esa-sceva/satcom-chunk-collection huggingface dataset:
# The Antimicrobial Effectiveness and Cytotoxicity of the Antibiotic-Loaded Chitosan: ECM Scaffolds
## 2 Materials and Methods  
### Preparation of Chitosan:ECM Scaffolds  
Chitosan solutions were prepared by dissolving 2% (_w/v_) chitosan powder (85% deacetylated, Sigma Aldrich, St. Louis, MO, USA) in dilute acetic acid (2% _v/v_) under clean conditions. Endotoxin content of the prepared chitosan solution was assessed using the Pierce Limulus Amebocyte Lysate (LAL) Chromogenic Endotoxin Quantitation Kit (ThermoFisher Scientific, Waltham, MA, USA) following the manufacturers recommended protocol. Endotoxin levels were expressed as EU/mL.  
ECM was prepared from porcine urinary bladders (UBM) as previously described [28,29]. Bladders were briefly harvested from market-weight pigs immediately after death. Excess connective and adipose tissues were removed from the serosal surface of the bladder and the apex of the bladder was removed. The bladder was then split longitudinally from the apical opening to the neck of the bladder, forming a rectangular sheet. Mechanical delamination was performed to remove the he tunica serosa, tunica muscularis externa, and tunica submuc, as well as the majority of the muscularis mucosa. The remaining tissue consisted of only the basement membrane and tunica propria. Decellularization and disinfection was completed by washing with 0.1% peracetic acid/4% ethanol solution for 2 h followed by phosphate-buffered saline solution (PBS) (pH 7.4) and water washes. The decellularized tissue was then frozen and lyophilized.  
Two forms of ECM were then prepared to be combined with the chitosan solution. First, the lyophilized ECM was comminuted into a powder using a Wiley Mill with a 40-mesh screen. The resulting powder was added to the chitosan solution at either 1% or 10% (_w/v_) to form two scaffold formulations. Second, the comminuted powder was digested with popsin for form a solubilized ECM hydrogel as previously described [27]. Briefly, the UBM-ECM powder was solubilized by partial enzymatic digestion in a 1 mg/mL repsin (Sigma-Aldrich) solution in 0.01 N HCl for 48 h at a concentration of 10 mg ECM/mL solution (dry wt/vol). To prepare chitosan:ECM scaffolds, the solubilized ECM was brought to physiologic pH by adding 1/10 the volume of 0.1 N NaOH, the solution was combined with the chitosan solution at either 80:20 or 50:50 ratios of chitosan to ECM. The resulting slurries of either chitosan and ECM powder or chitosan:ECM gel were poured into 60 mm petri dishes (approx. 5 mL per dish) and then frozen and lyophilized forming a sponge like scaffold. Scaffolds consisting of 100% chitosan a were also prepared in a similar way for comparison control. Scaffolds consisting of 100% ECM hydrogel were not used as the lyophilized material proved too friable to make a handleable dressing and would rapidly dissolve when placed in a wet environment.
# Advanced Technologies for Geosite Visualization and Valorization: A Review
## 4 Conclusions  
Especially in the last decade, there has been an increasing interest in geoineritage and its main expressions, geosites, which can have aesthetic, cultural, economic, touristic, educational and scientific relevance. Many papers have been dedicated to the assessment of geosites all over the world. Besides the assessment, there have been many efforts by authors to come up with interactive and multimedia tools aimed at the visualization and valorization of geosites. Such tools belong mainly to three categories: the building of Virtual Geosites (VGs), the Geographic Information System (GIS) environment, and Virtual Reality (VR). In our contribution, we have tried to shed light on the most relevant and cutting-edge papers dedicated to enabling the lay public, as well as Earth Science academics and students, to visualize geosites, almost always arranged in virtual geotours, so as to valorize the related geological heritage. It is also worth noting the importance and potential of a multidisciplinary and multicultural approach that the use of these technologies in a GIS environment supports, both in a geological and naturalistic context. In view of the above, VR plays a pivotal role: users have a chance to hover above ad-hoc created geological environments, by using ad-hoc devices that facilitate the overall experience. The viewers, in this way, experience an overall view of a VG, allowing them to examine particular elements  
Figure 3: The 3D model of a major geosite in Greece, the Metaxa Mine on the Island of Santorini. Modified after [PERSON] et al. [92].  
of geosites at diverse aerial scales. In this regard, the pioneering paper by [PERSON] et al. [118] has attempted to obtain a statistically relevant evaluation of how scientists and students experienced immersive VR for geology-related education. Their results indicate that most participants would be keen on repeating the VR experience; most importantly, the majority of students and academics who participated in the experience confirmed the high value of this method for geo-education purposes.  
Conceptualization, F.P.M., N.C. and K.D.; methodology, F.P.M.; writing--original draft preparation, F.P.M.; writing--review and editing, F.P.M., N.C., K.D. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
We wish to thank the anonymous reviewers, whose insightful comments and suggestions have significantly helped us to improve our work.  
The authors declare no conflict of interest.
# CFD-DEM Simulation of Dust Deposition on Solar Panels for Desert Railways
## 2 Coupled CFD-DEM Mathematical Equations  
### CFD Equations  
The governing equation of the airflow around the solar panel can be expressed by the Reynolds-averaged Navier-Stokes equation, i.e.,  
\[\frac{\partial\overline{u}_{i}}{\partial x_{i}}=0 \tag{1}\]  
\[\frac{\partial\overline{u}_{i}}{\partial t}+\overline{u}_{j}\frac{\partial \overline{u}_{i}}{\partial x_{j}}=-\frac{1}{\rho}\frac{\partial\overline{p}}{ \partial x_{i}}+\frac{1}{\rho}\frac{\partial_{i}}{\partial x_{j}}\Big{(}\tau_{ ij}^{m}+R_{ij}\Big{)}+f_{i} \tag{2}\]  
where \(i,j=1,\,2,\,3,\,\overline{u}_{i}\) is the \(i\)-th component of time average velocity, \(f_{i}\) is the \(i\)-th component of acceleration, \(\textbf{R}_{ij}=-\rho\overline{u_{i}u_{j}}\) is Reynolds stress, \(\tau_{ij}^{m}=2\mu S_{ij}\) is molecular viscosity stress and \(S_{ij}\) can be expressed as:  
\[S_{ij}=\frac{1}{2}\bigg{(}\frac{\partial\overline{u}_{i}}{\partial x_{j}}+ \frac{\partial\overline{u}_{j}}{\partial x_{i}}\bigg{)} \tag{3}\]  
Equations (1) and (2) is closed by the realizable \(k-\varepsilon\) equation, which can be used to describe the boundary-layer-separated flow under a reverse pressure gradient, and the non-equilibrium wall function was used for the boundary flow calculation.
# Predicting Freeway Travel Time Using Multiple-Source Heterogeneous Data Integration
## 1 Introduction
### Overview of Prediction Method of Single Source Data  
A single data source was an earlier method used to predict travel time. Many prediction results used in research were obtained using a single data source.  
In 1997, [PERSON] [1] used occupancy and arrival time to predict travel time on a road. [PERSON] and [PERSON] [2] set statistical predictive algorithms to predict future travel time. [PERSON] and [PERSON] [3] employed data obtained from freeway detectors. [PERSON] et al. [4; 5] used inductive loop detectors to obtain the front position and capture interactions between trucks and non-trucks. However, fixed detector devices are easily influenced by the external environment and cannot directly access some important parameters, such as travel time, etc.  
In addition, many researches consider using GPS data to predict travel time. [PERSON] et al. [6] and [PERSON] et al. [7; 8] considered the diversity of GPS data and investigated the application of Markov chain for travel time estimation and proved its accuracy. [PERSON], [PERSON] & [PERSON] et al. [9] used the GPS data from the current highest volume GPS data source and applied the TRIP method to predict travel time. Based on GPS data sets, [PERSON] and [PERSON] [10] proposed a prediction method for urban trunk lines that was only suitable for traffic conditions in India. GPS data only captures the sample vehicle speed and real-time position information and its' accuracy depends greatly on the number of sample vehicles.  
Clearly, these methods are innovations and improvements in travel time prediction, yielding more accurate results. However, many predictions that use a single data source do not consider the impact of other unexpected events, or the result was not accurate enough because a single data source cannot reflect the exact traffic state of a road network. It will result in certain errors between prediction results and true values.
# Smart Warehouses: Rationale, Challenges and Solution Directions
## 3 Review Protocol
### Research Questions  
With Industry 4.0, smart warehouses emerged, and many companies introduced different kinds of automation based on the smart warehouse concept. The main objective of our research is to present solid knowledge for companies that wish to build or transition to a smart warehouse in the near future. For this transition, first, there should be a clear motivation, and therefore, we defined our research question (RQ) 2 to identify these key motivations. As such, this RQ has the most impact compared to others. Once the motivation is established, the main features of smart warehouses must be identified. We defined RQ 3 to determine the distinctive characteristics of smart warehouses. When these features are selected, certain technologies are needed. Therefore, we defined RQ 4 to identify the adopted technologies for realizing smart warehouses. RQ 3 and RQ 4 are critical to know how to transition to a smart warehouse. We also wished to know which sub-domains adopted smart warehouses so far. The impact of this RQ (RQ 1) has the least impact compared to the other RQs. There are several challenges while transitioning to smart warehouses and therefore, we defined a RQ (RQ 5) to get more information on these challenges. This RQ has more impact than RQ 1. Solid knowledge on these issues is critical for successfully transitioning to smart warehouses. In this review, the following research questions are defined:  
RQ1--What warehouse sub-domains adopted smart warehouse technology?  
RQ2--What are the key motivations for adopting smart warehouses?  
RQ3--What are the current distinctive characteristics of smart warehouses?  
RQ4--What are the currently adopted technologies for realizing smart warehouses?  
RQ5--What are the current challenges and strategies for transitioning to smart ware houses?
## 2 Materials and Methods  
### Hydrodynamic Model  
#### 2.1.1 Governing Equation  
This research utilizes the multi-architecture (multi-CPU and GPU) open-source two-dimensional flood model, known as TRITON, developed by Oak Ridge Laboratory of the United States [39]. The model is based on the analysis of the source term full shallow water equation and is used to simulate the spread of dam break floods. The grid utilized in this model is a Cartesian square grid derived straight from the DEM file, eliminating the need for constructing a dedicated computing grid.  
In this paper, the two-dimensional shallow water equation is used to simulate the propagation process of dam break flood, and its vector form is as follows:  
\[\frac{\partial\mathbf{U}}{\partial t}+\frac{\partial\mathbf{F}}{\partial x}+\frac{ \partial\mathbf{G}}{\partial y}=\mathbf{S_{r}}+\mathbf{S_{b}}+\mathbf{S_{f}}\]  
\[\mathbf{U}=\begin{pmatrix}h\\ q_{x}\\ q_{y}\end{pmatrix}\quad\mathbf{F}=\begin{pmatrix}q_{x}\\ \frac{q_{y}^{2}}{\hbar}+\frac{1}{2}gh^{2}\\ \frac{q_{y}q_{y}^{2}}{\hbar}\end{pmatrix}\quad\mathbf{G}=\begin{pmatrix}q_{y}\\ \frac{q_{y}}{\hbar}\\ \frac{q_{y}^{2}}{\hbar}+\frac{1}{2}gh^{2}\end{pmatrix} \tag{1}\]  
\[\mathbf{S_{r}}=\begin{pmatrix}r\\ 0\\ 0\end{pmatrix}\quad\mathbf{S_{b}}=\begin{pmatrix}0\\ -gh\frac{\partial z}{\partial x}\\ -gh^{2}\frac{\partial z}{\partial y}\end{pmatrix}\quad\mathbf{S_{f}}=\begin{pmatrix} 0\\ -\frac{gh^{2}}{h^{2}/3}q_{x}\sqrt{q_{x}^{2}+q_{y}^{2}}\\ -\frac{gh^{2}}{h^{2}/3}q_{y}\sqrt{q_{x}^{2}+q_{y}^{2}}\end{pmatrix}.\]  
In the formula, \(\mathbf{U}\) is a variable vector, including water depth \(h\), discharge per unit width \(q_{x}\), and \(q_{y}\) in the \(x\) and \(y\) directions; \(g\) is the gravitational acceleration; \(r\) is runoff rate; \(n\) is Manning roughness coefficient; and \(\mathbf{F}\) and \(\mathbf{G}\) are flux vectors in the \(x\) and \(y\) directions, respectively. \(\mathbf{S_{r}}\) is the runoff source term. \(\mathbf{S_{b}}\) is the source term of the bed slope. \(\mathbf{S_{f}}\) is the friction source term. \(z\) is the elevation of the bed.
## Materials and Methods
### Static Degradation Tests  
The corrosion tests on reference and PHB-coated Magnesium AZ31 alloy were carried out at 37 \({}^{\circ}\)C in a simulated body fluid (SBF), the composition of which in mmol/L is reported in Table 1, following metal corrosion test standard ASTM-G31-72. Specifically, solution volume-to-sample area ratio should be between 20 mL/cm\({}^{2}\) and 40 mL/cm\({}^{2}\); in our test, the selected ratio was 30 mL/cm\({}^{2}\). Identical reference and coated samples were immersed in separate flasks containing the same corrosion fluid (SBF). We daily collected SBF solution samples to measure their pH values (by electrochemical pH meter) and magnesium ion concentration (by inductively coupled plasma-mass spectrometer) and as a culture medium to run the biological tests. After daily sampling, SBF solutions were renewed for all samples over the entire period of the corrosion tests (15 days). Uncoated and coated samples' individual measurements had four repetitions. At one-day time intervals, a group of PHB-coated and uncoated AZ31 disks was daily removed from the SBF, gently rinsed, and cleaned with 180-200 g/L chromium trioxide for 5-10 min and then air dried for individual gravimetric measure of their daily weight losses as a function of SBF exposure time.  
According to gravimetric measurements and inductively coupled plasma-mass spectrometry, corrosion rates were respectively evaluated as daily weight loss (mg/cm\({}^{2}\) per day) and daily released Mg\({}^{2+}\) ion mass (mg/cm\({}^{2}\) per day). Moreover, following the ASTM-G31-72, corrosion rate was also evaluated as \(\upmu\)m/day both from gravimetric and mass spectrometry measurements.
## 1 Introduction
As such, all efforts shall be made to minimize sample disturbance. Ideally, RQD should be logged in the splits prior to boxing or transportation. The following are important notes on the measurement of RQD length: \begin{table}
\begin{tabular}{c c c} \hline \hline
**RQD \%** & **Rock Mass Classification** & **Description** \\ \hline \(<\)25 & Very poor & Disintegrated/soil \\
25-50 & Poor & Shattered, very blocky \\
50–75 & Fair & Blocky and seamy \\
75–90 & Good & Massive, slightly blocky \\
90–100 & Excellent & Intact \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Using EUROCODE 7-1 classification of rock types and rock mechanical designations [10]. Figure 1: Method for calculating RQD with coring [8]. Figure 2: (**a**) Very poor and (**b**) excellent borehole from Mörägy Granite Formation. * RQD length is only measured in core samples that are deemed competent and have a Field Estimated Strength (FES) of at least 1 MPa (R1). * In core samples with an FES less than 1 MPa, the RQD length should be logged as 0, i.e., RQD = 0%. * Even if they are longer than 100 mm, core pieces that are not \"inherently intact and sound\" will not be included in the RQD length computation. * Any highly or extremely weathered intervals and/or extremely weak intervals shall be given an RQD length equal to zero. * Fractures parallel to the core axis or that can be attributed to handling breaks, shall be ignored when measuring for RQD length. Similar to Recovery Length, the percentage should be computed immediately rather than later, ensuring any evident calculation errors can be quickly identified and resolved (e.g., an RQD of greater than 100%). Recent advancements in geotechnical testing, computational modeling, and automated data analysis provide opportunities to address these limitations. Modified RQD methods, such as those incorporating weighted joint density (RQD\({}_{\text{wjd}}\)), weak zone adjustments (MRQD), and discrete fracture network (DFN) modeling, have been proposed to enhance the reliability of RQD. However, these developments remain underexplored in terms of their practical application and integration with traditional geotechnical workflows. Furthermore, there is a lack of systematic evaluation of these methods in real-world scenarios, particularly in addressing complex geological conditions like fractured, anisotropic, or weak rock masses. This study bridges these gaps by presenting a comprehensive review of RQD's evolution, from its original formulation to contemporary modifications, and evaluates their applicability in modern geotechnical practices. The novelty of this research lies in its systematic analysis of modified RQD methods, emphasizing their practical implications for drilling, design, and production. By integrating recent advancements such as DFN modeling, seismic RQD techniques, and machine learning-based approaches, this paper provides a framework for more robust and versatile assessments of rock mass quality.
## 3 Theoretical Analysis
### Complex Relative Permittivity  
The complex relative permittivity \(\widetilde{\epsilon_{r}}\) of the saline water is derived according to the model proposed in [37] by the International Telecommunication Union (ITU) and is validfor evaluating the effect of salinity on radio transmission [38]; in particular, the real and imaginary parts of \(\widetilde{\epsilon_{r}}\) are computed through the following formulas:  
\[\epsilon_{r}^{\prime}=\frac{\epsilon_{ss}-\epsilon_{1s}}{1+(\frac{(f_{\rm GHz}) }{f_{1s}})^{2}}+\frac{\epsilon_{1s}-\epsilon_{\infty s}}{1+(\frac{(f_{\rm GHz} )}{f_{2s}})^{2}}+\epsilon_{\infty s} \tag{9}\]  
\[\epsilon_{r}^{\prime\prime}=\frac{(\frac{f_{\rm GHz}}{f_{1s}})(\epsilon_{ss}- \epsilon_{1s})}{1+(\frac{f_{\rm GHz}}{f_{1s}})^{2}}+\frac{(\frac{f_{\rm GHz} }{f_{2s}})(\epsilon_{1s}-\epsilon_{\infty s})}{1+(\frac{f_{\rm GHz}}{f_{2s}}) ^{2}}+\frac{18\sigma_{r}}{f_{GHz}} \tag{10}\]  
where \(f_{GHz}\) is the working frequency in GHz and \(\epsilon_{ss},\epsilon_{1s},\epsilon_{\infty s},f_{1s},f_{2s}\) are derived as  
\[\epsilon_{ss}=\epsilon_{s}e^{-3.3333\times 10^{-3}S+4.74868\times 10^{-6}S^{2}} \tag{11}\]  
\[\epsilon_{1s}=\epsilon_{1}e^{-6.28908\times 10^{-3}S+1.76032\times 10^{-4}S^{2} -9.22144\times 10^{-5}TS} \tag{12}\]  
\[\epsilon_{\infty s}=\epsilon_{\infty}(1+S(-2.04265\times 10^{-3}+1.57883\times 1 0^{-4}T)) \tag{13}\]  
\[f_{1s}=f_{1}(1+S(2.3232\times 10^{-3}-7.9208\times 10^{-5}T+3.6764 \times 10^{-6}T^{2}+\] \[3.5594\times 10^{-7}T^{3}+8.9795\times 10^{-9}T^{4}))\ {\rm GHz} \tag{14}\]  
\[f_{2s}=f_{2}(1+S(-1.99723\times 10^{-2}+1.81176\times 10^{-4}T))\ {\rm GHz}. \tag{15}\]  
In the previous equations, \(S\) is the salinity (in g/L or ppt) and T is the operating temperature in Celsius degrees, while the dimensionless terms \(\epsilon_{1},\epsilon_{s},\epsilon_{\infty}\) and the Debye relaxation frequencies \(f_{1},f_{2}\) are calculated as  
\[\epsilon_{s}=77.66+103.3\Theta \tag{16}\]  
\[\epsilon_{1}=0.0671\epsilon_{s} \tag{17}\]  
\[\epsilon_{\infty}=3.52-7.52\Theta \tag{18}\]  
\[f_{1}=20.20-146.4\Theta+316\Theta^{2}\ {\rm GHz} \tag{19}\]  
\[f_{2}=39.8f_{1}\,{\rm GHz} \tag{20}\]  
with \(\Theta=\frac{300}{T+273.15}-1\). Moreover, \(\sigma_{r}\) is computed from  
\[\sigma_{r}=\sigma_{35}R_{15}R_{T15}\ {\rm S/m} \tag{21}\]  
with  
\[\sigma_{35}=2.903602+8.607\times 10^{-2}T+4.738817\times 10^{-4}T^{2}- 2.991\times 10^{-6}T^{3}\\ +4.3047\times 10^{-9}T^{4} \tag{22}\]  
\[R_{15}=S\frac{37.5109+5.452165+1.4409\times 10^{-2}S^{2}}{1004.75+182.283S+S^{2}} \tag{23}\]  
\[R_{T15}=1+\frac{\alpha_{0}(T-15)}{\alpha_{1}+T} \tag{24}\]  
\[\alpha_{0}=\frac{6.9431+3.2841S-9.9486\times 10^{-2}S^{2}}{84.850+69.0245+S^{2}} \tag{25}\]\[\alpha_{1}=49.843-0.2276S+0.198\times 10^{-2}S^{2}. \tag{26}\]  
Once \(\widetilde{\epsilon_{r}}=\epsilon_{r}^{\prime}-je_{r}^{\prime\prime}\) is calculated, and knowning the angular frequency \(\omega\) of the propagating signal and the magnetic permittivity \(\mu\), the amplitude and phase constants \(\alpha\) and \(\beta\) can be derived according to Equations (4) and (5).
# SC-YOLOv8 Network with Soft-Pooling and Attention for Elevator Passenger Detection
## 3 Method
### SC-ConvModule
The feature maps fed into the SC-CSP_2 Conv module are first processed by the SC-ConvModule for feature extraction. Subsequently, the feature map processed by SC-ConvModule enters the Split layer, which is divided into multiple parallel channels. The data in these channels are processed by four consecutive SC-Bottleneck layers, and all the output feature maps from SC-Bottleneck are recombined in the Concat layer to form a feature map with multi-channel information. Finally, this merged feature map passes through an SC-ConvModule again for final feature extraction and transformation to obtain the output of the SC-CSP_2 Conv module. network model. It leverages the residual concept, conducting two convolutional operations on the original input to extract features, followed by element-wise addition with the original input. The advantage of the residual concept lies in its ability to preserve the essential features of the original input and mitigate the vanishing gradient problem. The bottleneck employs multiple small convolutional kernels to replace a large convolutional kernel, deepening the network and simultaneously reducing the number of parameters compared to the original structure. This not only enhances the depth of the network but also decreases the overall parameter count. This paper replaces the ConvModule in the bottleneck with the SC-ConvModule. The transformed bottleneck is referred to as the SC-Bottleneck, as illustrated in Figure 8.
## 3 Test Results and Discussion  
### Strength of Mortar Produced with (D)PAOs and Zeolite  
The mixing ratio of (D)PAOs was determined in the preliminary characteristics experiment by analyzing and comparing fluidity and strength measurements. The results showed that the fluidity was not significantly affected by the addition of (D)PAOs, but the compression strength was 10% higher when (D)PAOs were added.  
In the present study, mortar was produced according to mixing ratios as follows: while the mixing ratio of (D)PAOs was set to 10%, the mixing ratio of zeolite was adjusted to be 0%, 5%, and 10%. Subsequently, the compression strength of each mortar produced was measured and compared. The strength test results of these mortar products mixed with (D)PAOs and zeolite are shown in Figure 6. Here, the mixing of 10% (D)PAOs led to an increase in compression strength by 6.2% due to the effect of flocs that were generated during the formation of microorganisms. When 5% or 10% zeolite was added along with 10% (D)PAOs, the strength was lower than that of the (D)PAOs-added mortar. This phenomenon is considered to be due to the delay of an early hydration reaction caused by the addition of zeolite, which has a lower density and smaller content of CaO than cement [5]. When compared to plain mortar, however, the product mixed with 5% zeolite and 10% (D)PAOs and the product mixed with 10% zeolite and 10% (D)PAOs showed strength increases of 1.02 MPa and 1.88 MPa, respectively [3]. In other words, these (D)PAOs/zeolite-added mortar products exhibited compression strength equivalent to or higher than that of plain mortar products.