esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 The Technologies
### Adsorption Processes to Remove Hazardous H\({}_{2}\)S from Gas Streams
An activated carbon-fiber mat containing both metal chlorides and metal oxides is used to investigate H\({}_{2}\)S (and ammonia) removal [43]. The adsorbent, containing NiCl\({}_{2}\) and MgO, has a hierarchical porous structure, large surface area, and a good dispersion of bimetallic active sites. Metal oxides promote the chemical adsorption of H\({}_{2}\)S. The use of these ternary hybrid materials increases gas uptake with breakthrough capacities up to 209.2 mg/g. Adsorbent materials from banana empty fruit bunch biochar (BEFBB) and banana peel biochar (BPB) wastes are used to remove H\({}_{2}\)S from biogas [44]. Both types of adsorbents have good characteristics to remove H\({}_{2}\)S (low performance in the case of CO\({}_{2}\) and methane), with breakthrough capacities of 7.65 mg/g and 5.85 mg/g, respectively. Pellet-sized adsorbent (0.5 cm) has a better gas removal efficiency than larger pellets (1.5 cm), this being attributable to its larger surface area. Both hydroxide and carboxylic groups present in the BEFBB material are responsible for the removal of H\({}_{2}\)S from the gas stream. Spent Fenton-like reagents are another type of material to be considered as potential materials to remove H\({}_{2}\)S. Rice straw biochar and a spent Fenton-like reagent are used to prepare an adsorbent to investigate the removal of toxic H\({}_{2}\)S [45]. With the inclusion of the Fenton reagent, the number of active sites on the biochar is increased, and the adsorbent specific surface area is decreased, the maximum gas uptake being up to 1000.6 mg/g at 120 \({}^{\circ}\)C. The presence of copper oxide in the adsorbent is responsible for the gas adsorption, whereas cuprous sulfide, copper sulfide, and elemental sulfur appeared in the adsorbent after hydrogen sulfide removal. The use of copper and biochar produced a low cost H\({}_{2}\)S adsorbent (Cu2.56 WRS300), which is used in the adsorption of H\({}_{2}\)S [46]. Again, the presence of copper leads to the formation of copper oxide sites on the surface of the hybrid material, and these active sites increase H\({}_{2}\)S uptake onto the adsorbent, with a gas uptake of 1191.1 mg/g. Chemisorption is the key step in the removal of the hazardous gas from the stream. The hybrid adsorbent presented convenient regeneration.
# A Goal-Driven Evaluation Method Based On Process Mining for Healthcare Processes
## 5 Case Study
### PM Analysis and Generating Answers to Questions
#### 5.5.2 Proposals for Process Improvement  
The surgery process consists of five main activities; admission to hospital, surgery started, surgery finished, transfer to a service, and discharged, which are expected to take place sequentially. Depending on the nature of process or state of patients, between these activities, other activities may also occur (e.g., change service/department or transfer to the intensive care unit or being ex) or loops between them.  
In this case study, the timeliness of the surgery process was associated with some basic indicators about the process, variants, deviations, bottlenecks, and inconsistencies. In some of the event logs, skipping of events, missing events, and rework loops (repetitions) of surgery process were detected. There were two bottlenecks resulting from the absence of certain records in pre-operative and post-operative process. Lack of resources and staff or nature of specialty would explain the steps of the process that were delayed. Inconsistencies related to admission and surgery of patient were also observed. Using and integrating RFID tags that record the times for starting and finishing surgery of the patient and visualizing this information could help to increase the quality of care in the hospital.  
Finally, new questions were defined based on the bottleneck and inconsistencies identified; e.g., \"What are the reasons for deviation from the defined process?\" and \"Are the records accurate?\" These questions allow checking conformance to specifications based on actual behavior, and determine or manage the steps of HIS in which deviations take place. Another question is \"How would the process look if we only included patients that stayed at the hospital for more than 21 days?\". Here, we could specify a performance target to examine and visualize the cases that did not meet the target value using the performance target analysis.
# Risk Factors for Distortion Product Otoacoustic Emissions in Young Adults
###### Abstract  
Young adults with normal hearing may exhibit risk factors for hearing loss. The purpose of this study was to evaluate how self-reported personal music (PM) system volume use, preferred listening level, and self-reported alcohol use affects distortion product otoacoustic emissions (DPOAEs). Two-hundred, sixteen young adults, 161 women and 55 men, participated. Questionnaire data included the PM system and alcohol use. DPOAEs were obtained from 1-6 kHz and collapsed into 1/3 rd octave bands and a probe microphone was used to determine preferred listening level. Alcohol was defined as drinks per month (DPM), categorized as No, Light (\(\leq\)14), and Heavy (\(>\)14). Men who reported loud/very loud volume use had statistically significant lower DPOAEs at 1.5, 2, and 3 kHz than men who reported lower volume use. Light and Heavy DPM men had lower DPOAEs at 1.5, 2, and 3 kHz than no DPM men, but this was not statistically significant. There were no DPOAE differences for either variable in women and there was no association between preferred listening level and DPOAEs for women or men. Men who reported loud/very loud volume use and any DPM had poorer mid-frequency DPOAEs. There was not an association for volume use or DPM and DPOAEs in women.  
distortion product otoacoustic emissions; recreational noise exposure; alcohol; young adults; normal hearing Article
# Soriasis: From Pathogenesis to Pharmacological and Nano-Technological-Based Therapeutics
## 5 Nanotechnological Approaches for Psoriasis
### Polymeric Nanoparticles (PNPs)
#### 5.1.3 Dendrimers  
Dendrimers are a type of nanoparticle, characterised by being spherical, monodisperse and multivalent macromolecules. Their structure allows some unique features, such as their good solubility, biocompatibility, and a good reactivity ratio [132]. Moreover, depending on which structure is designed, the drug delivered could be encapsulated or as a covalently conjugated drug form, preventing any chemical or biological degradation. In addition, their structure permits the use of different drug release mechanisms [133].  
[PERSON] et al. studied the ability of polypropylene imine dendrimers to transport dithranol for topical treatment [134]. Its formulation obtained a particle size of 8 nm, with the drug loaded in the dendrimers after an exposure of dendrimers to dithranol. There was a significant improvement in drug permeation, from 35% to 95% of drug penetration, and a decrease in skin irritation, compared to a solution of dithranol. Conclusively, dendrimers should be studied as possible candidates in drug delivery systems in the future.
# CQFormer: Cyclic Quasi-Dynamic Transformers for Hourly Origin-Destination Estimation
## 2 Literature Review
### Iterative State Estimations  
The iterative state estimation realizes continuous dynamic OD matrix estimation through auto-regressive error control, mainly using Kalman Filter (KF). This branch of methods has been widely explored since [PERSON] first introduced the concepts [37]. Since the assumption of linear correlation between variables required by ordinary KF is not satisfied in the OD estimation problem, the Extended KF (EKF) and Unscented KF (UKF) apply local linearization to the nonlinear problem by using local derivatives and sample points, respectively. The linearization process causes a considerable computational burden even for medium-size road network [38], and the Local Ensemble Transformed KF (LETKF) reduces the computational complexity by factoring the state variables into several parallellizable sub-states [39]. Inspired by [15], [PERSON] et al. introduced the quasi-dynamic assumption and presented quasi-dynamic EKF (QD-EKF) to improve the accuracy and stability of dynamic OD estimation [17]. Generally, the Kalman Filter provides an effective tool for tracking dynamic OD matrices, but the large-scale matrix multiplication in it is sensitive to the initial error, and the incomplete accuracy of the parameters (common in OD estimation) tends to result in amplification of the errors and ultimately leads to significant deviations.
# Performance Analysis of the Demand-Based Ventilation in a Nordic Apartment Building
## 2 Materials and Methods
### Building Description
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{
\begin{tabular}{c} **Room** \\ **Type** \\ \end{tabular} } & \multirow{2}{*}{**Component**} & \multicolumn{4}{c}{**4-Room Apartment**} & \multicolumn{4}{c}{**3-Room Ap.**} & \multicolumn{4}{c}{**2-Room Ap.**} & \multicolumn{4}{c}{**Studio Ap.**} \\ \cline{3-14}  & & **Size,** & **Airflow,** & **K-** & **Size,** & **Airflow,** & **K-** & **Size,** & **Airflow,** & **K-** & **Size,** & **Airflow,** & **K-** & **Size,** & **Airflow,** & **K-** \\  & & **mm** & **L/s** & **Value** & **mm** & **L/s** & **Value** & **mm** & **L/s** & **Value** & **mm** & **L/s** & **Value** & **mm** & **L/s** & **Value** \\ \hline \hline \multirow{3}{*}{Bathroom} & Air valve, 1 & \multirow{3}{*}{\(\mathcal{O}\)125} & N 1 & B 2 & \multirow{3}{*}{3.35} & N & B & \multirow{3}{*}{4.24} & \multirow{3}{*}{\(\mathcal{O}\)125} & N & B & \multirow{3}{*}{3.00} & \multirow{3}{*}{\(\mathcal{O}\)125} & N & B & \multirow{3}{*}{3.06} \\  & Air valve, 2 & & 15 & 0 & & & & & & & & & & & & \\ \cline{1-1} \cline{5-14}  & Air valve, 2 & & 14 & 14 & 2.86 & & & & & & & & & & & & \\ \cline{1-1} \cline{5-14}  & Air valve, 1 & & 13 & 13 & 2.91 & & & & & & & & & & & & \\ \cline{1-1} \cline{5-14}  & Range hood & & & & & & & & & & & & & & & & \\ \cline{1-1} \cline{5-14}  & Diffuser, 1 & & 17 & 17 & 4.91 & & & & & & & & & & & & \\ \cline{1-1} \cline{5-14}  & Diffuser, 2 & & 17 & 17 & 4.54 & & & & & & & & & & & & \\ \cline{1-1} \cline{5-14}  & Diffuser, 3 & & 100 & 8 & 8 & 2.41 & & & & & & & & & & & \\ \cline{1-1} \cline{5-14}  & Diffuser, 4 & & & & & & & & & & & & & & & & \\ \hline \hline \end{tabular}  
* N—normal airflow rate, 2 B—boosting airflow rate, 3 0.73—kitchen hood normal mode k-value, 4 2.44—kitchen hood boosting mode k-value.  
\end{table} 
Table 1: Airflow rates, k-values and the sizes of the main ductwork of the different size of apartment branches. Figure 3: The topology of ductwork in the apartment zones, and the locations of the control and balancing dampers, supply diffusers and exhaust units (valves and hoods) in the apartments.
## 7 Conclusions
**Funding:** This research was funded by FCT--Fundacao para a Ciencia e a Tecnologia, through projects UIDB/00532/2020 and UIDP/00532/2020 of CEFT (Centro de Estudos de Fenomenos de Transporte), and through PTDC/EMS-ENE/3362/2014 and POCI-01-0145-FEDER-016665--funded by FEDER funds through COMPETE2020--Programa Operacional Competitividade e Internacionalizacao (POCI) and by national funds through FCT. It was also funded by FCT through the CMAT projects UIDB/00013/2020 and UIDP/00013/2020 (CMAT--Centro de Matematica e Aplicacoes); and was funded by Fapesp-Fundacao de Amparo a Pesquisa do Estado de Sao Paulo, Fapesp-2017/21105-6 and Petrobras (Project 0050.0075367.12.9). Research was carried out using the computational resources of the Center for Mathematical Sciences Applied to Industry (CeMEAI), funded by FAPESP grant 2013/07375-0. **Institutional Review Board Statement:** Not applicable. **Informed Consent Statement:** Not applicable. **Data Availability Statement:** Not applicable. **Acknowledgments:** [PERSON] acknowledges the support by Faculdade de Ciencias Exatas e Tecnologicas--FACET--UNEMAT--MT. [PERSON], [PERSON], and [PERSON] acknowledge the support by ICMC--Instituto de Ciencias Matematicas e de Computacao, Departamento de Matematica e Estatistica, USP, Sao Carlos, SP. [PERSON] acknowledges the support by CEFT (Centro de Estudos de Fenomenos de Transporte). [PERSON] acknowledges the support by CMAT--Centre of Mathematics from the University of Minho. **Conflicts of Interest:** The authors declare no conflict of interest. **Abbreviations**  
The following abbreviations are used in this manuscript: \begin{tabular}{l l} PTT & Phan-Thien and Tanner \\ gPTT & generalised Phan-Thien and Tanner \\ Re & Reynolds \\ Wi & Weissenberg \\ \end{tabular}
# Research on Key Technologies of Elastic Satellite Optical Network Based on Optical Service Unit
## 2 Methods
### OISL-OSU Service Slicing
#### 2.2.4 Comparison of OISL-OSU and OISL-MPLS Technologies  
Table 4 displays the comparative analysis of OISL-OSU and OISL-MPLS. Since OISL-OSU implements hard-channel isolation, a stringent resource isolation mechanism ensures the exclusive transmission of service flows via fixed resource reservation (PBs) within the OPU. Thus, OISL-OSU eliminates the need for services to compete for resources, thereby reducing transmission jitter.
# Investigation of the Maturity Changes of Cherry Tomato Using Magnetic Resonance Imaging
## 1 Introduction
[PERSON] and [PERSON] [12] examined the pericarp regions of MR images of processing tomatoes classified as three different maturity groups (green, breaker-right red, and red) using a partial least square discriminant analysis and found the potential of MRI technique for tomato classification according to maturity. [PERSON] et al. [13] used a MRI technique to examine the morphological features and tissue characteristics of pericarp areas for three different cultivars of processing tomatoes. Tomatoes usually have four or five locules and a thick pericarp wall. However, cherry tomato varieties generally have two or three locules and have a thin pericarp wall. This is the first intensive trial on different cherry tomato cultivars harvested at six different maturity stages using MRI techniques. The objectives of this study were to examine the internal structure changes of intact cherry tomato harvested at six different maturity stages using MRI and to find a suitable MR image analysis method to classify tomatoes according to their maturity stages.
# Design and Implementation of a Multi-Band Active Radar Calibrator for SAR
## 1 Introduction  
Synthetic aperture radars (SARs) are an important remote sensing tool for the acquisition of quantitative information about the Earth's environment. Nowadays, there are L-, S-, C-, and X-band spaceborne SARs in orbit. Additionally, lower P-band and higher Ka-band spaceborne SARs will be launched in the near future. For quantitative application, external calibration should be executed, and the active radar calibrator (ARC) plays an important role in the external calibration of SAR instruments due to its adjustable radar cross section (RCS), capability of changing its internal delay, high RCS, and small size. So, ARCs have been developed for many famous spaceborne SARs such as Radarsat-1/2, TerraSAR, Sentinel, and Gaofen-3 [1; 2; 3; 4; 5; 6; 7].  
Most of the existing ARCs operate in a single band. For example, the ARCs developed for Radarsat-1/2, Sentinel, ERS-1, and Gaofen-3 operate in the C-band [1; 3; 4; 6; 8], the ARCs developed for TerraSAR operate in the X-band [2], the ARCs developed for ALOS/PALSAR operate in the L-band [9], and the ARC developed for Chinese HJ1C operates in the S-band [10]. Even for multi-band SAR systems, the ARCs are also developed as single-band instruments. For instance, the NASA/JPL DC-8 aircraft SAR operated at three frequencies (L-, C-, and P-bands), but the ARCs used for calibration operated at a single frequency (three L-band ARCs and three C-band ARCs were used) [11]. AnotherSIR-C/X-SAR operated in the L-, C- and X-bands; however, two L-band ARCs, two X-band ARCs, and three C-band ARCs were used for the calibration of the SIR-C/X-SAR [12].  
Single-band ARCs have been developed for SAR systems, even for multi-band SAR instruments. However, with the increase of spaceborne SARs, it is not convenient to develop special ARCs for a specific SAR or a specific frequency band SAR. Therefore, the concept of a new multi-band ARC for spaceborne radar calibration is proposed in this study. Of course, it is very difficult to develop such an ARC, especially when it has many operational frequency bands. Many key technologies such as multi-band antenna, multi-band gain stability, and a large receiver dynamic range must be implemented for the development of the multi-band ARC. We took many measures and overcame these key technologies to design and develop three multi-band ARCs, which work in the L-, C-, X- and Ka-, operational bands. The specifications such as RCS stability, RCS ripper, linearity, and dynamic range were confirmed by the measurements. The three multi-band ARCs will be used to implement the calibration campaigns of Chinese Gaofen-3 SAR, Shenzhen-1 SAR, Luojia-2 SAR, and so on.
## 2 Related Work  
### Water Demand Forecasting  
Water demand forecasting has been a popular research in recent decades. In earlier studies, statistics-based methods are widely used in water demand forecasting because of their good interpretability. For example, [PERSON] et al. [2] used ARIMA based on the harmony search algorithm to forecast water demand for a district metering area. [PERSON] [3] applied VAR to incorporate relevant factors affecting forecast results into the analysis to address short-term agricultural irrigation water use forecasting. While the results of these models are more interpretable, these models usually require the data to satisfy a certain mathematical distribution. In realistic forecasting scenarios, the data is often more complex, making the models less effective in predicting results.  
Compared to statistics-based methods, machine learning methods have the ability to deal with complex nonlinear relationships hidden in the data. According to the application of artificial intelligence, it can be divided into traditional machine learning and deep learning. Overall, traditional machine learning methods have made good progress in water demand forecasting. [PERSON] et al. [4] used Random Forest Regression to forecast monthly water deficit. [PERSON] et al. [5] designed parallel global optimization algorithms to optimize SVM parameters to solve complex water demand forecasting. However, such methods often require complex feature engineering and feature extraction, which is challenging in practice. In contrast, deep learning methods can learn features automatically in addition to handling complex non-linear relationships. As a result, more and more researchers are applying deep learning methods to water demand forecasting. [PERSON] et al. [6] predicted short-term urban water demands based on LSTM models. [PERSON] et al. [8] proposed a method to predict daily urban water demands by integrating multiple base models such as TCN and ARIMA to capture the complex non-linear correlation. [PERSON] et al. [7] proposed a hybrid CNN-Bi-LSTM model for predicting urban water demand, in which CNN was used to extract features from the weather data and Bi-LSTM used historical data and extracted features to make predictions.  
Most of the above methods are aimed at one-dimensional time series, so they only perform correlation analysis in the time dimension. When solving the current multi-dimensional time series, these methods are often unable to fully extract the data features due to model limitations, which makes the models have poor forecasting results.
# Rock Emissivity Measurement for Infrared Thermography Engineering Geological Applications
## 5 Results
### Igneous Rocks  
Igneous rock samples can be divided herein into two sub-groups: polished and smooth. The polished variety results from rock manufacturing processes aimed at their ornamental use, while smooth faces are proper of the natural aspect of rocks undergoing laboratory tests. Starting from the lower part of the range, polished granites are characterized by 0.83 \(<\)\(\varepsilon\)\(<\) 0.86, with no relevant distinction between the slightly different mineral composition and color of tested rocks. This result is in accordance with the available literature data on the same polished rock type, reporting an average emissivity of 0.849 [20]. Smooth light granite samples are characterized by an average 0.92 emissivity, while the darker variety  
Figure 4: (**a**) Emissivity ranges resulting for each tested rock type; (**b**) statistical plot of the emissivity ranges for the three sedimentary, igneous, and metamorphic rock groups. Whiskers represent maximum and minimum values, while dots indicate the average emissivity for each rock type (acronyms are listed in Table 2).  
(red granite) shows a wider range, partially overlapping with the previous one (Figure 4a), with a 0.935 mean value. Basalt rocks rank above the holocrystalline samples, with a mean \(\varepsilon\)= 0.94 for the massive variety and 0.965 for the vesicular one, followed by the highest tuff value (mean \(\varepsilon\) = 0.975).
## 2 Materials and Methods  
### Dynamic Equations of the Catenary Pantograph System  
The pantograph-catenary system is initially composed of two subsystems interacting with each other under restricted conditions. The dynamic equations for a time instant \(t_{n}\) are shown in Equation (1), where \(M\) is the mass matrix, assumed to be constant; \(C_{n}\) is the damping matrix; \(K_{n}\) is the stiffness matrix; \(\varphi_{n}\) is the matrix of the constraint conditions; \(R_{n}\) is the vector of external loads on the system; \(q_{n}\) is the vector of coordinates that define the position of the system; and \(\lambda_{n}\) is the vector corresponding to the restriction forces. All of the latter elements are assumed to be variables, either in totality or in some of their terms for each instant \(t_{n}\). This results in a non-linear system of differential equations of second order whose integration in time will yield the evolution of the coordinates \(q_{n}\) and contact forces \(\lambda_{n}\). The subscript \(n\) represents the value of variables at the instant \(t_{n}\).  
\[\left(\begin{array}{cc}M&0\\ 0&0\end{array}\right)\left(\begin{array}{c}\ddot{q}_{n}\\ \ddot{\lambda}_{n}\end{array}\right)+\left(\begin{array}{cc}C_{n}&0\\ 0&0\end{array}\right)\left(\begin{array}{c}\dot{q}_{n}\\ \dot{\lambda}_{n}\end{array}\right)+\left(\begin{array}{cc}K_{n}&\varnothing _{n}^{t}\\ \varnothing_{n}&0\end{array}\right)\left(\begin{array}{c}q_{n}\\ \lambda_{n}\end{array}\right)=\left(\begin{array}{c}R_{n}\\ 0\end{array}\right) \tag{1}\]  
When considering two series of spans, as shown in Figure 1, it is necessary to partition the system coordinates and the different elements of the equation. In the case of two pantographs, as represented in Figure 1, this results in the following structure:  
\[q_{n}=\left(\begin{array}{c}q_{c1}\\ q_{c2}\\ q_{p1}\\ q_{p2}\end{array}\right),\ \ \lambda_{n}=\left(\begin{array}{c}\dot{\lambda}_{1}\\ \dot{\lambda}_{2}\end{array}\right),\ \ K_{n}=\left(\begin{array}{c}K_{c1}&0&0 &0\\ 0&K_{c2}&0&0\\ 0&0&K_{p1}&0\\ 0&0&0&K_{p2}\end{array}\right),\ \ R_{n}=\left(\begin{array}{c}R_{c1}\\ R_{c2}\\ R_{p1}\\ R_{p2}\end{array}\right) \tag{2}\]  
where \(q_{c1}\), \(q_{c2}\), \(q_{p1}\), and \(q_{p2}\), represent, respectively, the coordinates of the two series of spans (subscript \(c\) for catenary) and the two pantographs (subscript \(p\)). A similar notation may be used for the stiffness matrix and the other terms of Equation (1). The mass and damping matrices will have a structure similar to that of the stiffness matrix according to Equation (2). The vector \(\lambda_{n}\) of the restraining forces or contact forces is divided into terms corresponding to the internal forces between each of the pantographs and catenaries of the two series of spans. The subscript \(n\) represents the value of variables at an instant \(t_{n}\).