esa-sceva/satcom-chunk-collection huggingface dataset:
# Simultaneous Electrochemical Removal of Selenium and Strontium from Aqueous Solution
## 4 Discussion
### EC Stage
It can be concluded that the number of electrodes alone does not directly affect the reaction kinetics; it only influences the available surface area for reactions. If the applied current is constant, the system may already be operating at a level where additional electrode surfaces have a diminishing impact, as can be seen for Se in Figure 13b. However, the number of electrodes can affect the current density, which is a key parameter in any EC process. For instance, in the case of Sr removal (as shown in Figure 13a), when a low applied current was used with eight electrodes, the achieved current density  
Figure 13: The effect of the electrode number on the removal efficiencies of (**a**) Sr and (**b**) Se depending on the applied current for treatment time of 25 min. Black line presents 4 electrodes, red 8 electrodes. was not sufficient for effective removal. While higher densities typically increased the effectiveness, there was a threshold beyond which further increases in the current density yielded negligible gains. The required current densities could be achieved through various combinations of the applied current strength and electrode surface used. The combinations used in this study resulted in current densities of 72, 96, 144, 216, 288, 360, 432, 480, and 720 A m\({}^{-2}\). The highest current density of 720 A m\({}^{-2}\) was achieved by applying 25 A of electric current to four electrodes in each EC reactor, and it was used in the top-performing experiment when considering the removal of both Se and Sr. Specifically for Se, the current densities of 480 and 432 A m\({}^{-2}\) both resulted in complete Se removal under the same treatment conditions (25 min, no pH control, Fe electrodes). This indicates that the current density threshold for these operating conditions was reached at 432 A m\({}^{-2}\), and a further increase was not needed for the removal of Se. However, under the same conditions, the current densities below 480 Am\({}^{-2}\) were not sufficient for effective Sr removal. Interestingly, that threshold current density applied for 25 min in maximum pH range resulted in similar efficiencies when applied to both the Al (97.26%) and Fe (96.10%) electrodes, with 97.26% being the highest removal rate of Sr overall.
## 5 Strain/Load Prediction Based on Deep Learning Model
### Flight Parameter-Strain Model Simulation Results  
Although it has been validated in the previous step that the LSTM deep learning model may offer better accuracy, both LSTM and RNN were still employed to train on all input data. Using AE, the 18 flight parameters (such as pitch angle, pitch rate, roll rate, yaw, angular velocity, longitudinal acceleration, lateral acceleration, normal acceleration, roll angle, ground speed, heading angle, altitude, inertia, etc.) were reduced (dimensionality reduction) to 13, and then RNN and LSTM prediction models were used separately to predict strain data. Figure 12a,b show the comparison results between real flight data and the predicted one by (a) LSTM model and (b) RNN model predicted data and real data. Generally, large strains are predominant in structural damage, so it is important that the predicted values should ideally cover the true values of large strain amplitudes as much as possible. Evidently, in Figure 12b, the RNN model significantly underestimated a large portion of the true strain values indicated by blue, potentially placing the structural design strength of the aircraft in an unsafe condition or uncontrolled risk.  
Figure 13 displays the results of LSTM with FMR and without FMR at different maneuvers (State 1 and 3). Observably, after FMR processing, the prediction accuracy of the data significantly improves, particularly in predicting the maximum amplitude of strain. Furthermore, the area of the 95% confidence interval decreases substantially, indicating a reduction in uncertainty. This highlights the indispensability of FMR as a preprocessing step in the deep learning strain prediction model based on a considerable amount of flight parameter data.  
Figure 11: MSE plot obtained by (**a**) RNN deep learning model and (**b**) LSTM deep learning model.  
Figure 12: Comparison results between real flight data and the predicted one by (**a**) LSTM model and (**b**) RNN model predicted data and real data.  
Figure 13: _Cont._
# Effect of Wheel Path in Raster Grinding on Surface Accuracy of an Off-Axis Parabolic Mirror
## 4 Discussion
### Future Prospects  
Combined with the current research status and actual research results, the authors of this paper believe that in terms of the factors causing surface shape errors, scallop height, grinding wheel wear, and positioning accuracy will produce surface shape errors. This paper only studies the influence of scallop height on surface shape accuracy, and the remaining factors need to be further studied. In terms of measures to improve surface accuracy, wheel path planning, grinding wheel dressing, and the in-situ compensation of machined surface are effective means to improve surface accuracy, which will be the focus of future research.
# Commercially Biochar Applied for Tartrazine Removal from Aqueous Solutions
## 2 Materials and Methods  
### Materials  
For this study, commercial biochar sold as horticultural soil amendment, produced for Dr. Soil GmbH Germany, was procured from the market and used as adsorbent material with no other synthesis steps involved. Tartrazine dye, the pollutant chosen to be investigated in this study, was purchased from Sigma-Aldrich (Schnelldorf, Germany). As pH-adjusting materials, HCl and NaOH solutions procured from Sigma-Aldrich (Schnelldorf, Germany) and VWR Chemicals (Wien, Austria), respectively, were used. Aqueous solutions were prepared using ultrapure water lab source (Direct-Q\({}^{\otimes}\) 3 UV Water Purification System, Merck, Darmstadt, Germany).  
The morphological characterization of the commercial biochar was investigated through scanning/transmission electron microscopy (STEM) using a Hitachi HD2700 microscope, operated at 200 kV and in cold field emission.  
The specific surface area of biochar was determined from the Brunauer-Emmett-Teller (BET) analysis.  
The electrostatic nature of the adsorbent surface was determined with the pH of point zero charge (pHpzc) using the so-called pH drift method [43]. The method of determining pHpzc is according to the one described in a previous article [44].  
Using the KBr pellet approach, FT-IR spectra were obtained in the 4000-400 cm\({}^{-1}\) spectral domain with a resolution of 4 cm\({}^{-1}\) on a JASCO 6100 FT-IR spectrometer. After dispersing each sample in roughly 300 mg of anhydrous KBr, the powder was pulverized in an agate mortar. The ground mixture was pressed into an evacuated die to produce the pellet. The software Jasco Spectra Manager v.2 was used to gather and examine the spectra.
### Analysis of System Application Scenarios
#### 2.1.1 Determination of Building Type  
The distribution characteristics of PEDF building applications are illustrated in Figure 2[9; 11; 12]. PEDF buildings are versatile, making them suitable for both new and existing buildings in most areas of China [8]. Regions with annual total solar radiation of \(\geq\)1400 kWh/m\({}^{2}\) and annual equivalent sunshine hours of \(\geq\)1200 h are classified as having abundant solar resources; regions with annual total solar radiation ranging from 1050 to 1400 kWh/m\({}^{2}\) and annual equivalent sunshine hours between 900 and 1200 h are designated as the average areas for solar power generation resources [9; 11; 12]. However, the significant variations in energy demand and usage among different buildings greatly affect the selection and configuration of PEDF systems and interactive devices.  
Figure 1: Optimization and evaluation framework for the PEDF system configuration.  
It is also important to consider the different methods of photovoltaic energy consumption across various buildings. A building is classified as a zero-energy consumption building when its annual photovoltaic power generation offsets its annual electricity consumption, as indicated by the diagonal line in Figure 3. If a building generates more electricity than it consumes, it is classified as a photovoltaic \"grid-output\" building (in the area above the diagonal line in Figure 3), while buildings that generate less are termed \"self-consumption\" buildings, found in the area below the diagonal line in Figure 3 [8]. The PV self-consumption rate in Figure 3 represents the proportion of photovoltaic electricity consumed locally compared to total generation, whereas the PV self-sufficiency rate measures how much of the building's overall electricity consumption is covered by locally generated photovoltaic power [9; 10; 14].  
Figure 3: Distribution of PV consumption method in PEDF building [9; 10; 14].  
Figure 2: Distribution characteristics of PEDF building applications: (**a**) building type; (**b**) climatic region [9; 11; 12].  
Therefore, when configuring the PEDF system, it is essential to conduct a thorough analysis of the specific building type, considering regional climate characteristics, building size, and load type. Following this analysis, it is crucial to evaluate the potential of local solar energy resources and determine the most effective strategy for utilizing photovoltaic power generation. The components of the PEDF system should then be allocated appropriately to meet the needs of the various systems involved.
# A UAV-Assisted Stackelberg Game Model for Securing IoMT Healthcare Networks
## 4 Simulation Setup and Experimental Results
### Network Simulator Two  
Network simulator two (NS2) [37] is an open-source network simulation software commonly used by researchers to evaluate the performance of different networking protocols and architectures. With a wide range of networking models and components, NS2 enables researchers to simulate various network topologies and conditions, allowing them to optimize their protocols and architectures. The simulation setup in NS2 involves defining the network topology, configuring network components, and specifying simulation parameters. NS2 supports the simulation of various network technologies, including wired and wireless networks, satellite networks, mobile ad hoc networks (MANETs), and sensor networks. Additionally, NS2 provides a range of protocols, such as transmission control protocol (TCP), user datagram protocol (UDP), and hypertext transfer protocol (HTTP). Researchers use NS2 to simulate different network scenarios and evaluate the performance of their protocols and architectures under different network conditions [38]. The software also offers data visualization and analysis tools, enabling researchers to study the impact of different network configurations on protocol performance.
## 2 Results and Discussion  
Preparation and Properties of Zr-Loaded Ion-Exchangers (L-Zr-X, X=Chloride (Cl), Nitrate (N) and Sulfate (S))  
The iminodiacetate-functionalized styrene-divinylbenzene copolymer was prepared via chloromethylation of STY-DVB copolymer containing 2% DVB exclusively at the meta-position of STY rings [11] with subsequent coupling with iminodiacetic acid diethyl ester and by alkaline hydrolysis (ESI Scheme S1). The sodium salt of the resin containing the iminodiacetate group was activated with 10% HCl, HNO\({}_{3}\) and H\({}_{2}\)SO\({}_{4}\) (Scheme 1a), resulting in L-X resins as starting materials.  
The Zr-loading of L-X resins was performed with zirconium salts containing X anions, namely 0.5% zirconyl chloride (ZrOCl\({}_{2}\).8H\({}_{2}\)O in a 3 M HCl solution), 0.5% zirconyl nitrate (in 3 M HNO\({}_{3}\)) and 0.5% zirconium (IV) sulfate (in 1.5 M H\({}_{2}\)SO\({}_{4}\)) (Scheme 1a). Due to their acidic pH, the formal chemical species of zirconium (IV) was the \"zirconyl\" ion, ZrO\({}^{2+}\)[12; 13; 14]. Therefore, the CHNS elemental analysis data of L-Zr-X resins, the zirconium content of the solution eluted during Zr loading, and the amounts of L-X resins/salts containing zirconium used provide sufficient information for the calculation of the Zr/iminodiacetate group ratio (loading rate), water content and the Zr/C ratios according to the method given by [PERSON] et al. [15] (Supplementary Materials, Table S1).  
The ratio of N content (x mole of iminodiacetate is located on the DVB and STY rings) and carbon content (x mole of methylene-iminodiacetic acid groups and the polymer chain consisting of DVB and STY) suggests that almost exactly one -CH\({}_{2}\)N(CH\({}_{2}\)COO)\({}_{2}\)-group is located on every aromatic (STY or DVB) ring (Table S1). This means that during chloromethylation of the base polymer (STY-DVB) [16] (and consecutive coupling with iminodiacetic acid ester), only one chloromethyl group (iminodiacetate group) was inserted into every aromatic ring and, according to the synthesis conditions, it was built exclusively into the meta-position (see the IR results below and Supplementary Materials Figure S1) (Supplementary Materials, Scheme S1).  
Knowing the ratio of STY/DVB/-CH\({}_{2}\)N(CH\({}_{2}\)COO)\({}_{2}\)\({}^{2}\)- groups, we calculated the number of hydrogens belonging to these building elements. The ratio of Zr loading and the amount of iminodiacetate groups in L-Zr-X samples was easy to calculate from the molar ratio of nitrogen and zirconium contents. Zr loading was 34.8%, 27.0% and 22.8% for the samples produced from zirconyl chloride, zirconyl nitrate and zirconium (IV) sulfate, respectively. The difference between the calculated and measured hydrogen content may be attributed to the presence of swelling water, whose amounts were around \(\sim\)20% (Supplementary Materials Table S1).
## 3 The CART-Based Methods
### CART-Based Integrated Algorithms
#### 3.1.2 CART-RF Algorithm  
As a representative of the bagging algorithm, the CART-RF algorithm is a meta-estimator. Numerous theoretical and case studies have demonstrated that the CART-RF algorithm offers high prediction accuracy and robust tolerance for abnormal data. Using  
Figure 4: The schematic illustration of the CART-based algorithms: (**a**) CART-RF algorithm; (**b**) CART-AdaBoost algorithm.  
bootstrap resampling technology, multiple classification decision tree models are built on various sub-samples of the dataset. The average of these models is then used to improve predictive accuracy and control overfitting. As shown in Figure 4a, bootstrap resampling extracts \(n\) subsets from the original dataset, which serve as the training data for \(n\) CART models. The final prediction is obtained by averaging the predictions of the \(n\) CART models. The output of the CART-RF algorithm can be expressed as follows:  
\[\mathrm{y}=\frac{1}{n}\sum_{i=1}^{n}y_{i}(x) \tag{3}\]  
where \(y_{i}(x)\) is the individual prediction of a tree for an input \(x\), and \(n\) is the total number of decision trees. By taking the average of the decision trees' predictions, some errors can be canceled out. The CART-RF algorithm achieves a reduced variance by combining diverse trees, sometimes at the cost of slightly increasing bias [20]. Therefore, it is still necessary to combine domain knowledge and select reasonable model parameters to lay a good foundation for subsequent model training.
## 2 Mobile Cellular Network Basics
### 3G Mobility Management for Data Communication
After this release, although the UE is still known by an SGSN, a significant signaling overhead is required to reinitiate the data transfer. Connection re-establishment can be initiated by either the UE or the SGSN. The SGSN triggered connection re-establishment occurs only if an SGSN receives data destined for a certain UE, from a GGSN. In order to realize this, the SGSN uses the paging procedure. In that case, the UE's location is known by the SGSN with an accuracy of an RA but not a cell. It is important to note that if there is a PDP context active, the given UE's cell is already known, so a paging message is no longer required. So, the UE and the SGSN have valid PMM contexts. The UE and SGSN should enter the PMM-CONNECTED state when the PS signaling connection is established between the UE and the SGSN. _PMM-Detached state:_ This state means that the UE is switched off, outside the service area or not attached to a PS network, _i.e._, the UE and SGSN contexts don't have any location and routing information for the UE, and hence the UE is not reachable by an SGSN. In this state, there is no active PDP context and assigned IP address to the UE. A GPRS attach procedure should be performed in order to transfer to the PMM-Connected state. If any traffic destined to the UE comes to the GGSN, it will be discarded and no paging procedure will be needed. _PMM-Connected State:_ In this state, the UE's location is known in the SGSN with an accuracy of a serving RNC. Also, the serving RNC tracks the UE with an accuracy of a serving cell. Therefore, if  
Figure 5: A User equipment (UE)’s packet mobility management (PMM) state transition. any traffic destined to the UE comes to the GGSN, it will be forwarded to the UE through a PDP context and no paging procedure will be needed.
## 2 Methodology
### Error Checking  
In this work, some statistical measurements, including relative percentage error (_RPE_), root mean squared error (_RMSE_), mean of average percentage error (_MAPE_) and precision rate (\(p\)), were applied to measure the performance of the proposed models. The formula of these measurements is introduced as follows:_RPE_ reflects the difference between the actual value and the predicted value. It measures the size of error as a percentage of the actual value. The formula of _RPE_ is expressed as follows [22]:  
\[RPE=\frac{\left|x^{(0)}(k)-\hat{x}^{(0)}(k)\right|}{x^{(0)}(k)}\times 100\% \tag{16}\]  
_RMSE_ is part of a standard for evaluating forecasting accuracy that presents the sample standard deviation of the differences between actual values and predicted values. _RMSE_ is calculated using the following equation:  
\[RMSE=\sqrt{\frac{\sum\limits_{i=1}^{n}\left(x^{(0)}(k)-\hat{x}^{(0)}(k)\right) ^{2}}{n}} \tag{17}\]  
_MAPE_ is an accuracy measurement which is popularly applied in forecasting. _MAPE_ denotes the average relative size of the predicted error [1]. _MAPE_ is defined as follows:  
\[MAPE=\left(\frac{1}{n}\sum\limits_{k=1}^{n}\frac{x^{(0)}(k)-\hat{x}^{(0)}(k)}{ \hat{x}^{(0)}(k)}\right)\times 100\% \tag{18}\]  
When _MAPE_ is close to 0, the forecasting model is highly accurate and has provided good performance, and _vice versa_.  
Besides this, in accordance with the value of _MAPE_, the precision rate of forecasting model can be classified into four levels: excellent, good, qualified and unqualified [23].  
Precision rate (\(p\)), which measures the level of the closeness of the statement of forecast quantity and the actual value. \(p\), is defined as follows:  
\[p=1-MAPE \tag{19}\]  
On the other hand, the developing coefficient \(a\) of the grey model is also used as a criterion to judge the forecasting capability of the grey model. The evaluative standard is described in Table 1[24].
## 3 Results and Discussion
### Scenario Verification
#### 3.1.1 Thermal Scenario  
As demonstrated in the Section 2.3, previous calculations of the thermal scenario for the actual MMRTG on Mars suggest that the Grashof number should be on the order of \(10^{6}\) to ensure proper test conditions. To achieve this, the laminar flow of the ink emitted from the model was measured under the condition of active resistance at 301 K (28 \({}^{\circ}\)C) (Table 2), using this value as the characteristic length in the calculation (Equation (3)). It was calculated that for flow rates greater 0.3 mL/s, the Grashof number reached the order of \(10^{6}\) (Table 3), confirming the correct execution of the thermal tests.  
As shown in Figure 4 and Table 2, it is important to highlight that there is a significant increase in the length of the laminar flow as the internal temperature of the model rises. This phenomenon occurs because while the temperature of the ink emission increases, its buoyancy relative to the water also increases due to a considerable decrease in its density. As it is shown in Table 4, this also leads to an increase in the height of the plume, which could potentially affect the MEDA station. It is also important to note that the increase in the height of the laminar flow appears to converge for flow rates greater than 0.7 mL/s.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ink Flow [mL/s]** & \
\begin{tabular}{c} **Laminar Flow Height** \\ **(289 K) [\%] 1** \\ \end{tabular} &
\begin{tabular}{c} **Laminar Flow Height** \\ **(301 K) [\%] 1** \\ \end{tabular} \\ \hline
0.3 & 47 & 88 \\
0.7 & 72 & 145 \\
1.4 & 120 & 198 \\ \hline \hline \end{tabular} \({}^{\dagger}\) The % of height is measured with respect to the height of the fins, which corresponds in our study to 32 mm.  
\end{table}
Table 2: Thermal plume height for non-active (289 K) and active (301 K) resistor.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Ink Flow [mL/s]** & **Grashof Number (301 K) [\(\times\)10\({}^{6}\)]** \\ \hline
0.3 & 0.64 \\
0.7 & 3.15 \\
1.4 & 7.86 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Grashof number for active (301 K) resistor as a function of ink flow rate.  
Specifically, when comparing the 289 K (16 \({}^{\circ}\)C) and 301 K (28 \({}^{\circ}\)C) scenarios, the increase remains consistent at 70% for both the 0.7 mL/s and 1.4 mL/s flow cases.  
Although the experimental conditions feature considerably lower temperatures than those expected in the actual MMRTG environment, this discrepancy does not invalidate this study. Even with the laboratory temperature around 28 \({}^{\circ}\)C significantly lower than the MMRTG's operating temperatures, the key to ensuring the experiment's relevance lies in maintaining the similarity of dimensionless parameters. For example, the Grashof number, exposed in this section, which relates buoyancy forces to viscous forces, remains of a similar order of magnitude. This guarantees that buoyancy-driven effects and the resulting radiative and convective patterns observed at lower temperatures are representative of those that would occur under higher temperature conditions.
## 2 Experimental Section
### Instrumentation  
A Saturn 2000 and/or Saturn Star 3 GC/ion trap MS (Varian, Mississauga, ON, Canada) instruments with identical setups were used for analysis, where both ionization and mass analysis occur in the same chamber. The GCs were equipped with a 1079 split/splitless injector with SPME glass inserts (Varian) and a 5%-diphenyl-95%-dimethylpolysiloxane HP-5 MS fused-silica capillary column (Agilent, Palo Alto, CA, USA) with dimensions: 30 m \(\times\) 0.25 mm ID with a 0.25 \(\upmu\)m phase. UHP helium carrier gas was set to a flow of 0.5 mL\(\cdot\) min\({}^{-1}\). THM standards and SPME fiber extractions were injected into the GC at 190 \({}^{\circ}\)C under 10 psi constant pressure and used an experimentally optimized column temperature program: 40 \({}^{\circ}\)C, held for 4 min, to 160 \({}^{\circ}\)C at a rate of 30 \({}^{\circ}\)C\(\cdot\) min\({}^{-1}\), and held for 4 min, then ramped to 210 \({}^{\circ}\)C at a rate of 50 \({}^{\circ}\)C\(\cdot\) min\({}^{-1}\), and held for 1 min, giving a total run time of 10 min. For the mass spectrometer (MS), the electron multiplier was operating at 2000 V and samples were analyzed by electron impact (70 eV) over an acquired mass range of 40-650 \(m/z\). The transfer line, source, and ion trap temperatures were 170 \({}^{\circ}\)C, 100 \({}^{\circ}\)C and 120 \({}^{\circ}\)C, respectively. Quantification of the THMs from real water samples were performed by selected ion monitoring with a 100-ms dwell time, for limit of detection (LOD) and calibration studies. Selected ions used were \(m/z\): bromoform (Mol. Wt. 252.75) 252, 173, 96; bromodichloromethane (Mol. Wt. 163.83); 163, 128, 93, 84; dibromochloromethane (Mol. Wt. 208.29); 208, 172, 128, 93; 1,2-dibromoethane (Mol. Wt. 187.86); 187, 108, 106; chloroform (Mol. Wt. 119.38): 119, 87, 85; and 1,2-dichloroethane (Mol. Wt. 98.96) 98, 63, 61.