esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Mesh Regularity: Geometric Quality and Gradient Reconstruction in MFAMs
### Gradient Reconstruction
In terms of the LSQR method, two aspects become clear: First, selection of the parameters sets the difference between unacceptable and acceptable errors in the calculation of gradients, as inferred from the results from Figure 12b. Second, there are some restrictions in the function that the GR method is able to reconstruct, as shown in Figure 12c: The function \(\phi_{3}\) presents relative errors larger than 1 because the mesh does not provide enough resolution for the LSQR method to correctly obtain the function gradients. This limitation is related to the particular wavelength of the function, and it is not a specific problem of using non-Cartesian meshes, since a similar one will arise in a regular structured mesh with low resolution. In view of the previous results, it can be concluded that a compromise must be made between the Taylor series order (which determines the minimum number of stencil points), the resolution of the mesh, and the computational resources, in order to ensure good accuracy in the gradient reconstruction.
# Novel Common-Mode Current Suppression Method in Transformerless PV Grid-Connected System
## 2 Analysis of the CM Current of Non-Isolated Grid-Connected PV System
### Analysis of CM Current
In summary, the CM currents of modulation frequency, of resonant frequency and of line frequency are the main components in the CM circuit loop.
# Statistical Edge Detection and Circular Hough Transform for Optic Disk Localization
###### Abstract  
Accurate and efficient localization of the optic disk (OD) in retinal images is an essential process for the diagnosis of retinal diseases, such as diabetic retinopathy, papilledema, and glaucoma, in automatic retinal analysis systems. This paper presents an effective and robust framework for automatic detection of the OD. The framework begins with the process of elimination of the pixels below the average brightness level of the retinal images. Next, a method based on the modified robust rank order was used for edge detection. Finally, the circular Hough transform (CHT) was performed on the obtained retinal images for OD localization. Three public datasets were used to evaluate the performance of the proposed method. The optic disks were successfully located with the success rates of 100%, 96.92%, and 98.88% for the DRIVE, DIARETDB0, and DIARETDB1 datasets, respectively.  
Keywords:optic disk; OD localization; OD detection; statistical edge detection; diabetic retinopathy detection; modified robust rank order +
Footnote †: journal: Applied Sciences
# Integrating Classical Preprocessing into an Optical Encryption Scheme
## 2 Background and Tools
### The AlphaEta Protocol  
Ciphertexts in AlphaEta are a sequence of light pulses, where each pulse consists of many photons. They are represented using coherent states, and throughout, we use the following notation:  
\(\circ\): average number of photons per pulse  
\(\circ\): number of bases used  
\(\circ\): number of pulses sent in one round of the protocol  
The two communicating parties are assumed to share a uniformly random \((b_{1},\dots,b_{s})\in\{0,\dots,\beta-1\}^{s}\) that is unknown to the adversary and determines the bases used. Given a plaintext \(a=(a_{1},\dots,a_{s})\in\{0,1\}^{s}\), the sender will transmit each bit \(a_{i}\) in phase angle  
\[\varphi_{a_{i}b_{i}}=\left(\frac{b_{i}}{\beta}+a_{i}\right)\cdot\pi \tag{1}\]to the receiver using the optical channel. Knowing the bases \(b_{i}\), the receiver measures \(\varphi_{a_{i},b_{i}}\) and maps it to the nearest phase angle to determine the original plaintext bit \(a_{i}\).  
The number of photons in each pulse follows a Poisson distribution with parameter \(\langle n\rangle\). This statistical fluctuation is the quantum noise, which is intrinsic and cannot be avoided. This \(\langle n\rangle\) is also related to the phase angle \(\varphi\) used to modulate light pulses. Hence, there are fluctuations in the phase angle \(\varphi\) as well. The security of a protocol using coherent states is based on the difficulty of determining the right phase angle if the basis is not known. An eavesdropper, not knowing the basis, faces an intrinsic error that can be bounded from below by a value that can be made close to \(1/2\). Whereas the intended recipient, knowing the shared basis in which to measure, can recover any plaintext bit almost perfectly (bit error rates are below \(10^{-9}\)).  
In [8], it is shown that if a plaintext bit is chosen uniformly at random, which is typical for a key transport application, then the minimum probability of error \(P_{e}\) that an eavesdropper can achieve in the bit determination can be arbitrarily close to \(1/2\) by choosing the appropriate parameters \(\beta\) and \(\langle n\rangle\) (see ([8] Figure 3)). Therefore, the entropy about a bit of the plaintext given the measurement is reduced by only a small quantity \(\epsilon\) and the mutual information of both random variables approaches that value \(\epsilon\) (see ([8] Figure 4)).  
In the next section, we recall a theoretical tool which will enable us to split a payload between two different types of channels--an optical one and a classical one--without jeopardizing security.
## 5 Discussion
### D Surfaces and Thickness Map  
The processing and interpretation of the TDEMs attempted to obtain the position of the hanging wall and footwall of the saline-evaporitic part of the Barbastro Formation. An interpolation processing using Surfer V 15.1 software (Golden Software LLC.; Golden, CO, USA) allows us to obtain both surfaces, defining the main structure of the formation and Barbastro-Balaguer Anticline.  
Figure 11 integrates all interpreted TDEM points, showing the spatial elevation of the hanging wall and footwall of the saline-evaporitic Barbastro Formation in the survey area. In case of the hanging wall surface, the outcropping mapped position based on the official geological cartography 1:50,000. Magna [47] is used in the interpolation process to define its morphology and limit due to its outcropping to the North. According to the obtained surface (Figure 11A), the hanging wall surface becomes progressively less steep as we move to the SW, with a dip varying between 40\({}^{\circ}\) in TDEM-2 zone to almost subhorizontal in the southernmost TDEM survey points area (TDEM-13 and TDEM-15). The hanging wall elevation ranges between \(-658\) and \(-1135\) m above sea level (masl) (996 and 1433 m depth). The footwall shows a subhorizontal morphology, with small  
Figure 8: Profile A. (**A**) TDEM-2, 6, 7 and 13 (parametric) resistivity-time curves and (**B**) resistivity-depth models with indication of the inferred position of the saline-evaporitic Barbastro Formation.  
Figure 9: TDEM Profile B. (**A**) TDEM-14, 4 and 10 resistivity–time curves and (**B**) resistivity–depth models with indication of the inferred position of the saline-evaporitic Barbastro Formation.  
Once the hanging and footwall surfaces have been obtained, the saline-evaporitic part of the Barbastro Formation thickness can be calculated, which ranges between 601 and 1103 m in the TDEM survey points, decreasing in the SW direction according to regional anticlinal defined structure (Figure 12) constrained by the hanging wall depth variation.  
No influence on the interpolated surfaces is observed due to the irregular distribution of the TDEM points (Figure 2), presenting a realistic structure in accordance with the accepted geological structural model.  
The surfaces generated show the structure of the southern flank of the Barbastro-Balaguer anticline in the survey area, and its affectation of the Barbastro Formation materials, generating a thinning of the formation towards the SW, as can be seen in a 3D representation in Figure 13 created with the commercial Voxler V 4.1  
Figure 12: Thickness of the saline-evaporitic Barbastro Formation in the survey area.
# Threat Analysis and Risk Assessment of a Driver Monitoring System
## 2 Related Work
### Security Aspects in Driver Monitoring Systems  
The integration of DMSs in modern vehicles has become essential for enhancing driver safety and compliance with regulatory standards. As these systems increasingly rely on sensor data, artificial intelligence, and network connectivity, ensuring cybersecurity has become a critical challenge. While safety has been a predominant focus, fewer studies have rigorously addressed the cybersecurity aspects of DMSs, which are vital for protecting against unauthorized access and potential cyberattacks.  
[PERSON] et al. [13] presented an approach using the AM62A System-on-Chip (SoC) for monitoring drivers and passengers. Their architecture emphasizes security measures to protect sensitive in-cabin data and prevent system breaches. Given the increasing use of in-vehicle cameras, sensors, and Internet connectivity, the risks of cyberthreats--such as data theft, unauthorized system control, or communication disruption--are significant. The authors implement security features, including a dedicated hardware security module, cryptographic accelerators, and secure boot processes to enforce a root of trust. This approach aligns with automotive cybersecurity standards such as E-Safety Vehicle Intrusion Protection Applications and ISO/SAE 21434.  
[PERSON] et al. [14] examined the challenges related to data privacy in DMSs. Their work highlights how the use of multiple cameras to monitor the driver and passengers raises concerns about capturing personally identifiable information (PII). How the accumulation of such sensitive data could expose occupants to privacy breaches if the system is compromised is discussed. However, they do not provide specific solutions for protecting against such risks.  
The complexity of modern DMS architectures, which often integrate with ADAS and other networked components, increases the attack surface for potential adversaries. [PERSON] et al. (2013) address this by implementing encryption and hashing to protect data integrity, along with specialized hardware configurations that limit physical access to critical interfaces, such as General Purpose Input/Output (GPIO) and Joint-Test Action Group (JTAG) ports. Their work also introduces the use of cryptographic keys injected during the manufacturing process to increase the resilience of the system.
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## 4 Results and Discussion
### Static LUT Airfoil Performance at Low Reynolds Number
#### 4.1.4 Lift-Drag Ratios of the LUT Airfoil
In addition, the MI-VAWT1 airfoil [9] is also a dedicated airfoil for VAWTs; it has a 21% thickness and 0.44% camber. Compared with the results for Table 1, the geometrical parameters of the LUT airfoil balance the strength of the blade and the aerodynamic performance for a VAWTs with the LUT airfoil. Especially, the LUT airfoil performs better in the maximum lift coefficient and the maximum lift-drag ratios at a low \(R_{e}\), it is very important, because VAWTs typically operate at a low \(R_{e}\)[19]. However, the stall angle of attack of the LUT airfoil is smaller than that of the airfoil in Table 1.  
Figure 11: The characteristics of lift–drag ratios for the LUT airfoil at different values of \(R_{e}\): (**a**) the relationship between the lift coefficient and the drag coefficient; and (**b**) the relationship between lift–drag ratios and the lift coefficient. The lines in black and red of the lift–drag ratios correspond to \(R_{e}\) = \(3\times 10^{5}\) and \(R_{e}\) = \(5\times 10^{5}\), respectively.
# Design of Mega-Constellations for Global Uniform Coverage with Inter-Satellite Links
## 3 ISLs Design
### Visibility Conditions between Satellites  
Figure 4 shows the spatial geometric relationship of the two satellites. Vector \(\mathbf{r_{1}},~{}\mathbf{r_{2}}\), denotes the positions of satellites A and B, respectively. \(h\) is the distance from the line of sight of two satellites to the Earth's center. Then, the visibility conditions of the two satellites can be expressed as follows: If h is greater than the radius of the Earth, the two satellites are visible to each other. \(h\) is calculated as:  
\[h=\frac{|\mathbf{r_{1}}|\cdot|\mathbf{r_{2}}|\cdot\sin\theta}{|\mathbf{r_{1}}-\mathbf{r_{2}}|}\]
## 4 Results and Discussions
### Reconfirmation of the Number of Inclusions in the Critical Volume  
As described in Section 4.1, since the number of inclusions included in the critical volume is unknown, the analysis was carried out by setting three conditions of \(n=2\), 5, and 20, as the inclusion number. Then, the condition of \(n=5\) was tentatively selected as a preferable number of inclusions. However, in order to confirm the most reasonable number of inclusions, the fatigue life distributions were additionally analyzed, under conditions of \(n=6\), 8, and 10, respectively. Based on the series of analytical results, percentile points of the fatigue life distribution corresponding to \(G=1\%\), 10%, 50%, 90%, and 99% were determined; the relationship between those percentile points and the number of inclusions are depicted in Figure 13. Comparing five solid curves carefully with the dispersion aspect of the experimental results in an interior-induced fracture in Figure 12, it was finally noted that the analytical result at \(n=6\) was well fitted compared to the result at \(n=5\).  
Figure 12: Fatigue life distributions and _P-S-N_ curves in a very high cycle regime.  
Figure 13: Number of inclusions versus number of stress cycles to failure.  
Based on this evidence, the statistical fatigue properties (_P-S-N_ properties) in Figures 11 and 12 were numerically analyzed by putting \(n=6\) for the number of inclusions included in the critical volume of the specimen.
# Rice Secondary Metabolites: Structures, Roles, Biosynthesis, and Metabolic Regulation
## 4 Metabolic Regulation of Secondary Metabolites
### Metabolic Regulation by Biotic Stresses
#### 4.2.2 Metabolic Regulation by Fungi  
The biosynthesis of rice secondary metabolites can be also regulated by fungi or their extracts and components. Fungal infection often results in the accumulation of phytoalexins in rice plants [194].  
In the rice resistant cultivar Selenio, the presence of the pathogen _F. fujikuroi_ induced a high production of sakuranetin (**81**), and symptoms of bakanae were not observed. On the contrary, in the susceptible genotype Dorella, the pathogen induced the production of gibberellin and abscisic acid and inhibited jasmonic acid production, and sakuranetin (**81**) content was very low [153]. Infection with the rice blast pathogen _M. oryzae_ induced the production of momilactones, with greater accumulation of momilactones A (**148**) and B (**149**) [195], and also induced sakuranetin (**81**) production [125]. Young rice leaves in a resistant rice line exhibited a hypersensitive reaction (HR) within three days after the inoculation of a spore suspension of the blast pathogen _M. oryzae_, and an increase of sakuranetin was detected three days post-inoculation (dpi), increasing to four-fold at 4 dpi. In the susceptible line, increased sakuranetin was detected at 4 dpi but not at 3 dpi, by which time a large fungal mycelia had accumulated without HR. The decrease and detoxification of sakuranetin (**81**) were detected in both solid and liquid mycelium cultures of the rice blast pathogen [125].  
Rice leaves accumulated serotonin (**259**) in response to infection by _Bipolaris oryzae_. If serotonin (**259**) was added to the culture media, it was converted into 5-hydroxyindole-3-acetic acid (5 HIAA), which may be a detoxification process in the interaction between _B. oryzae_ and rice [196].  
When rice leaves were infected with _Cochliobolus miyabeamus_, phenylamides were also induced. They included \(N\)-feruolyagmatine (**244**), \(N\)-feruolyputrescine (**245**), \(N\)-benzoylcorotonin (**249**), \(N\)-benzoytryptamine (**250**), \(N\)-benzoyttyramine (**251**), \(N\)-_trans_-cinnamoylserotonin (**252**), \(N\)-_trans_-cinnamoyltramine (**254**), \(N\)-_p_-counaroylserotonin (**255**), and \(N\)-feruolyserotonin (**256**) [116].
# Applications of Raman Microscopy/Spectroscopy-Based Techniques to Plant Disease Diagnosis
## 3 Raman Microscopy--A New Method Used to Study Cell Biology  
Microscopic Raman imaging and confocal Raman microscopy (CRM) are current and advanced analytical tools for biomedicine; they offer spectral fingerprint recognition, molecular presence and biochemical mapping. CRM was first developed almost 20 years ago, though its applications have mainly been restricted to surface investigations of cellular components and drugs at the micrometer scale. Its development in the past few years has been particularly advanced with respect to biological specimens, particularly in the case of soft tissues [37].  
A new method used to study cell biology is Raman microspectroscopy, which has become common due to the ability to provide analytical data with little prior sample preparation and to enable the visualization and biochemical characterization of live cells, subcellular structures and specific organelles [38; 39].  
Moreover, the Raman spectra of microorganisms are superpositions of spectra due to variations in various organelles and biochemical components inside the cell, such as peptidoglycan (the predominant polymer in the cell wall of Gram-positive bacteria), chitin (a fungal cell wall component), ergosterol (the steroid molecule in fungal cell membranes) and polymers (F-actin, microtubules and septins--fungal cytoskeleton polymers) [39]. Furthermore, since fungal hyphal growth is closely related to the ability to secrete large amounts of enzymes, polysaccharides, lipids and proteins, including cytochromes, Raman spectroscopic imaging provides more accurate spatially resolved information on their relative amounts and chemical states compared with other methods [40].  
The properties of Raman spectroscopy make it an interesting technique for non-invasive characterization and differentiation of certain cell types and tissues. Studies applying Raman scattering to living eukaryotic cells appeared in the late 1990s, with pioneering works in this decade mainly investigating the effect of Raman scattering in single cells and the spatial distribution of cell structures and their conformational status. Raman scattering characteristics have been used by biologists to identify cancerous cells and to differentiate malignancy levels in a noninvasive way, without the need for chemical markers [41; 42]. With the use of a lower-intensity wavelength, molecular damage can mostly be minimized, which provides a successful approach to studying in vivo soft tissues, which are sensitive to optical damage. Such a technique can be invaluable to cancer researchers for studying cell dynamics and metabolisms [41; 42; 43].
# Reliable User Datagram Protocol as a Solution to Latencies in Network Games
## 3 RUDP Implementation in the Transport Layer to Handle Latency
### Design of the Re-Transmission Function  
It is a normal routine for the Receiver Node to send back an Ack packet to the Sender Node once it has received any data packet but if this procedure has not been completed, the Sender Node will repeatedly transmit the same packet following the communication protocol being adopted. Such a function is embedded in the system to guarantee the reliability of the data being transmitted.  
\[Retransmission\ Timeout=\left(\frac{n-1}{2}+1\right)RTT \tag{1}\]  
\[Max\ Retransmission\ Count=ceiling\left(\frac{Data\ Packet\ Aging}{Retransmission\ Timeout}\right) \tag{2}\]  
The maximum cumulative Ack count and the average number of round trips are represented as n and Round-Trip Time (RTT) in the equation. The measurements are taken by the timestamp (extension) attached to the R UDP header. The number of re-transmissions will be smaller than that of the 'Go Back N' protocol or the 'One-Bit Sliding Window' protocol. One of the limitations of the Selective Repeat Protocol is the complexity of communications between the Sender and Receiver Nodes as each packet has to be acknowledged respectively. However, such a limitation is not a definitive obstacle considering the effectiveness and efficiency of R UDP over TCP or UDP. The Receiver Node returns an acknowledgment packet as a Positive Acknowledgment (P-Ack) when it has been received  
Figure 4: The architecture of R UDP.  
in a normal way along with its sequence number. This way, the RUDP will not have to use a Negative Acknowledgment (N-Ack) and avoid any complexity involved in the transmission process.
# A Modified Compact Flexible Vivaldi Antenna Array Design for Microwave Breast Cancer Detection
## 2 Materials and Methods
### Parametric Study of Modified Antenna
#### 2.3.1 Changing DGS Width, W1  
The DGS width affects the bandwidth at the first and second resonant frequency. As can be seen in Figure 9, W1 = 0.2 mm shows the best result.  
Figure 8: Two-dimensional Radiation Pattern at 4 GHz of Modified Antenna.  
Figure 9: Effects of Changing W1.
# Fooling in Membrane Distillation, Osmotic Distillation and Osmotic Membrane Distillation
## 4 Characterization Techniques for Fouling Analysis
### Attenuated Total Reflectance-Fourier Transform Infrared Spectroscopy (ATR-FTIR)  
ATR-FTIR offers quantitative and qualitative analysis of both organic and inorganic solid and liquid samples and it is a well-known technique for fouling analysis.  
It is a label free technique that provides information on the chemical composition of the fouling layer [129]. ATR-FTIR measurements only collect signals from the surface and to a depth of 1.6 mm inside the sample by using a beam splitter KBr and an infrared source employing an attenuated total reflectance. When the infrared radiation reaches the sample, part of the radiation is absorbed by the sample and the rest is transmitted through it. The resulting signal in the detector is a spectrum representing the molecular \"fingerprint\" of the sample. The utility of infrared spectroscopy is the fact that different chemical structures (molecules) produce different spectral traces. The Fourier transform converts the output of the detector into an interpretable spectrum that provides information about chemical bonds and functional group in a molecule. In this technique the sample does not need any specific type of preparation. Further details about this technique can be found in [103; 129].  
[PERSON], et al. [129] used ATR-FTIR technique to determine the composition of the fouling layers deposited on PP and PTFE membranes used for ammonia removal from model manure by MD. It was proved that the fouling layer was formed by only organic compounds, whereas no indication of inorganic scaling was detected [129]. [PERSON], et al. [103] used ATR-FTIR to detect functional groups in PP and PTFE membranes used in MD for the treatment of model manure solution. This technique showed that fouling layer composition was not uniform across the entire membrane surface and all the bands found by ATR-FTIR spectroscopy were assigned to organic fouling. [PERSON] and [PERSON] [130] also used ATR-FTIR method to analyze fouling composition during whey concentration process by MD. The ATR-FTIR spectra of both clean and fouled PP membrane confirmed the presence of whey proteins in the fouled membrane structure.