are not monitored by ground-based instruments because of the cost and difficulty of access. Since the development of interferometric synthetic aperture radar (InSAR) in the 1990s, recent advances have allowed the near-real-time detection of surface deformations, one of the earliest markers of volcanic activity. According to the Global Volcanism Program, Turkey's most recent eruption (involving gas and ash) occurred in the Tendurek volcano in 1885. An explosion in the Tendurek volcano, which continues to actively output gas and steam, would be a critical issue for the life and property of the people living nearby. In this context, we processed the Sentinel-1 data collected by the European Space Agency using the Stanford Method for Persistent Scatterers, and the surface deformations of the Tendurek volcano were investigated. In addition, we applied two different atmospheric correction approaches (linear phase-based tropospheric correction and the Generic Atmospheric Correction Online Service for InSAR) to reduce atmospheric effects and found that the linear phase-based tropospheric correction model produced lower standard deviation values. Subsequently, the mean deformation velocity maps, displacement time series, and deformation components in the line-of-sight direction were calculated. The results showed that the most severe subsidence was \(-11\) mm/yr on the upper slopes of the Tendurek volcano. Although the lower slopes of the subsidence region have a lower settlement rate, the subsidence has a peak-caldera-centered location.  
Sentinel-1; Stanford method for persistent scatterers; TRAIN; GACOS; Tendurek volcano 1
# Caching Placement Optimization Strategy Based on Comprehensive Utility in Edge Computing
## 2 Related Work
### The Cache Placement Overhead for Data Blocks
#### 3.2.2 The Placement Cost of Data Blocks  
To place the data blocks, placement cost is an important factor that should be considered. Placement cost depends on several factors, including placement time, the bandwidth associated with the transmission of the data block from the edge server to the user, and the load on the edge server's resources, where the hit rate of the cached data blocks on the server can be expressed as:  
\[hit_{i,j}=\frac{h_{i,j}}{request_{i,j}} \tag{12}\]  
In Equation (12), \(request_{i,j}\) illustrates the total amount of times from the edge server and \(h_{i,j}\) represents the number of times that the request for a data block was fulfilled by the user.  
The requested rate of users requesting data blocks from the edge server \(rate_{i,j}\) can be expressed as:  
\[rate_{i,j}=\frac{V_{i,j}}{Vall_{j}} \tag{13}\]In Equation (13), \(V_{i,j}\) represents the frequency of users accessing the data block and \(Vall_{j}\) denotes the total amount of requests for that data block from the server by the user. The bandwidth cost \(BC\) by edge servers transmitting data to users can be represented as:  
\[BC=\alpha\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}s*hit_{i,j}*rate_{i,j}*d*tbc*x _{i,j} \tag{14}\]  
In Equation (14), \(tbc\) shows the bandwidth cost of transmitting 1 bit of data over the unit distance. \(d\) represents the average distance from the users to the edge servers and \(x_{i,j}\) expresses the placement scheme of data blocks.  
To alleviate the load on the edge server, the resource utilization load is taken into account. \(sc_{j}\) indicates the load on resource utilization caused by placing a data block on the edge server, that is, the memory and CPU usage load during the cache storage. Thus, the resource utilization load \(UL\) of placing the set of data blocks being cached can be expressed in Equation (15).  
\[UL=\beta\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}s*sc_{j}*x_{i,j} \tag{15}\]  
The placing of data blocks on the edge server \(j\) is based on their physical location \(j\) about the caching server; thus, the placement time cost \(TC\) of placing the set of data blocks being cached can be shown as:  
\[TC=\gamma\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}\frac{s}{b_{j\mid j}}*x_{i,j} \tag{16}\]  
In Equation (17), \(\alpha,\beta,\gamma\) are non-negative weights for the bandwidth, the placement time, and server resource utilization load, respectively, \(\alpha+\beta+\gamma=1\).  
\[SC_{i}^{j}=(\alpha*s*hit_{i,j}*rate_{i,j}*d*tbc)+(\beta*s*sc_{j})+(\gamma*\frac{ s}{b_{j\mid j}}) \tag{17}\]  
Then, the placement cost \(C_{storage}\) can be shown as:  
\[C_{storage}=BC+UL+TC=x_{i,j}*\sum\limits_{i=1}^{n}\sum\limits_{j=1}^{m}SC_{i} ^{j} \tag{18}\]
# Structural Optimization of MicroMED Dust Analyzer
###### Abstract  
This research work describes the structural optimization of the MicroMED Dust Analyzer, an Optical Particle Counter developed for the ESA ExoMars 2022 mission. Topology Optimization, a non-conventional design technique was adopted to obtain a lighter component, a valuable achievement for aerospace and space scientific instruments design. In particular, two solutions for the instrument optical bench were proposed and assessed relying on a classical finite element approach, comparing the improved performance with the current design. The optimization outcome proved the adopted design workflow robustness and provided promising results in view of a possible mechanical design enhancement of the MicroMED Dust Analyzer instrument. Indeed, a mass budget saving of about 55% of the considered design domain was achieved, and the dynamic behaviour of the optical bench was improved by up to 50% of the first natural frequency value. Finally, a mockup of the lightened optical bench was manufactured, and the redesign effectiveness was proven by comparing the numerical mechanical resonances with the ones obtained experimentally. An error smaller than 5% was found on the first natural frequency, validating the performed optimization approach.  
MicroMED Dust Analyzer, ExoMars 2022; optical particle counters; topology optimization; thermomechanical design +
Footnote †: journal:  
_Applied_  
_sciences_
# Cyber-Security Challenges in Aviation Industry: A Review of Current and Future Trends
## 4 Cyber-Attack Surfaces and Vulnerabilities in the Civil Aviation Industry
### Aerospace and Avionic Systems
#### 4.1.2 Automatic Dependent Surveillance-Broadcast (ADS-B)  
Aircraft automatically transmit (ADS-B Out) and/or receive (ADS-B In) identification and positional data in a broadcast mode through a data link using Automatic Dependent Surveillance Broadcast (ADS-B), improving the safety and capacity of airport surveillance and thus enhancing situational awareness of airborne and ground surveillance in airports [67]. [PERSON] et al. [68] state that ADS-B Out provides a range of ground applications support, including Air Traffic Control (ATC) surveillance in both radar and non-radar airspace over the airport, as well as enabling enhanced surveillance applications through links to aircraft in order to receive ADS-B Out messages from other aircraft within their coverage (ADS-B In) areas. The integrity and availability of the ADS-B system is paramount as a result of its role in supporting key ground and airborne applications [69]. Furthermore, [PERSON] and [PERSON] in [70] stated that ADS-B employing global satellite navigation systems generates precise airspace mappings for air traffic management. Thus, the security of ADS-B has become a major concern as the system broadcasts detailed information about aircraft, their positions, velocities, and other data over unencrypted data links.  
[PERSON] [71] analysed the performance of ADS-B data received from Grand Fork International Airport. The data were in raw and archived Global Data Link (GDL-90) format. GDL-90 is designed to transmit, receive and decode ADS-B messages through an on-board data link by combining GPS satellite navigation with data link communications. The aim was to detect anomalies in the data and, in turn, quantify the associated risk. In the course of the research, dropout, low-confidence data, message loss, data jump, and altitude discrepancy were identified as anomalies, but the focus was on two of them, dropouts and altitude deviations. The conclusion drawn was that all failures relating to the anomalies have the potential of affecting ATC operation either from an airspace perspective, such as dropout, low-confidence data or from an aircraft perspective, such as data jump, partial message loss and altitude discrepancy. All are surfaces which an attacker can leverage to execute attacks such as eavesdropping, jamming, message injection, deletion and modification [70; 72].
# Reinforcement Learning Based Resource Management for Network Slicing
## 3 System Model
### Service and Traffic Model
The remaining resources are equally shared among the elastic flows as follows:  
\[r_{t}^{e}=\frac{R_{n,t}-\sum_{i=1}^{|I|}r_{t}^{i}}{|E|}, \tag{3}\]  
where \(r_{t}^{e}\) denotes the data rate consumed by elastic flow \(e\) at \(t\). Both inelastic and elastic traffic occur following a Poisson process, and the flows' lifetime follows exponential distribution in our system model.
## 4 Discussion
### Effect of Solar Pyrolysis Temperature on Biochar Production
The molar ratio of H/C can be used as an indicator of the carbonization of different biochar. Feedstock has the highest H/C because H is principally essential to the organic carbon of plants. The H/C ratio was less than one in the case of SB-2 and SB-3, meaning that SB-2 and SB-3 were carbonized significantly better than SB-1. Both biochar types showed a great formation of aromatic compounds. In addition, different decrements in this ratio of various biochar types compared to feedstock indicate that the pyrolysis condition is different [26]. During the pyrolysis process, the deprotonation of functional groups creates a negative charge on the surfaces of the biochar particles, which can form an electrical double layer in the solution phase near the surface [27]. Generally, many researchers have reported that the zeta potentials of the biochar increase with increased pyrolysis temperatures; therefore, the higher pyrolysis temperature of biochar production obtains higher negative surface charges consistent with reduced density of carboxylic and phenolic functional groups than those produced at the lower temperatures, thus lowering the electronegativity [19; 28; 29]. Generally, the FTIR bands and peaks of SB-1 differed from those of SB-2 and SB-3. This is because the pyrolysis temperature uniformity was not the same as in the cases of SB-1 and SB-2. There was a slight change in peak intensity and width between SB-2 and SB-3, while SB-1 was more related to feedstock. This mention indicates that the pyrolyzed biochar formed on the upper internal chamber (SB-2) was similar to the pyrolyzed biochar formed under control conditions (SB-3, conventional method). This is due to the thermal decomposition of the lignocellulosic structure (three main components) of plant residue [4; 30]. Moreover, the changes in the morphological structures were due to the elimination of easily degradable organic compounds in the form of volatile gases [12; 22]. This indicates that the thermal decomposition of cellulose and hemicellulose was higher for the conventional method (SB-3) than those produced by the solar parabolic dish collectors. [PERSON] et al. (2018) perceived the same result in the study of the effect of surface morphology of different biochar types produced at 350 \({}^{\circ}\)C on water holding capacity. They observed that biochar types have heterogeneous surface morphologies with excessive pores and channels [12].
# Development of Radio-Frequency Sensor Wake-Up with Unmanned Aerial Vehicles as an Aerial Gateway
## 6 Experimentation and Results
### RF Wake-Up Delay
Another study in [72] using the same chipset claimed the time between t\({}_{2}\) and t\({}_{3}\) was 45.87 ms, which was similar to our experiment.