esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Optical Phased Arrays for Beam Forming and Steering
## 5 Material Platforms
### Si Photonics Platforms
#### 5.1.2 OPAs Based on the Si\({}_{3}\)N\({}_{4}\) Platform
Another Si\({}_{3}\)N\({}_{4}\) WGA-based OPA enabled a beamwidth of 0.2\({}^{\circ}\)\(\times\) 0.6\({}^{\circ}\) and an FOV of 7.4\({}^{\circ}\) were shown by wavelength tuning from 1.53 to 1.63 \(\upmu\)m [71]. A 2D Si\({}_{3}\)N\({}_{4}\) OPA shown in Figure 11a enabled an FOV of 3\({}^{\circ}\)\(\times\) 17.6\({}^{\circ}\) at a fixed wavelength of 905 nm, where 2D beam steering was achieved by phase shifters and switching among various WGAs with different periods [62]. For the large-scale Si\({}_{3}\)N\({}_{4}\) OPAs, the moderate refractive index of Si\({}_{3}\)N\({}_{4}\) might lead to crosstalk among close placed antennas, which should be effectively suppressed. Figure 11: OPAs based on non-Si platforms. (**a**) Microscope image of a Si\({}_{3}\)N\({}_{4}\) OPA for 2D beam steering at a fixed wavelength of 905 nm. Reprinted with permission from [62] © The Optical Society. (**b**) A Si/Si\({}_{3}\)N\({}_{4}\) dual-layer OPA. Reprinted with permission from [88] © 2020 Chinese Laser Press. (**c**) A Si\({}_{3}\)N\({}_{4}\)/Ge-on-Si WGA for MIR OPAs. Reproduced from [72] published by IEEE, 2019. (**d**) A III-V/Si OPA. Reprinted with permission from [126] © The Optical Society.
## 7 Simulation Environment
### Packet delivery ratio calculation  
The packet delivery ratio is defined as the ratio between numbers of packets received by the destination with respect to number of packet sent by the sender node. Figure 14 compares the packet delivery ratio performance of the nodes for various protocols namely GA-LEACH, MW-LEACH, CSOGA, and MWCSGA. For this scenario, the simulation experiment is carried using 100 nodes. The packet delivery ratio performance of MWCSGA protocol is 98.25%. Whereas the earlier protocols such as GA-LEACH, MW-LEACH, and CSOGA protocols produce energy efficiency as 78.36%, 84.37%, and 90.99%, respectively. According to analyzes it proves that the performance of MWCSGA protocol performs better when compared with the others.  
Figure 14: Packet delivery ratio calculation.  
Figure 13: Throughput calculation.
## 2 Results and Discussion
### Toxin \(\zeta\) Phosphorylates a Fraction of UNAG In Vitro  
In the previous section was shown that \(\zeta\) requires the presence of UNAG to hydrolyze ATP, suggesting that UNAG or UNAG-3P might stimulate \(\zeta\)-mediated ATP hydrolysis. To further analyze this, UNAG and ATP were incubated in the absence or presence \(\zeta\) and the products of the reaction were analyzed by mass spectrometry as described in the Experimental section.  
In a mock reaction, lacking the \(\zeta\) toxin, phosphorylated UNAG was not observed (compare Figure 2A,B). In all conditions the nucleotide (or compound) and its Na-bound (e.g., M-H\({}^{+}\) [ATP, 506.06 peak], the M-2H\({}^{+}\)-Na\({}^{+}\) [ATP-Na\({}_{2}\), 528.05 peak] or even M-3H\({}^{+}\)-2 Na\({}^{+}\) forms (due to the contribution of the buffer used) were detected (Figure 2A). In the presence of \(\zeta\) toxin, a massive degradation of the ATP pool (506.06 Da peak, expected 507.18 Da) with subsequent accumulation of ADP (426.06 Da peak, expected 427.20 Da) was observed. However, the proportion of UNAG (606.15 Da UNAG peak, expected 607.35 Da) converted into a 687.35 Da product (UNAG-3P) with a small peak at 686.12 Da (UNAG-3P-Na\({}_{2}\)) was detectable but poor (Figure 2). A parallel TLC analysis of this reaction revealed that \(\zeta\) toxin hydrolyzed more than 95% of the ATP substrate (data not shown).  
As revealed in Figure 2B, in the presence of \(\zeta\) toxin traces of the ATP pool and a reduced fraction of UNAG were detected by mass spectrometry, when compared to the absence of \(\zeta\) toxin (Figure 2A). Since traces of ATP and a significant fraction of UNAG remained after 30 min reaction, it was assumed, as done in the previous section (Figure 1), that only a fraction of the P\({}_{i}\) was transferred to UNAG to generate UNAG-3P (Figure 2B). Alternatively, \(\zeta\) toxin phosphorylates UNAG in a coupled reaction, but under the conditions used we are loosing a fraction of the accumulated inactive UNAG-3P product.  
**Figure 2.**\(\zeta\) toxin phosphorylates _in vitro_ a fraction of UNAG. Samples containing 2 mM UNAG and 0.5 mM ATP were incubated in the absence (**A**) or presence (**B**) of \(\zeta\) toxin (0.5 \(\mu\)M) for 60 min at 30 \({}^{\circ}\)C in buffer B. The reaction products were analyzed by mass spectroscopy. The peaks corresponding to relevant products (ATP, ADP, UNAG and UNAG-3P) are indicated. The intensity (\(\times 10^{4}\)) was expressed in arbitrary units (a.u.). In panel B, the 675 to 740 \(m/z\) section is enlarged in the insert.
## 3 Bias Impact Analysis  
### Mathematical Model for a UAV-Based LiDAR Point Positioning  
For conducting the impact analysis of biases in the mounting parameters on the 3D mapping frame coordinates, the first and foremost step is to establish the mathematical relation between these entities, which can further be used to derive the partial derivatives of 3D coordinates with respect to the mounting parameters, thus quantifying the impact of biases in the mounting parameters. A UAV-based LiDAR system consisting of a spinning multi-beam laser scanner involves three coordinate systems--mapping frame, IMU body frame and laser unit frame--as shown in Figure 3. A point, \(I\), acquired from the system can be reconstructed in the mapping coordinate system using Equation (1). For the laser unit frame, the origin is defined at the laser beams firing point and the \(Z\)-axis is along the axis of rotation of the laser unit. For a spinning multi-beam laser unit, each laser beam is fired at a fixed vertical angle, \(\beta\); the horizontal angle, \(\alpha\) is determined based on the rotation of the unit; and the range, \(\rho\) is defined by the distance between firing point and its footprint, as shown in Figure 4. So, the coordinates of a 3D point relative to the laser unit coordinate system, \(r_{I}^{lu}(t)\), is defined by Equation (2). The laser unit (\(lu\)) is related to the IMU body frame by a rigidly defined lever arm, \(r_{lu}^{b}\) and boresight matrix, \(R_{lu}^{b}\). The GNSS/INS integration provides the time dependent position, \(r_{b}^{m}(t)\) and rotation, \(R_{b}^{m}(t)\), relating the mapping frame and IMU body frame coordinate systems, according to the optimized solution from the available GNSS and inertial measurements.  
Figure 2: Integration scheme for the M600 UAV-based system.  
\[r_{I}^{Ju}(t)=\ \left(\begin{array}{c}x\\ y\\ z\end{array}\right)=\left(\begin{array}{c}\rho(t)\cos\beta(t)\cos\alpha(t)\\ \rho(t)\cos\beta(t)\sin\alpha(t)\\ \rho(t)\sin\beta(t)\end{array}\right) \tag{2}\]
## 3 Results and Discussion
### Effect of Processing on the Volatile Compound Profiles of Wheat Grains  
As a result of the study on the composition of volatile compounds in whole and puffed wheat grains, 21 and 23 volatile compounds were identified, respectively. These compounds were grouped into the following organic classes: hydrocarbons, alcohols, phenolics, ethers, aldehydes, carboxylic acids, esters, heterocyclic, nitrogen-containing compounds, and other compounds. Significant differences in the composition and quantity of volatile compounds were observed between whole and puffed wheat grains (Figure 1).  
In puffed wheat, a significant decrease in the content of hydrocarbons such as tridecane, tetradecane, and hexadecane was observed compared to whole wheat. The most notable increase was in furfural, having bread, almond, and sweet aromas. Additionally, there was an increase in the levels of heptanoic, octanoic, and nonanoic acids, which are generally described as having sweat, green, and fat aromas. In the phenolic and heterocyclic compound groups, new compounds were formed, including methylpyrazine (which imparts popcorn aroma), 2,5-dimethylpyrazine (associated with roasted, cocoa aroma), and eugenol (which has clove and honey aromas). New volatile compounds in puffed wheat grains may have formed due to the breakdown of proteins and interactions between amino  
Figure 1: The content of volatile compounds in whole and puffed wheat grains.  
acids and reducing carbohydrates. During the Maillard reaction, the following processes and phenomena occur:  
* formation of phenols and carbonyl compounds;
* formation of aldehydes [52; 53];
* formation of pyrazines and furans upon heating, involving the reactions of reducing sugars, amino acids, proteins, and other nitrogen-containing compounds [17]; pyrazines such as methyl pyrazine and 2,5-dimethyl pyrazine contribute to the roasted aroma [54].  
In puffed wheat grains, a noticeable increase in benzyl alcohol (which has a sweet, flowery aroma) was observed, possibly driven by thermal processing and associated hydrolysis processes. This is corroborated by studies from other researchers [3; 34; 54]. The study results showed that the content of hydrocarbons, characteristic of durum wheat varieties [52; 53], significantly decreases in puffed grains due to thermo-oxidative processes occurring during puffing.
# A Miniaturized Circularly-Polarized Antenna for In-Body Wireless Communications
## 4 Experimental Results  
In order to validate the design strategy, the prototype of the proposed implanted antenna was fabricated and assembled. The measurement environment surrounding the proposed antenna is a piece of fresh streaky pork (shortly after slaughter) comprised layers of skin, fat, and muscle. Figure 12 shows the photos of fabricated antenna and measurement setup. The S-parameters of the antenna against frequency are measured with the aid of an Agilent N5230A vector network analyzer (VNA) (Keysight Technologies, Santa Rosa, CA, USA). The simulated and measured S-parameters are shown in Figure 13. The measured bandwidth for S11 \(<-\)10 dB is 12.9%, covering 2.32 to 2.64 GHz. There is a slight difference between the simulated and measured results mainly due to the tolerances in the fabrication process and measurement. A linearly polarized dipole, working as a receiver, is placed 150 mm away from the proposed antenna to testify the CP property of the proposed antenna. The S21 between the proposed antenna and the dipole was measured when the dipole was placed at the phi = \(-\)45\({}^{\circ}\), 0\({}^{\circ}\), 45\({}^{\circ}\), and 90\({}^{\circ}\), respectively. With reference to Figure 13, there is a maximum deviation only up to 3 dB for the measured S21, proving the CP property of the proposed antenna.  
Figure 12: Photograph of the fabricated antenna prototype and measurement setup.  
Figure 13: Measured S-parameters of the proposed antenna.
###### Abstract  
The aerospace industry is imposing increasingly strict dimensional tolerances, which is forcing continuous development in component manufacturing. Ensuring tight dimensional tolerances is difficult for thin-walled structures due to their reduced stiffness, which are increasingly used in the aerospace industry, where titanium alloys and nickel alloys, among others, dominate. Developments in this area are causing a search for machining conditions that provide sufficient quality characteristics including dimensional and shape accuracy. We discuss, herewith, thin wall deformations in the horizontal orientation of Inconel 625 nickel alloy samples in cross-sections perpendicular and parallel to the direction of tool feed motion. We measured dimensional and shape accuracy using a 3D optical scanner and also using a coordinate measuring machine to correlate these results. We compared the results obtained by the two methods and obtained the maximum discrepancy of the results equal to around 8%. Samples made with adaptive cylindrical milling had similar values of thin wall deviations, with the smallest deviations observed for the sample made with the tool for high-performance machining using adaptive cylindrical milling.  
horizontal thin-walled samples; milling; nickel alloy; Inconel 625; adaptive milling; deviation; optical method; contact method; 3D optical scanner; coordinate measuring machine +
Footnote †: journal: Applied Sciences
## 4 Analysis of Influencing Factors
### The Influence of Different Formation Conditions
#### 4.6.1 Tunnel Horizontal Displacement  
Figure 19(a),b show the horizontal displacement curve of the outer wall of the tunnel lining and the tunnel's maximum horizontal displacement upon completion of the foundation pit excavation under varying stratum conditions. The tunnel's horizontal displacement distributions remain consistent across different strata. The vault experiences the largest horizontal displacement, followed by the sidewall and floor; moreover, the horizontal displacement is higher on the south side compared to the north side. In the fine sand stratum, the horizontal displacement between the tunnel vault and the floor is smaller compared to other strata. The tunnel's horizontal displacement in the silty clay and muddy-silty clay strata surpasses the control value stipulated by the specification, while in the silty clay and silty sand strata, it exceeds the warning threshold. According to the tunnel's maximum horizontal displacement in each stratum, the hierarchy of influence of stratum types on tunnel displacement is as follows: muddy-silty clay \(>\) silty soil \(>\) silty clay \(>\) silty fine sand.  
Figure 19: Maximum structural stress of the tunnel for various horizontal bearing stiffnesses.  
Figure 20: Tunnel horizontal and maximum horizontal displacement for different strata.
## 4 Construction of the Sample Set
### Data Augmentation  
From the discussion in Section 4.1, it can be seen that constructing sample sets aiming to solve the design pattern detection problem can take much time and manpower, and thus these sample sets are usually not sizable. In addition, differing from conventional graph classification problems such as facial classification, cat and dog classification and medical image classification, the problem addressed in this research is mainly solved by using colors, line types, and geometric shapes to represent information of design patterns as well as of the system to be recognized. Thus, overfitting can easily occur in this research, that is, a very good recognition performance on the training set may fare poorly on the test set or new data. Then, it is imperative to perform data augmentation before model training.  
In this paper, the sample set is first expanded by conventional data augmentation techniques such as horizontal flip, vertical flip, and translation. In addition, the position of the class in the image of an instance of a design pattern does not affect whether the instance belongs to this pattern. Therefore, we also use the method of moving each class by pixel for data enhancement. The algorithm is as follows:  
**Step 1:** Let \(C_{\text{sample}}=\{C_{1},C_{2},\cdots,C_{n}\}\) as the set composed of all classes of the positive and negative pattern instance \(i\) in the sample library, where \(n\) is the number of classes in instance \(i\).  
**Step 2:** First, take the first Class \(C_{1}\) from the set \(C_{\text{sample}}\), with the locations of other classes remaining unchanged; new samples will be generated by moving the location of \(C_{1}\) from left to right and from top to bottom within the graph area one unit distance a time (which is set as 5 pixels here).  
**Step 3:** Repeat the above process, and sequentially take the Classes \(C_{2},C_{3},\cdots,C_{n}\) from the set \(C_{\text{sample}}\) and move locations to construct new samples.  
**Step 4:** At each time, take 2 classes from the set \(C_{\text{sample}}\) to form different combinations of classes, and keep the relative locations of these 2 classes unchanged and the locations of other classes unchanged, then move in accordance with the above-mentioned method to construct new samples.  
**Step 5:** Repeat the above process, take 2, 3,..., \(n-1\) classes from the set \(C_{\text{sample}}\) respectively to form different combinations of classes, and move locations to construct new samples.
# Multi-Device Security Application for Unmanned Surface and Aerial Systems
## 4 Results
### Experiment One--MAUI Chat
#### 4.1.2 Testing and Results  
During this phase of testing, we ensured that MLS was implemented according to the specifications outlined in Section 2. We used WireShark to capture and analyze packets to confirm the encryption of plaintext. Once the implementation was verified, we compared the plaintext packet size to the ciphertext size, which corresponds to the chosen MLS ciphersuite to calculate protocol overhead.  
We first sent an initial plaintext message of 15 bytes followed by the same message encrypted with each of the six ciphersuites available in MLS version 12. We also performed the same test with a plaintext message of 1000 bytes. Testing revealed that MLS encryption generated an overhead ranging from 171 to 277 bytes depending on the ciphersuite chosen, regardless of the plaintext size (15 B or 1000 B) as shown in Figure 7. The major difference in overhead seems to be generated by the size of the cryptographic hash function used in the selected ciphersuite (SHA256 or SHA512).
# On Analyzing Beamforming Implementation in O-RAN 5G
## 2 Related Work  
The packet structure of C-plane in O-RAN front-haul specifications has been discussed in \"Overview of O-RAN Fronthaul Specifications\" [PERSON], [PERSON], [PERSON], [PERSON], 2019, the O-RAN front-haul specifications that are expected to be the first standard to allow interoperability between various vendors. The paper introduced the packet structure of the U-, S-, and C-planes, where the author also touched on the benefit of small packet size in Category B and investigated Split Option 7-2x adopted in O-RAN front-haul, where the conclusion supports our proposal by confirming that the flexibility of hardware implementation in front-haul enables the low bandwidth requirement and high latency. In another research paper, \"Hybrid beamforming designs for 5G new radio with front-haul compression and functional splits\", [PERSON], [PERSON], [PERSON], 2020, the authors investigated the implementation of hybrid beamforming in O-RAN and discussed the designed problem in both categories (A and B). This paper's performance analysis and numerical results support our conclusions of using digital beamforming in Category B. It shows that the beamforming in the Category A scheme exceeds the beamforming in the Category B scheme in the large power management. Furthermore, the BF-B method is more sensitive to system parameter changes than the BF-A method, while it is less sensitive in small power management.  
The challenges of the open radio access network are related to implementation. Until now, much of the documentation related to this issue has been about the architecture proposed by the O-RAN alliance and the specification of the open interfaces, which should ensure the interoperability of solutions provided by different vendors.
## 3 Results and Discussion
### Variable Contribution  
On detecting burned areas using Sentinel-2, the RF variable importance exercise identified texture, NBR and NIR as variables that contributed most to the overall burned area classification (Figure 9).  
In summary, we presented a quick and affordable approach for monitoring burned areas within a GEE platform. The GEE offered a convenient environment for classifying burned areas and also facilitated the identification of variables that contributed most to burned area detection when using Sentinel-2. Our results successfully showed potential for GEE resources to estimate burned land use/cover classes with great fidelity. This is comparable to [PERSON] et al. (2019), who quantified burned classed using the same platform. The mapping of forest fires contributes a major component of forest fire management, and the rich information recorded by Sentinel's constellation of sensors allows much-improved characterization and identification of forest fires than previously, particularly when implemented in the GEE environment, which can be easily accessed and adjusted to select dates that best suit the ecosystem of interest. The methodology reported here may be readily replicated through time and can be used for other agricultural activities prone  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{2}{c}{Producer’s Accuracy (\%)} & \multicolumn{2}{c}{User’s Accuracy (\%)} & \multicolumn{1}{c}{Overall} \\ \hline Burned & Unburned & Burned & Unburned & Accuracy (\%) \\ \hline
98 & 97 & 97 & 98 & 98 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Accuracy assessments for burned and unburned classes.  
Figure 8: (**a**) Black carbon surface concentration and (**b**) Surface wind speed averaged 3-hourly over the Southern Cape from 26–29 October 2018.  
to fire damage. Moreover, this methodology holds much promise for the South African government to estimate burned land use/cover classes with greater accuracy.
# Channel Estimation Algorithm Based on Parrot Optimizer in 5G Communication Systems
## 3 Numerical Simulations
### Impact of Modulation Schemes
The MMSE algorithm consistently outperforms the LS algorithm across all SNR conditions, with significant advantages under low SNR conditions. At 0 Db SNR, the MSE of the MMSE algorithm is approximately 0.5, significantly lower than that of the LS algorithm. The PO algorithm's performance falls between the LS and MMSE algorithms under low SNR conditions, with an MSE slightly higher than the MMSE algorithm but lower than the LS algorithm at 0 Db SNR. As SNR increases, the MSE of the PO algorithm rapidly decreases, approaching the MMSE algorithm under high SNR conditions. In summary, the MSE trend clearly reflects the performance characteristics of different channel estimation algorithms. The LS algorithm is simple but has large errors under low SNR conditions; the MMSE algorithm provides the highest accuracy; and the PO  
Figure 6: MSE performance of LS, PO, and MMSE channel estimation algorithms under different modulation schemes. _X_-axis: SNR [Db]. The curves are the results of simulations. algorithm's performance accuracy falls between the two, with a performance close to that of the MMSE algorithm under high SNR conditions.