esa-sceva/satcom-chunk-collection huggingface dataset:
# Off-Resonance Control and All-Optical Switching: Expanded Dimensions in Nonlinear Optics
## 2 Conventional Nonlinear Optics
\begin{array}{ll}\beta_{ijk}(-2\omega;\omega,\omega)&=\frac{1}{2}\sum_{\tau \
eq\tau}\left\{\left(\frac{\mu_{i}^{0}\mu_{j}^{\omega}\mu_{k}^{\tau}\mu_{k}^{ 0}}{(E_{0}-E_{\tau}+2\hbar\omega)(E_{0}-E_{\tau}+\hbar\omega)}\right.\right.+ \left.\left.\frac{\mu_{j}^{0}\mu_{i}^{\omega}\mu_{k}^{\tau}\mu_{k}^{0}}{(E_{0} -E_{\tau}-\hbar\omega)(E_{0}-E_{\tau}+\hbar\omega)}\right.\right.\\ &+\left.\left.\frac{\mu_{j}^{0}\mu_{j}^{\omega}\mu_{k}^{0}\mu_{l}^{0}}{(E_{0} -E_{\tau}-\hbar\omega)(E_{0}-E_{\tau}-2\hbar\omega)}+\left\{j\leftrightarrow k \right\}\right\}\right\}\end{array} . \tag{6}\]  
The rate of SHG for a collection of \(N\) molecules is determined from the Fermi rate rule \(\Gamma=2\pi\hbar^{-1}\rho_{l}\left|M_{fi}\right|^{2}\), where \(\rho_{l}\) is the density of final states for the radiation. In terms of the second harmonic radiant intensity \(I\), we find the result  
\[2\hbar ck\frac{\text{d}\Gamma}{\text{d}\Omega}=I=\frac{\overline{l}_{0}^{2}g^ {(2)}k^{4}N^{2}}{2\pi^{2}\varepsilon_{0}^{3}c}\left|\overline{e}_{i}e_{j}e_{k }\left(\beta_{ijk}^{(\xi_{i})}(-2\omega;\omega,\omega)\right)\right|^{2} \tag{7}\]  
where \(\overline{l}_{0}\) is the mean irradiance of the input beam (note its quadratic dependence), \(g^{(2)}\) is the degree of second order coherence, and angular brackets denote the rotational averaging required only if the sample is a structureless fluid or an isotropic solid. The average of the third-rank hyperpolarizability tensor is then \(\left\langle\beta_{ijk}\right\rangle\approx\varepsilon_{ijk}\varepsilon_{ \lambda\mu\
u}\beta_{\lambda\mu\
u}\), which produces \(\varepsilon_{ijk}\overline{e}_{i}e_{j}e_{k}=0\) on account of contracting the totally antisymmetric isotropic third-rank Levi-Civita tensor \(\varepsilon_{ijk}\) with the \(j\), \(k\)-symmetric polarization component product. Hence, we secure the well-known result that SHG (and indeed all even-harmonic generation) is forbidden in randomly oriented or isotropic samples. In all other cases, the result is readily rewritten in terms of corresponding bulk quantities. Here, the latter implies the use of the second-order optical susceptibility, \(\chi\) (-2\(\omega\), \(\omega\), \(\omega\)). Second harmonic generation is the simplest nonlinear optical process, and the methods briefly outlined here form the basis of the analysis that follows [50].
## 4 Interface Selection
### Experimental Setup for Interface Selection  
_Raspberry Pi 4B_ is a small, low-powered, card-sized, and single-board computer. It can be used in a variety of practical systems requiring some computation or networking abilities. It has a built-in wireless network interface (NIC) that supports IEEE 802.11n and 11 ac. By using _hostapd_ software, it can be used as a _software AP_ in WLAN [53].  
_Archer T4U_ is a wireless adapter and is designed to work with the latest wireless standards, including IEEE 802.11 ac, to allow faster data transfers and wider ranges. It has a high-performance antenna that is designed to improve the signal strength and reduce interference. Therefore, we can realize the AP that has two network interfaces with a low cost.  
In WLAN, the throughput performance can be different by the protocol selections of the embedded NIC and the external one. We consider the two groups for _Raspberry Pi_ and _Archer T4u_ adapter for measurements as follows:  
**Group A**: Raspberry Pi for 11n at \(2.4\ GHz\) and external for 11 ac at \(5\ GHz\).  
**Group B**: Raspberry Pi for 11 ac at \(5\ GHz\) and external for 11n at \(2.4\ GHz\).  
Firstly, we conducted experiments by measuring the _single link throughput_ for two network topologies. Figure 6 illustrates the experimental field on the _3 rd floor of Engineering Building_ #2 at Okayama University. The host locations are represented by circles, and the AP locations are represented by triangles. The \(40\ MHz\) bonded channel is used for both interfaces. The dual interfaces were configured through the commands in [33].
## 6 Evaluation
### Ablation Study (RQ3)  
We conducted a series of ablation experiments to measure the impact of the various design choices behind SqliGPT. The comparisons included LLM4 Sqli-Sqlmap, SqliGPT-Strategy, which adds only the Strategy Selection Module to LLM4 Sqli-Sqlmap, and SqliGPT itself. After rigorous testing on the SqliMicroBenchmark, the performance of the three configurations was evaluated in terms of the number of SQLI vulnerability detections and HTTP requests, as shown in Table 3. Our key findings are as follows:  
1. In terms of efficiency, SqliGPT and SqliGPT-Strategy demonstrate superior results compared to LLM4 Sqli-Sqlmap, which clearly confirms the effectiveness of the Strategy Selection Module. Additionally, we observe a slight improvement in the number of detected SQLI vulnerabilities of SqliGPT-Strategy compared to LLM4 Sqli-Sqlmap for the base difficulty targets. This improvement can be attributed to the comprehensive enumeration of potentially injectable parameters by the Strategy Selection Module, which tests them individually. This approach ensures thorough testing of suspected injection points by LLMs and reduces the risk of missing potential injection points due to the random nature of LLMs generation.
2. In terms of effectiveness, SqliGPT stands out as a clear winner, significantly outperforming LLM4 Sqli-Sqlmap and SqliGPT-Strategy, particularly in the number of detected SQLI vulnerabilities for advanced difficulty targets. Without the Defense Bypass Module, LLM4 Sqli-Sqlmap and SqliGPT-Strategy are unable to accurately identify and effectively bypass defenses in advanced difficulty targets. The addition of the Defense Bypass Module equips LLMs with the ability to identify and bypass defense mechanisms, thereby increasing the number of detected SQLI vulnerabilities for advanced difficulty targets.
# Vision-Based Autonomous Vehicle Systems Based on Deep Learning: A Systematic Literature Review
## 3 Analysis of Domain
### Decision Making
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Ref.** & **Method** & **Outcomes** & **Advantages** & **Limitations** \\ \hline \multirow{2}{*}{[121]} & \multirow{2}{*}{MARL} & Obtained higher rewards than expert rule-based approach. & Resolved the limitation of coordination problem in autonomous driving. & Individual learning of RL agents involved high computational cost. \\ \hline \multirow{2}{*}{[122]} & \multirow{2}{*}{Weighted hybrid SVM} & Max 94.7\% accuracy for lane changing task. & Faster decision making in different traffic conditions. & Yet to demonstrate for critical and uncertain driving scenes. \\ \hline \multirow{2}{*}{[131,132]} & \multirow{2}{*}{AC-DRL with DDPG} & \multirow{2}{*}{1.43\% collision rate.} & Autonomous braking system while lane shifting with high accuracy. & Complexity with large training dataset and decreased the convergence rate. \\ \hline \multirow{2}{*}{[125]} & \multirow{2}{*}{DDQN, TD3, SAC} & In best case SAC achieved 86\% success rate. & Decreased sample complexity with visual encoding. & Lack of exploration caused failure cases of DDQN and TD3. \\ \hline \multirow{2}{*}{[126]} & \multirow{2}{*}{Dueling DQN} & Able to avoid collision after lowest & Develop lane-changing decision-making strategy for AVS on highway. & Still needed to improve training process for feasible decision making strategy. \\ \hline \multirow{2}{*}{[127]} & \multirow{2}{*}{Monte Carlo Tree Search} & 70\% success rate for highway exist case. & Combines planning stage to make efficient driving decision on highway. & Required huge training samples and did not consider lead vehicles’ behavior. \\ \hline \multirow{2}{*}{[128]} & \multirow{2}{*}{MOMD + SARSOP} & 91.67\% less collision and 25.9\% enhanced response rate. & Efficient overtaking decision without rule-based system and optimum actions. & Did not consider real-time speed and distance value. \\ \hline \multirow{2}{*}{[129]} & \multirow{2}{*}{MGRL} & Almost 100\% safety index to reach goal without collision. & Outperformed overtaking, collision avoiding, arriving at seven RL goals. & Insignificant performance to keep lane while overtaking. \\ \hline \multirow{2}{*}{[130]} & \multirow{2}{*}{Bayesian network} & Higher driver acceptance while & Collision avoidance with path prediction and threat assignation. & Testing in real and challenging driving scene was not mentioned. \\ \cline{3-5}  & & Better lateral and steering angle than ground truth. & Able to perform emergency braking decision at safe distance. & Only performed best in straight-road scenario. \\ \hline \multirow{2}{*}{[132]} & \multirow{2}{*}{LSTM with Fuzzy Logic} & \multirow{2}{*}{92.40\% accuracy.} & Decision based on the behavior of surrounding vehicles. & Urban or real-life traffic conditions were not considered. \\ \cline{3-5}  & & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Summary of multiple deep learning methods for decision-making process.
## 4 Results
### Models for Comparison  
To confirm the superiority of the proposed integration scheme, three state-of-the-art methods of PLF are adopted as comparisons, which are successively quantile regression, prediction interval and confidence interval methods.  
**Model 1** (\(QRNN\)): The quantile regression neural network (QRNN) model discussed in [48] is directly used for PLF. The Huber function [47] is the substitute of the loss function, which is a hybrid of the absolute value and squared error functions.  
**Model 2** (\(GELM\)+\(NN\)): The statement of this model can be found in [28]. A wavelet processing with Daubechies is applied to the generalized ELM for load forecasting. The total uncertainty for PLF is the summation of model uncertainty and noise uncertainty. The noise is predicted by a neural network.  
**Model 3** (_hybrid ELM+Bootstrapping_): This model is an extension of the load forecasting model published in [40]. The hybrid neural network with an ensemble feature selection method is used as the main part of load forecasting, while the bootstrapping method works for PLF. This model is also regarded as the reference of the proposed model that neglects the noise factor.  
Figure 8: Load data of the second and third quarters in 2005.
# Convolutional Adaptive Network for Link Prediction in Knowledge Bases
## 2 Related Works
### Translational Distance Methods  
The TransE [16] method is the most well-known translational distance method. For a given triplet (_head entity_, _relation_, _tail entity_), denoted as (\(h\), \(r\), \(t\)), TransE regards the relation as a translation from the head entity to the tail entity and assumes that \(\mathbf{h}+\mathbf{r}\approx\mathbf{t}\), provided that the triplet is correct. The embedding representation is denoted by the bold letters \(\mathbf{h}\), \(\mathbf{r}\), and \(\mathbf{t}\), respectively. Simply and quickly, TransE achieves outstanding performance for link prediction in comparison with previous methods. However, the method has issues when modeling complex relations, including the N-1, 1-N, and N-N relations.  
To address this issue, other strategies have been designed to split entities and relations into separate subspaces. For example, TransH [17] posits that entities should exhibit various characteristics in contrast to one another, and it projects the head and tail entities onto a hyperplane that is connected to the relation. Furthermore, TransR [18] considers that the embeddings of relations and entities should be in respective spaces. It projects the entity into the relation space by generating a projection matrix associated with each relation. TransD [19] further learns two projecting matrices to respectively project. Moreover, these matrices are initialized diagonally to reduce the method parameters. TorusE [20] embeds KB relations and entities in a lie group. The method points out that regularization is unnecessary during the training process if the embedding space is in a compact lie group. However, the results on several datasets showed that translational distance methods have weak improvements in terms of modeling complex relations. Furthermore, these methods only learn less expressive features, thus potentially limiting their performance.
# Halophyte Plants and Their Residues as Feedstock for Biogas Production--Chances and Challenges
## 5 Halophyte Composition
### Lignocellulose Content  
Halophyte biomass cultivated from their native environments, inland and coastal salt marshes contained 9-37% cellulose, 11-39% hemicellulose and 2-10% lignin based on the dry matter of the aerial fractions of the plant [1; 79]. However, the lignocellulosic contents were significantly lower in hydroponically grown halophyte biomass. For instance, on a dry matter basis, [PERSON] et al. [73] reported the aerial fractions of _Tripolium pannonicum_ grown under 0, 15 and 30 g L\({}^{-1}\) NaCl contained 6.7, 1.8 and 2.2% cellulose and 5.7, 8 and 9.5% hemicellulose, while [PERSON] et al. [74] reported in dry crop residues of the aerial part of _Chenopodium quina_ 16.6, 19.1 and 17.4% cellulose and 9, 8.9 and 10.1% hemicellulose in plants cultivated with 0, 10 and 20 g L\({}^{-1}\) NaCl, respectively.
# Parametric Analysis Towards the Design of Micro-Scale Wind Turbines: A Machine Learning Approach
## 1 Introduction
have studied an H-type vertical-axis wind turbine to improve the performance by changing the blade pitch angle. The power coefficient of the turbine for different Reynolds numbers and blade pitch angles was studied. They performed the simulations on QBlade software and validated the results with the experimental findings [17]. [PERSON] et al. have compared the results of QBlade simulations to CFD simulations over horizontal axis turbine blades to calculate the aerodynamic loads. They found a very good agreement between QBlade and CFDs, especially regarding the normal loads. The tangential loads were not in very good agreement with CFDs; however, for better modeling of wind conditions, the accuracy of simulating tangential loads increases [18]. Further studies have utilized QBlade for design and simulation. [PERSON] et al. have used QBlade to optimize the chord and twist angles of the blade. The blade load results from QBlade were used for a Finite Element Analysis (FEA) using Ansys software [19]. [PERSON] et al. have also relied on QBlade to design a vertical-axis wind turbine. Their main finding was the accuracy of QBlade software which was validated with an experiment, after changing many design parameters and wind conditions [20]. QBlade has also been used to optimize wind turbine design parameters. [PERSON] et al. have performed simulations by changing the blade lengths and wind speeds. The outputs of the simulation included important characteristics and outputs like the turbine power, and coefficients of lift, drag, and power. The work resulted in an optimized turbine with a small capacity for domestic applications [21]. [PERSON] et al. have also used QBlade to optimize a small-scale wind turbine but for different parameters like the twist and chord distributions of the blade. The optimized turbine could achieve a coefficient of power as high as 0.45. The extensive research on wind turbines made using QBlade proves that it is a reliable tool, and its results could be used for the effective design and simulations of different types of wind turbines with no further validations. To ease the design process, artificial intelligence could be of great assistance. It has been widely used in applications of renewable energy for the prediction of power output or design of renewable energy systems' related components [22; 23]. Machine learning and deep learning algorithms were also effectively used to analyze the energy potential and detect faults in renewable energy systems [24; 25], or for forecasting loads on smart grids or energy management in wind farms [26]. So far, machine learning has not been used as a design tool that can replace extensive simulations on the different wind turbine analysis software. In this work, different supervised machine learning models were employed to provide a simple, accurate, and fast method for preliminary designs of wind turbines.
## 3 Three-Dimensional OSD Finite Element Model
### ASME TTSL Method  
The ASME TTSL is claimed to be mesh insensitive, but SHSS results did not verify that statement, as shown in Figure 7a-d. There were some variations in the results, but stress values were more converged as compared to other methods, especially when the nodal averaging feature was turned on. The deck and the rib-toe stress results varied from 58-78 MPa and 14-20 MPa, respectively. This window was reduced to 60-66 MPa in the case of the deck-toe when the nodal averaging feature was used, which demonstrates its importance in this method. The TTSL method showed slightly higher results at the deck-toe as compared to other methods, and it was quite evident in 1/2-CH20E elements while the nodal averaging feature was turned off. At the rib-toe, two distinguished lines converged at one point, unlike in other methods (Figure 7a-d). Similar to deck-toe results, CH20E elements showed higher stresses at the rib-toe but, in this case, nodal averaging did not reduce them. Overall, SHSS results were more consistent as compared to other methods except for 1/2-CH20E and 8-CQ4 elements, which gave higher and lower stress results, respectively. Nodal averaging had a major influence on SHSS results, especially in the case of the deck-toe, where the results were significantly converged.
# Principles and Methods of Servomotor Control: Comparative Analysis and Applications
## 4 Control Methods of Servomotors
### Two-Loop Control  
Two-loop control connects feedback loops, typically consisting of positional and velocity loops. This method enhances the efficiency of motor control by ensuring precision in positioning and speed. Two-loop control allows a balance between system dynamics and control accuracy to be maintained, so the application of this control method is common in medical devices and automatic manufacturing lines.  
The positional loop is designed to control the position of the servomotor's output shaft and sets the direction of its movement to a specified point. A control signal for the electric motor is created by comparing the current position with the set position using a potentiometer. The control signal is generated to minimize positioning errors [42; 84].  
The velocity loop aims to stabilize the rotational speed of the servomotor's output shaft. The feedback from the velocity loop measures the current rotational speed and compares it with the desired speed, calculated based on the positioning error from the positional control loop [42; 84].  
The mathematical apparatus of the two-loop control is dance such as current control. The difference between these two apparatuses is the amount of regulators used for each loop.  
The advantages of two-loop control include the following [84; 85; 86]:  
1. Precision in positioning.
2. Stability of the speed control system.
3. Dynamic response. Interaction between both loops allows the servomotor control system to respond promptly to changes.
4. Integration with other methods. A two-loop control-based system easily integrates with control systems based on other methods, such as field-oriented control.  
The main drawback of two-loop control is the requirement for the precise tuning of the regulator coefficients in both loops. Achieving optimal control and stable system operation demands more complex mathematical calculations. However, the mathematical calculations required are much less than that of more advanced methods, such as FOC and fuzzy logic [84; 85; 86].
## 2 Related Work  
[PERSON] et al. [9] and [PERSON] et al. [11] put forward the Dlatonization process for protanopia (L-cone) and deuteranopia (M-cone) compensation, respectively. As shown in Figure 1, in [9, 11], a compensation image, \(\mathbf{I}_{COM}\), was generated by referring to the original image, \(\mathbf{I}_{SRC}\), and the CVD simulated image, \(\mathbf{I}_{SIM}\). The \(\mathbf{I}_{COM}\) is then superimposed over the \(\mathbf{I}_{SRC}\) to synthesize the target image, \(\mathbf{I}_{TAR}\); this image helps individuals with protanopia or deuteranopia identify red and green, which they often struggle to distinguish. In the deuteranopia case [11], \(\mathbf{I}_{SIM}\) was obtained by removing the information concerning the M-cone from the \(\mathbf{I}_{SRC}\). The difference between the \(\mathbf{I}_{SRC}\) and the \(\mathbf{I}_{SIM}\) was treated as the lost color information, particularly from the G channel, as a result of deuteranopia. Then, the \(\mathbf{I}_{COM}\) was generated by distributing the lost G channel information across the R and B channels through the following calculations:  
\[\begin{bmatrix}R_{COM}\\ G_{COM}\\ B_{COM}\end{bmatrix}=\begin{bmatrix}1&0.7&0\\ 0&0&0\\ 0&0.7&1\end{bmatrix}\begin{bmatrix}R_{SRC}-R_{SIM}\\ G_{SRC}-G_{SIM}\\ B_{SRC}-B_{SIM}\end{bmatrix}. \tag{1}\]  
In this paper, we applied [PERSON]'s model [18] to \(\mathbf{I}_{SRC}\) for the deuteranopia simulation and use the same process as [11] to compute the compensation image. With the appearance of augmented reality (AR) techniques, CVD compensation via wearable devices has become an emerging research topic. [PERSON] et al. [10] and [PERSON] et al. [13] both proposed CVD compensation solutions with OST-HMDs. In [10, 13], the \(\mathbf{I}_{COM}\) was shown on an OST-HMD superimposed over the realistic scene. However, as already mentioned, the issue of color distortion in OST-HMDs cannot be ignored. In addition, there must be subtraction of arriving light during the Dlatonization process, but current OST-HMDs only add light to the background and are not capable of subtracting light.
## 2 Materials and Methods
### Micro-Meteorological Conditions Measurement  
In 2015, continuous observations of carbon-water-energy-meteorological factors were made on building a flux tower in a cold-temperate larch forest. The radiation range radius of flux tower is 1000 m. The tower was equipped with passively shielded HMP155 probes (Vaisala, Vantaa, Finland) at 20 m, 14 m and 9 m above the ground for continuous observation of air temperature (Ta) and relative humidity (RH), and an NR01 sensor (Hukseflux, Delft, The Netherlands) was used for continuous observation of photosynthetically active radiation (PAR). Meteorological measurements were conducted in 5 min intervals and data were saved as 30 min averages by the CR3000 datalogger (Campbell Scientific, Logan, UT, USA).