esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Findings and Discussion
### Quantification of Waste  
Considerable demand for PPE and single-use plastic has resulted in an increase in waste production. A study about the spatial distribution of PPE debris has demonstrated that the most-abundant debris items have been disposable gloves (44%), followed by face masks (31%) and disinfect wipes (25%) [1]. Furthermore, the monthly number of face masks has been reported to have increased from 10 [47] to 129 million masks [48]. Other studies reported that more than 85 million face masks are disposed of each day in Brazil [16], with 65 billion gloves consumed monthly [1].  
The increased production of waste related to PPE has been accompanied by an increased use of other medical waste and single-use plastics [3]; there has been increases of 350% and 370% in the amount of medical waste in regions such as Catalonia (Spain) and China, respectively [49]. Moreover, consumers prefer fresh food acquisitions in plastic packages to prevent infections, and restaurants have implemented pick-up services, increasing the production of waste plastic [2; 7].  
This increase in the use of single-use plastics goes against environmental sustainability, as well as the objectives included in different international agreements [5]: (i) the first circular economy action plan adopted by the European Commission in 2015, which identified plastics as a key priority and resulted in the European Strategy for Plastics in a Circular Economy; (ii) the Basel Convention and its amendment in 2019 to regulate transboundary movements of plastic waste; (iii) the United Nations Convention on the Law of the Sea to control the pollution of the marine environment produced by plastics; (iv) the United Nations Global Partnership on Marine Litter, a multi-stakeholder association created to define strategies to reinforce a reduction in plastics in the marine environment based on innovation for sustainable and green plastic solutions and to achieve an ocean free of macrolastic and microplastic pollution.
# A Deep Learning Model to Forecast Solar Irradiance Using a Sky Camera
## 3 Methods
### Cloud Cover Algorithm  
To include further details of the cloud cover retrieval, we have created a simple flow chart of the algorithm used to detect the cloud and sky pixels on images, as shown in Figure 5. This flow chart outlines the process of obtaining the cloud cover from sky images by using the distribution of the pixel values in images. The first step is to collect the sky images from the sky camera. These images are converted into a one-channel image in order to improve the contrast and to reduce the noise of the image by applying the RBR method; this method was proposed by [PERSON] et al. (2013) and is able to successfully detect the cloud  
Figure 4: The variability of GHI measurement for clear-sky day (8 June 2020), partly cloudy day (4 June 2020), and overcast day (24 June 2020).  
Figure 3: Day classification based on the distribution of mean clear-sky index (\(\mu_{kl}\)) and clear-sky index variability (\(\delta_{kl}\)).  
and sky pixels in an image. Mathematically, the RBR method is defined as the ratio of the red and blue channels of an image with the pixel value ranging from 0 to 255. It should be noted that the value of the blue channel is increased by 1 if it equals 0 to avoid dividing by 0 [14].  
After that, the threshold value is applied to the one-channel image to distinguish the clouds from the sky. We calculated the value using the Otsu thresholding method that [PERSON] proposed back in 1979 [15]. This method was chosen on account of its flexibility and robustness in identifying the cloud and sky pixels. Another advantage of this method is the simple process involved in determining the threshold values: since the calculation requires one-dimensional intensity data, the other parameters, such as the shape or geometric components of an object, do not affect the accuracy of the threshold value.  
It is worth noting that the basic concept in thresholding is to segment an image based on the difference between the pixel value. Therefore, a fixed threshold value cannot be applied to all-sky images because the brightness in sky images differs on account of the ever-changing position of the sun. To address these problems, the threshold value is adapted to all images, and this makes the value different in each image.  
Finally, the cloud cover was calculated using the ratio of cloud pixels to total pixels based on the percentage of the cloud pixels in the binary image. The measurement of cloud cover is reported in the number of parts of the sky covered by clouds. The sky can be split into 8 (oktas) or 10 (tenths) parts representing the amount of cloud in a particular sky. It should also be noted that cloud cover does not describe the cloud thickness and that it only refers to the amount of the sky covered by clouds in a particular location.
## 3 Results and Discussion  
Four water parameters were routinely monitored during the toxicity testing procedures, including temperature, pH, dissolved oxygen, and conductivity. Since the test was commonly performed under controlled conditions, at constant temperature, water temperature was commonly the least varying factor. The pH, dissolved oxygen, and conductivity are shown in Table 3, before and after exposure.  
\begin{table}
\begin{tabular}{c c} \hline
**Metal** & **Freshwater FCV** \\ \hline Cu & 0.960[e\({}^{0.8545}\)[ln(hardness)]\({}^{-1.465}\)] \\ Pb & 0.791[e\({}^{1.273}\)[ln(hardness)]\({}^{-4.705}\)] \\ Ni & 0.997[e\({}^{0.8460}\)[ln(hardness)]\({}^{+1.1645}\)] \\ Zn & 0.986[e\({}^{0.8473}\)[ln(hardness)]\({}^{+0.7614}\)] \\ Cd & CF [e\({}^{0.7852}\)[ln(hardness)]\({}^{-3.490}\)] \\ \hline \end{tabular} \(\overline{\text{CF}}=1.101672\)—[(ln hardness)(0.041838)] (revised from [PERSON] et al., 2005).  
\end{table}
Table 1: Derived final chronic values from dissolved water concentrations.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Compound** & **FCV (\#g/L)** & **Compound** & **FCV (\#g/L)** \\ \hline Acenaphthene & 55.85 & Chrysene & 2.042 \\ Acenaphthylene & 306.9 & Dibenz[a,h]anthracene & 0.2825 \\ Anthracene & 20.73 & Fluorene & 39.3 \\ Benzo[a]anthracene & 2.227 & Fluoranthen & 7.109 \\ Benzo[a]pyrene & 0.9573 & Inden[1,2,3-c,d]pyrene & 0.275 \\ Benzo[b]fluoranthene & 0.6774 & Phenanthrene & 19.13 \\ Benzo[k]fluoranthene & 0.6415 & Pyrene & 10.11 \\ Benzo[g,h,i]perylene & 0.4391 & Naphthalene & 193.55 \\ \hline \end{tabular} Source: U.S. EPA (2003) [41].  
\end{table}
Table 2: The final chronic values for the 16 PAHs.
## 3 Results
### Identification of RAM Degradation Product  
Based on the obtained MS spectra, the degradation impurity of RAM was verified. The relevant HPLC-ESI(+)-MS-TIC and HPLC-ESI(\(-\))-MS-TIC chromatograms, recorded using three different scan modes, are provided in Figure 3. All of them show only one signal at t\({}_{\text{R}}\) of about 7 min obtained for a completely degraded sample of RAM under the conditions of dry air (t = 90 h, T = 373 K). It meant that the tested sample contained only one compound, which was identified as RAM degradation impurity. The corresponding mass spectrum, provided in Figure 4, demonstrated one pseudomolecular ion at \(m/z\) = 399 for ES+ and at \(m/z\) = 397 for ES\(-\) (Figure 3). The value \(m/z\) stands for mass-to-charge ratio. As per electrospray ionization principle, the ES+ spectrum was obtained secondary to the attachment of one proton to the analyzed molecule, thus the mass of the resulting pseudo-molecular ion was increased by the mass of proton [M + H]\({}^{+}\) relative to the analyte (thus m\({}_{\text{analyte}}\) = 399 \(-\) 1); the charge of the molecule was +1. The ES\(-\) spectrum was, in turn, the result of molecule deprotonation [M - H]\({}^{-}\); therefore, the mass of the pseudomolecular ion was actually reduced by the mass of proton relative to the analyte (thus, m\({}_{\text{analyte}}\) = 397 + 1); the charge of the molecule was \(-\)1. Consequently, the mass of the analyzed compound was established as m = 398 u, as depicted in Table 2.  
Figure 4: Mass spectra of RAM degradation impurity (\(\rm{t_{R}}\) about 7 min).  
Figure 3: HPLC–ESI(+)-MS-TIC (**A**), HPLC-ESI(-)-MS-Scan (**B**), HPLC-ESI(+)-MS-Scan (**C**). Chromatogram for the analysis of RAM degradation impurity (t = 90 h; 373 K) in solid state.  
The mass of the analyzed compound, established based on the obtained MS spectra, matched with the molecular mass of RAM intramolecular cyclization product-a diketopiperazine derivative (DKP, chemical formula C\({}_{23}\)H\({}_{30}\)N\({}_{2}\)O\({}_{4}\), molar mass M = 398 g/mol), as shown in Table 2.  
It was finally concluded that the degradation of RAM under dry air conditions followed a single and rapid first-order reaction of intramolecular cyclization, producing a DKP derivative, as demonstrated in Figure 5. The reaction is in two stages. The first stage involves cis/trans transformation to adopt appropriate molecular conformation. Then, the escape of water molecules and the formation of new bonds occurs. A detailed discussion of the mechanism of DKP formation and its two-stage nature is provided in the Section 4.
# Pathway to Carbon Neutrality in the Cement Industry: CO2
## 2 Research Significance
### Environmental Parameters Affecting Carbonation Reaction
This effect can be explained by the fact that at high pressure, the volume of CO\({}_{2}\) adsorbed in the surface of the material is much higher than the volume at lower pressures, promoting a faster interaction between the compounds that can carbonate. As mentioned, a higher carbonation rate can lead to the formation of carbonated \"barriers\" on the surface of the materials, decreasing CO\({}_{2}\) diffusion and the speed of carbonation. The use of high concentrations allows more CO\({}_{2}\) to diffuse into concrete, increasing the carbonation depth and the respective carbonation rate. However, at high concentrations of CO\({}_{2}\), pore clogging can occur more easily due to the formation of more carbonates, affecting CO\({}_{2}\) diffusion. According to some authors, the ideal CO\({}_{2}\) concentration to maximize the carbonation process is around 20-30% [29; 30]. [PERSON] et al. [31] studied three different concretes exposed to different CO\({}_{2}\) concentrations, namely 2%, 10%, 20%, 50% and 100%, at different periods of time (7, 14, 28 and 56 days). The results indicate that the carbonation depth increased until 20% of CO\({}_{2}\) concentrations. Above 20%, the \"clogging\" effect affected the microstructure, reducing the CO\({}_{2}\) diffusion into concrete. [PERSON] et al. [32] studied the chemical changes and phase analysis of Portland cement pastes exposed to different CO2 concentrations (3%, 10% and 100%) and compared it with natural carbonation (0.03%) after a specified carbonation time. For 3% of CO2 concentration, the formed phases were similar to those from natural carbonation. This conclusion was obtained by comparing the Ca/Si ratio of the C-S-H gel of the uncarbonated sample (Ca/Si = 1.87), with that of C-S-H after 0.03% and 3% CO2 concentration. The Ca/Si of C-S-H was lower for 0.03% CO2, but this compound was not totally consumed by carbonation (Ca/Si = 1.23 and 1.18 for 0.03% and 3% CO2, respectively). However, at 10% and 100% concentration of CO2, the C-S-H gel decomposed due to the progressive depolymerization of C-S-H (that increases with CO2 concentration) and the formation of Ca-modified silica gel and calcium carbonate. Moreover, when carbonating at 0.03% and 3% CO2, there was still some evidence of ettringite content, which means that it did not fully react during carbonation. However, at 10% and 100% concentrations of CO2, no ettringite content was detected, suggesting that it was fully consumed after carbonation.
## 2 State of the Art
### Additional Features of Industry 5.0
#### 2.6.4 Cyber Physical Cognitive Systems  
Cyber Physical Systems (CPS) have gained popularity in recent years as a result of advancements in technologies such as Smart Wearable Devices, IoT, Cloud Computing, and Big Data Analytics. The communication between CPS with the help of IoT is the foundation for Industry 4.0. Cloud technology is used to store and exchange a large amount of data in an efficient and secure manner. Additionally known as a Cyber Physical Cognitive System (CPCS), cognitive methods are used in a variety of applications such as surveillance, industrial automation, smart grid, vehicular networks, and environmental monitoring to improve the performance of the system. In CPCS, learning and knowledge are the most important factors in making decisions. For Human Robot Collaboration (HRC) in manufacturing, the CPCS has been introduced. In collaboration with a robot and a human, the HRC completes component assembly in the manufacturing division. The integration of machine-human cognition is modeled and applied for this real-time collaboration work. In Industry 5.0, robots and skilled laborers are allowed to collaborate to produce customized products and services in a more effective manner [62].
###### Abstract  
A facile and direct method for synthesizing magnetic periodic mesoporous organosilae nanoparticles from pure organosilae precursors is described. Magnetic ethylene- and phenylene-bridged periodic mesoporous organosilica nanoparticles (PMO NPs) were prepared by nanoemulsification techniques. For fabricating magnetic ethylene- or phenylene-bridged PMO NPs, hydrophobic magnetic nanoparticles in an oil-in-water (o/w) emulsion were prepared, followed by a sol-gel condensation of the incorporated bridged organosilae precursor (1,2 bis(triethoxysily))ethane or 1,4 bis(triethoxysily)benzene), respectively. The resulting materials were characterized using high-resolution scanning electron microscopy (HR-SEM), high-resolution transmission electron microscopy (HR-TEM), energy-dispersive X-ray (EDX) spectroscopy, powder X-ray diffraction (XRD), solid-state NMR analysis, and nitrogen sorption analysis (N\({}_{2}\)-BET). The magnetic ethylene-bridged PMO NPs were successfully loaded using a ruthenium oxide catalyst by means of sonication and evaporation under mild conditions. The obtained catalytic system, termed Ru@M-Ethylene-PMO NPS, was applied in a reduction reaction of aromatic compounds. It exhibited very high catalytic behavior with easy separation from the reaction medium by applying an external magnetic field.  
periodic mesoporous organosilae nanoparticles; magnetic nanoparticles; ruthenium nanoparticles; catalysis; hydrogenation of aromatic compounds +
Footnote †: journal: Applied Physics Letters
# 5G Positioning: An Analysis of Early Datasets
## 3 Methods  
The section focuses on a description of the algorithms implemented to process the \(ToA\) observations. The available GINTO5G datasets include 5G time of arrival (\(ToA\)) observations (Figure 5, left), their signal-to-noise ratio (SNR) values, local Cartesian coordinates of the base stations, and the ground truth output from the total station. In the processing, the time difference of arrival (\(TDoA\)) values will be utilized as the observations. As specified in Section 2, the underlying assumption is that all the base stations are synchronized; hence their clock offsets are set to zero. The clock term of the receiver can be eliminated by differentiating two simultaneous \(ToA\) observations. In fact, given the receiver \(R\) and the two \(ToA\) to two base stations  
\[ToA_{R}^{BS_{i}}(t)=\tau_{R}^{BS_{i}}(t)+dt_{R}(t),ToA_{R}^{BS_{i}}(t)=\tau_{R }^{BS_{i}}(t)+dt_{R}(t). \tag{2}\]  
choosing \(BSi\) as the reference station, the relevant \(TDoA\) is given by  
\[TDoA_{i,j}(t)=\tau_{R}^{BS_{i}}(t)-\tau_{R}^{BS_{j}}(t), \tag{3}\]  
Multiplying by the signal velocity \(c\), the observed \(ToA\) can be converted to metric pseudoranges:  
\[c\cdot T_{0}A_{R}^{BS_{i}}(t)=\rho_{R}^{BS_{i}}(t)+c\cdot dt_{R}(t), \tag{4}\]  
where \(\rho_{R}^{BS_{i}}\) is the geometric distance between the base station and the user receiver. Note that the differences of the metric observations, \(c\cdot TDoA_{i,j}\), are directly differences of distances.  
From a geometrical point of view, the pre-elimination of the clock in the \(TDoA\) values does not present any specific advantage with respect to the processing of undifferenced observations (see, for example, [34]); indeed, it is counter balanced by the reduction in the input observations. However, we decided to process the \(TDoA\) to investigate this specific approach, and also because it is very popular in the technical literature. Given these premises, our objectives in this study are:  
1. Compute the \(TDoA\);
2. Investigate possible strategies for the choice of the reference station in the \(TDoA\) (Section 3.1);
3. Estimate the positions using the least squares method in single epochs (Section 3.2);
4. Analyze the results and the statistics of the estimated trajectories with respect to the ground truth (Section 4).  
All the base stations of the experiment are at the same height, which is also approximately the height of the trolley. As was preliminarily discussed in the technical documentation of GINTO5G, with such a configuration, the estimation of the vertical  
Figure 5: \(ToA\) observations (**left**); processing flux diagram (**right**).  
coordinate causes an ill conditioning of the resulting system; therefore, only horizontal coordinates will be estimated.
Without a comprehensive energy management model, they may operate in less optimal conditions and add up the cost, or in uncoordinated ways and offset each other, either of which will harm the operation performance of the MES and waste these valuable assets of the MES. Meanwhile, rare cross-sectional researches that include them all are conducted. On these premises, based on the economic dispatch model of the power system, this paper proposes a novel day-ahead optimal energy management method for the MES with CCHP, P2G, and EV, to simultaneously promote the RES utilization, and the economic and energy-saving performance of the MES. At first, the operation of the CCHP plant, P2G plant, and the demand response model of the EV charging load under TOU schemes are modelled respectively. Among them, the modelling of the CCHP devices are based on the concept of \"Energy Hub\", and the demand response of EV charging load is based on the price elasticity matrix. Then, the model of these three ME applications are integrated in the holistic dispatch model of the MES. Based on the developed model, three objectives are formulated to simultaneously promote the RES utilization rate, and the economic and energy saving performance of the MES. Considering the proposed optimization model is mathematically a multi-objective, mixed integer non-linear model, which cannot be easily solved with classical mathematical techniques, the Elitist Non-dominated Sorting Genetic Algorithm II (NSGA-II) [26] is applied to find Pareto optimal results. The main contributions of this paper are threefold:  
1. The energy management of CCHP plant and P2H/G2P plant, and the demand side management of EV charging loads are modelled, and integrated in the energy management model of the MES as dispatch resources. 2. Base on the developed model, a multi-objective optimal energy management problem is proposed to facilitate the cooperation of the ME applications, and simultaneously promote the RES utilization, and the overall economic and energy-saving performance of the MES. The rest of this paper is organized as follows: Section 2 presents the operation models of the CCHP plants, P2H/G2P plants, and the demand response model of the EV charging loads. Based on the developed models, a multi-objective optimization model for the MES energy management is proposed in Section 3, and tested on cases studies in Section 4. At last, the paper is concluded in Section 5.
## 5 Conclusions  
In this paper, we have investigated the performances of thirteen existing QoE models over twelve open databases for QoE prediction in HTTP Adaptive Streaming. Based on the results of the evaluations, various findings were provided with important insights into the behavior/performance of the considered QoE models. In particular, it was found that the _LSTM_ model is most efficient, followed by the _SQI_ and _KSQI_ models. Interestingly, simple models such as _Guo_'s and _Vriendt_'s are also found to be stable and acceptable for most databases. For the three models of _P.1203_, _CQM_, and _biQPS_, they are quite effective for long sessions. It is expected that the findings presented in this paper are useful for researchers and service providers to assess the QoE of streaming services and evaluate delivery solutions in HAS. However, there are some open issues to be tackled in the future. First, the support for the popular HEVC coding format should be very much improved. Second, investigations with more databases of various viewing devices and session durations will be conducted to better understand and enhance the existing models. The impact of quality scores (e.g., different MOS scales) on database developments and QoE model performances should also be studied in more detail. In addition, emerging content types such as 360-degree videos and volumetric videos will be a new and hot direction to be considered.  
Conceptualization, D.N., N.P.N., T.C.T.; methodology, D.N., N.P.N., T.C.T.; software, D.N.; validation, D.N., T.C.T.; formal analysis, D.N., T.C.T.; investigation, D.N., N.P.N., T.C.T.; resources, D.N., T.C.T.; data curation, D.N., N.P.N., T.C.T.; writing--original draft preparation, D.N., N.P.N., T.C.T.; writing--review and editing, D.N., N.P.N., T.C.T.; visualization, D.N., N.P.N, T.C.T.; supervision, T.C.T.; project administration, T.C.T.; funding acquisition, T.C.T. All authors have read and agreed to the published version of the manuscript.  
This research was supported by JSPS KAKENHI Grant No. 22K12299 and the competitive fund of the University of Aizu.  
Not applicable.  
The authors thank [PERSON] for her kind helps in this study.  
The authors declare no conflict of interest
# An Autonomous Wireless Device for Real-Time Monitoring of Water Needs
## 3 Materials and Methods
### Hardware Description
#### 3.1.4 Gateway  
The Gateway is the element in charge of receiving the information from the nodes and taking it to the network. It consists of a microprocessor, a concentrator, and an antenna.  
First, the antenna receives the information sent by the node and communicates it to a Raspberry Pi 3 Model B+ (Sony, Pencoed, Wales) microprocessor through the IC880a concentrator, which includes a Semtech SX1301 chip.  
The base-station placements were at the edge of the rooftop on a building in the University of Huelva for the laboratory test, and on the rooftop of a cooperative for the field test. The base-stations employed in our experiments were ISM-868 MHz outdoor omnidirectional antennas (SIRIO GP 868 C and GP901C models).  
Then, the Raspberry Pi is responsible for processing all of the information necessary to upload the information to the network through the previously installed software.  
In this work, we estimated the consumption in a rural scenario using fixed positions from the end-device to the gateway, not under mobility conditions [32].