esa-sceva/satcom-chunk-collection huggingface dataset:
# Rebirth of Distributed AI--A Review of eHealth Research
## 7 Challenges of Federated Learning and Relevant Research Works
### Communication Cost  
Although in federated learning there is no need to communicate raw data with a central server, and hence there is less overhead on the network, there is still a need to transmit millions of parameters between the participants and the server. This poses an overhead on the communication channel, especially if we consider the multiple communication rounds needed to achieve model convergence. Moreover, unreliable network connectivity and limited bandwidth can add to the communication cost. In addition, complementing FL with other privacy-preserving techniques such as differential privacy or encryption adds to the size of the updates and hence to the communication costs. The FL training process encompasses different steps, and each has an impact on communication. Any of the following can cause either an increase or decrease in the number of communication rounds or in the size of the model (i.e., parameters exchanged):  
* The design of the deep network model being trained (e.g., number of layers)
* The participants' selection--their data quality, their computation power, and the number of participants at each round
* The decided local epochs for each vehicle
* The frequency of sending updates  
Based on the above, strategies adopted by research so far to reduce communication cost in FL are focused on:  
* reducing the total number of communication rounds, or
* reducing the size of transmitted messages at each round.  
Inspired by the trends in the research literature, we suggest three major solution categories, namely: (i) Techniques that define the load of training put on the servers and the clients to train the model (ii) Techniques that define the size of the model exchanged between the clients and the servers (iii) Techniques that define the sub-set of clients participating in the training process. Figure 10 depicts these solution dimensions.  
The first technique assigns more local updates on a device before sending for aggregation [68; 69]. On this same subject, another strategy focuses on finding a balance between global and local training. The strategy is based on partitioning the deep learning process into parts and outsourcing some of the parts to a more computationally powerful server, either in the edge or in the cloud such as in [70; 71; 72; 73].  
In regards the data compression techniques, quantization and sparsification, the aim of these techniques is to reduce the size of the updates that are exchanged between clients and servers. Research works [74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85], are some of the major approaches contributed in this regard. For the \"fewer rounds\" and \"relevant updates only\", algorithms are developed to keep a balance between the number of communication rounds and their relevancy, and the model accuracy. Research works in this area include [86; 87; 38].  
Figure 10: Solution approaches for communication relevant challenges.
_Appl. Sci._**2015**, \(5\), 1503-1527; doi:10.3390/app5041503  
_Applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
_Article_  
**Privacy-Preserving Smart Metering with Authentication in a Smart Grid**  
**[PERSON] 1, [PERSON] 2 and [PERSON] 3,* \({}^{\ast}\)**  
\({}^{1}\) Department of Computer Science and Engineering, Korea University, 145 Anam-ro,  
Seongbuk-gu 02841, Korea; E-Mail: [EMAIL_ADDRESS]  
\({}^{2}\) Department of Computer Science, Korea Advanced Institute of Science and Technology (KAIST),  
291 Daehak-ro, Yuseong-gu 34141, Korea; E-Mail: [EMAIL_ADDRESS]  
\({}^{3}\) National Security Research Institute, 1559 Yuseongdae-ro, Yuseong-gu 34044, Korea  
* Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS];  
Tel.: +82-42-870-2306.  
Academic Editor: [PERSON]  
_Received: 28 September 2015 / Accepted: 24 November 2015 / Published: 1 December 2015_  
**Abstract:** The traditional security objectives of smart grids have been availability, integrity, and confidentiality. However, as the grids incorporate smart metering and load management, user and corporate privacy is increasingly becoming an issue in smart grid networks. Although transmitting current power consumption levels to the supplier or utility from each smart meter at short intervals has an advantage for the electricity supplier's planning and management purposes, it threatens user privacy by disclosing fine-grained consumption data and usage behavior to utility providers. In this study, we propose a distributed incremental data aggregation scheme where all smart meters on an aggregation path are involved in routing the data from the source meter to the collection unit. User privacy is preserved by symmetric homomorphic encryption, which allows smart meters to participate in the aggregation without seeing any intermediate or final result. Aggregated data is further integrated with an aggregate signature to achieve data integrity and smart meter authentication in such a way that dishonest or fake smart meters cannot falsify data en route. Only the collection unit can obtain the aggregated data and verify its integrity while the individual plain data are not exposed to the collection unit. Therefore, user privacy and security are improved for the smart metering in a smart grid network.  
**Keywords:** smart grid; smart metering; privacy; security; data aggregation
## 4 Results and Discussion
### Hyperparameter Adjustment for the Student's Knowledge Level Prediction
Therefore, the greater the amount of data in the blue section, the more accurate the prediction of the model. The second type of prediction is taken as the observation object, and the accuracy of the prediction in the training set increases with the increase in the complexity of the model. The prediction results of SSA-SVM were similar to those of the KPCA-ISSA-SVM model, and the prediction accuracy of the second type of data reached 92.7%. This is possible because the SSA-SVM model predicts overfitting during the training stage. Observing the results of the test set, it is obvious that the accuracy of the composite model's prediction of students' knowledge level increases with the increase in the complexity of the model. The prediction results yielded by the training sets of the ISSA-SVM and KPCA-ISSA-SVM models were similar to those for the test sets, and the second type of prediction accuracy for the ISSA-SVM model was 89.9% in the training set and 92.3% in the test set. The second type of prediction accuracy for the KPCA-ISSA-SVM model was 92.7% for the training set and 94.9% for the test set. Through the analysis of the above data and pictures, we can conclude that the integration of the SSA algorithm and the KPCA data processing method with the initial machine learning model SVM can effectively improve the prediction accuracy of the model. In this study, by comparing the prediction results and prediction accuracy of the SVM, SSA-SVM, ISSA-SVM, and KPCA-ISSA-SVM models, a composite machine learning model that can be developed that can accurately predict students' knowledge level. This model can effectively analyze the influences of input parameters such as STG and SCG on the UNS results, and explore the internal relationships among them. Thus, a prediction model with high accuracy is constructed, which can effectively help to show students' knowledge level, and further provide powerful informational support for students' development and individualized teaching. Figure 5: Cont. \begin{table}
\begin{tabular}{c c c c c} \hline
**Accuracy Rate (\%)** & **SVM** & **SSA-SVM** & **ISSA-SVM** & **KPCA-ISSA-SVM** \\ \hline Training Datasets & 96.0584 & 96.4413 & 95.7295 & 96.7972 \\ \hline Testing Datasets & 91.8033 & 92.623 & 95.082 & 96.7213 \\ \hline \end{tabular}
\end{table} 
Table 3: Predicted UNS for the training datasets and testing datasets.
# Seismic Response Analysis of Prestressed Concrete
## 4 Rocking Motion Equation
\begin{array}{rl}T=&\frac{1}{2}I_{\rm c}\dot{\theta}_{1}^{2}+\frac{1}{2}I_{ \rm b}^{\rm G}\left(\partial_{\theta_{1}}\theta_{2}\right)^{2}\dot{\theta}_{1 }^{2}+\frac{1}{2}I_{\rm c}\left(\partial_{\theta_{1}}\theta_{3}\right)^{2} \dot{\theta}_{1}^{2}\\ &+\frac{1}{2}m_{\rm b}\left[\left(2R_{\rm c}^{\prime}\right)^{2}+4R_{\rm c}^{ \prime}R_{\rm b}\cos(\beta-\gamma-\theta_{1}+\theta_{2})\partial_{\theta_{1}} \theta_{2}+\left(R_{\rm b}\partial_{\theta_{1}}\theta_{2}\right)^{2}\right] \dot{\theta}_{1}^{2}\end{array}  \tag{26}\]  
During an admissible rotation \(\delta\theta_{1}\), Equation (27) can be derived by the principle of virtual work  
\[\delta W=Q\delta\theta_{1} \tag{27}\]  
The virtual work caused by the external field forces is  
\[\delta W=-\ddot{u}_{\rm g}\left(m_{\rm c}\delta x_{\rm AB}^{\rm G}+m_{\rm b} \delta x_{\rm BC}^{\rm G}+m_{\rm c}\delta x_{\rm CD}^{\rm G}\right) \tag{28}\]  
where  
\[ \begin{array}{rl}\delta x_{\rm AB}^{\rm G}=R_{\rm c}\cos(\alpha+\theta_{1}) \delta\theta_{1}\\ \delta x_{\rm BC}^{\rm G}=\left[2R_{\rm c}^{\prime}\sin(\beta-\theta_{1})+R_{ \rm b}\sin(\gamma-\theta_{2})\partial_{\theta_{1}}\theta_{2}\right]\delta \theta_{1}\\ \delta x_{\rm CD}^{\rm G}=R_{\rm c}\cos(\alpha+\theta_{3})\partial_{\theta_{1}} \theta_{3}\delta\theta_{1}\end{array}  \tag{29}\]  
Substituting Equations (28) and (29) into Equation (27) yields the generalized force \(Q\):  
\[Q=-\ddot{u}_{\rm g}\left\{m_{\rm c}R_{\rm c}\left[\cos(\alpha+\theta_{1})+ \cos(\alpha+\theta_{3})\partial_{\theta_{1}}\theta_{3}\right]+m_{\rm b}\left[2 R_{\rm c}^{\prime}\sin(\beta-\theta_{1})+R_{\rm b}\sin(\gamma-\theta_{2}) \partial_{\theta_{1}}\theta_{2}\right]\right\} \tag{30}\]  
The substitution of Equations (15), (16), (26) and (30) into Equation (13) results in the motion equation of the PCRF:  
\[I(\theta_{1})\ddot{\theta}_{1}+J(\theta_{1})\dot{\theta}_{1}^{2}+G(\theta_{1} )\rm g+K(\theta_{1})=S(\theta_{1})\ddot{u}_{\rm g} \tag{31}\]  
The terms \(I\), \(J\), \(G\), \(K\) and \(S\) in Equation (31) are nonlinear functions of the generalized coordinate \(\theta_{1}\) and are expressed as Equation (32):  
\[ \begin{array}{rl}I(\theta_{1})=&I_{\rm c}+I_{\rm b}^{\rm G}\left(\partial_{ \theta_{1}}\theta_{2}\right)^{2}+I_{\rm c}\left(\partial_{\theta_{1}}\theta_{3} \right)^{2}+m_{\rm b}\left[\left(2R_{\rm c}^{\prime}\right)^{2}+4R_{\rm c}^{ \prime}R_{\rm b}\cos(\beta-\gamma-\theta_{1}+\theta_{2})\partial_{\theta_{1}} \theta_{2}+\left(R_{\rm b}\partial_{\theta_{1}}\theta_{2}\right)^{2}\right]\\ J(\theta_{1})=&\left\{R_{\rm b}^{\rm G}\partial_{\theta_{1}}\theta_{2} \partial_{\theta_{1}}^{2}\theta_{2}+I_{\rm c}\partial_{\theta_{1}}\theta_{3} \partial_{\theta_{1}}^{2}\theta_{1}\theta_{3}\right.\end{array}  \tag{32}\]  
\[
# A Novel Hybrid Genetic-Wale Optimization Model for Ontology Learning from Arabic Text
## 4 Proposed Model for Arabic Ontology Learning
### Proposed Text Mining Algorithm
#### 4.2.1 Term Weighting  
The weight in text mining is a well-known statistical measure for evaluating how important a term (word) is for a textual document in a corpus. Thus, we assigned a weight to each term of a document. This procedure is called term weighting. Thereby, every document is expressed in a vector form relying on the terms encompassed inside. Formally speaking, the vector that characterizes the document will be in the following format:  
\[doc_{n}\ =\ \ \left\{TW_{1},\ TW_{2},\......\ TW_{a},\....\ TW_{|C|}\right\} \tag{10}\]  
where \(TW_{a}\) refers to the weighting of the term that has the number \(m\) in the _doc_ document of index \(n\), \(C\) represents the term set, and \(|C|\) denotes the cardinality of \(C\). To obtain a vector involves the terms of \(C\), the TF-IDF is utilized as weighting. Assume that the term frequency \(TF_{a}\) expresses the occurrences number of \(T_{a}\) within the document, and the document frequency \(DF_{a}\) is the document number in which the given term \(T_{a}\) can be seen at least once. Thus, we can compute the inverse document frequency \(IDF_{a}\), as illustrated in Equation (11) using \(DF_{a}\)[68]:  
\[IDF_{a}\ =\ \log\left(\frac{|DOC|}{DF_{a}}\right) \tag{11}\]  
where \(|DOC|\) denotes the number of documents assigned as a training set, and \(\text{TW}_{a}\) is computed by Equation (12):  
\[\text{TW}_{a}\ =\ \text{TF}_{a}\ \cdot\ \text{IDF}_{a}. \tag{12}\]  
Subsequently, the irrelevant and redundant features are eliminated from the text document, thus, we can represent the document set as a \"document-term\" matrix as follows:  
\[\left[ \begin{array}{c}T_{1}\\ T_{2}\\ \cdot\\ \cdot\\ T_{a}\end{array} \right]=\left[ \begin{array}{cccc}TW_{(1,1)}&TW_{(1,2)}& \......&TW_{(1,a)}\\ TW_{(2,1)}&TW_{(2,2)}&\......&TW_{(2,a)}\\.&.&.&.\\ TW_{(a,1)}&TW_{(a,2)}&\......&TW_{(a,A)}\end{array} \right]. \tag{13}\]  
Depending on the resulting weights for feature frequency, the algorithm maps the document's terms to corresponding concepts. As illustrated in Algorithm 1, \(TW\) and \(CW\) are two matrices to the same document, and \(S_{T}\) and \(C_{T}\) indicate the sets of terms and concepts, respectively. The algorithm reveals that through mapping, the document's terms to correlative concepts, the document's vector of terms will be converted into a vector of concepts. Thus, the algorithm will replace the document set of Equation (13) by the _document-concept matrix_ in Equation (14):  
\[\left[ \begin{array}{c}T_{1}\\ T_{2}\\ \cdot\\ \cdot\\ T_{a}\end{array} \right]=\left[ \begin{array}{cccc}CW_{(1,1)}&CW_{(1,2)}& \......&CW_{(1,a)}\\ CW_{(2,1)}&CW_{(2,2)}&\......&CW_{(2,a)}\\.&.&.&.\\ CW_{(a,1)}&CW_{(a,2)}&\......&CW_{(a,A)}\end{array} \right] \tag{14}\]  
where \(CW_{(l,m)}\) denotes the frequency weight for \"concept 1\" in document \(m\), \(a\) represents the documents number, and \(A\) is the concepts number.
# Exploitation of Antimicrobial Nanoparticles and Their Applications in Biomedical Engineering
## 2 Nanomaterials
### Nanoparticle Synthesis and Processing Methods
#### 2.1.1 Bottom Up
found that microwave-assisted synthesis can produce needle-like nanoparticles, but when the microwave power was increased, the morphology changed into flower-shaped nanoparticles [36]. 3. Biological synthesis: Biosynthetic methods can be divided into two classes, the mycosynthesis (utility of fungi) and phytonanotechnology (utility of plants) [39; 40]. These eco-friendly methods are also known to produce nanoparticles with active biological function (i.e., antimicrobial). This way, Nanoparticles can be synthesised without utilising toxic chemicals or concerns over generations of harmful by-products. For instance, it is possible to replace the reducing agent used in chemical methods with harmless microorganisms or plant extracts [40]. Well-established biosynthetic methods for NP preparations can also be very cost-effective [41]. The production of antimicrobial Ag NPs via mycosynthesis was reported by [PERSON] et al., where fungi _Apergillus niger_ and _Fusarium semitecum_ were utilised [42]. Although there are numerous advantages of using biological methods for nanoparticle synthesis, there are a few drawbacks. First, many of the mechanisms involved in these biosynthetic processes remain unclear. Second, it is not easy to manipulate the constituents in microorganisms or plant extracts to optimise the quality and quantity of nanomaterialproductions. Hence, nanoparticles formed using such synthetic methods often result in low production rates and yields.
## 3 Results and Discussion  
### Validation of the Method  
A quantitative analysis of the HM was carried out, and calibration curve equations were plotted between concentrations 1.0 and 100 mg/L. The calibration curves consisted of four concentrations. Table 2 shows correlation coefficients, calibration curve equations, ranges, LOD, and LOQ values for each HM. The correlation coefficients were more than 99%, confirming excellent response. The LOD value was between 0.06 mg/L and 0.10 mg/L, and the LOQ values varied between 0.18 mg/L and 0.31 mg/L, which showed that the method is sensitive.  
Table 3 shows the mean recovery and standard deviation results using reference standard solution for each HM. Results for HM were in agreement at a 95% confidence level with the certified values for each metal. Mean recovery results were between 99.6 and 100.5% for all metals.
# An Unsupervised Anomaly Detection Based on Self-Organizing Map for the Oil and Gas Sector
## 1 Introduction
The research study focuses on managing and detecting possible anomalies in the two-phase experimental plant in the Department of Industrial Engineering and Mathematical Sciences (DIISM) of the Universita Politecnica delle Marche (Ancona, Italy). An unsupervised algorithm called Self-Organizing Maps was chosen to conduct the research study. This algorithm allows for easy verification of the status of the system as the data from the plant are projected onto a two-dimensional output map where each node represents a particular state of the system. The article is organized as follows. After the introduction, a systematic review of the literature in the oil and gas sector is explored in Section 2. Section 3 accurately describes the experimental plant with all its components. In addition, the operation of the SOM-type algorithm used to conduct the study is described. The algorithm is trained based on the data collected on the plant, and the output map in Section 4 is then analysed. Finally, Section 5 summarises and outlines future research directions.
# Sustainable and Reliable Information and Communication Technology for Resilient Smart Cities
## 3 Sustainable Smart Cities
### Definition: Smart Cities  
We summarize the smart city understanding according to [19; 20; 21] as follows. A smart city is:  
1. Intelligent
2. Sustainable, but also
3. Adaptable meaning that it can adapt its action and process options according to social and/or economic needs,
4. User-oriented, meaning that the citizens of a municipality are at the center of attention; satisfying their needs and optimizing related processes and services using ICT is the main goal of a smart city
5. Responsive, meaning that both the administration and the optimized processes and ICT services are in constant interaction with the citizens of a smart municipality
6. Sensitive, using various types of sensor technology and data acquisition tools, a constant attempt is made to scan the situation and obtain the relevant data and to use it for new types of services, applications and process control options, and
7. Innovative, meaning that the smart city creates an eco-system in which constant innovation--based on data, information, networking and modern ICT--continuously optimizes and improves urban efficiency and the quality of life of citizens.
# A Novel Data-Aided Frame Synchronization Method Based on Hough Transform for Optical Communications
## 5 Conclusions  
In this work, we have conducted a detailed investigation on the techniques of frame synchronization, and give a brief review of some previous works. A typical frame synchronization method based on auto-correlation was introduced as a comparison. Our proposed algorithm based on Hough transform was finally presented. Simulations were carried out over a 56 Gbps QPSK coherent optical transmission system.  
The workflow of our algorithm was shown first and some key parameters were analyzed, including \(r\), the columns of interception matrix \(C\), and \(\alpha\), a threshold set for recognizing frame length. The results show that our algorithm still works well at BER = 0.3, and the optimal value for \(r\) is between 100 and 200, considering both the computation and bit-error resilience. The trend of the value of \(\alpha\) with the growth of BER reflects the suppression effect on noise of our algorithm. A comparison of recognition performance between our algorithm and an existing autocorrelation-based algorithm was also made. The results demonstrate that our algorithm outperforms the AC-based algorithm significantly, as the BER exceeds 0.03. It is noteworthy that our algorithm is also effective for the recognition of the frame synchronization word inserted in the frame at intervals.  
However, the proposed algorithm requires the frame synchronization word to be inserted periodically in the receiving frames, which does not apply to code-aided frame synchronization. On the other hand, the computation of our algorithm is not compared with other algorithms, which will be further studied.  
Conceptualization, H.Y.; methodology, J.C.; software, H.Y.; supervision, Z.H.; validation, S.L.; writing (original draft), H.Y.; writing, review and editing, S.L. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (General Program), Grant No. 51575517.  
The authors would like to acknowledge [PERSON] for providing high-performance computer as a simulation platform, and the authors also would like to thank editors and anonymous reviewers for giving valuable suggestions that have helped to improve the quality of the manuscript.  
Figure 9: Comparison between HT-based recognition algorithm and AC-based algorithm. HT, Hough Transform; AC, autocorrelation.
## 4 Energy Consumption Analysis  
### Energy Consumption According to the Passive Elements  
The energy consumption in Seoul and Busan for large offices and elementary schools was analyzed by building direction. In both Seoul and Busan, elementary schools with S/V ratios of 0.46, had a higher energy consumption than that of large offices with S/V ratios of 0.25. Additionally, the difference in energy consumption between the two building types is larger in Seoul than in Busan (Table 6). This means that in buildings with the same volume, the larger the area of cooling and heating energy, the greater the influence on the change in energy consumption according to the S/V ratio [25].  
As for the energy consumption according to the direction of the building, for both building types, the direction with the highest energy consumption is northeast, and the direction with the lowest energy consumption is south (Figure 5).  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{1}{c}{**Properties**} & \multicolumn{1}{c}{**Heating Energy**} & \multicolumn{1}{c}{**Cooling Energy**} & \multicolumn{1}{c}{**Latent Heat**} & \multicolumn{1}{c}{**Sum of Energy**} \\ \cline{2-6}  & Seoul & 2,323,063,516 & 364,261,629 & 402,608,542 & 3,089,933,688 \\ \cline{2-6}  & Busan & 1,642,495,287 & 728,342,476 & 313,254,886 & 2,684,092,649 \\ \hline Primary & Seoul & 2,908,380,002 & 126,817,459 & 432,126,624 & 3,467,324,085 \\ \cline{2-6} School & Busan & 2,072,689,046 & 319,291,962 & 342,133,536 & 2,734,114,545 \\ \hline Difference & Seoul & 585,316,486 & \(-\)237,444,170 & 29,518,082 & 377,390,398 \\ \cline{2-6} Value & Busan & 430,193,759 & \(-\)409,050,514 & 28,878,651 & 50,021,896 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparison of average energy demand (year sum, kJ/h).  
Figure 4: Simulation model of midrise apartment building: (**a**) perspective view; (**b**) elevation.