esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Morphology
Another observation from this SEM image is the presence of a large number of shallow and interlinked holes. Moving to the micropores produced by the symmetric silicon cathode in Figure 4c, and its pore size analysis in Figure 4d, we can see that the total area of the holes is 344.3 \(\upmu\)m\({}^{2}\), that gives a porosity of 24.68%. Although the total number of holes (694 for this image) and porosity are not so different from the Pt sample, the size distribution is notably different where most of the hole diameters are concentrated around 829 nm. This indicate a more uniform distribution compared with the Pt sample. The number of shallow and interlinked holes is fewer for the symmetric cathode sample. In this third sample we continue to explore the potential of using a Si substrate as cathode in the fabrication of PSi layers and assess its impact on the pore size. As a conclusion, the morphology analysis reveals that using silicon as cathode produce a more uniform and larger pore size distribution, both for nanoscale and microscale sizes. Using certain fabrication parameters and sample type silicon cathode can produce bilayer structure with microflakes/spongy form. Figure 3: Microstructural surface on S2 substrate using (**a–c**) Pt cathode and (**d–f**) Si cathode. It is important to examine the surface composition of the samples. To this end, we selected the S1 substrate samples for EDS analysis. The results shown in Figure 5a, for the Pt electrode sample and Figure 5b, for the Si electrode sample, where both reveal almost the same composition. Which indicates that we have only a structural modification rather composition changes. We thoroughly examined the morphology of the Si substrate used as a counter electrode after multiple uses in the etching process. The durability of the Si cathode is an important criteria to provide an effective alternative to the inert Pt electrode. To the naked eye, the Si substrate appears unchanged before and after the anodization process. However, SEM image is a better tool to confirm the maintain of the substrate surface without critical damage. Figure 6 demonstrates the stability of the surface morphology of the used Si electrode. the SEM image for the silicon substrate before anodization in Figure 6a show a smooth surface without any nanofeatures, while that in Figure 6b for the substrate after anodization show a similar surface with some particulates on the surface. This nano roughness on the surface of Si substrate used as cathode can be a result of multiple insertions of the substrate into diluted HF electrolyte. This remarkable stability can be attributed to the minimal impact of the applied current density and short etching time on the Si electrode. Figure 4: large pores fabricated on S3 substrate using (**a**) Pt cathode and its (**b**) pores size distribution, while (**c**) for Si cathode and its (**d**) pores size distribution. Figure 5: composition analysis using EDS technique for PSi fabricated on S1 substrate using (**a**) Pt cathode and (**b**) Si cathode.
# Noise Cancellation Method for Mud Pulse Telemetry Based on Discrete Fourier Transform
###### Abstract
We have also investigated interference elimination methods from other fields, including electric power [18; 19; 20], biomedical signal processing [21; 22], and speech recognition [23; 24]. These de-noising methods can be broadly classified into three categories, namely adaptive-filter-based methods, signal decomposition-based methods, and machine learning-based methods. However, these advanced methods often face significant challenges. A significant drawback of signal decomposition methods is their inability to adaptively recombine the decomposed components. Adaptive filtering and machine learning require careful adjustment of numerous parameters to achieve optimal performance. Improper tuning can lead to suboptimal interference suppression or even amplify the noise, reducing the overall effectiveness of the method. In this study, an adaptive noise cancellation method based on discrete Fourier transform (DFT) is applied to adaptively estimate the mud pump noise generated by two or more pumps operating at different frequencies. The system model and technological process of the proposed method are described in Section 2. Simulations and field tests were conducted to verify the effectiveness of the proposed method, and their corresponding results are presented in Section 3. Discussions and comparisons of various pump noise cancellation methods are shown in Section 4. Concluding remarks are given in Section 5.
# Battery Sizing for Mild P2 HEVs Considering the Battery Pack Thermal Limitations
## 2 Vehicle Performance Modelling
### Battery Performance and Electro-Thermal Model
#### 2.5.1 Static Component  
The static component of the model represents the open-circuit voltage, which is the measured cell voltage when the cell is subject to a zero-load condition. The OCV is approximated as a function of only SOC for the temperature operating condition considered in this work. For the cell under consideration, the OCV is obtained from the cell datasheet of Sanyo NCR 18650-618 lithium-ion cell [38] that has a rated capacity of 3200 mAh. Figure 7 shows the voltage variation across SOC under open and closed-circuit conditions.  
The middle blue curve shows the OCV as a function of SOC, as acquired from the datasheet. The red curve is the closed-circuit voltage at a constant charge load of 1.675 A. The yellow curve is the closed-circuit voltage at a constant discharge load of 0.66 A. It can be seen that the OCV taken from the datasheet approximates the true OCV at the mean of the closed-circuit voltages of the charge and discharge phases. The larger deviation of the charge phase results from the relative higher load with respect to the discharge phase.  
The SOC is computed through coulomb counting and it is analogous to a fuel gauge that expresses the amount of charge contained in a cell. The expression of SOC is shown in Equation (10) for a given time step \(k\).  
\[SOC(k+1)=SOC(k)-\frac{\eta\;i(k)k}{C_{tot}} \tag{10}\]  
where \(i(k)\) is current input to the model--positive at discharge, \(C_{tot}(\mathrm{Ah})\) is the total releaseable capacity in a complete cycle and \(\eta\) is the battery coulombic efficiency. \(SOC(k)\) is the SOC at \(k\)th time step. Only a percentage of current that is applied to the cell contributes to increasing the SOC in the charging phase. Hence, SOC is penalized with \(\eta\)[30] in the charge phase.
## 3 Results and Discussion
### Effect of the Wettability Alteration on the Dissolution Capacity of CA  
It was observed [41] that the efficiency of well stimulation depends on the accessibility of the rock's surface to the species which dissolve it. Accessibility of dissolving species can be estimated with the dissolution capacity (DC) of the well stimulation fluid towards the carbonate rock. DC is the weight of the carbonate rock which is dissolved by 1 g of dry CA [63]. A higher DC value means that a higher portion of the surface is accessible to the CA.  
We investigated the DC of plain CA and surfactant-CA mixture towards the marble, aged in stearic acid according to Section 2.2.7. Control experiments with CA and non-hydrophobized (original) marble were performed. The results of the study are shown in Table 2.  
Hydrophobicity of the carbonate surface decreases the DC of plain CA, which indicates the inaccessibility of the surface to dissolving species. Moreover, the formation of caverns on hydrophobic samples treated with plain CA at 120 \({}^{\circ}\)C is observed (Figure 10). It seems that dissolution occurs only on the surface patches where the stearic acid layer is absent or loose.  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & \multicolumn{2}{c}{**DC, g/g**} \\ \cline{2-3}
**Sample** & **25 \({}^{\circ}\)C** & **120 \({}^{\circ}\)C** \\ \hline Hydrophilic sample, CA & 0.057 \(\pm\) 0.003 & 0.084 \(\pm\) 0.002 \\ Hydrophobic sample, CA & 0.029 \(\pm\) 0.002 & 0.018 \(\pm\) 0.005 \\ Hydrophobic sample, CA-surfactant mixture & 0.050 \(\pm\) 0.011 & 0.086 \(\pm\) 0.002 \\ \hline \hline \end{tabular}
\end{table}
Table 2: DC of CA with and without surfactant towards marble.  
Figure 9: Mechanism of synergistic effect between CTAB and CA: (**a**) formation of ionic pair, (**b**) diffusion of ionic pair to the bulk volume with the following solubilization, (**c**) calcium ions leaching by the EDTA, (**d**) spontaneous desorption of stearate ions.  
However, the DC of the surfactant-CA mixture towards the hydrophobic sample is equal to the results obtained for CA towards hydrophilic samples. This indicated that wettability alteration enhanced dissolution of the carbonate rock. Other evidence includes the contact angle values of control samples (hydrophilic and hydrophobic) and samples after the treatment, which are shown in Table 3.  
A clear correlation between wettability alteration and DC is observed. It seems that the surfactant desorbs stearic acid from the surface, which is then dissolved by CA. Higher values of DC mean that a lower amount of CA is needed to dissolve the same amount of rock and stimulate a well.
# When Operation Technology Meets Information Technology: Challenges and Opportunities
## 4 A typical Use Case  
In this second part of the manuscript, we will focus on the specific problem of integration between the OT and the IT with an emphasis on communication capabilities and services. We believe this to be a very significant problem that can be successfully addressed with the logical approach enabled by the AAS.  
The focus will be on the use case of maintenance, which is a very typical problem in manufacturing environments, with scenarios such as the following [43]:  
* BM--Breakdown Maintenance -> sudden failure;
* TBM--Time-Based Maintenance -> performed on a calendar schedule;
* CBM--Condition-based Maintenance -> in advance of the failure.  
The pilot implementation described below can be successfully applied to all these situations, as we experienced working with industrial partners on research projects at national and international levels. In particular, we will consider, as an example, the case in which some assets must be configured to share a common time frame, using some sort of time synchronization over the network, which will involve ultra-low latency communications such as TSN. In this case, the problem is twofold:  
1. On one hand, an external entity must orchestrate which communication capability should be active in which asset in order to guarantee the proper service configuration;
2. On the other hand, an external entity must interact with the assets to perform the actions required to enable and configure the communication capabilities.
# Mycobacterial Biofilm: Mechanisms, Clinical Problems, and Treatments
## 3 Genes and Molecules Involved in Mycobacterial Biofilm Development
### c-di-Gmp  
Quorum sensing (QS) is a 'cell to cell' communication phenomenon. QS bacteria release 'autoinducers' (signal molecules) to regulate gene expression and alter a wide range of bacterial activities, including biofilm formation [53; 54]. Mycobacteria possess a QS system that is regulated by cyclic-di-GMP (c-di-GMP)--a second messenger that exists extensively in bacteria [53]. C-di-GMP targets cellulose, polysaccharides intercellular adhesion (PIA), pili, and many other factors that can influence biofilm formation [55]. Intracellular c-di-GMP levels are closely associated with biofilm. High levels of c-di-GMP facilitate biofilm formation, while low intracellular c-di-GMP levels intensify biofilm dispersal [56]. A recent study conducted by [PERSON] et al. found a reduced level of bacterial metabolite gamma-aminobutyric acid (GABA) in biofilm cells during biofilm dispersal. The decrease in GABA promoted cellulase expression, disrupting the main EPS component cellulose, and this function was achieved by downregulated the c-di-GMP level [34]. Recently, [PERSON] and colleagues clarified that c-di-GMP facilitates biofilm formation in _M. smegmatis_ through the nucleoid-associated protein Lsr2, suggesting that Lsr2 acts as a c-di-GMP receptor linking the second messenger's function to biofilm formation in mycobacteria [57].  
Besides the above genes and molecules, certain high-centrality proteins and regulators, like RegX3, Rv0081, Rv0020c, Rv0097, and Rv1996, also participate in Mtb biofilm formation [26; 58]. They are not isolated; instead, they establish complicated connections. In addition, a recent study on Mtb transcriptome indicates that non-coding RNAs (ncRNAs) are widely upregulated in Mtb biofilm and contribute to Mtb's adaptation to biofilm growth [59]. Therefore, deeper investigations into these genes will contribute to our understanding of the mechanisms of mycobacterial biofilm development.
## 3 Results and Discussion
### Mycotoxins Occurrence
After harvest, fungal development and mycotoxin production can be prevented in grain storage under controlled humidity and temperature conditions. Reducing the storage period to less than six months can also be beneficial. Keeping the temperature at 10-15 \({}^{\circ}\)C and relative humidity at 65% can be effective [72]. Pathogenic fungi pose a risk and can make infected grain unsuitable for human consumption or animal feed production. These toxic secondary metabolites can cause various adverse effects in animals and humans. Mycotoxins are a serious food and feed safety threat concerning cereal-based foods and feeds. Particularly, infants, small children, farm animals, and pets are at greater risk of mycotoxicosis. The molds producing mycotoxins can cause acute toxicity as well as initiate/contribute to the development of a number of non-communicable diseases, including immunity suppression, growth impairment, neural defects, and cancers [73]. For this reason, it is essential to research varieties and growing conditions of cereals that indicate a lower risk of mycotoxin contamination.
# Differentiation of Fresh and Processed Fruit Juices Using Volatile Composition
## Materials and Methods
### Fruit juice Samples  
Healthy mature-state and undamaged fruit samples from fresh grapes (_Vitis vinifera_ L), wild red fruits (_Fragaria x ananassa Duch. [PERSON]_ L., _Rubus ideas_ L.), orange (_Citrus sinensis_ L.), pear (_Pyrus communis_ L.), and apple (_Malus domestica Borkh._) were collected at maturity state based on the sugar/acid ratio, color, and pH from producers in the southwest region of Madeira Island, Portugal during the 2016 harvest and were immediately taken to the process line at 4 \({}^{\circ}\)C. For each sampling, fruits were picked randomly throughout the experimental vineyard, taking into consideration the balance between shadow and sun exposure in the different vineyard locations, following a z-shaped pattern to avoid edge and center effects. In the process line, the fresh fruits were screened, inspected, washed, mechanically crushed, and submitted to an enzymatic treatment using pectinase enzyme (concentration of enzyme: 5 mg/100 g; incubation time: 2 h; and temperature: 35 \({}^{\circ}\)C). Operations such as washing, sorting and inspecting require attention to mass and heat transfer. Cooling depends upon heat transfer from fruit to air (possibly water). Cleaning sometimes involved the physical removal of surface debris by brushes which reduced the number of microbes. Inspecting sometimes involved the removal of raw materials which did not meet the standards of the quality of analysis. The enzymatic activity was monitored by adding five milliliters of juice to 15 mL of HCl-acidified ethyl alcohol, observing the mixture for 5 min for gel formation. No gel formation meant that the depectimization was complete. After this, the juice was extracted at room temperature (25 \({}^{\circ}\)C), using the equipment appropriate for each fruit variety, and then filtrated (XP-201 polyvinylidene fluoride membrane) under vacuum. At this stage, fresh fruit juices were taken, in 250-mL amber glass vials and immediately transported to the laboratory at 4 \({}^{\circ}\)C, aliquoted in 20-mL amber glass vials and frozen at \(-80\)\({}^{\circ}\)C until analysis. The remaining fruit juice was submitted to a pasteurization process at 80-95 \({}^{\circ}\)C for 1 to 10 min in intermediate tanks to eliminate bacteria without changing the fresh flavor of the fruit juice. After this process, the fruit juice was submitted to a cooling batch (4 to 5 \({}^{\circ}\)C) and sterile storage. At this stage, the processed fruit juice was taken, transported to the laboratory, aliquoted, and frozen at \(-80\)\({}^{\circ}\)C until analysis. All analyses were performed in triplicate.
## 4 SAR Raw Signal Processing
### Omega-k Algorithm for LFM-CW SAR System
#### 4.2.5 Reference Function Multiply  
The general focusing is implemented using referential slant range, which consists of multiplying the data in the two-dimensional frequency domain by a reference function, as shown in Equation (47).  
\[H_{ref}(f_{t},f_{n})=exp\left(-1\mathrm{j}\omega_{ref}(f_{n},f_{t})\right) \tag{47}\]  
where:  
\[\omega_{ref}(f_{n},f_{t})=\frac{4\pi\alpha R_{ref}}{c}\sqrt{(f_{0}+f_{t})^{2}- \left(\frac{cf_{n}}{2\alpha V_{s}}-\frac{V_{s}}{c}(f_{0}+f_{t})\right)^{2}} \tag{48}\]
###### Abstract  
In this work, the resonance enhancement of magnetoelectric (ME) coupling at the two lowest bending resonance frequencies was investigated in layered cantilever structures comprising a magnetoactive elastomer (MAE) slab and a commercially available piezoelectric polymer multilayer. A cantilever was fixed at one end in the horizontal plane and the magnetic field was applied horizontally. Five composite structures, each containing an MAE layer of different thicknesses from 0.85 to 4 mm, were fabricated. The fundamental bending resonance frequency in the absence of a magnetic field varied between roughly 23 and 55 Hz. It decreased with the increasing thickness of the MAE layer, which was explained by a simple theory. The largest ME voltage coefficient of about 7.85 V/A was measured in a sample where the thickness of the MAE layer was \(\approx\)2 mm. A significant increase in the bending resonance frequencies in the applied DC magnetic field of 240 kA/m up to 200% was observed. The results were compared with alternative designs for layered multiferroic structures. Directions for future research were also discussed.  
magnetoelectric effect; magnetoactive elastomer; piezoelectric polymer; flexible composite; low frequency +
Footnote †: journal:  
Acticle Resonant Magnetoelectric Effect at Low Frequencies in Layered Polymeric Cantilevers Containing a Magnetoactive Elastomer
# Adsorption of Ibuprofen from Water Using Waste from Rose Geranium (_Pelargonium gravelens_) Stems
## 3 Characterisation
### Adsorption Results
#### 3.5.3 Concentration Effect and Isotherm Models for SPG  
The concentration effect was investigated using working standards with an initial concentration between 25 and 125 mg/L (Figure 7). The trends for IBU adsorption show that adsorption capacity increases when the initial concentration of the working standards is increased. The increase was recorded for standards between 25 and 100 mg/L. Thereafter, for standards with a concentration between 100 and 125 mg/L, no significant adsorption was recorded. This indicates that the active sites and binding points on SPG were saturated [27]. The mass transfer was limited and resulted in reduced uptake for the working standard of 125 mg/L.  
The equilibrium data were fitted to the isotherm models (Table 4) to investigate the appropriate model and determine the interaction between SPG and IBU. [PERSON] and Freundlich models are based on single-layer and multilayer adsorption, respectively [28]. The selection of the appropriate model is based on the comparison of the (\(r^{2}\)) values closest to 1. Table 4 shows that [PERSON] has an \(r^{2}\) value closer to 1 than the [PERSON] model, indicating that the equilibrium data follow [PERSON]'s isotherm model. This suggests that uptake occurs on the surface of SPG, with the active sites having the same affinity for IBU [29]. The statistical error analysis was calculated to evaluate the most effective model by determining [PERSON]'s percentage standard deviation (MPSD) and root mean square error (RMSE). The values obtained for MPSD and RMSE are 2.32 and 3.68 for Langmuir, respectively. The [PERSON] model showed greater errors with an MPSD value of 17.69 and an RMSE value of 22.57. The [PERSON] model showed lower error values for the adsorption of ibuprofen on SPG.  
Figure 7: Concentration effect on SPG for ibuprofen.
# Advancements in Glucose Monitoring: From Traditional Methods to Wearable Sensors
## 3 Minimally Invasive Methods  
Minimally invasive glucose-sensing methods aim to provide accurate blood glucose measurements with reduced discomfort as compared to traditional invasive techniques like finger pricing. These methods typically involve using sensors that penetrate the skin to a minimal depth or rely on non-intrusive technologies, offering patients a more comfortable and continuous monitoring experience. Technologies such as fluorescence-based sensing  
Figure 4: **(a)** (i) The top view of a common ISFET. **(ii)** The assembly of ISFET for the measurement of glucose. An Ag/AgCl electrode serves as the reference electrode, while the ISFET functions as the gate electrode, modified with functionalized Dex-AgNPs. Reprinted with permission from [43]. **(b)** The schematic illustration of the graphene/silk enzymatic sensor fabrication process. **(i)** Source and drain electrodes were prepared by depositing Au/Ti (500 nm/100 nm) at the two opposite ends of the graphene channel using an e-beam evaporator via a Ni shadow mask. **(ii)** The Gox-entrapped silk film was coated on graphene channels upon the silk substrates. **(iii)** Top gate electrodes were then patterned on GOx-entrapped silk film above graphene FET channels through a second shadow mask. **(iv)** The current between source and drain (Ids) of the graphene FET was continuously monitored while sweeping the gate voltage. The gate potential was applied on the top gate electrode, and its influence was transmitted through the silk/GOx layer. Reprinted with permission from [31] **(c)**. **(i)** The schematic diagram of the sensing membrane of EGFET. **(ii)** The measurement system of the EGFET. Reprinted with permission from [45]. **(d)** P-FET characteristics: **(i)** the SEM image of the liquid-gated P-GFET. The AFM images of the graphene channel before and after functionalization with Poly(AAm-AAPBA-DMAPAA). **(ii)** The transfer characteristic curve of pristine graphene does not show any shift with glucose concentrations ranging from 0 to 2 mM. After functionalization, the Dirac point shifts from \(-\)48.3 mV to \(-\)23.3 mV. **(iii)** Raman spectra of graphene before and after functionalization. AAm: acrylamide; AAPBA: 3-acryladiphenylboronic acid; DMAPAA: N-dimethylaminopropyl acrylamide; P-GFET: polymer-functionalized graphene field-effect transistor; SEM: scanning electron microscopy. Reprinted with permission from [46].  
methods [7, 8, 47], SERS-based sensing [9, 10, 48], microneedle-based sensors [49] are being explored to monitor glucose levels in real time while minimizing tissue disruption. In this segment, we will discuss various minimally invasive methods applicable for real-time glucose sensing.