esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions  
Fe\({}_{3}\)O\({}_{4}\), PANI, and their composite can be applied as effective adsorbents for the removal of hexavalent chromium and divalent nickel from water. The adsorption of both Cr(VI) and Ni(II) was higher for composite material than Fe\({}_{3}\)O\({}_{4}\) and PANI alone. This enhancement in adsorption might be due to the existence of electrostatic interactions between active sites on the surface of adsorbents with opposite charges of HCrO\({}_{4}\)- and Ni (II) ions. Uptake of Cr(VI) on all the materials was high in an acidic environment while that of Ni(II) was high in a slightly basic environment. Adsorption of both metals decreased in the presence of co-existing ions. Removal of HCrO\({}_{4}\)- was competed with by HSO\({}_{4}\)- in acidic environments while that of Ni(II) was competed with by Na+ ions when the solution was slightly basic. The maximum decrease in the adsorption of both HCrO\({}_{4}\)- and Ni(II) ions was observed at 0.1 ionic strength. The maximum amount of Cr(VI) adsorbed by PANI/Fe\({}_{3}\)O\({}_{4}\) composites was 174.09 mg\(\cdot\)g\({}^{-1}\), while that of Ni(II) was 134.88 mg\(\cdot\)g\({}^{-1}\). The Freundlich adsorption isotherm model fitted more closely to the adsorption data. The kinetics of adsorption was pseudo-second order. The negative sign with the values of Gibb's free energy changes and enthalpy and entropy changes confirmed the spontaneous and exothermic nature of adsorption.  
The presented tests were performed on a laboratory scale in very limited conditions of the external environment. As the effluents coming out from industries contain several types of ions, our future studies will focus on the implementation of this type of solution on an industrial scale in specific technical and climatic conditions.  
[PERSON] performed experimental work, formal analysis, and writing of the main draft. [PERSON] and [PERSON] supervised and contributed editing. All authors have read and agreed to the published version of the manuscript. The APC was funded by the German Research Foundation and the Open Access Publication Funds of the Technische Universitat Braunschweig.  
We acknowledge support from the German Research Foundation and the Open Access Publication Funds of the Technische Universitat Braunschweig. S.B. acknowledges support from the Alexander von Humboldt Foundation Germany. The authors declare no conflict of interest.
# Anti-Hyperglycemic Activity of Major Compounds from _Calea terifolia_
## 3 Materials and Methods
### HPLC Method Validation  
The method was validated according to ICH guideline for selectivity, linearity, accuracy, precision, LOD and LOQ [33]. Selectivity was checked using the CRF fraction and a mixture of standards **1** and **2**. Linearity of the method was performed by analyzing a standard solution of the markers in a concentration range of 20-65 \(\upmu\)g/mL for standard 1 and 10-80 \(\upmu\)g/mL for standard **2**. A calibration line was made and the least square line and correlation coefficient were calculated. The intercept and the regression coefficient were established using the Student's _t_-test. Both the calibration line and the residuals were graphically inspected and evaluated. The Accuracy was evaluated by means of recovery assays carried out by adding known amounts of the standards of **1** and **2** to the sample at three different levels of the initial concentration of the sample (20%, 40% and 60%). Average recoveries were calculated by the formula: recovery (%) = {(amount found--original amount)/amount spiked} \(\times\) 100. Precision was evaluated using repeatability (intra-day) and intermediate precision (inter-day). Intra-day and inter-day variations were established using six replicates within the same day and three consecutive days (a total of 18 determinations), respectively. The stability was tested by analyzing the sample solution at different time points (0, 24, 48 and 72 h) and peak areas of all standards were recorded and compared. The % RSD \(\leq\) 2.0 was taken as a measure of precision and stability. LOD and quantification LOQ were determined based on the standard deviation (\(\sigma\)) of the response and the slope (S) (LOD = 3.3 \(\times\)\(\sigma\)/S and LOQ = 10 \(\times\)\(\sigma\)/S, respectively).
# Treatment of Aqueous Bromate by Superparamagnetic BiOCl-Mediated Advanced Reduction Process
###### Abstract
For liquid-phase photocatalytic BiOX depollution processes, photoinduced holes h\({}^{+}\)/HO. (h\({}^{+}+\)OH\({}^{-}\)\(\rightarrow\) HO.) and superoxide radicals (O\({}_{2}^{\bullet-}\)), have been considered to play important roles [29]. Thus, photoexcited BiOX can be used as an efficient source of HO. In calculations for practical application, modest effort has been devoted to load BiOX onto magnetic materials (e.g., spinel [30], Fe\({}_{3}\)O\({}_{4}\)[31], and Fe\({}_{2}\)O\({}_{3}\)[32]). Recently, phosphate was used as photocatalyst support to improve a photocatalyst's resistance against water erosion and hydraulic shearing [33]. Therefore, magnetic hydroxyapatite (HAP; Ca\({}_{10}\)(PO\({}_{4}\))\({}_{6}\)(OH)\({}_{2}\)) may be a proper support in producing recoverable BiOX. Besides HO., SOCAs are other precursors of CO\({}_{2}^{\bullet-}\). In the photocatalytic system, two mechanisms may underlie the conversion of organic carboxylic acids into organic radicals, such as CO\({}_{2}^{\bullet-}\). First, SOCAs initially complex with the metal ion harboured by the photocatalyst. Subsequent intramolecular electron transfer from carboxylic acid to the photocatalyst band and photogenerated hole (h\({}^{+}\))-mediated oxidation lead to the formation of organic radicals [34]. This mechanism considers that the reduction process occurs at the catalyst surface. In this mechanism, the energy of the highest occupied molecular orbital and ionization potential of SOCAs determine the formation potential of the reducing organic radicals. Second, ROSs (such as h\({}^{+}\)/HO.) generated on the interface of the photocatalyst and then reacted with the free SOCAs. As a result, reducing organic radicals, such as CO\({}_{2}^{\bullet-}\), are produced [35]. To date, only a few works have distinguished these two aspects in the mechanism expounding. In particular, the contributions of the absorbed ROSs on the photocatalyst surface and free ROSs in bulk solution to the formation of reducing organic radicals have not been discussed. Given the above-mentioned literature review, an UV/BiOX/SOCA process was proposed to degrade BrO\({}_{3}^{-}\) in drinking water. BiOCl, which was loaded onto superparamagnetic HAP (HAP\({}_{\text{SM}}\)), was selected as a typical of BiOX for experimentation. This work intends to (1) probe the potential of combining photoexcited BiOX and SOCAs for CO\({}_{2}^{\bullet-}\) generation and BrO\({}_{3}^{-}\) removal; (2) clarify the formation mechanism of reductive radical and the main site where \(\mathrm{BrO}_{3}^{-}\) degradation occurs; and (3) evaluate the influence of several common electron acceptors (\(\mathrm{O}_{2}\), \(\mathrm{NO}_{3}^{-}\), and \(\mathrm{Fe}^{3+}\)), which may influence the decontamination efficiency of UV/BiOX/SOCA.
# Efficient Filtering for Edge Extraction under Perspective Effect
## 2 Optimal Scale and Orientation Assignment
### Local Edge Extractor Selection and Performance Analysis
The DoB is an acceptable local edge extractor that can be tuned by scale and orientation only when the real localized target size is less than the pre-known localized target size, which is usually acceptable in real applications. Figure 5: Angular response of DoB for different aspect ratio \(\lambda\). Having the appropriate local edge extractor that can be tuned by scale and orientation, the next problem is how to assign the proper scale and orientation for special application. The relative positions of the target and the camera, and the local features of the target can appear different in size and orientation in the image. This is the perspective effect. Pre-known information about the real size of the target, the relative positions of the target and the camera, and the perspective parameters of the imaging system, provide some clarity about the size, orientation and location of the target, which appeared in the image. Although this does not tell us where the target will appear next in the image, we do know what its size and orientation will be when it does. That information is also called the contextual information regarding the scene to be imaged. The problem of lane marking detection and lane boundary detection by on-board camera systems is typical in that it involves some known information about the target and the imaging system. In the next section, we use lane marking detection to test our proposed orientation and scale-tuned local feature detector, and we illustrate how to assign the scale and orientation of certain image locations for special target recognition.
# Fungi-Templated Silver Nanoparticle Composite: Synthesis, Characterization, and Its Applications
## Materials and Methods
### Preparation of Fungal Hyphae-Silver Nanoparticle (FH-AgNP) Composite  
The fungus (_Sclerotium roflsii_) was grown initially in agar medium maintained at room temperature. The fungal biomass was taken out from the medium before sporulation for templating. Later, the biomass was added to the AgNP suspension and was kept for incubation with stirring for four days. The colour of the biomass changed from white to black, indicating the complete deposition of AgNPs on the fungal hyphae. The biomass was then taken out and washed with deionized water 5 times to remove the loosely bound particles. The silver deposited biomass (FH-AgNPs) was then dried in a hot air oven at 50 \({}^{\circ}\)C for 5 h (Figure 1). The dried biomass was then used for characterization.  
Figure 1: Schematic representation of the synthesis of FH-AgNPs. (**a**) Fungal biomass incubated with AgNPs. (**b**) Fungi-templated AgNPs. (**c**) Optical microscopic image of fungi filament templated with AgNPs.
# The Conditional Probability of Correlating East Pacific Earthquakes with NOAA Electron Bursts
## 2 Materials and Methods
Being that the CR distributions were Poissonian in every interval of (t; L; \(\alpha\); B), an electron CR fluctuation was considered a statistical fluctuation with a probability of less than 1% if the value of the Poisson distribution(CR fluctuation) was less than 0.01. CRs that satisfy this relation were defined as EBs. When the satellite runs a semi-orbit, all detected EBs were labeled as only one EB. Including only EBs with L-shells in a restricted interval [27], a histogram of Correlation Events (CEs) between EQs and EBs was calculated. CE was defined by the time difference between EQs and EBs, \(\rm{T_{EQ}-T_{EB}}\), which permitted the filling of the histogram with a bin interval of 2 h. Whenever a CE peak appeared at a certain time interval for some kind of EQs, it was tested for its significance [26] thus determining whether it was a positive correlation. The conditional probability of an EQ event following the observation of an EB event was defined for binary events by [27; 28]:  
\[\rm{P(EQ\,|\,EB)=CE/N_{EB}.} \tag{1}\]  
The cross-correlation Pearson coefficient for binary events called the Matthews correlation coefficient [33] can be obtained by [27; 28]:  
\[\rm{P(EQ\,|\,EB)=P(EQ)+corr(EQ,EB)[P(EQ)[1-P(EQ)][1-P(EB)]/P(EB)]^{1/2},} \tag{2}\]  
and  
\[\rm{corr(EQ,EB)=[CE/N_{h}-P(EQ)P(EB)][P(EQ)[1-P(EQ)]P(EB)[1-P(EB)]]^{-1/2},} \tag{3}\]  
where \(\rm{P(EQ)=N_{EQ}/N_{h}}\), \(\rm{P(EB)=N_{EB}/N_{h}}\), \(\rm{N_{EQ}}\) was the number of considered EQs, \(\rm{N_{EB}}\) was the number of considered EBs, and \(\rm{N_{h}}\) was the number of time intervals. The corresponding ratio \(\rm{P(EQ\,|\,EB)/P(EQ)=G}\) is the probability gain of the correlation peak.
# A Novel Consistent Quality Driven for JEM Based Distributed Video Coding
## 4 Performance Evaluation
### Complexity Performance Evaluation  
In this work, we also evaluate the encoding complexity of the proposed JEM-DVC and other DVC solutions, since the encoding complexity is an important factor for most low-complexity constrained video applications, such as wireless sensor networks or wireless surveillance systems. Based on the processing time that is consumed for encoding video frames at the DVC encoder, the encoding  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Sequences**} & \multicolumn{2}{c}{**BD-Rate**} & \multicolumn{2}{c}{**BD-PSNR**} \\ \cline{2-5}  & **JEM-DVC vs.** & **JEM-DVC vs.** & **JEM-DVC vs.** & **JEM-DVC vs.** \\  & **H.265-DVC** & **DISCOVER** & **H.265-DVC** & **DISCOVER** \\  & **(anchor)** & **DVC (anchor)** & **(anchor)** & **DVC (anchor)** \\ \hline _Foreman_ & \(-8.56\) & \(-20.38\) & \(0.54\) & \(1.37\) \\ \hline _Soccer_ & \(-3.76\) & \(-11.39\) & \(0.25\) & \(0.76\) \\ \hline _Coastguard_ & \(-3.00\) & \(-14.10\) & \(0.15\) & \(0.74\) \\ \hline _Hall_ & \(-5.29\) & \(-23.68\) & \(0.40\) & \(1.98\) \\ \hline _Average_ & \(-5.15\) & \(-17.38\) & \(0.33\) & \(1.21\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparisons of BD-Rate and BD-PSNR [29] between the proposed JEM-DVC with H.265-DVC and DISCOVER DVC.  
Figure 12: Comparisons of overall visual quality measurement using SSIM metric obtained by H.265-DVC, DISCOVER DVC, and the proposed JEM-DVC with different test sequences: (**a**) _Foreman_, (**b**) _Soccer_, (**c**) _Coastguard_, and (**d**) _Hall Monitor_.  
complexity is evaluated in terms of encoding times for the proposed JEM-DVC and compared with that of the relevant benchmarks, notably: JEM-Intra and JEM-No motion. For detailed references, the complexities of DISCOVER DVC and H.265-DVC are also calculated and compared with that of H.264-Intra, and H.264-No motion; H.265-Intra, and H.265-No motion, respectively.  
In our experiments, the input video sequences are encoded while using a single personal computer (PC), which utilizes an Intel core i7-7700 HQ (2.8 GHz) processor, 16 GB RAM, and Windows 10- Home OS. Each video sequence has a long of 150 frames and the processing time of all the encoded frames in a sequence is calculated for complexity performance evaluations.  
Figure 13 reports the complexity performance of the proposed JEM-DVC, H.265-DVC, and DISCOVER DVC. As shown in Figure 13, the proposed JEM-DVC can save up to 38% encoding time as compared to that of the JEM-Intra and JEM-No motion modes. The proposed JEM-DVC introduces relatively higher encoding times as compared to the DISCOVER DVC and H.265-DVC; however, with the breakthrough on high power IC chip designs nowadays and the high coding efficiency gained by the JEM-DVC, it is worth inspiring and considering the proposed JEM-DVC as an effective solution for a new DVC based video coding codec.  
Figure 13: Comparisons of complexity for encoding time (s) obtained by each DVC scheme and video coding standards (Intra and No motion) with different test sequences: (**a–f**) _Foreman_ and _Soccer_.
###### Abstract  
Transarterial chemoembolization (TACE) is a first-line treatment for patients with hepatocellular carcinoma (HCC) in Barcelona Clinic Liver Cancer stage B (BCLC-B). There are two major techniques of TACE: conventional TACE (cTACE) using iodized oil and gelatin sponge particles, and TACE using drug-eluting beads (DEB-TACE). The latest randomized controlled trial proved the superiority of cTACE regarding local effects over DEB-TACE; however, cTACE also damages the liver more severely. Therefore, cTACE should be performed for localized HCCs as selectively as possible. On the other hand, DEB-TACE has less liver toxicity and is favorable for patients with an advanced age, large and/or bilobar tumors, or a poor liver function. However, some BCLC-B HCCs are TACE-resistant and the concept of TACE unsuitability (mainly up-to-7 criteria out) has been proposed by Asia-Pacific Primary Liver Cancer Expert Meeting. Systemic therapy is recommended for patients with TACE-unsuitable HCC; however, the condition of TACE-unsuitable HCC does not always rule out TACE monotherapy and some up-to-7 criteria out tumors may also be good candidates for superselective cTACE when localized in limited liver segments. The sequential therapy of an antiangiogenic and TACE is also a novel option for patients with TACE-unsuitable HCC, antiangiogenic-refractory HCC, or even down-staged HCC.  
hepatocellular carcinoma; transarterial chemoembolization; iodized oil; gelatin sponge particle; drug-eluting bead; chemotherapeutic; systemic therapy; antiangiogenic; treatment strategy
## 2 Control Algorithm
### Effectiveness of the Algorithm  
As aforementioned, there are only four parameters to tune in total for an arbitrarily high order system: \(\lambda\) and \(\alpha\) to construct \(s\)-surface, \(\eta\) to construct \(v_{sw}\), and \(\Phi\) to determine the saturation function. Usually, a higher order controller has more parameters to tune. Using only four parameters to control an arbitrarily high order system may sacrifice potential performance more parameters may bring, but it simplifies the tuning procedures.  
On the other hand, the proposed algorithm has more degrees of freedom to adjust the system's performance, compared to the conventional SMC algorithm. Although those characteristics may result in complexity in selecting parameters, with the aforementioned methods and analysis, one is able to tune parameters systematically and efficiently.  
Another concern is the cost of the control. Better performance may result from greater effort, such as a larger control signal or greater energy consumption. From our experiences, though not rigorously proven, in some cases the proposed algorithm actually costs less than the conventional SMC, which only tunes \(\lambda\) to build the sliding surface and \(\Phi\) to defined the boundary layer.  
An example is presented in Figure 6. The example was simulated using the aforementioned nonlinear system. In order to evaluate the control laws, a cost function \(J\) is defined as follows:  
\[J = \frac{1}{2}\int_{0}^{t}\tilde{u}^{2}d\tau \tag{40}\]  
Two control laws were employed to control the system: the SMC and our proposed PISMC. Parameters were tuned so that the two systems had similar settling times. A smaller overshoot occurred in the system using PISMC. Table 1 lists the parameters:  
The simulations in Figure 6 show that the proposed algorithm costs less while keeping similar transient performance. Notably, under the designated parameters, chattering occurs once using the control from SMC. As a result, it can be concluded that our algorithm performs better than the conventional SMC in this specific case.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Algorithm** & \(\mathbf{\lambda}\) & \(\mathbf{\kappa}\) & \(\mathbf{\eta}\) & \(\mathbf{\Phi}\) \\ \hline SMC & 30 & – & 28 & 0.1 \\ \hline PISMC & 1 & 4 & 0.001 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters used for comparison of cost functions.
###### Abstract  
This study theoretically explored the effects of parameter settings on thermoacoustic oscillations with a low-order model. Three factors were explored--combustor length, inlet gas temperature and thermal power. The research findings indicate that optimizing the parameter settings can yield better thermoacoustic oscillation suppression results. The sound pressure amplitude decreased from 3.2 \(\times\) 10\({}^{5}\) Pa to 2.1 \(\times\) 10\({}^{5}\) Pa as the combustor length increased from 1.2 m to 6.0 m. The triggering time increased from 0.32 s to 0.91 s when the combustion chamber length increased. The climb rate declined from 23.38 \(\times\) 10\({}^{5}\) Pa/s to 3.75 \(\times\) 10\({}^{5}\) Pa/s when the combustor length was elongated. The sound pressure amplitude decreased from 3.44 \(\times\) 10\({}^{5}\) Pa to 2.4 \(\times\) 10\({}^{5}\) Pa as the gas temperature rose from 0 to 100 \({}^{\circ}\)C. The triggering time and climb rate variation tendency were similar when the gas temperature increased--both declined as the gas temperature rose. The sound pressure amplitude experienced a slight fluctuation when the thermal power rose. However, the triggering time decreased from 0.26 s to 0.043 s when the thermal power improved. The climb rate increased from 18.72 \(\times\) 10\({}^{5}\) Pa/s to 27.65 \(\times\) 10\({}^{5}\) Pa/s when the thermal power rose. The oscillation frequency presented was completely different in three cases that had different wavelengths and oscillation intensities. The triggering time and climb rate fluctuated extensively in varying conditions, and the above two factors were interrelated and contradictory to each other when thermoacoustic oscillation was excited. This study explored parameters' effects on triggering time and climb rate, thereby providing references for constructing a model-based control system for thermoacoustic oscillation feedback control.  
t pacs: 28 November 2023
# C-SHAP: A Hybrid Method for Fast and Efficient Interpretability
## 4 How C-SHAP Improves SHAP's Performance
For example, the root node in Figure 3 displays a SHAP value of 6076.398, while the corresponding node in Figure 4 shows 5298.152, indicating differences in the values processed due to clustering in C-SHAP. * Dataset coverage (third row): This percentage indicates the proportion of the total dataset reaching the node. For instance, both root nodes in Figures 3 and 4 begin with 100% coverage, showing the full dataset before any splits. * Sample count (fourth row): This denotes the volume of data or number of samples processed at the node. In the root node of Figure 3, the count is 153.737, compared to 145.775 in Figure 4. This difference highlights how C-SHAP clusters data points, potentially altering sample sizes across nodes. Thus, Figure 3 illustrates the original decision thresholds and sample distributions between the nodes that SHAP creates. Here, the decision path dives into finer-grained details as it does not group similar data points, leading to a greater number of samples per node. Conversely, Figure 4 shows how the model structure changes with clustering. By clustering similar data points, C-SHAP reduces the sample size at each node, which leads to a more streamlined tree with fewer decision points while maintaining comparable error margins. So, in terms of performance, C-SHAP demonstrates advantages over SHAP by requiring fewer computations due to clustering, which can result in faster execution times and reduced error margins. In conclusion, by clustering data points, C-SHAP retains SHAP's ability to provide both local (individual predictions) and global (overall model behavior) explanations, ensuring that the transparency of the model's decision-making process is not compromised. The method maintains the high-quality interpretability offered by SHAP while addressing its execution time limitations, making it suitable for real-time applications that demand both speed and interpretability. Figure 4: Decision Tree of C-SHAP approach. Note: the BMI threshold \(bmi\leq-0.023\) observed in the decision tree reflects the scaled value of the BMI after preprocessing. The original BMI values were normalized using z-score normalization to improve model performance, and the thresholds should be interpreted in this context.