esa-sceva/satcom-chunk-collection huggingface dataset:
## 7 Conclusions  
This paper presented a new monopole antenna structure that is capable of a super-wide bandwidth, high bandwidth ratio, and high efficiency. The dimensions of the antenna with a high bandwidth ratio (13.009:1) were miniaturized compared to those described in the literature. The objectives were to achieve a super-wide bandwidth by using multiple techniques. This would mean that a single antenna would be suitable for multiple applications, replacing multiple antenna installations and saving antenna real state The structure is very simple in its design and cheap in its fabrication. Due to the end-fire properties of the radiation patterns, the antenna may be utilized in biomedical applications. Due to the satisfactory results (gain, bandwidth ratio, and efficiency) and time domain analysis, the proposed antenna may be used for various wireless communications systems like WIMAX, UWB, WLAN, ISM, and for other multiple defence applications.  
[PERSON] proposed the structure of the antenna and simulates it in CST-MWT designing software; fabrication of antenna and prepare the manuscript. [PERSON] is the head of the research lab, proposed the design and participated in paper revision. [PERSON], [PERSON], [PERSON] and [PERSON] participated in the measurement, recorded results, plots the data and in preparation of the paper. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the National Natural Science Foundation of China (Grant No. 61831001).  
**Conflicts of Interest:** Authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Ref. No’s** & **Bandwidth** & **Dimension** & **Efficiency** & **Lower Frequency** & **Bandwidth** \\  & **(\%)** & **(mm\({}^{3}\))** & **(\%)** & **(GHz)** & **Ratio** \\ \hline
[6] & 107.35 & \(34\times 20\times 1.6\) & 90 & 2.27 & 3.3:1 \\
[10] & 122 & \(31\times 28\times 1.6\) & — & 3 & 4.26:1 \\
[11] & 129.24 & \(14\times 18\times 1\) & — & 2.94 & 4.65:1 \\
[21] & 153 & \(25\times 17\times 1.6\) & 86 & 2.94 & 7.55:1 \\
[22] & 135.2 & \(32\times 32\times 1.6\) & 79.21 & 2.9 & 5.17:1 \\
[23] & 138 & \(50\times 50\times 1.52\) & 88 & 2.1 & 5.47:1 \\
[24] & 165.7 & \(30\times 35\times 0.49\) & 96 & 2.7 & 10.6:1 \\
[25] & 133 & \(60\times 60\times 1.524\) & — & 10 & 5:1 \\
[26] & 168 & \(38\times 55\times 1.6\) & — & 3 & 11.6:1 \\
[32] & 163 & \(40\times 30\times 1.6\) & 75 & 2.26 & 9.8:1 \\
[33] & 160.4 & \(25\times 35\times 0.83\) & – & 3.5 & 9.11:1 \\
[34] & 168.62 & \(13.4\times 5.2\times 1.6\) & 65 & 0.4 & 11.75:1 \\
[35] & 150 & \(38.5\times 36.6\times 0.8\) & 90.1 & 0.55 & 6.93:1 \\
[36] & 166.6 & \(30\times 28\times 1.6\) & 90 & 3.4 & 11:1 \\
[37] & 164 & \(30\times 45\times 0.175\) & — & 3.15 & 10.15:1 \\ Proposed work & 174.68 & \(35\times 35\times 1.57\) & 92.7 & 3.01 & 13.009:1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of Proposed Work with Previous Work.
## 2 Materials and Methods
### Synthesis of ligands
#### 2.2.2 2.3,4-Tri-O-benzyl-6-O-acetyl-\(\alpha\)-C-allyl-glucopyranoside 8  
A mixture of Ac\({}_{2}\)O/trifluoroacetic acid 4:1 (70 mL), prepared at 0 \({}^{\circ}\)C in argon atmosphere, was added via a double-tip needle to a round-bottomed flask containing 2 g (3.55 mmol) of 2,3,4,6-tetra-O-benzyl-\(\alpha\)-d-glucopyranoside 7. The solution was stirred vigorously at 0 \({}^{\circ}\)C and the reaction followed by TLC (Hexane/EtOAc 8.5:1.5). After 1.5 h, no more starting compound was present, and the solution was gently poured into ice water and stirred for 10 min. The aqueous solution was extracted with EtOAc (3\(\times\)), the organic phase was then washed once with a sodium hydrogen carbonate saturated solution and twice with distilled water. The organic phase was dried over sodium sulfate, filtered and concentrated. The resulting crude was purified by flash chromatography (silica gel, eluent: Hexane/EtOAc 9:1), affording compound **8** (1.61 g, 3.12 mmol, 88% yield).  
\({}^{1}\)H NMR (400 MHz, CDCl\({}_{3}\)) 5 7.43-7.24 (m, 15H, CH Ar), 5.79 (ddt, J = 17.2, 10.2, 6.9 Hz, 1H, H2\({}^{\prime}\)), 5.18-5.05 (m, 2H, H3\({}^{\prime}\)a,b), 4.98 (d, J = 10.8 Hz, 1H, CH\({}_{2}\)Ph), 4.89 (d, J = 10.7 Hz, 1H, CH\({}_{2}\)Ph), 4.83 (d, J = 10.8 Hz, 1H, CH\({}_{2}\)Ph), 4.72 (d, J = 11.6 Hz, 1H, CH\({}_{2}\)Ph), 4.65 (d, J = 11.6 Hz, 1H, CH\({}_{2}\)Ph), 4.57 (d, J = 10.8 Hz, 1H, CH\({}_{2}\)Ph), 4.24 (d, J = 3.5 Hz, 2H, H6a,b), 4.11 (dt, J = 9.3, 5.9 Hz, 1H, H1), 3.85 (t, J = 9.0 Hz, 1H, H3), 3.76 (dd, J = 9.4, 5.8 Hz, 1H, H2), 3.70 (dt, J = 9.8, 3.5 Hz, 1H, H5), 3.48 (dd, J = 9.8, 8.7 Hz, 1H, H4), 2.50 (t, J = 8.2 Hz, 2H, H1\({}^{\prime}\)a,b) and 2.04 (s, 3H, CH\({}_{3}\)CO).  
\({}^{13}\)C NMR (101 MHz, CDCl\({}_{3}\)) \(\delta\) 170.92 (CH\({}_{3}\)CO), 138.59 (Cq Ar), 138.15 (Cq Ar), 137.83 (Cq Ar), 134.40 (C2\({}^{\prime}\)), 128.80, 128.65, 128.58, 128.49, 128.29, 128.11, 128.05, 128.00, 127.95 and 127.85 (C Ar \(\times\) 15), 117.24 (C3\({}^{\prime}\)), 82.37 (C3), 80.04 (C2), 77.89 (C4), 75.65 and 75.21 (CH\({}_{2}\)Ph), 73.68 (C1), 73.23 (CH\({}_{2}\)Ph), 69.68 (C5), 63.62 (C6), 29.89 (C1\({}^{\prime}\)) and 21.00 (CH\({}_{3}\)CO).  
MS: \(m/z=\) 517.6 [M+H]\({}^{*}\).
# Mechanical and Dielectric Properties of Aligned Electrospun Fibers
## 2 Molecular Orientation and System Configurations of Nanofibers  
Two key factors affecting mechanical and dielectric properties are (a) molecular orientation due to elongation of fibers on the periphery of the rotating mandrel [98] and (b) system configuration improvement for obtaining improved properties due to better alignment [99]. Other properties, such as thermal and electrical properties [100] are also often improved by alignment.
# Affection Enhanced Relational Graph Attention Network for Sarcasm Detection
## 4 Experiments
### Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{4}{c}{**Tweets ([PERSON])**} & \multicolumn{4}{c}{**Tweets ([PERSON])**} \\ \cline{2-10}  & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Acc. (\%)** & **Precision (\%)** & **Recall (\%)** & **F1 (\%)** & **Acc. (\%)** \\ \hline NBOW & 71.28 & 62.37 & 64.13 & 79.23 & 80.02 & 79.06 & 79.43 & 80.39 \\ CNN & 71.04 & 67.13 & 68.55 & 79.48 & 82.13 & 79.67 & 80.39 & 81.65 \\ GRNN & 66.32 & 64.74 & 65.40 & 76.41 & 82.06 & 81.02 & 82.43 & 82.20 \\ CNN-LSTM-DNN & 69.76 & 66.62 & 67.81 & 78.72 & 79.65 & 79.12 & 79.20 & 79.94 \\ \hline ATT-LSTM & 69.76 & 66.62 & 67.81 & 78.72 & 81.62 & 81.45 & 81.56 & 81.56 \\ SIARN & 73.82 & 73.26 & 73.24 & 82.31 & 82.62 & 82.51 & 82.59 & 82.59 \\ MIARN & 73.34 & 68.34 & 70.10 & 80.77 & 82.34 & 82.72 & 82.78 & 82.78 \\ SAWS & 74.69 & 74.08 & 74.34 & 81.72 & 83.25 & 83.40 & 83.43 & 83.55 \\ \hline ADGCN & 74.81 & 76.22 & 75.45 & 81.75 & 83.85 & 83.85 & 83.85 & 83.86 \\ \hline ARGAT (**proposal**) & **83.19** & **76.24** & **79.78** & **85.81** & **84.28** & **84.28** & **84.28** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results on Tweets datasets. The best results are in bold and the results of four basic models are from [6]. too long (IAC) or too short (Reddit), this means that more ideas should be explored when the sentence contains little information or redundant information. The proposed ARGAT achieves comparison performances in both the small dataset (Riloff) and the big dataset (Ptacek). In contrast, the others only perform well in the big dataset, indicating that the graph-based method captures more features for sarcasm detection.
# Compact 3 \(\times\) 1 Matrix Converter Module Based on the SiC Devices with Easy Expandability
## 1 Introduction
The high-density sparse matrix converter is presented in [15] with a very compact design wherein SiC MOSFETs were used. The power output of the converter is 1500 W, and the demonstrated power losses by the authors are below 80 W with a switching frequency of 100 kHz. Several methods for cooling high-power surface mount technology (SMT) components are described in [16; 17], as well as the impact of the vias placement on the cooling performance [18]. The first uses the thermal vias in the two-layer or multilayer PCB as shown in Figure 2a. The generated heat is dissipated through vias to the heatsink or the inner layers of the PCB. This method needs an additional insulating heat-conducting pad between the FR4 substrate and heatsink to ensure the right insulation of the board if high voltage is present. The next approach shown in Figure 2b, which can reach potentially lower thermal resistances, is PCB with copper inlays. This method is attractive from the low thermal resistance point of view but faces the same insulation problem. Additionally, non-standard manufacturing technology must be used, which can be costly, especially in small batches. The next solution on the market is the insulated metal substrate (IMS) boards, shown in Figure 2c, which ensure a good thermal interface between the SMT component and the heatsink with low thermal resistance provided by the metallic core. These PCBs are widely used in the automotive industry due to their good performance in the cooling of power LED modules. If electrical insulation is needed, manufacturers can offer IMS boards with an insulation voltage up to 4 kV. According to the datasheet of the used IMS board with the market name Goldenmax G11, the breakdown voltage is above 3 kV, declared by the manufacturer. Figure 1: Possible types of the bidirectional switches: (**a**) diode bridge switch; (**b**) common-emitter (CE) switch; (**c**) common-collector (CC) switch; and (**d**) reverse blocking IGBT (RB-IGBT) switch. This paper focuses on the design, modeling, and practical realization of the \(3\times 1\) matrix converter module, with easy expandability to provide a matrix converter with various input/output configurations. According to the authors' research, no modules with similar parameters are now commercially available. The presented module can deliver power up to 1.7 kW. The designed module is part of the research project for the control of multiphase drives and the investigation of converter efficiencies. In the five-phase application, the presented modules in this configuration can deliver up to 8.5 kW. The output power of the module is adjustable by the change of power semiconductors and can be increased up to 3.5 kW per module.
## 4 Evaluation Results and Discussion
### Performance Analysis  
The employed experimental procedure covers two principal scenarios that both follow the introduction of user-centric VNRs, with antithetical performance-driven requirement profiles. The first scenario (\(VNR_{1}\)) corresponds to an emergency communications embedding to accommodate the first responders' actions in a disaster-stricken area. An application of this category demands stable and near-to-nonexistent jitter values and extremely low delays [31; 59; 73; 80]. However, it exhibits relaxed requirements for computing resources and networking speeds. The second scenario (\(VNR_{2}\)) anticipates a resource allocation operation suitable for industrial activities based on immersive technologies like XR. Although such an application consistently expects a baseline of balanced jitter and delay measurements to perform adequately, it nevertheless first and foremost requires robust CPU and BW profiles [53; 60; 61; 62; 74].  
The developed framework initially identifies the special features of the incoming VNRs and, by exploiting the researched thresholds of Tables 2-5, generates possible weighted combinations, as depicted in Table 6. Afterwards, by executing the Wii-based algorithmic steps, both VNRs are classified to the applicable tailored weighting groups and mapped to the corresponding appropriate Wii combinations, as Table 9 portrays.
## 3 Basic Theoretical Concepts
### Routing Protocols of Wireless Networks  
The main features of the nodes in WLAN are Dynamics and Robustness, which are controlled by different ad hoc routing protocols, such as OLSR, DSR, and AODV. However, the increasing demand for resources in networks leads to insufficient resources. Node mobility also results in link failure and significant routing overhead, which lowers the reliability of network communication [15].  
1. Reactive routing protocols: These are often called upon to route protocols for requests. A node can only find a path for sending a packet to another node. It uses a cache path if it does not have routing tables [16].
2. Proactive (Distance Vector) routing protocols: In these protocols, information about routing is maintained periodically for each node with one route recorded [16].
3. Hybrid routing protocol: This incorporates the benefits of both reactive and constructive routing. Initially, a proactive approach is used to obtain route information, and then the route requirement is reactively handled to the required node [16; 17]  
A Performance study of the two AODV, DSR reactive routing protocols, and OLSR proactive routing protocols is presented in this paper.  
AODV stores data routing in standard routing tables for wireless networks. The function of the timer in mobile node is the process of removing the routing table entry when the route has no use for a specific period and it works in two basic operations [18; 19].  
In the first process, it is necessary to locate and preserve the route. Subsequent neighbor messages are handled by the source and intermediate nodes in each flow to propagate material packets. In the route discovery stage, multimedia communication is implemented by sending an introductory message. In addition, error messages are sent when connection failure is observed. In the route maintenance process, an RREQ is sent by the root node to its neighbors and the data propagate until the destination node is reached.  
DSR: In this protocol, an on-request path is provided when a transmitting node requests one, and source routing is used instead of searching the routing table at each intermediate node. The transmission of introduction messages is not required to be periodic in order to notify neighbors of its availability, which is distinguished from other on-demand routing and DSR is an interactive routing protocol. An example of this is the route search and maintenance phase, where route request packets are produced within the network to perform a route check [19; 20]. The network with DSR is completely self-configuring and self-organizing, with no requirements for network administration [16].  
OLSR is a proactive routing protocol that is specifically designed for wireless networks. The protocol maintains one or more tables for each node. The nodes are available because of the proactive existence of this protocol. It also updates the routing information so that all the information on the nodes is announced and published within the entire network [21].
## 2 Experimental Investigation
### Workability Testing  
All SCC mixtures were proportioned to achieve acceptable levels of filling ability, passing ability and stability (static and dynamic). These properties, except dynamic stability, were assessed using standard test methods to assure the quality of the fresh SCC. Filling ability was evaluated using the slump flow test of the inverted cone in accordance with ASTM C1611. As an indication of the viscosity of the mixtures, the time of reaching 500 mm spread diameter (\(T_{50}\)) was also measured. The passing ability of fresh SCC was determined using the J-ring test method according to ASTM C1621. Two parameters were used to describe the passing ability of fresh SCC: (1) the difference between average slump flows (\(\Delta\)D) in restrained (with J-ring) and unrestrained conditions (without J-ring); (2) the difference between the height of concrete patty in the middle of the J-ring, and the average height of the patty at four points around the perimeter of J-ring (\(\Delta\)H) according to AASHTO T 345. The higher the \(\Delta\)D and \(\Delta\)H, the higher the probability of blockage when SCC flows around reinforcing bars. The filling capacity of fresh SCC was determined using the caisson test method and according to AASHTO T 349. The measured filling capacity represents the ability of fresh SCC to fill the forms while passing through cross bars. The static stability of SCC was determined using the four standardized test methods: penetration test according to ASTM C1712, column test according to ASTM C1610, visual stability index (VSI) according to ASTM C1611, and hardened visual stability index (HVSI) according to AASHTO PP 58. Dynamic stability was evaluated using the flow-through test according to [PERSON] et al. [23], as no standard test method is available for this property. Additionally, mortar and concrete rheometers were used to characterize the rheological properties of SCC mixtures. Dynamic yield stress and plastic viscosity (i.e., Bingham model parameters) were determined using Brookfield mortar rheometer according to ASTM C1749, while yield torque and slope were determined using IBB concrete rheometer according to [PERSON] and [PERSON] [24].
# Multimodal AutoML via Representation Evolution
## 4 Experiments
### Datasets  
We evaluate the performance of our approach on the task of multimodal classification from images and associated texts on five different datasets. A short overview of the considered datasets is provided in Table 2. _Tasty Recipes_[33] is a small collection of recipes, represented by a textual document, including the ingredient list and preparation instructions, and an image of the described dish. Each recipe is classified as one of 25 food categories, such as \"tacos\". _Caltech Birds_[34] is a multimodal extension of _Caltech-UCSD Birds (CUB-200)_[35] a popular fine-grained image classification dataset with images of birds belonging to one of 200 often visually similar species. Each image is augmented with textual descriptions of the given bird's physical features provided by human annotators in this dataset.  
In the above cases, data from different modalities can be seen as additional information about instances that can help improve prediction. _Fauxtography_[36] and _Fakeddit_[37] datasets, however, entail the task of _fact-checking_, which is inherently multimodal. Here, an image-text pair can be valid, meaning the image and text convey real and matching information. Conversely, a pair can be invalid, meaning either the image or the text are false, misleading or manipulated, or both the image and the text are real but mismatched, usually when a description makes a false claim about a real image. Borrowing its name from the practice of manipulating photographs in order to deceive, _Fauxtography_ depicts scenes from world news gathered from Snopes, a fact-checking website with a collection of both true and deliberately false news articles. The dataset is balanced with additional true image-text pairs coming from the _Reuters' Pictures of the year_ segment. _Fakeddit_ is collected from specific Reddit communities (called _subreddits_), where, for a given instance, its source subreddit determines its class. It offers binary, 3-way, and 6-way target variables, of which we only consider the first--predicting whether an image-text pair is true (real) or false (fake). Fakeddit is originally a very large dataset (1M examples). However, we take a 5 k sub-sample to approximately match the scale of Fauxtography since we focus primarily on small datasets. We call this dataset _Fakeddit 5k_.  
All considered datasets are organized as follows. Each training example is represented by one image, one textual document, and the associated target variable. We make these datasets available in the described consistent format along with the provided code.
# Performance Analysis of Internet of Things Interactions via Simulation-Based Queueing Models
## 4 Experiment Results
### Message Classes Assigned with Priorities  
We then leveraged the nonpreemptive priority and multiclass simulation model to compare response times of different classes assigned with priorities. As depicted in  
Figure 6: Response time for various ON/OFF model parameters.  
Figure 7: Success rates for various ON/OFF model parameters.  
Figure 8a, we defined 19 classes (i.e., \(|\mathcal{C}|=19\)) with different arrival (\(\lambda_{c_{4}}\)) and service (\(\mu_{c_{2}}\)) rates for each class \(c_{k}\). Depending on the values of \(\lambda_{c_{k}}\), \(\mu_{c_{4}}\), the queue (or IoT system that it represents) can be saturated. Let \(\rho=\sum_{c_{n}\in\mathcal{C}}\frac{\lambda_{c_{n}}}{\mu_{c_{n}}}\) be server utilization (i.e., probability that the server is busy) of the nonpreemptive priority and multiclass queue. The queue remains unsaturated (i.e., queue stability is ensured) when \(\rho<1\). In Figure 8a, we defined \(\rho=0.69\); thus, queue stability was ensured. As expected, lower priorities present higher response times. \(c_{4}\) experienced a lower response time than that of \(c_{9}\) despite the fact that \(c_{4}\) was assigned with a lower priority. This is because our model enables system designers to assign different arrival or service rates to different classes (e.g., different groups of IoT data sizes to be transmitted with diverse importance).  
In Figure 8b, we define 9 classes (i.e., \(|\mathcal{C}|=9\)) but with the server utilization defined as \(\rho=0.89\). Therefore, the system was close to the saturation point, and classes assigned with lower priorities thereby experienced high response times. On the other hand, despite the fact that the system was almost saturated, high-priority classes experienced low response times.