esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Experimental
#### 2.1.2 Instrumentation and Analytical Conditions  
An extraction apparatus with an ultrasonic bath (300 mm \(\times\) 240 mm \(\times\) 150 mm) (Hongxianglong Biotechnology Developing Co., Ltd., Beijing, China) was used for extraction experiments. The apparatus has a work frequency in a range of 18-200 kHz and the power rating of the ultrasonic bath is 500 W on the scale of 1-8, corresponding to the powers of 25, 50, 100, 150, 200, 300, 400 and 500 W, respectively. The actual electrical power or ultrasonic energy to the transducers was measured by using a digital wattmeter (JYK-96 var, Shanghai elite Instrument Electric Co., Ltd., Shanghai, China), to be 24.9, 50.4, 98.9, 199.4, 300.7 and 401.2 W, respectively for the setting of 1, 2, 3, 5, 6 and 8 tested in this study. Extraction temperature was controlled by the water temperature in the ultrasonic bath, which can be circulated and regulated at constant desired temperatures using a heating/cooling temperature controlling circulator included in the extraction apparatus. Moreover, time can also be set to a desired value by a digital timer. For HPLC analysis, an Agilent Model 1100 HPLC system (Agilent Co., Palo Alto, CA, USA), which consists of a controller, a G1311A pump and a G1314A multi-wavelength detector, was used to determinate quercetin and rutin. Meanwhile, calibration curve method was also applied for the HPLC analysis of quercetin and rutin from _Euonymus. alatus_.
###### Abstract  
This paper aims to provide a novel control framework for exactly regulating the output voltage of floating interleaved boost converters (FIBCs), which have been widely employed in fuel cell applications in recent years. Firstly, a mathematical model of the FIBC is constructed according to [PERSON]'s current and voltage loop principles. Then a cascade control structure with a current inner loop and voltage outer loop is developed to achieve the desired voltage regulation performance. The current controller is established based on the generalized super-twisting algorithm (GSTA) to ensure that the inductor current exactly follows the current reference, which is generated by the outer loop. Meanwhile, an active disturbance rejection control (ADRC) framework is utilized for robustly regulating the output voltage despite the presence of input variation and load change in the voltage control loop based on a nonlinear continuous GSTA-based extended state observer (GSTA-based ESO). The stability of a closed loop system based on the GSTA controller and the GSTA-based ESO is conclusively proven using the Lyapunov theory. The Simscape model of the FIBC is developed, which is used to verify the feasibility and the appropriateness of the recommended control algorithm. Finally, numerical simulations are carried out to demonstrate the effectiveness of the proposed method compared to several previous works.  
floating interleaved boost converter (FIBC); active disturbance rejection control (ADRC); generalized super-twisting algorithm (GSTA) +
Footnote †: journal: Applied  
12 November 2022  
**An Improved Voltage Regulation Performance of Floating Interleaved Boost Converters for Fuel Cell Applications Subject to Input Variation and Load Change.**_Applied_  
10.3390/app122211501
## 2 Materials and Methods
### Extract Characterisation  
#### 2.3.1 Bioactive Compounds  
The total phenolic (TC) content was determined using the Folin-Ciocalteu method, as before [9]. Diluted extract (0.5 mL) was mixed with Folin-Ciocalteu reagent (2.5 mL). After 3 min incubation, 7.5% Na\({}_{2}\)CO\({}_{3}\) (2 mL) was added and samples were incubated in a water bath (50 \({}^{\circ}\)C, 15 min). The results were read spectrophotometrically at 750 nm. TC content was calculated using the calibration curve of quercetin and presented as quercetin equivalent milligram per gram (mg/g) of extracts \(N=6\).  
The modified Ellman method was used to determine the glutathione content spectrophotometrically [10]. The procedure detects non-protein -SH groups in the reduction reaction of 5,5\({}^{\prime}\)-dithiobis (2-nitrobenzoic acid) (DTNB) by thiol compounds and the pro duction of coloured 2-nitro-5-mercaptoethanol acid. Extract (\(N\) = 6) was homogenised with 0.1 M phosphate buffer pH 7.4 and centrifuged (3500 rpm, 12 min). The supernatants (1.5 mL) were mixed with 50% TCA (78.96 \(\mu\)L) and samples were centrifuged (2500 rpm, 5 min). Next, the sample (25 \(\mu\)L) was pooled with 0.2 M phosphate buffer pH 8.0 (200 \(\mu\)L) and with DTNB (25 \(\mu\)L). The absorbance was read on a microplate reader (Infinite M200; Tecan, Mannedorf, Switzerland). The standard curve was obtained using different dilutions of GSH in 2.5% TCA. Analysis of \(\beta\)-carotene and vitamins A, D (group), K and E (\(\alpha\)-tocopherol) was conducted using an Agilent 1100 high-performance liquid chromatography (HPLC) system (Agilent, Waldbronn, Germany). Separations were performed at 25 \({}^{\circ}\)C on the Zorbax Eclipse XDB C8 column (Agilent). Parameters: solvent flow rate--1.2 mL/min in a linear gradient of 90:10 (vol./vol.) methanol to water, injection volume--10 \(\mu\)L, temperature of the column oven--30 \({}^{\circ}\)C. Detection was performed with a UV-vis detector at 265 nm for \(\beta\)-carotene and vitamin D and at 230 nm for \(\alpha\)-tocopherol. The standard solution was produced by dissolving each standard substance in methanol. Identification was carried out through comparison with standard substances (Sigma-Aldrich, Darmstadt, Germany) \(N\) = 2.  
The crude protein was determined by the Kjeldahl method according to AOAC International, as before [11]. The amino acid content was determined by ultra-performance liquid chromatography. The Acquity UPLC System with a PDA detector was used, and detection was performed at wavelength 260 nm (Waters Corp., Milford, MA, USA) and using an AccQ-Tag Ultra C18, 1.7 \(\mu\)m, 2.1 \(\times\) 100 mm column (Waters Corp., Milford, MA, USA). On the basis of the protein content of the extract, the results were converted into the content of the respective amino acid in the extract \(N\) = 2.  
Chemical score = (AA content in extract/AA content in FAO Proposed Pattern (1981)) \(*\) 100
# Incentive Price-Based Demand Response in Active Distribution Grids
## 5 Simulation Studies
### Case 1: Simulation without BESS Connected to the LV Grid  
The aim of this case study is to analyze the voltage profile of the LV grid in Figure 3 with typical PV power generation on a sunny day. A simulation is performed without any BESS connected to the LV grid and it will be a base case used for comparison of the results with that of the case with BESS. A typical daily profile of PV power generation and load consumption shown in Figure 4 is used for this study. An increased load consumption especially during evening is used in this simulation considering a future operational scenario. The PV systems are operated with unity pf, which is typically the case in few European countries such as Denmark.  
The plot of voltages at the end nodes (R7, R13 and R17) are shown in Figure 5. During the peak PV production from 10:00 to 15:30 h, the node voltages are higher than 1.1 pu which is the allowed limit as per EN50160 standard [32]. The nodes voltages are below the lower limit of 0.9 pu from 18:30 to 21:00 due to high load consumption. It is to be noted that the voltage violations are noticed in this simulation study because of an increase in the penetration of PV generation to 95% and the load consumption. However, the real LV grid, as of today, does not experience any voltage problems.  
Figure 4: Plot of (**a**) typical solar irradiation on a clear sky day and (**b**) typical power demand profile of a residential customer in the LV grid under study.  
Figure 3: Schematic diagram of LV distribution grid used for simulation studies [13].
## 4 Simulation and Practical Application  
Traffic flow at the signalized intersections is time-varying and random. It is also often difficult to use empirical models or mathematical models to accurately describe the characteristics of the traffic flow. In this Section, the microscopic traffic simulation software, PTV VISSIM 9.0 (2016, PTV, Karlsruhe, German) is utilized in studying the typical form of the integrated waiting area and the practical application. It is a microscopic and time-based traffic simulation program. It is employed in this research because of its ability to model unconventional lane uses and signal timing plans. The correlation evaluation indicators with or without integrated waiting area are compared and analyzed to evaluate the effect of the integrated waiting area setting at the signalized intersection.
## 2 Graph Neural Networks
### Graph Isomorphic Network  
GNN are typically composed of _Aggregate_ and _Combine_ functions [22], which determine the different variations in GNN.  
\[aa_{v}^{(n)}=Aggregate^{(n)}\Big{(}\Big{\{}h_{u}^{(n-1)}:u\in\mathcal{N}(v) \Big{\}}\Big{)} \tag{2}\]  
\[h_{v}^{(n)}=Combine^{(n)}\Big{(}h_{v}^{(n-1)},aa_{v}^{(n)}\Big{)} \tag{3}\]  
where \(h_{v}^{(n)}\) represents the feature vector of node \(v\) at layer \(n\) and \(h_{v}^{(0)}:=x_{v}\).  
The graph isomorphic network (GIN) is a significant variant of GNN. Traditional GNNs cannot efficiently discern features of two isomorphic graphs. The usage of sum aggregators instead of other functions enables GINs to learn more precise graph structure information. Thus, the sum aggregator serves as the AGGREGATE (\(\bullet\)) function, followed by employing a multi-layer perceptron to model the COMBINE (\(\bullet\)) function.  
\[h_{v}^{(n)}=MLP\Bigg{(}\Big{(}1+\Delta^{(n)}\Big{)}h_{v}^{(n-1)}+\sum_{u\in N (v)}h_{u}^{(n-1)}\Bigg{)} \tag{4}\]  
Figure 2: GNN model for graph-level fault diagnostics.  
where \(\text{MLP}(\cdot)\) represents a multi-layer perceptron, where the learnable parameter, denoted by \(\sigma\), is initialized to zero. To facilitate computations, Equation (4) can be expressed in a matrix format as follows:  
\[H^{(n)}=\sigma\Big{(}\Big{(}\Delta^{(n)}\cdot I+A\Big{)}H^{(n-1)}W^{(n)}\Big{)} \tag{5}\]  
where the identity matrix is denoted as \(I\), while \(A\) refers to the adjacency matrix that captures the relationship between graph features, the MLP network weight is represented as \(W\), and the variable \(\sigma\) represents a nonlinear function.  
The readout function in GIN uses updated node features \(h_{v}^{(n)}\), and it can be written as follows:  
\[h_{G}^{(n)}=Readout\Big{(}\Big{\{}h_{v}^{(n)}\Big{|}v\in G\Big{\}}\Big{)}. \tag{6}\]  
The readout function here can be further represented in the following matrix form:  
\[h_{G}^{(n)}=H^{(n)}\Omega_{\text{mean}} \tag{7}\]  
where \(\Omega_{mean}^{T}=[1/N,\dots,1/N]\).
applied sciences  
An Effective Rainfall-Panding Multi-Step Prediction Model Based on LSTM for Urban Waterlogging Points  
**[PERSON] 1,2, [PERSON] 3,4,*, [PERSON] 3, [PERSON] 3, [PERSON] 5 and [PERSON] 6**  
1 Hydrology and Water Resources Department, Nanjing Hydraulic Research Institute, Nanjing 210029, China  
2 The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Nanjing Hydraulic Research Institute, Nanjing 210029, China  
3 Department of Hydrology and Water Resource, Hohai University, Nanjing 210098, China  
4 The State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China  
5 Changzhou Branch of Jiangsu Hydrology and Water Resources Survey Bureau, Changzhou 213022, China  
6 Hydroology and Water Resources Survey Center of Shaanxi Province, Xi'an 710068, China  
*Correspondence: [EMAIL_ADDRESS]  
**Abstract: With the change in global climate and environment, the prevalence of extreme rainstorms and flood disasters has increased, causing serious economic and property losses. Therefore, accurate and rapid prediction of waterlogging has become an urgent problem to be solved. In this study, Jianye District in Nanjing City of China is taken as the study area. The time series data recorded by rainfall stations and ponding monitoring stations from January 2015 to August 2018 are used to build a ponding prediction model based on the long short-term memory (LSTM) neural network. MSE (mean square error), MAE (mean absolute error) and MSLE (mean squared logarithmic error) were used as loss functions to conduct and train the LSTM model, then three ponding prediction models were built, namely LSTM (mse), LSTM (mae) and LSTM (msle), and a multi-step model was used to predict the depth of ponding in the next 1 h. Using the measured ponding data to evaluate the model prediction results, we selected rmse (root mean squared error), mae, mape (mean absolute percentage error) and NSE (Nash-Sutcliffe efficiency coefficient) as the evaluation indicators. The results showed that LSTM (msle) was the best model among the three models, with evaluation indicators as follows: rmse 5.34, mae 3.45, mape 53.93% and NSE 0.35. At the same time, we found that LSTM (mae) has a better prediction effect than the LSTM (mse) and LSTM (msle) models when the ponding depth exceeds 30 mm.**  
**Keywords: LSTM model; deep learning; urban; waterlogging prediction**
## 1 Introduction
The most common are data-driven models that are relying on machine learning, but only a few approaches use real-time energy data streams for model training and prediction. The data-driven prediction models [17] should consider only raw energy data as features but also contextualized information and energy and non-energy features to improve the prediction accuracy. The features used for model training can be related to the energy and weather characteristics, indoor environmental conditions, building characteristics, and occupancy features [18]. Models for a specific type of prediction often achieve better performance because the patterns of energy consumption depend on the type of the system used by the prosumer (e.g., heating, cooling, and lighting) and indirectly on the resident behavior; thus, it is less predictable. Finally, a significant challenge to the accuracy of the energy prediction is the prediction horizon. As the time horizon grows, the prediction accuracy decreases; thus, efforts should be committed to improving the multi-step energy prediction, such as the hourly day-ahead forecasting. A significant amount of the literature considers the one-step-ahead energy prediction, and only a few address the multi-step prediction. To address the identified challenges in this paper, we bring the following novel contributions:  
* Definition of a prediction model for multi-steps ahead energy prediction of prosumers that integrates clustering and multilayer perceptron classification models used to detect the classes of energy profiles and multilayer perceptron (MLP) regression models used to fine-tune the energy prediction considering the energy data streams;
* Introduction of new features derived from the raw energy data collected from prosumers into the machine learning prediction, such as peaks and valleys, concerning the energy baseline;
* Development of a software infrastructure that allows for the integration of real-time energy data streams with the deep learning models' training and prediction;
* The evaluation tests are made not only by considering the offline datasets of energy data, as most state-of-the-art approaches do, but also using the real-time data streams of energy data from prosumers and their estimated impact on the prediction accuracy. The rest of the paper is structured as follows: Section 2 presents the state-of-the-art approaches related to machine-learning-based energy prediction models highlighting the main gaps identified, Section 3 presents the multi-step energy prediction model for prosumers, and Section 4 show the evaluation results considering datasets and real-time data streams for prosumers of different scales. Finally, Section 5 presents the paper's conclusions and planned future work.
# Radiation Hardened Read-Stability and Speed Enhanced SRAM for Space Applications
## 2 Proposed RHRSE-20T SRAM
### Hold Operation  
Throughout the hold operation, as neither read nor write actions take place, both WL and WWL stay in a low state. The SRAM maintains the stored value through negative feedback. Assume that the data stored in the SRAM is '1' (Q = S1 = S3 = 1, QB = S0 = S2 = 0), transistors P2, P4, P6, and N2 are turned on, while transistors N3, N5, and N8 are turned off to maintain the data '1'. Additionally, transistors N1, N4, and N7 are turned on, while transistors P1, P3, P5, and N6 are turned off, allowing the data '0' to be maintained.
## 3 Results and Discussion  
### Preparation of the Gossypol-MIPs  
The gossypol molecule is an acidic organic compound with p_Ka_ of 6.5 [50] containing six phenolic hydroxyl -OH functional groups in the structure. It can form an acid\(-\)base ionic pair interaction with the basic amino -NH\({}_{2}\) group, which is beneficial for selectively binding and rebinding with the template. Theoretically, functional monomers containing basic groups could interact strongly with acidic phenolic hydroxyl groups via acid-base interaction. In the work reported here, two different routes were employed to prepare MIPs by bulk polymerization and the sol-gel process, combining a surface molecular imprinting protocol. The schematic procedure of the preparation of the gossypol MIPs is illustrated in Figure 1.  
In our previous work [9], the bulk polymer was prepared using EGDMA as a cross-linker, DMAEMA as a functional monomer, and gossypol as the template. Compared with the bulk polymerization route in Figure 1A, the sol-gel surface layer imprinting is depicted in Figure 1B. Silica gel was chosen as the solid supporter for surface imprinting because it is non-toxic and stable under acidic conditions and elevated temperatures. Silica gel is an amorphous inorganic polymer that has siloxane groups (Si-O-Si) in the bulk and silanol groups (Si-OH) on its surface. The surface silanol groups facilitate the introduction of the organic groups, which covalently bind to the silane monomer. In order to increase the concentration of surface silanol groups, the activation of silica gel surface is necessary [47].  
In the first step, silica gel particles were treated with methanesulfonic acid to increase the number of surface silanol groups [35]. In the next step, the complex was formed between the template gossypol and the functional monomer, APTES. It is noteworthy that APTES was the functional monomer capable of covalently bonding onto silica particles [36]. Then, the template-monomer complex was grafted  
Figure 1: Schematic illustration of the synthetic procedure for gossypol-MIPs via bulk polymerization (**A**) and sol-gel surface imprinting (**B**).  
onto the surface of the silica in the sol-gel process through the hydrolysis of silane and condensation with the activated silica gel in the presence of HAc and TEOS. TEOS acted as a cross-linking reagent, which was easily hydrolyzed in the presence of the HAc catalyst. This allowed the formation of a polymeric network on the surface of the SiO\({}_{2}\). Finally, after removal of the template molecules from the surface imprinted layer, the MIP2 was obtained, leaving a large number of tailor-made cavities for gossypol at or close to the surface of the silica.
# In Vitro Compatibility of Three Native Isolates of _Trichoderma_ with the Insecticide Chlorpyrifos
## 1 Introduction
The compatibility between fungus and pesticides is known in other BA, for example, _Beauveria bassiana_ and _Metarthrizium anisopliae_[36; 37]. [PERSON] et al. [38] showed that non-toxicing _Aspergillus_ section Flavi strains could grow in soils contaminated with chlorpyrifos. [PERSON] showed that _T. asperellum_ is compatible with some fungicides [33]. Likewise, [PERSON] et al. [39] indicated that in the biological control of lettuce, _T. asperellum_ IBLF 914 could be used together with phytosanitary treatments. [PERSON] and [PERSON] [40] showed that Chlorpyrifos is compatible with indigenous _Trichoderma_ isolates. On the other hand, [PERSON] et al. [41] demonstrated that _Trichoderma_ is capable of using the pesticides chlorpyrifos and cypermethrin as the only carbon source, which demonstrates the versatility of these filamentous fungi when found in polluted environments. Finally, it is important to investigate the compatibility between BA and pesticides because, in this way, they can be integrated as sustainable strategies for the pest control of economically important crops. Based on this, the objective of the present investigation was to evaluate the in vitro compatibility of three native isolates of _Trichoderma_ with the insecticide chlorpyrifos, for which it was necessary to determine the effect of chlorpyrifos on spore germination, the mycelial growth, and in the antagonistic capacity of the three native _Trichoderma_ strains, isolated from agricultural soils.
## 2 Results and Discussion
### Optimization of the Magnetic Solid-Phase Extraction (MSPE) Procedure  
#### 2.4.1 Optimization of Adsorbent Amount Usage  
The effect of usage amounts of DETA-MPs-GO on adsorption ability was measured using the blank sample extracts spiking with nine fungicides at 5.0 \(\mathrm{\SIUnitSymbolMicro g}\)/kg. The spiking sample extracts were investigated by adding different usage amounts of DETA-MPs-GO, as shown in Figure 4b. It indicated that a significant influence could be seen on the adsorption efficiency of the fungicides due to the usage amount of DETA-MPs-GO adsorbent, and it was not difficult to observe a clear trend in the adsorption efficiency as the usage amount of DETA-MPs-GO ranged from 1 mg to 20 mg. When adding 1 mg or 2 mg of DETA-MPs-GO for the adsorption procedure, low recoveries of the nine fungicides were achieved, ranging from 31.2% to 82.3%. To increase DETA-MPs-GO, varying from 5 to 20 mg, the desired adsorption recoveries of the nine fungicides were achieved in the range of 85.5% to 101.4%. It could be easily discussed that the least usage amount (5 mg) of DETA-MPs-GO could availably concentrate the nine fungicides from fruit sample extracts.  
Figure 4: Extraction and desorption parameter optimization; each experiment was performed in sextulplicates: (**a**) extraction solvent; (**b**) adsorbent usage amount; (**c**) pH effect; (**d**) desorption solvent.
## 3 Results
### ERP Results  
#### 3.2.1 ERP Mean Amplitude Analysis at Occipital Electrodes  
The grand averaged waveforms in occipital sensors are shown in Figures 2 and 3. ANOVA revealed a significantly larger P100 amplitude in the right than in the left hemisphere, \(F\) (1, 17) = 13.12, \(p\) = 0.002, \(\eta_{\text{p}}^{2}\) = 0.43. No other significant effects were observed for P100 amplitude, _Fs_ < 1.4, _ps_ > 0.26.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline  & \multicolumn{2}{c}{**Self**} & \multicolumn{5}{c}{**Other Familiar**} \\  & **Original** & **Enhanced** & **Degraded** & **Original** & **Enhanced** & **Degraded** \\ \hline RT (ms) & 494.1 & 505.8 & 513.3 & 493.3 & 484.4 & 492.2 \\  & (73.7) & (87.0) & (104.8) & (68.9) & (84.1) & (68.3) \\ Accuracy (\%) & 93.6 & 93.7 & 93.7 & 95.1 & 93.6 & 94.9 \\  & (4.9) & (4.5) & (5.9) & (4.7) & (5.4) & (5.4) \\ \hline \end{tabular}
\end{table}
Table 1: Mean and standard deviation of RT and accuracy rate in each condition. Standard deviations are in parenthesis.