esa-sceva/satcom-chunk-collection huggingface dataset:
# Deep Learning for EEG-Based Preference Classification in Neuromarketing
## 3 Literature Review
### Preference Detection Using a BCI  
This section explains the design process of the neuromarketing experiment to predict the consumer preferences and choices. First, a BCI device must be placed on the head of a consumer. Next, the consumer is asked to look at the products. During the recording phase, the EEG data are recorded concurrently while the consumer views a product. After viewing each product, the user is asked for his or her preference toward the product in terms of a five- or nine-point scale of subjective rank. When all products are displayed, the subjective ranks must be manually labeled as liked or disliked classes. Next, the EEG signals undergo a signal preprocessing and feature extraction. The classification module is developed based on the ground truth completed by the consumer's selection (subjective ranks). Figure 3 presents a proposed BCI system for the preference detection composed of three main modules: signal preprocessing, feature extraction and selection, and classification. Figure 3: EEG-based consumer preference prediction system.
# An Enhanced Time Series Analysis to Improve the Performance of 5G Communication Systems +
## 2 Materials and Methods  
One of the major performance enhancement issues of 5G communication systems lies in the aggressive bandwidth demands [9]. To achieve the goal of faster transmission speed, 5G networks must use more spectrum and more antennae than ever before, which requires increased signal strength to ensure good signal quality and reliability. Additionally, the system is also faced with high spectral noise due to heavy interference from multiple sources [10; 11]. Moreover, to ensure that the 5G system meets its performance enhancement goals, the architecture and design must factor in the impact of environmental conditions. To prevent such scenarios, the implementation of enhanced propagation models, as well as better accountability and scheduling schemes, are necessary [12]. By properly accounting for the local conditions, improved performance can be attained. Finally, security has become an important aspect of performance enhancement of 5G networks. Due to the increased use of multiple antennas, frequency hopping, and higher mobility support, the security of 5G networks has become more vulnerable [13]. Performance enhancement issues require the use of advanced techniques and technologies, such as integrated antenna designs, propagation models, cryptographic methods, and authentication schemes [14]. The fifth generation of 5G wireless communication systems represents an evolution in connectivity, offering faster speeds, lower latency, and improved capacity over its predecessors. However, there are several challenges in the performance enhancement of 5G communication systems [15; 16]. One of the biggest challenges facing the performance enhancement of 5G wireless communication systems is network congestion [17]. While 5G networks offer faster speeds than their predecessors, they are more susceptible to interference, which leads to a decrease in the achieved data rate. Additionally, the longer the distance a signal must travel, the more likely it is to experience attenuation, resulting in a decrease in data rate [18]. The vast number of antennas needed to operate 5G networks has greater environmental impacts than earlier generations, leading to expressed concerns from some communities [19]. Current hardware is often not powerful enough to support 5G communications, requiring costly upgrades or replacement of existing hardware [20].  
The novelty of performance enhancement of 5G communication systems using time series analysis lies in the use of advanced analytics to gain insights about the data collected from the network. With time series analysis, 5G communication systems can identify trends, outliers, and changes in user behavior and improve network optimization algorithms that are capable of predicting performance before and after any changes take place. This can help 5G communication systems improve advanced features such as network QoS, better latency performance, better spectrum efficiency, and upgraded network security.
# Application of Thin-Film Microextraction to Analyze Volatile Metabolites in A549 Cancer Cells
## 3 Materials and Methods
### Sampling Optimization  
#### 3.4.1 Selection of Sorbent  
The pooled cell culture medium was used for extraction on pure PDMS, DVB, HLB and CAR TFME sheets to ensure a relevant sample matrix with the richest content of diverse VOCs. For this purpose, 5 mL sample was transferred to a 20 mL glass vial and sampling was conducted for 30 min according to protocol \"B\" described in Section 3.4.2. For each sample type, four independent replicates were used (\(n=4\)).
# A Self-Testing Platform with a Foreground Digital Calibration Technique for SAR ADCs
## 2 Proposed Self-Test Platform
### Testing Stimulus Signal  
Generally, the dynamic performance of the ADC, including the total harmonic distortion, signal-to-noise ratio, signal-to-noise and distortion ratio (SNDR), and effective number of bits (ENOB), is evaluated by stimulation using the input signal of a sinusoidal waveform and analysis using a digital signal processor [10]. Therefore, a high-accuracy DAC controlled by a digital code in the Arduino board is used to implement the sinusoidal testing signal. Figure 8 shows the basic block of the signal generator. A gain stage and a level shift are employed to adjust the signal amplitude and the required DC level. In addition, a low-pass filter is used to reduce the noise, eliminate the error, and enhance the signal performance. Figure 9 shows the experimental results, which reveal that this signal generator has a spurious dynamic range (SFDR) of 52 dB.  
Figure 8: Block diagram of the sinusoidal signal generator.  
Figure 7: Simulation result from the MATLAB tool with 2 mV DC offset only (**a**) before and (**b**) after calibration.  
Figure 9: (**a**) Output signal waveform of the signal generator. (**b**) Output power spectrum of the signal generator.  
The clock signal is required to control the ADC for most of the ADC testing platform. Figure 10 shows the experimental results of the clock signal, and the root mean square of the jitter is 0.808 ns.
# Optimized Clustering Algorithms for Large Wireless Sensor Networks: A Review
## 3 Optimized Hierarchical Based Routing Protocols  
The optimization problem of traditional hierarchical based routing has conducted to intelligent clustering strategies commonly known as optimized hierarchical based protocols [4]. The optimized clustering strategies are more recent and aim to be intelligent algorithms which improve the lifetime of a sensor network and make them energy efficient. For that, they are based on a recent development of ML/CI which is defined by [8] as an intelligent computational methodology that uses heuristic algorithms to obtain approximate solutions to NP hard problems efficiently. Several ML/CI paradigms used in clustering in WSNs can be classified as follows: Fuzzy Logic, Genetic Algorithm, Neural Network, Reinforcement Learning, and Swarm Intelligence paradigms [7; 8; 20; 21; 28; 29; 30]. Figure 2 presents the classification of optimized clustering algorithm for WSNs.
## 1 Introduction  
Underwater acoustic sensor networks (UASNs) have gained much attention in recent years due to their diversity of aquatic applications, such as monitoring, auxiliary navigation, unmanned exploration or surveillance, and mine reconnaissance [1, 2, 3]. Underwater acoustic channels are characterized by limited bandwidth, high and variable propagation delay, time-varying multi-pathing, and fading, which leads to high bit error rates and temporary losses of connectivity [4]. In addition, sensor nodes are mainly powered by batteries that are difficult to replace, making it hard to replenish the energy of the sensor nodes. So, constructing low-energy-consumption UASNs is extremely challenging.  
Clustering is a common technique that can increase the scalability of a network while reducing its energy consumption. Typical cluster-based protocols in wireless sensor networks include low-energy adaptive clustering hierarchy (LEACH) [5], hybrid energy-efficient distributed clustering (HEED) [6], power-efficient and adaptive clustering hierarchy (PEACH) [7], distributed energy-efficient clustering (DEEC) [8], etc. Among them, the LEACH protocol is one of the most classical cluster-based protocols and has been widely used in UASNs. In the LEACH protocol, a round of transmission consists of two consecutive phases, i.e., the clustering phase and the transmission phase. It can achieve energy equalization and reduce the network energy consumption effectively by periodically selecting cluster heads and re-clustering the sensor nodes [5]. However, the existing LEACH protocol for UASNs still has some shortcomings. First, cluster numbers are usually determined empirically in the clustering phase, which is not optimal in many cases. Second, the transmission efficiency is limited due to the low parallelism of the cluster head node and sensor node data transmission. Third, the signaling in the clustering and transmission phases should be reduced to lower the energy consumption and improve the clustering and transmission efficiency.  
In this paper, we propose an improved LEACH protocol based on the underwater energy propagation model, parallel transmission, and replication computing for cluster-based UASNs. The contributions of our work include the following:  
* A cluster number optimization method based on the underwater energy propagation model is proposed to minimize the transmission energy consumption of the network.
* A parallel data transmission method based on scheduling for the cluster head nodes and sensor nodes is proposed to achieve conflict-free parallel data transmission.
* Replication computing is introduced to the LEACH protocol to reduce the signaling in the clustering and transmission phases.  
The rest of this paper is organized as follows: in Section 2, the works related to cluster-based underwater routing protocols are reviewed. In Section 3, the improved LEACH protocol for cluster-based UASNs is proposed. Section 4 presents and analyzes the simulation results. Finally, Section 5 concludes the paper.
# Advances in Machine Learning Techniques Used in Fatigue Life Prediction of Welded Structures
## 2 Materials and Methods
### Classification and Definitions
#### 2.4.6 Random/Hybrid Based Methods
[123] performed a machine learning-assisted probabilistic fatigue assessment of RD joints in OSDs using the GPR surrogate model, DBN, and PFCG model in a subsequent study. The modified posterior model exhibits better concordance with the test data when compared to the old PFCG model. [PERSON] et al. [124] used RSM, GA, and Multi-Attribute Decision Making (MADM) to reinforce fatigue fractures at U-rib to diaphragm joints in OSDs. This improved comprehension of how to strengthen the joints with CFRP and permitted the best use of CFRP material in preserving aging steel structures. A novel method for assessing the mechanical performance of different welded joints was devised by [PERSON] et al. [125] by fusing the thermographic method, multiple-cut contour technique, and X-ray diffraction. [PERSON] et al. [126] showed the relevance of the risk-based maintenance of lock gates by combining failure probabilities with projected costs for various occurrences and DBN. An adaptive neuro-fuzzy-based system was created by [PERSON] et al. [127] for the forecast of surface roughness in wire arc additive manufacturing. In a study, [PERSON] et al. [128] used a trilinear interpolation method, an interactive Mixed Reality (MR)-based user interface, a Gradient Boosted Regression Tree (GBRT) model, and FEA to visualize the results of hand metal arc welding. In a cyclically loaded structural steel, the Acoustic Emission (AE) examination of proximate fracture was carried out by [PERSON] et al. [129] using AE data. A combination of criteria based on power spectrum and pattern recognition, as well as energy data and spectral density functions was used. In an attempt to upgrade the Eurocode and IIW codes for higher strength steels in certain fatigue classes such as butt welds and transverse stiffeners, [PERSON] et al. [130] subjected welded cruciform joints to experimental and finite element models. Subsequently, the reliability of the upgrade was upheld using Weibull models found in survival analysis. In research by [PERSON] et al. [131], they developed a particle-filter based Fatigue Crack Growth (FCG) prediction technique for a base metal and butt-welded specimen to deal with the uncertainty in the FCG process. [PERSON] et al. [132] developed a novel technique incorporating both ML and FEA to forecast F-N fatigue curves of high-strength steel RSW joints. The influences of thickness and width of the sheet as well as nugget diameters were measured.
# IoT and Blockchain Integration: Applications, Opportunities, and Challenges
## 8 Challenges of IoT, Blockchain and the Integration
### Interoperability, Standardization, Regulation, and Governance  
Interoperability is a question of enabling seamless communication among heterogeneous devices. Due to device and communication varieties related to IoT, there is a lack of IoT design and communication standards. Due to the explosion of the IoT business, privacy controls, user agreements, third-party applications, and system update procedures are not uniform and consistent across platforms and applications [43]. Multitudes of standards such as IEEE, IETF, W3C, ITU-T, ONeM2M, OMG, and ETSi have been proposed for different products, services, and systems [80]. A unifying standard for cross-domain IoT communications is still far from adoption.  
For example, contemporary, popular blockchain networks such as Bitcoin and Ethereum are struggling to be adopted as stable, efficient, and dependable technologies for financial transactions and health care management, among others [77]. Similarly, these technologies struggle to operate along with existing regulatory and governing bodies ([[https://bit.ly/3](https://bit.ly/3) QYnLYd]([https://bit.ly/3](https://bit.ly/3) QYnLYd) (accessed on 15 July 2022)).
# A Simple Neural-Network-Based Decoder for Short Binary Linear Block Codes
## 6 Experimental Results of Decoding by Proposed Multi-class NN-Based Decoder
### Experimental Results
#### 6.2.4 Results for BCH (31, 11) Code  
The code length of BCH (31, 11) is \(n=31\), the message length is \(k=11\), the error correcting capability is \(t=5\), and the code rate is \(11/31=0.35\). Figure 14a,b reveals the similar observations as the previous results. The BER and CER performance of the presented NN-based decoder falls between the OSD and Chase II algorithms. Note that only four candidate codewords are evaluated in our method.  
Figure 12: (**a**) BER for BCH (15, 5) Code, (**b**) CER for BCH (15, 5) Code.  
Figure 13: (**a**) BER for QR (23, 12) Code, (**b**) CER for QR (23, 12) Code.  
The decoding time for different decoding algorithms at the SNR of 0 dB is summarized in Table 2, where the colors of red, green and blue mean the first, second and third minimum decoding time. Here, the HDD utilizes the syndrome-weight decoder. The total number of candidate codewords for BCH (15, 7), BCH (15, 5), BCH (31, 11) and QR (23, 12) codes are 2, 4, 4 and 4, respectively. Results show that the proposed NN-based method is competitive as compared to the other DNN-based frameworks.
## 4 Experimental Results and Discussion
### Results and Discussion  
In order to assess the advantages of the proposed TSETMF denoising algorithm to improve the grayscale image quality, three kinds of standard images were selected as the denoising objects to conduct contrast verifications in MATLAB, including Lena (256 \(\times\) 256 pixels) [61], Cameraman (504 \(\times\) 505 pixels) [62], and COVID (1070 \(\times\) 1200 pixels) [63]. Lena and Cameraman are the classical images for grayscale image processing and COVID is the grayscale micrograph. By adding different densities of salt and pepper noise, the ability of four filter algorithms to address different high-density-noise grayscale images was examined. The density was continuously increased from 5% to 95% in increments of 5%.
## 2 Materials and Methods
### Methods
#### 2.2.2 Ansys Analysis  
To simulate the electrospinningning phenomenon, the ANSYS Maxwell software was used (version 2020 R2). The Electric Transient simulation was performed for three different supplied voltage values (25 kV, 30 kV, and 35 kV). The needle-to-collector distance was stated as 50 and 100 mm. To present the field distribution results, the Authors selected the exemplary cases. The generated was related to grid densities in regions of electric field concentration. The simulation time was 30 s.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Sample**} & \multicolumn{2}{c}{**Periclase**} & **Polydopamine** & **BMP-2** \\  & **Concentration [\%]** & **Concentration [ppm]** & **Concentration [\%]** \\ \hline
1 & 0 & 0 & 0 \\
2 & 1 & & \\
3 & 2 & & \\
4 & 3 & 50 & 0.1 \\
5 & 4 & & \\
6 & 5 & & \\ \hline \end{tabular}
\end{table}
Table 1: Chemical composition of prepared composites.  
Figure 1: XRD diffractogram confirming preparation of highly crystalline periclase.  
The simulation conditions were as follows:
* Needle top to collector distance: 50 and 100 mm, representing the working distance in the electrospinning setup;
* Grounded collector (the collector was set to ground potential to establish a stable electric field gradient between the needle and the collector);
* Number of mesh elements: 1,233,843 (a high-resolution mesh consisting of 1,233,843 elements was generated to ensure accurate field distribution analysis and capture detailed changes in the electric field);
* Needle material: stainless steel (stainless steel was selected as the needle material due to its high electrical conductivity and compatibility with the electrospinning process);
* Collector material: aluminum (aluminum was chosen as the collector material, leveraging its excellent conductivity and lightweight properties);
* Needle filling: acetone (the needle was modeled as containing acetone, reflecting the solvent used during the electrospinning process);
* Syringe material: plastic (plastic (PS) was used to model the syringe, consistent with the experimental apparatus).  
The analysis type was set as Electric Transient with a simulation time of 30 s. The simulation area was restricted to a 570 \(\times\) 600 \(\times\) 90 mm box. The voltage excitation applied to the needle was set as 25 kV, 30 kV, and 35 kV, with zero potential assumed for the collector. The crated model allows for changing the needle position and dimension without the new mesh settings. The vector and magnitude field intensity distribution display were chosen for the presentation of the results.
## 3 Results and Discussion
### Results of Abrasive Wear Resistance
#### 3.6.2 Change in Mass of Samples and Countersamples  
The tests carried out for the friction pair of the 20 MnCr5 steel countersample with the sheet samples showed that in the case of the countersamples (Figure 12a), a mass gain was observed, while in the case of the samples, a mass loss was observed (Figure 12b). The mass loss of the EN AW-6061 T0 alloy sample is the largest, as it is 19.96% (Figure 13a,b) of the initial mass. Such a large mass loss indicates the cutting character of the wear mechanism, which may be reflected in large fluctuations in the friction force change during the wear test after the running-in stage (Figure 11).  
The mass loss of the Inconel 625 alloy sample is the smallest (about 0.04%) compared to the other countersamples. The pure copper sample showed a mass loss of 2.79% (Figure 13b), which is more than six times smaller compared to the mass loss of the EN AW-6061 T0 sheet. Meanwhile, as shown in the previous section, the friction force for the 20 MnCr5/pure copper friction pair was about 40% lower than in the contact of the EN AW-6061 T0 sheet with the 20 MnCr5 steel countersample. This indicates a clear tendency of the aluminium alloy sheet to lose mass during friction with the 3D-printed 20 MnCr5 countersamples. In the 20 MnCr5/Inconel 625 friction pair, no significant increase in the countersample mass was recorded (0.00005 g), only in the case of the sample the minimal loss was 0.00017 g (0.04%). Despite the similar values of friction coefficients recorded during wear testing 20 MnCr5/321 steel and 20 MnCr5/Inconel 625 friction pairs, the mass increase in the countersample in the case of the first friction pair was about 20 times higher (0.00097 g) than for the second friction pair (0.00005 g).  
In the quantitative aspect of sample mass loss and countersample mass gain, there is a direct relationship with friction force values (Figure 11): the higher the friction force, the greater the sample mass loss and, consequently, the greater the countersample mass gain. The intensity of material wear is a function of various types of interactions and the topography of interacting surfaces [39,40]. The relative resistance to abrasive wear of different metals depends on the properties of the cooperating materials, but in each of the individual cases this resistance is proportional to the strength of these materials. In the context of tool wear, the 3D-printed 20 MnCr5 countersample is most suitable for forming Inconel 625 and 321 steel sheets. The results obtained for the 20 MnCr5/Inconel 625 friction pair indicate that the increase in the mass of the countersamples in the remaining friction pairs is directly proportional to the mass loss of the samples.
## 5 Numerical Results  
The applicability and correctness of the RFGG-FG-FFT with cube polynomial inter/extrapolation method will be tested by analyzing the scattering problems in this section. All the computation is carried out on \(M=2\) and \(h_{x}=h_{y}=h_{z}=h\), where \(M\) denotes the expansion order and \(h\) denotes the Cartesian grid spacing.
# An Efficient On-Chip Data Storage and Exchange Engine for Spaceborne SAR System
## 4 Hardware Implementation
### On-Chip Data Exchange Engine Architecture
#### 4.1.2 Dline_Buf Module  
The superscalar pipeline data exchange control with the DDR3 storage side and the compute core processing side during processing is controlled by the Dline buf module. The DDR3 output to be processed, the data buffering procedure to be stored after processing, and the feedback status to be monitored during the data exchange process are all controlled by the Data Exchange Unit Register (DEU Reg). The RAMSA_Ctrl and RAMSB_Ctrl modules mainly control the Ping-Pong RAM pipeline and data buffer corresponding to DDR3A and DDR3B. The data exchange paths corresponding to DDR3A and DDR3B are primarily under the command of the Data ManageA Ctrl and Data ManageB Ctrl modules.