esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Random Variables
### Uncertainties of Soil Parameters
#### 3.1.1 Sandy Soil Parameters  
Data on the COVs for \(C\) and \(R\) were calculated from tests conducted by [PERSON] et al. [37; 38] where 60-degree cone projectiles were launched vertically at an impact velocity of 200 m/s into soil targets. Soil targets of Ottawa sand, \(D_{50}=\sim 0.3\) mm, were prepared under loosely packed and densely packed conditions using dry pluviation into cylindrical barrels 0.31 m in diameter and 0.76 m tall. The samples' densities were consistent with depth, with a density range of 1.82 gm/cm\({}^{3}\) and 1.57 gm/cm\({}^{3}\) for the dense and loose soil targets. The range of relative densities was 95% to 41%, respectively. The projectile used in these experiments was a 14.3 mm diameter cylindrical rod with a 60-degree nose. It was precision-machined from 6061-T6 aluminum alloy with a length-to-diameter ratio of 6. Projectiles were launched by a vertical launcher into the soil targets.  
A Photon Doppler Velocimeter (PDV) was used to monitor the motion of the projectile in the soil target from impact to the final DoB [37]. The PDV resolves the velocity-time history of the moving projectile based on the Doppler shift in coherent laser light waves reflected from the tail of the projectile [37; 40]. The penetration versus velocity relations derived from the PDV measurements were fitted by the Poncelet model to obtain the \(C\) and \(R\) values for each experiment. [PERSON] et al. [38] reported the values for \(C\) and \(R\) for seven ballistic experiments. These values were used in the current study despite being a small sample and due to the scarcity of similar experiments at these velocities in the literature. The mean value and COV for each of \(C\) and \(R\) was calculated. The calculations showed that the COV for \(C\) is 33%, while the COV for \(R\) is 58% (Table 1).
###### Abstract  
In this paper, we investigate the so-called noise propagation effect in a mixed radio-frequency/free-space optical (RF/FSO) amplifying-and-forwarding (AF) relaying system that is applied for data transmission in wireless sensor networks. The noise propagation could be essentially severe when battery-charged sensor nodes have very limited transmit power. We provide an exact expression on the cumulative distribution function (CDF) of end-to-end signal-to-noise power ratio (SNR) for a dual-hop mixed RF/FSO AF relaying system. We assume a tightly power-constrained amplifying gain at the relay, which has been usually ignored in existing performance studies for the mixed RF/FSO AF system. It however should be considered to properly evaluate the noise propagation effect especially if the relaying power is not infinite or the sensor has a poor budget in transmit power. We apply the derived exact CDF to evaluate the system performances such as outage probability, average bit-error rate, and ergodic capacity. Numerical investigation is used to justify that the proposed analysis is exactly matched with the simulation and shows that the performance gap caused by the inclusion of the noise propagation effect is significant (about 2-12%) especially when the SNR per hop is in the medium- or the low-SNR ranges (i.e., at 10-20 dB).  
wireless sensor networks; mixed RF/FSO; AF relaying; power-constrained amplifying gain; noise propagation effect; exact CDF expression; performance evaluation
## 2 Materials and Methods
### Green Space Classification and Extraction System (U-net-SVM)
#### 2.3.2 Classification of Urban Green Zone Information  
Urban green zones can be divided into irrigation (composed of trees and shrubs) and grass, according to their types. Owing to the complexity of urban green space classification, the classification and extraction of irrigation and grass cannot be realized only by the spectral features of the images. Therefore, in this study, based on U-net to extract green zone information, the supervised classification method of traditional machine learning was employed, using GF-7 remote sensing image synthesis as the standard false color. According to the vegetation spectrum and texture decoding characteristics, the green space information in the image of the sample collection area was discreetly labeled into two types, namely irrigation and grassland, through artificial visual interpretation. In the standard false color composite images, the texture features of the irrigation areas appear coarser, while those of the grassland areas appear finer; the irrigation areas are depicted in red and dark red, whereas the grassland areas are shown in light red. The irrigation areas are mainly distributed within parks, residential areas, and along roadsides; the grassland areas are primarily found in parks, squares, and golf courses. The training sample was selected by drawing a polygon interest area. The separability between each sample type was represented by the Jeffries-Matusita distance and conversion separability parameters, and the combinations of areas of interest were listed from small to large, according to the size of the separability values. The values of these two parameters ranged from 0 to 2.0, and in general, a sample separability >1.8 was considered satisfactory, with greater separability indicating better separability. The separation degree between the collection of irrigation and grass samples was 1.81, indicating the qualification of the sample selection. The support vector machine (SVM), with a high classification accuracy, a better classification effect, and a capability to extract the target features more accurately, was used as a classifier [18].
## 3 Research Design
### Relevant Technological Concepts
#### 3.3.7 Web Crawling and Web Scraping Tools for Python  
As some of the authors have little programming experience, the following criteria were crucial for the selection of the tools:  
* Should run on Python;
* Easy to grasp and learn;
* Well documented;
* High community support;
* If possible, web crawling, web scraping and Natural Language Processing in the tool should be expandable according to the needs (libraries);
* Built in data storage;
* Can manage complex scraping operations in a powerful manner.  
Based on the above criteria, the authors prepared the decision table shown in Table 1 as a basis for their decision-making.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Scrapy** & **Mechanical Soup** & **PySpider** & **Beautiful Soup** \\ \hline \multirow{2}{*}{What is it?} & web scraping & \multirow{2}{*}{library} & \multirow{2}{*}{web crawler} & \multirow{2}{*}{library} \\  & framework & & & \\ \hline \multirow{3}{*}{Purpose} & web crawler and & to simulate human & & \\  & collaborative web & interaction with & web crawler & data parser \\  & scraping & websites & & \\  & data extraction from & automated storing and & ability to retry failed & \\  & websites, processes & sending of cookies, & pages, crawling pages & \\  & them as needed, & redirections following, & by age, prioritization, & \\  & the storage & link following and form & & \\  & in a preferred format & submitting, easy filling & & \\  & in of HTML forms & in of HTML forms & & \\ \hline \multirow{3}{*}{Ideal for} & high-level web & \multirow{3}{*}{simulating human} & \multirow{3}{*}{powerful web crawling} & \multirow{3}{*}{simple web scraping} \\  & \& scraping projects on a & behavior & system & tasks \\ \cline{1-1}  & large scale & & & \\ \hline \multirow{3}{*}{Speed} & very fast & fast with scraping & \multirow{3}{*}{fast} & \multirow{3}{*}{fast} \\  & simpleity & & & \\ \cline{1-1}  & \multirow{2}{*}{easy} & easy & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & Built-in Data & \multirow{2}{*}{Json, XML, CSV} & \multirow{2}{*}{own development} & \multirow{2}{*}{
\begin{tabular}{} \end{tabular} } & \multirow{2}{*}{own development} \\  & Storage Support & & & & \\ \hline \multirow{3}{*}{Documentation} & \multirow{3}{*}{excellent} & available, & & \\  & & unmaintained & & \\ \cline{1-1}  & & for several years & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: Decision table—overview of web scraping and web crawling libraries.  
Based on the content from Table 1, we decided to work with BeautifulSoup, because the documentation of Scrapy is not beginner friendly. The following points were crucial for the decision:  
* Runs on Python;
* Can be learned with a little more effort;
* Is well documented and beginner friendly;
* Has a high community support;
* Is suitable for web crawling and web scraping, is easily extensible;
# Lifting Activity Assessment Using Kinematic Features and Neural Networks
## 2 Materials and Methods
### Center of Mass and Lifting Energy Consumption (Lec)  
From kinematic data, we calculated CoM values for three different complexes: the load alone (CoM\({}_{\rm L}\)); the complex of the load together with the multi-segment upper body model (hands, forearms, upper arms, trunk, head) defined as CoM\({}_{\rm UPP+L}\); the complex of the load together with the whole body model (pelvis, thighs, shanks, feet and multi-segment upper body model) defined as (CoM\({}_{\rm Tot}\)) [29]. The barycentre of the crate was considered the CoM\({}_{\rm L}\).  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c} \hline \hline
**LC** & **H** & **H** & **V** & **VM** & **D** & **DM** & **A** & **AM** & **F** & **CM** & **L** & **(kg)** & **RWL** & **LI** \\
**(kg)** & **(cm)** & **HM** & **(cm)** & **VM** & **(cm)** & **DM** & **(c)** & **DM** & **(c)** & **AM** & **(lit/min)** & **FM** & **C** & **CM** & **L** & **(kg)** & **RWL** & **LI** \\ \hline
23 kg & 50 & 0.5 & 75 & 1 & 25 & 1 & 0 & 1 & \(\leq\)2 & 1 & good & 1 & 23 & 11.5 & 2 \\ \hline
23 kg & 63 & 0.4 & 30 & 0.87 & 45 & 0.92 & 0 & 1 & \(\leq\)2 & 1 & good & 1 & 22.09 & 7.36 & 3 \\ \hline \hline \end{tabular}  
* L: load weight; H and HM: horizontal location and corresponding value of the multiplier; V and VM: vertical locations and corresponding value of the multiplier; D and DM: vertical travel distance and corresponding value of the multiplier; A and AM: asymmetry angle and corresponding value of the multiplier; F and FM: lifting frequency and corresponding value of the multiplier; C and CM: hand-to-object coupling and corresponding value of the multiplier; RWL: recommended weight limit. LC: load constant (defined as 23 kg in RNLE).  
\end{table}
Table 1: NIOSH protocol values for each lifting task are reported.  
For each of the calculated CoMs, the kinetic energy (\(E_{k}\)), the potential energy (\(E_{p}\)) and the mechanical energy (\(E_{M}\)) during the lifting tasks were calculated as in [34]. For each CoM, Lifting energy consumption (LEC) values were calculated as the difference between maximum and minimum values of each E\({}_{k}\) (LEC\({}_{k}\)), E\({}_{p}\) (LEC\({}_{p}\)) and E\({}_{M}\) (LEC\({}_{M}\)) within the lifting cycle. In particular, we calculated these parameters for the three complexes: LEC\({}_{k\_L}\), LEC\({}_{p\_L}\), and LEC\({}_{M\_L}\) for CoM\({}_{L}\), LEC\({}_{k\_Tot}\), LEC\({}_{p\_Tot}\) and LEC\({}_{k\_Tot}\) for CoM\({}_{\text{Tot}}\) and LEC\({}_{k\_Upp+L}\), LEC\({}_{p\_Upp+L}\) and LEC\({}_{M\_Upp+L}\) for CoM\({}_{\text{Upp+L}}\).
## 2 Materials and Methods
### Numerical Simulation  
A three-dimensional finite element numerical model, as shown in Figure 6, was established based on the finite element numerical computation platform COMSOL Multiphysics(r) 6.5. The material parameters of the plate are listed in Table 1. To avoid the influence of boundary reflections on guided waves, a Rayleigh damping absorbing boundary with a width of 50 mm was set at the boundary of the aluminum plate structure. The mass damping parameter \(a_{dM}\) and the stiffness damping parameter \(\beta_{dK}\) transition smoothly at the structure/damping boundary, gradually increasing with the depth of wave propagation into the damping layer. The material parameters are detailed in Table 1. The model allows for the configuration of different scales and quantities of damage as needed. The ultrasonic guided wave transducer array consists of \(11\) piezoelectric elements and employs an alternating excitation/receiving measurement scheme to collect out-of-plane velocity signal data. This is done to maintain consistency with the guided wave signals extracted by SLDV in the experiment, resulting in a total of 121 sets of actuation/sensing measurement sets for numerical simulation imaging.  
Figure 6: Numerical model of ultrasonic guided wave in plate.  
In numerical simulations, to improve computational efficiency and reduce the physical fields that need to be coupled for solving the model, the ideal coupling effect between the piezoelectric transducer and the structure is equivalent to a localized force acting at the boundary of the transducer based on the pin force model proposed by [PERSON] et al. [1]. The excitation signal for the transducer unit is a sinusoidal guided wave signal modulated by a Gaussian window function.  
\[F(t)=A\cdot sin(2\pi f_{c}t)\cdot\exp\Bigl{\{}-[2\pi f_{c}(t-t_{0})]^{2}/B \Bigr{\}} \tag{18}\]  
where \(A\) is the amplitude control factor; \(f_{c}\) is the center frequency of the excitation signal which is set at 250 kHz; and \(B\) is the time/frequency-domain bandwidth control factor, set at 20. At this point, the excitation waveform is a tri-peak wave, which has good temporal concentration, with an effective frequency range of approximately 120 kHz to 380 kHz at \(-6\) dB, as shown in Figure 7; \(t_{0}\) is the time-domain delay control parameter for the excitation signal.  
To ensure the stability and convergence of the dynamic transient analysis, the 3D finite element model employs COMSOL's Time-Dependent Solver with the Generalized-\(\alpha\) method for time stepping. The solver time step of the finite element model must satisfy the Newmark time increment scheme, with at least 20 time steps within one period of wave propagation. According to this requirement, the 3D finite element model was discretized with a global mesh size of 0.23 mm (20 elements per wavelength at 380 kHz), refined to 0.01 mm near damage and transducers. The time step of 0.13 us ensured numerical stability. The initial calculation step was set to 0.1 us, and it automatically adjusts as the calculation process progresses.
# A Method to Produce vsiRNAs in Plants with Cross-Kingdom Gene Silencing Capacity
## 3 Results  
### vsiRNA Production  
A fragment of the Krt18 gene of _M. musculus_ was isolated via PCR. This fragment was cloned into pEuMVYP:\(\Delta\)AV1 [22] to produce the VIGS vector pEuMV-YP:Krt18. _N. benthamiana_ plants were inoculated with pEuMV-YP:Krt18, and symptoms of infection were visible after the first week and remained throughout the plants' life cycle. The symptoms consisted of deformed leaves in the first week; after that, chlorotic mosaic leaves appeared (Figure 2a-c), and vsiRNAs derived from the Krt18 fragment were detected with an accumulation of 0.386 pmol/g by leaf (28 days postinoculation), quantified by RT-PCR.
# A Practical Implementation of Post-Quantum Cryptography for Secure Wireless Communication +
## 5 Security Analysis of the Proposed Protocols
### Security Analysis of the PQC-AES Hybrid PKE Protocol
#### 5.2.4 Overall Security Argument  
The hybrid protocol combines the strengths of both PQC and AES-256.  
Formal Statement  
The overall security of the hybrid protocol is bounded by the security of its weakest component. By combining a post-quantum KEM with AES-256, the protocol achieves a defense-in-depth strategy. An attacker would need to break both the PQC KEM and AES-256 to compromise the confidentiality of the data. This provides a higher level of security against both classical and potential future quantum attacks. Since PQC encryption/decryption algorithms are often significantly slower than AES-256, the hybrid approach leverages the best aspects of both: post-quantum key exchange and fast symmetric encryption.
## 2 Theoretical Background
### AI Model  
In this study, wrinkles and cracks were predicted after sheet metal forming using machine learning and deep learning, as shown in Figure 3, and Support Vector Machine (SVM) was used in regression and classification. The regression process learns data so that they can be included within the support vector margin, and the classification process classifies data based on the margin [17]. SVM is outstanding in high-dimensional data and can effectively process data that are not linearly separated by applying kernels to nonlinear data. However, its speed can be slow for large datasets, and kernels and parameters must be modulated to obtain optimal results. Random Forest is an ensemble learning method that improves predictive performance by combining multiple decision trees. Each tree is trained with the random samples of the training data, and they provide averages or weights, which affect the final results [18]. Therefore, the ensemble of various trees can improve the prediction accuracy and reduce overfitting, and model interpretation is possible through variable importance. However, as the number of trees increase, learning and prediction speeds decrease, and memory consumption increases. The Gradient Boosting Machine (GBM) creates a powerful prediction model by sequentially adding continuously weak learners and improves the model by supplementing the errors of each stage [19]. It has very high prediction accuracy using the boosting technique and can optimize the model by adjusting parameters such as the loss function and learning rate. However, it requires considerable time for model training, as with other machine learning models, and more hyperparameters must be adjusted compared to the models introduced above. A deep learning model consists of the input, hidden, and output layers, as with ANNs [20]. Each node receives the input values and calculates the output by applying the weights and biases. The model can learn nonlinear relationships well and learns according to the data, as they can be applied to various problems. However, complex networks cause overfitting or difficult interpretation in some cases. The accuracy of the four models introduced above was compared and analyzed in this study by predicting the amounts generated and by classifying the wrinkles and cracks after forming.  
Figure 3: Machine learning and artificial intelligence model concepts.  
Figure 2: (**a**) FLD schematic and (**b**) post-forming geometry by region [16].  
DT is a technology to monitor and optimize the digital replica of a physical object in the manufacturing industry using real-time data, IoT, sensors, and AI. It detects and analyzes the operation and status of the production process in real time and identifies the potential problems in advance by predicting future operation [21]. It maximizes the process efficiency by determining optimal operation conditions through various simulations and helps in quality control and equipment maintenance. However, implementing this method requires advanced technology and infrastructure and may involve data security issues and high initial construction costs [22].
###### Abstract  
In this paper, a method of enhancing gain in a microstrip patch array antenna using two metallic plates for 24 GHz radar applications is presented. A 4 \(\times\) 1 linear microstrip square patch array antenna covering the 24.0 to 24.25 GHz frequency range and using a shunt-connected series feed network with a tapered power distribution was first designed with a measured maximum gain of 9.8 dBi and dimensions of 30 mm \(\times\) 12 mm. Two metallic plates were appended along the array axis of the antenna to double the gain in the 4 \(\times\) 1 array antenna. Effects on performance from varying the tilting angle and length of the metallic plates, such as the input reflection coefficient, the radiation patterns, and gain, were investigated through simulation. Gain enhancement in the 4 \(\times\) 1 patch array antenna with metallic plates was highest when the tilting angle was around 70\({}^{\circ}\), and gain kept increasing as the length of the metallic plates increased. A prototype of the 4 \(\times\) 1 patch array antenna was fabricated with plates at a tilting angle of 70\({}^{\circ}\), a length of 50 mm, and a measured maximum gain of 16.8 dBi. Therefore, a gain enhancement of about 7 dB was achieved by adding the metallic plates along the array axis of the 4 \(\times\) 1 patch array antenna.  
microstrip patch array antenna; gain enhancement; metallic plates; tilting angle; radar Article
## 3 Results and Discussion
### Performance of AASCHB Specimens  
Based on the above mix test results, an AASC mix with a water-to-slag ratio of 0.35, sand ratio of 0.64, silicate modulus of 1.2, and Na\({}_{2}\)O content of 6% was initially chosen as the optimal mixture (M1.2N6) to provide the maximum compressive strength and minimum drying shrinkage. However, mix M1.2N6 tended to set quickly and thus necessitated rapid placement and consolidation in the molds. According to a previous study [12], the setting time of the mortar corresponding to a silicate modulus of 1.2 and Na\({}_{2}\)O content of 6% is too short, limiting its potential applications. However,  
Figure 8: Effects of silicate modulus and Na\({}_{2}\)O content on (**a**) compressive strength of AASC specimens; (**b**) drying shrinkage of AASC specimens.  
the setting time of M1.2N8 was somewhat longer than that of M1.2N6, while the compressive strength of M1.2N8 was 96.8%, 99.2%, 98.8%, and 91.3% that of M1.2N6 after 3, 7, 14, and 28 days, respectively, and never less than 45 MPa. Additionally, the drying shrinkage of M1.2N8 was only 15% greater than that of M1.2N6, or about 0.02% after 28 days. As a result of these concerns and the relative suitability of its other material properties, Mix M1.2N8 was used to prepare the AASCHBs investigated in this study.  
The determined mechanical and physical properties of the AASCHBs are provided in Table 4. The average compressive strength was 17.2 MPa with a coefficient of variation of 7.4% and a minimum value of five compressive strength tests of 15.4 MPa. According to Chinese Standard GB/T 8239-2014, a strength grade of MU15 was determined for the AASCHB using the average and minimum compressive strengths as they were greater than 15.0 MPa and 12.0 MPa, respectively. The flexural strength (1.2 MPa) of the AASCHB was nearly 7% of its compressive strength. The water absorption of the AASCHB was 9.95% with a coefficient of variation of 5.4%. The water absorption and drying shrinkage of a load-bearing hollow concrete block should be less than 10% and 0.045%, respectively, according to Chinese Standard GB/T 8239-2014. Consequently, the AASCHB cast from Mix M1.2N8 can be classified as having a strength grade of MU15 and is therefore suitable for use as a load-bearing hollow concrete block.
## 9 Applications of Machine Learning
Some key ML applications in satellite communication include [157; 158; 159; 160]:
* Interference detection and mitigation: ML algorithms can identify and classify interference sources in satellite signals, enabling operators to maintain optimal system performance. * Flexible payload configuration: ML techniques, particularly deep learning, can optimize resource allocation within satellite payloads, allowing the system to adapt to changing user demands and improve capacity utilization. * Congestion prediction: ML-based forecasting can predict network congestion in satellite communication systems, enabling operators to proactively manage network resources and avoid service disruptions. * Spectrum management: ML can assist in dynamic spectrum allocation and interference mitigation, leading to more efficient use of available spectrum and better overall system performance. * Anomaly detection: ML algorithms can analyze telemetry data to detect anomalies or potential issues in satellite systems, enabling operators to address problems before they escalate. * Orbit determination and prediction: ML can improve satellite orbit prediction accuracy, facilitating better planning for satellite-based services, collision avoidance, and space debris tracking.