esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### ADMET Analysis  
The in silico analysis including absorption, distribution, metabolism, excretion, and toxicity (ADMET) is a useful tool to predict the bioavailability of the tested compound [50; 51]. The ADMET parameters such as solubility (logS), lipophilicity (logP), molecular mass (MW), and number of acceptors (HA) and donors (HD) of the hydrogen bond for compounds **1-3** and **6-7** were determined using the pkCSM software [35]. The toxicity was predicted by the lethal concentration values (LC\({}_{50}\)) and hepatotoxicityicity (Table 4).  
According to the ESOL solubility class [52], a logS value between \(-4\) and \(-2\) means that the substance is soluble in water. The tested compounds **1-3** and **6-7** show good solubility in water. The introduction of a methyl group or chlorine atom at the C2 position decreases the solubility. Compounds with the formyl (**3**) and hydroxyl (**6**) groups have a solubility comparable to that of 6,7-dichloro-5,8-quinolinedione **1**.  
All compounds (**1-3** and **6-7**) are characterized by a good oral availability according to the Lipinski rules, which means that they have low lipophilicity (logP \(<5\)) and molecular mass (MW \(<500\)). Moreover, the number of acceptors (HA) and donors (HD) of the hydrogen bond are below 10 and 5, respectively.  
The lethal concentration value (LC\({}_{50}\)) is a concentration of a compound causing the death of 50% of _fathead minnows_. If the LC\({}_{50}\) is less than 0.5 mM, the compound has high acute toxicity [35; 36]. The in silico analysis shows that the LC\({}_{50}\) for tested compounds (**1-3** and **6-7**) is in the range of 6.531 mM to 28.249 mM, which mean that they have low toxicity. It can be assumed that these derivatives do not interfere with liver function (Table 4).
# Earth-to-Earth Microwave Rain Attenuation Measurements: A Survey On the Recent Literature
## 4 Rainfall Measurements Through Backhaul Cellular Infrastructure
The length of the path links ranged from a few hundred meters up to 30 km, and the operating frequencies were ranged from 10 to 40 GHz based on the length of the path. For the validation of the data, measurement equipment such as a gridded gauge radar, which was adjusted every hour, had been already employed by the German Meteorological Service. Particular emphasis was given to the accurate determination of rain event by the signal attenuation signal and the wet antenna attenuation using schemes of [PERSON] et al. 2008 [63] [PERSON] et al. 2010 [64]. In Kuala Lumpur, Malaysia, rain attenuation and rainfall measurements were collected for 1.8 km of path link and for a period of one year from September 2017 to 2018 [65]. The measurements were conducted at 73.5 GHz (E-band), while three rain gauges had been installed along the path. The gauges were tipping buckets type with a sensitivity of 0.2 mm. A transmitter (Tx) and receiver (Rx) were equipped with Mini Link-6352 providing the minimum and maximum received signal level values every 15 min. This was the first experiment at E-band, and it was accomplished in a heavy rain tropical region. Another interesting research which used a deep learning approach relevant to recurrent neural networks (RNN) is reported in [66]. The key-points are (1) the rainfall estimation by taking into consideration only the minimum and maximum attenuation data in order to produce a constant weighted average method for calculating the actual attenuation and (2) that the proposed RNN model was developed by using a dishorometer's (OTT PARSIVEL1 located 20 km from Melbourne) data with an interval of 30 s that they are comparable to those data acquired from network operators. A more in-depth look reveals that the proposed model was far better than the typical estimation methods of attenuation from MLs. It is worth mentioning that very recent work by a research team in the Netherlands presented the possibility of monitoring general meteorological phenomena from various opportunistic sources including data from CML [67]. Several CML were used between 18 and 40 GHz, while maximum path links values were around 10 km. Data from smartphones, personal weather stations, and traditional sources were also used. The experiment took place in the Amsterdam metropolitan area, Netherlands, during 17 days between 6 June and 23 June 2017. Although each source presented limitations regarding the accuracy, it is a fact that the more data collected by sources, the better hydrometeorological monitoring can be achieved.
# CS Measures for Nuclear Power Plant Protection: A Systematic Literature Review
## 4 Literature Review
### Measures for NPP Protection
To support cyber attack response and effective risk assessment, reduce the risk of cyber attacks, and improve the resilience of NPPs, the authors of [36] proposed a finite-horizon semi-Markov general-sum game between a defender and an attacker to obtain the time-sensitive attack response strategy and the real-time risk assessment in NPPs. By applying the model and the method to a simplified digital feedwater control system for a generic pressurized water reactor, the authors showed that the defender's optimal strategy varies with different system states and different amounts of time remaining in the game. The authors concluded that more research must be conducted in order to collect datasets, understand real-world defender--attacker interactions, and integrate changes in operation due to malfunctioning or other unexpected occurrences. Unfortunately, it is not possible to conduct vulnerability tests directly on NPPs; this is because of the risks of adverse effects, which could provoke serious damages to the systems and interrupt their functioning. To circumvent this issue, testbeds are often used to test technical solutions. The authors of [18] suggested the design of a CS testbed for a Digital Plant Protection System (DPPS) and Plant Monitoring and Annunciator System (PMAS). Network connectivity was considered an important element for the analysis and design of the cyber security testbed. Unfortunately, the testbed has yet to be completed. Once completed, the authors have planned the development of a test to detect malware, such as APT for control systems, as well as a vulnerability test. The authors of [26] also presented the implementation of a cyber-physical testbed. Their testbed included the implementation of two simulated and interconnected Critical Infrastructures (CIs), namely, a power grid and a nuclear plant. Additionally, the following components were included in the testbed:  
* A simulated power market for providing the cost of the provided energy;
* An actual Programmable Logic Controller (PLC), which is interconnected with a specific bus of the power network;
* An emulated cyber network that interconnects and controls all of the aforementioned elements. The novelty of this system comes from interconnecting this diverse range of elements and, by doing so, aiding the understanding of the consequences of the interdependencies between the different systems. The authors showed that the effects of network parameters on a coordinated attack could be significant. The authors are planning to make use of the implementation to run more advanced experiments, which will include the actions of real operators in the cyber-physical testing/simulation process. Additionally, they plan to implement a set of countermeasures to tackle and mitigate the attacks based on the exchanged signals and their statistical analysis to detect anomalies.
## 3 Results
### Adsorption of Atenol and Propranolol  
The adsorption of AT and PR was carried out in the presence of AC and AC-OH. A commercial activated carbon (AC) was also evaluated for removing AT and PR from contaminated water. To investigate the parameter effects on the adsorption of pharmaceutical products, temperature, initial concentration, and pH were studied in terms of adsorption capacity. The results are shown in detail in the Supplementary File. As seen in Figures S2-S4, the high temperature was not suitable for the adsorption of AT nor PR molecules due to their limited temperature stability. The adsorption capacity increased as the concentration of pollutants increased to achieve the equilibrium phase, indicating its saturation [14; 26; 35]. Measurements on the effect of pH displayed that the adsorption of pharmaceutical molecules could have been performant in basic media, while the acidic solution avoided competition with a proton. The removal capacities of the samples, which had the same experimental conditions, i.e., the synthetic and the commercial AC, are reported in Figure 5. The results show that both samples adsorbed the pharmaceutical products in two distinct phases. A rapid adsorption process describes the first phase, while equilibrium steps characterize the second phase. The large number of active sites available at the AC surface for the adsorption of AT and PR explains this result. Meanwhile, over 60 min, the main part of these active sites became saturated by adsorbate, resulting in limited access to more molecules in the solution. Therefore, the second step was reached to achieve the adsorption equilibrium [14].  
Figure 4: DSC analysis of AC and AC-OH.  
A comparison of the adsorption capacity of commercial and prepared AC shows that AC synthesized from date stems produced a higher adsorption uptake (60%) than commercial AC (32%). This trend can be explained by the key role of treatment and carbonization in this work. It was found that our prepared AC's equilibrium time is longer than the commercial AC's. Interestingly, the best performance is observed for the AC-OH sample, where the adsorption capacity is over 93% for the removal of PR in 120 min. The adsorption kinetic is also faster with AC-OH because the equilibrium was reached after 60 min, and only 10 min is required to adsorb 56% of the PR. This result can be explained by the negative charge surface of AC-OH, as supported by the Zeta potential measurement. For the atenol molecule, the performance of AC and AC-OH is still higher than that of AC, but the adsorption was more difficult than for propranolol. Indeed, 67% of the AT was adsorbed on AC-OH compared to 36% and 23% for AC (from date stem) and commercial AC, respectively.
# Simulation Study on an ICT-Based Maritime Management and Safety Framework for Movable Bridges
## 1 Introduction
Recently in Australia, smart camera technology and data science methods have been implemented as a preliminary trial for water vehicle navigation control and security [17]. These studies motivate the implementation of advanced technologies for movable bridge navigation and security system. The current study proposes an ideal maritime management and safety protocol for movable bridges using the ship-to-bridge infrastructure communication system. In order to locate approaching ships and take corresponding decision measurements, the navigation zone of the movable bridge is defined into specific belts based on calculating the maximum ship speed limit and minimum bridge vehicle speed. An emergency clash avoiding control system is integrated with the proposed protocol that will prevent any probable collision between the bridge and ships. To evaluate the proposed protocol, a simulation system is developed specifically for maritime management of movable a bridge. Moreover, ships' transit behavior under adaptive cruise control (ACC) is demonstrated using the simulation system to ensure additional safety among ships. Furthermore, the transportation and litigation policy for this newly proposed maritime management and safety system is discussed with future improvement and potential implications. The subsequent sections in the paper elaborate on the proposed methods in detail and correlate each technology for ensuring spontaneous operation. The sections in this paper are organized as follows. Section 2 discusses the system components. Sections 3 and 4 specify the system considerations and principles of the study respectively. Section 5 explains the development of the simulation environment and the proposed methodology. Section 6 presents the results and discussion. The last section summarizes the outcomes with concluding remarks.
## 3 Use Case: Watson Analytics as AaaS to Identify HF Patients Analyzing Only the ECG Signal
### Material and Methods
#### 3.3.4 Data Analysis with Watson Analytics  
When HRV features dataset is ready, you can start the analysis with WA. Until now the actions are performed locally; from this point on, the dataset is ready to be uploaded to the cloud and analytics operations will be performed as AaaS. Watson Analytics accepts the most common matrix formats, such as.CSV,.XSL,.XSLX. After loading the dataset, the system assigns an index value of data quality, by considering the completeness of the fields, possible presence of constant values, low number of records compared to the columns and other qualitative factors.  
Now it is possible to process the dataset using the modalities offered by WA: \"Assemble\", \"Explore\", and \"Predict\" (see Section 2.10 above).  
In Figure 6 is shown an example of use of the Assemble feature, where the distribution of Target value (HF_State) is compared to an HRV parameter (pNN20).  
We can consider the Explore mode as a facilitator for the Predict mode. As seen in Figure 7, the Explore mode proposes some questions to the user in natural language. These questions are generated  
Figure 5: Abstract of current dataset in suitable format for analysis.  
Figure 6: Graphic representation of the distribution of a target, based on a parameter in Assemble mode.  
by relationships that WA automatically extracted from the dataset parameters (without setting any parameters as a target).  
In Explore mode, the user can also ask questions in natural language, as shown in Figure 8.  
What do you want to explore in \"long_analysis\"?  
The most interesting mode is \"Predict\" that allows supervised analyses by setting a prediction target. In this mode it is possible to inspect the predictive power of any other parameter.  
We created two different instances of the Predict module, one for the Long-Term HRV dataset parameters and one for Short-Term HRV dataset, as explained in Section 3.3.2.  
An interesting feature offered by WA is that, regardless of the type of dataset as target, it automatically chooses the most appropriate model to treat that type of data. In our case study, being HF_State a dichotomous variable, the system automatically selected the logistic regression model, as shown in Figure 9.  
Figure 8: Text questions typed by user to inspect data relations or distributions.  
Figure 7: Proposals for links automatically detected in “Explore” mode.
## 4 Engineering Application
### The Hybrid Model Verification
#### 4.2.3 Inversion of Mechanical Parameters  
First, an FE model was constructed using HYPERMESH (version 14.0) with the spatial coordinates of the model corresponding to those of the actual project, so that the node values of the model could be spatially interpolated according to the spatial position of the sensors to obtain the calculated values of the sensors. The foundation extends upstream and downstream, and the foundation depth is about two times the maximum dam height, with the X direction indicating downstream, the Y direction indicating cross-river, and the Z direction indicating vertically upward. The model was composed of hexahedral elements with a total of 5580 elements and 6234 nodes, as shown in Figure 8. Secondly, the material properties were initially defined, and the external load and constraints were set in GeHoMadrid (Version 1.0), the team's own FE analysis software, and then discrete iterations were performed in the time and space domains to obtain the calculated values under the actual boundary conditions. A linear elastic model was used in the simulation with fixed constraints around and at the bottom of the model. According to Section 4.2.1, the hydraulic component was separated from the displacement values, and therefore only the hydraulic load was considered in the forward calculation.  
In an FE simulation, multiple parameters can have an impact on the accuracy of the simulation, including density, elastic modulus, and Poisson's ratio. Therefore, it is necessary to find the most sensitive parameter for inversion. In this paper, the elastic modulus was chosen as the parameter of inversion by combining the engineering practice and the research of other scholars [30; 31; 32]. Based on the material parameters of the dam body and foundation, the elastic modulus of the dam body and the foundation in the inversion were set to 20-26 Gpa and 4-8 Gpa, respectively. The Poisson's ratios of the dam body and foundation were set to 0.167 and 0.25, respectively. The density rates of the dam and foundation were set at 24 and 25.7 kg/m\({}^{3}\), respectively. A multisensor inversion model was used with the following objective function:  
\[f=\frac{1}{mn}\sum_{i=1}^{m}\sum_{j=1}^{n}\left(c_{i,j}-d_{i,j}\right)^{2} \tag{19}\]  
where \(n\) is the time step of the simulation, \(c_{i,j}\) is the \(i\)-th sensor, with the calculated value of the \(j\)-th step; and \(d_{i,j}\) is the \(i\)-th device, with the measured value of the \(j\)-th step.
# Improving Performance of Cluster Heads Selection in DEC Protocol Using K-Means Algorithm for WSN
## 5 Simulation and Evaluation of DEC and DEC-KM Protocols
### Simulation and Evaluation of DEC-KM
#### 5.2.3 Simulation Results  
The results obtained for the DEC-KM protocol are shown in Figure 6.  
Comparing the simulation results in Figures 4 and 6, we see that network clustering is more effective for the DEC-KM algorithm. Distances between the nodes inside the cluster area are shorter than for the DEC protocol, and no single-node clusters exist.  
After applying the DEC-KM algorithm using the same parameters that implemented the original protocol, six cluster heads (in cluster areas CA3, CA6, CA7, CA8, CA9, CA10) remained in the same place as for the DEC protocol. However, some cluster areas have been changed. Cluster heads in CA1, CA2, CA4 and CA5 areas contain the newly chosen cluster heads depending on centroid points in the cluster areas and their energy level.  
Figure 5: Centroid points for selected cluster areas in the first K-means iteration.
# Education Mobility as a Service: A Study of the Features of a Novel Mobility Platform
## 2 Review of New Technologies in Education and Lifelong Learning
### E-Learning Platforms  
E-learning platforms are prevalent teaching aids among several dozen platforms. The two best known examples are Moodle and ILIAS. The Moodle platform (Modular Object-Oriented Dynamic Learning Environment) is one of the most popular e-learning platforms. It is an open type of software made available for free under the GNU GPL public license. It allows for course evaluations, surveys, questionnaires, tasks, and reviewing work, chats, forum posts, workshops, and also the ability to create collaborative texts [28,33]. Similarly, the ILIAS LMS (learning management system, i.e., a system that allows for the analysis of the users' progress (in tests, surveys, or reports)) platform is used to administer education and training processes. The platform includes the Personal Desktop and Available Resources (repository). While the resources available include courses and other structured materials described in the metadata, the Personal Desktop is the personal space of each platform user, teacher, author, or administrator. The personal dashboard includes selected items from the repository (e.g., courses a learner visits regularly or interesting forums) and tools such as e-mail, tags, calendar, e-portfolio, and private blogs. The essential functions of both platforms are student management (registration, progress reporting, evaluation of results, competency analysis), training management (access to training plans, access, and distribution of teaching materials). The platforms meet the requirements of both the SCORM (Sharable Content Object Reference Model--a technical standard that ensurescompatibility between e-learning and the learning platform). Currently, a modernized version of the SCORM that frees us from the obsolete constructs of the past is the Experience API--xAPI. It is designed as a successor to the SCORM at the most basic level. It allows educators to record any learning experience, including informal learning, giving us a much richer picture of an individual's learning path [34].  
Online e-learning platforms can be used to conduct independent online training or support or complement traditional forms of exercise. They are viable tools for teachers and administrators. They may be open-source software, available for download and installation on a given system, but it also happens that some organizations with the help of their own IT staff create plans solely for their own needs. They can adopt private solutions. It should be noted that most platforms are available in versions for mobile devices.
## 3 Establish Finite Element Numerical Model  
### Specimen Design and Loading Scheme  
Based on the traditional PSCFST pier, a PSCFST pier with an external replaceable energy dissipation ring is designed. The traditional PSCFST pier and the PSCFST pier with the external energy dissipation ring are named specimen A and specimen B, respectively. The test piece is composed of a bearing platform foundation, pier body and pier cap. The pier body of the precast assembled pier is divided into four segments: S1, S2, S3 and S4. The height of each segment is 334 mm. The rectangular section is adopted, the section size is 230 mm, the effective pier height is 1486 mm, and the axial compression ratio is 0.4 [18]. The pier cap and the bearing platform are connected by four 15.2 mm steel strands, the applied prestress is 103 kN (through calculation, we find that the ultimate bearing capacity of the PSCFST pier specimen is 1028 kN, so the working condition with initial prestress of 103 kN is adopted; that is, the axial compression ratio of the initial prestress is 0.1). The unbonded part is arranged in the center of the pier. A detachable energy dissipation ring member is set at the section interface, and the energy dissipation ring member and steel pipe section are fixed with a high-strength bolt cap. Q235 steel is used for the steel pipe section and energy dissipation ring, and the internal core concrete is C40 concrete. The structure of the test piece and the size of the energy dissipation ring are shown in Figure 7.  
For the traditional PSCFST pier, the size structure and prestressing tendon arrangement are the same as the PSCFST piers with external energy dissipation rings. The only difference is that the traditional PSCFST piers do not have external energy dissipation rings.  
Figure 7: Structure of precast assembled pier test piece (unit: mm).  
The specimen is loaded with low-cycle reciprocating loading, which is located in the center of the side of the pier cap, and the bottom of the pier column is fully restrained to form a cantilever structure. The loading mode adopts displacement control, the displacement to be loaded is 5 mm, 10 mm, 15 mm, 20 mm, 25 mm, 30 mm, 35 mm, 40 mm and the loading cycle is once at each stage. The loading curve is shown in Figure 8.