esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Control Strategy and Optimization Function
### PIDF Controller  
The first controller investigated in the LFC loops of the shipboard MG is a PID controller that incorporates a derivative filter coefficient \(N\). The controller's structure is illustrated in Figure 3a. The use of a filter on the derivative term is required to decrease the effect of noise in the system. More specifically, the majority of industrial processes employ traditional PI and PID controllers because of their straightforward and reliable design, low cost, and efficiency for linear systems. A derivative factor was applied to the PI controllers in order to speed up the system's reaction because PI controllers make the system respond slowly. However, this could also enhance the impact of system noise [48]. The addition of a filter to the derivative term is a practical solution to address these issues since it helps reduce high frequency noise and prevents chattering caused by noise. The PIDF operation is described mathematically as [45]  
\[G_{c}(s)=K_{p}+K_{i}\frac{1}{s}+K_{d}\frac{N}{1+N\frac{1}{s}} \tag{15}\]  
where \(K_{p}\), \(K_{i}\), \(K_{d}\) are the proportional, integral, and derivative gain, respectively, and \(N\) is the derivative filter coefficient. In this context, the optimization of the fitness function of the problem can be given as  
\[\begin{split} J_{\text{min}}(K_{p},K_{i},K_{d},N)=\int\limits_{0} ^{Tsim}t|\Delta f|dt\\ \text{subject to:}\\ K_{p}^{\text{min}}\leq K_{p}\leq K_{p}^{\text{max}}\\ K_{i}^{\text{min}}\leq K_{i}\leq K_{i}^{\text{max}}\\ K_{d}^{\text{min}}\leq K_{d}\leq K_{d}^{\text{max}}\\ N_{\text{min}}\leq N\leq N_{\text{max}}\end{split} \tag{16}\]
# Entropy-Based Ensemble of Convolutional Neural Networks for Clothes Texture Pattern Recognition
## 3 The Proposed Method
### Base Learner CNN Models
#### 3.3.2 ResNet50 with Coordinate Attention Block (ResNet_CA)  
Channel attention has a considerable effect on improving network performance. Coordinate attention [26] is an attention mechanism that embeds positional information into channel attention. First, the channel attention is factorized into two parallel 1D poolings to aggregate features along the width and height spatial dimensions to capture long-range interactions spatially with precise positional information. In the second step, the coordinate attention is generated by \(1\times 1\) convolutional transformations. We designed base learner ResNet_CA by incorporating this block into the pre-trained backbone ResNet50 as shown in Figure 6.
# A Novel GBSM for Non-Stationary V2V Channels Allowing 3D Velocity Variations
## 1 Introduction
### Main Contributions  
The concept of this paper was obtained from [31; 32] to model the V2V channels allowing 3D trajectories, but the proposed model only supported 3D constant velocity. The goal of this paper was to address this void. The key achievements and novelties are defined as follows:  
(1) Considering the idea of GBSM and the twin cluster proposal, a new 3D V2V channel model is introduced. In this proposed model, non-stationary V2V channels with the movement of scatterers for non-line of sight (NLoS) scattering scenarios were taken into consideration. Furthermore, each of the terminals allows 3D velocity variations according to the 3D scattering condition, which ensures that the model is much more usual and has the prospect of non-stationarity.  
(2) The time-evolving channel variable algorithms for 3D V2V communication scenarios, i.e., Doppler frequencies, AOA and AOD, path delays, and powers, were executed and facilitated.  
(3) The hypothetical terms of PDF, ACF, and DPSD of this introduced model according to the VMF scattering scenarios were examined and approved by the assumed and measured outcomes.  
This article is arranged as follows. Section 2 introduces the GBSM for V2V channels. Section 3 considers the proposed V2V channel model allowing 3D velocity variations. In Section 4, the time-variant channel parameter computation is described. In Section 5, the theoretical PDFs, ACFs, and DPSDs of this introduced model are executed in detail. The simulated outcomes are compared with the theoretical ones, and the calculated outcomes can be seen and contrasted in Section 6. The conclusions are eventually drawn in Section 7.
# SmartX Multi-View Visibility Framework
## 2 Background and Requirements of Flow-Centric Visibility
### Flow-Centric Visibility Requirement for Unified Monitoring
#### 2.2.3 Multi-Layer Visibility Data Integration  
Due to the growth in collected metrics from multiple layers of visibilities, the amount of visibility data generated is huge. Data integration is the process of combining data from different sources into a single unified view [17]. In typical scenarios of data integration, a user sends a request to the server for the data. The server gets the required data from internal or external datastores, consolidates it into a single cohesive data set, and sends it back to the user for use. Another key benefit of data integration is that it produces a summarized output that is more suitable for subsequent analysis. Flow-centric visibility requires scalable tools to analyze and integrate metrics from multiple layers together. Identification of key metrics that can facilitate the linking of these multiple layers is also required.
## 5 Experimental Verification  
The experimental setup was presented in Figure 9, which involved three fast steering mirror systems used in the disturbance isolation table. One was the controlled object named Tracking Mirror, one was Target Mirror for simulating target motion, and one was Disturbance Mirror for simulating external disturbances. The Tracking Mirror was fixed above the Disturbance Mirror. Since the fast steering mirror was a symmetrical two-axis system, we only need to pay attention to single-axis motion. The laser could emit light as a visual axis reference. Then, the light could be reflected by the Target Mirror and entered into the phase sensitive demodulator (PSD), a substitute for CCD. We could control the motion of Target Mirror and Disturbance Mirror to simulate the target's motion and the external interference. The controller could receive the boresight error from PSD and the platform's velocity detected by a FOG, to stabilize the boresight. The PSD could run in at the sampling rate of 100 Hz with an artificially added delay to imitate the CCD. The FOG could run at the rate of 5000 Hz.  
Before designing the closed-loop controller, the model of the platform should be acquired with a spectrum fitting method. In order to identify the parameters in Equation (19), the driver should output a sinusoidal signal of varying frequency to actuate the Tracking FSM. Then the FOG would be used to detect the motion state; comparing the output to the input, the open-loop bode response of the velocity can be drawn with the blue line, as shown in Figure 10. Finally, the parameters were adjusted to make the red curve of the model coincide with the blue one, and a high-precision transfer function could be acquired, as shown in Equation (24).  
\[\widetilde{G}_{p}(s)=\frac{2.3\text{s}}{0.00072s^{2}+0.0202s+1}\cdot\frac{1}{0.0005s+1} \tag{24}\]  
Figure 9: Experimental setup.  
The closed-loop bode response of the velocity was exhibited in Figure 11, in which the bandwidth passed 100 Hz, and the resonance peak of the open-loop velocity model under 7 Hz was eliminated, benefiting the controller design of the outer position loop. Below 10 Hz, the amplitude was close to 0 dB and the phase loss was less than 6\({}^{\circ}\), which indicated that the closed-loop transfer function could be regarded as 1 in this low frequency band. Therefore, it was enough to take a proportional link as the position controller.
## 2 Computational Details
### Binding Free Energy Calculations  
In the present work, the binding free energy was calculated employing the Molecular Mechanics-Poisson Boltzmann Surface Area (MM-PBSA) method, originally developed by [PERSON] [58]. In this method, the binding free energy between a protein and a ligand is estimated from the following contributions,  
\[G_{bind}=G_{PL}-G_{P}-G_{L} \tag{5}\]  
where \(G_{PL}\), \(G_{P}\), and \(G_{L}\) referred to the protein-ligand, protein, and ligand-free energies, respectively. The free energy of each contribution is estimated by the following sum:  
\[G=\left\langle E_{MM}\right\rangle+\left\langle G_{solv}\right\rangle+\left \langle G_{np}\right\rangle-T\left\langle S_{MM}\right\rangle \tag{6}\]  
In this expression, \(E_{MM}\) is the standard Molecular Mechanics energy, namely, the sum of all the contributions to the internal energy of the molecule (bonded terms, electrostatic and Van der Waals interactions); \(G_{solv}\) is the polar solvation energy of the molecule, obtained from the solution of the Poisson-Boltzmann (PB) equation (in this work, calculated using the APBS [60] package); \(G_{np}\) is the nonpolar solvation energy, estimated from the solvent-accessible surface area of the molecule (calculated using VMD), and, the last term is composed by the temperature T times the entropy contribution (normally obtained from a normal-mode calculation of the harmonic frequencies at the MM level). The last term was ignored in this study due to the well-known fact that normal mode analysis calculations are computationally expensive, and it tends to introduce significant uncertainty in the final results [61]. The \(\left\langle\ldots\right\rangle\) brackets in Equation (6) represent the ensemble averages, which are obtained from a given number of molecular dynamics snapshots (200 in this study).  
For practical reasons, all the terms were calculated from a single MD simulation corresponding to the protein-ligand complex, where the same geometry was used for all three systems (complex, ligand, and receptor). In this study, the total binding free energy was calculated using the CaFE [62] VMD plugin. For the PB part, CaFE used the APBS program with the following parameters: 1.0 and 80.0 for the internal and external dielectric constants, respectively, mdh (multiple Debye-Huckel model) for the boundary conditions, and the spl2 (cubic B-spline) method to map atomic partial charges onto grid points.
## 1 Introduction
Indeed, a special subset of Building Information Model (BIM) is dedicated for heritage buildings, dubbed the Heritage Building Information Model (HBIM) [13], which enables such operations to be conducted. Nevertheless, even before the 3D modeling for HBIM starts, the process of point cloud segmentation and classification is largely a manual process; an operator manually segments and labels each point cloud cluster as the intended entity. This process is similar and even analogous to traditional digitising in the 2D case of aerial or satellite images into 2D vectors with attributes in GIS (Geographical Information System). The manual segmentation and labeling is further complicated in the case of heritage objects due to its inherent complexity in terms of architectural style, materials, age of the structure, infinitely diverse decorations, etc. It is therefore more often than not the most time consuming part in the 3D pipeline [14] and therefore presents a bottleneck in the general workflow. This paper intends to present our work on a series of functions (collectively stored as a toolbox) which enables an automatic processing of some parts of the point cloud segmentation and classification problem in the case of heritage objects (see Figure 1). Taking into account the complexity of the problem (mainly due to the differing architectural styles and elements involved), the project addresses only the processing of several particularly important architectural classes, such as structural supports (pillars, piers, etc.) and framework supports (be it wooden or otherwise). The proposed toolbox also works in a multi-scalar approach, meaning that the input point cloud is processed on several levels of scale,from that of a heritage complex up to architectural elements. The flexibility of the toolbox is intended to enable an easier adaptation for different types of heritage sites. Furthermore, several comparisons with other existing approaches will also be presented to assess the reliability of the developed algorithms.
## 4 Firefly Optimization Algorithm
### Distance and movement  
We suppose a firefly located at \(x_{i}=\big{(}x_{1}^{i},x_{2}^{i}\ldots x_{k}^{i}\big{)}\) is brighter than another firefly located at \(x_{j}=\Big{(}x_{1}^{j},x_{2}^{j}\ldots x_{k}^{j}\Big{)}\), the firefly located at \(x_{i}\) will move towards \(x_{j}\). The distance between any two fireflies \(i\) and \(j\) at \(x_{i}\) and \(x_{j}\) is the Euclidean distance given by [64; 66] as follows:  
\[r_{ij}=\big{|}x_{i}-x_{j}\big{|}=\sqrt{\sum_{k}^{d}\Big{(}x_{i,k}-x_{j,k}\Big{)} ^{2}} \tag{14}\]  
where, \(d\) is the dimension, \(x_{i,k}\) is the \(k\)-th component of the spatial coordinate \(x_{i}\) of \(i\)-th firefly the movement of a firefly \(i\) is attracted to another more attractive firefly \(j\) and the update location is determined by  
\[x_{i+1}=x_{i}+\beta_{0}e^{-\lambda r_{ij}^{2}}\big{(}x_{j}-x_{i}\big{)}+\alpha \bigg{(}rand-\frac{1}{2}\bigg{)} \tag{15}\]  
The first term is the current position of a firefly [66], the second term is used for considering a firefly's attractiveness to light intensity seen by adjacent fireflies and the third term is used for the random movement of a firefly in case there are not any brighter ones. The coefficient \(\alpha\) is a randomization parameter determined by the problem of interest, while rand is a random-number drawn from a Gaussian distribution or uniform distribution at time t, if \(\beta_{0}=0\), it becomes a simple random walk. In the implementation of the algorithm we will use \(\beta_{0}=0\), \(\alpha=0.25\) and the attractiveness or absorption coefficient \(\lambda=1\) which guarantees a quick convergence of the algorithm to the optimal solution. The concept of the firefly-based algorithm is presented in Figure 3. Moreover, Figure 4 shows the here considered implementation of FA for the specific problem and cost function given in Equation (9), as defined in Section 3.  
Figure 3: A conceptual view of the firefly algorithm relationships, including locations \(x\), distance \(r\), brightness \(I(r)\), and attractiveness \(\beta(r)\).
# Research and Development of a Large-Scale Axial-Flux Generator for Hydrokinetic Power System
## 2 Materials and Methods
### Virtual Prototype of Preliminary Design  
The examined AFPM generator is based on an initially predefined geometry, with the concept of a stacked rotor that forms the magnetic circuit in the coreless generator and overall dimensions, as is shown in Figure 2. This initial conceptual design is rated for 20 MW at 13 rpm, having a 13.5 m overall diameter and 4 rotor stages. It has 48 permanent NdFeB magnets installed on each rotor at an air gap of 140 mm to the stator coils. Rotor magnets are embedded in a disk hole made of non-magnetic material, while the stator coils are embedded in the disk and made of insulating material to avoid generating an eddy current. The magnet polarity direction is aligned with that of the neighboring rotor, and it generates the magnetic field between the gaps of the rotors. The coreless coil is arranged in a ring shape in the stator, and the stator itself is positioned in the gap of the rotor to generate electromotive force by receiving the alternating magnetic field.  
This prototype is used to perform three-dimensional magnetic field analyses. The applied boundary conditions are the current and rotational speed of the system. The electromotive force was derived at different generated powers from the magnetic flux quantity that interlinks the coil. Additionally, the losses were estimated, and the major parameter of the design could be calculated--the efficiency. The efficiency (\(\eta_{G}\)) of a generator is the ratio of the electrical power produced via the generator (\(P_{G}\)) to the mechanical supplied to the generator (\(P_{M}\)).  
\[\eta_{G}=\frac{P_{G}}{P_{M}} \tag{1}\]The generated power (\(P_{G}\)) could be calculated directly from the simulation results, but it could also be presented as the difference between the input power (\(P_{M}\)) and the power loss (\(P_{L}\)).  
\[P_{G}=P_{M}-P_{L} \tag{2}\]  
The input mechanical power (\(P_{M}\)) could be presented by the torque (\(T\)) and rotational speed (\(n\)) of the rotor as follows:  
\[P_{M}=\frac{\pi*n}{30}*T \tag{3}\]  
Thus, the efficiency (\(\eta_{G}\)) could be shown as a function of the torque (\(T\)), rotational speed (\(n\)), and power loss as follows (\(P_{L}\)):  
\[\eta_{G}=1-\frac{30}{\pi*n*T}*P_{L} \tag{4}\]  
Generally, the simulation is set by adjusting the input parameter for the current (\(I\)) in the coils at a constant speed. Obtained values for the torque (\(T\)) and power loss (\(P_{L}\)) are further used to estimate the generator efficiency (\(\eta_{G}\)).
## 4 Results and Discussion
### Water Quality for Various Utilization  
#### 4.2.1 Overall Water Quality Based on EWQI  
To understand the overall groundwater quality in the basin, the entropy water quality index approach [15] was introduced in the present research. A comprehensive set of 13 hydrochemical parameters was considered in the assessment. The results show that the EWQI values of groundwater span from 26.7 to 143.4, with a mean of 64.05 (Figure 4a). The distribution of groundwater quality is as follows: 50% falls within Rank 1, 33.3% within Rank 2, and 16.7% within Rank 3. Collectively, 83.3% of the groundwater samples are deemed suitable for drinking, suggesting that the overall groundwater quality in the basin is relatively good. Additionally, a positive correlation was observed between the EWQI and TDS, implying that TDS can serve as an indicative measure of groundwater quality to a certain extent.  
The spatial distribution of the EWQI value indicates that groundwater with superior quality is predominantly found in the upper reaches of the basin (Figure 4b). This pattern is attributed to the fact that upstream groundwater sources are primarily recharged by precipitation, meltwater from snow and ice, and seepage from rivers, occurring in regions where human activities are minimal. Conversely, the downstream areas show a notable degradation in groundwater quality, with some areas no longer meeting the criteria for pottable water. Consequently, residents in the downstream regions of the basin should exercise caution when considering the use of local groundwater as a direct source of drinking water, due to potential water quality concerns.
## 4 Experimental Results
### Ablation Experiments and Analyses  
It can be seen from Table 6 that the ablation experiments on the network modules verified the importance of all the modules in the proposed EACNN network model, including the ECBAM and ECA attention modules and their combinations with different networks. Finally, the reliability and fairness of the ablation experiment were ensured by setting the hyperparameters consistently.  
In fact, the main limitation faced by the EACNN algorithm is the accuracy and robustness of the endmember bundle extraction algorithm. After consideration of the inherent attributes of the endmembers in the EACNN method, the abundance estimation was significantly improved.  
Using the same endmember bundles after removing the attention modules in the ECBAM and ECA, the EACNN obtained the worst unmixing result, which shows that the single two-stream network had some limitations with regard to HU. Thus, adding the ECBAM and ECA attention modules to the two-stream network improved the abundance and endmember estimation, especially the estimation of abundance. For the ablation experiment, it should be noted that the ECBAM attention module comprehensively obtained the spatial and spectral information from the HSI and had a better effect on the endmember bundles aggregating the HSI spatial information than the HSI itself. Hence, the ECBAM attention module was combined with the EN to add the endmember bundles and share its parameters with the UN, which can reasonably embed more detailed endmember information. In addition, especially for targets with complex ground feature distributions and serious SVs, when the HSI dataset is large, its role in obtaining spatial information is limited, and capturing the dependencies between all bands is not efficient and unnecessary. The introduction of the ECA to the UN obtained more effective spectral information for unmixing from the HSI and increased the complexity of the model while posting obvious improvements. It brought about further improvements to the network in terms of the abundances and endmembers. The results in the above experiments explained the proposed point of view well and also showed that the EACNN's multi-attention joint learning obtained the information that the network needed to pay attention to and help guide the network in improving unmixing results.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Structure** & **RMSE** \\ \hline EN + UN (no spectral bundles) & 0.0351 \\ EN(ECBAM) + UN(ECA) (no spectral bundles) & 0.0286 \\ EN + UN (no feature extraction module) & 0.0232 \\ EN(ECA) + UN(ECBAM) & 0.0203 \\ EN(ECBAM) + UN(ECBAM) & 0.0197 \\ EN(ECA) + UN(ECA) & 0.0195 \\ EN(CBAM) + UN(ECA) & 0.0175 \\
**EN(ECBAM) + UN(ECA)** & **0.0171** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Analysis of the ablation experiment for the Samson dataset.  
Figure 7: The abundance results of the Samson Dataset estimated using the proposed method.
## 3 Results
### Improved SIF-Based GPP Estimation by Considering the Influence of Temperature
By considering the influences of \(T_{a}\), \(tSI_{F60}\)-based GPP estimation was improved (RMSE dropped from 5.43 to 4.65 umO CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), while R\({}^{2}\) values increased from 0.47 to 0.61; Figure 10d). \(tSI_{F688}\)-GPP estimation was also improved (RMSE decreased from 4.41 to 4.01 umO CO\({}_{2}\) m\({}^{-2}\) s\({}^{-1}\), while R\({}^{2}\) values increased from 0.65 to 0.71; Figure 10b) by integrating \(T_{a}\) with \(tSI_{F688}\). This indicated that considering the effects of temperature could improve the SIF-based GPP estimation at different time scales. Overall, the above results demonstrate that the SIF-GPP models should consider the influence of temperature. Figure 9: The relationships of GPP with \(tSI_{F688}\) (**a**), \(tSI_{F688}*f(Ta)\) (**b**), \(tSI_{F60}\) (**c**), and \(tSI_{F760}*f(T_{a})\) (**d**) based on half-hour measurements. The blue dotted line is the 1:1 line. The red line is the fitted line.
# A Millimeter-Wave 4 th-Harmonic Schottky Diodes Mixer with Integrated Local Oscillator
## 2 Mixer Design  
The schematic configuration of the 300 GHz whole fourth-harmonic mixer is shown in Figure 1. It consists of an MMIC-based local oscillator and a microstrip Schottky-diode-based mixer. The top view of the designed prototype can be seen in Figure 1. The detailed description of the different components is given below.