esa-sceva/satcom-chunk-collection huggingface dataset:
# An On-Demand Charging for Connected Target Coverage in WSRNs Using Fuzzy Logic and Q-Learning +
## 5 Performance Evaluation
### Comparison with Existing Algorithms
#### 5.2.4 Non-Monitored Targets and Dead Sensors over Time  
We present the number of non-monitored targets and the number of sensors over time caused by different algorithms in Figure 9(a),b, respectively. In Figure 9(b), when the time elapses, the number of sensors exhausting energy and becoming dead nodes increases. Accordingly, more targets become non-monitored, as shown in Figure 9(a). Fuzzy Q-learning outperforms the other algorithms concerning both metrics. There is a huge gap between the performance of Fuzzy Q-learning and the others in Figure 9(a). Fuzzy Q-charging with better charging strategies slows down the increase of non-monitored targets over time. Another interesting observation is that while the gaps between the number of dead sensors caused by using Fuzzy Q and that of INMA and GSA are relatively small (Figure 9(b)), the gaps concerning the number of non-monitored targets are huge (Figure 9(a)). The reason is INMA and GSA do not consider target coverage and connectivity constraints. Therefore, the next charging location is not optimized to prioritize the sensors with an essential role. Those sensors may be dead in INMA and GSA, leading to the targets being non-monitored. Meanwhile, in Fuzzy Q-charging, the charging location determination algorithm can identify the sensor nodes with a specific priority. Therefore, the dead sensors caused by Fuzzy Q-charging are the less important ones. In many cases, the death nodes may not affect or have minor impacts on the monitored targets.  
Figure 10: A comparison of non-monitored targets and dead sensors over time. (**a**) Non-monitored targets over time. (**b**) Dead sensors over time.  
Figure 9: Network lifetime vs. the packet generation frequency.
### Case Study No. 3: OpenPLC Linked with Node-RED Acting as a Software IIoT Gateway
#### 6.3.4 Communication from OpenPLC to Factory I/O  
First we need to describe how the communication from OpenPLC runtime towards Factory I/O works. From OpenPLC, Factory I/O needs to read the values of the outputs. Therefore, we need to read the outputs using the **Modbus Read- %QX0.0-7** node, since the PLC program in OpenPLC evaluates whether a given Conveyor should be started or not. We connect this _Modbus Read_ to our server, which we have called OpenPLC local, where we have again (as in the first case study) set the corresponding address 127.0.0.1 with port 502. Next, we again set the quantity to 1 (because we are reading 1 byte), the address to 0 (the outputs are available from address %QX0.0). This is a reading, so we set the function to _FC: Read Coil Status_.  
Figure 25: Fragment of discrete-event system.  
Once we have read the values, we need to obtain them to OPC UA server so that Factory I/O can read them from it. Using the **Conveyors: Modbus to OPC UA namespace** custom function (Figure 27), we assign a specific _msg.topic_ to the Modbus messages to ensure that the Modbus data are placed in the correct variable in the OPC UA address space. Sending the data to the OPC UA server is handled by the _OPC UA client_ node.  
The nodes at the bottom of Figure 27 are for program testing purposes only and specifically return the current value of the inputs from the Modbus server (addresses %QX1.0 and %QX1.1).  
In Factory I/O, the input and output variables of the address space of the OPC UA server must be correctly assigned to the Factory I/O components (Figure 28).  
Figure 28: Factory I/O—connection.  
Figure 27: Communication from OpenPLC to Factory I/O.
## 2 Preliminaries
### Data Distribution Service (DDS)  
DDS middleware is widely considered to be the best middleware for containment control of UAVs owing to the fact that it is able to manage information due to real-time subscription publishing and mission-critical applications. It also consists of a flexible set policy for Quality of Service (QoS) that can be employed for different purposes. Since DDS provides almost whole performance metrics, DDS middleware is considered in order to conduct our study for the containment control of UAVs. The DDS middleware is structured on architecture for a publishing subscription by assuming certain UAVs as subscribers and other as publishers to collaborate with each other. A flexible list of Quality of Service Policy requirements that need to be met prior to cooperation between the publisher and the subscriber includes:  
* **Durability**: Specifies whether the publisher previously transmitted data to a new subscriber or not. This QoS strategy helps separate the system from reliance on start-ups.
* **Reliability**: Determines whether or not the DDS will re-send data missing across the middleware. Reliability includes two configurations: BEST EFFORT (do not resend data missing) or RELIABLE (resend data missing).
* **History**: Specifies that the data received or sent by a publisher will be retained for a subscriber. There are dual settings: KEEP ALL or KEEP LAST. KEEP ALL does not indicate that the middleware is storing unlimited data.
* **Deadline**: For subscribers: determines the maximum time for the arriving samples. On the publisher side: establishes a pledge to publish data not exceeding this time.
## 2 Related Work
### Web Ecosystems for the Treatment of Hypertension  
Telemedicine research applied to hypertension started focusing on the use of web platforms. [PERSON] et al. [9] implemented a web-based hypertension platform where patients can introduce self-reported health variables, and doctors can provide feedback about the treatment. This platform presented reminders of medical appointments to the patients, who could also ask predefined questions that the platform solved. This work represented an incipient approach to include web ecosystems for the control of HBP. However, the measurements were not required to be provided frequently since the platform notified the user only when data was not introduced during a consecutive period of one month. Research in this direction has allowed the development of web platforms with more functionality. [PERSON] et al. [7] implemented a cloud-based system for managing hypertension, offering a web platform capable of managing blood pressure data uploaded by patients, offering diagnoses services, and instant messenger service with the doctors.  
[PERSON] et al. [10] reviewed the state of the art concerning the application of web ecosystems over hypertension. The authors highlighted that these platforms allow the registration of patients' clinical data and blood pressure measurements and their communication with doctors. These systems usually introduce educational support, medication reminders, and online consultations with doctors. This work highlighted that web ecosystems used as a support technology for hypertension could improve patients' adherence to the treatment, generating a reduction of blood pressure values, thus improving patients' quality of life. These advantages may also reduce healthcare costs, especially in chronic treatments.  
Although these web platforms are interesting to bring patients closer to doctors, most of their features are reduced to the transmission of relevant information and communication with the doctors. Additionally, one of the main disadvantages of these platforms is on usability, where users need to access the website to register the measurements. To improve this situation, in recent years, these ecosystems have included the use of smartphone applications. Smartphones are continually increasing their computational capabilities, and they can also be linked to blood pressure monitors to register the measurements automatically [8]. Although the usage of these applications has proven to help reduce blood pressure values [11], there is nowadays a lack of clinical trials in this field. Moreover,the diversity of monitoring devices complicates the development of applications, also introducing issues regarding the accuracy of the measurement and a lack of standardization of technologies used in nowadays wearables.
## 3 Mission Overview
### Orbit Specification  
The appropriate orbit for this mission concept is a circular low Earth orbit (LEO). Crucial to the implementation is a phased homogeneous constellation of retroreflectors on board multiple microsatellites, counter-rotating with respect to the monostatic active spacecraft, and in the same orbital plane. A single plane is required for the occultation geometry, to achieve collocated nadir and limb measurements and for relaxed requirements to the pointing and tracking system described in Section 5.4.  
Relative precession between both orbits must be avoided; for a mission which requires global coverage, this can only be achieved with satellites in a polar orbit. Furthermore, the altitudes of the primary and retroreflector spacecraft should be close, to ensure that the descent of the tangent point throughout the atmosphere is as close to vertical as possible, though a minimum altitude separation of 20 km is recommended to minimize collision risks [47]. We suggest that the constellation of retroreflector spacecraft is injected onto the lower orbit to avoid collision with the primary satellite in case of orbit maintenance problems for the microsatellites.  
Five retroreflector spacecraft on a 550 km LEO (270\({}^{\circ}\) inclination, 5,738.8 s period) in combination with a 582 km LEO (90\({}^{\circ}\) inclination, 5,778.6 s period) for the primary satellite have been identified to provide an optimal configuration. This involved a trade-off study between the number of occultations required, the measurement sequence timing, the amount of atmospheric drag and implications for orbit maintenance, as well as considerations about the nadir LiDAR SNR, which decreases with increasing path length (altitude). The altitude has been set to produce a 10 day return period of the ground track. Active orbit control will guarantee high accuracy inclination maintenance and prevent relative drift of the orbital planes (see Section 5.5 for more details).
# Fronthaul Design for Wireless Networks
## 3 Fronthaul Design Methodology
### Link Design Algorithm  
The link design algorithm determines, out of all the user-specified equipment characteristics associated with one or more of the communication technologies (MRT, FSO, and FO), which is the most cost-effective solution to connect two points under user-specified link conditions; for a set of specifications, namely the equipment features and other installation aspects (such as link distance, required bit rate, and surrounding environment characteristics), this algorithm computes the cheapest solution that is able to deliver the necessary bit rate, while satisfying a certain link margin and error criteria.  
With respect to the wireless technologies addressed herein (MRT and FSO), it is important to mention that only single-hop links are considered by the algorithm, because the inclusion of relay stations is not straightforward (e.g., sites for relay deployment may not be available) and leads to more complex business models (e.g., the addition of rental expenses for the extra sites). Accordingly, link distance is henceforth regarded as hop distance when MRT and FSO technologies are under consideration.  
The following subsections present the details of the link design algorithm, namely the adopted models for the communication technologies, the economic analysis methodology, and finally, the workflow of the algorithm.
## 2 Material and Methods
### Eulerian Steady-State Model  
A 3D Eulerian steady-state model was also developed based on the short length simplification, as shown in Figure 3. In the Eulerian short model, the material was drawn into the inflow border, underwent deformation, passed through the fixed mesh, and finally was pushed away through the outflow border. The Eulerian model comprised a 50 mm long fixed mesh (Figure 3a), through which material flowed over a 500 mm distance during the rolling analysis. The Eulerian boundary conditions regulated the material flow at the inflow and outflow borders, where adaptive mesh constraints were applied to specify the material velocity perpendicular to the borders, as shown in Figure 3b. The model consisted of 27600 C3D8R elements (8-node linear brick, reduced integration, hourglass control) and the element dimensions were 1.881 \(\times\) 0.625 \(\times\) 0.667 mm. An adaptive mesh domain was specified near the roller to improve mesh quality for large deformation.  
The interface between the roller and the wall was specified as the Eulerian sliding surface. On the bottom surface, the nodal displacement was constrained in the vertical direction to represent the clamping, while on the inflow and outflow borders, all the nodal displacements were fixed. The rolling load of 50 kN was imposed by prescribing a vertical displacement of the roller. Translation of the roller was constrained in all directions, while rotation was allowed only around the X axis. An angular velocity boundary condition was applied to the roller around of the spinning axis in order to provide the rolling torque. A predefined field of translational velocity was applied to the wall and substrate, as required to initiate contact and minimise the transient effect of instantaneous movement at the beginning of analysis. The model was solved using Abaqus/Explicit, and the steady-state detection option was used in the model. The steady-state detection plane was positioned perpendicular to the rolling direction and behind the last point of contact (Figure 3a). As soon as a constant deformed shape was achieved on the steady-state detection plane, it was considered that the rolling had reached the steady state and the analysis was terminated.  
To improve computational efficiency, rolling speed was artificially increased, i.e., the analysis step time was approximately 1% of the actual rolling time, which did not affect the result under the steady state. In addition, a fixed mass scaling factor of 2750 was used in the analysis, close to the maximum value recommended by Abaqus Example Problems Guide [52] for similar problems. This scaling factor was deemed optimal as a result of iterative trials to minimise the computational time and the potential adverse effect of mass scaling on the accuracy.  
Figure 3: Short Eulerian steady-state model: (**a**) dimensions and mesh; (**b**) material flow and boundary conditions.
###### Abstract  
This study aimed to detect damage based on thermal and electrical resistance properties by fabricating composites in which multi-walled carbon nanotubes (MWCNTs) and cement-based materials are mixed. The experimental parameters used were the cement-based material type, MWCNT concentration, curing period, and presence of damage. The experimental results showed that damage in cement paste can be detected using the heat property at every MWCNT concentration, and damage in mortar can be detected at MWCNT concentrations of \(\leq\)0.25 wt%. However, damage to concrete is difficult to detect using the heat property. Damage to cement paste, mortar, and concrete can be detected at every concentration using the electrical resistance property. Furthermore, field emission scanning electron microscopy (FE-SEM) results revealed uniformly dispersed MWCNTs inside the composites without agglomeration or the formation of carbon nanotube (CNT) networks.  
Keywords:multi-walled carbon nanotube; damage detection; cement-based materials; thermal property; electrical resistance property; microstructure +
Footnote †: journal: Applied Sciences
## 2 Relative Position and Attitude Measurement of Maglev Vibration Isolation System
### Relative Position and Attitude Measurement Based on Eddy Current
(2) The position vector from the origin of the base coordinate to the center of the reflection surface of the eddy current sensor is \(R\). (3) In the floating platform coordinate system, the position vector from the floating platform coordinate origin to the reflection surface of the eddy current sensor is defined as \(p\). (4) In the sensor coordinate system, the position vector from the origin of the reflective surface coordinate system to the measurement point is defined as d.  
In the floating platform coordinate system, according to the geometric position relationship of the spatial vector, the following can be obtained:  
\[{}^{F}\mathbf{p}={}^{F/S}\mathbf{C}({}^{S/C_{pi}}\mathbf{C}^{C_{pi}}\mathbf{d}+{}^{S}\mathbf{R}-{} ^{S}\mathbf{r}) \tag{5}\]  
The distance from the origin of the base coordinate to the origin of the reflection surface coordinate of the eddy current sensor can be written as:  
\[\mathbf{R}= \begin{bmatrix}0&0&R_{y1}&-R_{y2}&R_{z1x}&-R_{z2x}&R_{z3x}&-R_{z4x}\\ -R_{x1}&R_{x2}&0&0&-R_{z1y}&R_{z2y}&R_{z3y}&-R_{z4y}\\ 0&0&0&0&0&0&0\end{bmatrix}  \tag{6}\]  
In the floating platform coordinate system, the distance from the origin of the floating platform coordinate to the irradiation point of the eddy current sensor can be expressed as:  
\[\mathbf{p}= \begin{bmatrix}0&0&p_{y1x}&-p_{y2x}&p_{z1x}&-p_{z2x}&p_{z3x}&-p_{z4x} \\ -p_{x1y}&p_{x2y}&0&0&-p_{z1y}&p_{z2y}&p_{z3y}&-p_{z4y}\\ -p_{x1z}&-p_{x2z}&-p_{y1z}&-p_{y2z}&0&0&0\end{bmatrix}  \tag{7}\]  
The position matrix in the coordinate system of the eddy current sensor is:  
\[\mathbf{d}= \begin{bmatrix}d_{x1}&d_{x2}&0&0&0&0&0&0\\ 0&0&d_{y1}&d_{y2}&0&0&0&0\\ 0&0&0&0&d_{z1}&d_{z2}&d_{z3}&d_{z4}\end{bmatrix}  \tag{8}\]  
It can be concluded that:  
\[ \begin{bmatrix}0\\ -p_{x1y}\\ -p_{x1z}\\ \end{bmatrix}  =\mathbf{C} \begin{bmatrix}-d_{x1}-x\\ -R_{x1}-y\\ -z\\ \end{bmatrix} , \begin{bmatrix}0\\ p_{x2y}\\ -p_{x2z}\\ \end{bmatrix} =\mathbf{C} \begin{bmatrix}d_{x2}-x\\ R_{x2}-y\\ -z\\ \end{bmatrix} , \tag{9}\] \[ \begin{bmatrix}p_{y1x}\\ 0\\ -p_{y1z}\\ \end{bmatrix}  =\mathbf{C} \begin{bmatrix}R_{y1}-x\\ -d_{y1}-y\\ -z\\ \end{bmatrix} , \begin{bmatrix}-p_{y2x}\\ 0\\ -p_{y2z}\\ \end{bmatrix} =\mathbf{C} \begin{bmatrix}-R_{y2}-x\\ -d_{y2}-y\\ -z\\ \end{bmatrix} ,\] \[ \begin{bmatrix}p_{z1x}\\ -p_{z1y}\\ 0\\ \end{bmatrix}  =\mathbf{C} \begin{bmatrix}R_{z1x}-x\\ -R_{z1y}-y\\ d_{z1}-z\\ \end{bmatrix} , \begin{bmatrix}-p_{z2x}\\ 0\\ \end{bmatrix} =\mathbf{C} \begin{bmatrix}-R_{z2x}-x\\ R_{z2y}-y\\ d_{z2}-z\\ \end{bmatrix} ,\] \[ \begin{bmatrix}p_{z3x}\\ p_{z3y}\\ 0\\ \end{bmatrix}  =\mathbf{C} \begin{bmatrix}R_{z3x}-x\\ R_{z3y}-y\\ d_{z3}-z\\ \end{bmatrix} , \begin{bmatrix}-p_{z4x}\\ -p_{z4y}\\ 0\\ \end{bmatrix} =\mathbf{C} \begin{bmatrix}-R_{z4x}-x\\ -R_{z4y}-y\\ d_{z4}-z\\ \end{bmatrix} \]
## 3 Results and Discussions
### Parametric Analysis
#### 3.3.4 Effect of the Scattering Coefficient of the Cancerous Region  
Figure 11 shows the effect of the scattering coefficient of the malignancy \(\sigma_{s}\) on the temporal reflectance profiles from human skin with CM spears in the basal layer. In the present work, four different coefficients are considered: 9.185 (the same value as the previous case), 20.00, 30.00 and 40.00 mm\({}^{-1}\). It is notable that because this layer is entirely full of this of this malignancy, the CM malignancy and basal layer have the same \(\sigma_{s}\).  
The modification of the scattering coefficient of the malignancy reveals a significant variation in the peak magnitude of the reflectance, as presented in Figure 11. This peak magnitude increases with an increase of \(\sigma_{s}\), and while an opposite trend is obtained in the decaying region, the effect of \(\sigma_{s}\) is important. The normalized reflectance is higher when the scattering coefficient of the CM is higher than that for the normal human skin, although in the case of comparable scattering coefficients, the reflectance signals are practically the same. When \(\sigma_{s}\) of the CM is smaller in comparison to its value for the normal human skin, the normalized reflectance is lower. These conclusions are explained by the fact that the reflectance is principally dominated by the scattered diffuse radiation that is received at the boundary of incidence. When \(\sigma_{s}\) increases, the scattering increases, and thereafter a higher reflectance is obtained. The significant augmentation of the scattering character of the damaging malignant lesion has been well-verified. This can be explained by the augmentation of the size of scatterers and/or modification of the volume density of scatterers in the affected cells [21; 27; 28].
# Multilevel Privacy Assurance Evaluation of Healthcare Metadata
## 1 Introduction
The quality BDA directly depends upon the provenance records, i.e., the genuine documentation of the metadata pipeline. The metadata as a final output are then stored in data warehouses in either remote or onsite servers for metadata retrieval and interoperability. The data analysts encounter ethical issues at each phase of the metadata pipeline: from data collection to metadata formation, storage, and interoperability [2]. To counter ethical issues, data analysts abide by principles such as FACT (i.e., Fair, Accessible, Confidential, and Transparent) [4; 21]. Conversely, to broaden the scope of healthcare metadata utility, data analysts have to adapt to FAIR (Findable, Accessible, Interoperable, and Reusable) principles. Thus, the ultimate goal determines the following path while handling metadata. The privacy preservation of sensitive healthcare metadata is legally desired at each phase of the metadata pipeline. In this wake, Privacy by Design, privacy by policy (via ISMS), and informed consent are fundamental privacy-preserving measures to ensure the ethical usage of healthcare metadata. However, the concerned authorities encounter the Privacy Utility Tradeoff (PUT) while performing fundamental privacy-preserving measures. PUT is the performance impairment of data utility in ensuring data privacy [20]. This research work focuses on exploring this problem area. Figure 1 shows the significance of responsible data science and its direct influence upon EHR and BDA. The figure portrays how privacy is an essential measure of responsible data science for the stakeholders and affectees alike. The research aims to highlight PUT's influence across multilevel privacy assurance measures.