esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Literature Review and Data Extraction
### Context-Aware Task Allocation  
Previous research [52] highlighted the need for high-performance computing in smart environments. In this work, the authors proposed an effective task allocation algorithm for global heterogeneous systems that are DFD (Data-Flow-Driven). In order to achieve local computation and low latency, the authors suggested fog computing and edge computing topologies. To address the work distribution issue for large-scale scientific computing systems leveraging remote data sources, this paper provides two optimization techniques based on mixed-integer linear programming (ILP). Compared to the optimum matching model, the staged optimization model's solutions were shown to be nearly ideal in all situations. In conclusion, the authors acknowledged the need for more study in the areas of availability monitoring, predicting application behavior, and finding nearly ideal solutions in a very short amount of time.  
A collaborative edge computing system presented by [PERSON] et al. [69] is a solution tailored to increase application loading, workload distribution, and resource allocation while preserving application service provider profitability. The advent of mobile edge computation, which offers computation, storage, network, and communication resources at the edge for various applications, has made it feasible to connect telephony with Internet services. Physical memory limitations prevent the edge server from running many application services at once, and uneven traffic distribution over the mobile network makes it challenging to make the most out of the edge network's resources. The suggested approach utilized a profit-aware technique for minimizing system latency in long-term stochastic optimization issues with assignment problem (ASP) profit constraints. It also ensured ASP profitability and used genetic algorithms to achieve a near-optimal approach for each time slot. The simulation results showed that the algorithm reduced the system latency in the long term while maintaining application service provider profits.
## 4 Discussion  
### Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA)  
Table 3 shows the PCA results of concrete curing methods based on the data features shown in the Table A1 in Appendix A. The compressive strengths of the cylinders and the flexural strengths of the beams are the dependent variables for the multivariate regression and variance analysis. The influence factors for concrete strengths are curing days, specimen type, casting date, method, replicate, max air temperature, and min air temperature. The negative value of a component in Table 3 means that the component is not significant. The first principal component accounts for 28.5% of the total variance. The variables that correlate the most with the first principal component (PC1) are min air temperature (0.685), max air temperature (0.670), method (0.060), and curing days (0.030). The first principal component is positively correlated with all the variables except the casting date. The first four principal components explain 79.5% of the variation in the data. Therefore, these components can be used to analyze concrete strength prediction. In this research, the method variable was used to classify observations. Table 4 lists the squared distance between groups. The more distinct the value is, the better the classification in the LDA.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Eigen Analysis of the Correlation Matrix**} \\ \hline Eigenvalue & 1.9976 & 1.3152 & 1.1975 & 1.0525 & 0.8313 & 0.5378 & 0.0680 \\ \hline Proportion & 0.285 & 0.188 & 0.171 & 0.150 & 0.119 & 0.077 & 0.010 \\ \hline Cumulative & 0.285 & 0.473 & 0.644 & 0.795 & 0.913 & 0.990 & 1.000 \\ \hline \multicolumn{6}{c}{**Eigenvectors**} \\ \hline Variable & PC1 & PC2 & PC3 & PC4 & PC5 & PC6 & PC7 \\ \hline Curing days & 0.030 & \(-\)0.094 & \(-\)0.406 & \(-\)0.693 & 0.578 & 0.104 & 0.017 \\ \hline Specim type & 0.012 & \(-\)0.712 & \(-\)0.223 & 0.028 & \(-\)0.342 & 0.570 & \(-\)0.002 \\ \hline Casting date & \(-\)0.276 & 0.034 & \(-\)0.658 & \(-\)0.097 & \(-\)0.475 & \(-\)0.489 & 0.121 \\ \hline Method & 0.060 & \(-\)0.610 & 0.447 & \(-\)0.276 & \(-\)0.017 & \(-\)0.585 & 0.082 \\ \hline Replicate & 0.008 & 0.326 & 0.329 & \(-\)0.655 & \(-\)0.546 & 0.241 & 0.022 \\ \hline Max air temperature & 0.670 & 0.013 & \(-\)0.178 & \(-\)0.032 & \(-\)0.148 & \(-\)0.157 & \(-\)0.686 \\ \hline Min air temperature & 0.685 & 0.067 & \(-\)0.113 & 0.054 & \(-\)0.058 & \(-\)0.010 & 0.712 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Principal component analysis.
# Ad Hoc Data Foraging in a Life Sciences Community Ecosystem Using SoDa +
## 3 SoDa Architecture
### Accessing Resources  
Once an external SoDa resource is identified, a wrapper needs to be generated to facilitate real-time online access and ensure successful extraction. In SoDa, resources are of two types--a deep web database (a database that can only be accessed via query form submission or through an API), or an online analysis tool that returns a table on the appropriate submission of input parameters, again likely using forms. In SoDa, the underlying data model is relational, and thus all its operations are conceived using a tabular representation of data, although the engine is capable of processing TXT, CSV, XML, and JSON formatted data.  
Figure 2: Resource discovery and resource description generation in ResCom: These figures show how ResCom’s Resource Specification Analyzer finds relevant resources from PubMed database and helps generate an execution plan and resource descriptions for the QBuild query builder using an LLM with the help of users and the wrapper generation system CroW.  
However, developing an access protocol for such resources is mostly a manual process, largely because each one is unique, but follows a simple mathematical relation as follows.  
\[\gamma:\mathcal{U}\rightarrow(\mathcal{T}\rightarrow\mathcal{T})\]  
where \(\gamma\) is a function (the wrapper generator) that maps a resource \(u\in\mathcal{U}\) (represented by a URL) into a function \(\omega\in\Omega\) of the form  
\[\Omega\subseteq\{\omega|\omega:\mathcal{T}\rightarrow\mathcal{T}\}\]  
Thus, \(\gamma(u):\mathcal{T}\rightarrow\mathcal{T}\) or, \(\gamma(u)(t)=t\in\mathcal{T}\), where \(u\in\mathcal{U}\) is a URL and \(t\in\mathcal{T}\) is a table. The access plan we develop is a learned function, \(\omega\) from \(\gamma(u)\), that would transform an input table \(t\) into a result table \(t^{\prime}\) using the resource at the URL \(u\).  
To be able to develop the function \(\omega\), we use a GUI-enabled public wrapper generation system called _CroW_ (stands for Crowd Wrapper Generator). CroW supports visual tools and functions to help CroW to learn the form of behavior and data layouts so that a formula can be learned, which CroW can then use to recreate the access plan for a site \(u\) when requested by a query in real time (the wrapper generation process for the DisGeNET database is shown in Figure 3). Once a wrapper is generated and tested, it can be archived in a searchable wrapper bank (a wrapper bank is a storage, or a database, of wrapper definitions in a machine-readable format, and thus can be reused as needed to retrieve information from a deep web database) for community use, as shown in Figure 4.  
Figure 3: CroW wrapper generation toolkit: CroW allows a deep web database page to be loaded in HTML and manually identifies interesting features by marking them with a pointing device. It also allows users to characterize the marked elements on the page. Once marked and requested, CroW can generate resource descriptions in Needle, which SoDa can use to extract the data. This picture shows such a process for the DisGeNET database.
# Novel Faulted-Section Location Method for Active Distribution Networks of New-Type Power Systems
## 2 Fault Region Location
### Fault Region Location Method  
The suspicious fault region is the connected region where all boundary nodes have alarm information. A suspicious fault region location algorithm is proposed to obtain the boundary nodes, which uses the idea of depth-first traversal in the tree search method.  
The algorithm sets two variables and arrays: \(m,p\), \(X\), \(B\). Variable \(m\) represents the node number of the temporary detection. Variable \(p\) is the auxiliary iteration parameter. Array \(X\) marks the node search state. Array \(B\) records boundary nodes. The procedure is explained in detail in the following steps, and the flow chart is shown in Figure 2.  
1. Set the initial value of \(m\) as 1; the arrays \(X\) and \(B\) are all zero-number arrays; the initial node \(B[1]=1\), \(X[1]=1\), \(p=2\);
2. Assign the search state as \"0\" in the downstream node of node \(m\) to \(m\), set \(X[m]=1\);
3. If node \(m\) has a downstream node, go back to step 2; otherwise, check whether the equipment status information of node \(m\) is not \"0\". If it is not \"0\", set \(B[p]=m\), \(p=p+1\), then go to step 6; otherwise, delete node \(m\) from the node status list and go to step 4;
4. Assign the upstream node number of node \(m\) to \(m\), and check whether the array \(X\) is a one-number array. If not, run the next step; otherwise, go to step 8;
5. Check whether a downstream node search status as \"0\" exists on node \(m\). If it exists, go back to step 2; otherwise, check whether node \(m\) has a downstream node. If not, check whether the equipment status information of node \(m\) is not \"0\". If it is not \"0\", set \(B[p]=m\), \(p=p+1\), go to step 6; otherwise, go back to step 4. If it exists, go to step 6;
6. Assign the upstream node number of node \(m\) to \(m\), and check whether the array \(X\) is a one-number array. If not, run the next step; otherwise, go to step 8;
7. Check whether a downstream node whose search status as \"0\" exists on node \(m\). If not, go back to step 6; otherwise, go back to step 2;
8. Terminate the program, output the array \(B\), and obtain the boundary nodes.  
Figure 2: Flow chart of the suspicious fault region location algorithm.  
The suspicious fault region is determined through the boundary nodes in array \(B\). The protection action information obtains the protection action region. Check whether the suspicious fault region contains the protection action region. If it does, the suspicious fault region is the fault region; otherwise, the node farthest away from the system power in the protection action region is regarded as a boundary node. The fault region is determined together with the boundary nodes in array \(B\). The fault node status list is generated from the fault region.
## 5 Conclusions  
In this study, an effective control model is developed for the stack thermal management system to solve the issues of unsatisfactory thermal control accuracy and response speed under complex operating conditions with constantly changing load current and preset stack temperature.  
First, a dynamic mechanistic model of the fuel cell stack and thermal management system was created. It was then verified by comparing the simulation and experimental results, with a stack performance error of no more than 1.65% and a temperature error of no more than 0.90%. Then, we designed the mapping for setting the coolant flow rates according to the currents to control the coolant inlet/outlet temperature difference and decoupled the two parameters (coolant inlet/outlet temperature difference and coolant inlet temperature). The simulation results showed that the coolant inlet/outlet temperature difference was always within 5 \({}^{\circ}\)C, with an average temperature difference of 4.3 \({}^{\circ}\)C. Finally, a control model was developed based on the fuel cell's dynamic mechanistic model and PSO+ANN algorithm. The ANN showed excellent training results, and the R\({}^{2}\) from each sample was greater than 0.969. It was applied to compare with the traditional PID algorithm, and we found that the regulation time of coolant inlet temperature as the current changed was reduced by more than 74%, and the overshoot was reduced by more than 50%. Therefore, we can conclude that the control model can control the temperature better than the traditional PID algorithm.  
We focused on one-dimensional control modeling in this study. In future work, the model can be extended to multi-dimensional to gain more accurate and comprehensive temperature distribution information inside the stack, further improving the thermal management effectiveness.  
Conceptualization, H.T. and C.Y.; Investigation, B.D., X.Z., C.Y. and Y.L.; Methodology, B.D. and X.Z.; Software, Y.L.; Validation, X.Z.; Writing--original draft, B.D., X.Z. and Y.L.; Writing--review and editing, C.Y. and H.T.; Supervision, H.T.; Funding acquisition, H.T. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research was funded by the National Natural Science Foundation of China (No. 52376169), Science and Technology Program of Sichuan Province (No. 2022 NSFSC1218 and No. 2023 ZHCG0061), and Science and Technology Program of Suzhou (No. ST202204).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The data presented in this study can be available on request from the corresponding author. The data are not publicly available due to privacy.  
**Conflicts of Interest:** Author [PERSON] was employed by the company New Energy Technology of CAERI Co., Ltd. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
# External Field Response and Applications of Metal Coated Hemispherical Janus Particles
## 1 Introduction
This new Janus particle has a larger anisotropy in shape than the spherical Janus particle, with a prospect of exhibiting new functions. It is reported that spherical Janus particles are self-assembled under an electric field [28]. Research that is focused on the property of active matter has been extensively performed [29; 30], where the direction of movement is manipulated by further controlling the electric field [31]. It is interesting to study the behavior of metal coated hemispherical Janus particles under an electric field. We have investigated the response of the particles that are dispersed in water to an applied AC electric field. Experiments of applying not only an electric field, but also a magnetic field, are reported on Janus particles. The orientation of the particles is controlled [32] and interesting self-organizing behavior is observed [33; 34; 35], where ferromagnetic parts are connected like a chain. We have examined what kind of phenomenon occurs with ferromagnetic-metal coated hemispherical Janus particles, too. In this paper, we report the optically interesting properties of the particles. Previously, we reported that surface plasmons can be excited in metallic coated hemispherical Janus particles. Here, we have found that the particle whose flat surface that is coated with metal can be utilized as a microscopic mirror. Further, the external field response of silver or nickel coated hemispherical Janus particles that are dispersed in water is investigated. When an AC electric field is applied, it is found that the equatorial metal surfaces of both silver and nickel coated particles are aligned parallel to the electric field. When a stationary magnetic field is applied, the nickel coated particles show self-assembled behavior to make a chain-form in the direction of the field. These properties can be utilized as a shutter. In addition, when the AC electric and stationary magnetic fields are simultaneously applied, the hemispherical particles can be uniquely oriented so that the equatorial plane is parallel to both electric and magnetic fields, and the direction of the particle is reversed when the magnetic field is reversed. As future prospects, the applicability to micro optical elements such as micromirrors and microfilters, and to valves and pumps in microfluidic channels is demonstrated.
## 1 Introduction
### Organization of the Paper  
The rest of the paper is organized as follows. The channel and system models for a downlink NOMA-based 5G network are developed in Section 2. The throughput maximization problem is formulated in this section together with the description of a dataset generation. In Section 3, a brief introductory explanation of the DNN model is included. In the same section, a novel DNN-UC scheme is designed together with the proposed DNN algorithms for training and testing phases. An efficient power allocation strategy is derived based on the output of the DNN-UC. Furthermore, simulation results with in-depth analytical discussion and justification are presented in Section 4. Various simulation plots showing the optimization of hyper-parameters of the proposed DNN architecture are illustrated in this section. Last but not least, the paper ends with some insightful concluding remarks in Section 5, which also navigates the readers to some possible future research avenues related to this work.
## 1 Introduction
[31] numerically simulated the downburst wind field on a quadratic curved hill using three RANS turbulence models: REA, RNG, and SST. By comparing their results with wind tunnel test data, they found that the REA model was optimal. By reviewing the research on numerical simulations of flat wind fields and wind fields over hills under downbursts, it is evident that while there are many studies on flat wind fields simulated using different turbulence models, there are fewer studies on wind fields over hills. The main contributions and novelties of this study are shown below. 1. This work aims to provide a more comprehensive reference for the selection of turbulence models. The most suitable turbulence model for simulating the mean and transient wind fields when downburst over hills is determined. 2. The evolution mechanism of the transient wind field structure over the quadratic ideal hill under a downburst is revealed. 3. The flow separation characteristics and the acceleration phenomena of wind fields over hills and flatlands are analyzed, and the flow separation obtained by different turbulence models is compared. The rest of this paper is organized as follows. Section 2 introduces the parameter settings for the numerical simulation, including the turbulence model, computational domain, boundary conditions, grid division, working condition setting, and solution algorithm. This section also provides grid independence verification and a description of the wind tunnel test layout. In Section 3, different turbulence models are used to carry out numerical simulations and their results are compared with impinging jet model tests. The accuracy of each turbulence model is then analyzed both qualitatively and quantitatively. Additionally, the turbulent flow characteristics are further analyzed using the POD method. Finally, the conclusions of this study are presented in Section 4.
# Equatorial Plasma Bubbles: A Review
## 2 Observations of EPBs and Associated Irregularities
### Ionospheric Scintillation Measurements  
It has been mentioned in the Introduction that out of the vast range of scale sizes for the structures associated with EPBs, extending over six orders of magnitude, only irregularities with scale sizes in the intermediate range extending from about 100 m to a few km give rise to scintillations on VHF and higher frequency radio signals propagating through the equatorial and low-latitude ionosphere. The reason for this is that maximum contribution to fluctuations in signal amplitude comes from scattering caused by irregularities with scale sizes around the Fresnel scale [39]. The Fresnel scale defined as \(d_{F}\) equals \(\sqrt{2\,\lambda\,z_{R}}\), where \(\lambda\) is the wavelength of the radio signal and \(z_{R}\) is the average distance of the layer of irregularities from the receiver along the path of the signal. A VHF or higher frequency signal transmitted from a satellite and recorded by a ground receiver is essentially a forward scattered signal, since the scale sizes of the irregularities which give rise to scintillations are in the range of approximately 100 m to a few km, which is much greater than the signal wavelength. In the case of radar observations, the radar signal is backscattered by irregularities of a scale size that is half the signal wavelength. Forward scattering of the signal by ionospheric irregularities produces a pattern of spatially varying intensity on the ground. This pattern moves across a receiver either due to irregularities drifting across the signal path, as in the case of signals transmitted from a geostationary satellite, or due to the signal sweeping across the irregularities as well, as is the case for signals transmitted from orbiting satellites such as the GNSS satellites. This movement of the pattern across a receiver causes the receiver to record a signal with temporally varying amplitude and phase. Thus, ionospheric scintillation data contain a wealth of information about ESF irregularities. This is an inexpensive technique for studying the intermediate-scale irregularities that develop within EPBs and has been used extensively in different longitudinal zones around the globe [5]. Developments in the theory of scintillations and analysis of ionospheric scintillation data have yielded important information about the characteristics and evolution of intermediate-scale irregularities in EPBs [39; 40].
## 8 Home Automation System Based on 5G  
With the rapid growth of the IoT, 5G is the next generation of wireless communication that significantly increases the speed and responsiveness of wireless technologies [100]. This technology creates new possibilities for various applications such as agriculture, transportation, etc. This article discusses the connection of the device to the device of this technology [101]. Also, 5G technology, which uses the millimeter wave spectrum, includes:  
* High-band waves (above 6 GHz);
* Medium-band waves (2 to 4.5 GHz);
* Low-band waves such as the fourth generation (below 3 GHz).  
Figure 9: Home automation system based on Wi-Fi protocol.  
Also, one of the essential goals of this new technology is to maximize the data transfer rate up to 20 Gb/s [102; 103]. Furthermore, 5G technology is critical to developing smart cities because 5G creates excellent opportunities for connected devices in buildings and cities to help track, monitor, and control energy. It allows buildings and cities to better manage their energy resources, save costs, and become more sustainable [104].  
Review of 5G Network Architecture  
Traditionally, physical network hardware components for the 5G Core Network, such as servers, switches, and storage, will be offloaded entirely to the cloud and orchestrated in a virtual environment through intelligent software tools. Figure 10 demonstrates how everyday consumer devices such as smartphones, smart sensors, or connected automobiles cannot connect directly to the 5G Core Network on their own. In order to reach the 5G Core Network, User Equipment must first connect through 5G-enabled equipment on the Radio Access Network (such as a cell tower or small cell array) that will route traffic to the network core and the internet.