esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Prototype System
### Representation Through Template Matching
#### 5.2.2 Template Collection  
Templates are configurations of available multimodal features. In theory, any permutation of available features can be represented (detailed in Section 4.2.1). However, not all permutation may make sense for a representation. Therefore, an implementation may not have some possible templates in the collection. Furthermore, an implementation may only include a few permutations because of application design choices.  
Template collection can be implemented in any format depending on the technology used.  
For the prototype, the template collection is embedded in the representation engine source code since the design of the user study (Section 6) only required a subset of possible permutations of feature set.  
The feature set entailed within this implementation included the following (see Section 5.1 for details):  
* Extracted Keyframes from the video recording of the segment.
* Text transcript and textual summary generated from the transcript.
* Word Cloud (generated from transcript).
* Video recording of the segment.  
This enabled the implementation of template matching as a series of simple if-else statement. The following code snippet shows two of the if-else branches implemented in C# language.  
```
publicclassTemplate { StringtemplatID; ExpenseNameexpanse; ModalityNamemodality; List<FeatureName>featureset; publicstringTemplateID { get{returntemplatID;} set{templatID=value;}
```]....] FeaturePrimFeature,SecondFeature,defaultFeature,ontefeature; if(videoSeg.Relevant==true&& userPreferece== ModalityName.Textual&&hasSummary==true) { PrimFeature=videoSeg.AvailableFeatures.Where(F=>F.Name== FeatureName.Text_Summary).FirstOrDefault(); PrimFeature.RepPlace=RepresentationPlace.Primary; segRep.FeatureSet.Add(PrimFeature);....  
elseif(videoSeg.Relevant==false&&userPreferece== ModalityName.Visual&&hasKeyFrames==true) { PrimFeature=videoSeg.AvailableFeatures. Where(F=>F.Name==FeatureName.Keyframes).FirstOrDefault(); PrimFeature.RepPlace=RepresentationPlace.Primary; segRep.FeatureSet.Add(PrimFeature);  
The above code snippet shows the implemented template selection. As per the pseudo-code in Section 4.7, relevant segments are represented with deep features like text summary/transcript or video recording of that segment, depending upon the user preference for modality while non-relevant segments get efficient features such as word cloud or key-frames depending upon the user preference for modality.  
The actual placement of the represented features and any other information is dependent upon the user interface. Section 5.2.4 describes the implemented user interface.
## 2 Materials and Methods
### Synthesis
#### 2.2.9 Spiro-fluorene[3,4]-5-terphenylnaphthalene (TP-ASF)  
Spiro-fluorene[3,4]-5-bromonaphthalene (0.27 g, 0.54 mmol), 4,4,5,5-tetramethyl-2-[1,1', 3',1'']terphenyl-5'-yl-[1,3,2]-dioxaborlane (0.23 g, 0.65 mmol), tetrakis(triphenylphosphine) palladium(0) (0.03 g, 0.02 mmol) were placed in a 3-neck round flask, and anhydrous toluene (25 mL) was added. 2 M sodium hydroxide 10 mL was added. The mixture was heated under reflux overnight. After reaction, the solution was extracted using chloroform and water. Using MgSO4, the remaining water in the organic layer was removed. Column refinement under chloroform: hexane = 1:3 eluent was proceeded after evaporation of product in the organic layer. Chloroform and methanol were used for re-precipitation and then, by evaporating the filtered solid, 0.17 g of white power was obtained: (48% Yield) \({}^{1}\)H-NMR (300 MHz, THF) \(\delta\)(ppm): 9.65 (s, 1H), 8.84-8.81 (d, 1H), 8.39-8.37 (d, 1H), 8.07-8.06 (t, 1H), 7.92-7.90 (d, 2H), 7.78-7.76 (q, 5H), 7.74-7.69 (d, 2H), 7.62-7.53 (q, 3H), 7.44-7.27 (m, 9H), 7.19-7.15 (t, 1H), 7.08-7.05 (t, 2H), 6.82-6.78 (d, 1H), 6.71-6.66 (d, 2H), 6.64-6.60 (d, 1H). \({}^{13}\)C NMR (100 MHz, THF-d\({}_{8}\)) \(\delta\)(ppm): 142.04, 141.63, 140.01, 139.24, 137.48, 136.37, 136.31, 135.07, 133.73, 133.56, 131.67, 130.18, 129.33, 128.08, 127.85, 127.84, 127.71, 127.63, 127.15, 126.95, 126.78, 126.52, 126.14, 125.56, 125.03, 124.29, 123.80, 64.28. Fab+-MS: 644 \(m/z\).
# NOMA-Based VLC Systems: A Comprehensive Review
## 4 Potential Challenges and Open Research Issues
### Mimo  
Indoors, enough lighting is typically provided by arrays of LEDs, which drive the need for MIMO in VLC systems. When MIMO methods are used in VLC systems, the limited modulation bandwidth constraint of the LEDs may be circumvented, leading to improved spectral efficiency [151]. Using spatial multiplexing, users may be divided into groups, with each group being assigned to a different light-emitting diode (LED). As in the power domain, users from the same group that share the same modulation bandwidth are overlaid. Multiple users of MIMO-VLC systems employ precoding methods to partition incoming signals into their respective MIMO subchannels [76]. As an added bonus, SIC may be used to prevent intra-group interference among multiplexed users. Exploiting MIMO-VLC has the potential to greatly increase the system's capacity by catering to a significant number of clients all at once [152]. However, the construction complexity of MIMO-PD-NOMA-aided VLC systems is increased because PD-performance NOMA is highly dependent on the power allocation approach and the user grouping approach was chosen. Generally, system performance improves as the rank of the MIMO channel matrix grows because more de-correlated channels are produced. Because of this, the rank of the channel matrix is crucial for judging the performance of MIMO-NOMA. On the contrary, existing research in MIMO-NOMA takes into account full-rank channel matrices to study the performance of the system.
## 3 Micro-Vibration Test Principle
### The Image Point Offset Extraction and LOS Jitter Calculation of the HPSOP
#### 3.2.2 LOS Jitter Calculation  
The LOS jitter caused by micro-vibration can be divided into translation and angular jitter. Compared with angular jitters, the effects caused by translational jitters can be ignored in deep space. Moreover, the angular micro-vibration around the Z-axis has little effect on image stabilization because of the rotational symmetry of the primary mirror (PM) and the second mirror (SM). Hence, this paper pays more attention to the LOS jitter around the non-optical axes.  
As shown in Figure 6, with the optical payload jitter around the non-optical axes causing the jitter of the incident light, the image point will be offset on the CCD. In Figure 6, \(P_{0}\), \(P\), and \(P^{\prime}\) stand for the object point, the image point without jitter, and the image point with jitter.  
The LOS jitter angles of the HPSOP around the X-axis and Y-axis are as follows:  
\[\left\{\begin{array}{l}\theta_{x}=\frac{L_{y}\cdot f}{f^{2}+y^{2}}\\ \theta_{y}=\frac{L_{y}\cdot f}{f^{2}+x^{2}}\end{array}\right. \tag{6}\]  
where \(\theta_{x}\) and \(\theta_{y}\) indicate the jitter angles around the X-axis and the Y-axis; \(x\) and \(y\) are the image point coordinates before micro-vibration; \(L_{u}\) and \(L_{v}\) represent the image point translation caused by the micro-vibration; and \(f\) expresses the focal length.  
Notably, Equation (6) can be simplified to Equation (7) when \(x\) and \(y\) are both equal to 0.  
\[\left\{\begin{array}{l}\theta_{x}=\frac{L_{y}}{f}\\ \theta_{y}=\frac{L_{y}}{f}\end{array}\right. \tag{7}\]  
Because the image point is usually corrected to the center of the CCD when there is no micro-vibration, this paper uses Equation (7) to calculate the LOS jitter angles of HPSOP.
# Bending Performance of a Prestressed Concrete Composite Girder Bridge with Steel Truss Webs
## 6 Calculation Method of the Flexural Bearing Capacity
### Cracking Bending Moment \(M_{\text{ctu}}\)  
When the strain at the edge of the bottom plate reaches the concrete cracking strain, the bottom plate begins to crack. However, the composite girder remains in the elastic stress stage, and the section strain distribution conforms to the 'plane cross-section assumption'. Figure 18 illustrates the force diagram of the girder section at the cracking stage.  
According to the 'plane-section assumption' and the cracking strain \(\epsilon_{\text{c}}\) of the bottom plate concrete, the stress increment of prestressed strands and steel reinforcements when the bottom plate cracks can be obtained from Formulas (1) and (2):  
\[\sigma_{\text{s}}=E_{\text{s}}\frac{h-x-a_{\text{s}}}{h-x}\epsilon_{\text{c}} \tag{1}\]  
\[\Delta\sigma_{\text{p}}=E_{\text{p}}\frac{h-x-a_{\text{p}}}{h-x}\epsilon_{ \text{c}} \tag{2}\]  
According to the force balance of the section, the formula for calculating the cracking bending moment when the bottom plate cracks can be obtained, as shown in Formula (3),  
\[M_{\text{ctu}}=\big{(}\sigma_{\text{pe}}+\Delta\sigma_{\text{p}}\big{)}A_{ \text{p}}\big{(}\text{h}_{\text{p}}-h_{\text{c}}\big{)}+\sigma_{\text{s}}A_{ \text{s}}(h_{\text{s}}-h_{\text{c}}) \tag{3}\]  
In Formula (1) to Formula (3), \(A_{\text{p}}\) and \(A_{\text{s}}\) are the cross-sectional areas of the external prestressed strand and tensile reinforcement in the bottom plate, respectively, and \(E_{\text{p}}\) and \(E_{\text{s}}\) are their elastic moduli. \(\epsilon_{\text{c}}\) is the cracking strain of the bottom plate concrete. \(h\), \(h_{\text{c}}\), \(h_{\text{p}}\), and \(h_{\text{s}}\) are the section height, the distance between the resultant point of concrete pressure and the sectional top edge, the distance between the prestressed strand center and the sectional top edge, and the distance between the center of reinforcement in the bottom plate and the sectional top edge, respectively. \(x\) is the depth of the section compression zone. \(a_{\text{p}}\) and \(a_{\text{s}}\) are the distances from the center of the external prestressed strand and the reinforcement in the bottom plate to the sectional bottom edge, respectively.  
Figure 17: Magnification factors caused by eccentric loading.  
Figure 18: Mechanical schematic diagram at the cracking stage.
## 7 Conclusions  
In this paper, we addressed the problem of the efficient and scalable evaluation of content-matched range monitoring queries (CM range monitoring queries). Given a set of geographically distributed moving objects, the primary goal of our study is to keep the results of queries up to date, while incurring the minimum communication cost and server workload by letting the moving objects evaluate several queries that are relevant to them. To achieve this, we used the resident domain concept and proposed a novel query indexing structure, namely the group-aware query region tree (GQR-tree). For the tight integration of the spatial and the non-spatial specifications of the CM range monitoring queries, The GQR-tree groups the queries according to their non-spatial query values (_i.e._, non-spatial information) when being built on their spatial query ranges (_i.e._, spatial information). We carried out a series of comprehensive simulations and demonstrated that the GQR-tree method outperform the existing methods, validating the effectiveness of the GQR-tree.
## 2 The TASIPPS Conceptual Framework  
This paper proposes the \"Technical and Semantic Interoperability, Preserving Privacy and Security\" (TASIPPS) conceptual framework to address multiple aspects of the previously discussed interoperability challenges by leveraging the strengths of several existing technologies in a novel way, in addition to incorporating new technologies. The main existing technologies that are used include the Service-Oriented Architecture (SOA), Fast Healthcare Interoperability Resources (FHIR), and Security Assertion Markup Language (SAML), to ensure a holistic, robust, and comprehensive interoperability solution. New technologies that are developed include an AI module that enables network monitoring and security, and a semantic interoperability module incorporating a novel medical (disease) ontology. The TASIPPS conceptual framework consists of six major components/modules, as illustrated in Figure 1: a Middleware server module; a semantic interoperability module; a privacy module; a security module; and the policy module.  
The framework components are described in greater detail in the next section.
## 4 Results and Discussion
### Comparative Evaluation
#### 4.7.1 Analysis Based on YOLO Object-Detection Dataset  
This analysis section describes the assessment of FP-SFOA-DQN-FL based on the YOLO object-detection dataset depicted in Figure 9. Figure 9a specifies the evaluation of FP-SFOA-DQN-FL with respect to accuracy. If the time step is 200 s, the accuracy gained by FP-SFOA-DQN-FL is 0.950, while the existing models gained an accuracy of 0.816 for the federated object-detection algorithm, 0.889 for FedCV, 0.880 for DRFL, and 0.896 for active learning. While considering the time step as 200 s, the loss function attained by the designed approach illustrated in Figure 9b is 0.104, and MSE gained by FP-SFOA-DQN-FL is 0.122, as shown in Figure 9c. However, the existing models delivered the MSE as 0.249, 0.185, 0.182, and 0.156, respectively, for the federated object-detection algorithm, FedCV, DRFL, and active learning. Figure 9d implies the evaluation of RMSE. By considering the time step as 200 s, the RMSE received by the proposed FP-SFOA-DQN-FL is 0.035, and the FPR attained by the designed scheme is 0.140, as illustrated in Figure 9e. Also, the FPR provided by conventional schemes, like the federated object-detection algorithm, is 0.264, FedCV is 0.201, DRFL is 0.199, and active learning is 0.173. Figure 9f signifies the comparative evaluation of FP-SFOA-DQN-FL in terms of mean average precision. When the time step was 200 s, FP-SFOA-DQN-FL delivered a mean average precision of 0.909. Figure 9g depicts the comparative evaluation of communication cost. When the time step was 100 s, the communication cost of the federated object-detection algorithm, FedCV, DRFL, active learning, and FP-SFOA-DQN-FL was 0.137, 0.094, 0.097, 0.088, and 0.064, respectively.  
Figure 9: \(Cont.\)
## 1 Introduction
Since traditional linear data space conversion methods including principal component analysis (PCA) [17], fisher discriminant analysis (FDA) [18], and other algorithms may ignore the convex and concave characteristics of nonlinear feature data under some conditions, the nonlinear feature space conversion technologies based on manifold learning have become a hotspot of current research, which mainly include the Isometric map algorithm (Isomap) [19], Laplacian Eigenmaps algorithm (LE) [20], local linear embedding algorithm (LLE) [21], and so on. Laplacian Eigenmaps constructs the relationship between the data from the local point of view. If two data instances i and j are similar to each other, then I and j would be as close as possible in the subspace transformed by LE, it is therefore, LE is competent to reflect the intrinsic manifold structure of data. After comparing the existing shortcomings of the proposed techniques, this paper reports a novel method that integrates LE into DNN to assess machine running conditions, which mainly includes four steps: (1) Original features ex-traction; (2) LE feature space conversion; (3) normal DNN optimization and training; and (4) obtaining the evaluation results with the well trained DNN. The comprehensive contrast experiments with two other important feature space conversion algorithms PCA and Isomap, and two other intelligent evaluation models HMM and BPNN highlighted the advantages of the method proposed in this paper. Other sections of the article are arranged as follows: Section 2 introduced executive steps of the proposed method in detail, in which all the related algorithms and theories are illustrated. In Section 3, the experiments of extracting original features of the vibration signal, feature space transformation and the comparison experiments with some other popular feature space transformation methods and intelligent assessment algorithms were carried out, and the assessment results are also introduced in this section. Then, in Section 4, the conclusions were drawn.
# Telecommunication Technologies for Smart Grid Projects with Focus on Smart Metering Applications
## 4 Smart Metering Applications--Smart Grid Projects
### Grid Management
#### 4.5.8 Model City Manheim  
Grid management and control from power generation to power consumption has been studied in the project \"Model City Manheim\", in Germany. One of the objectives is to accommodate fluctuating energy generation from renewable energy sources. At the customer side, an intelligent controller is used, in order to assure that the generated energy is consumed. This device has information about energy consumption characteristics of several devices and it helps the end-user to decide about the moment of their functionality even when he/she is absent. For this purpose, communication through IP is used for the interaction of the devices in the distribution network, while data is transmitted via broadband powerline technology through the electricity grid [105].
## 8 Conclusions  
In this work, we developed a modular architecture, a methodology, and a dashboard, that provide insights when a GTSMLFM forecast can be trusted or not and the reasons behind anomalous forecasts. The architecture, methodology, and dashboard support the development and engineering of GTSMLFMs, providing means to enhance their performance. We evaluated our approach through a series of experiments conducted on a reduced version of two publicly available datasets and measuring the performance improvements of the GTSMLFM when following the methodology described in Section 4. Our research confirmed that removing particular instances from the train set can lead to a better GTSMLFM performance and compared several approaches to achieve the best outcome.  
As future work, we envision extending the current application to support explainable anomaly detection algorithms and include a semantic model to enrich the explainability of any anomaly detection model. Such a semantic model can provide additional insights based on domain knowledge regarding the inner workings of the anomaly detection model and the data of a particular time series and forecast. Finally, we would like to explore different policies to deal with problematic train instances. In particular, we are interested in studying if replacing the values of a subset of features of interest with some imputation criteria can be an effective alternative to removing such data instances. Such a technique would retain the advantages of keeping all the data to train a global model, while removing patterns that create outlier forecasts. Moreover, to avoid losing valuable information in the noisy instances, we could leverage generative adversarial sampling to enrich the dataset with synthetic train instances that resemble noisy ones. Such enrichment could help the model better learn the decision boundaries, increasing the overall model's performance.  
Conceptualization, J.R.; methodology, J.R.; software, J.R. and E.T.; validation, J.R. and E.T.; formal analysis, J.R. and K.K.; investigation, J.R.; resources, J.R., E.T., K.K. and B.F.; data curation, J.R. and E.T.; writing--original draft preparation, J.R.; writing--review and editing, J.R., K.K., B.F. and D.M.; visualization, J.R.; supervision, K.K., B.F. and D.M.; project administration, B.F. and D.M.; funding acquisition, B.F. and D.M. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Slovenian Research Agency and the European Union's Horizon 2020 program project STAR under grant agreement number H2020-956573.  
Not applicable.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript
# Best Relay Selection Strategy in Cooperative Spectrum Sharing Framework with Mobile-Based End User
## Appendix C Derivation of \(F_{\gamma_{D}}(\gamma)\)  
The conditional CDF \(F_{\gamma_{R}}(\gamma/Y_{SP})\) can be obtained by substituting Equations (19) and (21) into Equation (16), resulting in being expressed as follows:  
\[F_{\gamma_{R}}(\gamma/Y_{SP})=1-\left(1-\frac{2F_{1}(m,m;1+m;1)}{m\beta(m,1-m )}+\psi_{\tau_{k}p_{\tau}d_{\tau}}(\gamma)\right)e^{-\left(\frac{\gamma_{SP} \lambda_{SR_{k}}}{Id_{1}}\right)} \tag{118}\]  
On the other hand, Equation (118) can be simplified as follows:  
\[F_{\gamma_{R}}(\gamma/Y_{SP})=1-\phi(\gamma)e^{-\frac{\gamma\lambda_{SR_{k}}} {Id_{1}}y_{SP}} \tag{119}\]  
where  
\[\phi(\gamma)=1-\frac{2F_{1}(m,m;1+m;1)}{m\beta(m,1-m)}+\psi_{\tau_{k}p_{\tau} d_{\tau}}(\gamma) \tag{120}\]  
Furthermore, by substituting Equation (119) into Equation (15), one gets:  
\[F_{\gamma_{D}}(\gamma/Y_{SP})=\prod_{k=1}^{N}\left(1-\phi(\gamma)e^{-\frac{ \gamma_{k}S_{R_{k}}}{Id_{1}}y_{SP}}\right) \tag{121}\]  
For i.n.i.d realistic channels, one may further simplify Equation (121) as follows:  
\[F_{\gamma_{D}}(\gamma/Y_{SP})=\left(1-\phi(\gamma)e^{-\frac{\gamma_{k}S_{R_{k} }}{Id_{1}}y_{SP}}\right)^{N} \tag{122}\]  
Using Equation (1.111) [63], the power series of the binomial can be exploited where:  
\[(a-b)^{n}=\sum_{k=0}^{n}{(-1)^{k}\binom{n}{k}a^{n-k}b^{k}} \tag{123}\]  
Hence, \(F_{\gamma_{D}}(\gamma/Y_{SP})\) can be expressed as follows:  
\[F_{\gamma_{D}}(\gamma/Y_{SP})=\sum_{k=0}^{N}{(-1)^{k}\binom{N}{k}\left(\phi( \gamma)e^{-\frac{\gamma_{k}S_{R_{k}}}{Id_{1}}y_{SP}}\right)^{k}} \tag{124}\]where \(N\) represents the number of relays. \(F_{\gamma_{D}}(\gamma)\) can be obtained by averaging the conditional CDF \(F_{\gamma_{D}}(\gamma/Y_{SP})\) as follows:  
\[F_{\gamma_{D}}(\gamma)=\int_{0}^{\infty}F_{\gamma_{D}}(\gamma/Y_{SP})f_{Y_{SP}}( y_{SP})dy_{SP} \tag{10}\]  
Finally, substituting Equations (10) and (5) into Equation (10) yields:  
\[F_{\gamma_{D}}(\gamma)=\sum_{k=0}^{N}{(-1)^{k}{N\choose k}(\phi(\gamma))^{k}} \lambda_{SP}\int_{0}^{\infty}e^{-y_{SP}\left(k^{\frac{\gamma_{1}\lambda_{SR}}{ \lambda_{1}}+\lambda_{SP}}\right)}dy_{SP}=\sum_{k=0}^{N}\frac{(-1)^{k}{k \choose k}(\phi(\gamma))^{k}}{1+\frac{\gamma_{3}\lambda_{SR}}{\lambda_{1} \lambda_{SP}}} \tag{11}\]