esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
For the first part of the study, the best performance for cultivating Ma-B flocs was obtained by digestate diluted to 30%, with 93 \(\pm\) 1.1% for ammonium removal and 37.7 \(\pm\) 4.0% for COD removal, and by slurry diluted to 10% with 84 \(\pm\) 2.9% for ammonium removal and 83.1 \(\pm\) 6.0% for COD removal. A higher dilution seems to give the most advantageous effect, likely due to greater access of Ma-B flocs to nutrients and light, as mentioned before. Regarding anaerobic digestion, for both slurry and digestate controls, the best results of methane production after 30 days were achieved by slurry with 726.7 mL CH\({}_{4}\cdot\)g\({}^{-1}\) VS substrate  
Figure 4: (a) Cumulative methane production for digestate control (Dg 0) and different ratios of Ma-B flocs / Digestate (Ma-B flocs/Dg). (b) Daily and cumulative methane production for digestate control (Dg0) and ratio 0.2 Ma-B flocs / Digestate.  
Figure 5: (a) Cumulative methane production from slurry as control and different ratios of Ma-B flocs/Slurry (Ma-B flocs/SL). (b) Daily and cumulative methane production for Slurry control and ratio 0.2 Ma-B flocs /Slurry.  
compared to 245.6 mL CH\({}_{4}\cdot\)g\({}^{-1}\) VS substrate for the digestate). Digestion with Ma-B flocs at different ratios showed that the best results were with a ratio of 0.2 with 317.2 mL CH\({}_{4}\cdot\)g\({}^{-1}\) VS substrate compared to 165.7 mL CH\({}_{4}\cdot\)g\({}^{-1}\) VS substrate. However, in both cases of slurry or digestate used as the inoculum, methane production did not reach high values after 30 days. The anaerobic digestion of Ma-B flocs is then proven, but further studies should question these results to determine the favorable conditions for better biogas production by Ma-B flocs.  
Conceptualization, O.B. and N.A.; methodology, O.B. and N.A.; validation, N.A., S.P. and H.-Z.L. formal analysis, O.B., N.A., S.P. and H.-Z.L.; investigation, O.B.; resources, O.B. and N.A.; data curation, O.B., N.A., S.P. and H.-Z.L.; writing--original draft preparation, O.B.; writing--review and editing, N.A.; visualization, N.A., S.P. and H.-Z.L.; supervision, N.A., S.P. and H.-Z.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Part of the data related to this manuscript can be found via this link: [[http://docnum.univ-lorraine.fr/public/DDOC_T_2018_0289_BEJI.pdf](http://docnum.univ-lorraine.fr/public/DDOC_T_2018_0289_BEJI.pdf)]([http://docnum.univ-lorraine.fr/public/DDOC_T_2018_0289_BEJI.pdf](http://docnum.univ-lorraine.fr/public/DDOC_T_2018_0289_BEJI.pdf)) (accessed on 20 April 2022).  
The authors declare no conflict of interest.
# An Overview of Biopolymers for Drug Delivery Applications
## 2 Types of Biopolymers Used in Drug Delivery Systems
### Polysaccharides
#### 2.1.4 Cellulose  
As the most prevalent biopolymer, cellulose exhibits biodegradability, renewability, and high strength. It is composed of glucose units, and it has abundant hydroxyl groups in its backbone. Some of the disadvantages of cellulose are its very low solubility in water and common organic solvents, thus having limited applicability [71; 72; 73]. Because it is environmentally friendly, simple, and presents low cost and toxicity, an aqueous sodium hydroxide (NaOH)/urea solution is frequently used to dissolve cellulose [74; 75]. Cellulose from different and diverse sources represents an adaptable and adequate material for DDSs [76; 77]. [PERSON] and [PERSON] developed a sustainable synthesis method for a thermoresponsive Pluronic F127 composite hydrogel reinforced with cellulose taken from empty fruit clusters of oil palm for silver sulfadiazine drug delivery. Their study demonstrated that the developed material from rich agricultural biomass is sustainable, environmentally friendly, and cost-effective for being used as DDSs [78].
# Comprehensive Optimization of Air Quality in Kitchen Based on Auxiliary Evaluation Indicators
## 4 Optimization Analysis of Kitchen Air Quality
### Simulation Results of Different Working Conditions
After a comprehensive evaluation of all indicators, condition 4 was selected for further optimization. To investigate the impact of airflow rate on kitchen air quality, the original airflow rate settings were adjusted. The simulation cases are shown in Table 10.  
The auxiliary evaluation index for each condition was calculated by integrating the average values of various air quality indicators at different airflow rates, as shown in Table 11. The data indicate that increasing the airflow rate generally improved air quality more effectively than reducing it, when the airflow rate was set at 2.16 m\({}^{3}\)/min. Among the four conditions, conditions 9 and 10 had the lowest Q, with no significant differences. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Conditions** & **Case 7** & **Case 8** & **Case 9** & **Case 10** \\ \hline airflow rate (m\({}^{3}\)/min) & 2.052 (\(-\)5\%) & 1.944 (\(-\)10\%) & 2.268 (\(+\)5\%) & 2.376 (\(+\)10\%) \\ \hline \hline \end{tabular} Note: The airflow rate is 2.16 m\({}^{3}\)/min, supply air temperature is 293 K.  
\end{table} 
Table 10: Simulation conditions. Figure 14: Scatter plot of COFP over time for different operating conditions. (**a**) Average concentration at breathing area of occupant; (**b**) Indoor average concentration. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Conditions** & **100\%-** & **Forehead** & **Subnasal** & **Inhalation** & **Entire** & **Inhalation** & **Entire** & **Forehead** \\
**ADPI** & **PMV** & **PMV** & **Area CO2** & **Kitchen** & **Area COFP** & **Kitchen** & **Q** \\
**Case 1** & 78.41 & 2.194 & 2.081 & 736.3 & 740.9 & 29.67 & 28.93 & 1.634 \\
**Case 2** & 64.47 & 1.888 & 1.779 & 659.1 & 688.0 & 28.41 & 27.24 & 1.414 \\
**Case 3** & 57.99 & 1.637 & 1.455 & 619.3 & 667.9 & 25.71 & 27.87 & 1.274 \\
**Case 4** & 55.60 & 1.520 & 1.335 & 595.6 & 651.5 & 23.73 & 26.74 & 1.209 \\
**Case 5** & 53.48 & 1.490 & 1.523 & 623.6 & 648.5 & 26.70 & 26.44 & 1.226 \\
**Case 6** & 77.19 & 1.713 & 1.673 & 680.9 & 679.9 & 30.55 & 27.29 & 1.483 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Average air quality indicators and evaluation index for different conditions. As the airflow rate increased, the COFP concentration in condition 10 did not decrease but rather increased, indicating a complex trajectory of the COFP over time. The simulation results suggest that higher airflow does not always equate to more effective air quality improvement, and beyond a certain threshold additional airflow yields no significant benefits. Considering energy consumption, condition 9 emerged as the most energy-efficient option for optimal air quality improvement in the kitchen.
# Evaluating the Effects of Deep Excavation on Nearby Structures Through Numerical Simulation
###### Abstract  
Traditional numerical analyses often overlook the potential impact of adjacent building basements on ground surface deformation. This study investigated the influence of neighboring structures on diaphragm walls and ground surface deformation during deep excavation for building foundations using PLAXIS 3D finite element software. This study simulated the top-down construction method with plate elements for diaphragm walls retaining H-shaped steel for support and pre-stressed anchors. The adjacent structures were modeled using plate elements. Numerical analysis results were compared with field observations for model validation. The results show that the lateral displacement of the retaining wall varies with the depth of neighboring basements. At 0.5 times the excavation depth, displacement was significant, and it stabilized at 1.0 times the depth. When the distance between adjacent buildings and the retaining wall was about twice the excavation depth, the deformation curve converged, indicating negligible influence beyond this distance. Ground surface settlement increased as the neighboring basement depth reached half the excavation depth, and stabilized at 1.6 times the depth. A closer proximity resulted in greater ground surface settlement. These findings offer practical references for deep excavation design and assessment, aiding engineers in ensuring project stability and safety.  
finite element method; deep excavation; neighboring buildings; scenario simulation; lateral displacement; ground settlement +
Footnote †: journal: Applied sciences
## 3 Results and Discussion
### Hydraulic Data Analysis  
The new time-based pressure management SARIMA model to keep a target pressure in the critical point of the area has been applied on real operation for 12 days, from 12 December to 23 December 2022. Starting from critical point pressure values defined by the water utility operator, the _P3_ target, shown in Table 1, and applying the model, the pressures _P2_ at the outlet of the PRV were calculated and adjusted. By setting these pressures _P2_ on the controller commanding the PRV, the pressures at the critical point _P3_ were measured. Figure 10 describes the performance of the WDN under the SARIMA model, compared to the former pressure management scheme for CPC based on the flow measurement. In order to make the comparison, a similar period of days, between 21 November and 2 December, just before the SARIMA model was implemented, was chosen.  
Under the new SARIMA working scheme, the pressure at the outlet of the PRV, _P2_, changes according to the day and the time of the day to keep the defined target pressure in the critical point, _P3_. Under the previous flow-based PM scheme, _P2_ varies according to the instantaneous flow measurement following a control model which relates headloss between _P2_ and _P3_ and the _flow_ with the same target to keep a fixed pressure value in the critical point. The flow signal is not needed when applying the SARIMA PM model and it is shown in the graph just for illustration purposes.  
The average, maximum and minimum pressure _P1_, _P2_, _P3_ and _flow_ during the two phases described in Figure 10 are summarized in Table 4. The data in SARIMA PM show between brackets the difference compared to the previous phase in absolute value and in percentage. The table shows that there is almost no difference in the hydraulic variables of the area when the new SARIMA PM model is applied.  
Figure 10: WDN performance under critical point time-based pressure management model.
## 3 Results and Discussion
### Level Analysis of Influencing Factors of SSCFC's Fluidity and Compressive Strength  
For an in-depth analysis of how varying levels of three factors (W/C ratio, SS substitution ratio, ceramics replicate replacement ratio) govern SSCFC's properties, with factor levels  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Performance**} & \multirow{2}{*}{**Source**} & **Class III Sum** & **Degrees** & **Error Degrees** & \multirow{2}{*}{**Mean Square**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**F**} & \multirow{2}{*}{**\(\alpha\)(2,6)**} \\  & & **of Squares** & **of Freedom** & & & & & \\ \hline \multirow{4}{*}{Liquidity} & A & 1204.667 & 2 & 6 & 602.330 & 0.961 & 5.143 \\  & B & 3192.667 & 2 & 6 & 1596.333 & 5.407 & 5.143 \\  & C & 548.667 & 2 & 6 & 274.333 & 0.373 & 5.143 \\  & A & 65.136 & 2 & 6 & 32.568 & 8.199 & 5.143 \\ \multirow{4}{*}{7d compressive strength} & B & 0.682 & 2 & 6 & 0.341 & 0.023 & 5.143 \\  & C & 6.376 & 2 & 6 & 3.188 & 0.232 & 5.143 \\ \multirow{4}{*}{14d compressive strength} & A & 78.587 & 2 & 6 & 39.293 & 8.613 & 5.143 \\  & B & 1.607 & 2 & 6 & 0.803 & 0.046 & 5.143 \\ \multirow{4}{*}{28d compressive strength} & C & 8.780 & 2 & 6 & 4.390 & 0.271 & 5.143 \\  & A & 82.149 & 2 & 6 & 41.074 & 8.682 & 5.143 \\ \multirow{4}{*}{28d compressive strength} & B & 2.069 & 2 & 6 & 1.034 & 0.057 & 5.143 \\  & C & 7.722 & 2 & 6 & 3.861 & 0.225 & 5.143 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Variance analysis of the fluidity and compressive strength of SSCFC.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Performance** & **Factor** & **K1** & **K2** & **K3** & **R** \\ \hline \multirow{4}{*}{Fluidity (mm)} & A & 164.7 & 188.3 & 190.0 & 25.3 \\  & B & 206.7 & 174.3 & 162.0 & 44.7 \\  & C & 174.7 & 176.3 & 192.0 & 17.3 \\  & A & 13.6 & 12.4 & 7.4 & 6.2 \\
7d Compressive strength (MPa) & B & 11.5 & 10.9 & 10.9 & 0.6 \\  & C & 10.5 & 12.3 & 10.6 & 1.8 \\  & A & 15.1 & 14.0 & 8.4 & 6.7 \\
14d Compressive strength (Mpa) & B & 13.0 & 12.0 & 12.5 & 1.0 \\  & C & 11.6 & 13.9 & 12.1 & 2.3 \\  & A & 15.7 & 14.6 & 8.8 & 6.9 \\
28d Compressive strength (Mpa) & B & 13.5 & 12.4 & 13.2 & 1.1 \\  & C & 12.1 & 14.3 & 12.6 & 2.2 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Range analysis table of SSCFC’s performance.  
plotted along the abscissa and flow characteristics combined with compressive strengths at 7d/14d/28d curing ages as ordinate values, interaction diagrams correlating factor levels with performance metrics were developed (Figure 8).
## 3 Experimental Results and Discussions
Figure 10: The curves of the BER versus transmission rate for **(a)** the MLSE case and **(b)** the NN equalization case when the RoP ranged from \(-9.5\) to \(-2.5\) dBm (with an optical backplane). a three-layer fully connected neural network can be considered as an optimal choice between system performance and algorithm complexity.
# Polar Codes and a M-ary Modulation-Based OFDM-PLC System
## 5 Performance Simulation and Results Analysis
### Noise Immunity Using Polar Codes and LDPC Codes  
Finally, by simulating the analysis in Figures 6-8, the transmission performance of different coding schemes under the OFDM-PLC-based system is focused on by selecting the high-order 16 QAM modulation in the final system model and setting the improved impulse noise parameter \(m=10\times 5^{-3}\). The BER performance of the polar and LDPC code schemes with three different coding rates (1/2, 5/8, and 3/4), respectively, and code lengths of 512 are compared under Gaussian white noise and improved impulse noise, where the LDPC code scheme is a combination of Approximate Lower Triangular (ALT) encoding and  
Figure 8: BER performance under different modulation schemes.  
BP algorithm decoding has a maximum number of iterations of 50. As shown in Figure (a)a, with the same code length, the decoding performance of both polar codes and LDPC codes deteriorates as the code rate increases under Gaussian white noise only. At BER of \(10^{-3}\), the LDPC code and polar code with a code rate of \(1/2\) both have approximately \(1\) dB better performance than those with a code rate of \(3/4\). In the high SNR region, such as at a code rate of \(3/4\) and SNR \(>3\) dB, the decay slope of the polar code scheme increases significantly, and its error correction performance is enhanced. Similarly, for code rates of \(1/2\) and \(5/8\), the advantages of the polar code scheme over the LDPC scheme become more pronounced with increasing SNR. As shown in Figure (b)b, when improved impulse noise is added on top of Gaussian white noise, at BER of \(10^{-3}\) and code rate of \(1/2\), both polar code and LDPC code schemes show approximately \(2\) dB performance reduction compared to that under Gaussian white noise only. However, at BER of \(10^{-2}\) and code rate of \(5/8\), the performance of LDPC codes with added impulse noise is reduced by more than \(2\) dB over Gaussian white noise only, which is greater than that of PC codes under the same conditions, and the trend becomes more pronounced as the code rate and SNR increase. The decay slope of the polar code scheme under impulse noise is greater than that of the LDPC code, and its error correction capability is significantly enhanced compared to that of the LDPC code scheme. Therefore, the polar code scheme shows better suppression of impulse noise than the LDPC code scheme.
# Support Vector Machine-Based EMG Signal Classification Techniques: A Review
## 2 EMG Signals  
Pattern recognition-based myoelectric signal classification consists of logging a specific time interval of EMG signals coming from the muscles and performing many repetitions with different movements, to segment them later. The classification is performed by extracting the features in each interval of the signal to recognize the characteristic information of each movement. From these data, the training is accomplished--which varies according to the method--and, in this way, it is possible to classify the type of movement.  
However, the myoelectric signal acquisition is not a simple procedure since the EMG signals have a high noise content since they are not extracted directly from the muscles, but they need to go through the different layers of the skin between the electrode and the pulse generated by the muscle. Besides, the signal acquisition instruments introduce noise themselves by the parasitic frequencies on the power line.
## 3 Optimization Formulation
### Constraints  
#### 3.2.1 The First-Stage Constraints  
In this model, the CVPP's trading strategy in the DA market is accomplished under first-stage constraints. Equation (17) describes that RES and ESS participate in selling electricity to the system in the DA market, so their rating power should limit the CVPP's selling power. Similarly, Equation (18) shows that the CVPP purchases power from the system to supply the customers and accumulates in the ESS; consequently, the CVPP's buying power is limited by the sum of customers' demand and the ESS's maximum charging power. The binary variable \(u_{sell,DA}^{t}\) ensures that the CVPP cannot sell and buy electricity simultaneously in the DA market. Besides, the DA trading contract is determined for each 1-h interval, while each time block in the optimization model is 15 min. Hence, Equations (19) and (20) ensure that the CVPP's trading power should be similar for four 15-min blocks of a 1-h interval.  
\[0\leq P_{sell,DA}^{t}=P_{RES\_grid,\ DA}^{t,s}+P_{ESS\_grid,DA}^{t,s}\leq u_{ sell,DA}^{t}(P_{ESS\_max}+P_{RES\_etal}) \tag{17}\]  
\[0\leq P_{buy,DA}^{t}=P_{grid\_load,DA}^{t,s}+P_{grid\_ESS,DA}^{t,s}\leq\Big{(}1 -u_{sell,DA}^{t}\Big{)}(P_{ESS\_max}+Load_{max}) \tag{18}\]  
\[P_{sell,DA}^{t}=P_{sell,DA}^{t-\tau}\quad\forall\ t\dot{:}4\dot{:}\ \tau=\{1,\ 2,\ 3\} \tag{19}\]\[P^{t}_{buy,DA}=P^{t-\tau}_{buy,DA}\ \ \ \forall\ t\hat{:}4;\ \tau=\{1,\ 2,\ 3\} \tag{20}\]
# Identification of High-Order Nonlinear Coupled Systems Using a Data-Driven Approach
## 1 Introduction
### Contribution  
With this in mind, in the context of a known structure for a high-order nonlinear system with coupled dynamics, this work presents a significant contribution by introducing a simple modification of the SINDY methodology. Specifically, this modification allows the identification of missing coefficients within the known system structure. What distinguishes this contribution is not only its effectiveness but also its minimal computational cost. By seamlessly integrating into the existing SINDY framework, this modification offers a practical solution to a challenging problem, improving the applicability and accuracy of the method without imposing significant computational overhead. Therefore, in addition to improving SINDY's capabilities for system identification, this work also underlines the importance of efficiency in algorithmic improvements within the field of nonlinear dynamical systems [19; 20]. A comparison between the performance of the original algorithm and modified one was carried out by applying both to the identification of the mathematical model of a commercial quadcopter.
# Memory-Based LT Codes for Efficient 5G Networks and Beyond
## 2 Preliminaries and Related Work
### Degree Distributions of LT Codes  
As can be seen from the encoding and decoding processes of LT codes, the degree distribution significantly influences LT codes' performance [9; 29]. As a result, designing an optimal degree distribution is crucial for code performance, such as the code rate, reliability, complexity, and decoding success probability (DSP). The first 'degree distribution' was designed by [PERSON], and it is referred to as ideal soliton distribution (ISD), \(\rho(i)\), and it is given as [9]:  
\[\rho(i)=\begin{cases}\frac{1}{k}&\text{for }i=1\\ \frac{1}{i(i-1)}&\text{for }i=2,3,\ldots,k.\end{cases} \tag{1}\]  
Note that the ISD is the optimal distribution in terms of the code rate, where only \(k\) encoded symbols are required to recover the \(k\) source symbols. Thus, only one source symbol is recovered at each decoding step. Simultaneously, a new encoded symbol will be changed to have only one neighbor. That is, there is only one degree-one encoded symbol at a time. In practice and while using the BEC, if any encoded symbol is erased, this will stop the decoder's peeling process, and then the decoder will announce decoding failure. To overcome this problem of the ISD, Luby introduced another degree distribution called robust soliton distribution (RSD), \(\Omega_{d}\), where the expected number of degree-one encoded symbols is increased from 1 to \(R\) (\(R>1\)). Thus, RSD requires more encoded symbols (i.e., \(n>k\)) but is asymptotically optimal in terms of capacity-achieving and practical. Moreover, its average degree distribution is \(\mathcal{O}(\log k)\). The RSD, \(\Omega_{d}\), is given by [9]:  
\[\Omega_{d}=\frac{\rho(d)+\tau(d)}{\beta},\ d=1,2,\ldots,k, \tag{2}\]  
where \(\beta=\sum\limits_{i=1}^{k}\{\rho(i)+\tau(i)\}\) is used for normalization and \(\tau(i)\) is given by  
\[\tau(i)=\begin{cases}\frac{R}{k}\frac{1}{i},&\text{if }i=1,2,\ldots,(\lfloor \frac{k}{R}\rfloor-1)\\ \frac{R}{k}\ln{(R/\delta)},&\text{if }i=\lfloor\frac{k}{R}\rfloor\\ 0,&\text{if }i=\lfloor\frac{k}{R}\rfloor+1,\ldots,k,\end{cases} \tag{3}\]  
where \(R=c\ln(k/\delta)\sqrt{k}\) is the modified ripple size, \(c>0\) is a suitable constant, and \(\delta\) is the allowable failure probability of the decoder once it receives \(n\) encoding symbols. As one can notice, there is a spike at the position of \(i=k/R\).  
As a result, the LT code can be defined as its parameters \((k,\Omega(x))\), where \(\Omega(x)=\Omega_{1}x+\Omega_{2}x^{2}+\ldots+\Omega_{k}x^{k}\).  
The probability of decoding failure, \(\mathrm{P}_{F}\), is calculated as the probability that a given source symbol is not covered at the encoding process, and it is given by [18]  
\[\mathrm{P}_{F} \geq(1-\frac{1}{k})^{m\Omega^{\prime}(1)} \tag{4}\] \[\simeq e^{-n\Omega^{\prime}(1)},\]  
where \(\Omega^{\prime}(x)\) is the first derivative of the degree distribution \(\Omega(x)\). Thus, \(\Omega^{\prime}(1)\) is the average degree of the encoded symbols.