esa-sceva/satcom-chunk-collection huggingface dataset:
# The EU Directive on Electromagnetic Fields--Practical Experience of Field Measurements
## 3 Frequency Converters  
In 2016, the Swedish National Electrical Safety Board published a report authored by [PERSON] [11] where it was reported that 69% out of 39 installations of frequency converters had EMC-related deficiencies. From the report: \"Remarkably often, the right type of equipment has been used in the installation, but in an incorrect way. Several mistakes are constantly repeated, where the absence of zoning is the most important, caused by inappropriate construction of control cabinets, incorrect wiring and inappropriate placement of constituent components. The shortcomings indicate that installers lack a basic understanding of the concept of electromagnetic compatibility (EMC)\".  
We have often at our site visits to check compliance with the EMF EU directive noticed high electric fields from the installation of frequency converters, often near the cable connecting the converter to the motor. As with the EMC problem, this is due to the installation not being completed in a correct way. Examples are that the motor cable is not shielded 360\({}^{\circ}\) or not correctly bonded for high frequencies--i.e., only using ordinary earthing. Sometimes a switch is installed between the converter and the motor, and if the box is not EMC-safe, the fields will be leaking.  
Not only is this an EMC problem but it also conflicts with the action levels for electric fields from the converter. Since the operation of the converter is based on chopping the current into pulses with short rise and fall times, the voltage will not only contain the ELF frequencies, but the spectrum will cover both the kHz and MHz range. Because of this, when the electric field is measured with the weighted peak method, including frequencies up to 400 kHz, the action level is exceeded.  
In view of this, it is of importance to make sure the frequency converter in use at the factory is correctly installed both from an EMC point of view as well as regarding occupational exposure to electric fields.
## 5 Numerical Simulations  
In this section, numerical simulations are implemented to verify the correctness of the main results. A vehicular platoon including five followers and one leader is considered under PF topology. The main parameters, including the time constant of the drivetrain [35],time headway [36], and controller gains [28] are provided in Table 1. These parameters satisfy Lemma 1, so the vehicular platoon is internally stable for the delay-free case.  
In the following, we conduct two types of simulations to evaluate the internal stability and string stability of the vehicular platoon with sensing delay and communication delay.
# Biodegradable and Biocompatible Systems Based on Hydroxapatite Nanoparticles
## 4 Functional HAp Biocomposites through Modification/Encapsulation Techniques
### Hydroxyapatite Nanoparticles As Carriers for Drug Delivery Systems
Metallic nanoparticles of high electron density (e.g., gadolinium-based, titanium-based, silver-based, and gold nanoparticles) are considered appropriate as ROS-enhanced radiosensitizers [171]. The direct use of such particles has inherent problems derived from the interaction of their reactive surface with healthy tissues and subsequent damage [172]. [PERSON] et al. [173] have recently reported that HAp nanoparticles could be effectively doped with Hf ions giving rise to significant ROS formation in cells after irradiation with \(\gamma\) rays, cell apoptosis and in vivo growth inhibition of lung cancer tumors. Preliminary in vivo results showed specifically that tumors grew promptly after seven days of treatment except for being treated by Hf (15 mol %):HAp, of which approximately 30% was inhibited relative to the starting tumor volume, and exhibited a relative delay of tumor growth until Day 14.
## 3 RLDA and Severity Analysis
### RLDA Data Analysis
#### 3.3.2 Damage Definition and Results  
The failure modes considered in this study were fatigue failure in terms of basic strength and oil leakage, which causes an important reduction in damping force in the ECS system. The damage determination method was to estimate damage using pseudo stress and define relative motion related to oil leakage.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Facility** & **Manufacturer** & **Specification** \\ \hline Strain Gauge & KYOWA, Tokyo, Japan & 120 \(\Omega\) \\ Accelerometer & DYTRAN 3016A, Chatsworth, CA, USA & 0.5 V/G \\ Height Sensor & DATRON, Milton Keynes, UK & - \\ Amplifier & M/M, Milano, Italia & 4-Channel \\ Recorder & TEAC, Tokyo, Japan & 16-Channel \\ \hline \hline \end{tabular}
\end{table}
Table 1: Acquisition facilities’ specifications.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Road Classification** & **Ratio (\%)** \\ \hline City road & 53.6 \\ Country road & 28.6 \\ Highway & 18.1 \\ Unpaved road & 1.5 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Acquisition facilities’ specifications.  
Because the front and rear road damage vary greatly, the equivalent amplitude was applied for analysis, and damage was defined and calculated from the results of rain flow counting using damper speed. Here, the \(k\) value corresponds to steel.  
\[D=\sum\frac{n_{i}}{N(S_{i})} \tag{1}\] \[E.A(\textit{Equavalent Amplitude})=\sqrt[k]{D}\]  
where  
\(D\) = Damage  
\(n_{i}\) : Actual counting number that occurred  
\(N(S_{i})\) : Number of repetitions until failure for each load level  
As can be seen from the above Figure 7, each data group shows linearity, so they can be judged to have normality, and the discrimination between groups is clear.  
The rain flow matrix of the front damper for each driving road surface is displayed in Figure 8. The rain flow matrix represents the stress range and the number of occurrences of the cycle for each stress range. It can be seen that the speed band of the damper is widely distributed on the unpaved road and that it is distributed in the following order: (1) the unpaved road, (2) the city road, (3) the county road, and (4) the highway. The same trend can be seen in the rear wheel damper described in Figure 9.  
Figure 8: _Cont._  
Figure 7: Equivalent amplitude of front and rear damper with respect to road conditions.  
Based on the above data analysis, pseudo damage was calculated by applying cycle counting using damper speed. It can be seen that unpaved driving conditions dominate the severity level, and the damper speed and displacement are critically affected by the pavement condition or roughness of the road.  
Figure 8: Rain flow matrix of the damper velocity (front): (**a**) Highway; (**b**) Country road; (**c**) City road; (**d**) Unpaved road.  
Figure 9: _Cont_.
# Mechanisms of Fluoride Toxicity: From Enzymes to Underlying Integrative Networks
## 4 The Implication of Fluoride Toxicity on Human Health  
Early reports about fluoride toxicity in humans appeared in the journal Physiological Reviews already in 1933 [58]. Aluminum smelter workers and persons living near the factory where fluoride was in high concentration in the atmosphere suffered with psychiatric and neurological disturbances. Endemic fluorosis is caused by persistent fluoride exposure through ingestion or inhalation, and most commonly, as a result of high fluoride levels in drinking water and beverages [90; 91]. The WHO's drinking water quality Guideline Value for fluoride is 1.5 mg/L [92].  
Dental fluorosis results after excess fluoride ingestion, most commonly in drinking water, during tooth formation. For example, dental fluorosis appears in 43-63% of schoolchildren in endemic areas of China with total fluoride intake 2.7-19.8 mg/day [5]. The rate of dental fluorosis also increases in countries with CWF. In the 2010-2012 survey, dental fluorosis was reported among adolescents aged 12-15 years in the USA with a surprisingly high rate of 65% [93]. Dental fluorosis might be used as a sensitive indicator of excessive fluoride exposure [94].  
Skeletal fluorosis impacts millions of people in regions with high natural levels of fluoride, like India, China, Pakistan, Iran, and the Gulf region, to name a few [91]. The U.S. Environmental Protection Agency (USEPA) sets a maximum contaminant level of 4.0 mg/L to protect against skeletal fluorosis. WHO indicates a clear risk of skeletal fluorosis for a total intake of 14 mg fluoride per day. Nevertheless, the recent findings revealed that consumption of fluoride at even 10 times lower concentrations of 1.5 mg/L caused its high incidence in India [95]. The chemical analyses show that 80% of water sources in rural areas exceed the WHO fluoride permissible limits and residents are affected by skeletal fluorosis.  
[PERSON] et al. examined about 500 people affected by chronic fluoride intake from CWF [96]. These authors observed chronic fatigue, headaches, loss of the ability to concentrate, depression, gastrointestinal symptoms, and deterioration of muscular coordination.
## 3 Simulation Results and Experimental Verification
### Validation of Simulation Models  
The simulated sound power curve is compared with the experimentally measured sound power curve from Section 2.2 as shown in Figure 10. It can be seen from Figure 10 that the simulation results are in general agreement with the experimental results. The two main peak frequencies are at 855 Hz and 1589 Hz, with errors of approximately 1.79% and 0.25% compared to the experimental results, which is within a reasonable range. In different frequency ranges, the differences in sound power levels between the simulation and experiment are characterized by the simulation results having larger peaks, while being slightly lower at non-peak frequencies. This can be attributed to possible errors in the preparation process and the experimental environment of the sample, while the results of the simulation model are more idealized. The non-uniformity caused by manufacturing processes can lead to structural changes and increased internal damping in experiments. While this increases the dissipation of vibration energy and suppresses peaks, it can also cause noticeable vibrations at frequencies not predicted by theory, leading to sound  
Figure 9: Acoustic finite element modeling of small plates: (**a**) top of the model; (**b**) underneath the model.  
radiation. In conclusion, the finite element model developed in this study is reliable and accurate.
## 3 Results and Analysis
### Test Results of Internal and External Water Pressure in the Water Conveyance Tunnel  
In experimental group S\({}_{1}\)-S\({}_{2}\), the SSS coating was placed on the bottom or top surface of the specimen. The average seepage height and relative permeability coefficients are shown in Figure 7. Regardless of whether the SSS coating was on the bottom or top surface, the specimens were permeable under a long-term water pressure of 3 MPa. With an increase in time, the average water seepage height of the specimens coated on the bottom surface and those coated on the top surface increased. However, the relative permeability coefficient of the former increased, while that of the latter decreased. This result occurred because the two permeation mechanisms are different. When the SSS coating is on the bottom surface of the specimen, high-pressure water must pass through the SSS coating before entering the concrete. The SSS coating acts as a barrier with an ultra-low permeability coefficient, isolating water permeation. Therefore, the permeability coefficient is relatively low in the early stages of permeation. In the later stages of permeation, high-pressure water enters the concrete, showing a relatively high permeability coefficient. When the SSS coating is on the top surface, high-pressure water directly enters the concrete, and the seepage regulation is the same as that of concrete and the concrete coated with organic sodium silicate, as shown in Figure 6. However, the relative permeability coefficient is much smaller. The SSS coating improves the airtightness of the concrete, resulting in the pores' inability to release gases. As high-pressure water enters the pore channel, the pore pressure inside the channel increases, balancing the pore water pressure and inhibiting further water permeation. In summary, the SSS coating on the top surface of the specimen indirectly enhances concrete impermeability. By contrast, the SSS coating on the specimen's bottom surface directly prevents water permeation. The average penetration height and  
Figure 6: The average seepage height and relative permeability coefficient of group C-O-S\({}_{1}\).  
relative permeability coefficient of the specimens coated on the bottom surface decreased by 49.6% and 71.2%, respectively, compared with those of the specimens coated on the top surface. This finding indicates that the direct waterproofing effect of the SSS coating positively affects concrete impermeability.
## 2 Background  
### Process Mining  
Process mining serves as a bridge connecting the process-focused aspects of business process management (BPM) with the data-centric elements of machine learning and data mining. The foundation of process mining lies in the analysis of recorded process executions, which are preserved in event logs.  
As a field of study, process mining aims to discover, monitor, and refine actual processes by leveraging knowledge extracted from readily accessible event logs in systems [24]. Figure 3 provides a comprehensive overview of the research domain encompassed by process mining. As illustrated in Figure 3, a cloud represents organisations, their business processes, and people. It is the real-world setting where processes happen. The information system supports/controls the operations/activities performed in an organisation (cloud), and it records all events and stores them as event logs, which are detailed records of all the activities that have occurred. A process model is a formal representation of the business processes, and it is derived from the event logs using process-mining techniques, e.g., discovery, conformance, as shown in Figure 3.  
As illustrated, process mining establishes a connection between the theoretical behaviour model and the actual observed behaviour.  
Process-mining techniques can be categorised into three types: discovery, conformance, and enhancement. There are various types of modelling notations to represent a process model, with _transition systems_, _Petri nets_[25; 26], _BPMN_[27], and _causal net_ being widely used notations.  
Figure 2: Statistical model checking for process-mining models.
# Adjustment Algorithm for Free Station Control Network of Ultra-Large Deepwater Jacket
## 3 Results and Discussion
### Comparison of Different Adjustment Schemes
#### 3.2.3 Adjustment Results via the Proposed Method
For the traditional method, if S7 and S8 are directly constrained, one cannot find this problem. They may even come to the opposite conclusion. Only by repeatedly transforming the constraint points for adjustment and comparing and analyzing the results of multiple schemes can S7 be identified as unstable. This method, however, effectively avoids differences in results due to different starting data by introducing coordinate similarity transformations and quasi-stable adjustments.
# Leukoencephalopathy with Calcifications and Cysts--The First Polish Patient with Labrune Syndrome
## 3 Genetic Analysis  
DNA from the proband and her parents was extracted from peripheral blood using the standard protocol. Library preparation for WES was performed using the DNA sample of the proband with the SureSelectXT Human kit All Exon v7 (Agilent, Agilent Technologies, Santa Clara, CA, USA) and paired-end sequence (2 \(\times\) 100 bp) on HiSeq 1500 (Illumina, San Diego, CA, USA) to obtain 64,810,369 reads (89.4% of target bases were covered at a minimum of 20x, whereas 95.2% had coverage of min. 10x). The obtained raw data were processed with the pipeline previously described [3]. WES data were inspected with an Integrative Genomics Viewer (IGV). Two heterozygous variants in the _SNORD118_ gene were prioritized for further verification (NR_033294.1:n.19C > G and NR_033294.1:n.* 5C > G). Both disease-causing variants were validated in the proband, and studied in his parents by amplicon deep sequencing (ADS) (Figure 4).  
The transmission of the variants was consistent with the autosomal recessive pattern, as revealed by family assessment. The population frequency for the variants: n.19C > G and n.* 5C > G was  
Figure 4: Pedigree of the studied family (**A**) and the results of WES and ADS for the variants in the _SNORD118_ gene; (**B**) WES results; (**C**) results of amplicon deep sequencing in the proband, her parents (I–proband, II–mother, III–father). The obtained coverage of the ADS for the n.19C > G position in the _SNORD118_ gene was: 9508 reads for the proband (II.1); 8923 reads for the mother (I.2); and 7578 reads for the father (I.1). The obtained coverage of the ADS for the n.* 5C > G position in the _SNORD118_ gene was: 11,529 reads for the proband (II.1); 7721 reads for the mother (I.2); and 8440 reads for the father (I.1).  
Figure 3: Third brain MRI two years after the first imaging, axial plane: (**a**) T2; (**b**) Flair T2; (**c**) T1; (**d**) DWI; (**e**) T1 + CE; (**f**) SWI (used for the assessment of blood metabolites and calcifications) sequences; white matter hyperintensity progression, cyst enlargement with the mass effect and calcification artifacts.  
0.00003492 and 0.0006978, respectively, in gnomAD (database, v.3, [[https://gnomad.broadinstitute.org/](https://gnomad.broadinstitute.org/)]([https://gnomad.broadinstitute.org/](https://gnomad.broadinstitute.org/))) and 0.000834725 and 0.000554939 in in-house datasets of \(>\)3500 WES of Polish individuals. Both variants were previously described, although these were only single reports [2; 4]. They were reported as causative of LCC, although no comprehensive characteristics of patients with n.19C \(>\) G variant were raised.  
The suspicion of [PERSON] syndrome was raised due to cyst progression in the degenerated white matter and increasing calcifications in the follow-up studies (Figure 5). Identified variants in the _SNORD118_ gene in correlation with radiological changes confirmed this diagnosis.
# An Optimization Framework for Data Collection in Software Defined Vehicular Networks
###### Abstract
As a solution for that, we propose to check for optimization frequently and make optimization decisions by inspecting the network link entropy change. For that, we formulate heterogeneous network link-state entropy by considering links with Road Side Units (RSUs) and vehicular nodes as two different types of links. We make the optimization decision by comparing the heterogeneous network link-state entropy change with a threshold value. Furthermore, we present a systematic way to collect both data and metadata (metadata are the data required to solve the optimization model, which consists of a timestamp, Node Identifier (ID), and a 1 hop Neighbor set IDs of each node in the vehicular network) in the initial data-gathering cycle and subsequent data-gathering cycles avoiding redundant data transmissions. In each subsequent data-gathering cycle, first, the broadcasting nodes should broadcast a status and neighbor ID set, while agent nodes should broadcast metadata during the same time step. Once the broadcasting is over, the agent nodes should unicast the collected data along with its own data to the controller, where the controller will extract metadata using the data unicast from agents to find a solution to the optimization problem. Finally, at the end of the cycle, the optimization solution will be broadcast to the nodes. Furthermore, we discuss the probable solutions of the proposed data collection optimization framework for a couple of vehicular network topologies. We further analyze the performance of the data collection of different SDVN architectures, including the proposed optimization framework, as a platform for future researchers to investigate. As contributions to the existing literature, we present a novel optimization framework for data collection in the hybrid architecture of software-defined vehicular networks, which has a much lower communication cost, resource utilization, and end-to-end latency than existing data collection methodology for a centralized SDVN architecture. The proposed framework will be useful for making data-driven decisions using Machine Learning (ML)/Deep Learning (DL) in future software-defined vehicular networks. The limitation of the proposed method is that it is only applicable to the SDVNs, where both the data plane communication and control plane communication exist such that agent nodes and broadcasting nodes can be selected using optimization. The rest of the paper is organized as follows. Section 2 reviews the existing literature providing a background for an SDVN and its existing architectures, Section 3 presents the proposed data collection mechanism in detail, Section 4 presents performance evaluation metrics, simulation environment configuration, and all the research experiments conducted with their results and analysis, Section 5 cross analyzes the results and compares them with the existing literature while discussing the results, and, finally, Section 6 concludes the paper and presents future work.