esa-sceva/satcom-chunk-collection huggingface dataset:
# Viscosity Prediction of Different Ethylene Glycol/Water Based Nanofluids Using a RBF Neural Network
###### Abstract  
In this study, a radial basis function (RBF) neural network with three-layer feed forward architecture was developed to effectively predict the viscosity ratio of different ethylene glycol/water based nanofluids. A total of 216 experimental data involving CuO, TiO\({}_{2}\), SiO\({}_{2}\), and SiC nanoparticles were collected from the published literature to train and test the RBF neural network. The parameters including temperature, nanoparticle properties (size, volume fraction, and density), and viscosity of the base fluid were selected as the input variables of the RBF neural network. The investigations demonstrated that the viscosity ratio predicted by the RBF neural network agreed well with the experimental data. The root mean squared error (_RMSE_), mean absolute percentage error (_MAPE_), sum of squared error (_SSE_), and statistical coefficient of multiple determination (\(R^{2}\)) were respectively 0.04615, 2.12738%, 0.46007, and 0.99925 for the total samples when the Spread was 0.3. In addition, the RBF neural network had a better ability for predicting the viscosity ratio of nanofluids than the typical Batchelor model and Chen model, and the prediction performance of RBF neural networks were affected by the size of the data set.  
n  
nofluids; viscosity; RBF neural network; ethylene glycol/water
# Analysis of Information Availability for Seismic and Volcanic Monitoring Systems: A Review
## 2 Methodology Validation
### Study Selection
However, these articles do not point to seismic data processing or seismic data acquisition. These do not refer to practical or experimental cases for EEW either. As an example, we can mention WSN mobile solutions, as well as LoRaWan applications for networks in urban areas with a high demand for data traffic. * _Relevant studies._ In this group, we selected 51 highly relevant works because these identified proposals and solutions directly related to the seismic-volcanic monitoring networks, as well as improvements and alternatives to optimize the seismic data acquisition and processing systems. On the other hand, we also identified ad hoc point solutions for seismic-volcanic monitoring networks. It is important to recognize that some studies do not consider the approach proposed in this research. The scope of these studies is partial. In other cases, the proposed solutions cannot be applied to different seismic network environments. The documents were analyzed, compared, and used as referents to identify knowledge gaps in the security of information-oriented data acquisition systems and data transmission networks for seismic-volcanic monitoring. Figure 2 shows each of the stages in the review process designed for this study. It starts in the database search, passing by the string strategy that offers 1938 articles until December 2021. After the selection criteria (inclusion and exclusion), 282 articles were selected and reduced to 51 when only relevant studies are extracted. Finally, filters based on the content of papers were applied to selected articles. To identify the possible research gaps that fit the objectives of this review, we addressed four groups: (a) articles oriented to seismic data transmission networks, (b) seismic data acquisition and processing systems, (c) seismic data standards, and (d) early warning trends.
# An Intuitionistic Fuzzy-Rough Set-Based Classification for Anomaly Detection
## 4 Proposed Algorithm  
For generating classification rules, we choose a suitable value of the correlation coefficient (\(\alpha\)) to define the \(\alpha\)-relation. The correlation coefficient used to define the relation is given in Section 3. The procedure of finding classification rules is given as follows. We have a collection of \(m\)-data instances, each of which is described by \(n\)-intuitionistic fuzzy attributes and is represented as an intuitionistic fuzzy matrix [54], where each entry is \(<\!\!x_{ij}\), \(y_{ij}\!\!>\), \(x_{ij}\in[0,1]\), \(y_{ij}\in[0,1]\), and \(0\leq x_{ij}+y_{ij}\leq 1\), \(I=1,2,\ldots\ m\) and \(j=1,2,\ldots\ n\). Usually, the dataset can be expressed as an information system (\(U,C\cup D\)), where \(C\) and \(D\) are conditional and decision attributes, respectively, and are expressed as intuitionistic fuzzy sets. The method is described below.  
The first step of the proposed method is to compute \(\alpha\)-relation of the conditional attribute using correlation coefficient, and compute the equivalence classes of decision attributes using the same formula of the correlation coefficient. The value of \(\alpha\) is taken to be 0.4. Then, the \"infimum\" operator is applied on fuzzy knowledge granules of conditional attributes. Then, intuitionistic fuzzy nano lower approximation and intuitionistic fuzzy nano upper approximation are constructed using decision class. Then, the boundary regions are also found. With the help of two approximations, two sets of fuzzy rules, namely, the certain fuzzy rules and possible fuzzy rules, can be generated. The proposed method is also explained with the help of the flowchart given in Figure 1 below.  
constant time. Therefore, the overall time-complexity of the proposed algorithm is O(\(m^{2}\).\(n\)), which shows that the proposed algorithm is quite efficient.
# Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
## 5 MA with Code Separation
### Grassmannian-Sequence-Based NOMA Protocols Founded on the Generalized Welch Equality
The NOCA scheme from Nokia also employs quantized sequences using elements from the QPSK signal constellation.
# Repositioning Bikes with Carrier Vehicles and Bike Trailers in Bike Sharing Systems
## 2 Related Work
### Dynamic Repositioning Using Bike Trailers  
To consider the self-sustaining and environment issues, instead of using vehicles, [PERSON] et al. proposed a pricing mechanism that takes the global view of the repositioning requirements and incentives the execution of bike trailer tasks (based on crowdsourcing) within the budget constraints [10; 20]. [PERSON] et al. proposed a centralized pricing-based dynamic incentive mechanism to mobilize the participants via crowdsourcing with regarding to reposition the indiscriminately parked bikes [21]. Despite the success of those approaches, bike trailers can only take a few bikes at once and the distance of movements is limited. To bridge this gap, [PERSON] et al. proposed an ethically aligned incentive optimization approach that maximizes the rate of success for bike repositioning while minimizing cost and prioritizing users' wellbeing [15]. Furthermore, [PERSON] et al. developed a meteorology similarity weighted \(k\)-nearest neighbor regressor to predict the station pickup demand based on large-scale historic trip records and proposed an interstation bike transition model to predict the station drop-off demand [18]. The value of crowdsourcing tasks may be high (over the available budget).  
Different from previous approaches, our DRRPVT approach aims to leverage the advantage of using both carrier vehicles, which are able to take a large number of bikes and move to longer distance, and bike trailers, which are able to move to short distance with limited cost and allow self-sustaining, by considering the expected profit and the loss demands reduction of repositioning and routing solution [22; 23].
## 3 Results and Discussion
### Consistency Verification of Observation and Simulation Results
To investigate the effect of bubbles formed by the thermal decomposition of molten polymer on the bonding process, the results of interfacial observations and temperature simulations were extracted for the laser powers of 600 W, shown in Figure 7, and 700 W shown in Figure 8, where the gray area in the temperature distribution was the area beyond the thermal decomposition temperature of the polymer (520 \({}^{\circ}\)C). When the laser power was 600 W, the surface temperature of the polymer layer exceeded the thermal decomposition temperature, and bubbles were observed on the corresponding surface of  
Figure 6: Observed interfacial bonding processes and the corresponding temperature distributions when the interface was filled with polymer: (**a**) laser power 500 W, (**b**) 600 W, and (**c**) 700 W.  
the polymer during the last laser scan. The temperature simulation results showed that the local temperature of the polymer surface exceeded the thermal decomposition temperature at 10.75 s, and the peak temperature of the polymer surface decreased as the laser scanned from the edge to the center. However, from the observations of the corresponding moments, it was found that the bubbles moved toward the laser scanning direction, resulting in the presence of bubbles in all the moments, which may be related to the thermal convection caused by the uneven temperature. Meanwhile, similar fiber position changes were found in the CFRTP composite, but the fiber position change was smaller than the bubbles in the molten polymer because of the constraints from the fiber axial direction. When the laser power was 700 W, as shown in Figure 8, the surface temperature of the thermoplastic polymer exceeded the thermal decomposition temperature at 9.00 s, and bubbles appeared not only on the surface of the molten polymer, but also in the contact area with the CFRTP composite. Since the thermal conductivity of the gas in bubbles was significantly lower than that of the molten polymer, the bubbles reduced the heat transfer from the polymer to the CFRTP composite, which on the one hand made it easier to generate high temperatures in the molten polymer and exacerbated thermal degradation, and on the other hand reduced the number of melted piles of the CFRTP composite, which resulted in a lower number of melted piles than that of the simulation results.
## 3 The Methods  
### Overview  
We propose a Nighttime Road Glare Suppression Network (NRGS-Net) to enhance the visibility and clarity of glare-affected regions in nighttime road scenarios for autonomous driving. As illustrated in Figure 1, NRGS-Net adopts an encoder-decoder structure with an improved Uformer as its backbone. Specifically, we design lightweight downsampling modules (L-Downsampling) and lightweight upsampling modules (L-Upsampling) to preserve spatial details and enable structural restoration with low computational cost. Within the vanilla Transformer blocks, we incorporate a Local Context-Aware Feed-Forward Network (LCA-FFN) to adaptively adjust channel-wise feature weights, effectively suppressing irrelevant feature interference and further recovering structural details in glare-affected regions, with the new block incorporating LCA-FFN referred to as LCAWin-Transformer. Additionally, we also introduce a gated positional attention (GPA) module in the skip connections. It combines positional encoding and local context to guide the network in locating and suppressing glare, improving visibility in affected areas. To facilitate effective learning, paired training data consisting of flare-corrupted images, and their corresponding clean background images and estimated flare images are utilized. This allows the network to learn to separate glare components from the background explicitly during training. The network jointly predicts the restored background and the estimated flare component, with background loss and flare loss used as supervision signals to ensure the effectiveness of glare suppression while preserving scene details. The following sections provide a detailed description of the NRGS-Net architecture and the working principles of each module.
## 5 Experiment Results and Analysis
### Experiment for Generalization in Low-Resource Scenarios (Answer to RQ3)
While the Soft-prompt model shows potential adaptability on large-scale datasets, its effective deployment requires more precise hyperparameter tuning strategies and sufficient training data support. Consequently, future research will explore integratingthe strengths of both approaches by investigating the potential of hybrid-prompt paradigms in sentiment analysis for requirements elicitation from app reviews. Specifically, we will examine how the explicit semantic constraints of Hard-prompts can guide Soft-prompt parameter optimization and how domain adaptation mechanisms may enhance cross-domain transfer performance. This exploration is expected to adjust the balance between the two prompt methods according to task complexity and data characteristics, while preserving model flexibility and robustness in low-resource and cross-domain scenarios.
# A Circularly Polarized Millimeter Wave Radar for Wind Turbine Sensing
## 3 Antenna Design and Experimental Test
### The Experimental Test of Proposed Antenna  
According to the antenna simulation model in Section 3.1, the fabricated physical diagrams of the circularly polarized transmitting and receiving antennas are shown in Figure 8, with dimensions of 43 \(\times\) 18 mm\({}^{2}\) (11.4 \(\lambda_{0}\)\(\times\) 4.8 \(\lambda_{0}\)) and 43 \(\times\) 6 mm\({}^{2}\) (11.4 \(\lambda_{0}\)\(\times\) 1.6 \(\lambda_{0}\)), respectively. Figure 6(a),b present the simulated and measured radiation patterns of the circularly polarized transmitting and receiving antenna arrays. The peak gain of the measured circularly polarized transmitting antenna array is 21.4 dBi, with a sidelobe level of approximately \(-\)18.3 dB. The beam widths of the y'o'z' and x'o'y' planes are \(11^{\circ}\) and \(6^{\circ}\), respectively. For the circularly polarized receiving antenna array, the peak gain is 18.4 dBi, with sidelobe levels lower than \(-\)20.0 dB. The beam widths of the y'o'z' and x'o'y' planes are \(30^{\circ}\) and \(6^{\circ}\), respectively. Despite the inherent deflection angle of the antenna's main lobe, the mechanical down-dip angle of the radar can effectively eliminate any beam shift in the antenna.  
The simulated and measured bandwidths of the circularly polarized transmitting and receiving antennas are illustrated in Figure 10. The simulated and measured S-parameter results are shown in Figure 10a. The measured impedance bandwidths of the circularly polarized transmitting and receiving antennas cover 76-81 GHz, exceeding the 5 GHz bandwidth. In Figure 10b, the measured axial ratio bandwidths of the antennas are 78.5-80.3 GHz and 78-81 GHz, respectively. Within this frequency range, the circularly polarized antenna array exhibits good characteristics of circularly polarization for both transmission and reception.  
Figure 7: Configuration of (**a**) transmitting and (**b**) receiving antenna.
## 5 Automated Processing Procedure
The image on the left shows the boundary of the detected panel and the image on the right shows the binary mask, where the white area shows the surface of the panel. The correction of the perspective of the PV panels is a crucial step, because the correspondence between the pixels and the real positions inside the PV panel can be established. Once the panels are detected and the perspective is corrected, the next step is to independently detect the thermal defects in each panel. More specifically, each panel is treated as an independent image. In this publication, and according to the aforementioned considerations in the previous section, two different types of thermal anomalies are identified: (a) single-cell anomalies, and (b) module or row (sub-string) anomalies. Additionally, and according to the severity, the thermal defects are categorized into: (a) severe failures (differences higher than 30 \({}^{\circ}\)C), (b) medium severity failures (differences from 10 \({}^{\circ}\)C to 30 \({}^{\circ}\)C), and (c) minor failures (differences lower than 10 \({}^{\circ}\)C). This can be seen in the Figure 13. Severe failures are shown in red, medium severity failures are shown in orange, and minor failures are shown in green. The final step of the algorithm is to convert from pixel coordinates to GPS coordinates for every defect found in the image panels. A similar procedure than the algorithm proposed in [29] is carried out. This way, a complete output is achieved containing both the GPS information of (1) every defect, (2) the center of each thermal panel, and (3) the center of the thermal image, and also all the generated images: (1) full thermal image and (2) the image of each thermal panel. The images used for training and adjusting the parameters for the different steps of the algorithm were collected from previous UAV-based field inspections that were performed in two PV plants also owned by TSK. In these previous inspections, another camera was used, but the same GSD was achieved, decreasing the relative altitude of the UAV from the PV panels. More specifically, the FLIR VUE PRO camera was used. This camera is also based on an uncooled VOx Microbolometer providing a resolution of \(640\times 512\) pixels. This camera can be configured with three different lenses (9 mm,  
Figure 12: Perspective correction of the detected panels. Figure 13: Defect detection and categorization based on three classes of severity. 13 mm, 19 mm). In these two inspections, the FLIR VUE PRO 13 mm was used, providing a FOV of \(45\times 37\). The spectral band is also in the range of 7.5-13.5. In total, 500 images were collected with no overlap between them. In these images, a total of 63 hotspots and 15 bypass defects were manually identified. Finally, it should be commented that the acquisition of these images for training and adjusting the algorithms were necessary due to the lack of available public datasets, which is something that previous authors have also stated [30].
## 2 Related Work
### Medical Time Series Analysis  
Medical time series data analysis is an active area of research in clinical research and practice. Some common statistical methods, like autoregressive integral sliding average  
Figure 1: Basic Variational Autoencoder Model, consisting of an encoder and a decoder. The input data is analyzed by the encoder to produce means and variances, and reparameterization is used to obtain latent variables. These latent variables are fed into the decoder to reconstruct the original data, with the aim of minimizing the sum of reconstruction error and KL divergence to optimize reconstruction performance.  
Figure 2: CoTD-VAE is an advanced VAE model tailored for medical time series data. It uses three encoders to disentangled input data into latent variables \(Z_{\text{static}}\), \(Z_{\text{trend}}\), and \(Z_{\text{event}}\). During backpropagation optimization, it adds minimizing trend smoothness loss and event sparsity loss.  
models (ARIMA) [15] and Kalman filtering [16], have been used to study and predict medical data. However, traditional linear models often have problems with modern healthcare big data that has complex nonlinear patterns and long-term dependencies. In recent years, deep learning models have been making progress in medical time series analysis because they can automatically learn features. Recurrent neural networks (RNNs) and their variants, such as long short-term memory networks (LSTMs) and gated recurrent units (GRUs), are now common methods in healthcare time series prediction. They are able to effectively model temporal dynamics in sequence data [17]. Different architectures, such as GRUs, LSTMs, and their bi-directional and multilayered variants, as well as feature-specific networks and target replication strategies, have specific advantages in different scenarios. Convolutional Neural Networks (CNNs) have a lot of potential in the field of recognizing and classifying electroencephalography (EEG) signals. This is expected to provide efficient solutions to practical problems in medical and brain-computer interface systems [18]. A new Transformer model called ETHOS uses a zero-sample learning approach to predict future health trajectories. It analyzes high-dimensional, heterogeneous, and intermittent health data, such as patient health timelines (PHTs) [19]. The Transformer architecture was used to train a large amount of EHR data ahead of time. This was carried out to predict the risk of serious lung problems in patients with SARS-CoV-2. It worked better than traditional machine learning models [20]. These methods have been very successful in improving how well predictions can be made. However, not enough attention has been paid to how easy it is to understand the representations learned by the model. This is especially true when it comes to disentangled latent factors, which is one of the things this study looks at.
## 6 PSK Modulation and Demodulation
### PSK Modulation for Various Stimulator Implants  
A stimulator is an agent that excites some functional activity of the human body. Accordingly, [PERSON] and [PERSON] [70] developed a fully integrated neuromuscular stimulator with a BPSK demodulator using COSTAS loop topology. The demodulator consists of comparator, multipliers, loop filters, VCO and 90\({}^{\circ}\) phase shifter. The BPSK demodulator can be modified as a QPSK demodulator to get double the data transfer rate. These types of demodulators are complex in function. To reduce the complexity of the system and to achieve low power and high data rate transmission, [PERSON] and [PERSON] [71] designed a system with two modulators and two demodulators using OQPSK modulation. The circuit was integrated in 0.18 \(\upmu\)m CMOS technology and operated with 13.56 MHz carrier frequency to achieve 8 Mbps data rates, _i.e._, very fast transmission. The power consumption of the modulators and demodulators is relative low. The BPSK modulation was also used to transmit data and power from the external part to the implant and the load shift keying (LSK) modulation is used to transmit the received data from the implant to the external part [72]. The implanted device consists of a BPSK demodulator that functions as a brain stimulator-based PLL. [PERSON] _et al_. [73] proposed a bio-device for transcutaneous wireless telemetry using the near-field inductive coupling technique with round-wire coils for a cardiac micro stimulator. PSK modulation is used to decode the transmission data.
# Novel Fuzzy Logic Scheme for Push-Based Critical Data Broadcast Mitigation in VNDN
## 4 Proposed Push-Based Data Forwarding Scheme with Fuzzy Logic  
In order to mitigate the critical data packet broadcast storm, we propose a push-based data forwarding scheme using fuzzy logic that comprises several steps.
## 1 Introduction
The incident electromagnetic radiation is collected and subsequently focused by the antenna on the absorber, which then goes through the SIN tunnel junctions. (**b**) The energy diagram shows the main heat flow processes: absorption of a photon by an electron in a normal absorber, thermalization of electrons, and tunneling of hot electrons. The read-out scheme is shown in a current-bias mode. (**c**) AFM image of the CEB showing the normal metal absorber (red), the superconductor (cyan), and the gold electrode, which also serve as the electron traps. The inner and outer SIN tunnel junctions are also shown. The black scale bar on the lower right corner corresponds to a dimension of 1 \(\upmu\)m.  
electron-phonon interactions, where \(\Sigma\) is a coupling constant, \(v\) is the absorber volume, and \(T_{e}\) and \(T_{ph}\) are the electron and phonon temperatures, respectively. The third term is the NEP associated with the SIN tunnel junction and is attributed to the shot noise, heat dissipation through the tunnel junctions, and the correlation between these two processes. Theoretically, the CEB should achieve a NEP of \(10^{-19}\) W/Hz\({}^{1/2}\)[6,18]. The detection of a 1-30 pW signal by a CEB with an NEP of photon noise greater than the CEB NEP has been shown very recently for arrays of CEBs [11,12]. This paper demonstrates a voltage response to power of \(10^{9}\) V/W and a NEP of better than \(10^{-18}\) W/Hz\({}^{1/2}\) at 100 mK in the current-bias mode for a single-pixel CEB.
## 5 Results and Validation
### Validation with Ground Component Temperature Measurements
In addition, there may be errors from the fraction of vegetation cover and neglecting the scale affection in the non-isothermal pixels when using Equation (26).