esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### LA-ICP-MS  
Following electron microscopy study, two polished sections from the stibnite mineralization were analyzed by LA-ICP-MS. Trace element concentrations in stibnite were measured using a PerkinElmer ELAN DRC-e ICP (PerkinElmer Inc., Waltham, MA, USA) mass spectrometer combined with a New Wave UP193-FX excimer laser ablation system (New Wave Research, Inc., Fremont, CA, USA) at the Geological Institute, Bulgarian Academy of Sciences, Sofia, Bulgaria. The ablation was conducted in He medium. In order to maximize sensitivity, the ICP-MS was optimized daily with respect to the oxide production rate of ThO/Th (0.5%). Operating conditions of the laser system include 5 Hz depletion rate, 35 \(\upmu\)m spot size, and energy density on analyzed minerals and standards of 5.0 to 5.2 J/cm\({}^{2}\). The nebulizer gas flow rate was 0.8 L/min, while auxiliary and make-up gas flows rates were 0.92 L/min. The analysis time was 100 s (background: 40 s, laser-on the sample: 60 s). The acquisition dwell time was set to 0.02 s for \({}^{74}\)Ge, \({}^{82}\)Se, \({}^{115}\)In, \({}^{125}\)Te, and \({}^{202}\)Hg, to 0.03 s for \({}^{107}\)Ag, to 0.04 s for \({}^{197}\)Au, and to 0.01 s for all other elements. The monitored isotopes include: \({}^{27}\)Al, \({}^{31}\)Pb, \({}^{34}\)S, \({}^{49}\)Ti, \({}^{51}\)V, \({}^{53}\)Cr, \({}^{55}\)Mn, \({}^{57}\)Fe, \({}^{59}\)Co, \({}^{60}\)Ni, \({}^{65}\)Cu, \({}^{66}\)Zn, \({}^{71}\)Ga, \({}^{73}\)Ge, \({}^{74}\)Ge, \({}^{75}\)As, \({}^{77}\)Se, \({}^{93}\)Nb, \({}^{95}\)Mo, \({}^{107}\)Ag, \({}^{111}\)Cd, \({}^{115}\)In, \({}^{118}\)Sn, \({}^{121}\)Sb, \({}^{125}\)Te, \({}^{139}\)La, \({}^{140}\)Ce, \({}^{146}\)Nd, \({}^{147}\)Sm, \({}^{157}\)Gd, \({}^{128}\)W, \({}^{185}\)Re, \({}^{197}\)Au, \({}^{202}\)Hg, \({}^{205}\)TI, \({}^{208}\)Pb, \({}^{209}\)Bi, \({}^{232}\)Th, and \({}^{238}\)U. The targeted areas in the polished sections were predefined to try and avoid mineral inclusions. Repeated external standardization was conducted by analyzing NIST SRM 610 glass standard, and the USGS Mass 1 sulfide standard.  
Data reduction was performed by using the Sb contents of stibnite measured by SEM-EDS as internal standards and the SILLS software [46]. During data reduction, peak-shaped fluctuations of the intensity signal of some isotopes were investigated in order to exclude the influence of other minerals on the chemical composition of the studied minerals. The full list of the LA-ICP-MS data, including elements ascribed to nano-scale mineral inclusions, is included in Table S3.  
For the statistical analysis of the bulk geochemistry and the LA-ICP-MS analytical results, univariate analysis (minimum, maximum, standard deviation, and average), and bivariate analysis (Pearson's correlations: Table S4) were obtained. The SPSS (version 25.0.0) statistics software (IBM, New York, NY, USA) was used for the statistical analyses. Statistical graphs and time-resolved analytical profiles of LA-ICP-MS analyses were plotted using Grapher (version 16.1.335) software (Golden Software LLC, Golden, CO, USA).
## 3 Basic Principles of the Proposed Method
### Gray-Level Co-Occurrence Matrix  
Gray-level co-occurrence matrix (GLCM) is an effective texture feature extraction approach. GLCM considers not only the distribution of intensities but also the relative positions of pixels in an image [25]. Let \(Q\) be an operator that defines the position of two pixels relative to each other, and consider an image, \(I_{f}\), with possible intensity levels. Let \(G\) be a matrix whose element is the number of times that pixel pairs with intensities \(z_{v}\) and \(z_{h}\) occur in \(I_{f}\) in the position specified by \(Q\). A matrix formed in this manner is referred to as a gray-level co-occurrence matrix. Generally, GLCM is not directly regarded as a texture feature, but it is represented by some descriptors such as energy, contrast, entropy, homogeneity, and correlation.
Appl. Sci._**2015**, \(5\), 706-727; doi:10.3390/app5040706  
_applied sciences_  
**ISSN 2076-3417**  
www.mdpi.com/journal/applsci  
Trends and Potentials of the Smart Grid Infrastructure: From ICT Sub-System to SDN-Enabled Smart Grid Architecture  
**[PERSON] 1,2,\({}^{\dagger}\), [PERSON] 1,\({}^{\dagger}\) and [PERSON] 2,\({}^{\star}\)**  
1 Qatar Mobility Innovations Center, Qatar Science and Technology Park, Doha 210531, Qatar; E-Mails: [EMAIL_ADDRESS] or [EMAIL_ADDRESS] (J.K.); [EMAIL_ADDRESS] (F.F.)
2 Graduate School of Computer Engineering, Ajou University, 206 World Cup-ro, Suwon 16499, Korea  
\({}^{\dagger}\) These authors contributed equally to this work.  
\({}^{\star}\) Author to whom correspondence should be addressed; E-Mail: [EMAIL_ADDRESS]; Tel.: +82-031-219-1841.  
Academic Editor: [PERSON]  
_Received: 12 July 2015 / Accepted: 29 September 2015 / Published: 10 October 2015_  
**Abstract:** Context and situational awareness are key features and trends of the smart grid and enable adaptable, flexible and extendable smart grid services. However, the traditional hardware-dependent communication infrastructure is not designed to identify the flow and context of data, and it focuses only on packet forwarding using a pre-defined network configuration profile. Thus, the current network infrastructure may not dynamically adapt the various business models and services of the smart grid system. To solve this problem, software-defined networking (SDN) is being considered in the smart grid, but the design, architecture and system model need to be optimized for the smart grid environment. In this paper, we investigate the state-of-the-art smart grid information subsystem, communication infrastructure and its emerging trends and potentials, called an SDN-enabled smart grid. We present an abstract business model, candidate SDN applications and common architecture of the SDN-enabled smart grid. Further, we compare recent studies into the SDN-enabled smart grid depending on its service functionalities, and we describe further challenges of the SDN-enabled smart grid network infrastructure.  
**Keywords:** software-defined networking; smart grid network; Advanced Metering Infrastructure (AMI); context awareness
## 3 Results and Discussion
### Implications of Climate Change on Sediment Yield
Spatial Distribution of Precipitation, Streamflow and Sediment Yield under Dry-Warm and Cold-Wet Models  
The bias corrected rainfall, maximum and minimum temperature data were used as inputs in the calibrated SWAT model to investigate the future consequences of the Dry-Warm and Cold-Wet models on streamflow and sediment yield in the Nagavali and Vamsadhara watersheds. According to Figure 8, historical precipitation, surface runoff, and sediment yield in these watersheds ranged from 1054 to 1473 mm, 7 to 182 mm, and 0 to 25 t/ha/yr. The upper sub-watersheds received the most precipitation while the lower sub-watersheds received the least. The catchment areas of the Nagavali and Vamsadhara watersheds are 9200 and 10,450 sq.km, respectively, with 2438 and 5120 sq.km of watershed areas subjected to high soil erosion. In the Nagavali watershed, out of 2438 sq.km, 635 and 645 sq.km of area belong to agricultural and barren land, respectively. In Vamsadhara watershed, out of 5120 sq.km, 1476 and 1066 sq.km of area belong to agricultural and barren land, respectively. [PERSON] et al. (2022) [47] found that sub-watersheds with the highest sediment yield, representing 26.5% and 49% of the total watershed area, were wastelands, followed by fallow land, agricultural land, and degraded and deciduous forest land with steep slopes in both watersheds. The near-level slope (0-2%) represents 19.21% and 14.32% of the watershed area in Nagavali and Vamsadhara watersheds, respectively. The medium slope (2-8%) represents 23.26% and 19% of the watershed area, while the steep slope (>8%) represents 57.33% and 66.68% of the watershed area in the Nagavali and Vamsadhara watersheds, respectively. These figures suggest that Vamsadhara watershed has more undulated areas than Nagavali watershed. Figure 9 shows the projected annual average precipitation, surface runoff, and sediment yield under the Dry-Warm model. The range for these variables under Dry-Warm and SSP245, SSP370, and SSP585 is 1116 to 1669 mm, 13 to 216 mm, and 0 to 30 t/ha/yr, respectively. These values are higher than the historical period. Figure 10 shows the same variables under the Cold-Wet model. The range under Cold-Wet and SSP245, SSP370, and SSP585 is 1130 to 1878 mm, 10 to 333 mm, and 0 to 49 t/ha/yr, respectively. Under Cold-Wet, the SSP585 scenario showed 7468 and 9426 sq.km of watershed area subjected to high soil erosion over Nagavali and Vamsadhara watersheds, respectively, in far future. Therefore, based on the results of the Dry-Warm and Cold-Wet scenarios, it is important to implement soil water conservation measures in the observed critical sediment source areas in the Nagavali and Vamsadhara watersheds to mitigate the potential impact of climate change. Figure 8: Spatial distribution of annual average precipitation, surface runoff, and sediment yield for the period of 1975–2014. Figure 9: Spatial distribution of annual average precipitation, surface runoff and sediment yield based on the Dry-Warm (ACCESS-CM2) model.
## 4 Performance Comparison of Observability Indices
### Results of Observability Index Performance Comparison  
The results of performance comparison of observability indices are shown in Table 4 and Figure 5. Table 4 lists the mean integrated errors of calibration with different observability indices. Figure 5a-d show the histogram superimposed PDF curve of integrated error under each measurement condition. The horizontal coordinate axis in the figure is the value of the integrated error. The left axis is frequency, which corresponds to the histogram, and the right axis is the probability density, which corresponds to the PDF curve.  
When analyzing the graphs and tables, taking measurement condition I as an example, Figure 5a shows that after 100 calibrations with five observability indices, respectively, the calibration with \(O_{1}\) obtains the smallest integrated error overall. Table 4 records that, in measurement conditions I, the mean integrated error of 100 calibrations with \(O_{1}\) is 0.01050, which is smaller than the mean integrated error corresponding to the other 4 observability indices. Figure 5a also shows that the calibration with \(O_{4}\) has the largest integrated error. Figure 5b-d show that calibrations with \(O_{1}\) also have the smallest integrated error in measurement conditions II, III, and IV.  
According to the above analysis, the simulations of calibrations of the six-hardpoint positioning mechanism with \(O_{1}\) obtains the minimum integrated error and the highest accuracy. Therefore, we believe that \(O_{1}\) has better performance than the other four observability indices, and \(O_{1}\) is the most suitable observability index for calibration of six-hardpoint positioning mechanisms. In the proposed calibration method, the index \(O_{1}\) is used to select the optimal measurement poses.  
\begin{table}
\begin{tabular}{c c c} \hline
**Measurement Condition** & **Observability Index** & **dX\({}_{\text{mean}}\)** \\ \hline \multirow{4}{*}{I} & \(O_{1}\) & 0.01050 \\  & \(O_{2}\) & 0.02936 \\  & \(O_{3}\) & 0.03388 \\  & \(O_{4}\) & 0.04805 \\  & \(O_{5}\) & 0.01727 \\ \hline \multirow{4}{*}{II} & \(O_{1}\) & 0.15785 \\  & \(O_{2}\) & 0.24408 \\  & \(O_{3}\) & 0.15933 \\  & \(O_{4}\) & 0.22564 \\  & \(O_{5}\) & 0.24932 \\ \hline \multirow{4}{*}{III} & \(O_{1}\) & 0.02415 \\  & \(O_{2}\) & 0.06181 \\  & \(O_{3}\) & 0.04541 \\  & \(O_{4}\) & 0.05333 \\  & \(O_{5}\) & 0.02554 \\ \hline \multirow{4}{*}{IV} & \(O_{1}\) & 0.21662 \\  & \(O_{2}\) & 0.36087 \\ \cline{1-1}  & \(O_{3}\) & 0.27937 \\ \cline{1-1}  & \(O_{4}\) & 0.32318 \\ \cline{1-1}  & \(O_{5}\) & 0.27659 \\ \hline \end{tabular}
\end{table}
Table 4: Mean integrated error of calibration with five observability indices.
# Forest Fire Detection and Notification Method Based on AI and IoT Approaches
## 6 Conclusions  
In this paper, we have introduced our new technology to reduce wildfires by using AI and IoT devices and sensors. Therefore, with our system, we believe that the proposed system can be effectively used to end the rapid increase in the world climate crisis and the loss of lives. The system can be installed in forests and start detecting smoke to let the AI model detect the exact fire location and notify the fire department to disallow the fire to continue for days. Finally, we hope that this technology will be effective in other countries to prevent wildfires worldwide.  
Recent studies have shown that, in order to promote safety in our daily lives, it is critical to quickly identify fire accidents in their early phases. As a result, we hope to carry out more research in this area and enhance our findings. Our goal is to identify fire occurrences in real time with fewer false positives using the YOLOv6 and YOLACT models. Future goals include improving the accuracy of the approach and addressing wrongly detected situations in the same color cases with fire regions. Using 3D CNN and 3D U-Net in the IoT environment, we intend to create a compact model with reliable fire-detection performance and without communication issues.  
This manuscript was designed and written by [PERSON] and [PERSON]. [PERSON] conceived the main idea of this study. [PERSON] wrote the program and conducted all experiments. [PERSON] and [PERSON] supervised the study and contributed to the analysis and discussion of the algorithm and the experimental results. All authors have read and agreed to the published version of the manuscript.  
This study was funded by Korea Agency for Technology and Standards in 2022, project numbers are K_G012002073401, K_G012002234001 and by the Gachon University research fund of 2021 (GCU-202106340001).  
**Data Availability Statement:** Not applicable.  
**Acknowledgments:** The authors would like to express their sincere gratitude and appreciation to the supervisor, [PERSON] (Gachon University), for her support, comments, remarks, and engagement over the period in which this manuscript was written. Moreover, the authors would like to thank the  
Figure 6: Small size fire region detected images.  
editor and anonymous referees for their constructive comments toward improving the contents and presentation of this paper.
###### Abstract  
Named Data Networking (NDN) has emerged as a promising architecture to overcome the limitations of the conventional Internet Protocol (IP) architecture, particularly in terms of mobility, security, and data availability. However, despite the advantages it offers, producer mobility management remains a significant challenge for NDN, especially for moving vehicles and emerging technologies such as Unmanned Aerial Vehicles (UAVs), known for their high-speed and unpredictable movements, which makes it difficult for NDN to maintain seamless communication. To solve this mobility problem, we propose a Distributed Mobility Management Scheme (DMMS) to support UAV mobility and ensure low-latency content delivery in NDN architecture. DMMS utilizes decentralized Anchors to forward proactively the consumer's Interest packets toward the producer's predicted location when handoff occurs. Moreover, it introduces a new forwarding approach that combines the standard and location-based forwarding strategy to improve forwarding efficiency under producer mobility without changing the network structure. Using a realistic scenario, DMMS is evaluated and compared against two well-known solutions, namely MAP-ME and Kite, using the ndnSIM simulations. We demonstrate that DMMS achieves better results compared to Kite and MAP-ME solutions in terms of network cost and consumer quality-of-service metrics.  
named data networking; producer mobility management; distributed mobility +
Footnote †: journal: Future Internet
# Comparative Assessment of Shear Demand for RC Beam-Column Joints under Earthquake Loading
## 1 Introduction  
Post-earthquakes surveys revealed that beam-column joint failure is recurrent in RC buildings showing structural deficiencies such as inadequate joint reinforcement [1]. When a beam-column joint fails prematurely, the RC frame is both not able to sustain significant displacement demand and, eventually, loses its gravity load carrying capacity because of the buckling of column reinforcement [2].  
The stress state of a beam-column joint is prevalently characterized by shear developed at the boundaries of the joint and at the interfaces between the joint concrete and the beam or column reinforcement [3]. Given its shear nature, design check is based on equilibrium between shear demand and shear strength [4]. Nonetheless, compatible models, defining the shear stress vs shear distortion constitutive law, are used in more sophisticated numerical applications [5].  
According to [PERSON] [6], a beam column joint is defined as that portion of the column within the depth of the deepest beam that frames into the column. This definition implies that more than one beam can frame into the column and, consequently, force input should be considered bi-axial.  
This paper focuses on the evaluation of bi-axial shear demand for beam-column joints, comparing different numerical methods to carry out seismic structural analysis. The selected benchmark structure is the SPEAR building [7]. First, the definition of joint shear demand and joint shear strength are summarized in the section titled \"Fundamentals of RC beam-column joint\". Second, the section \"Numerical study\" presents the SPEAR frame by recalling the associated literature; details of the implemented numerical models are given as well. Third, the section \"Results and Discussion\" focuses on the shear demand at beam-column joints, highlighting the differences between modal-response-spectrum analysis,non-linear-static-analysis and non-linear-time-history. Strength assessment according to building codes targeted to existing structures is discussed as well. Finally, some conclusions summarize the work.
# Data-Driven Social Security Event Prediction: Principles, Methods, and Trends
## 3 Research on Data-Driven Predicting of Social Security Events
### Technical Methods
#### 3.2.2 Deep Learning  
With the advancement of neural networks, research on such methods has been increasing, with the main idea being to extract features from text using natural language processing techniques and input these features into pre-trained neural networks to predict social security events.  
[PERSON] et al. [27] employed a predictive model based on Long Short-Term Memory (LSTM) combined with event table data from the GDELT database to analyze and forecast the number of conflict events in the United States. [PERSON] et al. [29] utilized a two-stage sentiment analysis approach based on deep neural networks to predict whether regular group gathering behaviors would evolve into social security events. [PERSON] et al. [30], based on GDELT data, used a recurrent neural network GRU sequence model to aggregate and concatenate fields including Date, Actor1, Actor2, EventCode, etc., from the GDELT dataset on a daily basis to form feature vectors to determine whether a country is experiencing one of six conflict events, such as domestic political crises, riots, racial violence, leadership regime changes, etc. [PERSON] et al. [25] applied a multiple-instance learning approach to transfer supervised information at the group level to individuals, establishing a multiple-instanceclassifier through learning from labeled multiple-instance bags and applying it to predict social security events in three Latin American countries. Additionally, [PERSON] et al. [25] first applied multi-task learning to social security event prediction, developing predictive models for specific cities and identifying evidence precursors for multiple cities from past news articles. [PERSON] et al. [21], using hierarchical geographic spatial multi-task learning, predicted whether unrest events would occur on specified dates and proposed a hierarchical task numerical conjunction feature learning model to improve the model's generalization in 2019. [PERSON] et al. [7] designed a mechanism that maps historical news reports onto dynamic graph structures and then used graph convolutional networks to model the graph structure and predict the occurrence of future social security events. [PERSON] [31] employed similar ideas to predict riots in Hong Kong, generating evolutionary graphs representing the temporal evolution process. [PERSON] et al. [12] made the first attempt to combine the representation learning of event graphs with time series prediction using RNN-like models for civil unrest prediction. [PERSON] et al. [32] constructed event-centered semantic correlation graphs based on structured event information and employed the idea of graph convolutional networks to aggregate node neighborhood information, predicting trends in North Korean nuclear behavior.
## 7 Experiment Design
### Feedback Capturing, Annotations and Data For Analysis
#### 7.6.2 Summary Evaluation Task (Performed by Reviewers)  
In synopsis writing task users produced a total of 24 summaries, 12 of them were created by using the RAAVE system while the rest were created using the baseline video player. Since 4 TED talks were used in the experiment there are 6 summaries created for each video.  
There are two types of techniques to compare summaries, researchers often use automatic tools such as ROUGE [76]. The other technique is to use human evaluators. [PERSON] et al. [77] observed that automatic techniques fall short in evaluating certain factors of summary quality compared tohumans. Therefore in order to evaluate the user produce synopses, the current experiment used a similar approach.  
All 7 reviewers evaluated the summaries of all four TED talks. For each TED talk, they were provided with the video and the six summaries created of that TED talk. They were asked to do the following.  
1. Watch the TED talk.
2. Evaluate each summary individually according to the characteristics listed in Table 5.
3. Rank the summaries in order of preferences (1 and 6).  
Reviewers were asked to assign a rank between 1 to 6 to the synopsis of each video with the most preferred summary being 1 st and the least preferred summary being 6 th.
## 3 Capacity Enhancement by Channel State Information  
Assuming that the channel matrix \(H\) is known to the receiver (e.g., through training sequences) but is unknown at the transmitter due to the long round-trip delays in optical transport networks. In this case, the transmitter sends uncorrelated signals of equal power on all modes. The average capacity of the FMF channel with equal power allocation \(C_{ep}\) is then given by [12; 17]:  
\[C_{ep}=\mathbb{E}_{\lambda_{i}}\left(\sum_{i=1}^{M}\log\left(1+\frac{P_{t}}{ M}\frac{\lambda_{i}}{2\sigma^{2}}\right)\right) \tag{5}\]  
\(P_{t}\) is the total power at the transmitter. In the last equation, the average is taken over the eigenvalues of the channel \(H\). The analytical computation of the average capacity requires the knowledge of the distributions of \(\lambda_{i}\)'s which is not always available, consequently we compute the capacity by extensive numerical simulations. Note that this is a linear capacity that increases with the amount of power, if nonlinearities are taken into account the capacity drops after a non-linear power threshold [19].