esa-sceva/satcom-chunk-collection huggingface dataset:
# Displacement-Dependent Damping Inerter System for Seismic Response Control
## 3 Characteristics of DDIS
### Parametric Analysis of DDIS
#### 3.2.3 Influence of Structural Parameters  
Physical and model uncertainty of the primary structure may cause the practical behavior of the DDIS to deviate from theoretical expectations. The sensitivity of the vibration control effect of the DDIS to uncertainties of the primary structure is also worth analyzing. Therefore, the stiffness of the primary structure was considered to be variable owing to errors yielded by imprecise construction and deterioration during service.  
Figure 11 illustrates the effect of the variation of \(k_{new}/k\) and \(\zeta\) on the vibration control effect (i.e., \(\gamma_{Dis}\) and \(\gamma_{Acc}\)) of the DDIS, where \(k_{new}\) is the changed stiffness of the SDOF frame structure mentioned in Section 3.2.1. The \(k_{new}\) is set in the range of 50% variation of the stiffness \(k\) of SDOF frame structure. The increase of \(\mu\) from 0.00 to 0.40 causes the displacement-dependent damper to evolve into the proposed DDIS with a larger inertance. In accordance with the earlier analysis, the structural responses \(\gamma_{Dis}\) and \(\gamma_{Acc}\) of the SDOF structure equipped with the DDIS are all lower than those equipped with the displacement-dependent damper, implying that the addition of the inerter improves the structural performance. This improvement is observed in structures with different stiffness and is not significantly affected by a rapid change of \(k_{new}\). For instance, in Figures 11 and 12b-d (\(\mu=0.10,\ 0.25,\ 0.40\), respectively), the degradation of \(k_{new}\) and increase of \(\zeta\) diminishes the acceleration mitigation effect of the DDIS to some extent; however, \(\gamma_{Dis}\) and \(\gamma_{Acc}\) still remain lower than those for the displacement-dependent damper depicted in Figures 11a and 12a.  
Figure 12: Sensitivity analysis of \(\gamma_{Acc}\) with variation of the structural circular frequency for \(\kappa=0.50\) and \(\lambda=1.00\) with (**a**) \(\mu=0\), (**b**) \(\mu=0.10\), (**c**) \(\mu=0.25\), and (**d**) \(\mu=0.40\).
# Ontological Representation of Smart City Data: From Devices to Cities
## 2 Research Methodology
### Conducting the Review  
The review process was performed by one of the authors and validated by the rest of the authors (the findings of this review process are available at [[https://delicias.dia.fi.upm.es/nextcloud/index.php/s/Nc8](https://delicias.dia.fi.upm.es/nextcloud/index.php/s/Nc8) gxJAQFm6X7 RM]([https://delicias.dia.fi.upm.es/nextcloud/index.php/s/Nc8](https://delicias.dia.fi.upm.es/nextcloud/index.php/s/Nc8) gxJAQFm6X7 RM)). Figure 2 shows the workflow of the review process. In this figure, numbers followed by letter \"p\" refer to publication results from journals and databases, and those followed by letter \"r\" refer to ontology registries (catalogs and indexes) results. On the one hand, using the search strings, 119 studies were retrieved from the databases and specific journals. Then, 34 studies were discarded because they did not include an ontology. Next, 46 duplicate studies were removed, and 49 studies remained. After, 35 studies were excluded because they did not relate to the IoT domain, and finally, 2 studies remained, which included 2 ontologies.  
On the other hand, 49 results were retrieved from the ontology registries. Then, 13 duplicate ontologies were eliminated, and 36 ontologies were kept. Next, 8 ontologies were excluded because they did not relate to the IoT domain. After, 8 ontologies were removed because they were not available online, and one ontology was also discarded because it was available only in Italian. Finally, 6 ontologies were excluded because they were not released after 2012, and 13 ontologies remained at the end of the review process. The results from both journals and ontology registries were merged, and the at end, 15 ontologies were obtained. These ontologies are listed in Table 3 as part of the ontology characterization section.  
It is worth mentioning that at the beginning of this search process, the Ontology Design Patterns' catalog was also considered as a potential source. This catalog collects a list of ODPs in a catalog classified by types of patterns. The patterns classified as Content Patterns (CPs) were checked in order to find those available for the smart city domain. As a result, the \"Smart City Strategy Design\" CP was found, but it has not been reviewed since it is used for the design of strategies to transform a city into a smart one, a topic that is out of the scope of this paper. In addition, this catalog does not have a domain for sensors or actuators; thus, it was excluded from the review.  
Figure 2: Systematic literature review workflow with the detail of the number of studies found. Numbers followed by letter “p” refer to publications results from databases and journals, and those followed by letter “r” refer to ontology registries’ results.
# Developments on Perovskite Solar Cells (PSCs): A Critical Review
## 7 PSC Encapsulation  
One of the recent effective methods/solutions towards PSC degradation lies in the tight encapsulation of the perovskite material. This method prevents outgassing of the volatile perovskite decomposition products, protecting against moisture and UV radiation. The technology behind encapsulation uses thin-film materials of epoxy, SnO\({}_{2}\), butyl rubber, and dyads surrounding the PSC from the top, bottom, and edges [87; 88; 89; 90]. In a recent study, [PERSON] (2020) reported a breakthrough in PSC encapsulation surviving extreme standard test conditions specified for commercial modules for about 1800 h of thermal cycle and 75 humidity freeze test cycles [91]. The PSC devices were encapsulated between transparent conductive oxide substrates and the inexpensive polyisobutylene rubber; a similar approach has been demonstrated in an early study by [PERSON] (2017), as shown in Figure 16[92].  
Figure 15: Characterization of perovskite device degradation upon exposure to **(a)** 40% relative humidity (RH) and **(b–d)** 365 nm UV light. **(a)** i–iii SEM micrograph images of device exposed for t = 20 min, 24 h and 48 h of the 40% humidity condition. **(b)** I–V curve, **(c)** XRD, and **(d,e)** PCE and FF perovskite response to UV light degradation [84; 86].
# Mutual Authentication Protocol for D2D Communications in a Cloud-Based E-Health System
## 5 Security Analysis
### Mutual Authentication  
In our protocol, each entity calculates a MAC to perform mutual authentication with the cloud server and vice versa. For example, in the HUP phase, the health center calculates \(\mathrm{MAC_{HC}}=\mathrm{h_{2}(ID_{H}\parallel R_{H})}\) and sends it to the server cloud, which calculates \(\mathrm{MAC_{HC}}^{\prime}=\mathrm{h_{2}(ID_{H}\parallel R_{H})}\) and verifies if \(\mathrm{MAC_{HC}}^{\prime}=\mathrm{MAC_{HC}}\). If the verification is successful, the server cloud authenticates the health center, calculates its own \(\mathrm{MAC_{CH}}=\mathrm{h_{2}(ID_{H}\parallel R_{CH})}\) and sends it to the health center, which calculates \(\mathrm{MAC_{CH}}^{\prime}\)-\(\mathrm{h_{2}(ID_{H}\parallel R_{CH})}\) and verifies if \(\mathrm{MAC_{CH}}^{\prime}=\mathrm{MAC_{CH}}\). If the verification succeeds, the health center authenticates the server cloud and the mutual authentication procedure is complete. A similar procedure is performed in the PUP, TP and CP phases.
# Super-Resolution Remote Imaging Using Time Encoded Remote Apertures
## 6 Conclusions  
In this paper, we present a novel imaging method that does not depend on the fundamental diffraction limit. The method makes use of hardware and algorithm to break the resolution limit under certain conditions of the scene (point-cloud assumption of the scene). In conventional imaging systems, the fundamental diffraction limit is governed by the size of the aperture, wave-length, and distance to the target. With fixed wave-length and distance, our proposed method does not depend on the size of the aperture, but instead, depends on the time resolution of the imaging system. The method is robust to change in surface materials. Currently, the main limitation is that the method is only applicable to the point-cloud scenes. Such sparse scenes commonly occur in aerial or space imaging scenarios. The paper gives base theory and introduces a method to use multiply scattered light (second bounce). As a next step, we are exploring possibilities of using multiply scattered light for continuous surfaces. As well as using machine learning to do the classification of objects below resolution limit while using multiply scattered light.  
[PERSON] contributed to the method, implemented the algorithms, developed simulations and conducted experiments. [PERSON] conceived the method and advised all parts of the project. All authors contributed to writing the paper. All authors have read and agreed to the published version of the manuscript.  
This work was funded by Air Force Research Laboratory through program AFRL-AFOSR (VA-TR-2018-0422).  
We appreciate help of [PERSON] on the hardware setup, and helpful discussion with [PERSON] and [PERSON].  
The authors declare no conflict of interest.
## 1 Introduction
Figure 1: Scheme of mixed or extended reality and augmented and virtual reality. Regarding motivation, numerous studies have demonstrated that the use of virtual reality (VR) enhances students' motivation towards the subject matter and activities they need to engage with [18; 22; 24; 25]. In a related vein, some investigations have focused on optimizing the design of VR technology to improve its performance efficiency [4; 26]. Specifically, researchers have explored the effectiveness of incorporating \"hot spots\", signals, or informative cues to enhance user experience and reduce cognitive load associated with the use of VR [27; 28]. The growing adoption of VR in educational institutions can be attributed to several factors, including the increasing capabilities of computers to deliver high-quality audiovisual and multimedia information, as well as the decreasing cost of VR equipment such as headsets. However, it is important to acknowledge that cost, novelty, and complexity still pose limitations to its widespread use. It is worth noting that VR technology has primarily been utilized in the field of health sciences. Extensive research has demonstrated its effectiveness in simulating various processes and contexts, providing students with realistic experiences and access to challenging environments [12; 22; 29; 30; 31; 32; 33]. In conclusion, the application of VR in education has proven beneficial, particularly in terms of motivating students and creating immersive learning experiences. Ongoing advancements in technology and continued research will likely address the limitations and further expand the potential of VR in education.
## 1 Introduction
As a result, the main goal of this research is to assess the impact of various guidance techniques (i.e., AR-based and paper-based guidance) and task difficulty on human performance and physical stress. Human performance as a function of different training methods was measured by total guidance time, while physical stress was evaluated using HRV. This research aims to answer the following question: How do AR-based guidance techniques influence human physical stress, perceived workload, and user task performance (i.e., task completion time) across a range of task difficulties? For maintenance guidance, it was hypothesized that AR-based engagement would be more efficient and successful than traditional paper-based techniques. Therefore, AR-based interactions were expected to result in lower physical stress and perceived workload and improved performance in the experimental maintenance tasks compared to paper-based interactions.
## 3 Results
### Pollution Assessment of Heavy Metal in the Soil Planted Hulless Oats  
Figure 3b shows the \(P_{i}\) value of the eight heavy metals. The mean values of \(P_{i}\) of heavy metals were Ni (0.50) \(>\)Cd (0.48) \(>\) Cr (0.38) \(>\) Cu (0.34) \(>\) As (0.32) \(>\) Zn (0.28) \(>\) Pb (0.09) \(>\) Hg (0.03), all of which were less than 1, indicating no pollution in oats soil as a whole. Cd presents a slight degree of pollution in 4% of the samples, while it reaches medium pollution in 4% of the samples; 4% of Hg and 8% Ni, respectively were at the level of slight pollution. The average value of \(NIPI\) of soil samples was 0.52, which belonged to the safety level. The \(NIPI\) values ranged from 0.52 to 2.37, suggesting that there are point sources of pollution in some areas (Figure 3c); 12% and 4% of \(NIPI\) achieved warning and medium level, respectively.  
The highest mean value of \(I_{\rm{cg0}}\) (Figure 4b) was Ni (0.52), followed by Cd (0.30), Cu (0.01), Hg (\(-\)0.01), Cr (\(-\)0.28), Zn (\(-\)0.62), As (\(-\)1.17), and Pb (\(-\)1.27). The mean values of \(I_{\rm{cg0}}\) of eight heavy metals corresponded to no pollution. For the samples, \(I_{\rm{cg0}}\) values of 68%, 44%, 12%, 76%, 8%, and 56% of Cd, Cu, Zn, Cr, Hg, and Ni are at low pollution level, respectively; 4%, 4%, and 28% of samples are moderately polluted by Cd, Hg, and Ni, respectively; 4% and 4% of samples are moderately to seriously polluted by Cd and Hg. The Ni at 4% of the sampling points exhibited heavy pollution, suggesting that there could be human activities overlapping with the geological accumulation processes in this sample point.  
The \(E_{i}\) of the eight heavy metals in oats soils were as follows: Cd (14.34) \(>\) As (3.18) \(>\) Ni (2.48) \(>\) Cu (1.45) \(>\) Cr (0.66) \(>\) Pb (0.49) \(>\) Hg (0.33) \(>\) Zn (0.24) (Figure 5b). The mean values of \(E_{i}\) for each sampling point were less than 40, indicating the level of ecological risk was at the light degree. Cd reaches a degree of severe pollution in 4% of sampling sites; 4% of Hg values were at the medium pollution level. \(RI\) results revealed that 96% of sampling sites exhibited light potential ecological risk, and 4% of sampling sites were at the level of medium ecological risk. Mean contribution rates of the \(E_{i}\) to the \(RI\) values of eight heavy metals were as follows: Cd (59%) \(>\) As (13%) \(>\) Ni (10%) \(>\) Cu (7%) \(>\) Hg (5%) \(>\) Cr (3%)\(>\) Pb (2%) \(>\) Zn (1%).
## 3 Results and Discussion
### Eggs Quality Changes
#### 3.3.1 Changes of Haugh Unit (HU)
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Sample** & **Parameter** & **Initial Value** & **9 Days** & **18 Days** & **30 Days** \\ \hline \multirow{3}{*}{n-3 PUFAs enriched eggs} & Weight & 59.34 \(\pm\) 2.81 a & 59.03 \(\pm\) 2.13 a & 58.18 \(\pm\) 2.31 b & 56.75 \(\pm\) 2.05 c \\  & HU \(\tt\copyright\) & 95.66 \(\pm\) 1.44 a & 88.97 \(\pm\) 3.68 a & 76.62 \(\pm\) 6.33 c & 54.89 \(\pm\) 4.03 d \\  & YI \(\tt\copyright\) & 0.46 \(\pm\) 0.01 a & 0.43 \(\pm\) 0.01 b & 0.38 \(\pm\) 0.03 b & 0.30 \(\pm\) 0.01 c \\  & albumen pH & 8.40 \(\pm\) 0.14 a & 8.67 \(\pm\) 0.73 a & 9.04 \(\pm\) 0.15 b & 9.36 \(\pm\) 0.12 c \\ \hline \multirow{3}{*}{Conventional eggs} & Weight & 66.45 \(\pm\) 3.71 a & 66.43 \(\pm\) 3.87 a & 65.32 \(\pm\) 3.25 b & 64.18 \(\pm\) 3.43 c \\  & HU \(\tt\copyright\) & 90.56 \(\pm\) 0.43 a & 86.93 \(\pm\) 1.52 a & 72.69 \(\pm\) 2.31 b & 52.14 \(\pm\) 2.37 d \\  & YI \(\tt\copyright\) & 0.45 \(\pm\) 0.02 a & 0.42 \(\pm\) 0.01 a & 0.36 \(\pm\) 0.02 b & 0.27 \(\pm\) 0.03 c \\  & albumen pH & 8.24 \(\pm\) 0.13 a & 8.38 \(\pm\) 0.08 a & 8.99 \(\pm\) 0.23 b & 9.41 \(\pm\) 0.29 c \\ \hline \hline \end{tabular}  
* Values with different letters (a–c) within the same row are significantly different (p \(<\) 0.05);
* See Table 2 for details.  
\end{table} 
Table 3: The quality * changes of n-3 PUFAs enriched and conventional eggs in refrigeration. Figure 6: _Cont._
###### Abstract  
Seed purity is a key indicator of crop seed quality. The conventional methods for cultivar identification are time-consuming, expensive, and destructive. Fourier transform near-infrared (FT-NIR) spectroscopy combined with discriminant analyses, was studied as a rapid and nondestructive technique to classify the cultivars of sweet corn seeds. Spectra with a range of 1000-2500 nm collected from 760 seeds of two cultivars were used for the discriminant analyses. Thereafter, 126 feature wavelengths were identified from 1557 wavelengths using a genetic algorithm (GA) to build simplified classification models. Four classification algorithms, namely K-nearest neighbor (KNN), soft independent method of class analogy (SIMCA), partial least-squares discriminant analysis (PLS-DA), and support vector machine discriminant analysis (SVM-DA) were tested on full-range wavelengths and feature wavelengths, respectively. With the full-range wavelengths, all four algorithms achieved a high classification accuracy range from 97.56% to 99.59%, and the SVM-DA worked better than other models. From the feature wavelengths, no significant decline in accuracies was observed in most of the models and a high accuracy of 99.19% was still obtained by the PLS-DA model. This study demonstrated that using the FT-NIR technique with discriminant analyses could be a feasible way to classify sweet corn seed cultivars and the proper classification model could be embedded in seed sorting machinery to select high-purity seeds.  
FT-NIR; discriminant analysis; KNN; SIMCA; PLS-DA; SVM-DA; cultivars; sweet corn seed _Article_
# A Design for a Manufacturing-Constrained Off-Axis Four-Mirror Reflective System
## 2 Working Principle
### Aberration Analysis for Four-Mirror System  
According to the particular constraints proposed by the previous section, we chose a four-mirror structure that contains a plane mirror. The plane mirror can fold the beam path to meet the special manufacturing method mentioned above and also provides the degree of freedom for the final aberration correction. Figure 5 shows the layout of the four-mirror reflective system.  
This system is composed of three curved mirrors and one plane mirror: a primary mirror \((M_{1})\), a secondary mirror \((M_{2})\), a tertiary mirror \((M_{3})\) and a quaternary mirror \((M_{4})\), in which \(M  
According to the paraxial optical theory [25], the obscure ratios and magnification ratios defined by the Equation (6), the curvature radius of the mirror and the distance between the mirror \(i\) and the mirror \(i+1\) can be deduced as follows:  
\[\left\{\begin{array}{l}R_{1}=\frac{2f^{\prime}}{\beta_{1}\beta_{2}}\\ R_{2}=\frac{2\alpha_{1}f^{\prime}}{\beta_{2}(1+\beta_{1})}\\ R_{4}=\frac{2\alpha_{1}\alpha_{2}f}{1+\beta_{2}}\\ \end{array}\right., \tag{7}\]  
\[\left\{\begin{array}{l}d_{1}=\frac{(1-\alpha_{1})f^{\prime}}{\beta_{1}\beta _{2}}\\ d_{2}+d_{3}=\frac{\alpha_{1}(1-\alpha_{2})f^{\prime}}{\beta_{2}}\\ d_{4}=\alpha_{1}\alpha_{2}f^{\prime}\\ \end{array}\right., \tag{8}\]  
where \(f^{\prime}\) is the focal length of the four-mirror optical system.  
Based on the primary aberration theory [26], we can obtain the third-order aberrations expressed in terms of the structure parameters \(\alpha_{1},\alpha_{2},\beta_{1},\beta_{2},k_{1},k_{2}\) and \(k_{4}\)[27]. Due to space limitation, the implicit functions are as follows:  
\[\left\{\begin{array}{l}S_{1}=S_{1}(\alpha_{i},\beta_{i},k_{i})\\ S_{2}=S_{2}(\alpha_{i},\beta_{i},k_{i})\\ S_{3}=S_{3}(\alpha_{i},\beta_{i},k_{i})\\ S_{4}=S_{4}(\alpha_{i},\beta_{i},k_{i})\\ S_{5}=S_{5}(\alpha_{i},\beta_{i},k_{i})\\ \end{array}\right.. \tag{9}\]
# SMS: A Secure Healthcare Model for Smart Cities
## 1 Introduction
### Main Contributions  
The manuscript proposes an MEC-based healthcare system for smart cities. The main contributions of the paper include:  
* Identifying the limitations of classical healthcare systems and their inapplicability in smart cities ecosystems.
* Identifying the need for transitioning from the cloud to edge and the role of mobile edge computing in providing real-time healthcare services.
* Identifying security vulnerabilities in MEC and the possible solutions.
* Proposing a smart healthcare model for smart cities based on MEC, IoT, and blockchain consensus mechanisms for the reliable, faster, secured, and transparent interconnection of participating entities of the healthcare system.
# Implementing GDPR-Compliant Surveys Using Blockchain
## 3 Background  
### General Data Protection Regulation  
In recent decades, considerable amounts of personal data have been collected and processed online, frequently without proper authorization or adequate ethical considerations. This situation has led to the introduction of privacy-oriented legislation, such as the General Data Protection Regulation (GDPR) [23].  
The GDPR took effect across all European Union member states (EU) in May 2018 [30]. Its purpose is to \"protect the rights, privacy and freedoms of natural persons in the EU\" and to reduce \"barriers to business by facilitating the free movement of data throughout the EU\" [31].  
The regulation considers personal data as \"any information which is related to an identified or identifiable natural person\" [23] and sensitive data as \"data revealing racial or ethnic origin, political opinions, religious or philosophical beliefs, or trade union membership [...] genetic data, biometric data, data concerning health or data concerning a natural person's sex life or sexual orientation\" [23].  
Sensitive data cannot be processed by default, although paragraph two of article 9 lists a few exceptions. The first is when the data subject gives explicit consent for one or more specific purposes. Other exceptions are narrower; most are connected to health/medicaland legal purposes. Article 89 of the GDPR provides other exceptions (safeguards and derogations) relating to processing personal data for archiving purposes in the public interest, scientific or historical research, and statistical purposes [23].  
GDPR identifies four stakeholders: the data subject (DS) (the person that the data refers to), the data controller (DC) (who determines the purpose and the processing that will be performed), the data processor (DP) (who processes the data), and the supervisor authority (an independent public authority that is responsible for the enforcement of the GDPR) [31]. It applies to all companies that process the personal data of European citizens, regardless of where they are headquartered [23], and includes hefty sanctions of up to 20 million Euros or 4% of a company's global revenue (whichever is higher)--see article 83 [23].  
According to this regulation, the data subject (DS) must consent to the processing and can revoke consent or even request the deletion of the data if there is no legal purpose/reason for the organization to continue using it. Nowadays, any system within the scope of GDPR must be compliant. Blockchain is no exception. When using this technology to store and process personal data, it is imperative to make the required adaptations to comply with the regulation.