esa-sceva/satcom-chunk-collection huggingface dataset:
_Article_  
Statistical, Spatial and Temporal Mapping of 911 Emergencies in Ecuador  
[PERSON] 1,2,*, [PERSON] 1 (c), [PERSON] 2,3 and [PERSON] 2,3 (c)  
Departamento de Electrica y Electronica, Universidad de las Fuerzas Armadas ESPE, 171103 Sangolqui, Ecuador; [EMAIL_ADDRESS]  
Departamento de Teoria de la Senal y Comunicaciones, Sistemas Telematicos y Computacion, Universidad Rey Juan Carlos, 28943 Fuenlabrada, Spain; [EMAIL_ADDRESS] (S.M.-R.); [EMAIL_ADDRESS] (J.L.R.-A.)  
Center for Computational Simulation, Universidad Politecnica de Madrid, Boadilla del Monte, 28660 Madrid, Spain  
Correspondence: [EMAIL_ADDRESS]; Tel.: +593-9-398-9400  
Received: 9 January 2018; Accepted: 26 January 2018; Published: 29 January 2018  
**Abstract:** A public safety answering point (PSAP) receives alerts and attends to emergencies that occur in its responsibility area. The analysis of the events related to a PSAP can give us relevant information in order to manage them and to improve the performance of the first response institutions (FRIs) associated to every PSAP. However, current emergency systems are growing dramatically in terms of information heterogeneity and the volume of attended requests. In this work, we propose a system for statistical, spatial, and temporal analysis of incidences registered in a PSAP by using simple, yet robust and compact, event representations. The selected and designed temporal analysis tools include seasonal representations and nonparametric confidence intervals (CIs), which dissociate the main seasonal components and the transients. The spatial analysis tools include a straightforward event location over Google Maps and the detection of heat zones by means of bidimensional geographic Parzen windows with automatic width control in terms of the scales and the number of events in the region of interest. Finally, statistical representations are used for jointly analyzing temporal and spatial data in terms of the \"time-space slices\". We analyzed the total number of emergencies that were attended during 2014 by seven FRIs articulated in a PSAP at the Ecuadorian 911 Integrated Security Service. Characteristic weekly patterns were observed in institutions such as the police, health, and transit services, whereas annual patterns were observed in firefighter events. Spatial and spatiotemporal analysis showed some expected patterns together with nontrivial differences among different services, to be taken into account for resource management. The proposed analysis allows for a flexible analysis by combining statistical, spatial and temporal information, and it provides 911 service managers with useful and operative information.  
**Keywords:** spatial-temporal analysis; emergency alerts; public safety answering point; first response institutions
# Advanced Default Risk Prediction in Small and Medium-Sized Enterprises Using Large Language Models
## 4 Experiments and Results
### Quantitative Analysis  
#### 4.1.1 Indicator Selection and Model Design  
Due to the high dimensionality of enterprise data, including all indicators in the regression analysis is impractical, and some dimensions exhibit endogeneity issues. Therefore, this study prioritizes the selection of core indicators to mitigate high endogeneity.  
This research posits that paid-in capital is related to a firm's risk resilience, the number of defendants in legal judgments correlates with enterprise risk, the proportion of negative news is associated with risk, and restrictions on high consumption are significant risk indicators. Additionally, historical default records reflect the long-term creditworthiness of the firm. Consequently, the core explanatory variables include enterprise age, the number of defendants, the proportion of negative news, last year's default status, and the number of high consumption restrictions. Given that the variables underwent logarithmic transformation, the regression model is as follows:  
\[Sr=\ \ \beta_{0}+\beta_{1}Age+\beta_{2}Pla+\beta_{3}Nnr+\beta_{4}Frn+\beta_{5} Lcr+\varepsilon \tag{2}\]  
In this model, Age symbolizes company age, Pla represents the number of times a company has been a plaintiff, Nnr portrays the ratio of negative news (negative news ratio), Frn stands for the previous year's default status (first refusal number), and Lcr signifies the count of individuals subject to luxury consumption restrictions. Sr is the dependent variable, indicating the current year's default status (second refusal number).
## 2 Methods
### Term Frequency-Inverse Document Frequency (TF-IDF)  
The Term Frequency-Inverse Document Frequency (TF-IDF) concept has been applied frequently in information retrieval. The method basically evaluates the importance of a Tokenized unit in a collected corpus by considering both the number of times a word appeared in a single document and the frequency with which a word has been used among all corpora. Search engines usually rely on versions of the TF-IDF weighting scheme as a critical technique in retrieving and rating the significance of a text given a keyword [16; 17].
## 4 Simulation and Performance Analysis
The output of a circuit with high AOP can stably transmit values to subsequent circuits, so it is one of the important performance indicators that is directly related to the scalability and connectivity of the circuit. AOP calculates the average of the highest and lowest values of output polarization, as shown in Equation (9) [52]. As shown in Table 6, the highest meaningful AOP of the proposed circuit was measured at all outputs. The total AOP, calculated as the average value of AOP of all outputs, improved by more than 3.8% compared to the best existing circuit in [37].
###### Abstract  
Optical remote sensing data are freely available on a global scale. However, the satellite image processing and analysis for quick, accurate, and precise forest above ground biomass (_AGB_) evaluation are still challenging and difficult. This paper is aimed to develop a novel method for precise, accurate, and quick evaluation of the forest _AGB_ from optical remote sensing data. Typically, the ground forest _AGB_ was calculated using an empirical model from ground data for biophysical parameters such as tree density, height, and diameter at breast height (_DBH_) collected from the field at different elevation strata. The ground fraction of vegetation cover (_FVC_) in each ground sample location was calculated. Then, the fraction of vegetation cover (_FVC_) from optical remote sensing imagery was calculated. In the first stage of method implementation, the relation model between the ground _FVC_ and ground forest _AGB_ was developed. In the second stage, the relational model was established between image _FVC_ and ground _FVC_. Finally, both models were fused to derive the relational model between image _FVC_ and forest _AGB_. The validation of the developed method was demonstrated utilizing Sentinel-2 imagery as test data and the Tundi reserved forest area located in the Dhanbad district of Jharkhand state in eastern India was used as the test site. The result from the developed model was ground validated and also compared with the result from a previously developed crown projected area (_CPA_)-based forest _AGB_ estimation approach. The results from the developed approach demonstrated superior capabilities in precision compared to the _CPA_-based method. The average forest _AGB_ estimation of the test site obtained by this approach revealed 463 tons per hectare, which matches the previous estimate from this test site.  
Sentinel-2; regression modeling; fraction of vegetation cover; forest _AGB_ 23 November 202011 January 20211 January 202113 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211 January 20211
## 3 Proposed Scheme
### Overview of Proposed SDPCACM Corresponding to NDN
#### 3.2.1 Initialization Phase  
Herein, we explain a scenario in the case where an MC is first connected with an edge router, which is linked to the NAR that the network controller has configured to enable NDN transactions. Moreover, the PIT, CS, and FIB make up the NDN node. When a mobile consumer contacts the router with an interest in content orientation, the router uses their name in the CS to examine the content data. The customer can then be provided with the desired info if it is discovered in the CS. The pending interest table (PIT), comprising an interest list awaiting data, is where the router searches if the data is not found in the CS. It is sent to the consumer if the operation inside the look-up table is effective; if not, it is checked with the PIT before sending the interest packet to the provider. In this scenario, the interest packet is ignored since we know the router does not have the content and does not know how to deliver it from the source if no concerned entity is located. The forwarding strategy is maintained by the FIB, which also chooses how and when to convey interest.  
The edge router connects to the NAR via an IP router. The way it operates is as follows: primarily, once a mobile user creates an interest packet, it is further enclosed in an IP header, then sent to the node of an NDN over the connected AP. The packet is decapsulated when it gets to an NDN node by removing the IP header, and it is then forwarded in the NDN layer. The returning data packets are then once again wrapped with an IP header at the NDN node and sent to mobile users [18].  
Inter-domain handovers and intra-domain handovers are the two forms of handovers that are illustrated below.
# Graphene-Based Grid Patterns Fabricated via Direct Ink Writing for Flexible Transparent Electrodes
## 3 Results and Discussions
### Formation Mechanism of Well-Aligned Graphene Flakes in DIW Films
This leads to a significant improvement in film uniformity and conductivity, with the highest conductivity reaching 5.22 \(\times\) 10\({}^{5}\) S/m, surpassing many traditional modification methods.
## 2 Antenna Array Structure
### MTM Based UWB Antenna Array with Wide Rejection Bands  
As the UWB frequency band is shared with other wireless systems such as WLAN operating across 5.150-5.825 GHz it can experience interference. To prevent this the RF front-end of UWB systems require a filter which can be costly and affect the size of the system. To overcome the effects of interfering signals various techniques have been previously proposed [3; 19; 20; 21; 22; 23; 24] that introduce notching function on the UWB antenna; however, their rejection band is narrow to completely reject wideband interfering signals. Dual wideband rejection band is introduced here by applying electromagnetic bandgaps on the back side of the MTM antenna array. This technique does not affect the array's physical dimensions. The proposed EBG is realized by implementing arrangement of circular slots on the antenna's ground-plane under the patches and interconnecting microstrip-lines, as shown in Figure 4a-c. The geometrical parameters of the proposed antenna array are given in details in Table 3. The exact positions of the EBG slots were optimized using CST Microwave Studio. The results in Figure 4d show the two notched bands are highly selective and realized between 4.90-6.04 GHz and 7.09-8.50 GHz. Therefore, the proposed antenna array is capable of suppressing interference affects from WLAN (5.15-5.825 GHz) and X-band satellite downlink communication band (7.10-7.76 GHz). Two different 3-D full-wave electromagnetic simulation tools were used to validate the design, that is CST Microwave Studio and HFSS. There is excellent agreement between both tools which confirms the feasibility of the proposed technique.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameters** & **Dimensions (mm)** \\ \hline Radius of hexagonal slots & 1.25 \\ Width of hexagonal slots & 0.25 \\ Radius of via-holes & 0.25 \\ Height of via-holes & 0.85 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Geometrical parameters of the MTM inspired ultra-wideband (UWB) antenna. (Note, all other parameters are given in Table 1.).  
Figure 4: Layout of the proposed MTM based antenna array loaded with electromagnetic bandgap (EBG) slots: (**a**) top-side of substrate, (**b**) back-side of substrate (ground-plane), (**c**) isometric view showing the structural components and their locations, (**d**) reflection coefficient response comparison of the reference, MTM, and MTM with EBG loading, and (**e**) radiation gain of the reference, MTM, and MTM with EBG loading.
## 1 Introduction
The Gaussian pulses transmitted through UWB channel conditions from anchors to tag and estimated the TOA of UWB pulses. * We formulated a TOA-distance model and estimated the tag distance from anchors. The estimated tag distances from anchors are used for predicting the user position. * We introduced the LSTM deep learning architecture for UWB localization. The performance of the LSTM architecture was analyzed and we estimated the best parameters for the LSTM model. The rest of the paper is organized as follows--Section 2 summarizes the previous work on UWB localization, Section 3 discusses the proposed UWB indoor localization approach using deep learning LSTM networks. The analysis of simulation results is provided in Section 4. Finally, the conclusions are summarized in Section 5.
## 4 Wind Tunnel Testing
### Sensitivity of Numerical Outputs with the Spatial and Temporal Discretizations
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Case** & **No. Elements** & \(y_{1}\)**/\(C\)** & \(x_{1}\)**/\(C\)** & \(n_{\text{bl}}\) & \(r_{\text{bl}}\) & \(h_{\text{bl}}\)/\(C\) \\ \hline g01 & 31,9467 & \(2.42\times 10^{-4}\) & \(9.69\times 10^{-4}\) & 15 & 1.104 & \(7.95\times 10^{-3}\) \\ g02 & 294,175 & \(2.52\times 10^{-4}\) & \(1.01\times 10^{-3}\) & 15 & 1.104 & \(8.27\times 10^{-3}\) \\ g03 & 356,262 & \(2.41\times 10^{-4}\) & \(9.63\times 10^{-4}\) & 15 & 1.104 & \(7.90\times 10^{-3}\) \\ g04 & 368,172 & \(2.35\times 10^{-4}\) & \(9.39\times 10^{-4}\) & 15 & 1.104 & \(7.70\times 10^{-3}\) \\ g05 & 362,604 & \(2.47\times 10^{-4}\) & \(9.87\times 10^{-4}\) & 15 & 1.104 & \(8.10\times 10^{-3}\) \\ g06 & 368,871 & \(2.37\times 10^{-4}\) & \(9.46\times 10^{-4}\) & 15 & 1.104 & \(7.76\times 10^{-3}\) \\ g07 & 295,849 & \(2.53\times 10^{-4}\) & \(1.01\times 10^{-3}\) & 15 & 1.104 & \(8.32\times 10^{-3}\) \\ g08 & 331,608 & \(2.38\times 10^{-4}\) & \(9.52\times 10^{-4}\) & 15 & 1.104 & \(7.81\times 10^{-3}\) \\ g09 & 306,320 & \(2.32\times 10^{-4}\) & \(9.27\times 10^{-4}\) & 15 & 1.104 & \(7.61\times 10^{-3}\) \\ g10 & 308,364 & \(2.33\times 10^{-4}\) & \(9.31\times 10^{-4}\) & 15 & 1.104 & \(7.64\times 10^{-3}\) \\ g11 & 290,430 & \(2.55\times 10^{-4}\) & \(1.02\times 10^{-3}\) & 15 & 1.104 & \(8.37\times 10^{-3}\) \\ g12 & 295,644 & \(2.49\times 10^{-4}\) & \(9.98\times 10^{-4}\) & 15 & 1.104 & \(8.19\times 10^{-3}\) \\ g13 & 298,393 & \(2.48\times 10^{-4}\) & \(9.91\times 10^{-4}\) & 15 & 1.104 & \(8.13\times 10^{-3}\) \\ g14 & 334,589 & \(2.40\times 10^{-4}\) & \(9.59\times 10^{-4}\) & 15 & 1.104 & \(7.87\times 10^{-3}\) \\ g15 & 296,766 & \(2.44\times 10^{-4}\) & \(9.75\times 10^{-4}\) & 15 & 1.104 & \(8.00\times 10^{-3}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Characteristics of the finite volume meshes for the set of 15 samples (\(y_{1}\)/\(C\) is the first layer mesh height, \(x_{1}\)/\(C\) is the first layer element size along the wall, \(n_{\text{bl}}\) is the number of layers in the boundary layer (bl) mesh, \(r_{\text{bl}}\) is the inflation ratio in the bl mesh and \(h_{\text{bl}}\) is the total height of the bl mesh). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Max Co** & \(C_{D}\) & \(C_{L}\) & \(C_{M}\) & \(\widetilde{C_{D}}\) & \(\widetilde{C_{L}}\) & \(\widetilde{C_{M}}\) & \(\mathcal{St}\) \\ \hline
0.5 & 0.060 & \(-0.014\) & 0.021 & 0.012 & 0.110 & 0.025 & 0.139 \\
1.0 & 0.061 & \(-0.010\) & 0.021 &
# Evolving Strategies and Materials for Scaffold Development in Regenerative Dentistry
## 4 Discussion
### Materials for Scaffold Fabrication in Regenerative Dentistry
On top of that, [PERSON] highlighted the importance of 3D-printed scaffolds, including cells and signaling molecule selection, in promoting increase in root dimensions, which greatly reduced incidence of tooth fractures in necrotic immature permanent teeth. He calls for an innovation of endodontic treatment protocols, shifting towards regenerative apexification techniques [115]. Another element that is used in regenerative dentistry is porous boron-modified bioactive glass that possesses dentin regenerative properties, while having good biodegradation and inducing calcium phosphate formation [72]. Another promising actor in the studied area is baghdadite I--calcium silicate reinforced with zirconium ions. It increases apatite formation, mineralization, and ossification, which plays a crucial role in dentofacial tissue regeneration [110]. Gelatine methacrylate (Gel-MA) is another biomaterial used in tissue engineering, drug delivery systems, and 3D bioprinting applications. An example of such an application would be fabrication of patient-specific hydrogels with tissue healing properties and controlled release of bioactive molecules or substances [126]. In relation to drugs-doped scaffolds, [PERSON] et al. described simvastatin (SV) as a promising agent in odontoblastic marker overexpression and matrix mineralization enhancement when in contact with dentin [127]. Another study explored deproteinized bovine bone minerals (DBBM) in the form of granules, and blocks and their effect on osteoblasts and macrophages, showing promising outcomes--even more pronounced in the case of DBBM granules [128]. [PERSON] et al. compared mineralization rate, properties, and indications of cell-free biomimetic mineralization and cell-dependent scaffold mineralization in skeletal and dental hard tissue regeneration [129]. Biomimetic principles are significantly contributing to advances in this field [130,131]. Further research on materials for regenerative dentistry is crucial, especially regarding their structure, degradation, biocompatibility, stem cell interaction, vascularization, and antimicrobial properties.
# TOAR: Toward Resisting AS-Level Adversary Correlation Attacks Optimal Anonymous Routing
## 4 Design of TOAR
### Second Stage Algorithm for Full Security Policy Function  
Based on the first phase of the algorithm, which finds the set of routes that comply with the security policy, the second phase of the optimization algorithm performs a local search in the vicinity of the path obtained in the first phase. It progressively removes shared nodes, searches for a new optimal path near the removed node, and updates the iterative basis from a priori to a posteriori knowledge. The goal is to improve security without increasing cost. If the iterative conditions are not met, a different shared node is selected for removal. The algorithm terminates when the set number of iterations is reached, all shared nodes have been removed, or no further improvement in the path can be obtained. Considering the worst-case scenario, the algorithm may enumerate every possible route. Therefore, similar to the PCSR algorithm [19], the iteration number is set as a termination condition for the search, controlling the overall runtime of the entire search process.  
The second stage uses the complete security policy as in Equation (3). The security policy on which the algorithm based is jointly determined by the length of the path and the number of shared nodes, and \(d(r)\) is subsequently used instead of \(d(p,k,m,n)\) to simplify the expression. Define \(I_{d}(r,r^{*})=d(r)-d(r^{*})\) to denote the improvement in the security of the newly sampled paths compared to the previous paths. Define \(I_{path}(r,r^{*})=length(r^{*})-length(r)\) to denote the increase in the length of the newly sampled obtained path compared to the previous path. Define the price policy \(c(r,r^{*})\), as in Equation (16) to denote the unit cost of routing security enhancement.  
\[c\left(r,r^{*}\right)=\frac{b\cdot I_{path}\left(r,r^{*}\right)}{I_{d}(r,r^{ *})} \tag{16}\]  
The specific practice of Iteration Optimal Searching Routing, as in Algorithm 2, begins with the shortest path as \(r\) and uses the optimal path obtained in the first stage of the algorithm as \(r^{*}\). The value of \(c(r)\) is calculated as the maximum value of \(c(r)\) that is acceptable to the user. Thereafter, in the second stage algorithm, \(c(r)\) decreases as an iterative condition for route optimization. If the condition is satisfied, the route is saved as \(r^{*}\), and the last \(r^{*}\) is used as \(r\), continuing the iteration. If the condition is not satisfied, the path is discarded and resampled.  
The complexity of Algorithm 2 is \(\mathcal{O}(k\times T_{PCSR})\). The reason for not using Algorithm 2 directly and instead completing the process in two stages is to balance the routing search space. Algorithm 1 searches for routes that meet the conditions within a larger search space, while Algorithm 2 then refines the results by searching in the nearby region of each route found.
## Appendix C Survey Questions (Sample)  
Survey participants were presented with questions about demographics and from four topics based on their survey participation selection. The categories and a sample of questions are listed below.  
**Path 1--Students with remote work experience completed the following question categories:**  
1. DE--Demographics
* DE01--What is (or was) your regular remote work location?
* DE06--How long have you been working as an intern/employee?
2. SD--Smart devices
* SD02--Are you aware of any formal cyber security company requirements relating to smart home devices?
* SD03--What is the level of cyber security support, relating to smart home devices, that you would expect from your company?
3. RW--Remote work
* RW04--Did you get any cyber security company training in the past 12 months to cover remote work requirements?
* RW08--Was the initial password for your home Wi-Fi network at least once updated? (Initial password is provided by the Wi-Fi router manufacturer.)
4. SE--Social engineering attacks
* SE03--What is the level of cyber security support, relating to phishing and other social engineering attacks that you would expect from your company?
* SE07--Did you report the phishing email attacks to your company, if you received any in the past 12 months?
5. SI--Shadow IT
* SI03--What is the level of cyber security support, relating to shadow IT/BYOD that you would expect from your company?
* SI09--Are you using personal cloud based services (i.e., Google Drive, Amazon Cloud, Microsoft Cloud,...) to store work related data?