esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### CFD Simulation
#### 2.4.1 Governing Equations  
For each simulated problem, ANSYS Fluent solves the mass and momentum equations. The differential form of mass and momentum equations is given, respectively, by Equations (12) and (13). Additionally, the energy Equation (14) is solved for the heat transfer problems. Therefore, the energy equation is not included in this study.  
\[\frac{\partial\rho}{\partial t}+\
abla\cdot\rho\overrightarrow{V}\ =\ 0 \tag{12}\]  
\[\rho\left[\frac{\partial\overrightarrow{V}}{\partial t}+\overrightarrow{V} \cdot\
abla\overrightarrow{V}\right]\ =\ -\
abla p+\mu\
abla^{2} \overrightarrow{V}+\frac{1}{3}\mu\
abla\left(\
abla\cdot\overrightarrow{V} \right)+\overrightarrow{F_{b}} \tag{13}\]\[\frac{\partial(\rho e_{t})}{\partial t}+\
abla\cdot\left[\stackrel{{ \rightarrow}}{{V}}(\rho e_{t}+p)\right]\ =\ \
abla\cdot\left[\lambda\
abla T+\left(\stackrel{{ \rightarrow}}{{\overline{\tau}}}\cdot\stackrel{{ \rightarrow}}{{V}}\right)\right]+\dot{S}_{g} \tag{14}\]  
where \(\rho\)--the fluid density; \(t\)--the time; \(\
abla\)--the Nabla operator; \(\stackrel{{\rightarrow}}{{v}}\)--the velocity vector; \(p\)--the pressure; \(\mu\)--the viscosity; \(\stackrel{{\rightarrow}}{{F_{b}}}\)--the body force per unit volume; \(e_{t}\)--the total internal energy; \(\stackrel{{\rightarrow}}{{\overline{\tau}}}\)--the viscous stress tensor; \(\lambda\)--the thermal conductivity; \(\dot{S}_{g}\)--the energy of other processes that is not represented in the total energy.
# Green Hydrogen Production--Fidelity in Simulation Models for Technical-Economic Analysis
## 3 Experimental Phase with Electrolytes  
During the experimental phase for obtaining hydrogen fuel by electrolysis of water, the research was based on the results of a preliminary study [37]. Initially, solutions were prepared with sodium hydroxide (NaOH) at a 1% concentration, sulfuric acid (H\({}_{2}\)SO\({}_{4}\)) at a 37% concentration, and sodium bicarbonate (NaHCO\({}_{3}\)) at a 1% concentration. It is worth noting that the electrochemical analysis was carried out in the Laboratory of the Faculty of Chemical Sciences. The results are summarized in Table 1, which presents the outcomes of the experimentation with sulfuric acid for electrodes both with a size of 50 cm\({}^{2}\).  
According to Figure 6a for an electrode area of 50 cm\({}^{2}\) and Figure 6b for an electrode area of 100 cm\({}^{2}\), it can be confirmed that at the moment hydrogen is generated, its weight is approximately double that of oxygen, as demonstrated by the graphs. Furthermore, as the distance between electrodes decreases from 39 cm to 1 cm, hydrogen production increases. In Figure 6a, there is a peak of 606 cubic centimeters (c.c.) at a concentration of 26.92%. Meanwhile, for Figure 6b, the maximum limit reaches 1217 c.c. at a concentration of 24.21%.  
Based on the study of the ELS500 Hydrogen Generator electrolyzer of the Micro-Grid of the University of Cuenca, to prepare a 1% potassium hydroxide (KOH) solution in 4 L of water, we dissolved 40 g of KOH. This is based on the following Equation (29):  
\[Concentration=\frac{\text{mass of solute}}{\text{volume of solution}}\times 100\% \tag{29}\]  
For a 1% concentration, the mass of KOH should be 1% of the solution volume in milliliters (4000 mL), resulting in 40 g of KOH required to achieve the desired concentration in 4 L of water.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Distance**} & \multicolumn{4}{c}{**cm**} \\ \hline  & & & **39** & **30** & **20** & **10** & **1** & **39** &  
Finally, in Figure 7, the three electrolytes under experimentation, NaHCO\({}_{3}\), NaOH, and H\({}_{2}\)SO\({}_{4}\), are synthesized. The experimental results indicate that the best electrolyte is sulfuric acid, with a 1 cm electrode spacing, an electrode area of 100 cm\({}^{2}\) each, and a hydrogen production of 1217 cc. The lowest quality electrolyte was sodium bicarbonate (NaHCO\({}_{3}\)). From the practices, it was determined that hydrogen production is also proportional to the electrode area; for example, with NaOH, an electrode area of 50 cm\({}^{2}\) produces 298 cc, while an area of 100 cm\({}^{2}\) produces 596 cc of hydrogen.
## 4 Discussion
### Comparisons with Past Studies  
Previous studies that examined 90-day readmissions after stroke have found readmission rates ranging from around 18% to 26% [12; 24; 29; 30] even though the inclusion criteria and outcome measures  
Figure 3: Performance of various ML models in predicting 90-day readmission or mortality in patients hospitalized for stroke or transient ischemic attack. Prediction models are ordered according to the area under the receiver operating characteristic curve (AUC).  
Figure 2: Heat map showing the area under the receiver operating characteristic curve (AUC) across the ML techniques and resampling methods. In the heat map, the red color indicates high, yellow intermediate, and green low values of AUC.  
varied slightly across studies. The rate of readmission or mortality in this study was similar to those in previous reports. However, this study differed from previous readmission models in that only the variables available upon stroke admission were used to build the prediction models.  
Several prior studies have investigated readmissions in patients with stroke admitted to post-acute-care or inpatient rehabilitation facilities [21; 22; 31]. Even though their predictor variables were more informative and generally included past medical history, comorbidities, complications during acute care, and factors related to post-acute care [21; 22; 31], their values of AUC were not much different from those in this study. For example, a large retrospective study of 803,124 patients with stroke using inpatient rehabilitation facility functional outcome data achieved AUC values ranging from 0.553 to 0.694 in predicting 30-day readmissions [21]. In other words, the prediction of readmission after stroke is not a trivial task. This is probably because various patient clinical and social characteristics are associated with readmission and these characteristics are not always routinely collected in clinical databases [32].  
In addition to readmission models for stroke, a systematic review found that readmission models for various disease populations based on administrative data, clinical data, or both generally performed inadequately [33]. Those tested in large populations had a particularly poor discriminative ability with AUCs between 0.55 and 0.65. A study that developed readmission models for heart failure also reported that the use of ML algorithms did not improve prediction performance compared with traditional statistical models [34].
## 3 Results and Discussion
### The Best Values of Parameters
#### 3.2.3 Influence of Flow Rate  
Changing the flow rate has the slightest effect on the sorption process of silver and copper ions. The effect of feed flow rate was investigated by changing the feed flow rate from 2 and 3 to 4 and 6 cm\({}^{3}\)/min for silver and copper ions, respectively. The breakthrough curves at a constant bed height and constant initial concentration of metal cations are shown in Figure 10. The slope of the breakthrough curves increases as the flow rate increases. [PERSON] et al. conducted the biosorption of copper(II) ions. They noted that the flow rate had a significant influence on the course of the breakthrough curve [34]. Analyzing the effect of the flow rate on Ag\({}^{+}\) and Cu\({}^{2+}\) adsorption showed that full bed saturation was faster at higher flow rates of solutions. It follows that the degree of adsorption decreases as the flow rate of the adsorbate solution increases. At a low rate of influent, metal cations had more time for contact with vermiculite, which resulted in the increased removal of silver and copper ions from the column, and the efficiency of the sorption process improved. Furthermore, the lower the flow of the feed solution, the less wastewater is produced.  
Figure 8: Breakthrough curves for different initial concentrations of metal cations: (**a**) Ag\({}^{+}\) adsorption (h= 6 cm, Q = 3 cm\({}^{3}\)/min), (**b**) Cu\({}^{2+}\) adsorption (h = 6 cm, Q = 4.5 cm\({}^{3}\)/min).  
Figure 9: Breakthrough curves for different bed heights: (**a**) Ag\({}^{+}\) adsorption (C\({}_{0}\) = 4000 mg/dm\({}^{3}\), Q = 3 cm\({}^{3}\)/min), (**b**) Cu\({}^{2+}\) adsorption (C\({}_{0}\) = 4000 mg/dm\({}^{3}\), Q = 4.5 cm\({}^{3}\)/min).
## 3 Proposed Model: ABCDE Method and Integration with SSI Concepts
### Final Considerations  
As just mentioned, the dApp described in this section can be easily customized to be applied to the several phases present along the lifecycle of a building. So a system that notarizes the information flow associated with a building over its entire lifecycle can be developed. In addition, the integration of such an application with the concepts of the SSI paradigm allows users to realize a system for which all data associated with an entity--hence, with a person or a building--become verifiable credentials. Verifiable credentials guarantee that information is certified by an authorized entity that stores on-chain the proofs to allow a third party, the so-called verifier, to prove its validity; thus, the information is accepted as true and valid.  
In this model, all information is stored off-chain. So all documents/information associated with a building during its lifecycle are stored off-chain--for example, in CDEs--and also in digital wallets belonging to the owner of the building's DID. Information about all stakeholders--associated, hence, with a natural or legal, person--is stored off-chain in the digital wallets of the entity.  
On the contrary, the model stores on-chain only the data necessary for the notarization and for the exchange of verifiable credentials. Precisely, it stores only the data's hash and the cryptographic proofs of the issuers.
## 3 Methodology
### Dry Beach Length Measurement
#### 3.2.3 Calculation of Dry Beach Length  
The three-dimensional coordinates of the boundary, including the beach top and waterline, are projected onto the horizontal plane (XY plane), as shown in Figure 8. A three-dimensional point \(M(x_{M},y_{M},z_{M})\) on the waterline is projected onto the XY plane with the projection point \(m(x_{m},y_{m})\). A straight line \(l:y=kx+b\) is fitted using the beach top (L) projected onto the XY plane. The direction vector \(\overset{\rightarrow}{n}=(1,k,0)\) of the line is the direction of the dam axis. The horizontal distance from the waterline point M to the beach top, representing the length of the dry beach at point M, can be calculated using Equation (2), as shown in Figure 8.  
\[dis_{m}=\left|\frac{kx_{m}-y_{m}+b}{\sqrt{(k^{2}+1)}}\right| \tag{2}\]  
Figure 7: Pixel coordinates to 3D coordinates.
# An IoT-Based COVID-19 Prevention and Control System for Enclosed Spaces
## 1 Introduction
In addition, a path-planning algorithm presented thereinafter also seriously considers congestion control [15], i.e., as people attempt to move to an enclosed space, the algorithm provides a navigation service that conducts background congestion control. The congestion control also utilizes the booking algorithm. Furthermore, it is necessary to prevent COVID-19-positive individuals from entering several locations. If a COVID-19-positive individual idles, a great many virus particles may exist in the places where he passes. A path-planning algorithm accelerated by a Graphics Processing Unit (GPU) is introduced, ensuring that the individual swiftly reaches his destination. The system tracks users, and related information is sent to the risk assessment model. There is a high risk of infection in an area where a COVID-19-positive individual is located, but after he leaves, this risk will gradually decrease over time. The \"Sliding Window-Timer\" algorithm is responsible for risk level assessment. If a patient has entered an enclosed space before, other related people will be identified, and an infection risk level is assigned. In addition, the authorities can also obtain an analysis report for subsequent measures. The fundamental aspect of the system is the indoor navigation service. Considering cost-effectiveness and convenience, Bluetooth Low-Energy (BLE) devices (iBeacon) are selected as hardware support [16; 17]. With the received signal strength indication (RSSI)-based indoor positioning algorithm, the tracking of the real-time location of an individual becomes possible. Positioning technology based on the fingerprint database is introduced. Initially, in the offline stage, Bluetooth beacons are deployed; then, the RSSI values of the Bluetooth beacons are collected at reference points; the fingerprint of each reference point is confirmed step by step [18]. In the online positioning stage, the collected signals are transformed into real-time positioning fingerprints, and then the trained model is used to match the fingerprint in the RSSI fingerprint database; the positioning coordinates are determined with the matching results [19; 20]. We use KNN as the matching algorithm in the online stage. In this paper, we first review studies related to COVID-19 and indoor positioning. Then, a booking algorithm is introduced. After this, a means to establish an indoor positioning system is presented, which is the fundamental navigation service, and we also introduce the risk assessment model. By testing these parts, the reliability of the indoor positioning system and the performance of the path planning algorithm are demonstrated. Furthermore, privacy and compliance are considered in depth. The structure of the system is shown in Figure 1. The first layer illustrates functions from a user or manager's perspective. The middle layer presents related algorithms and models supporting the functions. The bottom is the hardware layer in which programs run.
## 3 Results and Discussion
### Quadrupole Time-of-Flight Mass Spectra  
The obtained oligomer's Total Ion Chromatogram (TIC) QTOF-MS spectra are depicted in Figure 4, with additional information being provided in Table 3.  
It can be seen that all but two oligomers present the [M + H]\({}^{+}\) molecular ion, with the exceptions being the PET pentamer and heptamer. For these two the sodium adjunct is instead present, with the m/z 983.199 and 1367.282, respectively. In addition, using Electrospray Ionization mode in positive mode (ESI+), an on-source ionization of all the oligomers to their open linear dimer (m/z of 273.095) was observed. This identification was confirmed using the ACD/Labs platform, which resulted in a matching score of 89.3%. The \"all ion MS-MS\" mass fragmentation patterns observed in the spectra of Figure 4 hint at a potential trend among the different PET cyclic oligomers. In fact, the even-numbered oligomers (dimer, tetramer and hexamer) seem to present specific patterns, fragmenting to the dimethyl terephthalate ion (the most abundant m/z 193.023, Figures 5 and 6). The only difference is that, while the dimer and tetramer fragment directly to that ion, the hexamer instead is fragmented either directly or in two-steps via the trimer (m/z 577.134). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**PET**} & \multicolumn{3}{c}{**1H ***} & \multicolumn{3}{c}{**13C ***} \\ \cline{2-7}  & **a** & **b** & **a** & **b** & **d** & **e** \\ \hline
**Cyclic dimer** & 7.66 & 4.84 & 129.88 & 62.10 & 133.29 & 167.19 \\ \hline
**Cyclic trimer** & 8.13 & 4.79 & 130.12 & 63.50 & 133.48 & 167.10 \\ \hline
**Cyclic tetramer** & 8.13 & 4.80 & 130.06 & 63.99 & 133.43 & 167.31 \\ \hline
**Cyclic pentamer** & 8.12 & 4.79 & 130.04 & 63.83 & 133.43 & 167.08 \\ \hline
**Cyclic hexamer** & 8.11 & 4.78 & n/a & n/a & n/a & n/a \\ \hline
**Cyclic heptamer** & 8.11 & 4.84 & n/a & n/a & n/a & n/a \\ \hline \hline \end{tabular}  
* For signal contribution, see Figures 2 and 3, respectively.  
\end{table} 
Table 2: Chemical shifts in ppm for the aromatic area in 1H and 13C NMR spectra, according to signal contribution of the molecule. Figure 3: 13C APT NMR spectrum (B) of the PET cyclic dimer (**1**), trimer (**2**), tetramer (**3**) and pentamer (**4**), recorded in CDCl3/TFA.
# A Blockchain Protocol for Authenticating Space Communications between Satellites Constellations
### Problem Statement  
Most of the global space agencies have begun to rely on launching satellites in the form of a constellation (or swarm) that work together to accomplish a specific space mission instead of relying on a single satellite [22]. Unlike a single satellite, a constellation can grant permanent global coverage everywhere at any time on Earth. Therefore, at least one satellite is available to respond to ground instructions at any time everywhere. Many types of satellite constellations have been launched for various missions, for example, navigational satellite constellations (e.g., global positioning system (GPS)). But, a satellite constellation is vulnerable to some cyber-attacks that aim to disrupt one satellite or all satellites of the constellation such as hacking, spoofing, interference, and jamming attacks [23]. For instance, hacking the very small aperture terminal (VSAT) (VAST is a small-sized ground station utilized to transmit/receive data, audio, and video signals over a satellite communication network, excluding television broadcasting) is the most common hacking technique which aims to access uplink and downlink data from/ to VSAT device [24]. Moreover, the failure to encrypt uplink/downlink data can expose satellites and ground stations to spoofing attacks [25]. In addition, jamming the control link of the uplink data may noise ground stations' commands sent to a satellite or a constellation system. On the other hand, hijacking and control of satellite signals transmitted between two satellites or between satellites and ground stations represent another important security challenge in space communication. Therefore, authenticating space transactions, especially regarding satellite constellation networks is one of the important issues. In response to these challenges, a novel blockchain protocol for managing and securing satellite transactions called Proof of space transactions (PoST) is proposed. This protocol is based on a new concept called space digital tokens (SDT). The main objective of the proposed PoST is to manage and authenticate satellite communications using blockchain.  
To achieve these objectives, the following assumptions have to be existing:  
1. Satellites revolve around the earth within a swarm (or a constellation). An international association or a space agency can manage each swarm.
2. The satellite communication within/between constellations is conducted through a peer-to-peer network.
3. The Blockchain statue is shared with all satellites in the same swarm.
## 6 Results and Discussions
### Packet Evaluation at Access Point 2  
The transmitted and received packets and the energy consumption of Access Point 2 when using each of the evaluated routing protocols are as follows:  
* Shortest-path routing method: Access Point 2 receives packets from Access Point 1. The cumulative number of packets received is shown in Figure (a)a. The number of received packets constantly increases until the simulation time reaches 200 s. Consequently, the cumulative receiving energy increases, as shown in Figure (c)c. At 200 s, the gain terminates since there are no more packets received. The transmitted packets from Access Point 2 to Access Point 4 are shown in Figure (b)b. The transmission expands until the simulation time is 200 s. The energy transmitted increases accordingly, as shown in Figure (d)d.
* Proposed SDN-based energy-aware routing method: Access Point 2 receives packets from Access Point 1 and transmits them to Access Point 4, as shown in Figure (a)a,b, respectively. The number of received packets increases cumulatively until 60 s, then arrests because the SDN controller decides to shift the routing path to the energy-aware one. At 160 s, the SDN controller returns to the shortest-path routing method. The packets are transmitted from Access Point 2 to Access Point 4 until 240 s; then the SDN controller shifts to the energy-aware routing path again, as shown in Figure (d)d. The transmission and receiving energies of Access Point 2 when using the SDN-based energy-aware path are shown in Figure (c)c,d, respectively. These energies increase cumulatively when the SDN controller shifts the routing path to the shortest one, being stable when the energy-aware path is used.  
Figure 11: Cumulative Packets and Energy of Access Point 2.
## 4 Simulator Development
### Converter Model  
In the HPS, a converter is required to regulate the DC bus voltage (low-side) while limiting the fuel cell current (high-side). In this manner, the HPS can maintain the DC bus voltage higher than the rated voltage of the load. Additionally, the fuel cell can operate at the desired operating point, despite significant variations in load demand. Based on these requirements, the buck converter block [26] in the Simscape library was modified with voltage and current regulators, and a current limiter.  
The modified converter block is a non-isolated type in which a switch such as a chopper regulates the current flow and an inductor stores energy when the current flows through it (see Figure 7). The output voltage of the converter can be adjusted according to a switching frequency. Since the switching frequency typically reaches several kilohertz, the time interval required to simulate the switching behavior is infinitesimal. Therefore, the switching model is extremely time-consuming.  
In contrast, another type of model, called the average model, is computationally inexpensive. This is because pulse-width modulation generator-based switches are replaced with a simple switching function in the buck converter block, which can be directly controlled by a duty cycle (see Figure 8). To adjust the duty cycle, the current regulator is installed in series with the voltage regulator. The voltage regulator block takes the reference voltage as an input and outputs the current demand to compensate for the voltage error.  
\begin{table}
\begin{tabular}{c c c} \hline Nominal voltage (V) & 23.1 & \(3.3\times 7\)S \\ \hline Rated capacity (Ah) & 20 & \(2.5\times 8\)P \\ \hline Initial state-of-charge (\%) & 50 & \\ \hline Battery response time & 30 & \\ \hline Max. capacity (Ah) & 20 & \\ \hline Cut-off voltage (V) & 17.5 & \(2.5\times 7\)S \\ \hline Fully charged voltage (V) & 29.4 & \(4.2\times 7\)S \\ \hline Nominal discharge current (A) & 20 & 1C \\ \hline Internal resistance (ohm) & 0.01 & \\ \hline Capacity at nominal voltage (Ah) & 16.8 & \(2.1\times 8\)P \\ \hline Exponential zone (V, Ah) & 26.18, 5.84 & \(3.74\times 7\)S \\  & & \(0.73\times 8\)P \\ \hline \end{tabular}
\end{table}
Table 5: Battery model parameters.  
Figure 7: Circuit schematic for converter model.  
The current demand is limited before being inputted into the following block. The current regulator block takes the limited reference current as an input and outputs the duty cycle that the switching function in the buck converter block takes as an input. Table 6 lists the parameters of the converter model.  
Through the converter block, the fuel cell stack block is connected to the battery block and load block (see Figure 9). The load block takes the power demand as an input and outputs the current demand that the controlled current source takes as an input. In this manner, the controlled current source simulates the load in the form of power versus time, which is called a power profile.