esa-sceva/satcom-chunk-collection huggingface dataset:
# Review of Energy Management Systems in Microgrids
## 3 Energy Management Systems Applications in Microgrids
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Ref.** & **Proposed Method** & **Energy Management Application** \\ \hline
[66] & Generative adversarial network & It aims at managing uncertainties in the output power of renewable energy sources with a data-driven, artificial intelligence-based energy management strategy for isolated microgrids. \\ \hline
[67] & Deep learning & Models with two different recurrent neural network architectures (RNN), long short-term memory (LSTM) and gated recurrent unit (GRU) were used to predict wind speed and solar irradiance. \\ \hline
[68] & Fuzzy logic controllers & An application has been developed for energy management in microgrids with multiple operating modes. \\ \hline
[69] & Fuzzy logic controllers & For the best possible control of the energy storage system in a residential microgrid, a fuzzy logic controller-based energy management system is suggested. \\ \hline
[72] & Artificial neural networks & Energy management system-based studies aim to reduce production costs, increase the use of distributed energy resources, and reduce emissions. \\ \hline
[73] & Reinforcement learning & It manages uncertainties by looking at real-time data with online energy management systems. \\ \hline
[74] & Game theory & Fuel/transaction costs are minimized, and the grid operator’s distributed generation benefit is maximized at its price. \\ \hline
[75] & Deep learning & To overcome demand-side uncertainties, a program was developed to provide participants with optimal incentive rate strategies, as different customers have different attitudes toward paid incentives. \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Artificial intelligence methods-based energy management systems applications. was developed and implemented in the C++ programming language, based on the client-server architecture. In Ref. [82], an intelligent and sustainable energy management system for a microgrid based on a multi-agent system is examined. The system is designed to address issues brought on by the intermittent availability of renewable energy resources. Furthermore, the system optimizes the utilization of available AC and/or DC renewable energy sources by leveraging load flexibility and the complementarity of renewable resources. An evaluation of this proposed multi-agent framework is conducted through a co-simulation for a microgrid linked to the main grid, utilizing the MATLAB and JADE platforms. A summary of multi-agent energy management systems applications in microgrids is given in Table 6.
# Joint Embedding of Semantic and Statistical Features for Effective Code Search
## 3 Approach
### Feature Extraction with Different Granularities
#### 3.1.2 Feature Extraction at the Class Level  
In Java, a method exists as part of a class. This work extracts class-level semantic information from three features: class name, association attribute, and association method. The class-level semantic information is used as a supplementary description of the token collection in the method-level semantic information. The specific methods are as follows:  
* class names: The scope covered by some descriptive names (such as class names, method names, variable names) varies according to the objects they describe. The smaller the coverage, the more accurate the semantic information can be represented in the name. Thus, our scheme uses the class name as a supplement to the token collection in the method description information.
* Associated attributes and associated methods: A class contains several attributes and methods, in which class methods can directly access class attributes or invoke other class methods. To extract class-level semantic information for methods, it is necessary to dig deep-level sub-method calls and attribute access to analyze the functions of upper-level methods. Since the semantic information at the class level is less important than the semantic information at the method level, the upper layer and the bottom layer method name are parsed and added to the token collection of the middle layer. At the same time, the class name of the attribute accessed by the upper-level method is also added to the token collection.  
The deep semantic features of the code are the ternary features generated after filtering the method name, API invocation, and token collection extracted from the method level features. Code searching based on statistical features extracts code semantics via statistical methods, and then matches them with the semantic features of the query language. Statistical features, which are Java code features extracted at the class level, include class names, associated attributes, and associated methods. In addition to considering the information of the method itself, the statistical features in the class where the method is located are a complementary description of the semantic information of the method.
# An Optimization Methodology for Adapting Legacy SGX Applications to Use Switchless Calls
## 5 Evaluation  
To validate our optimization on our target application, we evaluate the performance of SGX-enabled network middleboxes with micro-benchmarks and end-to-end performance analysis. We use the same machine specification used for determining the adaptiveness of switchless calls in Section 4.3. Also, we use 10 Gbps link in a lab environment for connecting servers, clients, and an SGX-enabled middlebox to avoid network bandwidth becoming a bottleneck. Finally, we use TLS v1.2 encryption protocol and select AES256-GCM-SHA384 as a cipher suite for packet encryption and decryption.
# Room-Temperature Formation of Hard BC\({}_{\rm x}\) Films by Low Power Magnetron Sputtering
## 2 Materials and Methods  
### Film Deposition  
BC\({}_{\rm{x}}\) films tested in this study were deposited on Si (100) and fused silica substrates by single- and dual-target magnetron sputtering processes at room temperature. The deposition system consists of two cylindrical two inch magnetron cathodes (Torus 2\({}^{\prime\prime}\) HV) powered by an RF generator RF-3-XIII (13.56 MHz) through the matching unit and DC generator APEL-M-1.5 PDC-800-2. The B\({}_{4}\)C target (99.5% purity) sputtered in RF and the C target (99.95% purity) sputtered in DC mode were used as the cathodes for preparing BC\({}_{\rm{x}}\) films at single and dual magnetron sputtering. The rotation of the substrate holder at 20 rpm was used in the case of dual sputtering to provide thickness and element distribution uniformity in the film. The targets were mounted on a water-cooled Cu holder. The deposition chamber was initially pumped down to the base pressure of 5 \(\times\) 10\({}^{-5}\) Torr using the combination of an oil-sealed rotary vacuum pump MVP36 and a turbomolecular pump nEXT 400D. The power supplied to the RF magnetron for sputtering the B\({}_{4}\)C target varied in the range of 50-150 W, while the power supplied to the DC magnetron for sputtering the graphite target was 75 and 100 W (Table 2). This corresponds to a specific power density at the target of 2.47-7.41 W/cm\({}^{2}\) in RF mode and 3.71 and 4.94 W/cm\({}^{2}\) in DC mode. Gas distribution rings, through which Ar was supplied to the cathode, were placed on the top of the dark space shield of magnetrons. Sputter deposition was performed at the constant Ar flow in all experiments, and it was maintained at 50 sccm through a mass flow controller Horiba SEC-Z512. Total gas pressure during film deposition was 3\(\times\)10\({}^{-3}\) Torr. The distance between the target and the substrate was 50 mm. The substrates were degreased and chemically cleaned before layer deposition.
# Triple-Band Uniform Circular Array Antenna for a Multi-Functional Radar System
## 2 Materials and Methods  
The configuration of the proposed antenna is shown in Figure 1a-c. Two substrates with \(\varepsilon_{r}\) = 2.2 (Taconic TLY-5) were used in order to implement the triple-band array antenna and its feed network. The size of the proposed array antenna was 275 mm \(\times\) 275 mm. The thickness of the two substrates was selected as 3.2 mm, which is advantageous to create a low-profile antenna. The ground plane between the two substrates is commonly used for the array antenna and the feed network. The proposed antenna is made up of the three sets of radial array antennas which operate at different frequencies. Each outer, middle, and inner radial array operates at an S-band, C-band, and X-band, respectively. The radius of the radial configuration is set as 1.05 \(\lambda\). Each array antenna consists of eight elements of microstrip patch antennas. The inter-element spacing is set as 0.8 \(\lambda\), which is the minimum distance preventing collapse of the elements. The minimum inter-element spacing is helpful in terms of reducing the appearance of grating lobes. The length of the patch element was approximately set as 0.5 \(\lambda\), and the polarization direction of the electric field was parallel to the x-axis for S- and X-band radial array antennas, while it was parallel to the y-axis for C-band radial array antenna. For port 1 and the port 3, coaxial probe feeds were used to deliver power to C- and X-band array antennas. The substrate and ground plane were perforated to construct 16 vials which were used to feed the array antennas, and the diameter of the vias was set as 1 mm. For port 2, a microstrip feedline was used for the C-band array antenna. A T-shaped power divider was used to divide the magnitude of the input signal. The length of the feed line to an element antenna was identical to provide the signal in phases to the all-element antennas. The width of the feed line gradually decreased as the length of the feed line to an element antenna was reduced, in order to minimize the reflection due to the power divider.  
Figure 1: \(Cont.\)
# AE-CGAN Model based High Performance Network Intrusion Detection System
## 5 Experimental Environment
### Model Parameters  
The hardware experimental environment was tested on a desktop with an Intel(R) core I9-7900X CPU of 3.30 GHz, 64 GB RAM, and Linux Ubuntu 16.04 operating system.  
Experiment simulations were performed using Tensorflow and scikit-learn, the most commonly used of the machine learning frameworks, and Python was chosen as the programming language.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Number** & **Percentage** \\ \hline Benign & 2,273,097 & 80.3004\% \\ Distributed Denial-of-service (DDoS) & 128,027 & 4.5227\% \\ Port Scan & 158,930 & 5.6441\% \\ Bot & 1,966 & 0.0695\% \\ Infiltration & 36 & 0.0013\% \\ Brute Force & & \\ Web Attack & Structured Query Language (SQL) Injection & 2,180 & 0.0770\% \\ Cross-site Scripting (XSS) & & \\ File Transfer Protocol (FTP)–Patator & 7,938 & 0.2804\% \\ Secure Shell (SSH)–Patator & 5,897 & 0.2083\% \\ Denial-of-service (DoS) GoldenEye & 10,293 & 0.3636\% \\ DoS Hulk & 231,073 & 8.1630\% \\ DoS Slowhttptest & 5,499 & 0.1943\% \\ DoS Slowloris & 5,796 & 0.2048\% \\ Heartbleed & 11 & 0.0004\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Canadian institute for cybersecurity intrusion detection system (CICIDS)2017 dataset.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & \multicolumn{2}{c}{Actual} \\ \cline{3-4}  & & Normal & Attack \\ \hline \multirow{2}{*}{Predicted} & Normal & TP & FP \\ \cline{2-4}  & Attack & FN & TN \\ \hline \hline \end{tabular}
\end{table}
Table 2: Confusion matrix.  
CICIDS2017 dataset consists of 15 classes in total, but in this paper, brute force, Structured Query Language (SQL) injection, and XSS are grouped into web attack classes, making up a total of 13 classes.  
In AE for dimension reduction of features, 77 features are used as inputs to find the optimal parameter while adjusting the number of hidden layers. The parameters of AE, AE-CGAN, and random forest (RF) are as shown in Table 3.
## 3 Results
### Sorption Tests
#### 3.2.7 Metal Desorption and Sorbent Recycling  
The rationale for using urea is based on the ability of this reagent to bind with metal cations. Combining urea with acidic solutions allows both reversing metal sorption (effect of unfavorable pH conditions) and favoring the competition effect between free urea and reactive groups at the surface of the sorbent for metal binding. Therefore, the tentative mechanisms of sorption and desorption are illustrated by Scheme 2.  
Sorption performances (sorption efficiency and sorption capacity) were compared for three successive cycles for Ni(II), Zn(II) and Hg(II) (Figure 8). The most stable performance is observed for Hg(II), where the loss in sorption is less than 1.2% at the third cycle. On the other hand, the recycling of the sorbent has more impact on Ni(II) removal; the decrease in sorption reaches up to 9%. The desorption efficiency was not quantified; the slight decrease in sorption efficiency and sorption capacity at sorbent recycling may be explained by two causes: (a) the incomplete desorption of metals ions that contribute to the progressive saturation of the material, and (b) the partial degradation of the sorbent during the alternating sorption and desorption cycles. This deserves complementary investigation.  
The stability of magnetite NPs in chitosan-based composites was investigated in acidic solutions [56; 71]. After 24 h exposure to 0.5 M HCl solutions, the loss of iron (as a tracer of magnetite) did not exceed 1.1%. This is first evidence for sorbent stability. It is noteworthy that in this study, the acidic conditions are less \"aggressive\" than in previous tests (pH 2.7 and sulfuric acid solutions vs. pH \(\sim\)1 and HCl solution). In addition, the FTIR characterization and the semi-quantitative EDX analysis confirmed the stability of the sorbent; herein, Figure S1 illustrates this stability.
## 2 Circular Cavity Metasurface Antenna with a Diameter of 0.25 \(\lambda_{0}\)  
### Description of the Proposed Antenna  
The metasurface cavity antenna, linearly polarized, is described in Figure 1. Its design fulfills the practical needs of manufacturing. Instead of a single circular patch, the metallization is divided into multiple metallic strips whose dimensions are much smaller than the wavelength (width \(<0.1\)\(\lambda_{0}\)). A design example with three strips is provided in Figure 1a. The shape of these strips is chosen to follow the E and H field lines of the TE\({}_{11}\) mode excited inside the cavity, as illustrated in Figure 2. Such a shape allows for minimizing the disturbance of the field distribution at the aperture while effectively working as a distributed capacitance at the aperture. The gap \(g\) between the strips is adjusted to reach the right value of capacitance loading at the antenna aperture, as is the same for the rectangular case [8]. Increasing this gap \(g\) leads to a decrease of the capacitance value. This value is optimized in order to increase the bandwidth and keep the antenna well matched (reflection coefficient \(|S_{11}|<-10\) dB). The substrate is bonded to the cavity wall using an organic glue (\(\varepsilon_{\mathrm{r}}=3.5\) and tan \(\delta\)= 0.03) whose thickness is labelled \(g_{\mathrm{r}}\). The glue degrades the antenna radiation performance, especially when the capacitance at the aperture is high. It is therefore desirable to make the gap \(g\) between the metasurface elements and the cavity wall as wide as possible, to keep the capacitance relatively low and to minimize the glue impact. In practice, \(g_{\mathrm{r}}\) is set to 0.2 mm here. The aperture capacitance can be lowered by using a thin high-permittivity substrate on which the metasurface is etched, or it can be increased by using multiple layers, as shown in Figure 1b with two layers. The antenna is fed by a coaxial probe, and the outer conductor of the coaxial cable is soldered to a short tapered line to further enlarge the antenna BW (Figure 1b).  
Figure 1: (**a**) Metasurface antenna with three strips at the aperture; and (**b**) cross-section view of the cavity showing the multilayer nature of the design.
# A Synergistic Elixir-EDA-MQTT Framework for Advanced Smart Transportation Systems
## 3 Architecture Design
### MQTT for Event Transport in Edge Computing  
Edge computing necessitates efficient communication protocols for real-time data processing, resource-constrained environments, and seamless device/sensor interactions. Choosing the right protocol significantly impacts component interaction, latency, bandwidth utilization, scalability, and security--all crucial factors in edge computing.  
Given these demands, MQTT (Message Queuing Telemetry Transport) [27] emerges as a prime candidate. Its lightweight footprint aligns well with the resource limitations of edge devices. Built on a publish-subscribe model, MQTT inherently facilitates event-driven communication, where events are transported through clients subscribing and publishing messages to the broker. Moreover, MQTT offers various quality of service (QoS) levels, ensuring reliable message delivery. This combination of features makes MQTT a well-balanced choice for edge computing requirements.  
The following discussion details the rationale behind selecting MQTT for our proposed architecture.
# The Next Generation Platform as A Service: Composition and Deployment of Platforms and Services
## 4 Virtual Network Function as A Service, Overview
### The Telco PaaS Platform: Central Office Re-Architected as A Datacentre (CORD)  
The selected platform for the Telco PaaS use case is CORD and the current prototype uses version 6.0 [6]. The primary reason for selecting CORD was that it was designed to provide edge network access, by acting as a virtualised Central Office. This implies that the core functionalities of the CORD platform could be effectively reused to fit the needs of the VNFaaS use case. For example, other platforms (e.g., OSM), have a more generic implementation which would make their integration into the VNFaaS use case a more complex task.  
CORD's architecture comprises three main functional elements, implemented as open source projects: (1) The XOS orchestrator, responsible for the joined control of ONOS and OpenStack. This joined control is facilitated by well-defined service models, which describe the supported services and service synchronisers. The synchroniser's role is to align the operational state of CORD with the desired state, as defined by the administrator, in a programmatic way; (2) The ONOS SDN Controller (SDNC) is responsible for managing the physical and virtual networks; (3) The OpenStack platform is responsible for managing the lifecycle and resources of the deployed VNFs. In the used version of CORD, all the individual CORD components are deployed as sets of Docker containers, managed by Kubernetes and Helm. Figure 8 illustrates a high-level overview of the CORD architecture.  
Figure 8: High-level overview of the CORD architecture.  
Figure 7: VNFaaS Proof of Concept.
## 3 Results and Discussion
### Peak Gain  
The peak gain of the UWB three-port MIMO is presented in Figure 8. The gain of the antenna varies from \(-7\) dBi to 5.5 dBi. At the band notching frequencies (3.2 GHz and 5.2 GHz), the antenna gain is sufficiently reduced to suppress the transmitted or received signal. The gain is significantly high at in-band frequencies, especially at the high frequency end.  
Figure 8: Simulated and measured antenna gain of the proposed antenna.  
Figure 7: Simulated and measured radiation pattern of proposed antenna for notch and in-band frequencies at the principal planes (E and H). (**a**) 3.2 GHz; (**b**) 5.2 GHz; (**c**) 6 GHz; and (**d**) 8.9 GHz.
## 7 Experimental Results
### Performance of Classification  
The dataset included 827 sharp images and 89 blurred ones including 47 with mixed blur, 22 with motion blur, and 15 with defocus blur. Three classifications were performed: (a) between sharp and mixed blur images, (b) between sharp and motion blur images, and (c) between sharp and defocused images, where the level of imbalance in the classifications was 5.38%, 2.59%, and 1.78%, respectively. A single class was selected to be the positive class in the classifications, while the remaining classes were combined to make up the negative class. All classifications were performed using a tenfold cross-validation. For each classification, the data set was split into ten partitions, nine of which were used to train the  
Figure 10: A typical example of histogram calculation on \(\mathrm{v_{1}}\) depth data. The blue and red lines represent the data from the sharp and blurred image, respectively.  
Figure 9: A typical example of a \(\mathrm{v_{1}}\) representation of depth data. The blue and red lines represent the data from the sharp and blurred image, respectively.  
model, while the remaining partition was used to test the model. This process was repeated ten times so that each partition acted as test data once. In addition, ten independent runs of this procedure were performed to eliminate any biasing that may have occurred during the random partitioning process.  
We use four quantities to evaluate the results: true positive rate (TP\({}_{\text{rate}}\)) which is the number of true positives divided by the total number of positives in the dataset, true negative rate (TN\({}_{\text{rate}}\)), which is the number of true negatives divided by the total number of negatives in dataset, false positive rate (FP\({}_{\text{rate}}\)), which is the number of false positives divided by the total number of negatives in the dataset, and false negative rate (FN\({}_{\text{rate}}\)), which is the number of false negatives divided by the total number of positives in dataset. Table 1 shows the classifications results. The classification accuracy was calculated as the mean of the true positive rate and true negative rate. According to Table 1, the classification accuracies between sharp and mixed blur, between sharp and motion blur, and between sharp and defocused blur were 0.9506, 0.9464, and 0.9638, respectively.