esa-sceva/satcom-chunk-collection huggingface dataset:
# Assessment of Catalyst Selectivity in Carbon-Nanotube Silylesterification
## Materials and Methods  
### Diazonium-Mediated Modification  
Multiwalled carbon nanotubes (MWNTs) were purified prior to modification by diazonium in order to remove alumina (a catalyst support). Purification was achieved by refluxing 4 g of MWNTs into 500 mL sodium hydroxide solution (6 moles) for 12 h. The purified MWNT sample was then thoroughly washed with deionized water [15]. MWNT functionalization by diazonium chemistry was carried out by modifying a previously reported procedure [15]. Briefly, 20 mg of purified CNTs was dispersed in 10 mL of deionised water containing \(6.0\times 10^{-4}\) moles of the appropriate aniline derivative (depending on the desired functional group, refer to Table 1), along with \(6.0\times 10^{-4}\) moles of sodium nitrite and \(8.0\times 10^{-4}\) moles of perchloric acid. This mixture was stirred for 60 min under IR irradiation and then washed with deionized water. It was then washed with acetone and dried at room temperature.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 3 ITS Sensing
### Infrastructure-Based ITS Sensing
#### 3.2.2 Travel Time Estimation  
Coupled with traffic flow detection, travel time estimation is another task in ITS sensing. Accurate travel time estimation needs multi-location sensing and re-identification of road users. Bluetooth sensing is a primary way to detect travel time since Bluetooth detection comes with a MAC address of a device so it can naturally re-identify the road users that carry the device. Vehicle travel time [102] and pedestrian travel time [103] can both be extracted with Bluetooth sensing. Bluetooth sensing has generated privacy concerns. With the advance in computer vision and deep learning, travel time estimation has been advanced with road user re-identification using surveillance cameras. Deep image features are extracted for vehicles and pedestrians and are compared among region-wide surveillance cameras for multi-camera tracking [104; 105; 106; 107; 108]. An effective and efficient pedestrian re-identification method was developed by [PERSON] et al. [108], called KISS+ (Keep It Simple and Straightforward Plus), in which multi-feature fusion and feature dimension reduction are conducted based on the original KISS method. Sometimes it is not necessary to estimate travel time for every single road user. In those cases, more conventional detectors and methods could achieve good results. [PERSON] et al. [109] proposed a method to estimate link travel time, as early as in the year 2002, using loop detectors. The key idea was based on road section density that can be acquired by observing in-and-out traffic flows between two loop stations. While no re-identification was realized, these methods had reasonably good performances and provided helpful travel time information for traffic management and users [109; 110; 111].
# A Neural-Symbolic Approach to Extract Trust Patterns in IoT Scenarios
## 1 Introduction
### Advantages of T-Patterns in the IoT Context  
As discussed, most existing models represent trust and reputation as independent scalar values aggregated into a trust vector. This assumption simplifies real-world interactions, where trust dimensions (e.g., reliability and honesty) can be interdependent. In highly dynamic IoT environments, where agent populations evolve rapidly and newcomers frequently join, information about certain trust dimensions may be initially unavailable, while other dimensions can be inferred from initial interactions. Our research addresses this issue by introducing the following advantages:  
* The T-pattern model enables IoT systems to infer unknown trust dimensions, such as reliability, based on available information from related dimensions like honesty.
* It employs an interpretable logical formalism that supports interactions between smart objects at the IoT edge, while the inference process runs in the background at the cloud level.
* T-patterns can help smart objects in an IoT environment to operate in presence of noisy or incomplete information since the smart object can use T-patterns to derive an unknown or uncertain information from another one available or more reliable. Our experiments, although limited to a simulated environment, demonstrated that T-patterns can represent the logical links by the different trust dimensions with an high degree of precision, due to the observation of the whole agent community; thus, an inexpert agent, a newcomer, or an agent that operates in presence of noise and that has not a complete information on a given trust dimension can be advantaged by the possibility to derive it form the T-patterns.
* Our work presents the first real implementation of the T-pattern model, utilizing a neural-symbolic inference engine.
* CILIOS is specifically designed to extract logical rules by directly observing the behavior of a set of agents in a multi-agent system, regardless of the particular network environment and trust conditions, as the logical rules are extracted from a neural network trained at the cloud layer of the multi-agent system and are continuously updated as agents move from one given network environment to another. This feature made CILIOS particularly suitable to be applied to extract T-patterns in an IoT context.
# Exploring Image Decolorization: Methods, Implementations, and Performance Assessment
## 6 Results
### Method Complexity  
All assessed algorithms were run on an x64 architecture with an Intel i5 1.60 GHz processor and 16 GB of RAM. The average execution speed per image in seconds for each method is shown in Table 8.  
Figure 6: Quality metrics for the Rec. 2020 method.  
It is evident that the basic methods are the fastest, but the CDF and GD methods also yield acceptable results for wide use in various systems. The DL method is significantly slower, with an average time of approximately 2 h on this type of computer, which is unacceptable for most practical applications. The DL method requires more powerful computational resources, which implies the use of a graphics processor. The authors in [30] state that executing the same algorithm on an NVIDIA GeForce GTX 1080 GPU (NVIDIA, Santa Clara, CA, USA) takes about 30 s per image.  
Execution speed is crucial for real-time applications like telehealth, where timely diagnosis and decision-making require fast processing of large medical image volumes. Efficient algorithms that maintain image quality are essential in such cases. In contrast, other applications, e.g., satellite imaging, prioritizes accuracy, and fidelity over speed.
# When Intelligent Transportation Systems Sensing Meets Edge Computing: Vision and Challenges
## 4 Edge Computing: Opportunities in ITS Sensing Challenges
### Objectives  
#### 4.1.1 Large-Scale Sensing  
ITS sensing in smart cities is expected to cover a large network of microsites. Without edge computing, the cost for large-scale cloud computing services (e.g., AWS and Azure) is significant and will eventually reach the upper limit of network resources (bandwidth, computation, and storage) (2019). Sending network-wide data over a limited bandwidth to a centralized cloud is counterproductive. Edge computing could significantly improve network efficiency by transporting non-raw data in smaller amounts or providing edge functions to eliminate irrelevant data onsite. Systems and algorithms will need to be developed to address concerns in high probability of sensor failure in a high variety of large scale real-world scenarios and maintenance and support facilities.
# Bio-Inspired Dielectric Resonator Antenna for Wideband Sub-6 GHz Range
## 2 Bio-Inspired Spiral Shell DRA
### Gielis Parameters: Impact on the SsDRA Design  
The design initiator was defined by considering that the variation of the \"a\" and \"b\" parameters affects the sizes and volume of the DRA, and so the footprint it can actually occupy. The angular position of the shell wave ridges remains constant, while the shell wave amplitudes increase proportionally as \"a\" and \"b\" increase. The \"m\({}_{1}\)\" and \"m\({}_{2}\)\" parameters influence the frequency of the shell wave over the base spiral: when these coefficients are increased, the shell wave number increases along the spiral. The parameters \"n\({}_{1}\)\", \"n\({}_{2}\)\" and \"n\({}_{3}\)\" affect the shell wave amplitude on each loop of the shell: in particular, when n\({}_{1}\) = n\({}_{2}\) = n\({}_{3}\) = 2, the output geometry becomes the well-known Archimedean spiral. For values of \"n\({}_{1}\)\", \"n\({}_{2}\)\" and \"n\({}_{3}\)\" higher than 2, alternate waves are overlapped on the spiral. When these parameters are lower than 2, the waves resemble a rectified sine (or cosine) with an outward concavity, thus orienting the wave ridges inward. The constant scaling factor K regulates the overall amplitude of the geometry and, as such, it affects the DRA radius and volume. The planar exponential function f(\(\theta\)), expressed by the following Equation (2):  
\[\text{f}(\theta)\ =\ \text{e}\left(\text{c}_{\text{theta}}\ast\theta\right) \tag{2}\]  
acts on the closeness of the wavy spiral loops, thus on the space actually filled by the DRA. In particular, the parameter c\({}_{\text{theta}}\) (which is a rational number) has a sharp influence on the radial expansion of the spiral loops, i.e., the aperture velocity \(\Delta\text{R}/\Delta\theta\).  
On the basis of these considerations, we fixed the starting initiator values reported in Table 1 where the angle parameter \(\theta\) is varied in the range [\(-3\pi\), \(3\pi\)] (i.e., three complete rounds).  
The defined parameters lead to a SsDRA footprint smaller than the ground plane whose dimensions are 100 \(\times\) 100 mm\({}^{2}\), thus with L\({}_{\text{SUB}}\) = 100 mm.
# A Spatio-Temporal Schedule-Based Neural Network for Urban Taxi Waiting Time Prediction
## 3 Materials and Methods
### Multi-Source Data Fusion Based on a Spatio-Temporal Grid
#### 3.4.2 Spatio-Temporal Grid Structuration  
Considering the urban planning layout, geographical topography, resource allocation and other factors affecting the waiting time of taxi passengers, we choose to mesh the map into several grids of unit size [33; 34]. Taking into account that, if the map grid is set too large, the actual meaning of the recommended waiting point will be lost. This is because, on the one hand, people are unwilling to move too far, and on the other hand, it takes too long to move to a better waiting point. Time is not necessarily the time difference between the two waiting points. When the map grid is set too small, the prediction may be inaccurate. This may be due to the small difference between the waiting time of the adjacent area or the unreachable location that is too close (if possible, in the house or the middle of the road, etc.). In view of the above considerations, this article sets the map grid size at 50 m \(\times\) 50 m. The details of the map grid are shown in Figure 2[35].  
The minimum and maximum values of longitude and latitude in the grid are calculated based on the length and width of the grid and the longitude and latitude values of the center point of the grid, that is, the range of the grid. The form and meaning of grid information are shown in Table 2.  
The continuous trajectory of the taxi can be discretized by the grid operator of the administrative map. Match and compare the spatio-temporal data of waiting time data, POI data, taxi trajectory data, rest time data and geospatial data, so as to map the multi-source data to each grid respectively. The specific mapping method will be shown in Section 3.4.3.  
Figure 1: Track triangle for checking the drift track points.  
Table 3 is a data fragment intercepted from the map grid information table read from the database.
# A Survey on IoT-Edge-Cloud Continuum Systems: Status, Challenges, Use Cases, and Open Issues
## 3 Indicative Use Cases
### Smart Manufacturing  
The IoT concept is widely applicable to industry 4.0 environments for process optimization, where sensor nodes are deployed and the collected data are offloaded to edge or cloud nodes for processing (Figure 7, [67]). In this context, the industrial Internet of Things (IIoT) is a physical network of things, objects, or devices (that contain embedded technology) for sensing and remote control in an industrial context that allows much better integration between physical and cyber worlds. To this end, there are three primary pillars that support a fully IIoT-enabled operation: (i) smart machines equipped with sensors and software that can collect and transmit various types of data; (ii) robust edge and cloud computer systems that can store and process the data; and (iii) advanced data analytics systems based on appropriate ML techniques [68; 69].  
Therefore, with respect to Figure 7, latency-critical tasks can be offloaded to local edge servers, and production processes can be updated accordingly. In this context, typical scenarios include failure minimization functionalities, where, for example, high-definition three-dimensional (3D) images of the products are captured by the IoT devices and compared to well-known prototypes for potential deviations.
# Acoustic and Flow Aspects of Synthetic Jet Actuators with Chevron Orifices
## 3 Results and Discussion
### Noise Level  
In Figure 8, the noise level as a function of real power for all cases is presented. The SPL(A) for all cases can be interpolated as the exponential function (\(SPL(A)\ =\ a\cdot e^{b\cdot p}\)) with different \(a\) and \(b\) parameters and the coefficient of determination \(R^{2}>0.9\). The highest value of SPL(A) is obtained in Case 3. The noise generated in Case 1 is always lower than in Case 0. The SPL(A) in Case 2 is bigger than in Case 0 at \(p=5\), 6 W, and for greater real power, it is lower. To sum up, in only Case 1, the noise is reduced by about 1-2% when compared to Case 0.  
It must be noted that, generally, the higher the chevron cutouts, the highest the SPL (except in Case 1 at \(p=12\) W). Along with increasing chevron cutouts, the height of the angle between the walls formed after \"removing\" the chevron decreases. On this basis, we assume that at this point (the vertex of chevron cutouts), the sound may be, in some way, amplified. This hypothesis is supported by the fact that for small power levels, the SPL generated in Case 3 is much bigger than in Case 0. Along with the increase in power, this effect is less and less important, and, finally, at \(p=12\) W, it is reduced by the benefits of the overall shape of the chevron orifice. However, this hypothesis should be tested and confirmed/rejected by thorough research of the sound field around the orifice.  
Most often, during investigations of SJAs with chevron nozzles or orifices, it is assumed that the chevron nozzle generates less noise than the classic circular nozzle. However, this issue has not been verified (e.g., in [15; 19]). There has been a simple transfer of assumptions from aviation to SJA techniques. The present paper shows that it is not always true (Figure 8), and the chevron nozzle can increase the generated noise level. It depends on the input parameters and chevron geometry. [PERSON] and [PERSON] [25] tested six  
Figure 8: SPL(A) of SJA at different power.  
different types of chevron nozzles, and only two of all the tested geometries reduced the generated noise level.  
It is very important that in this paper, chevron orifices have been tested and, in most publications known to the authors, chevron nozzles (the Bendemann nozzle) have been used (except in [21; 23]). However, the results indicate compliance with other works, and, in our opinion, the change from orifices to nozzles will not significantly change the results if the primary parameters of the SJA are preserved (e.g., orifice diameter, actuator high). This has also been confirmed by the comparison of a chevron nozzle and a chevron orifice in [21].
## 4 Experiments
### Impact of Input MRI Sequences  
To demonstrate the impact of the input MRI sequences on the pCR prediction performance, we compare the pCR prediction performance of five types of input MRI sequences in Table 2: {T2}, {DWI/ADC}, {CE}, {T2, DWI/ADC}, {T2, DWI/ADC, and CE}. Note that pCR prediction performance is affected not only by the input MRI sequences but also by the feature vector extraction method and the pCR classification model. For a fair comparison, we use both radiomics features and MSFI embedding extracted by the same architecture, 3D-ResNet, and apply different dimension reduction methods, as in {no dimension reduction, PCA, and Kernel PCA}. We report the AUC of an ensemble classifier because it corresponds to the average performance of five different pCR classification models.  
Table 2 shows that the AUC of pCR prediction using three MRI sequences, {T2, DWI/ADC, and CE}, as the input is higher than that using one of these MRI sequences separately, regardless of the dimension reduction method. In particular, while T2 is widely known to be the most effective in evaluating rectal cancer, pCR prediction performance can  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Input MRI Sequences** & **No Dimension Reduction** & **PCA** & **Kernel PCA** \\ \hline \{T2\} & 0.765 & 0.791 & 0.787 \\ \{DWI/ADC\} & 0.721 & 0.801 & 0.791 \\ \{CE\} & 0.716 & 0.800 & 0.801 \\ \{T2, DWI/ADC\} & 0.764 & 0.793 & 0.800 \\ \{T2, DWI/ADC, and CE\} & 0.811 & 0.804 & 0.819 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of pCR prediction performance of five types of input MRI sequences. For a fair comparison, we extract both MSFI embedding and radiomics features from input MRI sequences, apply three different dimension reduction methods in {No dimension reduction, PCA, and Kernel PCA} and report the AUC of an ensemble classifier.  
be further improved when DWI/ADC and CE are used simultaneously. When comparing {T2, DWI/ADC} and {T2, DWI/ADC, and CE}, we observe that pCR prediction using three input MRI sequences, {T2, DWI/ADC, and CE}, outperforms {T2, DWI/ADC}.  
Among the three possible pairs of MRI sequences from {T2, DWI/ADC, and CE}, we include only {T2, DWI/ADC} in Table 2, because using T2 and DWI together is already known to be highly effective in evaluating rectal cancer. Radiologists achieve higher pCR prediction accuracy by using both T2 and DWI than by using T2 alone [21] and simple classification methods, such as logistic regression and random forest on radiomics features from T2 and DWI, outperform MRI assessment by radiologists [8].
# Cloud-Assisted UAV Data Collection for Multiple Emerging Events in Distributed WSNs
## 1 Introduction  
With rapid advances of Information and Communication Technology (ICT), aerial robots, especially UAVs, have attracted the attention of both industry and academia. Because UAVs are flexible, portable, inexpensive, and convenient to use, they have been applied to perform various tasks in the domains of transportation, agriculture, healthcare, and heavy industry [1; 2; 3; 4; 5; 6]. Certain characteristics such as being wireless, unmanned, and remotely-operable make them appropriate for use in domains which might be dangerous or difficult for humans. There have been several research studies for the application of UAVs for the protection of powers plants, inspection of electric power, disaster rescue, and recording aerial videos for the protection of the environment [7; 8; 9; 10]. Traditionally, wireless sensor networks have been used and one drawback is lack of moving ability for fix-installed wireless nodes [11; 12; 13]. Although many studies have introduced mobile robots to address the problem, the locomotive capacity of these ground robots can often be limited by the terrain or obstacles of monitoring sites [14; 15]. Therefore, more studies are integrating UAVs or other aerial robots into WSNs. These systems are called UAV-WSNs and they are well suited for remote surveillance and emergency response [16; 17; 18; 19; 20], especially for events such as landslides, wildfire, flood, etc. In these scenarios, real-time event surveillance is the basis of the right decision. Furthermore, to some extent, real-time event detection could save lives and cost.  
However, UAV-based WSNs suffer from two major limitations: (a) due to the limited on-board computational capacity and storage, WSNs based on UAVs cannot perform global optimizationalgorithms for desired factors such as flying trajectory, and network topology control, (b) since WSNs and UAVs are powered by batteries [21], energy consumption is a major constraint, (c) there is a limited consideration of events priority. Therefore, we leveraged the cloud computing infrastructures to store and manipulate a large amount of data so as to undertake computational optimization for the behaviors of a WSN and UAV. Next, we provided a novel technique to minimize the energy utilized by the WSNs and UAVs to improve the duration of the data collection.  
The rest of the paper is organized as follows: Section 2 discusses the related work on WSNs, data gathering using UAVs, current challenges and problems. In Section 3, an efficient architecture for data aggregation using a UAV-based WSN is presented. The problem description, the mathematical model and the algorithm based on the emerging event and user interests are given in Section 4. The goal in this section is to find the optimal flying parameters and the corresponding WSN nodes and clusters. In Section 5 we discuss the simulation results by comparing our method with other approaches. Finally, we provide our conclusions and discuss possible future work in Section 6.
## 3 Output Design  
In terms of output design, the simplest and most direct approach is to use the aerostatic three-component force coefficient as the output of the prediction model. However, the mapping logic is very complicated, and it directly skips multiple data processing links such as local sampling of the pressure field and surface pressure integration. Although deep learning networks have the ability to implement the mappings, this is bound to increase the burden on the network, requiring more hidden layers and more complex structures. On the other hand, the pressure field can not only provide information on the aerostatic three-component force coefficient but also on the surrounding flow field structure, providing support for other scientific research scenarios such as flow control and heat source analysis. Therefore, this paper adopts a two-step strategy to obtain the aerostatic performance of bluff body sections. First, the neural network is used to output the pressure field \(O_{pressure}\) of the steady flow field, and then, the surface pressure is integrated to obtain the aerostatic three-component force coefficient. Figure 5 shows the output of the neural network for an example rectangular section with an aspect ratio of 4.