esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Mathematical Model of Permanent Magnet Synchronous Motor
 \tag{7}\]  
where \(L_{\text{d}},L_{\text{q}}\) is the stator inductance, \(u_{\text{d}}\), \(u_{\text{q}}\) is the stator voltage, \(\psi_{\text{d}}\), \(\psi_{\text{q}}\) is the stator magnetic chain, \(\omega_{\text{e}}\) is the rotor speed, p is the number of pole pairs, \(T_{e}\) is the electromagnetic torque, and \(\psi_{\text{f}}\) is the permanent magnet chain. Based on the voltage equations, the dynamic equivalent circuit of the permanent magnet synchronous motor is obtained, as shown in Figure 3.  
Figure 2: The equivalent circuit of a permanent magnet synchronous motor in the d-q coordinate system. Omitting the complicated derivation process, the standard mathematical model of SPMSM is delay-compensated using the first-order [PERSON]'s formula, whose discrete mathematical equations in the two-phase rotational d- and q-axis coordinate system are as follows:  
\[i_{s}(k+1)=\text{Ai}_{s}(k)+\text{B}u_{s}(k)+\text{C} \tag{8}\]  
\[\psi_{s}(k+1)=\psi_{s}(k)+[u_{s}(k)-R_{s}i_{s}(k)+\omega(k)D]T_{s} \tag{9}\]  
\[T_{e}(k+1)=\frac{3}{2}p\big{(}\psi i_{\text{q}}(k+1)+\big{(}L_{d}-L_{q}\big{)} i_{d}(k+1)i_{\text{q}}(k+1)\big{)} \tag{10}\]  
where \(A= \begin{bmatrix}1-\frac{R_{s}T_{d}}{L_{d}}&\frac{L_{d}T_{\omega}(k)}{L_{d}} \\ -\frac{L_{d}T_{\omega}(k)}{L_{d}}&1-\frac{R_{s}T_{\omega}}{L_{q}}\end{bmatrix} \), \(B= \begin{bmatrix}\frac{T_{s}}{L_{d}}&0\\ 0&\frac{T_{s}}{L_{q}}\end{bmatrix} \), \(\text{C}= \begin{bmatrix}0\\ -\psi_{\text{T}}T_{s}\omega(k)/L_{q}\end{bmatrix} \), \(D= \begin{bmatrix}\psi_{q}\\ -\psi_{d}\end{bmatrix} \), \(i_{s}= \begin{bmatrix}i_{d}\\ i_{q}\end{bmatrix} \), \(u_{s}= \begin{bmatrix}u_{d}\\ u_{d}\end{bmatrix} \), \(\psi_{s}= \begin{bmatrix}\psi_{d}\\ \psi_{d}\end{bmatrix} \), and \(T_{s}\) is using the period. In conventional FCS-MPTC, the cost function is designed as follows:  
\[g=\Big{[}T_{e}^{ref}-T_{e}(k+1)\Big{]}^{2}+\lambda\Big{[}\psi_{s}^{ref}-\psi_{ s}(k+1)\Big{]}^{2} \tag{11}\]  
where \(T_{e}^{ref}\) is the reference torque, obtained through the PI speed outer ring, \(\psi_{s}^{ref}\) is the reference magnetic chain, obtained through the MTPA, and \(\lambda\) is the weighting factor of the stator chain.
## 4 Conclusions  
This study demonstrated that agricultural biomass residues, such as soybean meal, and olive and hemp cultivation wastes can serve as effective substitutes for the traditionally used wheat flour in pMDI-based formulations for plywood panels. Of the various residues tested, the bio-ingredient-free olive fruit skin residues and oil-free (defatted) hemp seeds showed the best overall combined performance in terms of shear strength and wood failure, which are the most important properties for the relevant industrial sector. Similarly, the adhesive system with non-extracted olive skin (ROS-NE) showed the highest resistance in the three-point flexure tests, with a flexural strength and modulus of around 17 MPa and 650 MPa, respectively, while all ROS-based formulations were among the most thermally stable formulations. The use of such waste materials, which have no other use, in this application was investigated for the first time in this work, and the results of the study pave the way for their use in this value-added application.  
Figure 7: (**a**) Mass loss curves and (**b**) dTG curves of the plywood panels manufactured with the various adhesive mixtures.  
**Author Contributions:** Conceptualization, E.P. and K.C.; methodology, E.P., D.M., L.M. and K.C.; validation, S.K., R.O.I., E.X. and L.M.; formal analysis, A.L.S. and P.S.; investigation, E.P., S.K., L.M. and R.O.I.; resources, A.A., P.S. and A.L.S.; data curation, L.M.; writing--original draft preparation, E.P., P.S., A.A., L.M. and R.O.I.; writing--review and editing, E.P., D.M., L.M., A.A., P.S. and E.X.; supervision, K.C.; project administration, K.C. and E.P.; funding acquisition, K.C. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research has been co-financed by the European Regional Development Fund of the European Union and Greek national funds through the Operational Program Competitiveness, Entrepreneurship and Innovation, under the call Special Actions Aquaculture-Industrial Materials-Open Innovation in Culture (project code: T6 YBP-00161).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
**Conflicts of Interest:** Authors [PERSON], [PERSON] and [PERSON] were employed by the company Chimar Hellas S.A. Author [PERSON] was employed by the company PharmaGnose S.A. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# A Calculation Model of Grout Migration Height for Post-Grouting Technology
## 3 Results and Discussion  
### Parameter Influence Analysis  
The parameter influence analysis was carried out to explore the correlations of slurry migration height with the input parameters. It is assumed that the surrounding soil is isotropic and homogeneous, the unit weight of soil is \(\gamma\)\(=18\) kN, and the yield shear strength of cement slurry is \(\tau_{0}=6\) Pa \(\cdot\) s. A total of 15 samples with different geometric conditions were selected and classified into three sets, as listed in Table 1. The influencing factors include pile length, pile radius, and thickness of weak layer in the following section.  
Figure 2(a) presents the relationship between pile embedment depth and migration height. A linear decreasing trend was observed for these two parameters, in which migration height decreases with an increase in pile embedment depth. It can be deduced that for the bored pile with short length, its bearing capacity is more sensitive to the treatment of post-grouting. The correlation of migration height with pile diameter is shown in Figure 2(b), where migration height increases with the increasing of pile diameter, and this increment of height becomes moderate as the pile diameter exceeds 1.0 m. This phenomenon reveals that the migration height would be a stable value as pile diameter further increased. For the bored piles with large diameters, the discrepancy of migration height is expected to be insignificant for the same post-grouting technology. The relationship of migration height with grouting pressure is presented in Figure 2(c). It can be expected that a higher grouting pressure would result in a larger migration height as shown in figure. The high value of grouting pressure imposes the strict requirements on the grouting equipment and construction cost. In the actual grouting construction of cast-in-place pile, due to the complexity of the geological conditions and difference in pile length and mud thickness, the actual  
\begin{table}
\begin{tabular}{c c c c} \hline
**Set** & **Grouting Pressure/kPa** & **Pile Radius/m** & **Grouting Depth/m** \\ \hline
1 & different value & 0.4 & 20 \\
2 & 1500 & different value & 20 \\
3 & 1500 & 0.4 & different value \\ \hline \end{tabular}
\end{table}
Table 1: Grouting pressure, pile radius, and grouting depth of 15 samples for calculation.  
migration height values of cement slurry in piles are different from each other. Thus, the actual cement slurry migration height should be determined in situ.
# UWB-Based Real-Time Indoor Positioning Systems: A Comprehensive Review
## 4 Commercial UWB Modules and Chips for IPSs  
Many companies that produce and market real-time location and indoor positioning systems use off-the-shelf ultra-wideband modules to reduce the time, cost, and complexity of manufacturing these modules themselves. In this section, we will discuss the most popular commercial ready-made units that are used in indoor positioning systems.
## 3 Results and Discussion
### Changes in Metabolic Capacity  
The metabolic capacity in viable biofilm cells was quantified by the resazurin. As shown in Figure 5, the percentage of metabolic capacity exhibited a continuously decreasing tendency with the prolonged exposure to plasma. For the biofilms without PAH treatment, the percentage of metabolic capacity was 98.23% and decreased to 23.27% when biofilm was immersed in PAH for 30 min. Combined with ROS results, it was found that the balance of the oxidation-reduction system in the biofilms was disturbed by PAH, leading to a significant (\(p<0.05\)) decline in metabolic activity. Meanwhile, compared with the result as shown in Figure 1B, more than 99.9% of the bacteria lost the ability to reproduce in the biofilm after PAH treatment for 30 min, but only about 76% of these bacteria lost the ability to metabolize. This result is similar to the percentage of bacteria with intact cell membrane structure shown in Figure 4B. The metabolic capacity depends on the electron transport chain on the surface of bacteria, which can provide energy. When the bacterial membrane is damaged, the cell will lose the ability to metabolize.
# Dual-Slope Diffuse Reflectance Instrument for Calibration-Free Broadband Spectroscopy
## 5 Conclusions  
In this article, we have presented a new Dual-Slope Continuous Wave broadband Diffuse Reflectance Spectroscopy (DS CW-bDRS) instrument. Experiments on highly scattering liquid phantoms demonstrated the instrument's capability of measuring absolute absorbance spectra without any need for instrumental calibration. By combining DS CW-bDRS and Frequency-Domain Near-Infrared Spectroscopy (FD-NIRS; to account for scattering contributions to the absorbance spectrum), we were able to measure absolute absorption spectra of the liquid phantoms that contained a combination of three dyes. These experiments demonstrated the technique's ability to perform absolute spectral absorption measurements and retrieve the correct spectral features of various dyes. Future work will focus on further validation of chromophore concentration measurements. The importance of this work lies in the development of the DS CW-bDRS instrument. This DS CW-bDRS instrument is novel in that it combines DS and bDRS to achieve calibration-free measurements and provides a valuable tool for absolute spectral measurements of highly scattering media, including biological tissues.  
Conceptualization, G.B. and S.F.; methodology, G.B., R.D., S.M., H.N., L.V., C.F., A.S., T.P., and S.F.; software, G.B., R.D., S.M., H.N., and L.V.; validation, G.B., R.D., S.M., H.N., L.V., and C.F.; formal analysis, G.B.; investigation, G.B., R.D., S.M., H.N., L.V., and C.F.; resources, A.S. and S.F.; data curation, G.B., A.S., and S.F.; writing--original draft preparation, G.B., A.S., and S.F.; writing--review and editing, G.B., C.F., A.S., T.P., and S.F.; visualization, G.B.; supervision, G.B., C.F., A.S., and S.F.; project administration, G.B. and S.F.; funding acquisition, S.F. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the National Institutes of Health (NIH) grant R01 NS095334.  
Data Availability Statement: Data and supporting codes are available upon request.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
# Comprehensive Survey on VLC in E-Healthcare: Channel Coding Schemes and Modulation Techniques
###### Abstract  
The integration of information and communication technologies in the field of healthcare has generated a positive transformation in the traditional way of providing patient care, optimizing medical services, and streamlining healthcare operations. Traditionally, healthcare systems have relied on radio frequency (RF) signals for data transmission. However, the conventional RF wireless network faces ever-increasing challenges, such as spectrum shortage and a congested frequency spectrum. Moreover, issues such as interference, security vulnerabilities, and potential health risks associated with prolonged exposure to RF electromagnetic radiation complicate its application in healthcare environments. To address these challenges, visible light communication (VLC) systems, which offer dual functionalities, data transmission, and illumination, have emerged as a promising complementary solution to traditional RF-based options. VLC provides secure, high-speed data communication that is immune to RF interference, making it particularly suitable for healthcare settings. This research examines the advancements in e-health systems that utilize VLC technology, considering various modulation and channel coding techniques, with a focus on evaluating the effectiveness and limitations of these techniques to determine their impact on overall performance.  
channel coding; visible light communication (VLC); VLC modulation schemes; E-Healthcare
# A Multimode Fusion-Based Aviation Communication System
## 2 System Design
### Link Calculation
To simplify the calculation, we will change it to the decibel (dB) expression here:  
\[P_{r}(\mathrm{dBm})=P_{\mathrm{f}}(\mathrm{dBm})+G_{\mathrm{f}}(\mathrm{dB})+ G_{r}(\mathrm{dB})-L(\mathrm{dB}). \tag{8}\]  
Furthermore, by converting the power unit to mW and comparing it with the participating power of 1 mW, and then taking the logarithmic operation of the comparison value, it can be obtained that  
\[P(\mathrm{dBm})=10 lg\frac{P(\mathrm{mW})}{1(\mathrm{mW})}. \tag{9}\]  
The Friis equation is the basic link budget equation, which describes the relationship between received power and transmitted power when considering the transmission characteristics of wireless links. According to the Friis equation, the budget for the ad hoc network communication, 5G public network communication, BeiDou satellite communication, RTK high-precision positioning and navigation, and ADS-B air broadcast communication link of the new aviation multimode fusion communication system can be calculated, as shown in \[\Phi_{d}=\frac{P_{t}}{4\pi d^{2}} \tag{1}\]0  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Communication Parameters**} & \multirow{2}{*}{**Ad Hoc Network Communication**} & \multirow{2}{*}{**5G Public Network Communication**} & \multirow{2}{*}{**BeiDou Satellite Communication**} & \multirow{2}{*}{**RTK High-Precision Positioning and Navigation**} & \multirow{2}{*}{**ADS-B Air Broadcast Communication**} \\  & & & & & **Navigation** & \\ \hline Working Frequency & \(1.4\,\mathrm{GHz}\) & \(3.5\,\mathrm{GHz}\) & \(1.6\,\mathrm{GHz}\) & \(1.5\,\mathrm{GHz}\) & \(1\,\mathrm{GHz}\) \\ \hline Transmission Power & \(33\,\mathrm{dBm}\) & \(23\,\mathrm{dBm}\)
# A DQN-Based Multi-Objective Participant Selection for Efficient Federated Learning
## 3 System Model
### Problem Formulation for Node Selection  
The selection of nodes is influenced by multiple factors. Firstly, the differential computing and communication capabilities of end devices have a direct impact on local training and data transmission latency. Secondly, the datasets carried on the end devices vary in size, and the data may not satisfy the independent homogeneous distribution property, which can cause variations in the training quality of local models. The purpose of incorporating DRL into FL is to intelligently leverage the collaboration between end devices and servers to exchange learning parameters, thereby improving the training of local models.  
Therefore, in this paper, we propose a model that achieves optimal accuracy through node selection.  
Accuracy: For an FL task \(i\in I\), the training quality is determined by the test accuracy of the aggregated global model on the test dataset. In this paper, the test accuracy is represented by the sum of the loss functions computed on the test dataset.  
\[A_{i}=L^{i}\left(x_{test},y_{test};\omega_{g}\right) \tag{1}\]  
Time delay: Let there be \(n\) clients, and after node selection, we obtain client \(N^{\prime}=\{1,2,\ldots,d\}\) corresponding to FL task \(i\in I\). We assume that the time required to broadcast the global model, update the local model, and upload the local model is \(T_{i}\), denoted by  
\[T_{i}\geq\max\left\{T_{d}^{\text{local}}+T_{d}^{\text{trans}}\right\}\text{, where }\text{i}\in I,d\in N^{\prime} \tag{2}\]  
We assume that each client has an independent CPU cycle frequency denoted by \(f_{d}\) and a wireless bandwidth denoted by \(r_{d}\). The selected client and the corresponding training data require the local model to be updated in parallel, and each iteration requires \(|H_{d}|G\) CPU cycles, where \(G\) is the number of CPU cycles required per data. Therefore, the required local computation time is denoted by:  
\[T_{d}^{\text{local}}=\frac{|H_{d}|G}{f_{d}},\text{ where }d\in N^{\prime} \tag{3}\]The additional time required for the global model update (also known as transmission time) can be denoted as:  
\[T_{d}^{\text{trans}}=\frac{D}{r_{d}},\text{ where }d\in N^{\prime} \tag{4}\]  
where \(D\) represents the size of the global model.  
We assume that the time required for global model transfer and local model training depends on the local model training latency and the local model upload latency, denoted as:  
\[T_{i}\geq\text{max}\bigg{\{}\frac{|H_{d}|G}{f_{d}}+\frac{D}{r_{d}}\bigg{\}}, \text{ where i}\in I,d\in N^{\prime} \tag{5}\]  
For an FL task \(i\in I\), the node selection problem aims to choose a set of nodes \(Z_{i}\in Z\) at each iteration to optimize the training accuracy, i.e., the total loss function is minimized, while ensuring that the training and transmission latency remain within a certain range.
# Hydrological Drought Frequency Analysis in Water Management Using Univariate Distributions
## 2 Methodology
### Parameter Estimation
#### 2.2.3. Pearson V (IPV)  
The equations needed to estimate the parameters with MOM have the following expressions [20]:  
\[\mu=\gamma+\frac{\beta}{\alpha-2} \tag{43}\]  
\[\sigma^{2}=\frac{\beta^{2}}{\alpha-2}\cdot\left(\frac{1}{\alpha-3}-\frac{1}{ \alpha-2}\right) \tag{44}\]  
\[C_{s}=4\cdot\frac{(\alpha-3)^{0.5}}{\alpha-4} \tag{45}\]  
For estimation with MOM, parameter \(\alpha\) can be evaluated numerically with the following approximate forms, depending on \(C_{s}\geq 0.2\):  
\[\alpha=\exp\left(\begin{array}{l}3.04183273-1.532335271\cdot\ln(C_{s})+0.350 524466\cdot\ln(C_{s})^{2}+\\ 0.114266418\cdot\ln(C_{s})^{3}-0.018372078\cdot\ln(C_{s})^{4}-0.026493586\cdot \ln(C_{s})^{5}-\\ 0.000919818\cdot\ln(C_{s})^{6}+0.004375959\cdot\ln(C_{s})^{7}+\\ 0.000310213\cdot\ln(C_{s})^{8}-0.000357638\cdot\ln(C_{s})^{9}\end{array}\right) \tag{46}\]  
The scale parameter \(\beta\) and the position parameter \(\gamma\) are determined with the following expressions:  
\[\beta=\sigma\cdot(\alpha-2)\cdot\sqrt{\alpha-3} \tag{47}\]  
\[\gamma=\mu-\frac{\beta}{\alpha-2} \tag{48}\]  
The parameter estimation with the L-moments method is performed numerically (definite integrals) based on the equations using the quantile of the function.  
An approximate form of parameter estimation can be adopted. Thus, for the estimation with the L-moments, the shape parameter \(\alpha\) can be evaluated numerically with the following approximate forms, depending on L-skewness (\(\tau_{3}\)) [20]:  
\[\alpha=\exp\left(\begin{array}{l}0.692740135-0.466055586\cdot\ln(|\tau_{3}| )+0.460508385\cdot\ln(|\tau_{3}|)^{2}+\\ 0.470172127\cdot\ln(|\tau_{3}|)^{3}+0.938027763\cdot\ln(|\tau_{3}|)^{4}+\\ 0.805363294\cdot\ln(|\tau_{3}|)^{5}+0.359846034\cdot\ln(|\tau_{3}|)^{6}+\\ 0.08945247\cdot\ln(|\tau_{3}|)^{7}+0.011775528\cdot\ln(|\tau_{3}|)^{8}+0.00064 1664\cdot\ln(|\tau_{3}|)^{9}\end{array}\right) \tag{49}\]  
The scale parameter \(\beta\) and the position parameter \(\gamma\) are determined with the following expressions [16]:  
\[\beta=\frac{L_{2}}{\frac{1}{\alpha-2}-2\cdot z} \tag{50}\]  
\[\gamma=L_{1}-\frac{\beta}{\alpha-2} \tag{51}\]where \(z\) has the following expression:  
\[z=\exp\left(\begin{array}{l}4.083852739-11.442833473\cdot\ln(\alpha)+10.70179328 7\cdot\ln(\alpha)^{2}-\\ 6.513639901\cdot\ln(\alpha)^{3}+2.527142191\cdot\ln(\alpha)^{4}-\\ 0.640389591\cdot\ln(\alpha)^{5}+0.105546499\cdot\ln(\alpha)^{6}-\\ 0.010903032\cdot\ln(\alpha)^{7}+0.000641094\cdot\ln(\alpha)^{8}-0.000016371\cdot \ln(\alpha)^{9}\end{array}\right) \tag{52}\]
## 2 Experiment Section
### Adsorption Measurement  
In the next step, 20 mg MRGO@ZIF7-MIP, MRGO-MIP, and MRGO@ZIF7-NIP were dispersed to 0.08 mmol L\({}^{-1}\) BPA (20 mL) in aqueous solution, and the kinetic processes of BPA adsorption by these three materials were investigated. The experimental procedure was performed under continuous shaking at 25 \({}^{\circ}\)C, and samples were collected for testing at different time intervals of 5, 10, 20, 30, 40, 50, 80, and 60 min. The adsorption amounts of MRGO@ZIF7-MIP, MRGO-MIP, and MRGO@ZIF7-NIP were calculated based on the difference in concentration before and after the adsorption of BPA. In order to investigate the equilibrium process of the adsorption of MRGO@ZIF7-MIP, MRGO-MIP, and MRGO@ZIF7-NIP on template molecules, 10 mg of the three materials were adsorbed into different initial concentrations (0.01-0.2 mmol L\({}^{-1}\)) of BPA solution for 1 h, and the sample concentrations were detected at 279 nm with a UV-Vis spectrophotometer (Helios-8). To evaluate the selectivity of experimental materials for BPA [35], 10 mg MRGO@ZIF7-MIP and MRGO@ZIF7-NIP were dispersed in 10 mL of mixed aqueous solution containing 0.08 mmol L\({}^{-1}\) of BPA, 178-E\({}_{2}\), phenol, and progesterone (P4). The concentrations of BPA, 178-E\({}_{2}\), progesterone (P4), and phenol in the samples were measured by a UV-Vis spectrophotometer after 1 h of adsorption equilibration. In addition, 10 mg MRGO@ZIF7-MIP and MRGO@ZIF7-NIP were dispersed in running water, lake water, or Haihe river solution of 0.08 mmol L\({}^{-1}\) BPA (10 mL). After 1 h, The concentration of the BPA was measured by a UV-Vis spectrophotometer. At the same time, we analyzed the different pH values,temperatures and catalysts on the material, taking note of their influence on adsorption performance. The amounts of the adsorbed BPA were calculated and expressed as \(Q\) (mg/g) according to Equation (1).  
\[Q=(C_{0}-C_{\mathrm{e}})V/W \tag{1}\]  
where \(C_{0}\) and \(C_{\mathrm{e}}\) (mg/mL) are the initial and equilibrium concentrations of the BPA, respectively, \(V\) (mL) is the volume of the BPA sample solution, and \(W\) (g) is the mass of adsorbent material used [63].