esa-sceva/satcom-chunk-collection huggingface dataset:
# Ad Hoc Data Foraging in a Life Sciences Community Ecosystem Using SoDa +
## 3 SoDa Architecture
### Workflow Query Construction  
SoDa supports workflow orchestration using a cognate and separate system called VisFlow (For a detailed discussion on VisFlow, we invite readers to consult the first edition  
Figure 9: QBuild SQL query builder interface: table returned by SoDa upon execution of the query in Figure 7.  
of VisFlow article in ACM TCBB. We note that the new and second edition of VisFlow is currently under construction). While VisFlow has its own user base and maintains a resource registry, SoDa shares its resource registry (wrapper bank: all stored data and their Needle description) with VisFlow, and helps users construct workflow queries using the full power of VisFlow. In the case of SoDa, simple workflows may involve a successive set of BioFlow/SQL queries written using the QBuild application builder. Users build a query, likely using multiple resources (SoDa treats each of the resources as a table or one that returns a table) and save the results in a named view, then reusing the view in a later query. All workflows in SoDa have a named script that can be retrieved from the left side panel. In the event that VisFlow is used to construct a workflow, it can be stored in VisFlow, but cannot be executed in SoDa. The query processor of SoDa can be viewed as a fusion of the QBuild and VisFlow workflow systems, and together they form the _SoDaPro_ system in which VisFlow is accessed through QBuild. The Workflow option in Figure 7 can be used to sequence named queries already built by dragging them from the left bar under the query tab. Or, users can hop over to VisFlow system to use the SoDa resources to construct more powerful workflows.  
Since queries in SoDaPro potentially involve deep web databases, or online tools, that are accessed at query time, the process may fail for various reasons. The reasons for this include poor construction of the wrappers, failure of the schema matcher to resolve heterogeneity, resource site evolution or modification, logical errors, or simply network errors. All of the preceding causes are non-database operations that largely depend on software or tool applications with non-deterministic behaviors. In the event that they breakdown, debugging and fixing could be complicated simply because the clues could be hidden inside many moving parts of the query execution apparatus. One less frustrating way to fix such breakage or query errors is to seek help from more experienced or willing users in the community using crowd computing, as discussed in Section 3.7.
# A Survey of Data Representation for Multi-Modality Event Detection and Evolution
## 4 Single-Modality Data Representation and Event Detection
### Text Based Data Representation
#### 4.1.4 Deep Learning Based Methods  
With the development of deep learning techniques and natural language processing techniques, deep learning techniques achieved excellent performance on text representation and event detection tasks. The word embedding technique is the first step of data representation based on deep learning. Word embedding techniques learn the representation for each word and words with a similar meaning are close in space [42]. By using word embedding techniques, the problem of sparseness and dimension disaster can be avoided in text representation. Based on word embedding techniques, a series of deep learning based event detection methods have been proposed and achieved excellent performance. For example, [PERSON] et al. [1] presented a novel text representation method for event detection task based on word embedding. They first transform words into continuous low-dimensional vectors based on word embedding. After that, K-means clustering is used to cluster words that have similar meaning and then a latent semantic space can be obtained. Their proposed representation method can reduce the dimension of document representation and alleviates the sparse semantics, thus improved the efficiency and accuracy of event detection. [PERSON] et al. [43] also proposed a novel neural model for event detection and prediction, which apply a standard Long Short-Term Memory (LSTM) model [44] to learn the shared event representation between different tasks. The LSTM encoder reads the words within the event sequentially and updates its hidden state iteratively. The method classifies the events to predefined types as well as predicts the next probable event by generating a sequence of words describing it. [PERSON] et al. [45] proposed a novel approach that incorporates an assessment of the word proximity of texts, combined with text encoding model that is based on the system of sentence embedding. Their approach combines Universal Sentence Encoder (USE) with data pre-processing, agglomerative hierarchical clustering and the Markov stopping moment to optimize clustering. The proposed model performed better than traditional text representation methods.
###### Abstract  
Treatment efficacy for age-related macular degeneration relies on early diagnosis and precise determination of the disease stage. This involves analyzing biomarkers in retinal images, which can be challenging when handling a large flow of patients and can compromise the quality of healthcare services. Clinical decision support systems offer a solution to this issue by employing intelligent algorithms to recognize biomarkers and specify the age-related macular degeneration stage through the analysis of retinal images. However, different stages of age-related macular degeneration may exhibit similar biomarkers, complicating the application of intelligent algorithms. This article presents a hybrid and hierarchical classification method for solving these problems. By leveraging the hybrid structure of the classifier, we can effectively manage issues commonly encountered with medical datasets, such as class imbalance and strong correlations between variables. The modifications to the intelligent algorithm proposed in this work for staging age-related macular degeneration resulted in an increase in average accuracy, sensitivity, and specificity of 20% compared to initial values. The [PERSON]'s Kappa coefficient, used for consistency estimation between the regression model and expert assessments of the intermediate class severity, was 0.708, indicating a high level of agreement.  
age-related macular degeneration; optical coherence tomography; staging; computer vision; deep learning; hierarchical classification; semi-supervised learning Article
# Emergency Repair Scheduling Model for Road Network Integrating Rescheduling Feature
###### Abstract  
When a natural disaster occurs, road maintenance departments always face the challenge of how to assign repair resources properly to recover damaged road segments as soon as possible. From the literature review, most studies treat such problems as a vehicle routing problem (VRP). In those studies, repair resources are always dispatched as complete crews, and cannot be divided into smaller scales. Furthermore, each disaster point is only allowed one group of resources to recover it, without considering the possibility of accelerating the production rate subjected to specific objectives. Such limitation restricts required resources in an inflexible manner. Therefore, this study defines all repair works as an emergency repair project and adopts the framework of the Resource-Constrained Project Scheduling Problem (RCPSP), which can resolve such complicated resource assignment issue. A novel emergency repair scheduling model for the road network is proposed based on Constraint Programming (CP) as the searching algorithm to facilitate model formulation. According to the RCPSP concepts, disaster points are set as repair activities and resource travel routes between disaster points are set as transit activities. All the repair activities are linked by transit activities and the required resources are assigned accordingly. In order to consider the second-wave hazard events of where new disaster points may occur, and new resources may be added into emergency repair projects, a rescheduling feature is integrated into the proposed model. Through two case studies, research findings show that this model can be easily modulated to adapt to different situations satisfying practical disaster management goals and solving emergency repair scheduling problems for road networks efficiently.  
road network; emergency repair; project scheduling; rescheduling; constraint programming 1  
[PERSON], 1,1447  
11 Department of Civil and Construction Engineering, National Yunlin University of Science and Technology, Yuxlin 640, Taiwan; [EMAIL_ADDRESS] (W.T.C.); [EMAIL_ADDRESS] (Y.-H.H.)  
2 Graduate School of Engineering Science and Technology, National Yunlin University of Science and Technology, Yuxlin 640, Taiwan; [EMAIL_ADDRESS] (Y.-H.H.)  
Y.-H.H.  
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
## 3 Selected Results of Optical Metrology Techniques
### Multiple Simultaneous Viewing Directions Using Four Digital Holographic Microscopes (MuVie)
#### 3.1.3 Multiple Aperture Shear Interferometry (MArS)
To solve the inverse problem, we use a method based on simulated annealing [38]. Figure 7 shows an example of a measurement [35]. The SUT in this case is an aspheric lens with a diameter of 25.4 mm and a design curvature radius of 49.8 mm. Figure 7a shows the aggregate of \(k\)-vectors, where different colors indicate that the light originated from different light sources. A total of 500 measurements with varying shears have been recorded to achieve this result. The object has been illuminated by 156 LEDs arranged around the SUT and 11 LEDs that have been projected through the lens. In Figure 7b, we see the measured shape of the SUT after 2000 iterations of the inverse ray-tracing approach. After subtraction of the best fit sphere, the deviation of the measured residual from the residual design topography was Peak-to-Valley (PV) 4 \(\upmu\)m. These results are promising and show that the principle works. Currently, we are working on an improved calibration procedure in order to reduce the PV value by at least one order of magnitude, which is the requirement to introduce the system into the market of aspheric lens testing. Figure 7: Measurement results for an aspheric lens as the surface under test: (**a**) An aggregate of wave vectors extracted from the measured coherence function. Different colors indicate light from different light sources. (**b**) Calculated profile of the lens after 2000 iterations of the inverse ray-tracer. Reprinted with permission from Reference [35].
# New Generation Compact Linear Accelerator for Low-Current, Low-Energy Multiple Applications
## 2 LINAC 7 Accelerator Component Descriptions
### Drift Tube Linac (DTL)  
The final energy desired for the accelerator is 7 MeV as this is sufficient to produce radio chemicals for medical diagnostics, and, if impacted against a lithium target, it can be used to produce a low flow of neutrons. This is important for the design process of the DTL because these applications only require the right energy and not as high a quality beam as one that is to be further accelerated. The design of the DTL will be concurrent with the decision on the need of the MEBT, with the goal of finding the lowest cost solution that minimizes particle loss because, unlike at the beginning of the accelerator, here particle loss will result in unwanted radiation production. The DTL is a standing wave structure devoted to accelerate particles based on the Alvarez drift tube. The idea was proposed a long time ago but remains in use in most proton and other ion linacs. The resulting standing wave is employed to accelerate the particles. Drift tubes are suspended by stems and the beam passes through them at the locations at which the oscillating standing wave would produce beam deceleration. Drift tubes include focusing magnets which help to avoid beam spatial dispersion. In order to reduce the number of power supplies needed, the proposed DTL will use permanent magnet quadruples in the drift tubes.
# CFD Investigations of Cyclone Separators with Different Cone Heights and Shapes
## 2 Materials and Methods
### Validation  
For validation, we use the experimental data of [PERSON] et al. [54] in which the Stairmand [46] with main body diameter, \(D\) = 0.205 m was used. The geometrical details have already been presented in Table 1, with the ST2 variant resembling the one used in [54]. At the inlet, a uniform velocity profile has been prescribed amounting to \(Re=2.58\times 10^{5}\), as used in the experiment.  
Figure 4 elucidates the comparison of the present numerical simulations against the experimental data by [54]. The first column represents radial profiles of mean axial velocity, and the second column shows the mean tangential velocity at axial locations \(Z/D\) = 0.811, 1.104, 2.128, and 3.006 (from top to bottom), respectively. At all the axial locations, the mean axial velocity agrees well both qualitatively and quantitatively  
the cyclone axis and the local maxima on either side of the axis are very well captured. The tangential velocity is also in good agreement with the reference data. Although a mild difference is observed in the upper part of the cyclone, in the conical section the local peaks match very well. Since the cyclonic flows are known to constitute a high level of anisotropy followed by the Rankine vortex, the numerical results presented here may be considered to fit well with the measured data. Two levels of mesh consisting of nearly 385K and 624K hexahedra were tested. Since no appreciable difference was noticed, the results of level 2 have been presented in Figure 4.  
Figure 4: Comparative analysis of CFD results with experimental data [54]: (**a**) the first column—mean axial velocity; the second column—mean tangential velocity, and (**b**) from top to bottom: radial profiles at axial locations \(Z/D\) = 0.811, 1.104, 2.128, and 3.006 (\(D=2R=0.205\) m is the barrel diameter) from the cyclone roof, respectively.  
A mild difference is observed in the axial velocity profiles and tangential velocity profiles when compared to the reference data due to a high level of anisotropy and complex flow phenomenon. Such differences are also reported in several studies even with the advanced closure LES; for instance [1; 5; 6; 7; 8; 9; 10; 12; 13; 14; 15; 16; 17; 18; 19; 20].
# LDM-Ex-FDM: A Novel Multi-Service Transmission Scheme for the ATSC 3.0 System
###### Abstract  
In order to improve system coverage performance, in this paper, a multi-service transmission scheme on the basis of the puncturing technique, namely layered division multiplexing extension frequency-division multiplexing (LDM-Ex-FDM), is proposed. The key idea of the proposed scheme is that the symbols punctured from the enhanced layer (\(EL\)) of fixed services will be independently transmitted on a certain number of subcarriers orthogonal to the \(LDM\) signal of the core layer (\(CL\)) and most of the \(EL\). By doing so, the punctured symbols will be demodulated with a higher signal-to-noise ratio (SNR) on the receiver side and can be recovered well, thus improving the reception performance of the \(EL\) fixed services. Moreover, two puncturing strategies based on bit-interleaved coded modulation (BICM) symbols of the LDM-Ex-FDM scheme, namely equal interval symbol puncturing (EISP) and non-equal interval symbol puncturing (NESP), are developed to improve the performance of the \(EL\) series. Simulation results are given to show that, when the puncturing rate in the LDM-Ex-FDM scheme is configured as \(1/12\), the performance of the fixed service can be improved by \(1.8\) dB, meanwhile the bit error rate (BER) performance of the mobile service is not affected. Thus, the overall system coverage performance can be improved. Furthermore, to reduce the computational complexity and the demodulation delay of fixed service carried in the \(EL\), at the receiver, a direct interference cancellation (DIC) detector is proposed as well. Simulation results are given to show that, under high injection level and high modulation order, the proposed DIC scheme is able to achieve almost the same BER performance as that of the traditional successive interference cancellation (SIC) scheme while with lower computational complexity.  
LDM; ATSC 3.0; FDM; Multi-service transmission; SIC
## 3 Results and Discussion
### PdM Assessment of Case Study
#### 3.1.2 Step 2: Critical Components and Failure Modes  
The empirical case study data for failures experienced during operation show the maintainable items of rotor and bearings are two of the most critical components with associated failure modes of looseness (LOO) and vibration (VIB), respectively.
# Developing Security Assurance Metrics to Support Quantitative Security Assurance Evaluation
## 3 Our Methodological Approach
### Assurance Metrics Calculation: The Core Concept
#### 3.2.2 Assurance Criteria Evaluation Phase  
The second phase of assurance evaluation is responsible for the calculation of assurance criteria scores. Based on the previous discussion, the actual score of the \(i\)-th SRC, represented by \(ActSRCi\), is measured based on the average value of its respective SRE and obtained by multiplying a weight factor to express the levels of importance. The scale of the weight factor ranges from 1 to 10, where 1 is assigned to SRCs that are least essential, while 10 is the maximum expressing a vital requirement. The formula to calculate \(ActSRCi\) is defined as:  
\[ActSRC_{i}=WghSRC_{i}\times\frac{\sum_{j=1}^{n}ActSRE_{ij}}{n},\ \forall WghSRC_{i}\ \in\ [1,\ 10], \tag{3}\]  
where:  
\(ActSRCij\): the actual score of the \(j\)-th SRE associated with the \(i\)-th SRE;  
\(WghSRCi\): the weight factor that corresponds to the \(i\)-th SRC;  
\(n\): the number of SREs associated with the \(i\)-th SRC.  
Based on Equation (3), it can be derived that \(ActSRC\) has a maximum value, equaling its weight factor when all the underlying security requirements are fulfilled (i.e., \(ActSREi\) = 1).  
The assurance metric \(ActVUCi\), represented by the \(i\)-th vulnerability criteria, can be calculated using the average value of correspondent VUEs, considering the risk factor of vulnerabilities as well. It has to be mentioned that the risk is usually derived using the standard risk model: \(Risk=Likelihood\times Impact\) Risk. With this flexible model, the scale of the resulting risk value could range from 0 to 10, where 0 represents that the corresponding VUC is least likely to fail, while 10 is considered the maximum risk. The formula to derive the \(i\)-th \(ActVUC\) is defined as:  
\[ActVUC_{i}=\ RskVUC_{i}\times\frac{\sum_{j=1}^{n}ActVUE_{j}}{n},\ \forall RskVUC_{i}\ \in\ [0,\ 10], \tag{4}\]  
where:  
\(ActVUCij\): the actual score of the \(j\)-th VUE associated with the \(i\)-th SRC;  
\(RskVUCi\): the risk that corresponds to the \(i\)-th VUC;  
\(n\): the number of VUEs associated with the \(i\)-th VUC.
###### Abstract
The licensed/primary users (PUs) and unlicensed/secondary users (SUs) of the network can agree on interference limits that allow a variety of devices from the primary and secondary networks to share the same frequency band. More specifically, CRN allows the SUs to use its spectrum based on certain conditions, which can be occupied by the PUs, or their slot might be empty, hence accommodating more users [20; 21; 22]. Dynamic Spectrum Access (DSA) [20] technology, which divides the currently unoccupied spectrum between the licensed PUs and unlicensed SUs in a dynamically changing radio environment, is the foundation of the CRN concept. Based on how the SUs acquire and use the primary spectrum, CRN transmissions can be divided into three main branches: interweave, underlay, and overlay DSA methods [23; 24]. In both the interweave and underlay schemes, the PU has no knowledge of the secondary transmission because the SU transmits in a spectrum hole in the interweave DSA mechanism, while the SU transmits its information within the interference threshold of the primary transmitter in an underlay scheme. Both interweave and underlay will introduce slight degradation to the primary transmission. While cognition is employed to reduce interference at the destination of both the PU's and SU's destination in the CRN, the overlay transmission model focuses on the interference reduction or cancellation method. The SU should have access to the PU's codebooks in order to cancel out the interference [23]. A two-stage Stackelberg game was proposed in [25] to analyze the participation level of the mobile users and the optimal incentive mechanism of the CSP using backward induction. Similarly, ref. [26] proposed a two-stage Stackelberg game-based computational offloading mechanism in which the mobile edge nodes (leaders) set the appropriate price for their computational resources and the industrial IoT devices (followers) formulate their utility functions by considering the social interaction among each other. The work proposed a dynamic iterative algorithm for Nash equilibrium. In [27], the authors exploited the concept of a social tie in CJs, where the source and jammers are smart phones carried by humans who have diverse social relationships. The jammers would be more willing to become the CJs for the source-destination pair that has a closer tie with them.
## 4 Simulation Results and Discussion
### System under Study and Data  
The studied system is a nine-bus electrical network, the number of lines of which is twelve, and it has four buses and five pipelines in the gas network and the urban heating network has nine buses and also has nine pipelines (Figure 2). Line/pipeline data, by assuming 1 MW as base power, a 1 kV base voltage, a 10-bar base pressure, and a 100 \({}^{\circ}\)C base temperature, are presented in Table 2[31]. Furthermore, the amplitude of voltage, pressure, and temperature can vary between 0.9 and 1.1 p.u. Moreover, the data of the system load in the electric load peak hour [31] is considered. These data include active and reactive load power (p.u) and heating load power (p.u). In the rest of the hours, the load percentage value is multiplied by the load value at 20:00.  
The load curve (the share of peak load) of electric and heating networks is shown in Figure 3. Furthermore, Figure 4 illustrates the daily energy price curve in the day-ahead market for electricity, gas, and district heating energy. The size of electricity, gas, and heating stations is assumed to be 7, 11, and 3 p.u. The system under study consists  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Algorithm** & **Parameter** & **Value** \\ \hline FLA [29] & C & 0.1 \\  & C1 & 2 \\ PSO [32] & C2 & 2 \\  & Inertia weight & Linearly reduction from 0.9 to 0.1 \\ MRFO [33] & S & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Control parameters of different algorithms.  
Figure 2: Schematic of the studied system.  
Hubs' renewable energy sources include photovoltaic and wind sources, the predicted power of which is presented in Figures 5 and 6, respectively [31]. These figures show the forecasted changes in photovoltaic and wind power for the day-ahead. In addition, the number of EVs for hubs 1, 2, 3, 5, 6, and 7 is considered to be 60. The data of battery SOC, BC, charge/discharge rate and EV efficiency, and penetration rate are considered every hour [28; 34]. For an electric storage system in which the charger size is 0.5 p.u, minimum and maximum sizes of 0.5 and 2 p.u, charge/discharge rate, and efficiency are considered to be 0.3 and 0.88, respectively. Furthermore, the size of the CHP in EGHNs is 2.5, 1, 1 with an overall efficiency of 80% [32]. The size and efficiency of the boiler system are