esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
For example, the mixture 1825 LC (Institute of Chemistry, Military University of Technology, Warsaw, Poland) exhibits quite high birefringence (\(\Delta n\sim\)0.38) at THz frequencies [21]. Therefore, the thickness of LC can be reduced by more than 50% in comparison with the widely used E7 (Merck, KGaA, Darmstadt, Germany, \(\Delta n\sim\)0.14 [5]) and MDA-00-3461 (\(\Delta n\sim\)0.2 [8]), which both need to achieve a 90\({}^{\circ}\) phase shift at 1.2 THz. Moreover, the pristine PEDOT: PSS electrode layer, while the conductivity is lower than doped films, may be suitable enough to reorient the LC molecules properly prepared in such a TN-LC cell with reduced thickness and to achieve the 90\({}^{\circ}\) phase shift with reasonable driving and threshold voltage. Thus, a pristine PEDOT: PSS electrode-based TN-LC cell combined with LCs with high birefringence, such as mixture 1825, could be attractive for THz applications such as phase shifters. For this paper, we first investigated the transmittance properties of spin-coated pristine PEDOT: PSS layers with different thicknesses for the frequency range of 0.2-1.2 THz. In addition, we measured the THz optical properties such as ordinary refractive index (\(n_{\mathrm{o}}\)), extraordinary refractive index (\(n_{\mathrm{e}}\)), birefringence (\(\Delta n\)), absorption coefficient (\(\alpha\)), and extinction coefficient (\(\kappa\)) of two types of LC, namely, MDA-00-3461 and mixture 1825, for use in the TN-LC-based THz phase shifters. Next, we demonstrated three different types of LC phase shifters using pristine PEDOT: PSS as TCE. Furthermore, we made a comparative study of TN-LC-based phase shifters with two different MDA-00-3461 thicknesses. Finally, we improved the TN-LC phase shifter performances using the mixture 1825 LC, which made possible the use of thinner cells. A figure of merit (FOM) was defined and used to compare the relative merit of LC THz phase shifters.
## 3 Experiment Results and Analysis
### Analysis of Soil Displacement  
The settlement of the soil is observed by the PIV, Figure 8a shows that the displacement contour obtained by the PIV when the relative settlement \(d\) = 5% of the H3 group. Eleven position points of \(h\) = 0.0, 0.1,..., 1.0 m of the center line of the subsidence area are used as reference points for vertical displacement of the soil.  
Figure 7: Effect of the subsidence width on the tensile force of the geotextile.  
Figure 8b shows the comparison of the vertical displacement curves at the different soil heights on the center line of each model at the end of the settlement. The vertical displacement of the soil of each group has a maximum value at \(h\) = 0 m and decreases gradually with the increase of \(h\), which indicates the vertical displacement of the soil is transmitted from bottom to top. When \(h\) = 0.2 m, the maximum vertical displacement of the H4 group is 12.75 mm and the minimum vertical displacement of the H2 group is 5.62 mm, which is 56% lower than the H4 group. The results show that the vertical displacement of each group is different at the same soil height, which decreases with the decrease of the subsidence width. The vertical displacement of the soil of group H2 at \(h\) = 0.8 m is close to zero, indicating that the influence height of the soil vertical displacement of group H2 within the subsidence area is 0.8 m. When \(h\) = 1.0 m, the vertical displacement of the soil of the H3 and H4 groups are 7.12 mm and 9.89 mm, respectively. It indicates that the influence height of the soil has not been reached, which is related to the early tension failure of the geotextile. It shows that using the geotextile can effectively control the upward development of the vertical displacement of the soil. Based on the observation, when the settlement with a large subsidence width is reached, the vertical displacement of the soil is reduced to 12.75 mm and the minimum vertical displacement of the soil is reduced to 12.75 mm and the minimum vertical displacement of the H2 group is 5.62 mm, which is 56% lower than the H4 group. The results show that the vertical displacement of the soil is reduced to 12.75 mm and the minimum vertical displacement of the soil is reduced to 12.75 mm and the minimum vertical displacement of the H2 group is 5.62 mm, which is 56% lower than the H  
is treated, it is suggested to choose a geotextile with a large tensile stiffness to prevent the geotextile from being pulled and damaged in advance.
## Appendix A
* Step 5: Initialize the customer self-similarity \(s_{\rho}(U_{j,E_{\text{new}}},U_{j,E_{\text{new}}})=\mu_{m,E_{\text{new}}}\) in \(E_{new}\), where \(U_{j,E_{\text{new}}}\) is the jth customer in \(E_{new}\) and \(\mu_{m,E_{\text{new}}}\) is the median value of the customer portrait similarity in \(E_{new}\). * Step 6: Perform AP clustering on \(E_{new}\) to obtain the number of stable clusters as \(Q_{E_{\text{new}}}\), and determine whether customer \(U_{i}\) belongs to the clustering center \(U_{j,E_{\text{new}}}\) based on Equation (A9): \[U_{i}=argmax_{U_{j}}\big{\{}s_{\rho}\big{(}U_{i},U_{j}\big{)}\big{\}}\] (A9)
* Step 7: Based on the clustering results obtained in Step 6, calculate the clustering quality indicator using Equation (A10). \[Z_{Q,av}=\frac{1}{I}\sum\limits_{i=1}^{I}Z_{Q}(U_{i})=\frac{1}{I}\sum\limits_ {i=1}^{I}\frac{d_{out}(U_{i})-d_{in}(U_{i})}{max(d_{out}(U_{i}),d_{in}(U_{i}))}\] (A10)
* Step 8: Update the customer self-similarity using Equation (A11). \[b_{U_{i},mu}=\frac{Q_{U_{i},sum}}{P_{U_{i},max}}=\frac{\int P_{U_{i},v}dt}{P_{ U_{i},max}} \tag{2}\]0 (A11)
* Step 9: Determine whether the number of clusters satisfies \(Q_{E_{\text{new}}}\leq 2\). If so, the iteration ends and goes to step 10; otherwise, return to step 6 to continue the iteration. * Step 10: Compare the corresponding clustering quality indicators under different number of clusters and determine the optimal number of clusters \(c^{*}\) from Equation (2).
# Analytic Binary Alloy Volume-Concentration Relations and the Deviation from Zen's Law
## 2 Theoretical Background

Table 2: The volume size factor data: \(\omega_{1}\) is the volume size factor when the first element of the alloy system is the solute, and \(\omega_{2}\) when the second element is the solute [11]. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Alloy** & \(\Omega_{1}\) (Å\({}^{3}\)) & \(\Omega_{2}\) (Å\({}^{3}\)) & \(\Omega_{1}^{*}\) (Å\({}^{3}\)) & \(\Omega_{2}^{*}\) (Å\({}^{3}\)) \\ \hline Al-Ag & 16.6036 & 17.0578 & 15.3642 & 16.6193 \\ Al-Cu & 16.6036 & 11.8072 & 15.0820 & 11.8247 \\ Al-Mg & 16.6036 & 23.2396 & 17.2056 & 27.4045 \\ Al-Mn & 16.6036 & 12.2129 & 14.5684 & 10.4510 \\ Al-Ti & 16.6036 & 17.6542 & 13.8034 & 14.4677 \\ Al-Zn & 16.6036 & 15.2123 & 14.2961 & 15.6559 \\ Cu-Ag & 11.8072 & 17.0578 & 13.0147 & 18.4090 \\ Cu-Au & 11.8072 & 16.9618 & 12.4972 & 17.8913 \\ Cu-Fe & 11.8072 & 11.7771 & 14.1683 & 12.3236 \\ Cu-Ni & 11.8072 & 10.9415 & 11.8423 & 10.9082 \\ Cu-Zn & 11.8072 & 15.2123 & 9.5991 & 14.6320 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The real (experimental) atomic volumes (\(\Omega_{1}\) and \(\Omega_{2}\)) and _apparent_ atomic volumes (\(\Omega_{1}^{*}\) and \(\Omega_{2}^{*}\)) for the selected binary alloys. We introduced _continuum_ and _terminal_ approximations because, according to [11], the apparent Wigner-Seitz radius of the solvent atom was assumed to be constant, Equation (9). This is a reasonable assumption for the limited solubility of the solute component in the solvent matrix. However, in the case of continuous solid solutions, especially in the region around the vicinity of the equiatomic composition, the definitions \"_solvent_\" and \"_solute_\" are meaningless. Thus, we introduce the _continuum_ approximation, Equations (33) and (34), which ensures continuity of the atomic volume of the alloy within the whole compositional range. In the case of the limited solubility of the alloy components, the _terminal_ approximation, it is reasonable to consider the compositional change of the atomic volume of the solute only, Equation (36), assuming that the atomic volume of the solvent does not undergo a significant change with the alloy composition, which validates Equation (35).
## 3 Results and Discussion
### Straight Path-Tracking Test  
The straight path-tracking test site is shown in Figure 11, where the reference path is indicated by a straight line calculated between two points. The initial position of the tractor was obtained by manually driving it into a marking box designated by stones, which ensured that the initial lateral error and heading error between the tractor's \(m\)-frame and the reference path could be measured accurately. The tractor was controlled at speeds of 0.5 m/s and 1 m/s to demonstrate the adaptability of the FSM. The initial lateral error was defined as 2.5 m, and the stabilization time \(T_{s}\) was used as an indicator of the performance of the SM and FSM in guided trajectory. \(T_{s}\) is defined as the adjustment time of the lateral error from 2.5 m to 0.1 m, and the distance of the guided trajectory is written as \(D_{guide}=T_{s}\times V_{f}\), where \(V_{f}\) is the average speed of the tractor during the time interval \(T_{s}\).  
The path-tracking results are shown in Figures 12 and 13, and the specific error details are listed in Table 7. Notice that both the SM and FSM converged to the reference path quickly, and, in the case of 0.5 m/s, the \(D_{guide}\) of the FSM was 2.8 m, which is a decrease of 47.2% compared to the SM in the case of a 2.5 m initial lateral error. With an increase in the tractor speed, both the MTE and ATE of the SM and FSM increased for straight path tracking; the STDE increased for the FSM by 9.7% and for the SM by 25%, which indicate that the FSM can handle the stability of path tracking better than the SM under different tractor speeds. The ATE of the FSM was 3.3 cm with the STDE at 3.1 cm, which satisfies the tractor self-driving requirements such as tillage and spraying. \(D_{guide}\) increased with increases in the average speed, whereas the FSM still outperformed the SM with a reduction of 36.6% in terms of \(D_{guide}\). However, the FSM did not handle the large guiding trajectory distance better compared to the SM. With an increase in the travel speed, \(D_{guide}\) for the SM increased by 30.2%, and, for the FSM, \(D_{guide}\) increased by 57.2%. As the gain coefficient of the FSM was adjusted by the fuzzy logic control according to the tracking errors, which neglected the variation in speed and its effect on the curvature change, the convergence rate of the tracking error corresponding to the guiding trajectory could not be fully addressed.  
Figure 11: Straight path-tracking test site.
## 1 Introduction
### Research Objectives  
The majority of the aforementioned research studies focused solely on improving either attitude control performance or altitude controller response. In addition, it must be noted that some DRL algorithms are more efficient for attitude control (e.g., PPO), while another group of DRL algorithms (e.g., DDPG) shows better performance in improving trajectory tracking [46]. In order to compensate for fluctuations in the dynamics of the aerial robot, in the proposed controlling architecture, a reinforcement learning algorithm interacts with the system and learns adaptation policies for actively updating the gains of the controllers. To adjust the parameters of the attitude PID controller, the trained policy actively creates appropriate control gain values. Similarly, the scaling factor of the compensator is updated using actions generated by the RL agent. The aerial RL-based adaption algorithm is trained in a simulated environment in MATLAB software.  
The rest of this paper is organized as follows: In Section 2, the dynamic model of a quadrotor is discussed. In Section 3, the control problem of the aerial robot is addressed, where the altitude and attitude control of the robot is discussed before introducing the proposed RL-based adaptive control framework. The applicability and efficacy of the proposed control strategy are evaluated in a simulated environment in Section 4. Finally, Section 5 summarizes the research findings.
# An Effective Scheduling Algorithm for Coverage Control in Underwater Acoustic Sensor Network
## 3 Energy-Efficient Coverage Control Using Memetic Algorithm
### Algorithm Simulation and Analysis  
#### 3.3.1 Genetic Evolution  
The selection operation selects good individuals from the current group, allowing them to breed the next generation as fathers. According to the above ESACC coding rules, the gene represents the status of the node: \(1\) indicates active, and \(0\) indicates sleep. The chromosomes with higher adaptability have better scheduling methods in the UASNs to handle the SCP problems, with a greater probability of being selected through the selection operation. This paper uses the roulette method, by which independent solutions are selected from a circle, and each individual's probability of selection (reproduction probability) is proportional to its adaptability. The so-called reproduction probability is defined as  
\[P_{i}=f_{i}/\sum_{j=1}^{K}f_{j} \tag{14}\]  
where \(f_{i}\) is the adaptability of each individual \(a_{i}\), and \(k\) is the population size. Interleaving in genetic algorithms means that new individuals can be created. In ESACC, each gene represents the state of a network node. In the scheduling algorithm of network nodes, the state changes of nodes are unrelated. Therefore, this paper selects the random-point crossover method to reproduce. Mutation applies  
Figure 3: Relationship between values of \(F^{k}\), \(\alpha\) and \(\beta\).  
only to offspring individuals that have been born from two parents. In a genome set composed of all offspring individuals, the genome is selected for mutation with mutation probability \(P_{m}\). Mutation is an important flexible search operator, which can force the algorithm to search for new regions, thereby helping the genetic algorithm avoid premature convergence and resulting in a local optimal solution. In this paper, the mutation probability is the reciprocal of the length of the chromosome, which is theoretically proven to be optimal [27].
# Fully Open-Source Meeting Minutes Generation Tool
## 4 Selection of Open-Source Models
### Selection of Audio-to-Text Models  
The audio from each segmented video is extracted and converted into text. Selecting an accurate model is essential to ensure that subsequent processes accurately reflect the information conveyed in the audio. Additionally, model efficiency is essential to reduce computational time. Experiments were conducted to measure the _WER_ and the computational duration (in seconds) of models from _HuggingFaceHub_ to identify the most suitable model. Table 2 shows the results.  
Table 2 compares the accuracy of _WER_ and the computational duration. The _Whisper_ model achieved the lowest _WER_ and the shortest duration. While _Wav2 Vec_ had a relatively short duration, its _WER_ was the highest. Therefore, the _Whisper_ model is the most suitable and, thus, is selected for the system's _audio-to-text_ conversion.
# Vibro-Acoustic Performance of a Sandwich Plate with Periodically Inserted Resonators
###### Abstract  
The vibro-acoustic performance of a sandwich plate with periodic locally resonant (LR) units is examined in this paper, with specific focus on the effect of periodic resonators on the average radiation efficiency and the acoustic radiation to the far field. In order to assess the radiation performance, the band-gap properties of an infinite periodic structure and the vibrational response of a finite periodic structure are first studied with closed-form solutions. Subsequently, the acoustic radiation efficiency of the LR sandwich plate is obtained using the concepts of modal radiation. It is shown that the acoustic radiation power can be reduced significantly, not only in the band-gap but also at frequencies close below the band-gap, due to either the decrease in radiation efficiency or the decrease in the vibration response. Thus, the periodic resonators provide a broader attenuation band for the purposes of noise reduction than for vibration reduction. However, for frequencies close above the band-gap, the acoustic performance became worse, owing to the increase in acoustic radiation efficiency. Fortunately, the increased sound radiation above the band-gap can be reduced by adding a small damping to the resonator, which further broadens the attenuation frequency band. The reason for the variation of acoustic radiation efficiency is also studied and can be physically explained by the effective mass of an LR unit, where increased mass corresponds to decreased radiation efficiency and decreased mass corresponds to increased radiation efficiency. Thus, the effective mass can be a useful parameter for designers to estimate which frequency component will be acoustically reduced or acoustically enhanced in a practical design.  
periodic sandwich plate; periodically inserted resonators; radiation efficiency; vibro-acoustic reduction; local-resonance band-gap +
Footnote †: journal: Applied Physics Letters
## 3 Results
### Flatness  
We measured and comparatively analyzed different frequency spacings of OFCs, as shown in Figure 3. The OSNR test was performed in the optical resolution bandwidth of 100 MHz. Figure 2, the number of flat comb lines is related to the maximum power shift with different frequency spacing. It is shown that the flatness of the 25 GHz OFC is better than the 37.5 and 75 GHz OFC by adjusting the modulation depth applied on the DD-MZM. The optical signal to noise ratio (OSNR) of the optical modes is also investigated, and the OSNR of the frequency spacings at 25, 37.5 and 75 GHz within the 10 dB bandwidth are shown in Figure 3. The results show that the OSNR at 25 and 37.5 GHz frequency spacing is significantly lower than that at 75 GHz frequency spacing.  
Apparently, the comb lines generated are not flat in broadband, which is partially due to the characteristics of the DD-MZM. Restricted by the characteristics of commercial devices, the power deviation of the comb lines cannot be eliminated.
###### Abstract  
The aim of this study was to produce several flavor esters via esterification of octanoic acid with different commercial short-chain alcohols (methanol, propanol, isoamyl alcohol, hexanol and benzyl alcohol) and fused oil in solvent-free systems. Lipase Eversa(r) Transform 2.0 immobilized via mechanism of interfacial activation on poly(styrene-divinylbenzene) (PSty-DVB) beads was used as heterogeneous biocatalyst and its catalytic performance was compared with that of the soluble lipase. The heterogeneous biocatalyst was prepared by employing 5 mmol-L\({}^{-1}\) buffer sodium acetate at pH 5.0 and 25 \({}^{\circ}\)C using an initial protein loading of 40 mg-g\({}^{-1}\). The maximum amount of immobilized protein reached was 31 mg-g\({}^{-1}\), corresponding to an immobilization yield of 80%. Mass transfer studies demonstrated that the lipase was preferentially adsorbed inside the pores of the support, which was confirmed by scanning electron microscopy analysis. Lipase immobilization can be described by a pseudo-first-order kinetic model via a physisorption process. When used as biocatalysts of the target reactions, the highest conversion percentage (between 65% and 85% of acid conversion after 60-90 min of reaction) values were achieved for esterification reactions catalyzed by immobilized lipase. Reusability tests revealed high retention of the original activity of the immobilized lipase after six successive batch reactions using isoamyl alcohol (47%) and fused oil (72%). The proposed reaction systems can be considered green processes (EcoScale score above 80), with exception of methanol medium, classified as an acceptable green process (EcoScale score of 68). These results show that the heterogeneous biocatalyst prepared can be an economic and sustainable option for flavor esters production on an industrial scale.  
lipase Eversa(r) Transform 2.0; immobilization; adsorption studies; flavor esters; environmental assessment
# Detection of Precipitation and Fog Using Machine Learning on Backscatter Data from Lidar Ceilometer
## Preliminaries  
### Random Forest  
Random forest, an ensemble learning method used in classification and regression analysis, is designed to output the mode of the classes or the average forecast value of each tree by training multiple decision trees. The first proper random forest was introduced by [PERSON] (1975). To build a forest of uncorrelated trees, random forest uses a CART (Classification and Regression Tree) procedure combined with randomized node optimization and bagging. The two key elements of random forest are the number of trees and the maximum allowed depth. As the number of random trees increases, random forest generalizes well, but the training time increases. The maximum allowed depth is the number of nodes from the root node to the terminal node. Under-fitting may occur if the maximum allowed depth is small, and over-fitting may occur if it is large. This study set the number of trees to 100 and did not limit the maximum allowed depth.
## 4 Results and Discussion
### Fit of Durations Simulation
#### 4.2.4. Santiago Hydroelectric Project  
The evaluation was performed in months 10, 20, 30, 40, 50, and 60 after starting the simulated execution of the project, and in months 10, 8, 5, and 1 before the simulated completion of the project.  
Figure 4: Evolution of the probability of fit of the EVM and CTCR methods to the forecast of the final period of the Sopladora hydroelectric project.  
Figure 3: Evolution of the probability of fit of the EVM and CTCR methods to the forecast of the final period of the Mazar Dudas hydroelectric project.  
Figure 5 presents the time evolution of how the EVM and CTCR methods fit each other in their forecasts of the final completion time of the Santiago project, according to the ten thousand scenarios of the simulation model. It is apparent from the first months of execution that the CTCR methodology presents a greater probability of fit than the EVM method. This indicates that CTCR predicts the period to completion of the Santiago project more efficiently than EVM. It is apparent that in month 10 before completion of the project, CTCR has a probability of 43.6% of correctly predicting the project's period of completion, while in the same period, EVM achieves a probability of only 33.9%. Similarly, in month 5 prior to completion, CTCR shows a probability of 92.1% compared with 59.8% for EVM.
# Data Processing and Analysis of Eight-Beam Wind Profile Coherent Wind Measurement Lidar
## 2 Materials and Methods
### Calculation of the Wind Profile Lidar's Carrier-to-Noise Ratio  
The carrier-to-noise ratio (CNR) is an important index used to detect the performance of Lidar. The CNR directly determines the Lidar detection height. The CNR is dependent on both the instrument and the atmospheric variabilities within the resolution volume. In the radar system, the carrier-to-noise ratio refers to the ratio of the under modulated echo signal to the noise power. The accuracy of the velocity estimation is mainly determined by the CNR [48], that is, the carrier-to-noise ratio can be expressed as  
\[\text{CNR}=\frac{P_{s}}{P_{n}}, \tag{9}\]  
where \(P_{s}\) is the power of the unmodulated echo signal, and \(P_{n}\) is the noise power. For each range-gate signal, subtract (para minus) the base noise signal of the first range-gate, and add the mean value of the base noise, and the resulting spectrum will be a flat base spectrum as shown in Figure 5. Then, find the maximum value of the power spectrum after denoising, and find the first point with a different downward trend on both sides successively (Figure 5). Divide the area of the red part by the area of the entire purple part, and the result is the CNR. In this paper, a threshold technique based on the CNR was applied to verify the validity of the data. The manufacturer of the Stream Line Doppler Lidar suggests using a threshold signal-to-noise ratio (SNR) of \(-18.2\) dB (0.015) [49].