esa-sceva/satcom-chunk-collection huggingface dataset:
# Characterization and Optimization of PLA Stereocomplexed Hydrogels for Local Gene Delivery Systems
## 3 Results and Discussion
### Hydrogel Analysis by Solid-State NMR  
To further investigate the PLA crystal structures in the hydrogel from L-/D-micelles, CP/MAS \({}^{13}\)C NMR spectra were obtained for the polymer and lyophilized hydrogel samples at different stages. Figure 11 shows the spectra of PLLA-PEG-PLLA (800-2000-800) and stereocomplexed hydrogels (mixture of L- and D-micelles) which was immediately lyophilized, slowly air-dried, and treated in buffer at pH 7 and pH 4.5 for 5 day. The advantage of CP/MAS NMR is to identify the different polymer packing state. The spectrum of PLLA-PEG-PLLA (a) shows PLLA-methyl peak around 20 ppm, PLLA-methine peak around 72 ppm, PEG peak at 74 ppm, and PLLA-carbonyl peak around 175 ppm. Most of PLLA carbons display as multiple peaks due to the semicrystalline nature. For example, the inserted spectra are the expansion of the carbonyl peak region in which amorphous and crystal carbon peaks are distinctly separated [30]. The freshly made and lyophilized hydrogel  
Figure 10: \({}^{1}\)H NMR spectra of the lyophilized supernatants from the samples: pH 4.5, after 1 day (**blue**), pH 4.5, after 7 day (**red**), and pH 7.4, after 7 days (**green**).  
Figure 9: GPC profiles of the supernatants at (**a**) pH 7.4 and (**b**) pH 4.5, after lyophilized and re-dissolved in THF.  
from enantiomeric micelles of PLLA-PEG-PLLA (800-2000-800) and PDLA-PEG-PDLA (800-2000-800) (**b**) showed all three forms for the carbonyl carbon; homocrystal, amorphous, and stereocomplex crystal at 173, 174.5, and 176 ppm, respectively. This indicates that relatively low degree of chain exchange and stereocomplex formation still leads transformation of the micelle solution into a gel form. When the hydrogel was slowly dried up in air (c), we observed only distinguished peak of stereocomplexed PLA for carbonyl carbon. The hydrogels remained in buffer solutions after 5 days maintained good structure of stereocomplex PLA (d, e) although PEG and small PLA oligomers already started to release into the buffer solutions. Since both hydrolysis and biodegradation of PLA start from amorphous region, it is reasonable that stereocomplexed PLA remains longer in the hydrogel. Note that the small peaks at 30 and 184 ppm are attributed to the acetate buffer used for degradation test.
## 2 Material and Methods
### Inclusion and Exclusion Criteria  
A set of inclusion, exclusion and withdrawal criteria were established and had to be strictly adhered to (Figure 2). The inclusion criteria were (i) age between 60 and 90 years; (ii) clinical symptoms suggestive of AD by the National Institute on Aging--Alzheimer's Association workgroups on the diagnostic guidelines for AD [22] (in the case group, Mental State Examination (MMSE) < 20 points and Montreal Cognitive Assessment (MoCA) < 18 points; Activity of Daily Living (ADL) Scale was impaired (ADL \(\geq\) 22 points); and Clinical Dementia Rating (CDR) = 1 point; except for changes in AD, there were no other abnormalities);(iii) normal elderly people with no clinical diagnosis of AD in the CG, no memory deficit or other cognitive impairment and no mental or neurological disease (MMSE \(\geq\) 26 and MoCA \(\geq\) 26); (iv) individuals who voluntarily consented to participate free of charge; and (v) singed written consent form. The exclusion criteria were (i) individuals whose analytical results were not available or presented inconsistencies that prevented their inclusion in the study; and (ii) individuals who did not meet the inclusion criteria.
# Design of an Isolated Bidirectional Symmetric Resonant Converter
## 6 The Measurements and Operating Efficiency Analysis  
Based on the parameters, the resonant components are calculated with \(L_{T1}\) = 59.9 \(\mu\)H, \(L_{T2}\) = 41.6 \(\mu\)H, \(L_{m}\) = 209.65 \(\mu\)H, \(C_{r1}\) = 42.29 nF, and \(C_{r2}\) = 60.9 nF. Owing to the winding transformer's windings and resonant inductor being handmade, there definitely exists some differences from the calculated ones. Indeed, the transformer magnetizing inductance \(L_{m}\) was measured to be 219.85 \(\mu\)H, and the primary and secondly leakage inductances are \(L_{R1}\) = 6 \(\mu\)H and \(L_{R2}\) = 4 \(\mu\)H, respectively. Moreover, two resonant inductances are \(L_{r1}\) = 55.2 \(\mu\)H and \(L_{r2}\) = 40.36 \(\mu\)H. The resonant capacitors are connected in parallel; it not only can divert the current flow, but it also can reduce the current stress of each capacitor and decrease equivalent series resistance (ESR) loss with C\({}_{r1}\) = 41.4 nF and C\({}_{r2}\) = 53.7 nF.  
Table 2 lists the selected parameters of the important devices in the practical circuit. The microcontroller unit (MCU) TMS320F28335 developed by TI was selected as the control core of bidirectional power flow to effectively dwindle the amount of device and reduce the size and cost. Considering the cost and the fast switching feature, the wide-band gap SiC (C2M0160120D) is used to serve the power switches (\(Q_{1}\)-\(Q_{8}\)) to promote the converter's performance.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Device Symbol** & **Quantity** \\ \hline Transformer turn ratio \(N_{P}\):\(N_{S}\) & 1.2:1 \\ Magnetizing inductance \(L_{m}\) & 219.85 \(\mu\)H \\ Resonant inductance \(L_{r1}\)(including primary leakage inductance) & 61.2 \(\mu\)H \\ Resonant inductance \(L_{r2}\)(including secondary leakage inductance) & 44.36 \(\mu\)H \\ Resonant capacitance (primary) \(C_{r1}\) & 41.4 nF \\ Resonant capacitance (secondary) \(C_{r2}\) & 53.7 nF \\ DC grid capacitor tank \(C_{DC,Grid}\) & 540 \(\mu\)F \\ Battery capacitor tank \(C_{Bat}\) & 540 \(\mu\)F \\ Power switches \(Q_{1}\)-\(Q_{8}\) & C2M0160120D \\ Trigger controller & TMS320F28335 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Selected parameters of the important devices.  
Figure 15: The voltage state under abrupt load current fluctuation: (**a**) load current changes from 1.5 A to 2.5 A; (**b**) load current changes from 2.5 A to 1.5 A.
## 4 The FO PI\({}^{\lambda}\)D\({}^{\mu}\) Controller of a Ship's Course
### Autotuning of the FO PI\({}^{\lambda}\)D\({}^{\mu}\) Controller Based on the IPSO Algorithm
The parameter settings are shown in Table 1, and the test results are shown in Figure 6.  
From the simulations, it can be seen that the test results using the Griewank function show that the PSO and IPSO algorithms could converge to the optimal solution, but the number of iterations used to find the optimal IPSO algorithm was significantly smaller than that of the PSO algorithm. The results of the Schaffer function test show that the PSO and IPSO algorithms could converge to the optimal solution, but the number of iterations used to find the optimal solution was significantly smaller for the IPSO algorithm than for the PSO algorithm. Therefore, it is reasonable to believe that the addition of dynamic learning factors to the PSO algorithm in the process of finding the best search faster and with higher convergence accuracy and higher stability to solve the PSO algorithm easily fell into the local optimum problem. In this part, the IPSO algorithm is used to autotune the five parameters (\(K_{p}\), \(K_{i}\), \(K_{d}\), \(\lambda\), and \(\mu\)) of the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller in Equation (6), which is used for course-keeping of the USV. The working principal diagram and model of the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller with the IPSO algorithm are shown in Figures 7 and 8. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & **Particles** & **Weight** & **Learning Factors** & **Maximum** & **Maximum** \\  & \(m\) & \(\omega\) & \(c_{1}\), \(c_{2}\) & **Speed \(V_{max}\)** & **Iterations \(T_{max}\)** \\ \hline PSO & 20 & 1 & 2, 2 & 1 & 100 \\ IPSO & 20 & 1 &
\begin{tabular}{c} Equations (30) \\ and (31) \\ \end{tabular} & 1 & 100 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter settings for different PSO algorithms. Figure 5: Distribution chart of test functions. The IPSO algorithm is used to autotune the five parameters (\(K_{p}\), \(K_{i}\), \(K_{d}\), \(\lambda\), and \(\mu\)) of the FO PI\({}^{\lambda}\)D\({}^{\mu}\) control, which is a five-dimensional solution space. Considering the system  
Figure 8: Process of IPSO optimizing the FO PI\({}^{\lambda}\)D\({}^{\mu}\) controller. Figure 6: Performance comparison of PSO and IPSO algorithms. overshoot and the square integral of the control loop error, the equation of the optimization objective function is as follows:  
\[J=\varpi_{1}\int_{0}^{+\infty}e^{2}(t)dt+\varpi_{2}\sigma \tag{35}\]  
where \(e(t)\) is the systematic error, \(\sigma\) is the system overshoot, and \(\varpi_{1}\) and \(\varpi_{2}\) are weights.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 3 Results and Discussion
### MIMO Performance Parameters
#### 3.3.2 Total Active Reflection Coefficient (TARC)  
For the evaluation of MIMO performance, the TARC is an important parameter that considers all s-parameters. The TARC for the N number of antennas for the MIMO array can be defined according to [27].  
\[\text{TARC}=\frac{1}{\sqrt{N}}\sqrt{\sum_{i=1}^{N}\biggl{(}\sum_{k=1}^{N}S_{ik} e^{i\theta_{k-1}}\biggr{)}^{2}} \tag{2}\]  
Figure 10 shows the simulated and measured MIMO parameters of the TARC. It is noticeable that in the relevant bands, both the simulated and observed values are less than \(-6\) dB. The plot indicates that there is a modest shift in the measured response towards lower frequencies. This small discrepancy between the observed and simulated results could be caused by many factors, such as assembly mistakes and fabrication tolerance, but it still encompasses the entire bandwidth of the necessary bands of interest.
## 5 Applications of SSNs
This provides the farmer the ability to take good decisions regarding optimal plants placement, water level requirements, energy and pesticide usage, etc. This will produce a suitable environment that is based on the diverse conditions that are required for different types of crops. * Industrial Monitoring [86] Attaching a SSN to the industrial process will improve it by providing a diverse of applications that facilitate the system management and control. Offering dynamic data share and analysis for everything in the industrial environment, as energy consumption, machine status, product quality, and alerts for critical equipment parameters (system temperature, electricity, etc.) give an early detection of unstable conditions that cause risk reduction and removal of the related cost as a result. Therefore, it directly affects the manager decisions and makes industrial operations more stable, predictable, better energy consumption, safer, and cost effective. Natural Monitoring [87; 88] Observation of nature to predict disasters as ice avalanches, rock falls, volcanic eruptions, the migration of animals, etc. Is a sensitive issue, as it involves securing human beings, animals, and plants. Thus, a SSN provides diverse applications for that purpose, it offers reusing the old deployed infrastructure in these places and this reduces the overall cost and gives a chance for new applications to be deployed in that places, while sharing the gathered information among them.
# An Innovative Internal Calibration Strategy and Implementation for LT-1 Bistatic Spaceborne SAR
## 2 Internal Calibration System
### Echo Compensation
\begin{array}{c}\frac{RX_{A}^{a}\cdot RX^{a}}{RX_{A}^{a}\cdot RX^{a}}=\frac{RX_ {dal}^{a}}{\sqrt{C}\mathbb{E}_{cal}^{a}}\cdot\frac{\sqrt{ST_{cal}^{a}}}{\sqrt{ SR_{cal}^{a}}}\cdot\frac{\sqrt{H_{s}^{hu}}}{\sqrt{H_{s}^{ab}}}\cdot\frac{\sqrt{CE_{ cal}^{b}}}{RX_{cal}^{b}}\cdot\frac{\sqrt{SR_{cal}^{b}}}{\sqrt{ST_{cal}^{b}}}\\ \cdot\frac{\sqrt{CR_{1}^{a}\cdot CR_{3}^{b}}\cdot CN_{A}^{b}}{\sqrt{CR_{4}^{b} \cdot CR_{3}^{b}\cdot CN_{A}^{b}}}\cdot\frac{\sqrt{CR_{4}^{b}\cdot CR_{3}^{b}} \cdot CN_{A}^{b}}{\sqrt{CR_{1}^{b}\cdot CR_{5}^{b}}}\end{array}  \tag{7}\]  
The transfer function \(H_{\text{C}}\) that compensates for the echo to eliminate the impact of different receiving paths in BiSAR, can be written as:  
\[H_{\text{C}}=H_{sys}\cdot K_{T}=\frac{RX_{A}^{a}\cdot RX^{a}}{RX_{A}^{b}\cdot RX ^{b}} \tag{8}\]  
and,  
\[H_{sys}=\frac{RX_{cal}^{a}}{\sqrt{CE_{cal}^{a}}}\cdot\frac{\sqrt{ST_{cal}^{a} }}{\sqrt{SR_{cal}^{a}}}\cdot\frac{\sqrt{H_{s}^{hu}}}{\sqrt{H_{s}^{ab}}}\cdot \frac{\sqrt{CE_{cal}^{b}}}{RX_{cal}^{b}}\cdot\frac{\sqrt{SR_{cal}^{b}}}{\sqrt{ ST_{cal}^{b}}} \tag{9}\]  
\[K_{T}=\frac{\sqrt{CR_{1}^{a}\cdot CR_{5}^{a}}}{\sqrt{CR_{4}^{a}\cdot CR_{3}^{a} \cdot CN_{A}^{a}}}\cdot\frac{\sqrt{CR_{4}^{b}\cdot CR_{3}^{b}\cdot CN_{A}^{b}} }{\sqrt{CR_{1}^{b}\cdot CR_{5}^{b}}} \tag{10}\]  
Based on Equation (8), the echo compensation for the auxiliary satellite (receiver only) can be calculated. The first and second terms of Equation (9) are calibrated by the primary satellite in orbit, the third term is calibrated through inter-satellite synchronization transfer to compensate for phase noise and frequency offset caused by oscillator discrepancies, and the fourth and fifth terms are calibrated by the auxiliary satellite in orbit. The parameters in Equation (10) are related only to the characteristics of the internal calibrator, antenna calibration network, and coaxial cables. Considering the stability of these components, relevant temperature compensation data can be obtained through ground temperature experiments. The internal calibration accuracy of the system is largely determined by the measurement accuracy of the calibration network temperature compensation data and the accuracy of in-orbit temperature telemetry.
## 2 Materials and Methods
### Mathematical Modelling of Biofilm Growth  
To study the biofilm development in time, two levels of inoculum density, being \(10^{4}\) and \(10^{7}\) CFU/mL, were investigated. Biofilms obtained from different inoculum levels were quantified at each time point. Every 1.5 h or 3 h (for inoculum levels of \(10^{4}\) and \(10^{7}\) CFU/mL, respectively) the natural logarithm of the CFU per biofilm area was determined (as described in Section 2.4.1). Two to five replicates were performed at each time point. To study the biofilm development and estimate the growth parameters, the Baranyi and Roberts model (1994) was used [23]. To use this model, it is assumed that the population within the biofilm is homogenous and represented by \(N(t)\). The model equations are:  
\[\frac{dN(t)}{dt}=\frac{Q(t)}{1+Q(t)}u_{max}\bigg{(}1+\frac{N(t)}{N_{max}} \bigg{)}N(t) \tag{2}\]  
\[\frac{dQ(t)}{dt}=u_{max}\text{-}Q(t). \tag{3}\]  
Equation (1) describes the cell density as a function of time \(N(t)\), (CFU/cm\({}^{2}\)), which depends on \(Q(t)\), which represents the physiological state of the cells, and which determines the duration of the lag phase. \(\mu_{max}\) represents the maximum specific growth rate (1/h) and \(N_{max}\) the maximum cells density (CFU/cm\({}^{2}\)). Equation (2) describes the evolution of the state \(Q(t)\) with time. Data resulting from an inoculum levels of \(10^{7}\) and \(10^{4}\) CFU/mL were fitted with this model. The parameter estimation was implemented using the function _lsqtonlin_ of MATLAB R2016a (The Math Works, Inc.).  
The initial conditions are N(t = 0) = N\({}_{0}\) and \(Q(t=0)=Q_{0}\) and the following equation contains the parameter \(\lambda\), which represents the lag time (h):  
\[\lambda=\frac{1}{u_{max}}\cdot ln\bigg{(}1+\frac{1}{Q_{0}}\bigg{)} \tag{4}\]  
The root mean squared error (RMSE) between the natural logarithms of the observed (N\({}_{\text{exp}}\)) and predicted (N\({}_{\text{mod}}\)) values at the time points t\({}_{i}\) was estimated as follows  
\[\text{RMSE}=\sqrt{\frac{\Sigma_{1}^{\text{n}}\Big{(}\text{ln}(\text{N}_{\text {exp}}(\text{t}_{i}))-\text{ln}(\text{N}_{\text{mod}}(\text{t}_{i}))\Big{)}^{2 }}{\text{n}-\text{p}}} \tag{5}\]  
where n is the number of measurements and p is the number of estimated parameters. It was used to evaluate the goodness of the fit.
## 3 Study Data  
The Gyeongbu Expressway in South Korea was taken as the study site. Our study section was approximately 12.6 km in length in the vicinity of the Greater Seoul Area (GSA), with 12 vehicle detection systems (VDSs). The geographical information, the number of VDSs, and the absolute and relative milepost (unit is km) are shown in Figure 2. Traffic speed-data were collected from the Korea Expressway Corporation. The speed-data of each VDS was collected at the 5-min aggregate level. The observation period was between 1 March 2016 and 31 August 2016. All data were from real-life operations and the total number of days used was 158 days.  
The proposed model was developed using the speed-data from the neighboring VDS at the seven-time steps of the imputation variable, i.e., three-time steps of the past and future respectively in addition to a current time step. Since there were speed-data in 12 detectors at seven-time steps, there were 84 spatiotemporal variables as independent variables when there was no missing value. The missing data patterns in this study used a missing at determinate (MD) approach. The MD was known to be one of the difficult patterns of missing data for the matrix-based imputation methods to solve. The MD strategy implied that the missing traffic data have specific patterns. The patterns were normally caused by the long-term malfunctioning of the detectors and the detectors were no longer useful [27]. When the MD strategy was applied, there were 77 spatiotemporal variables as independent variables. The training data were arbitrarily divided into 70%, 80%, and 90%. The training dataset had no missing data but the validation dataset had missing data with the MD strategy.
# Application and Evaluation of Energy Conservation Technologies in Wastewater Treatment Plants
## 1 Introduction  
### Wastewater Treatment Plant  
The growth of the population and the development of economy make water pollution more common than ever before. Nearly 80% of the world's population is facing serious water security threats, and the world will face a 40% water deficit by 2030 [1; 2]. To deal with the challenge of water pollution, the European Union promulgated the Water Framework Directive to provide some guidance in water basin management and water quality restoration [3]. However, the instruction can only provide guidance, and sewage still needs to be treated to achieve water quality recovery. In order to protect the living environment of human beings, it is necessary to purify the wastewater and remove the pollutants in the wastewater.  
According to the different treatment objectives, wastewater treatment processes can be divided into three levels to select the appropriate treatment methods. The primary treatment uses physical methods like screening, interception, and gravity separation to remove suspended solids (SS), fat, and grease in wastewater [4]. The removal rate of biochemical oxygen demand (BOD) is only 30-40% and SS is 50-60% in primary treatment [5; 6; 7]. Secondary treatment, the main body of wastewater treatment, uses biological treatment and some chemical treatment methods to remove degradable organic matterand some colloidal pollutants from wastewater [8]. The activated sludge method is a biochemical combination method, which is the most widely used method for secondary treatment of wastewater [9]. Tertiary treatment is used to remove pollutants that cannot be removed by secondary treatment such as P, N, and biodegradable organic pollutants.  
WWTPs enrich pollutants into sludge, which contains many organic substances and pathogens while purifying wastewater. Sludge treatment is a process of sludge stabilization, reduction, and harmless treatment, including sludge drying, aerobic, anaerobic digestion, and adding chemical agents. Sludge disposal is the process of reasonable storage and resource utilization [6]. Sludge disposal mainly includes sludge landfill, land use, composing and thermochemical treatment technology. Common wastewater and sludge (WW&S) treatment processes are shown in Figure 1.
###### Abstract
The increase in remote work activities highlights the necessity of reliable power and energy security, areas where microreactors can contribute significantly [46; 47; 48]. Employing microreactors to enhance resilience can improve readiness and recovery from disruptions, ensuring the continuity of operations in critical sectors such as essential government functions, energy-intensive industries, emergency healthcare facilities, and high-speed networks for remote workers [42; 43; 44; 45; 46; 47]. Although economic challenges remain for microreactors to be competitive with other low-carbon energy sources, certain cost projections suggest that microreactors could perform well in markets where large nuclear technologies are not feasible [49; 50; 51; 52; 53; 54]. For example, microreactor designs support completely different operational strategies, such as mobile power generation for disaster recovery or potential semi-remote operations [55; 56; 57; 58; 59]. In terms of low-carbon energy production, large nuclear power plants are not suitable for smaller-scale applications, integration with microgrids, or small cogeneration uses, and they necessitate extensive emergency planning zones [60; 61; 62; 63; 64; 65; 66]. This study examines the status and adoption potential of microreactors by first discussing the profile of microreactor technology, highlighting differences from large nuclear power plants (NPPs) and small modular reactors (SMRs), and examining technological developments and commercialization aspects of microreactors. The core research into the costs and economic feasibility of microreactors thoroughly outlines the challenges of commercialization. Subsequently, the examination shifts to potential markets by considering the distinctive attributes of microreactors and their complementary application technologies, pinpointing niche markets ideal for their deployment. This paper provides an overview of recent studies on assessing the global market potential of smaller nuclear technologies and adapts these methodologies to microreactors. It encompasses the latest updates on microreactor technology, the economics of small reactors, and market dynamics. The analysis addresses deployment potential, regulatory hurdles, and institutional require ments in a balanced manner. The paper concludes with essential discussion points and final reflections.