esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Problem Description and System Models
### Constrained Markov Decision Process
#### 2.4.1 State Space  
This study leverages GCNs to extract spatial graph features such as node voltage and power-related information. In the framework of the CMDP, the state space is jointly defined by the node feature matrix and the topological adjacency matrix, denoted as \(G(C,\Lambda)\). The node feature matrix \(C=[O_{1,t},O_{2,t},\ldots,O_{N,t}],O_{N,t}=[V_{i,t},P_{i,t}^{L},Q_{i,t}^{L},Q_{ i,t}^{PV},Q_{i,t}^{CB}]\) includes the voltage magnitudes of all nodes, the active and reactive power outputs of PV units, and the reactive power support from capacitor banks. Additionally, it encompasses branch current magnitudes and the operational states of all controllable devices. The adjacency matrix \(\Lambda\subset C\times C\) captures the connectivity among nodes in the distribution network, thereby reflecting the system's topological structure.  
The state space in the CMDP includes the voltage magnitudes at all nodes, the active and reactive power generations from PV units, and the reactive power supply from capacitor banks. It also includes the current magnitudes in network branches and the operational status of all devices.
## 2 Related Works
### Related Methods
* Data Preprocessing: Historical stock data was transformed, recalculating the weights of key companies like TSMC, and technical indicators were normalized for the LSTM model. * Model Training: LSTM models were trained using the transformed data. As a comparison, models such as CNN, SVR, and BPNN were also tested, using a variety of technical indicators. * Optimization Process: GAs and GFSs were applied to refine trading strategies by optimizing the buy, sell, and hold signals. The Sharpe ratio was used as the fitness function to evaluate portfolio performance. * Evaluation: The results were evaluated based on prediction accuracy, annualized returns, and the robustness of the trading strategies developed. In the literature, the Z-Score model [9] has been widely applied in predicting corporate bankruptcy risk, forming the foundation for many Expert Systems (ES) and Neural Networks (NNs). For instance, the Z-Score Analyzer is based on [PERSON]'s model for evaluating bankruptcy risk, S&P Global Credit Assessment is used to assess corporate credit risk, and the Riskturn expert system focuses on equity portfolio planning. Additionally, Deloitte's BEAT (Bankruptcy Early Alert Tool) and the SAS Credit Scoring for Banking are widely used in the banking sector for credit and bankruptcy risk assessment. While many risk management systems and tools are already in use globally, the innovation in this study lies in the application of LSTM and GFS combined with GAs to optimize investment strategies and achieve stock price predictions on the Taiwan Stock Exchange. This novel approach is not only applicable to the Taiwan market but also has the potential to be applied to other countries and markets, further enhancing the management of stock price volatility and bankruptcy risk. In the area of bankruptcy risk assessment, [PERSON] [23] provides a detailed discussion of the Z-Score model in _Corporate Financial Distress and Bankruptcy_. Additionally, [PERSON] [24] offers a modern perspective on practical risk management. In the field of NNs, [PERSON] [25] introduces recent advances in deep learning methods, which are increasingly applied in financial markets. We also reference [26], which provides insights into the development and application of AI in financial risk management. Moreover, [PERSON] [27] and [PERSON] [28] offer theoretical support for the GA components of this study.
# Extensive Benchmarking of DFT+\(U\) Calculations for Predicting Band Gaps
###### Abstract  
Accurate computational predictions of band gaps are of practical importance to the modeling and development of semiconductor technologies, such as (opto)electronic devices and photoelectrochemical cells. Among available electronic-structure methods, density-functional theory (DFT) with the Hubbard \(U\) correction (DFT+\(U\)) applied to band edge states is a computationally tractable approach to improve the accuracy of band gap predictions beyond that of DFT calculations based on (semi)local functionals. At variance with DFT approximations, which are not intended to describe optical band gaps and other excited-state properties, DFT+\(U\) can be interpreted as an approximate spectral-potential method when \(U\) is determined by imposing the piecewise linearity of the total energy with respect to electronic occupations in the Hubbard manifold (thus removing self-interaction errors in this subspace), thereby providing a (heuristic) justification for using DFT+\(U\) to predict band gaps. However, it is still frequent in the literature to determine the Hubbard \(U\) parameters semieppirically by tuning their values to reproduce experimental band gaps, which ultimately alters the description of other total-energy characteristics. Here, we present an extensive assessment of DFT+\(U\) band gaps computed using self-consistent _ab initio_\(U\) parameters obtained from density-functional perturbation theory to impose the aforementioned piecewise linearity of the total energy. The study is carried out on 20 compounds containing transition-metal or \(p\)-block (group III-IV) elements, including oxides, nitrides, sulfides, oxynitrides, and oxysulfides. By comparing DFT+\(U\) results obtained using nonorthogonalized and orthogonalized atomic orbitals as Hubbard projectors, we find that the predicted band gaps are extremely sensitive to the type of projector functions and that the orthogonalized projectors give the most accurate band gaps, in satisfactory agreement with experimental data. This work demonstrates that DFT+\(U\) may serve as a useful method for high-throughput workflows that require reliable band gap predictions at moderate computational cost.  
DFT+\(U\); Hubbard correction; Lowdin orthogonalization; band gap; self-interaction +
Footnote †: journal: Applied Sciences
## 4 Experiment and Result Analysis
### Scene Generation  
The experiments were set up in a rectangular area of \(10\times 10\) Km, without considering the height. In order to better present the cooperative effect of the UAVs and observe the operation effect, we set the total number of UAVs and tasks to 30, respectively. The proportions of four types of UAVs are 6:20:6:2, respectively, and the specific number is the total number of UAVs multiplied by the ratio and then rounded down, and the excess number is added to the mini scouter. Therefore, when the total number of UAVs is set to 30, the specific number of each UAV type is: 7, 17, 5, 1. Similarly, the ratio between the five types of tasks is set to 15:7:4:2:1, and when the total number of tasks is 30, the number of corresponding tasks is 16, 7, 4, 2, 1, respectively.  
When generating a scene, the tasks are first randomly assigned to the experimental area with a uniform distribution, each task is randomly assigned a task type, and the quantity requirements of each task type is met. Similarly, initial positions and UAV types are randomly assigned to each UAV. Then, the generated UAVs and tasks are registered into the simulation control module of the experimental environment, and the information of all UAVs and tasks is broadcast to each UAV as the initial information for decision-making.  
The maximum single-hop communication distance of radio between UAVs is set as \(d_{\delta}=3\) Km, and multi-hop transmission is supported. The synchronization of state information and the negotiation of task assignments can only take place between two UAVs when there is a communication link. Under the above constraints, the generated initial scenario contains 30 UAVs and 30 tasks, as Figure 3 shows, and the corresponding task types are shown in Table 4.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Task Type** & **Tasks** \\ \hline Fake target & T1, T5, T6, T7, T8, T12, T13, T18, T19, T20, T21, T22, T23, T24, T27, T28 \\ Target type1 & T9, T10, T11, T14, T16, T17, T29 \\ Target type2 & T3, T4, T15, T25 \\ Target type3 & T0, T26 \\ Target type4 & T2 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Task IDs and corresponding task types in the scenario.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Task Type** & **Area Size (m2)** & **Required Capability Vector** \\ \hline Fake target & \(1\times 10^{6}\) & — \\ Target type1 & \(5\times 10^{5}\) & [25; 30] \\ Target type2 & \(2\times 10^{6}\) & [100; 80] \\ Target type3 & \(2\times 10^{6}\) & [40; 150] \\ Target type4 & \(4\times 10^{6}\) & [200; 200] \\ \hline \hline \end{tabular}
\end{table}
Table 3: Task type and its parameter setting.
## 3 Digital Beamforming General Considerations
### Amplitude and Phase Errors Estimation  
A complex cross-correlation is used to estimate the amplitude and phase errors among receivers. The performance of the DBF requires only a relative calibration, that is having all the amplitudes and phases equalized. It does not require an absolute calibration, _i.e._, set all the phases to 0 \({}^{\circ}\)and all the amplitudes to a predetermined value. To implement the relative calibration, a reference receiver is chosen, and the amplitude and phase of the other receivers are adjusted accordingly. To estimate the errors, all receivers have to be driven by the same input signal, which is injected through a correlated noise input port. A Controlled Noise Source (_CNS_), capable of driving three different noise power levels using two control bits (Section 4.1), is used to inject this common signal to all receivers. As this error information is derived from the result of a complex correlation, this process is performed by pairs. The reference receiver and Receiver Under Test (_RUT_) are the names of the current pairs of receivers from which the relative error estimation is obtained. Voltage signals from the pair of receivers that have been considered for the error estimation are shown in Equations (2,3):  
\[S_{REF} =A_{REF}\left(S_{CNS}+S_{W-REF}\right)e^{j_{REF}}+S_{receiver- REF}\,, \tag{2}\] \[S_{RUT} =A_{RUT}\left(S_{CNS}+S_{W-RUT}\right)e^{j_{RUT}}+S_{receiver- RUT}\,, \tag{3}\]  
where:  
* \(S_{REF}\) is the output complex signal of the reference receiver;
* \(S_{RUT}\) is the output complex signal of the _RUT_;
* \(S_{CNS}\) is the common signal coming from the _CNS_, which is uncorrelated to the other noise terms;
* \(S_{receiver-REF}\) and \(S_{receiver-RUT}\) are the reference and _RUT_ receiver's noise, which are uncorrelated among them, and to the other terms;
* \(S_{W-REF}\) and \(S_{W-RUT}\) are the Wilkinson power splitters' noise of the reference and _RUT_ receivers, which are uncorrelated among them and to the other terms;
* \(A_{REF}\) and \(A_{RUT}\) are the reference and _RUT_ amplitudes, respectively; and
* \(\phi_{REF}\) and \(\phi_{RUT}\)are the reference and _RUT_ phases, respectively. From Equations (2,3), the _RUT_'s relative phase and amplitude can be obtained straightforwardly as shown in (4-6).
# Directional Active Noise Control with a Local Minimax Error Criterion
## 5 Experimental Results and Discussion
The parameters \(\mathbf{P_{xx}}\), \(\mathbf{E_{1}}\), and \(\mathbf{E_{2}}\) are the average power spectrum density of primary noise, residual noise with the MSE method, and local minimax method obtained in the experiments. Now, we compare the reductions of the two methods at the desired, monitor and all error points. Compared with the  
Figure 8: The overall noise control performance of the two methods with two directional areas. (**a**) The overall reductions of the two methods with the Directional Area 1. (**b**) The overall reductions of the two methods with the Directional Area 2. Figure 7: Comparison of two optimal filter coefficients with two directional areas. (**a**) Comparison of the amplitude of filter coefficients with Directional Area 1. (**b**) Comparison of the phase of filter coefficients with Directional Area 1. (**c**) Comparison of the amplitude of filter coefficients with Directional Area 2. (**d**) Comparison of the phase of filter coefficients with Directional Area 2.  
reduction of ANC system with the MSE method, the extra noise reduction at the desired points \(Rd_{1}\) and the extra noise reduction at the monitor point \(Rd_{2}\) can be expressed in local terms as:  
\[Rd_{1}=\max(10*\lg(\textbf{E}_{{}_{1d}}))-\max(10*\lg(\textbf{E}_{{}_{2d}})) \tag{9}\]  
where \(\textbf{E}_{{}_{1d}}\) and \(\textbf{E}_{{}_{2d}}\) are the average power spectrum density of two residual noise at the desired points. \[Rd_{2}=\max(10*\lg(\textbf{E}_{{}_{1m}}))-\max(10*\lg(\textbf{E}_{{}_{2m}})) \tag{10}\]  
where \(\textbf{E}_{{}_{1m}}\) and \(\textbf{E}_{{}_{2m}}\) are the average power spectrum density of the two residual noise at the monitor points. The minimum of overall reductions with the proposed method \(Rd_{3}\) can be expressed in global terms as:  
\[Rd_{3}=\max(10*\lg(\textbf{P}_{\textbf{\infty}}))-\max(10*\lg(\textbf{E}_{{}_{2 }})) \tag{11}\]  
The values of the parameters mentioned above in the experiments are shown in Figure 6.0  
In summary, the ANC system with the proposed method can achieve an extra 7.8-dB reduction at desired points and a 15.1-dB reduction overall when controlling Area 1, as well as an extra 9.8-dB reduction at desired points and a 15.5-dB reduction overall when controlling Area 2. The reductions of monitor points were respectively 3.5 dB and 5.9 dB, indicating the extent of the controlled area. The results showed that the system can realize directional reductions in a specified area with overall reductions, as well.
# Characterization of Narrowband Noise and Channel Capacity for Powerline Communication in France
###### Abstract
2. The second one consists in estimating the channel capacity in various LV sites. This study has a major interest, particularly to support new IoT dedicated to smart grids' applications and services. In this manuscript, the methodological aspects of this study are described in Section 2. In particular, the measurement sites located in France are presented. Moreover, the experimental measurements' setup and scenarios in time-frequency representation are explained. Section 3 discusses the main results: the spectral analysis of the NB-PLC noise in the various locations, the analysis of the long-time variation of the power spectral density of noises using time-frequency algorithms, the cyclo-stationary behavior of noise, the measurement of the transfer function and the signal-to-noise ratio (SNR), and the estimation of the channel capacity in the CENELEC A and FCC bands. Finally, a discussion is presented in Section 4 to compare our results with existing ones recently described in the literature.
## 2 Zeolites
### Ion Exchange Process
The main method for CEC determination uses an NH\({}_{4}\)NO\({}_{3}\) solution following a determination of exchangeable NH\({}_{4}\)\({}^{+}\) by Nessler's method using colorimetry. However, the use of NH\({}_{4}\)\({}^{+}\) as exchanging agent is not useful for industrial application and may be underestimated, due its incapacity to remove some ions from the structure. The ion exchange property of zeolites has been explored for inhibition of biofilm formation that is responsible for producing acids that generate secondary caries. In this scenario, EMT zeolites presenting silver ion exchange have been used for the inhibition of metabolism and growth of biofilm from _Streptococcus mutans_, _Streptococcus gordoni_, and _Streptococcus sanguinis_. Biofilm CFU was also revealed as an anti-caries material [83]. Another relevant application of ion exchange capacity from zeolites in odontology is in production of dental implants, where titanium substrates have been modified with Ca\({}^{+2}\)-exchanged nanosized EMT zeolites, improving some undesirable characteristics of classic Ti implants, such as poor osteointegration leading to long treatment and implant failure [84]. The chemical and physical modification of structure may cause modification in cation exchange rates: in general, chemical modification generates higher cation exchange rates than physical modification. Chemical activation is achieved by acid washing the surface of the material, removing the impurities, and making the zeolite pore surface wider. Instead, the physical activation (modification) occurs by heating [85; 86]. The synthesis parameters that may affect CEC are reaction temperature, liquid/solid ratio, NaOH concentration and reaction time. Its value may increase with temperature and the concentration of an alkali activator, affecting zeolite formation. The influence of CEC in open window size and pore structures has been verified in other types of structures, such as RHO, LTA, and FAU [87]. The increase in liquid/solid ratio following the increased CEC is justified for the improvement of zeolite formation. The same is observed in increased CEC with reaction time, related to the increase in zeolite content in the medium [88]. Some zeolites, such as chabazite, stilbite, and heulandite, have the capacity to adsorb radioactive metallic species such as \({}^{137}\)Cs. Correlating this property with the CEC, the differences in values are related to a multistep adsorption process [89]. Figure 6 shows an experiment developed to study the cation exchange capacity of a zeolite with an electrochemical approach. Figure 6: Ion Cu\({}^{2+}\) migration under electric field toward electrodes and zeolite slice to trap them. Reproduced from [81]. Copyright 2006 Elsevier.
# Continuous Control Set Predictive Current Control for Induction Machine
## 3 Control Structure  
When modeling induction machines several state variables can be chosen to represent the state. For the proposed control structure, stator current, and rotor flux \(\alpha\beta\)-components are chosen. Additionally, the control structure is developed as cascade regulation with inner and outer control loops. Outer control loop is classic speed regulation via \(PI\) regulator that generates torque reference, while inner loop is the predictive current control via the proposed method. Any kind of model predictive control should be comprised of cost function that is minimized by selecting the optimal input to the system. Classic cost function that is used for predictive current control for induction machines driven by two-level voltage inverters can be found in papers, such as [32] or [11], and is shown by Equation (8):  
\[g(v_{i})=\sum_{j=0}^{h}(|i_{\alpha}^{*}-i_{a}[k+j]|+|i_{\beta}^{*}-i_{\beta}[k+ j]|),\;\;i\in\{0,1,...,7\} \tag{8}\]  
where variables with the star superscript represent reference values, \(i_{a}[k+h]\) and \(i_{\beta}[k+h]\) represent stator current components calculated at \(k\)-th sample for \(h\) future samples and \(v_{i}\) is one of eight possible voltage vectors applied to the machine during one switching period that minimizes the cost function. This kind of PCC is also called _finite control set_ PCC and it has the before-mentioned drawbacks. In order to derive continuous control set PCC, a continuous control signal should be generated by the regulator that can be modulated by the inverter. Induction machine model derived for this purpose is explained in the following subsection.
# Advances in Toughening Modification Methods for Epoxy Resins: A Comprehensive Review
## 5 Flexible Chain Segment Toughening Method
### Current Research  
Toughening epoxy resins with flexible chain segments involves incorporating various flexible chains into the cured system through molecular design strategies, either by modifying the curing agent or directly altering the epoxy resin. There are two primary methods for this approach [92]: (1) incorporating flexible chain segments into the curing agent; (2) directly introducing flexible chain segments into the epoxy resin.
# Multiple-Diffraction Subtractive Double Monochromator with High Resolution and Low Stray Light
## 2 Principle
### Subtractive Double Monochromator  
A monochromator decomposes polychromatic light by dispersion elements and utilizes a slit to select monochromatic light output within a specific narrow wavelength range. Two monochromators can be connected in series by two arrangements: additive dispersion for enhanced resolution and subtractive dispersion for superior stray-light rejection [34]. Here, we focus on the subtractive dispersion configuration and study stray-light suppression.  
In a single monochromator configuration, stray light inevitably reaches the exit slit through scattering from various surfaces. Let \(B^{\prime}(\lambda^{\prime})\) represent the spectral energy distribution when the monochromatic light of wavelength \(\lambda^{\prime}\) passes through a monochromator configured for wavelength \(\lambda\). The response of the monochromator to incident polychromatic light corresponds to the convolution of this energy distribution with the instrument function. The instrument function \(W\) arises from the finite slit width, grating characteristics, and geometric aberrations introduced by instrument components:  
\[B(\lambda)=\int_{0}^{\infty}B^{\prime}(\lambda^{\prime})W(\lambda^{\prime}- \lambda)d\lambda^{\prime} \tag{10}\]  
A subtractive double monochromator is shown in Figure 3a, and it can suppress stray light effectively [35]. When the incidence angle of the rear monochromator matches the diffraction angle of the front monochromator, the rear unit produces an opposite dispersion effect to the front unit. Such serially connected monochromators are referred to as a subtractive double-monochromator configuration [36].  
For the front monochromator, the angular dispersion is expressed as  
\[d\beta_{1}=\frac{m}{d\cos\beta_{1}}d\lambda \tag{11}\]  
where \(m\) denotes the diffraction order, and \(d\) represents the grating constant. The rear monochromator exhibits angular dispersion expressed as  
\[d\beta_{2}=\frac{\cos\beta_{1}}{\cos\beta_{2}}d\beta_{1}-\frac{m}{d\cos\beta_ {2}}d\lambda=0 \tag{12}\]  
If stray-light components at wavelength \(\lambda^{\prime}\) exit the front monochromator and enter the rear monochromator, they become coupled into the optical path of the signal light at wavelength \(\lambda\), where the stray light undergoes further suppression. When the optical components of the front and rear monochromator are identical, the stray-light response of the double-monochromator mount becomes the square of that of a single monochromator, as demonstrated in Figure 3b. This methodology proves to be highly efficacious in spectrometers demanding a stringent optical signal-to-noise ratio (OSNR) [37; 38].  
Figure 2: Angular dispersion comparison between single, double, and triple diffractions.
# Cross-Domain Fusion Constellation Design of Communication, Navigation and Remote Sensing
## 4 Experimental Analysis  
### Algorithm Implementation  
Using the MDCRO proposed in the paper, the convergence process of the number of satellites in the three functional domains and CDFC is shown in Figure 3. The convergence process has obvious two-stage characteristics, and the number of satellites optimized in the second stage is not as large as that in the first stage, because, after MDO with CO, the results have been constrained to a smaller feasible solution range, such as the red oval range in Figure 4. Figure 4 is the pareto front diagram of optimization objectives corresponding to the three functional domains. Under the constraint of meeting the service indicators, the CDFC reduced the number of satellites from 1354 to 347, about a decrease of 73.37%. Since 1354 satellites are initial constellations with redundant values, we can use 451 to compare the average number of constellations in the three functional areas. At this point, the number of satellites has decreased by 23.06%. That is, the result of one third of the 1354 satellites mentioned above and taking an integer, and 1354 satellites are the initial values for the iteration of the three functional domain constellations in Figure 3. It can be used to evaluate the improvement of constellation design optimization. Therefore, it can be seen that it is still a large improvement gain.