esa-sceva/satcom-chunk-collection huggingface dataset:
# Cyber-Physical-Social Frameworks for Urban Big Data Systems: A Survey
## 5 Data Processing
### Clustering and Classification of Data Streams  
[PERSON] et al. [32] provide an approach of assessing air quality from social media and structured air pollution data. The air pollution data are analyzed by different technologies: association rules, clustering, and classification. Association rules are used to infer the impacts caused by the interplay of the different parameters. Clustering partitions the data into different groups based on the numeric attributes, while classification classifies datasets based on categorical attributes. Finally, the numeric and categorical attributes are mapped together. The results of these processing methods are used for sentiment analysis of tweets expressing public opinion on air quality.  
A supervised classification approach is employed in [64], where the call data records are mined for features that are then assigned to the most representative label of the activity type of the nearby venues. The cell towers attributed to the call data records are also clustered by using the Density-based Spatial Clustering Algorithm (DBSCAN) [93] that clusters geographically proximate towers, based on the 2-dimensional geo-coordinate information. A clustering algorithm (k-nearest neighbors) is also used in [66] to identify groups from the user behavior modeled as an approximate tensor.  
A density-based clustering algorithm is used in [33] for determining the nearest parking space candidates for public buses. The algorithm defines a candidate selection method that detects the possible parking spots based on location parameters (GPS coordinates) and historical data consisting of points-of-interest (POI) vectors within a defined range. The identified parking points are used as passenger pick-up and drop-off locations. The k-Nearest Neighbors (kNN) algorithm [94] is used to discover groups in a tensor model in [66].  
In terms of data streams processing, Sentilo provides a pub/sub infrastructure with agents that support alerts based on the sensor data. However, more mechanisms are needed in the infrastructure for the actual processing, with the specific use cases deciding on the choice of techniques, which are not disclosed currently for the analyzed applications. Likewise, the commercial systems analyzed in Section 4.2.1 do not expose details of the specific algorithms used in their data processing mechanisms. However, Nest claims to be using machine-learning algorithms to gather insights into usage of the devices, which helps to optimize the configuration of the Smart Home based on the users' preferences. In the case of SmartThings, a rule engine based on the sensors/actuators present in the system provides the data processing aspect.
# Optimization of Mechanical and Setting Properties in Acrylic Bone Cements Added with Graphene Oxide
## 3 Results and Discussion
### Setting Properties
#### 3.1.2 Setting Time  
According to Table S2 (supporting information), for the setting time variable, only the interactions sonication time * BPO/solid ratio, and sonication time * GO/liquid ratio were significant, at levels below 0.076. A determination coefficient of 97.2% was obtained, indicating an adequate contribution of the controlled effects to the total variation.  
Following Figure 5, higher values in sonication time and BPO/solid ratio reduced \(t_{set}\), while the GO/liquid ratio increase increased \(t_{set}\). ISO 5833-02 indicates that the \(t_{set}\) should be between 180 and 900 s (3-15 min), which means that all formulations studied are within the acceptable range. Setting time is one of the most representative setting parameters of the ABCs (5,55), and in orthopedics, it is expected to have a moderate value (7) because it is directly related to the time the surgeon has to mold and apply the cement during the procedure (37).  
The concentration of BPO plays a vital role in the kinetic parameters of ABC (1,55,56), and it is found that the decrease in \(t_{set}\) with the increase of the BPO content is due to the higher amount of free radicals available to initiate the polymerization reaction faster (5,56). The longer exfoliation time when generating smaller GO sizes with reduced heat dissipation capacity promotes that the cement hardening reaction occurs in less time.  
According to the research of [PERSON] et al. (34), the presence of the double bonds C=C in the GO generates extensive electron donor-acceptor capability, so it becomes remarkably reactive toward free radicals, acting as polymerization retardation and polymerization inhibition simultaneously during the polymerization reaction of the MMA in the ABC. The same behavior has been reported by other authors who studied the radical polymerization of some typical vinyl monomers (61).  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Sonication Time * BPO /Solid** & \multicolumn{3}{c}{**Mean (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**Grouping *} \\ \hline
5-2.5-0.0 & 58.3 & A & & & & \\
5-2.0-0.0 & 56.8 & A & B & & & \\
1-2.0-0.2 & 55.8 & A & B & C & & \\
1-2.5-0.0 & 55.5 & A & B & C & & \\
5-2.5-0.1 & 54.8 & A & B & C & & \\
5-2.0-0.2 & 54.7 & A & B & C & & \\
1-2.0-0.0 & 54.5 According to [PERSON]'s post-ANOVA test for the \(t_{set}\) variable (Table 3), although all the formulations are within the range allowed by ISO 5833-02, group A provides higher values in both interactions for cement setting time. Formulation with 1 h of sonication time, 2.0 wt. % of BPO and 0.5 wt.% of GO presented the highest setting time.
# Ad Creative Discontinuation Prediction with Multi-Modal Multi-Task Neural Survival Networks
## 4 Methodology
### Two-Term Estimation Technique: For Short- and Long-Term Discontinuations  
We employ a two-term estimation technique to improve the performance for the two types of discontinuations (cut-out and wear-out). To account for the difference between the two types of discontinuations, we train two prediction models by setting different time intervals for each model. We call these two models the _short-term model_ and the _long-term model_, respectively.  
The short-term model learns the cut-out features, and the long-term model learns the wear-out features. In the short-term model, for time interval \(\mathcal{T}_{S}\), which has \(L_{\mathcal{T}_{S}}\) intervals, the target log-likelihood is defined as \(\ell(\mathcal{T}_{S},\mathbf{x})\). In the long-term model, for time interval \(\mathcal{T}_{L}\), which has \(L_{\mathcal{T}_{L}}\) intervals, the target log-likelihood is defined as \(\ell(\mathcal{T}_{L},\mathbf{x})\). Training with these different time intervals for each model is expected to enable learning of different properties of short- and long-term discontinuation, which will provide better predictions.  
The MTL allows us to train short-term and long-term models in a unified single model. In applying this technique to our framework to train the model, we used the following two likelihoods:  
\[\ell_{\text{multi}}(\mathcal{T}_{S},\mathcal{T}_{L},\mathbf{x})=\lambda\ell( \mathcal{T}_{S},\mathbf{x})+(1-\lambda)\ell(\mathcal{T}_{L},\mathbf{x}), \tag{7}\]  
where \(\lambda\) is a regularization coefficient that controls the balance between the two loss functions. In this study, we set \(\lambda\) to 0.5. As the fraction of short-term discontinuation is much larger than that of long-term discontinuation, tuning hyperparameter \(\lambda\) would yield better performance. The two hazard probabilities for the short- and long-term time intervals outputted from the MTL technique can be merged, multiplying by the last interval of the short-term time interval and the beginning of the long-term time intervals, to the last time interval of the long-term time intervals. We used this result as the overall model of MTL with short- and long-term time intervals.  
In particular, \(\mathcal{T}_{S}\) and \(\mathcal{T}_{L}\) were set to be as follows:  
\[\mathcal{T}_{S}=(1,3),(3,5],(5,7],(7,10],\quad\mathcal{T}_{L}=(1,10],(10,30],(30,60],(60,90],(90,120]. \tag{8}\]  
The time intervals for the short- and the long-term model were defined based on observations of a dataset in which most ads were discontinued in about 10 days or 120 days, respectively, under the assumption [28] that it was effective to make the interval width exponential.
## 3 Results  
### Optimization of Chromatographic Condition  
The UPLC chromatographic condition for the analysis of F2 was optimized on the basis of certain conditions such as column type, column temperature, mobile phase, elution system, flow rate, injection volume, running time, and detection wavelength. Various conditions were examined to obtain a suitable method. The best condition was found to be as follows: column type: Halo C18 RP-amide column (150 mm \(\times\) 2.1 mm, 2 um particle sizes); column temperature: 40 \({}^{\circ}\)C; mobile phase: Acetonitrile (A) and 0.5% Phosphate buffer (B); elution system: gradient flow (A)/(B) = 1/99 (0 min) \(\rightarrow\) (A)/(B) = 16/84 (30 min; held for 20 min); injection volume: 5 \(\upmu\)L; flow rate: 2 mL/min; running time: 53 min; detection wavelength: 210 nm and 250 nm. As presented in the previous report [22], the typical UPLC chromatograms of F2, along with standard compounds and individual plant ingredients obtained from the optimized condition, are also shown in Figure 1. The retention times of five marker compounds, fustin, ellagic acid, astragalin, fisetin, and sulfuretin were 14.792, 25.718, 29.445, 33.657, and 42.800 min, respectively.
## 4 Test Results and Discussion
### Particle Breakage Characteristics
#### 4.2.1 Particle Gradation  
Standard heavy compaction tests on eight weathered granite samples with different gradations (four red-brown samples and four gray samples) were carried out under optimum moisture content conditions. Sieving tests were implemented on the samples before and after compaction, and the uniformity coefficient (\(C_{u}=d_{60}/d_{10}\)) and relative breakage (\(B_{r}\)) of the samples before and after the compaction test were calculated. The relationship between the uniformity coefficient and the relative breakage is shown in Figure 15. It can be seen from the figure that the relative breakage of the samples decreased with the increase of the uniformity coefficient, indicating that as the uniformity coefficient of weathered granite increased, the degree of particle breakage was reduced because of the enhancement of the intergranular locking effect. When the uniformity coefficient was small, the contact area among particles was smaller, and the stress concentration among particles was easy to be produced in the process of compaction, which leaded to the particle being crushed easily. For the sample with a high uniformity coefficient, due to the good intergranular locking structure and the larger contact area among particles, there was no excessive stress concentration during compaction, and the probability of particle breakage was small because the particles might undergo moved, approached and overturned processes in the process of compaction. In addition, it can be seen that the rate of decrease of relative breakage of the red-brown sample was higher than that of the gray sample, reflecting that because the degree of weathering of red-brown weathered granite is higher than that of gray weathered granite, the strength of the red-brown sample was less than that of the gray sample. This shows that the sensitivity of the red-brown sample to particle heterogeneity was greater than that of the gray sample.  
Figure 15: \(B_{r}\)-\(C_{u}\) curves.
## 1 Introduction
As the proposed method uses a probabilistic method, it is first necessary to model the input uncertainties (RES output power); however, as stated earlier, and is discussed in detail in subsequent sections, most probabilistic models of RES either contain discontinuous terms or are incomplete, which makes them difficult to use in probabilistic analysis. Thus, we also propose a procedure to obtain a probabilistic RES model that is continuous and can be conveniently applied with probabilistic methods related to the power system. Lastly, as mentioned in the preceding paragraphs, the modern electric grid continues to integrate huge amount of RES, and the sole use of PSS and BESS controllers may be insufficient to improve the SSS margin. Thus, we also propose a strategy to use PSS and BESS controllers in a coordinated manner and study their effect on PSSS. BESSs, along with controllers, are modeled in detail in DIGSILENT for this purpose. The paper is organized as follows: Section 2 discusses the proposed method to enhance PSSS, followed by the PSSS assessment method in Section 3. Section 4 describes different power-system controllers utilized in this paper to enhance dynamic stability, as well as the optimization method to tune them. The detailed results and discussion under different scenarios are provided in Section 5. Conclusions drawn are described in Section 6.
# Solar Energy in Urban Planning: Lesson Learned and Recommendations from Six Italian Case Studies
## 2 Background: Italian Legislative Framework
### Municipal Standards  
The targets set at the national level are pursued by Italian Regions, which can enhance the minimum quota or specify the renewable energy systems to use. Therefore, depending on regional laws, municipalities can set their own energy-related regulations in the building code. Generally, the building codes consider two major categories of built environment: historical city centers and non-historical urban areas [42].  
Historical city centers are subjected to strict regulations about solar system applications due to the need to preserve the visual effect of their views and panoramas. Concerning this category, buildings codes set either prohibition of installing solar systems or restrictions on the visual effect and location of the systems (e.g., modules have to be located on roof surfaces not visible from main streets and to be building integrated). In addition to these restrictions, the approval of the Superintendence for Architectural Heritage and Landscape is required for installing solar systems on constructions subjected to landscape or heritage protection [43]. In the second category--non-historical urban areas--solar systems are generally permitted as building-integrated. For new buildings or buildings subjected to major renovations, it is required that PV or ST plants are installed integrated or adherent to the roofs, following their same orientation and inclination.  
Further regulations on solar energy systems are sometimes included in municipal building codes, deriving from national laws on building energy efficiency and the promotion of renewable energy production. However, the standards regard only the design of buildings at the architectural scale. Energy performance certification is mandatory for new constructions and for selling or renting a dwelling in existing buildings [44].
# A Decentralized Blockchain-Based Trust Management Framework for Vehicular Ad Hoc Networks
## 4 Conclusions  
In this paper, a decentralized blockchain-based trust management framework is proposed. It aims to compute for each vehicle a global and accurate trust metric stored in the blockchain. The proposed BC-TMF is built upon three phases: trust metrics evaluation, trust metrics aggregation, and blocks generation and validation. Each vehicle assesses the trust metric of its neighbors from which it receives alerts. The trust metric calculation relies on the authenticity of the received messages. Then the miners aggregate the trust metrics received from other vehicles to compute an aggregated trust metric for each vehicle. The aggregated trust metric will be packed in a block signed by the miner, then added to the blockchain after solving the PoW. Sharing the aggregated trust metrics in a distributed ledger will solve the cold-start problem.  
A set of simulations has been conducted to evaluate the efficiency and accuracy of the proposed BC-TMF in terms of the average trust metric for both malicious and non-malicious vehicles and the variance of the trust metrics as a function of many parameters. The obtained results show that the proposed BC-TMF has excellent capability in computing accurate trust metrics for malicious and non-malicious vehicles. The proposed BC-TMF outperforms not only classic schemes but also existing blockchain-based frameworks. In future work, it is expect to refine the miners' selection process. Besides, it is expected to conduct further simulations to tune the different parameters of the proposed framework.  
**Author Contributions:** Methodology, T.G.; Resources, O.A.; Writing--review & editing, A.M. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the University of Jeddah, Saudi-Arabia, grant number UJ-20-124-DR.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
## 4 Result and Discussion
### Compressive Strength of Cubic Specimens
#### 4.1.1 Effect of Rubber on Compressive Strength and Peak Strain  
Stress-strain curves of the tested specimens of each group including both pre-peak and post-peak behavior are presented in Figure 8 showing that all the stress-strain curves followed a similar trend. The pre-peak behavior of the curve was almost linear, then the stress slowly increased with different curvatures up to the peak stress. Obviously, the curvature was reduced with a decrease in rubber particle size and, especially, an increase of rubber content. The post-peak behavior of rubberized concrete was strongly influenced by the rubber replacement of the fine aggregates. With an increase of rubber content or a decrease in rubber particle size, the post-peak axial compression behavior exhibited increased softening properties, which contributed to an increase in the peak strain corresponding to uniaxial compressive strength. In addition, the descending portion tended to reach a constant stress level at high strains when adding high content rubber particles into concrete.  
The effects of rubber content and rubber particle size on compressive strength of specimens are shown in Figure 9. As shown in Figure 9a, there was a remarkable reduction in the compressive strength with an increase of rubber content. For instance, when the particle size was 3-5 mm, the average compressive strength of specimens with 10%, 20%, and 30% rubber was 24.6, 13.7, and 10.4 MPa, reduced by 8.2%, 48.9%, and 61.2%, respectively, as compared with the control group. When the particle size was 1\(\sim\)3 mm, the average compressive strength of specimens with 10%, 20%, and 30% rubber was 19.2, 13.6, and 6.8 MPa, decreased by 28.4%, 49.3%, and 74.6%, respectively. As shown in Figure 9b, the compressive strength systematically declined with a decrease in rubber particle size. For example, when rubber content was 10%, the average compressive strength of a specimen with 1\(\sim\)3 mm rubber particles was 19.2 MPa, which was 22.0% lower than a specimen with 3\(\sim\)5 mm particles (24.6 MPa). Furthermore, the average strength of a specimen with 40 mesh rubber particles was 19.0 MPa, which was 22.8% lower than a specimen with 3\(\sim\)5 mm particles. When the content was 20%, the average compressive strength of a specimen with 1\(\sim\)3 mm rubber particles was 13.6 MPa, which was 0.7% lower than a specimen with 3\(\sim\)5 mm particles (13.7 MPa). Moreover, the average strength of a specimen with 40 mesh rubber particles was 9.6 MPa, which was 29.9% lower than a specimen with 3\(\sim\)5 mm particles. The comparative analysis found that increasing rubber content sharply reduced the compressive strength of cubic specimens. Whereas, when the rubber particle size was 40 mesh, although the strength was reduced with an increase of rubber content, the rate of decline gradually decreased.
# Usability Measures in Mobile-Based Augmented Reality Learning Applications: A Systematic Review
## 3 Threats to Validity  
A study by [7] highlighted four major threats to validity (TTV) in SLRs, and this research acknowledges these shortcomings by strategizing to minimize the risks of these TTVs. The four threats of validity were resolved in four separate strategies:  
\begin{table}
\begin{tabular}{c l} \hline \hline
**QA No.** & **Quality Assessment Questions** \\ \hline
1 & Does the paper clearly describe the method/methods of usability used? \\
2 & Does the paper highlight the usability evaluation process clearly? \\
3 & Does the paper clearly present the contribution of study? \\
4 & Does the paper clearly present the metrics used relating to types of subject study \\  & (between-subjects, within-subjects, or both)? \\
5 & Does the paper add value to contributions towards academia, industry or community? \\ \hline \hline \end{tabular}
\end{table}
Table 3: Quality assessment questions.  
* Construct validity was confirmed through the implementation of an automated and manual (snowballing) search from the very beginning of data collection aimed to mitigate calculated risks. In order to further restraint this TTV, major steps of scrutiny plus additional QAs were carried out complementing existing RQs and clear selection criteria.
* Internal validity was solved by adopting a method used by [7]. In order to eliminate biases in paper selection through an exhaustive search, a combination approach of automated search and snowballing was carried out for a more inclusive selection approach. Every extracted study underwent strict selection protocols after being extracted from all major databases in similar research areas [1; 7; 11].
* External validity was mitigated with a generalizability of results by incorporating a 10 years' timeframe in MAR studies with a usability evaluation. The incremental collection of papers by year was parallel to the number of available papers by year, which can be an indicator that this SLR is able to maintain a generalized report aligned with the research's external validity requirements.
* Conclusion validity was managed by implementing SLR methods and techniques used in this study following the established, specific, and well-defined guidelines explored by scholars from credible publications such as [8]. It is therefore possible for each and every research chronology in this SLR to be replicated with measurable and near-identical outcomes.