esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Conclusions  
This paper analyzes the noise characteristics of the photoelectric detection circuit in the frequency domain for a cesium optically pumped magnetometer. Considering the characteristics of the photodiode and the associated operational amplifier, the equivalent noise of the low-noise pre-amplifier detection circuit is calculated. The results have good accordance with the measurements. The calculations can serve to guide the low-noise circuit design. The influences of the RF power, the incident light power and the cell temperature are also investigated and improved. With the help of all the mentioned optimizations, a single-beam optically pumped cesium magnetometer with a sensitivity of lower than 100 fT/\(\sqrt{\text{Hz}}\) is realized in a 53 \(\upmu\)T magnetic field condition, which is suitable for geomagnetism applications.  
The optimizations in this paper can serve to improve the detection performances for all the optically pumped magnetometers with a photodiode and current amplification process, not limited to the ones with cesium source. According to the frequency band of the Larmor signal, the appropriate photodiode and low-noise amplifier can be chosen for the optimization. For the other types of optically pumped magnetometers, the optimization process in this manuscript is also applicable.  
Conceptualization, L.L., Y.L., X.Z., Q.Z. and G.F.; Funding acquisition, G.F.; Methodology, L.L. and Y.L.; Supervision, Q.Z.; Validation, L.L. and Y.L.; Writing--original draft, L.L.; Writing--review & editing, X.Z. and Q.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences, grant number XDA22020301, the National Natural Science Foundation of China, grant number 41704177, and the National Key R&D Program of China, grant number 2018 YFC0603201.  
The authors declare no conflict of interest.
# Performance of Dense Wireless Networks in 5G and beyond Using Stochastic Geometry
## 5 Outage Analysis in the Presence of NLOS, Noise and Blockage
### Noise  
Although the paper analyzes the aggregated interference power, in a 6G network with high path-loss it is inevitable to consider also the effect of the noise power. The SINR is defined as:  
\[\text{SINR}=\frac{|\beta_{0}|^{2}G^{2}(\phi_{0},\theta_{0})}{(I+\sigma_{w}^{2} /N)(R_{o}^{2}+h^{2})^{6}}, \tag{47}\]  
where \(I=|\iota|^{2}\) and \(\iota\) is defined in (44). For the service probability in the presence of noise, it is easy to prove that relation (44) must be modified as  
\[\mathbb{P}_{\textit{service}}=\mathbb{P}_{I}\Bigg{(}I_{tot}+N_{tot}<\frac{1}{T }\Bigg{(}\sum\limits_{\ell=1}^{L}\frac{|\beta_{\ell}|^{2}}{I_{\ell}D_{\ell}^{ 2b}}\Bigg{)}^{2}\Bigg{)}, \tag{48}\]  
where \(I_{tot}=\sum\limits_{\ell=1}^{L}\alpha_{\ell}I_{\ell}\) and \(N_{tot}=\sigma_{\pi}^{2}\sum\limits_{\ell=1}^{L}\alpha_{\ell}\) are the total interference power and noise power respectively, after MRC at the AP with \(\alpha_{\ell}=|\beta_{\ell}|^{2}/(I_{\ell}+\frac{\sigma_{\pi}^{2}}{N})^{2}D_{ \ell}^{2b}\).  
Figure 15: Average users served \(M_{s}\) within square area \(200\,\rm{m}\times 200\) m vs. UCA height \(h\), and solid lines are the no NLOS (\(L=1\)), while dashed lines with a marker are the with NLOS for \(L=2,3,4\), with maximal ratio combining (MRC) and selection combining (SC) receivers for \(N_{c}=500,\lambda=10^{-2}\,\rm{m}^{-2}\), \(2b=2.6\), SIR threshold \([T]_{dB}=0\), \(R_{max}=400\) m, the normalized amplitude\(|\beta_{\ell}|=1\) for every path \(\ell\).
# State-of-All-the-Art and Prospective Hydrogel-Based Transdermal Drug Delivery Systems
## 6 Some Important Applications of Hydrogels in Transdermal Drug Delivery Systems
### Diabetes Treatment  
Most diabetic patients rely on insulin for maintaining controlled glycemia levels, often necessitating multiple daily administrations. Despite their associated pain and inconvenience, subcutaneous needle injections are commonly employed for insulin delivery. As an alternative to this traditional method, transdermal drug delivery holds promise. [PERSON] et al. conducted a study in which they developed a lyophilized hydrogel patch system for insulin delivery through microneedles. The hydrogel patch, created within a crosslinked matrix of poly (acrylamide-co-acrylic acid) through precipitation polymerization, was infused with recombinant human insulin. The results observed after 12 h of insulin injection revealed a proportional reduction in blood glucose levels in relation to the concentration of insulin incorporated into the lyophilized hydrogel patches, demonstrating a longer action duration than subcutaneous injection [85].  
\begin{table}
\begin{tabular}{p{142.3 pt} p{142.3 pt}} \hline \hline
**Physically Crosslinked Hydrogel** & **Chemically Crosslinked Hydrogel** \\ \hline Involve non-covalent interactions & Crosslinks formed by covalent bonds between polymer chains Irreversible Used for stimuli-responsive behavior, injectability, or biodegradability. & Crosslinks formed by covalent bonds between polymer chains Irreversible Used for mechanical strength, stability, and resistance to degradation. \\ \hline \hline \end{tabular}
\end{table}
Table 4: Physical vs. Chemical Crosslinked Hydrogel [30].
# Precipitable Water Vapor Retrieval Based on DPC Onboard GaoFen-5 (02) Satellite
## 2 Materials and Methods  
### Dataset  
#### 2.1.1 DPC Data  
The Chinese GF5-02 satellite was launched on 7 September 2021, at the local time of descending node of 13:30. The DPC is one of seven payloads of GF5-02 and is mainly used for the retrieval of atmospheric parameters. The DPC has multi-angle observation capabilities and can achieve a maximum of 17 effective observations in different views of one target. A DPC image has a swath width of 1850 km and a spatial resolution of 1.62 km at the nadir point. The DPC has eight spectral channels with a spectral range from visible to near-infrared wavelengths. Only the channels at 490 nm, 670 nm, and 865 nm have extra polarization observation, and the other five channels at 443 nm, 565 nm, 763 nm, 765 nm, and 910 nm can only realize intensity observation. Among them, the laboratory calibration uncertainty of DPC intensity data is better than 3.94%, and the detection accuracy of polarization data is better than 0.0066. In this study, we used data from two near-infrared bands of the DPC (865 nm and 910 nm) to retrieve water vapor. The signal received in the 865 nm band is not affected by atmospheric absorption, and the signal received in the 910 nm band is strongly affected by water vapor absorption. The spectral response functions (SRFs) of these two bands and the WVAT spectrum are shown in Figure 1. The blue line in the legend indicates the WVAT when the view zenith angle and the solar zenith angle are both equal to 0\({}^{\circ}\) and PWV is equal to 1 cm. The yellow line is the WVAT when the zenith angle of the satellite and the sun is equal to 0\({}^{\circ}\) and PWV is equal to 3 cm. In this study, we used 10 days of DPC data from 20 January 2022 to 29 January 2022 provided by the China Centre for Resources Satellite Data and Application (CESDA) during the on-orbit testing of the GF5-02 satellite.  
Figure 1: The SRFs of two near-infrared bands of DPC are illustrated by a purple line (denotes 865 nm) and a black line (denotes 910 nm), respectively. The blue line and yellow line represent the WVAT when PWV is equal to 1 and 3 cm, respectively [22].
## 3 Relative Position Measurement Calculation Method Using IRA Output
### Proposed Method
\begin{array}{c}\cos\xi\\ \sin\xi\end{array} \right]=-L_{2}\sin\theta \tag{29}\]  
where \(p=R\left[ \begin{array}{ccc}\sin\alpha\sin\beta&\cos\beta\\ \sin\alpha\cos\beta&-\sin\beta\\ -\cos\alpha&0\end{array} \right]\left[ \begin{array}{c}\cos\xi\\ \sin\xi\end{array} \right]=RS\left[ \begin{array}{c}\cos\xi\\ \sin\xi\end{array} \right]\), as shown in Equation (11). Therefore, if both sides of Equations (28) and (29) are divided into \(L_{1}\) and \(L_{2}\), respectively, the following single expression for \(\xi\), is acquired. \[\left[ \begin{array}{ccc}-1&0&0\\ \end{array} \right]C_{n}^{b}S\left[ \begin{array}{c}\cos\xi\\ \sin\xi\\ \end{array} \right]=\left[ \begin{array}{ccc}t_{1}&t_{2}\\ \end{array} \right]\left[ \begin{array}{c}\cos\xi\\ \sin\xi\\ \end{array} \right]=\sin\theta \tag{30}\]  
where \(t_{1}\)_and_\(t_{2}\) are calculated as follows,  
\[\left[ \begin{array}{ccc}t_{1}&t_{2}\\ \end{array} \right]=\left[ \begin{array}{ccc}-1&0&0\\ \end{array} \right]C_{n}^{b}S=\left[ \begin{array}{ccc}-1&0&0\\ \end{array} \right]C_{b}^{\mu T}S \tag{31}\]  
where \(C_{b}^{n}\) is a transformation matrix from the body frame to the navigation frame, shown in Equation (8), \(S=\left[ \begin{array}{ccc}\sin\alpha\sin\beta&\cos\beta\\ \sin\alpha\cos\beta&-\sin\beta\\ -\cos\alpha&0\\ \end{array} \right]\), as shown in Equation (12). Dividing both sides by \(\sqrt{t_{1}^{2}+t_{2}^{2}}\) we obtain,  
\[\frac{t_{1}}{\sqrt{t_{1}^{2}+t_{2}^{2}}}\cos\xi+\frac{t_{2}}{\sqrt{t_{1}^{2}+ t_{2}^{2}}}\sin\xi=\frac{\sin\theta}{\sqrt{t_{1}^{2}+t_{2}^{2}}} \tag{32}\]  
Substituting \(\sin\eta\) for \(\frac{t_{1}}{\sqrt{t_{1}^{2}+t_{2}^{2}}}\) and \(\cos\eta\) for \(\frac{t_{2}}{\sqrt{t_{1}^{2}+t_{2}^{2}}}\) we obtain,  
\[\sin\eta\cos\xi+\cos\eta\sin\xi=\sin(\eta+\xi)=\frac{\sin\theta}{\sqrt{t_{1}^{ 2}+t_{2}^{2}}} \tag{33}\]  
\[\eta+\xi=\sin^{-1}\frac{\sin\theta}{\sqrt{t_{1}^{2}+t_{2}^{2}}} \tag{34}\]  
\[\xi=\sin^{-1}\frac{\sin\theta}{\sqrt{t_{1}^{2}+t_{2}^{2}}}-\eta=\ \sin^{-1}\frac{\sin \theta}{\sqrt{t_{1}^{2}+t_{2}^{2}}}-\tan^{-1}\frac{t_{1}}{t_{2}} \tag{35}\]  
The relative position of the closest point can be calculated using the derived effective look angle \(\xi\) as follows. \[p=\left[ \begin{array}{c}R\cos\xi\sin\alpha\sin\beta+R\sin\xi\cos\beta\\ R\cos\xi\sin\alpha\cos\beta-R\sin\xi\sin\beta\\ -R\cos\xi\cos\alpha\\ \end{array} \right] \tag{36}\]
## 3 Markup languages
### Markup languages for text documents  
A mark-up language is a formalised way of providing markups [18]. There are dozens or maybe hundreds of mark-up languages in use today. The first structured markup language is IBM's Generalized Mark-up Language (GML) [19]. Based on the GML, the Standard Generalized Mark-up Language (SGML) was developed in the 1970s and became an international standard in 1986. SGML is not a language _per se_ but rather a meta-language - a rule set or toolkit for developing markup languages, such as HTML. HTML allows the markup of electronic documents for manipulation with regard to formatting and presentation, but the presentation specialisation makes HTML documents difficult to be processed for other purposes. To overcome the limitations of HTML, XML was created. Different from HTML, XML allows users to define their own tags (_i.e._, the labels that are embedded within text to distinguish individual/groups elements for display or identification purposes) based on the specific needs of a document, and therefore it is extensible. Further, as XML tag names are normally transferred from the meaning of the data and therefore they are readable. Each XML tag immediately precedes the associated data, which makes the data structure easily understandable by both humans and computers. In addition, one of most important powers of XML lies in the separation of specifications of valid tags; valid structure for data contained in elements; the valid type of data that each element may contain and what manipulations should be applied to elements identified by the tag, the information and its presentation. This offers the possibility to manipulate XML-based documents in multiple ways based on their markup. Thus, XML has become one of the most common computer-interpretable markup languages and been widely employed and implemented in engineering design.  
Further work in this stream are the many modifications and versions of mark-up languages based on XML which have been developed for different engineering scenarios. For example, DML (Dimensional Mark-up Language) was discussed for closed-loop CAPP/CAM/CNC process chain based on STEP and STEP-NC [20]. To provide designers with a product data exchange standard and support product lifecycle management, [PERSON] _et al._ proposed a Product Information Mark-up Language (PIML) [21]. A framework of XRML (eXtensible Rule Mark-up Language) developed by [PERSON] and [PERSON] allows the identification of rules on Web pages and extracts the identified rules automatically from Web pages to maintain consistency of text and table information [22]. In [23], a Motion Capture Mark-up Language (MCML) was proposed. The purpose of the MCML is not only to facilitate the conversion or integration of different formats, but also to allow for greater reusability of motion capture data, through the construction of a motion database storing the MCML documents.
# Proof of Optimum (PoO): Consensus Model Based on Fairness and Efficiency in Blockchain
## 5 Application and Findings
### Values Used in Determining the Winning Coefficient  
5.6.1. Fitness Value  
The fitness value is a measure of how well an individual performs in a given optimization problem. In the case of TSP, the fitness value is typically calculated as the length of the tour represented by an individual, to minimize this length. The fitness value is used to evaluate the performance of each individual in the population and to select individuals for reproduction and mutation based on their fitness. The better the fitness value of an individual, the higher the probability that they will be selected for the next generation. The fitness values produced by the miners as a result of optimization are given in Figures 4-9.
# Research Progress of Zero-Busbar Technology Based on Heterojunction Photovoltaic Modules
###### Abstract  
In order to reduce manufacturing costs, the design of silicon-based solar modules is changing from a super-multi-busbar design to a zero-busbar (0 BB) design. In this study, two different 0 BB technologies based on heterojunction with intrinsic thin-layer solar cells--conventional soldering, and Integrated Film Covering (IFC)--were investigated. IFC-based 0 BB technology was found to have a lower contact resistance, which well matches the theoretical calculations and module power testing results. To further measure module reliability, a series of tests on solders and silver pastes were carried out. The results show that Sn43 Pt43 Bi14 solder is more suitable for soldering-based 0 BB technology, whereas Sn32 Pt42 Bi26 solder is more suitable for IFC-based technology. Additionally, silver paste, which is used for solder ribbon contact areas (SRCAs), is suitable for soldering-based 0 BB technology. When Ag@Cu paste is used in SRCAs with IFC-based 0 BB technology, a reliable connection can also be achieved. After optimization, modules using both techniques were subjected to and passed lifetime tests, including the thermal cycling, humidity freeze, and hot-spot tests required in IEC standards, as well as more rigorous tests such as thermal-dynamic and thermal-static mechanical loading. The results show that the two technologies have great potential for future mass production.  
HJT; 0 BB; IFC; soldering; Ag@Cu
# Biochars as a Solution for Silver Removal and Antimicrobial Activity in Aqueous Systems
## 1 Introduction
Cotton, containing over 90% cellulose, is another promising feedstock for biochar production through direct carbonization or activation processes [13; 31; 32]. Unlike some chemical methods, the use of biochar generates less secondary waste, further enhancing its environmental benefits. Ongoing research and development in this area are critical to fully harness the potential of biochar-based adsorbents. In the present research, the efficiency of biochars prepared by the carbonization of desiliconized rice husks and cotton fabric in adsorbing silver ions from aqueous solutions was investigated. The kinetics, equilibrium, and thermodynamics of the adsorption process were analyzed in detail. As has been stated above, silver and silver ions have impressive antimicrobial and antiseptic properties, making them valuable for various applications. Silver has been used since ancient times for its ability to inhibit the growth of bacteria, viruses, and fungi. These properties have found applications in medical devices, wound dressings, and water purification systems. Silver nanoparticles are also being incorporated into textiles, coatings, and consumer products to impart antimicrobial properties, reduce the spread of pathogens, and improve hygiene. Research continues to explore the exact mechanisms of silver's antimicrobial action [33; 34]. Considering the increasing resistance of microorganisms to antibiotics and the urgent need for alternative solutions, this study investigated the antimicrobial activity of biochar with adsorbed silver ions. In vitro studies were conducted to evaluate the antimicrobial properties of biochar produced by the pyrolysis of desiliconized rice husks that had adsorbed Ag\({}^{+}\) ions.
# Spectroscopic Techniques Application for Wine and Wine Byproduct Authentication
## 2 Food Spectral Print Approaches
### Infrared (IR) Spectroscopy
#### 2.2.1 Mid-Infrared (MIR) Spectroscopy
The technique is also efficient; integrating it with other methods, such as NIR, electronic tongues, and GC-MS, can enhance wine authentication processes. These combined approaches provide a comprehensive analysis and mutual corroboration, improving the reliability of authenticity assessments [58].
## Appendix A  
**Proof of (20).** Since we assume the \(\alpha\)-\(\mu\)/IGA RVs are independent of each other, and the MGF of the \(l\)th RV can be given by using (9), then the MGF of \(\gamma_{M\!RC}\) can be obtained as  
\[MGF_{\gamma_{M\!RC}}(s)=\prod\limits_{l=1}^{L}MGF_{\gamma_{l}}(s)=\prod\limits _{l=1}^{L}A_{l}H_{2\perp 1}^{1.2}[\frac{\lambda_{l}}{s^{0.5\alpha_{l}}}|_{(l_{l},l )}^{(1-n_{l},0.5\alpha_{l}),(1,0.5\alpha_{l})}]. \tag{10}\]  
To get the desired results, with the aid of the definition of the univariate Fox H-function in [38], (10) can be expanded as  
\[MGF_{\gamma_{M\!RC}}(s)=\Theta_{1}\bigg{(}\frac{1}{2\pi j}\bigg{)}^{L}\bigg{(} \int_{\mathbb{C}_{1}}\cdots\int_{\mathbb{C}_{L}}\prod\limits_{l=1}^{L}\bigg{(} \Delta_{l}\Big{(}\lambda_{l}s^{-0.5\alpha_{l}}\Big{)}^{t_{l}}\bigg{)}dt_{1} \cdots dt_{L}\bigg{)}, \tag{11}\]  
where \(\Theta_{1}=\prod\limits_{l=1}^{L}\alpha_{l}/[2\Gamma(\mu_{l})\Gamma(n_{l})]\), \(\Delta_{l}=\Gamma(\mu_{l}-t_{l})\Gamma(0.5\alpha_{l}t_{l})\Gamma(n_{l}+0.5 \alpha_{l}t_{l})\), \(j=\sqrt{-1}\), and \(\mathbb{C}_{l}\) denotes the lth appropriate contour which starts at the point \(\tau_{l}-j\infty\) and goes to the point \(\tau_{l}-j\infty\) with \(\tau_{l}\) is a constant value, \(l\in\{1,\cdots,L\}\).  
Mathematically, the PDF of one RV can be found by applying the inverse Laplace transform of its MGF, then the PDF of \(\gamma_{M\!RC}\) can be written as  
\[f_{\gamma_{M\!RC}}(\gamma)=L^{-1}\{MGF_{\gamma_{M\!RC}}(s);\gamma\}=\frac{1}{ 2\pi j}\!\int_{\mathbb{C}}MGF_{\gamma_{M\!RC}}(s)\exp(s\gamma)ds, \tag{12}\]  
where \(\mathcal{L}^{-1}\{\cdot\}\) represents the inverse Laplace transform. Thus, by substituting (11) into (12), the PDF of \(\gamma\) can be expressed as  
\[f_{\gamma_{M\!RC}}(\gamma)=\Theta_{1}\bigg{(}\frac{1}{2\pi j}\bigg{)}^{L} \bigg{(}\int_{\mathbb{C}_{1}}\cdots\int_{\mathbb{C}_{L}}(\gamma)\prod\limits _{l=1}^{L}\Big{(}\Delta_{l}(\lambda_{l})^{t_{l}}\Big{)}dt_{1}\cdots dt_{L} \bigg{)}, \tag{13}\]  
where \(\gamma=\frac{1}{2\pi j}\!\int_{\mathbb{C}}s^{-\Delta_{0}}\exp(s\gamma)ds\), \(\Delta_{0}=\sum\limits_{l=1}^{L}0.5\alpha_{l}t_{l}\). By using [36] (Equation (8.315.1)), Y can be solved as  
\[\gamma=\gamma^{\Delta_{0}-1}/\Gamma(\Delta_{0}). \tag{14}\]  
By plugging (14) into (13), we can re-express (13) as  
\[f_{\gamma_{M\!RC}}(\gamma)=\Theta_{1}\gamma^{-1}\bigg{(}\frac{1}{2\pi j}\bigg{)} ^{L}\bigg{(}\int_{\mathbb{C}_{1}}\cdots\int_{\mathbb{C}_{L}}\Gamma^{-1}( \Delta_{0})\!\prod\limits_{l=1}^{L}\!\bigg{(}\Delta_{l}\Big{(}\lambda_{l} \gamma^{0.5\alpha_{l}}\Big{)}^{t_{l}}\bigg{)}dt_{1}\cdots dt_{L}\bigg{)}. \tag{15}\]  
With the help of the definition of the multivariate H-function [37] (Equation (10)), the multiple Barnes-type contour integrals in (15) can be written as the compact form in (20), which completes the proof. \(\square\)
## 2 Materials and Methods  
### Data  
Two databases were used for training and validating the proposed models. The first database was taken from ESMAP [20], which provide the measurements from different automated solar stations in Vietnam. Data for each site included one-minute averages of solar irradiance, PV module temperature, ambient temperature, and wind speed. Three data sets were selected from this database corresponding to three climatic zones in Vietnam: Hanoi in the north with subtropical with dry winter climate (Cwa), Danang in the center with equatorial monsoon climate (Am), and Tri An in the south with equatorial savannah with dry winter climate (Aw) [21]. The second database provides measured data for different PV modules in the USA, including crystalline silicon and thin film technologies, and is supplied by Marion [22]. Those modules were deployed in different locations: Cocoa, Florida, with subtropical climate; Eugene, Oregon, with marine west coast climate; and Golden, Colorado, with a semi-arid climate. Different from the former datasets, the data for these sites were measured immediately at a certain time of recording. The general characteristics of these databases are presented in Table 1.  
The data in Group 1 were used for both training and validating the proposed models, while the data in Group 2 were used for evaluating the accurate prediction of those models on different PV module technologies compared to other literature models. The PV module technologies and their characteristics used in this study are presented in Table 2. Due to the lack of wind measurements, which needs to be measured on-site at the same conditions as other parameters, measurements of the wind speed at the 10-m height [24] and the nearest measured station [23] were applied.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Group**} & \multirow{2}{*}{**Location**} & **Latitude** & **Interval Sampling** & \multirow{2}{*}{**Observed Period**} \\  & & (\(\lx@sectionsign\)**N**) & & (Minute) & \\ \hline \multirow{4}{*}{**1**} & Tri An 1 ** & 11.01 & 1 & 01/2018–11/2019 \\  & Da Nang 1  & 16.01 & 1 & 01/2018–11/2019 \\  & Ha Noi 1  & 21.20 & 1 & 01/2018–09/2019 \\  & Cocoa 2  & 28.39 & 5 & 01/2011–03/2012 \\
**2** & Golden 2  & 39.74 & 15 & 08/2012–09/2013 \\  & Eugene 2  & 44.05 & 5 & 10/2012–01/2014 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected databases for training and validating models.