esa-sceva/satcom-chunk-collection huggingface dataset:
## 8 Future Directions
### Metamaterials in THz Transceivers  
Using metamaterials to design high-gain, compact THz antennas can enhance signal propagation and reduce losses. Metamaterials can be engineered to focus and direct THz waves more efficiently than conventional materials. Developing a THz antenna with a metamaterial lens that focuses the THz beam can reduce diffraction and improve the signal-to-noise ratio (SNR) for long-distance communication.
## 3 Gear Train Efficiency and Meshing Force Model Analysis
### The Meshing Force Analysis of the Five Star Gears  
In order to facilitate the mechanical analysis of the gear train, the center of the sun wheel is taken as the coordinate origin and the orientation of the center of star wheel 1 is taken as the positive direction of the x-axis, and the fixed coordinate system _xoy_ is established, as shown in Figure 4. The five star wheels are evenly arranged, and the sun wheel rotates clockwise. Assuming that the input torque on the sun gear is \(\mathbf{T_{\rm{in}}}\), the meshing force between the sun gear and the star gear \(i\) is \(\mathbf{F_{\rm{spi}}}\), and the torque generated by the force relative to the center of the sun gear is \(T_{\rm{spi}}\). The meshing force between the star gear \(i\) and the inner ring gear is \(\mathbf{F_{\rm{pir}}}\), the torque generated by the force relative to the center of the sun gear is \(T_{\rm{pir}}\), and the output torque of the inner ring gear is \(\mathbf{T_{\rm{out}}}\). \(r_{\rm{bs}}\), \(r_{\rm{bpi}}\), and \(r_{\rm{br}}\) respectively represent the radius of the base circle of the sun gear, the star gear, and the inner ring gear, and \(\mathbf{F_{\rm{pis}}}\) and \(\mathbf{F_{\rm{rpi}}}\) are the reactions of \(\mathbf{F_{\rm{spi}}}\) and \(\mathbf{F_{\rm{pir}}}\), respectively.  
Figure 4: Force analysis of gear trains.  
Figure 3: Power splitting diagram of the star gear train.  
Under the ideal condition, the load of the star gear train is evenly distributed, and the meshing force between each star wheel and the sun wheel is equal. At this time, the sun gear is taken as the research object for mechanical analysis, the meshing force on the sun wheel forms a closed regular pentagon, and the meshing force is always tangent to the base circle of the sun wheel. The five meshing forces form a torque equal to the input torque in the opposite direction, which can be expressed as:  
\[r_{\text{bs}}\sum_{i=1}^{n}F_{\text{spi}}=T_{\text{in}} \tag{3}\]  
Similarly, the internal gear is taken as the research object for mechanical analysis, and the meshing force on the inner ring gear is always tangent to the base circle of the inner ring gear. The five meshing forces on the inner ring gear result in a torque equal to the output torque in the opposite direction, which can be expressed as:  
\[\left(d_{\text{bp}i}+r_{\text{bs}}\right)\sum_{i=1}^{n}F_{\text{pir}}=T_{ \text{out}} \tag{4}\]  
It is worth noting that this closed regular pentagon force model only exists in a completely ideal state. Even if the load-sharing mechanism is adopted, the load cannot be completely evenly distributed. In engineering practice, various methods are adopted to improve the load-sharing performance of the gear train. The purpose is to make the load model of the sun gear infinitely close to a closed regular pentagon.
# SoK: Delegated Security in the Internet of Things
## 5 Security Evaluation of Existing Protocols
### Process and Results of Security Evaluation
#### 5.2.4 Computation Delegation  
In this domain, the application of our mathematical model on the example of Lightweight Revocable Hierarchical Attribute-Based Encryption for the IoT [15] (see Section 2.4, Figure 6) is presented. Based on Definition 6, \(\mathcal{A}_{weak}\) initially receives as an input list \(F\) identified parties which are remotely reachable by \(\mathcal{A}_{weak}\). \(\mathcal{A}_{middle}\) and \(\mathcal{A}_{strong}\) receive as input the access matrix \(M\); hence, they are aware of all participants of the system, and their legitimate access. As ThP is not available for this application domain, \(\mathcal{A}_{weak}\) can query only Del. The absence of the capability of corruption does not allow this kind of adversary to acquire any secrets s of the system parties and to tamper with information within the system. However, \(\mathcal{A}_{weak}\) can perform DoS attacks against the devices of data owners. Hence, the given system can be proven secure against \(\mathcal{A}_{weak}\) in terms of confidentiality and integrity, but insecure in terms of availability.  
The authors assume that all system elements are trusted and cannot be compromised except for data users, but even they are not able to disclose information and tamper with data. Additionally, all communication channels between system parties are considered secure against confidentiality and integrity violations, except three channels (between domain authorities and data owners, the cloud service provider and data owners, and the cloud service provider and data users) that can be susceptible to eavesdropping. Hence, the adversaries \(\mathcal{A}_{middle}\) and \(\mathcal{A}_{strong}\) can violate the confidentiality of the system by listening to these three channels, but they are not able to perform attacks against the integrity of the system data. Considering the possibilities of disrupting system availability, we see the potential for \(\mathcal{A}_{middle}\) and \(\mathcal{A}_{strong}\) to perform DoS attacks against the cloud service provider, causing SPoF. Concluding the above, the given system is not secure against attacks from \(\mathcal{A}_{middle}\) and \(\mathcal{A}_{strong}\) aimed at the availability property.
## 2 Materials and Methods  
In this study an evaluation of three MeteoSwiss sources of precipitation data (conventional in situ measurements, remotely sensed observations, and a geostatistical merging of these two) is conducted using the GPM-DPR level 2 estimated surface precipitation products as a reference. As stated in the introduction, we have tried to minimize the huge differences in the sampling modes by integrating over time several hundreds of synchronous observations (to be precise, 528 satellite overpasses) from 9 March 2014 (when the first GPM precipitation data became available over Switzerland) to 29 February 2016.
## 3 Methods
### Effects of Changes in Weather and LULC on NPP
In fact, the effects of changes in climate on NPP are significant, as are changes in LULC. Therefore, the main effects should consider the three contributions of LULC change, climate change and the common interactions of both [35].The third one probably contains factors such as stand age effect, plant growth and atmosphere CO\({}_{2}\)[58]. Considering the evergreen needleleaf forest (ENF) as an example, if \(S_{1}\), \(S_{2}\), \(NPP_{1}\) and \(NPP_{2}\) express the LULC area and NPP per unit area, respectively, in times \(t_{1}\) and \(t_{2}\), the variation of total NPP (\(\Delta NPP_{\rm T}\)) should be expressed as Equation (4):  
\[\Delta NPP_{\rm T}=NPP_{2}\times S_{2}-NPP_{1}\times S_{1}=\Delta NPP\times S _{1}+\Delta S\times NPP_{1}+\Delta S\times\Delta NPP \tag{4}\]  
Thus, the fractional variation in \(\Delta NPP_{\rm T}\) includes three parts in Equation (4): the fractional variation affected by climate change equals the first item (\(\Delta NPP\times S_{1}\)), the fractional variation affected by LULC change equals the second item (\(\Delta S\times NPP_{1}\)),and their common interaction term which is the last item (\(\Delta NPP\times\Delta S\)).Not considering their common interactions, the following equations show the relative effects of changes in weather variables (Equation (5)) and LULC (Equation (6)) on total NPP (\(R_{\rm C}\), \(R_{LULC\rm change}\)). \[R_{\rm C}=\frac{\left|\Delta NPP\times S_{i}\right|}{\left|\Delta NPP\times S _{i}\right|+\left|\Delta S\times NPP_{i}\right|+\left|\Delta S\times\Delta NPP \right|}\times 100\% \tag{5}\]  
\[R_{LULCC}=\frac{\left|\Delta S\times NPP_{i}\right|}{\left|\Delta NPP\times S _{i}\right|+\left|\Delta S\times NPP_{i}\right|+\left|\Delta S\times\Delta NPP \right|}\times 100\%\]
# Error-Correction Coding Using Polynomial Residue Number System
###### Abstract  
There has been a tendency to use the theory of finite Galois fields, or GF(2n), in cryptographic ciphers (AES, Kuzmyechik) and digital signal processing (DSP) systems. It is advisable to use modular codes of the polynomial residue number system (PRNS). Modular codes of PRNS are arithmetic codes in which addition, subtraction and multiplication operations are performed in parallel on the bases of the code, which are irreducible polynomials. In this case, the operands are small-bit residues. However, the independence of calculations on the bases of the code and the lack of data exchange between the residues can serve as the basis for constructing codes of PRNS capable of detecting and correcting errors that occur during calculations. The article will consider the principles of constructing redundant codes of the polynomial residue number system. The results of the study of codes of PRNS with minimal redundancy are presented. It is shown that these codes are only able to detect an error in the code combination of PRNS. It is proposed to use two control bases, the use of which allows us to correct an error in any residue of the code combination, in order to increase the error-correction abilities of the code of the polynomial residue number system. Therefore, the development of an algorithm for detecting and correcting errors in the code of the polynomial residue number system, which allows for performing this procedure based on modular operations that are effectively implemented in codes of PRNS, is an urgent task.  
residue number system (RNS); polynomial residue number system (PRNS); detecting and correcting errors; positional characteristic; polynomial interval +
Footnote †: journal: Applied sciences
# Detection of Wind Turbine Failures through Cross-Information between Neighbouring Turbines
## 3 Materials and Methods  
In the following sections, the experimental database is described. Because not all the sensors have all the data times, a temporal alignment method is also presented. Then, an outlier removing block is described and after that, the reference signal for all the WTs can be calculated, from which the deviation of each individual turbine will be extracted. This residue will be used to identify possible failures in the turbines. Finally, the Gearbox subsystem is analysed using the above-mentioned procedure.  
Figure 1: Plot of the pattern of variation of four variables of all the WTs in a common time interval. It can be seen that although the variation is not identical, it follows a regularity when the turbines work correctly but substantially differ if one of them does not, as is the case of WT83 (shown in green) at the beginning of the figure.
# Resilience in the Internet of Medical Things: A Review and Case Study
## 6 A Case Study: Remote Patient Monitoring of a Heart Patient in the IoMT Networks
### Proposed Methodology
#### 6.3.3 Third Combination: MSA + SDN  
The use of software-defined networking provides the centralization of the system by having the global view of the entire IoMT network. However, the single centralization of the IoMT networks also brings a single point of failure. Traditionally, SDN is realized as a monolithic system for a single functionality of the entire network, which contains the issues of latency, scalability, and failure due to different cyber-attacks. The collaboration of SDN with microservices architecture brings the optimization into the IoMT networks [83; 110]. Ref. [81] combined microservices architecture with SDN on fog nodes. The use case belongs to the healthcare sector with a data transmission of patients' BP and ECG monitoring. The objective of [81] is to find QoS in terms of reduced response time and low latency. Mixed Integer Linear Programming (MILP) is used to formulate this MSA and SDN combination. The results show that the response time and latency remain stable as the number of nodes scale up with the larger topology in the IoMT networks.  
If microservices architecture with the combination of SDN is containerized on routing devices, it can diagnose the failure in the IoMT networks by restoring the data transfer operations among the active nodes. The distributed deployment of these services in IoMT networks can mitigate the issue of a single point of failure and can make the network more flexible and reliable.  
These three different combinations indicate the possible benefits of the distributed functionality and resilience in the expected framework shown in Figure 6. However, an integration of SDN, ML, and MSA can bring almost all the potential features together to make a system resilient. The next subsection explains the procedure through the proposed architecture by using this integration.
# A Survey on Big IoT Data Indexing: Potential Solutions, Recent Advancements, and Open Issues
## 4 Big Data Indexing Requirements  
Some recent studies on indexing techniques have highlighted how to optimize the search performance in large datasets with greater efficiency. This section discusses and describes some of the indexing needs that are more challenging than traditional data. Then, each indexing technique is analyzed based on these constraints to determine its applicability on a large scale.  
The challenges of designing the indexing techniques are related to the need for generalization of multidimensional spaces to metric spaces and the additional constraints on the set of feasible solutions. The constraints in Figure 5 may include the constraints on data independence, scalability, or efficiency [49].  
Figure 4: Big IoT data’s characteristics.  
Figure 5: Big Data indexing requirements.  
In general, the objects which require indexing are more complex than mere vectors [50; 51; 52; 53; 54]. This shifts the focus of indexing from multidimensional spaces to metric spaces. Formally, a metric space is defined for quantifying similarities or different elements through a given distance in such a way that smaller distances may correspond to more similar elements. Metric spaces are therefore a very general concept and can be applied to vectors as well as objects such as strings and graphs that can not easily be represented as vectors [55]. Several similarity measures exist for various types of objects, such as Minkowski distances. Manhattan and Euclidean distances are best known and can be used for any form of vector data, such as color histograms in multimedia databases.  
Similarity queries are a very important operation in IoT applications involving large data volumes and complex objects. They focus on finding objects in a dataset similar to a query object, based on a similarity measure. In metric space, the similarity query refers to the selection of objects in a dataset \(\mathcal{O}\) that are at a certain distance \(d(\cdot,\cdot)\) from a given point \(o_{\mathcal{G}}\).  
The main factor that affects the efficiency of the indexing algorithm when the dimension is increased is the dimensionality-curse problem [56; 57]. The approaches available in the literature have proven to be unreliable, making it difficult to index, manage and analyze large volumes of data. This is due to the inherent deficiencies of spatial partitioning and also to the factor of overlap between regions. This question therefore remains open for future research.
## 7 Results and Discussion
### Underlying IoT Infrastructure  
In the tests, the total time until the resources and its locations from the CoAP network are obtained has been measured. This process includes the resources and end point collection from observation of the RD discovered, and the location collection from each end point obtained. Tests have been done with access control enabled (thereby with secure communication) and disabled in order to evaluate Appdaptivity in the different environments that it can be deployed in. Each CoAP server used in the CoAP network has 4 associated resources: temperature and humidity simulated sensors, a light simulated actuator and a location resource to obtain its physical location. Figure 9 shows average response times obtained from the performance tests done. As can be seen, the discovery time with access control enabled varies a little, whereas in the secure communication the discovery time increases more notably with respect to the number of CoAP servers.  
Secure communications use DTLS which implies higher delays than UDP due to the necessary steps to secure the channel. However, Appdaptivity can manage these CoAP networks without large delays, and therefore it can be applied in large IoT deployments. In this test, it was also indented to evaluate the requirements of portable application development, thanks to the portability of IoT infrastructure (done thanks to the discovery) and personalised applications (secure configuration).  
Deploying complex behaviours which involve a large number of PoCo components can reduce the scalability of the system. For that reason, the location discovery response time has once again been measured with 7 CoAP servers (4 resources each) and a different number of PoCo components. Figure 10 shows the results obtained with respect to a different number of PoCo components. In the same chart, the location discovery response time obtained in Figure 9, is also included which has fewer less than 20 PoCo components. As can be seen, the number of deployed PoCo components does not affect, to a great degree, to the scalability of Appdaptivity.  
Figure 10: Location discovery response time with respect to the PoCo components deployed.  
Figure 9: Location discovery response time with respect to the CoAP end points discovered.
## Appendix B  
This section presents the effectiveness of the IMGAMO algorithm. Several tests were performed to analyze and check the effectiveness of IMGAMO algorithm with respect to one of the most popular metaheuristic optimization algorithms (NSGA II and SPEA2). For the comparison, the set of DTLZ test functions were chosen [44]. Formulations of the DTLZ problems are presented in Table 1.  
The experiments were configured to run 100,000 function evaluations and DTLZ is configured with 12 variables (if it is not stated otherwise). The results of IMGAMO algorithm were compared to \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# of Criteria & **Algorithm** & **GD (StdDev)** & **S (StdDev)** & **FrontSize(StdDev)** \\ \hline \multirow{4}{*}{3} & IMGAMO & 0.08 (0.01) & 0.06 (0.03) & 104.6 (2.77) \\  & NSGAII & 1.69 (2.36) & 0.14 (0.1) & 97.1 (6.01) \\  & SPEA2 & 49.71 (124.23) & 1.59 (3.95) & 97 (8.58) \\ \hline \multirow{4}{*}{5} & IMGAMO & 0.03 (0.01) & 0.07 (0.02) & 143.6 (16.86) \\  & IMGAMO & 0.03 (0.02) & 0.14 (0.03) & 69 (11.01) \\ \cline{1-1}  & IMGAMO & 0.03 (0.03) & 0.18 (0.1) & 19.8 (9.86) \\ \cline{1-1}  & IMGAMO & 0.01 (0.01) & 0.17 (0.14) & 11 (9.2) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of DTLZ3 problem results. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \# of Criteria & **Algorithm** & **GD (StdDev)** & **S (StdDev)** & **FrontSize(StdDev)** \\ \hline \multirow{4}{*}{4} & IMGAMO & 0.07 (0.04) & 0.17 (0.24) & 97 (26,19) \\  & NSGAII & 110.53 (55.01) & 13.98 (6.08) & 99.9 (0.29) \\  & SPEA2 & 150.81 (47.48) & 12.96 (2.85) & 100 (0) \\ \hline \multirow{4}{*}{5} & IMGAMO & 0.03 (0.02) & 0.04 (0.01) & 116 (26.32) \\  & NSGAII & 564.09 (29) & 65.15 (4.66) & 100 (0) \\  & SPEA2 & 635.18 (25.31) & 37.44 (1.98) & 100 (0) \\ \hline \multirow{4}{*}{6} & IMGAMO & 0.04 (0.03) & 0.16 (0.08) & 33 (5.66) \\  & NSGAII & 570.94 (34) & 77.94 (7.38) & 100 (0) \\  & SPEA2 & 674.02 (10.74) & 40.8 (3.62) & 100 (0) \\ \hline \multirow{4}{*}{7} & IMGAMO & 0.04 (0.09) & 0.31 (0.24) & 12.5 4.92 \\  & NSGAII & 520.99 (25.19) & 78.74 (12.05) & 100 (0) \\ \cline{1-1}  & SPEA2 & 618 (6.86) & 25 (4.86) & 100 (0) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of results for DTLZ1 problem. For the last problem analyzed--DTLZ5--the experiments were performed for four, five, six and seven criteria in comparison to SPEA2 and NSGA-II, and additionally, for eight, nine and 10-criteria formulations (Table A8). All experiments proved that the IMGAMO algorithm deals with such problems, finding fronts close to the real ones. To summarize, the biggest strength of this approach is in dealing with problems with higher numbers of criteria. Algorithm IMGAMO has successfully found the fronts close to the real ones for problems with 4-10 criteria, dealing with several difficulties:  
* Concave set of feasible solutions (DTLZ2-DTLZ5);
* Front in curved form (DTLZ5);
* Space with many local fronts (DTLZ1);
* Uniform arrangement of solutions over the front.
## 4 Self-Tuning Exploratory Data Analytics
### Probabilistic Approach
#### 4.2.1 Latent Dirichlet Allocation  
The latent Dirichlet allocation (LDA) is a generative probabilistic model for collections of discrete data such as text corpora [36].  
Using Bayesian inference (posterior inference), LDA infers the hidden structure to discover topics inside the collection under analysis. Documents are treated as mixtures of topics and topics as mixtures of words. For each document in the collection, words are generated through a two-stage process:  
1. Firstly, a distribution over a topic is randomly chosen;
2. Then, for each word in the document: 1. a _topic_ is randomly chosen from the distribution defined at the previous step (Step 1); 2. a _word_ is randomly chosen from the corresponding distribution over the dictionary.  
Each document shows topics in different proportions (Step 1); then, each word in each document is drawn from one of the topics (Step 2b), where the selected topic is chosen from the per-document distribution over topics (Step 2a).  
In order to generate each document in the corpus, two steps are performed [3]:  
1. The choice of the number of terms from a Poisson distribution;
2. After that, for each of the document's words: - The choice of a topic \(z_{n}\) from multinomial (\(\mathbf{\theta}\)), where \(\mathbf{\theta}\) is a Dirichlet (\(\mathbf{\alpha}\)), representing the document-topics distribution; - The choice of a word \(w_{n}\) from multinomial (\(\mathbf{\phi}_{z_{n}}\)), where \(\mathbf{\phi}\) represents the topic-words distribution (\(\mathbf{\phi}\sim\text{Dirichlet (}\mathbf{\beta}\))), conditioned on the previously chosen topic \(z_{n}\).  
So, if we consider a collection of \(K\) topics \(\mathbf{z}\), a collection of \(N\) terms \(\mathbf{w}\), and a document-topics distribution \(\mathbf{\theta}\), the joint multivariate distribution can be defined as:  
\[p(\mathcal{D}|\mathbf{\alpha},\mathbf{\beta})=\prod_{d=1}^{K}\int p(\mathbf{\theta}_{d}| \mathbf{\alpha})\left(\prod_{n=1}^{N_{d}}\sum_{z_{dn}}p(z_{n}|\mathbf{\theta}_{d})p(w _{dn}|z_{dn},\mathbf{\beta})\right)d\mathbf{\theta}_{\text{d}},\]  
where  
* \(\mathbf{\alpha}\) describes the concentration for the prior placed on documents' distributions over topics (\(\theta\)). Low \(\mathbf{\alpha}\) values will create documents that likely contain a mixture of only few topics;
* \(\mathbf{\beta}\) represents the concentration for the prior placed on topics' distributions over terms. Low \(\mathbf{\beta}\) values will likely produce topics that are well-described just by few words.  
Generally, it is unfeasible to compute these distributions; thus, this posterior Bayesian inferential problem cannot be solved exactly. In order to bypass such an issue, it is possible to exploit different approximate inference algorithms: the online variational Bayesalgorithm [49] is the one that ESCAPE uses, while \(\alpha\) and \(\beta\) are set to maximize the log likelihood of the data under analysis.