esa-sceva/satcom-chunk-collection huggingface dataset:
# Design of Swarm Intelligence Control Based on Double-Layer Deep Reinforcement Learning
## 3 Double-Layer Deep Reinforcement Learning Design for Swarm Intelligence Control
### Outer-Layer Manager Optimization Mechanism for System Control  
The outer-layer manager optimizes the control strategy by learning from its hidden states and historical data. The manager observes the agents' rewards, positions, task completion status, and competitive states, but cannot directly assess the agents' intrinsic abilities or attributes. The manager employs a unified strategy, selecting the optimal control strategy based on historical experience to optimize the performance. The manager's objective is to maximize the system's overall task effectiveness, as shown in the following formula:  
\[\forall i:max\ E_{\tau\sim\tau_{c},A\sim\tau,s^{\prime}\sim\tau}\left[\sum_{t= 1}^{H}\gamma^{t}*\underbrace{CTE_{t}}_{\tau_{c,t}}+CTE_{1}\right] \tag{4}\]  
\[Cra(z)=\frac{z^{1-\eta}-1}{1-\eta},\ \ \eta>0 \tag{5}\]  
\[TCE_{t,i}=Cra(Reward_{t,i})-Cost_{t,i} \tag{6}\]  
\[CTE_{t}=\frac{\sum_{t=1}^{i=N}TCE_{t,i}}{N}\ \frac{\sum_{i=1}^{i=N}Task_{t,i}}{N} \tag{7}\]  
where the \(Cra\) function exhibits marginal effects, and the parameter \(\eta\) controls the degree of nonlinearity: the higher \(\eta\) is, the more pronounced the nonlinearity. \(Reward_{t,i}\) represents the reward of the agent, \(i\), from \(t-1\) to \(t\); \(Cost_{t,i}\) is the corresponding energy cost; and task completion efficiency, \(TCE_{t,i}\), is a concave function that increases the agent's reward while nonlinearly reducing the energy cost. In this paper, task completion efficiency is also referred to as energy efficiency. To balance the task completion amount and efficiency in the system, this paper draws on the definition of social welfare [35] in sociology, and proposes an evaluation metric, which is the comprehensive task effectiveness \(CTE_{t}\) at time \(t\), whose value is equivalent to the manager's instantaneous reward, \(r_{c,t}\).  
At the system level, the manager is responsible for the global management and performance optimization of the entire swarm intelligence control system. It utilizes historical control strategies and interaction behavior data to adjust the strategy network, altering the agents' income structure to indirectly influence their rewards and maximize comprehensive task effectiveness. Under this strategy, the manager and agents each pursue the maximization of comprehensive task effectiveness and the optimization of their behavior to adapt to the environment. This process reflects a Stackelberg game, where the manager acts as the leader and the agents as followers responding to strategies [36].
## 2 Materials and Methods
### Numerical Simulation Method  
For site seismic response, the characteristics of ground motion can be represented by the amplitude and frequency spectrum of the ground motion. In the site seismic response analysis, acceleration time-history and response spectrum acceleration are important parameters to characterize ground motion. For numerical analysis, DEEPSOIL (1D ground response analysis software) and ABAQUS (finite element analysis software) were used to analyze and compare different types of seismic input motions. In this study, the seismic input motions used are 10 Hz with peak ground acceleration (PGA) equal to 0.07 g and 8 Hz with PGA equal to 0.05 g.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## 3 Prediction of Long-Term Settlement
### Statistics of Volume Loss  
In the case of metro construction in Changzhou, the surface settlement data were carefully recorded and collected. The measured settlement data of 59 cross sections collected from Line 1 and Line 2 were used as a dataset. The \(S_{\text{max}}\) and \(i\) are obtained by performing Gaussian distribution fitting on the selected data. Then the \(V_{l}\) and \(K\) are converted using Equations (2) and (3). Figure 4a shows the distribution of volume loss \(V_{l}\) for different soil types. Because of the minor differences in shield diameter for different projects, the buried depth \(H\) was normalized by the tunnel diameter \(D\), which is a common presentation way in other studies [18]. As given in Figure 4a, all of the observed \(V_{l}\) results are below 1.5%. It is clear that the \(V_{l}\) within 3H/D is lower than 1%. Beyond 3H/D, the \(V_{l}\) of several cross sections exceeds 1%. It can be concluded that the highest \(V_{l}\) is more likely induced in the deeper silty sand layers, such as \(\textcircled{S}_{2}\) and \(\textcircled{S}_{3}\).  
Figure 4: Statistical result of the volume loss \(V_{l}\). (**a**) volume loss \(V_{l}\) vs. buried depth \(H\), (**b**) probability distribution of \(V_{l}\).  
Figure 4b shows the statistics of the distribution of \(V_{I}\) for different ground types. What can be noticed is the fact that the silt with silty sand layer and silt layer shows no strong, obvious distinction. In general, most \(V_{I}\) induced in these layers is below 0.75%, with half of the results between 0 and 0.25% and 30% of the results between 0.25% and 0.50%. Not surprisingly, the \(V_{I}\) induced in silty sand is slightly higher than that in the silt layer, with more than 40% of the \(V_{I}\) exceeding 0.50%.  
Although Figure 4 presents detailed statistics of \(V_{I}\), it is still a challenge to predict \(V_{I}\) before construction as a lot of factors, such as the shield machine performance and the quality of work, are uncertain. Despite these difficulties, the empirical data in Figure 4 provide the basis for finding out the variability of \(V_{I}\), which can aid in selecting its representative values for design objectives.
## 2 Operation Principle of the Conventional DCM-CRM Cuk PFC
The critical value of the \(K\) value when the inductor operates in DCM mode under the entire AC input voltage range and given load can be determined by the limiting condition Equation (17) for the input inductor operating in DCM mode and the relationship Equation (19) for the intermediate capacitor voltage under the conventional control method. When \(K\) equals or exceeds this threshold, the inductor operates in DCM mode; if \(K\) is below this threshold, the inductor operates in CCM mode. Therefore, the variation curve can be obtained from the obtained \(K\) value and Equation (19), as shown in \[\frac{di_{L1}}{dt}=\frac{v_{\text{Rec}}}{L_{1}} \tag{1}\]5 It can be seen that \(V_{CI}\) is only related to the value of \(K\) under a certain load condition. In the region of \(K<1.4\), \(L_{1}\) operates in CCM mode, and in the region of \(K\geq 1.4\), \(L_{1}\) operates in DCM mode. To ensure that the input inductor \(L_{1}\) operates within the DCM mode constraints, and based on the analysis from Figure 3, it was observed that when \(1.4<K<3.6\), the PF remains above 0.96. As the input voltage is fixed, the intermediate capacitor voltage \(V_{\text{C1}}\) increases with the rise in the \(K\) value, necessitating a higher withstanding voltage rating for the intermediate storage capacitor \(C_{1}\). According to the above derivation of the \(V_{\text{C1}}\) and PF value of the conventional converter, it can be known that both the PF and the intermediate capacitor voltage are influenced by the ratio \(K=L_{2}/L_{1}\). An increase in the ratio \(K\) leads to a higher \(PF\), however, it simultaneously results in an elevation of the \(V_{\text{C1}}\). Based on the comprehensive comparison of PF and the intermediate capacitor voltage, \(K=2.05\) was selected in this paper. However, Capacitors rated above 450 V are expensive and offer lower cost-effectiveness. Consequently, achieving both a high PF and a low intermediate capacitor voltage becomes challenging. To address this problem, a DCM-CRM Cuk PFC converter based on variable inductor control which dynamically adjusts the input inductor in response to variations in the transient rectified input voltage is proposed in this paper. Since both the \(PF\) and \(V_{\text{C1}}\) are related to the ratio \(K\), it is possible to change the inductance of \(L_{1}\) during the half-line cycle and change the ratio \(K\) in real time. In contrast to the conventional Cuk PFC converter, the converter proposed in this paper utilizes a variable inductor as the input inductor. The inductance is dynamically adjusted according to a specific control strategy to achieve a high power factor while effectively reducing the voltage of the intermediate capacitor. Concurrently, the output inductor current remains constant, leading to a reduction in peak inductor current compared to the approach. This decrease in current stress and switching losses subsequently enhances the overall efficiency of the converter.
# Bandwidth Enhancement of a V-Band Klystron with Stagger-Tuned Multiple Radial Re-Entrant Cavities
## 4 Conclusions  
This article suggests a novel approach of staggered tuning for enhancing bandwidth with an 11-cavity Klystron configuration. In this presented configuration, each of the periodically allocated RF cavities operating at 60.1 GHz (synchronous tuning case) has been systematically tuned for bandwidth enhancement. The design and analysis were performed using a commercial 3D electromagnetic simulation tool. The proposed structure  
Figure 11: (a) The \(-1\) dB bandwidth plot of the RF section with IC1 tuned at 60.4 GHz (all others are tuned at 60.1 GHz) and (b) \(-3\) dB bandwidth plot of the same configuration.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **Synchronous Tuning** & **Stagger Tuning** & **Units** \\ \hline Beam Voltage & 20 & 20 & KV \\ Beam Current & 200 & 200 & mA \\ Operating Frequency & 60.1 & 60.1 & GHz \\ Input RF Power & 0.5 & 0.5 & W \\ Output RF Power & 256 & 186 & W \\ Power Gain & 27.13 & 25.61 & dBs \\ \(-1\)dB Bandwidth & 66 & 272 & MHz \\ \(-3\) dB Bandwidth & 165 & 540 & MHz \\ Focusing Magnetic Field & 0.2 & 0.2 & T \\ Length of RF Circuit & 16 & 16 & mm \\ \hline \hline \end{tabular}
\end{table}
Table 3: PIC simulated parameters and results.  
Figure 12: FT magnitude vs. frequency.  
can be fabricated using microfabrication techniques. The proposed approach demonstrates a 260% increment of \(-3\) dB bandwidth (compared to a synchronous tuned configuration) and achieves a \(-1\) dB bandwidth of 270 MHz of the device. Although the achieved bandwidth of the proposed device is less than that offered by traveling wave tubes (TWTs), the proposed device is simpler in design, has a compact form factor, and is compatible with microfabrication, which are the main advantages of this device. The proposed device can be used for wireless high data rate mm-wave communication. The reported approach of staggered tuning of Klystrons employing many cavities may be applied to similar devices operating in other frequencies for their bandwidth enhancement.  
Investigation, M.S.K., S.M. ([PERSON]) and S.M. ([PERSON]); Supervision, A.K.B., C.K. and D.P. All authors have read and agreed to the published version of the manuscript.  
This research was partially funded by IIT Guwahati Technology Innovation and Development Foundation sponsored by Department of Science & Technology (DST) grant number TH/TD/0303. The APC was funded by THH/TD/0303.  
Not applicable.
# Electro-Thermal and Aging Lithium-Ion Cell Modelling with Application to Optimal Battery Charging
## 2 Cell Modelling
### Description of the Thermal Part of the Model  
The kinetics of charge transfer during the intercalation process depends in particular on cell temperature. Cell temperature also has an impact on the kinetics of the side reactions. It is thus essential to associate a thermal model to the electrochemical sub-model. In this work, the efficient and simple thermal model presented in [21] was used to estimate the cell temperature. This thermal model results from a heat balance within the cell, and is described by  
\[mC_{p}\frac{dT(t)}{dt}=Q_{gen}(t)-Q_{loss}(t) \tag{7}\]  
The thermal parameters associated to Relation (7) are given in Table 1. The generated heat is defined by  
\[Q_{gen}(t)=U_{pol}(t)I(t)+R_{f}I(t)^{2} \tag{8}\]  
In Relation (8), \(U_{pol}(t)\) is the irreversible heat generation due to electrode polarization. \(R_{f}I(t)^{2}\) models heat losses over the internal resistor of \(R_{f}\).  
The convective heat exchanged with the environment is:  
\[Q_{loss}(t)=\alpha A(T(t)-T_{amb}) \tag{9}\]
# Ionic Liquids Hybridization for Carbon Dioxide Capture: A Review
## 4 Application of ILs Hybrid Material in Carbon Dioxide
### ILs/Anines
The addition of [EMIM][CoSO\({}_{4}\)] to the binary mixture of amine/water resulted in a decrease in CO\({}_{2}\) absortion capacity, while the opposite effect was observed upon the introduction of [BMIM][Oac]. Furthermore, a notable effect can be observed upon addition of ILs into a highly basic amine such as DBU. Analysis of NMR data revealed the formation of a newly attached carboxylic group to the C2 carbon of the imidazolium ring in [EMIM][Oac], which indicates the existence of chemisorption mechanism. Meanwhile, CO2 was specifically reacting with the bicarbonate generated through the reaction between the amine and water in DBU: [EMIM][OcSO4]: water (1:1:1) mixture. An additional study on amine degradation was carried out by focusing on [EMIM][OcSO4]/amine mixtures. The alkylation of [OcSO4]\({}^{-}\) anion was found to cause the rapid degradation of MEA and DEA. This degradation was observed to occur more rapidly in amines in the absence of CO2. However, the degradation of DBU and DABCO were not affected by the presence of CO2. Moreover, the recyclability study demonstrated that the mixture containing DBU maintained its absorption capacity with minimal loss over multiple cycles. This marks it as a highly promising absorbent system for CO2 capture.
## 2 Background
### [PERSON] et al. [33] Theoretical Formulation  
In their work, to provide a theoretical formula for a multi-neck Helmholtz resonator's resonance frequency, [PERSON] et al. [33] considered that the fluid is compressed due to the volume change inside the cavity, creating a pressure amplitude that may differ significantly from the pressure amplitude outside the cavity, given that the acoustic wavelength at the resonance frequency is lower than the Helmholtz resonator cavity's dimensions. Consequently, \(\mathrm{p_{0}}\) may be determined using the fluid's bulk modulus under the assumption that the pressure inside the cavity is uniform [41]. After a mathematical process, they arrived at the following formula for the resonance frequency of a Helmholtz resonator with multiple necks:  
\[f_{0}=\frac{c_{0}}{2\pi}\sqrt{\frac{\rho_{0}}{V_{0}}\sum_{i=1}^{N}\frac{S_{i} }{M_{i}}} \tag{1}\]  
where \(\mathrm{c_{0}}\) and \(\mathrm{p_{0}}\) are the speed of sound and density of the fluid inside the cavity and \(\mathrm{V_{o}}\) is the cavity volume. A total of N necks attached to the cavity, each neck i, with \(1\leq\mathrm{i}\leq\mathrm{N}\). \(\mathrm{S_{i}}\) is the cross-sectional area of each neck. Finally, \(\mathrm{M_{i}}\) can be interpreted as the inertial mass of the neck air volume. For cylindrical necks with diameter \(\mathrm{d_{i}}\), the neck air volume mass \(\mathrm{M_{i}}\) is approximately: \(\mathrm{M_{i}}=\rho_{0}(\mathrm{I_{i}}+\mathrm{a_{i}}\mathrm{d_{i}})\). Taking into consideration the inertia of a certain fluid volume surrounding the neck, \(\mathrm{a_{i}}\) is the so-called end correction coefficient and \(\mathrm{I_{i}}\) is the neck length [34]. The end correction coefficient accounts for the 'induced mass' in the oscillatory flow in the vicinity of the two ends of the neck [42]. Values for the end correction coefficient can be found in various manuscripts. For a circular aperture, the end correction is \(\mathrm{a_{i}}=\pi/4\) (\(\approx\)0.785) [43] or 0.75 for an unflanged pipe [44] and 0.85 for a flanged pipe [44].  
Adding the resonance frequencies that correspond to the Helmholtz resonance frequencies allows Equation (1) to be written in a more condensed form, with all, except the i-th, openings closed. Therefore, Equation (1) becomes:  
\[f_{i}=\frac{c_{0}}{2\pi}\sqrt{\frac{\rho_{0}}{V_{0}}\frac{S_{i}}{M_{i}}} \tag{2}\]  
\[f_{leaks}=\frac{c_{0}}{2\pi}\sqrt{\frac{\rho_{0}}{V_{0}}\sum_{i=2}^{N}\frac{ S_{i}}{M_{i}}} \tag{3}\]  
\[f_{0}=\sqrt{\sum_{i=1}^{N}f_{i}^{2}}=\sqrt{f_{1}^{2}+f_{leaks}^{2}} \tag{4}\]  
According to Equation (4), the resonance frequency of a Helmholtz resonator with multiple necks is given by the square root of the sum of the squared resonance frequency without leaks \(f_{1}^{2}\) and the squared resonance frequency with leaks only (\(f_{leaks}^{2}\)).
## 2 Preliminary Experiments  
An automotive component (Figure 1) (GlobalTek, Taoyuan, Taiwan) made of steel (SAE 1018) was chosen for the experiment. Two finish turning processes for the automotive component were used as an implementation object in this study: (1) T03--finishing for the inner diameter; (2) T04--finishing for the outer diameter. Different tungsten turning tools with Chemical vapor deposition (CVD) coatings of TiCN + Al2O3 + TiN were used in the two turning processes. The automotive components was produced on a mass production line. According to the manufacturing requirement, production needs to have a quality inspection of 100%, especially for the inner and outer diameters. Therefore, after the T04 and T03 processes finished, the work pieces were removed from the turning machine for outer/inner-diameter inspection with four air gauge instruments (GlobalTek, Taoyuan, Taiwan). The differences in inspected errors between two consecutive work pieces were regarded as the influence of tool wear, and were used as the tool wear compensation values which were manually inputted in the CNC controller (FANUC, Tokyo, Japan).  
The preliminary experiments were designed to understand three phenomena: (1) the characteristics of the cutting vibration caused by machining force and tool wear; (2) the correlation between the depth of cut and cutting vibration; (3) the correlation between vibration signals and machining quality. The experimental results were used to design the algorithms for dimension inspection and tool wear compensation. The experimental analysis includes: (1) the correlation between dimension error and cutting vibration; (2) an investigation of the vibration pattern caused by tool wear. In order to carry out on-line collection the cutting vibration signals for analysis, two accelerometers (PCB Piezatronics Inc., Depew, NY, USA) were attached to the tool posts (Figure 2), which are close to the work piece to collect the actual vibration signals.  
Figure 1: Automotive component used in the study.
# Neural Network-Assisted Interferogram Analysis Using Cylindrical and Flat Reference Beams
## 3 Results
### Neural Network Training
#### 3.3.1 Modeling Dataset  
In several numerical experiments, the recognition errors of the superposition of aberrations were obtained from interferograms with a tilt angle of the reference beam in the range from \(2\pi\) (Figure 9) to \(6\pi\) (Figure 10). The results are presented in Table 6.  
It has been found that a cylindrical interferogram makes it possible to reduce the aberration recognition error by 25% compared to a linear one, regardless of the beam tilt angle \(\alpha\). Moreover, it is confirmed that the recognition error is linearly related to the calculated parameter (informativeness) based on the RMSE of the reference interferogram compared to the aberrated one.  
Thus, the use of cylindrical interferograms makes it possible to improve the results of the recognition of types and levels of aberrations--the average absolute error decreases by more than 30% (from 0.0068 for linear interferograms to 0.0047 for cylindrical interferograms).  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Type Angle** & **Linear** & **Cylindrical** \\ \hline \(\alpha=2\pi\) & 0.0063 & 0.0050 \\ \(\alpha=4\pi\) & 0.0065 & 0.0047 \\ \(\alpha=6\pi\) & 0.0068 & 0.0050 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mean absolute recognition error (MAE) for model interferograms of various types.  
Figure 10: The neural network learning process dynamics using model interferograms of a cylindrical type with the parameters \(\alpha=4\pi\) (**a**) and \(\alpha=6\pi\) (**b**) expressed by the dependence of MAE on the number of epochs.  
Figure 9: The neural network learning process dynamics using model interferograms with the parameter \(\alpha=2\pi\): linear type (**a**) and cylindrical type (**b**) expressed by the dependence of MAE on the number of epochs.
## 3 Results and Discussion
### LgAC is an Optogenetic Modulator of cAMP in E. coli
#### 3.2.1. _LgAC_ Regulates Growth and Energy Metabolism in _E. coli_
In bacteria, the matrix-producing cells are essential for effective biofilm formation [58]. In _E. coli_ cells, HfIC and HfIK (chaperons) oligomerizes FtsH protein and make it a functional unit [59]. [PERSON] and coworkers showed a direct interaction between FloT/YqfA (HfIC/HfIK homologues) and FtsH. Inhibition of FloT/YqfA negatively affected the FtsH concentration and further reduced the differentiation of matrix producer cells and biofilm formation [59]. In this study, the cAMP-dependent biofilm formation ability of _E. coli_ cells expressing LgAC was estimated, and the result showed a significant reduction (_p_ < 0.05) in the biofilm formation ability in BL-illuminated cells (Figure 3B). However, the alteration in the cAMP-dependent biofilm formation ability of untransformed _E. coli_ culture (negative control) was not significant (_p_ > 0.069) when compared between dark-adapted and BL-illuminated conditions (Figure S3), suggesting that the reduction in the biofilm formation ability of _E. coli_ was partially associated with the light-dependent elevation of cAMP. Safranin-based qualitative detection and confocal microscopy revealed a reduction in the biofilm formation ability of _E. coli_ expressing LgAC upon BL illumination (Figure 3C,D). In the confocal microscopy, the fluorescent property of the protein was exploited for the visualization of biofilm formation. Cell aggregation, which is characteristic of biofilm-forming microorganisms, was reduced or absent in the BL-illuminated LgAC-expressing _E. coli_ culture (Figure 3D).
## 8 Current Challenges and Future Prospects
### Communication  
The actual achievement of the IoT in farming depends mostly on increased connectivity. From a telecommunications perspective, connectivity and more value-added essential services have a vast perspective and can significantly affect the whole chain [187]. Almost all telecommunication operators worldwide provide connectivity facilities, but such public services represent only a small portion of the total advanced agriculture market. Given its worth, especially in remote regions, smartphone operators must offer a range of the latest services to fulfill growers' needs. Accepting the truth that most people in the community are not highly educated, and, therefore, they do not fully understand new technologies, operators must give end-to-end resolutions, not only connectivity. Therefore, it will enhance the market share of smartphones and telecommunication operators. [PERSON] [119; 188] considered 23 experiments mainly in developing countries and concluded that cellular services and smartphone technology had brought a bright future to small farmers capable of increasing yields. It is very important to give crucial connectivity in the agriculture sector.