esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Results and Discussion
### Prediction Error Correction  
Although PSVM has a higher prediction accuracy than the other three MLAs and the two design expressions (see Section 5.2), there is still a certain error between estimated results and measured \(N_{u}\) values. Thus, prediction error correction becomes the key to further improve the prediction  
Figure 7: Coefficient of determination (\(R^{2}\)) of the measured and predicted \(N_{u}\) values in the test set using (**a**) decision tree (DT); (**b**) Gaussian process (GP); (**c**) multiple linear regression (MLR); (**d**) PSVM; (**e**) AIJ 1997 expression; (**f**) GJB 4142-2000 expression.  
performance of PSVM. To this end, a prediction error correction method based on PSVM is proposed. (1) The output variable was changed from \(N_{u}\) to the relative error (abbreviated as \(\xi\)) obtained by subtracting the predicted value from the measured value, and seven input variables \((D,t,L,f_{y},E_{s},f_{c},E_{c})\) remain unchanged. According to Section 4.1, the prediction error dataset was also divided into training set (70%) and test set (30%), and the data order remained the same. (2) According to the parameter optimization process in Section 4.2, when the iteration number is 55, the fitness value reaches the minimum of 0.068, and the optimal parameters, \(C_{best}=16.429\) and \(\gamma_{best}=0.391\), are obtained. (3) The PSVM model with optimal parameters was used to make a prediction of relative error \(\xi\). The predicted \(\xi\) values were added to the corresponding predicted \(N_{u}\) values in the test set (in Section 5.1) to obtain the predicted \(N_{u}\) results after error correction (see Figure 8). It can be found that the \(N_{u}\) value after error correction is closer to the measured value, especially the box selected points in Figure 8. Additionally, the evaluation indexes of corrected \(N_{u}\) values are better than those of original prediction results, as shown in Table 4. The improvement of prediction performance shows that the prediction error correction method is feasible and effective.
# Towards Collaborative and Dynamic Spectrum Sharing via Interpretation of Spectrum Access Policies
## 1 Introduction
One way to achieve this is to express policies in a formal declarative language with formal syntax and semantics and incorporate a policy interpreter that can interpret policies for as long as they are expressed in that language. There are two kinds of knowledge that needs to be represented using such a formal declarative language: (1) the shared concepts between radios and networks; (2) the policies that are used to control the behavior of the radio. The shared concepts between radios and network can be defined in common ontologies encoded in a (formal) ontology representation language, such as Web Ontology Language (OWL) [4]. In artificial intelligence, computer science and information science, the term \"ontology\" refers to a formal, explicit specification of a set of concepts in a specific domain and relationships between these concepts. The term \"formal\" means that the ontology is \"machine processable for the purpose of knowledge reuse and sharing\" [5]. In this paper, we describe policies that are represented in a language that uses ontological concepts. Figure 1: A cognitive radio in context.
# A Light-Weighted Machine Learning Approach to Channel Estimation for New-Radio Systems
## 3 Preliminaries on Channel Estimation
### Existing ML Method  
#### 3.2.1 Structure  
To compensate for the shortcomings of existing channel estimators, ML-based channel estimation that performs as well as the MMSE method without utilizing the statistical information of the channels is being studied. Figure 3 shows the simplest form of the ML method [7] among the existing ML methods used to compare with the proposed method in this paper. The ML-aided channel estimation is designed to minimize the MSE between the actual channel and the estimated channel obtained by the LS method to overcome the LS and MMSE methods, and the number of nodes in the input layer and output layer is \(2N\). The number of hidden layers is 3, and the number of nodes in the hidden layers is designed to be \(4N\). In detail, the input data use the estimated CFR \(\hat{\mathbf{G}}_{\text{LS}}\) by the LS method, and when these input data are input to the ML, the ML outputs a new estimated CFR \(\hat{\mathbf{G}}_{\text{ML}}\) corresponding to \(\hat{\mathbf{G}}_{\text{LS}}\). Since the ML does not accept complex numbers, \(\hat{\mathbf{G}}_{\text{LS}}\) must be input by dividing it into real and imaginary numbers and \(\hat{\mathbf{G}}_{\text{LS}}\) is also output divided into real and imaginary numbers as follows  
\[\mathbf{X}_{\text{in},d}=\begin{bmatrix}Re(\hat{\mathbf{G}}_{\text{LS},d}(0)) \\ Im(\hat{\mathbf{G}}_{\text{LS},d}(0))\\ \vdots\\ Re(\hat{\mathbf{G}}_{\text{LS},d}(N-1))\\ Im(\hat{\mathbf{G}}_{\text{LS},d}(N-1))\end{bmatrix},\]  
\[\mathbf{Y}_{\text{out},d}=\begin{bmatrix}Re(\hat{\mathbf{G}}_{\text{ML},d}(0) )\\ Im(\hat{\mathbf{G}}_{\text{ML},d}(0))\\ \vdots\\ Re(\hat{\mathbf{G}}_{\text{ML},d}(N-1))\\ Im(\hat{\mathbf{G}}_{\text{ML},d}(N-1))\end{bmatrix}, \tag{8}\]  
\[d=\{1,\ \ldots\,\ D\},\]  
where \(N\) is the length of the pilot signal, and \(\mathbf{X}_{\text{in},d}\) and \(\mathbf{Y}_{\text{out},d}\) are the input and output for the \(d\)-th data, respectively. Since the \(\mathbf{Y}_{\text{out},d}\) output by the ML is divided into real and imaginary numbers, it is converted to a complex number to obtain \(\hat{\mathbf{G}}_{\text{ML},d}=[\hat{G}_{\text{ML},d}(0),\ \ldots,\ \hat{G}_{\text{ML},d}(N-1)]^{T}\).
# AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
###### Abstract  
Lung cancer is a deadly cancer that causes millions of deaths every year around the world. Accurate lung nodule detection and segmentation in computed tomography (CT) images is a vital step for diagnosing lung cancer early. Most existing systems face several challenges, such as the heterogeneity in CT images and variation in nodule size, shape, and location, which limit their accuracy. In an attempt to handle these challenges, this article proposes a fully automated deep learning framework that consists of lung nodule detection and segmentation models. Our proposed system comprises two cascaded stages: (1) nodule detection based on fine-tuned Faster R-CNN to localize the nodules in CT images, and (2) nodule segmentation based on the U-Net architecture with two effective blocks, namely position attention-aware weight excitation (PAWE) and channel attention-aware weight excitation (CAWE), to enhance the ability to discriminate between nodule and non-nodule feature representations. The experimental results demonstrate that the proposed system yields a Dice score of 89.79% and 90.35%, and an intersection over union (IoU) of 82.34% and 83.21% on the publicly available LUNA16 and LIDC-IDRI datasets, respectively.  
artificial intelligence; computer-aided diagnosis; computed tomography; lung cancer; deep learning; lung nodule detection; lung nodule segmentation
## 3 Chaos Oscillator Synchronization Noise Immunity  
This study investigates the noise immunity of chaotic synchronization for both the Colpitts and Vilnius chaos oscillators. Different noise levels and circuit configurations are considered for synchronization performance assessment. Knowing how synchronization works in noisy environments is important for utilizing it in secure communication and signal processing.
## 4 Solution Problem
### Load Prediction Algorithm (LPA)
#### 4.1.1 Generalized Regression Neural Networks  
The GRNN proposed in [46] is based on a standard statistical method called Gaussian kernel regression. The main difference is that it is based on nonparametric regression, while the artificial neural network (ANN) is based on parametric regression. It also differs in the number of layers, which in the GRNN model is divided into four: input layer, default layer, summation layer, and output layer, where the last layer generates the predicted value corresponding to an unknown input vector. At the same time, the ANN model has three layers in a row: input layer, hidden layer, and output layer. The input layer of the GRNN model, which is known as the distribution unit and stores the information as vector X, is fully connected to the standard layer, which passes the information and uses the Gaussian activation function to calculate an output for the next sum layer.  
The sum layer receives the data from the standard layer to perform the appropriate processing. In this layer, there are two summations to smooth the output of the model. The first sums all the outputs of the model layer and the second calculates the weighted sum of the outputs of the model layer, where there is a connection weight between the layers to adjust the calculation. Finally, the output layer calculates the output Y of the GRNN modelby dividing the outputs of the summation layer. This model has been widely studied for predictions, as in [47; 48; 49]. The architecture of the GRNN model is shown in simplified form in Figure 3. The main advantages of the model are fast learning and convergence to an optimal regression value as the number of samples increases. Working with real-time data is particularly advantageous since the regression result is determined immediately after the process is started [50].
### Conceptual Comparison with Representative State-of-the-Art (SOTA) Techniques  
Our study focused primarily on validating the effectiveness of masking and reconstructing DMT signals using a deep learning-based self-supervised model, specifically a transformer-based MAE, which is a novel approach in the context of optical wireless Li-Fi systems. As such, there are few existing works that employ the same kind of signal-level reconstruction via learned masking strategies, especially in the time domain of DMT signals.  
In contrast, many SOTA Li-Fi enhancement techniques focus on  
* Hardware improvements, such as LED driver optimization or photodiode sensitivity enhancement.
* Equalization/filtering methods, such as RLS/FIR/DFE adaptive filters.
* Modulation techniques, like CAP, OFDM with high-order QAM, or VLC-optimized schemes.  
These methods, while effective, differ fundamentally in their domain of operation (e.g., analog front-end, frequency-domain digital processing) and are not directly comparable at the waveform compression-reconstruction level, which is the key innovation of our method.  
Table 2 shows the conceptual and qualitative comparison below, focusing on performance attributes.  
Our method supports waveform compression up to 85% masking, which traditional methods do not address.
* Unlike FNN/CNN models that are limited to local features or final-stage demapping, the MAE captures long-range temporal dependencies and reconstructs full time-series waveforms.
* Our approach also works without requiring strict channel models or Nyquist compliance, which many traditional DSP techniques depend on.  
The remainder of this paper is organized as follows. Section 2 explains how the proposed sparse coded masked model encodes by masking the DMT signal and then decodes to recover it. Section 3 describes the implementation of the Li-Fi optical wireless link used to experimentally verify the proposed technique. Section 4 presents various experimental results on the transmission performance of the proposed technique measured  
through the implemented Li-Fi optical wireless link. Finally, Section 5 provides a summary and conclusion.
## 3 Results
### Fourier Transform Infrared (FTIR) Spectrum  
The chemical structure of the films was analyzed using Fourier Transform Infrared (FTIR) spectroscopy. Figure 3 presents the FTIR results. The FTIR spectrum of the P.P film reveals a significant absorption band at approximately 3270 cm\({}^{-1}\), attributed to the stretching vibrations of OH groups, indicative of the presence of hydroxyl functionalities. Furthermore, characteristic absorption bands at 1727 cm\({}^{-1}\) and 1620 cm\({}^{-1}\) are clearly observed and correspond to the carbonyl (C=O) and carbon-carbon double bond (C=C) vibrations, respectively. These bands highlight the presence of anthocyanins within the P.P film [29]. Additional notable absorption features are present at 1210 cm\({}^{-1}\), associated with C-O bond vibrations characteristic of anthocyanins, and at 995 cm\({}^{-1}\), linked to C-O-C ring vibrations from starch content present in the pure P.P material [30].  
Upon blending P.P with PVA to form the P.P-PVA film, spectral contributions from both individual components become clearly evident. This composite material exhibits characteristic PVA absorption bands at 1420 cm\({}^{-1}\), associated with CH bending vibrations, 1318 cm\({}^{-1}\) corresponding to CH deformation, 1080 cm\({}^{-1}\) related to CO stretching vibrations, and 840 cm\({}^{-1}\) linked to C-C group vibrations [31; 32]. These bands confirm the successful integration of both polymeric materials into a homogenous blend.  
Figure 3: FTIR spectra for pure PVA, pure P.P, P.P film, P.P–PVA film, and P.P–PVA–AgNP films.  
It is noteworthy that the FTIR spectrum of the P.P-PVA-AgNP film closely resembles that of the P.P-PVA film. This similarity is attributed to the inert nature of silver nanoparticles (AgNPs), which, being metallic, do not possess functional groups capable of producing distinct bands in FTIR spectroscopy [33].
## 2 AR/VR Applications
### Healthcare  
AR/VR is gaining popularity in the healthcare sector, and the market for AR/VR in healthcare isexpected to reach nearly USD 9.7 billion in value in the next 5 years [36]. The usage of HMDs, such as goggles or headsets, enables users to interact with a computer-generated environment responsible for stimulating multiple sensory modalities, including visual, auditory, or haptic experiences [37]. The introduction of an entirely new perspective in AR/VR in the practice of medicine has enabled a systematic strategy for training physicians and other medical professionals. Medical professionals highly benefit from the AR/VR training procedure because it augments their ability to practice medicine through telehealth and telemedicine. The trainee can gain surgical experience using a VR surgical simulation system [37; 38] that generates the scenario of performing an actual operation and is highly proven to reduce the error during an actual operation in the future. With the steady growth of three-dimensional avatars in the VR industry, they can be used to represent three-dimensional stereoscopic visual effects to gain an understanding of the positional relationship between objects and simulations. They can simulate the process of intravenous injection to deliver knowledge related to human anatomy [39] to medical professionals or students. Recently, the Autism Glass Project has been initiated by the Medical School of Stanford University, which uses the Google Glass AR technology to help interpret the emotions of children with autism to help them develop normal social relationships [40]. While performing surgery or during any severe injuries, pain can be controlled by a less complicated adjunctive pain control mechanism performed in the clinic on an outpatient basis provided by VR [41]. These AR/VR techniques rely on cognitive-behavioral techniques by generating VR environments to relieve pain [42].
## 2 DQL Method  
In this section, we introduce the framework of the DQL method. At each iteration, the method starts from an initial search point \(x_{0}^{k}\) and a search step length \(\delta^{k}\). Note that at the first iteration, the initial search point and the search step length are given by the inputs \(x_{0}\) and \(\delta_{0}\), so \(x_{0}^{1}=x_{0}\) and \(\delta^{1}=\delta_{0}\). The initial search point and the search step length are used to initiate three distinct search steps: the direct step, the quadratic step, and the linear step. A variable \(x_{\text{best}}^{k}\) is used to track the current best solution at iteration \(k\). If an improvement is found in the search step length at iteration \(k\), the method updates \(x_{\text{best}}^{k}\). Three Boolean values are used to track the results from each search step: Direct_Flag, Quadratic_Flag, and Linear_Flag. If a search step succeeds at finding an improvement, it sets the corresponding Flag to true; otherwise, the corresponding Flag is set to false. These search steps are then followed by the update step. In the update step, the search step length is updated according to the search results and the method uses the current best solution as the starting search point of the next iteration.  
As mentioned, the DQL method utilizes two different stages: the exploration stage and the convergence stage. In the exploration stage, the method enables all the search steps, and it accepts the improvement whenever the evaluation candidate yields a lower value than the current best solution. If the iteration counter \(k\) reaches the given max_search, then the method proceeds with the convergence stage, the method disables the quadratic and the linear step and the solution acceptance implements a sufficient decrease rule.  
This framework allows various search strategies to be implemented. We provide some basic strategies for performing the search steps. The analysis of the convergence and the performance of these search steps are discussed in the next section.
## 4 Discussion
### The Critical Load Factor of Linear and Nonlinear Thermal Buckling  
In eigenvalue buckling analysis, the Nth-order buckling load factors are extracted. The equation is concretely expressed as:  
\[\begin{array}{l}P_{f}=\lambda_{cr-linear}P_{cr-linear}\\ P_{L}=\lambda_{cr-nonlinear}P_{cr-nonlinear}\end{array} \tag{17}\]  
Here, \(\mathbf{P_{cr}}\) is represented as the thermal buckling load, \(\lambda_{cr}\) is set as the load factor and \(\mathbf{P_{C}}\) is the calculated load. For linear and nonlinear buckling analysis, the first four different load factors are extracted and defined as \(P_{cr-linear}\), \(P_{cr-nonlinear}\) and \(\lambda_{cr-linear}\), \(\lambda_{cr-nonlinear}\). The load factors of the first four orders of blades are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order** & **1** & **2** & **3** & **4** \\ \hline critical load factor \(\lambda_{cr-linear}\) & 1.6067 & 2.5933 & 2.9089 & 3.2833 \\ \hline critical load factor \(\lambda_{cr-nonlinear}\) & 1.3316 & 2.1233 & 2.2631 & 2.6947 \\ \hline \hline \end{tabular}
\end{table}
Table 3: The critical load factor of the first four orders.  
Figure 15: The radial distribution buckling deformation along the blade. (**a**) The radial distribution along the blade; (**b**) the axial distribution along the blade.  
In the first order response, either linear buckling \(\lambda_{\sigma-\text{linear}}\) (\(\sim\)1.6067) or nonlinear buckling \(\lambda_{\sigma-\text{linear}}\)(\(\sim\)1.3316), are both larger than 1, which is a direct illustration of \(\mathbf{P_{f}}>\mathbf{P_{cr-linear}}\) and \(\mathbf{P_{L}}>\mathbf{P_{cr-nonlinear}}\). It is also verified that critical temperature-rise load will be greater than the maximum calculating load. Therefore, in linear thermal buckling, the blade does not lose stability under thermal shock load, and the structure also meets the stability requirements. Meanwhile, it is confirmed that the critical thermal buckling load is greater than the maximum load borne by the blades. Overall, the load factor \(\lambda_{\sigma}\) at each order are different, as depicted in Figure 16.  
Hence, the results of linear buckling are often used as critical reference values, while nonlinear buckling is more suitable for accurate results. Compared with the conclusions in Ref. [15], under thermal shock load, obvious torsional deformation and large radial deformation of the blade will increase the probability of thermal buckling instability, resulting in an important impact on the rub-impact response of blade-casing structure.
# An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
## 6 Conclusions  
In this paper we have presented a new scheduler, SOVANET, to address V2N communication for critical automated driving services of high SAE automation driving level. The difference between SOVANET compared to other proposed schemes for autonomous driving is twofold:  
* SOVANET takes into consideration real-time information about the load on both the UL and DL and balances the resources so that the performance on the uplink and the downlink are equally balanced
* SOVANET considers each session's needs and over-provides resources for the critical services while reducing corresponding resources for non-critical services.  
As we have presented, through extensive simulation results, SOVANET outperforms the compared baseline PF scheduler. It is worth noting that SOVANET offers more significant gains in the UL case. Considerable gains are also present on the DL when the network is overloaded with many vehicles and their services.  
In very congested scenarios, meeting the strict requirements of critical services is still difficult. Thus, further cross-layer solutions that will allow some adaptability of the V2N applications need to be developed. As future steps, we plan to evolve SOVANET further to identify a more adaptive calculation of the alpha and beta weighting factors using different simulation environments and a wider plethora of critical and non-critical applications. This will further improve the evaluation of the related KPIs (e.g., throughput, delay, etc.) in more realistic scenarios. Finally, as future networks are expected to operate in a dense radio access network environment, we are planning to extend the SOVANET algorithm to support smooth handovers.  
Figure 8: BSM DL mean throughput and delay.  
**Author Contributions:** Conceptualization, A.K. ([PERSON]); methodology, A.K. ([PERSON]); software, A.K. ([PERSON]); validation, A.K. ([PERSON]) and S.B.; formal analysis, A.K. ([PERSON]) and S.B.; investigation, A.K. ([PERSON]); data curation, A.K. ([PERSON]); writing--original draft preparation, A.K. ([PERSON]); writing--review and editing, A.K. ([PERSON]) and S.B.; visualization, A.K. ([PERSON]); supervision, A.K. ([PERSON]); project administration, A.K. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Data Availability Statement:** The data used for supporting the reported results can be found upon request to the corresponding author.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Effect Mechanism of Material Ratio on Ultrasonic P-wave Velocity in Coal Based Paste Fill Materials
## 2 Experimental Procedures
### Characteristics of Experimental Raw Materials  
#### 2.1.1 Coal Grains  
Underground raw coal, retrieved from the Shuiliandong coal mine, serves as the primary experimental substance. It undergoes elementary crushing and screening before being amalgamated with a high-water substance and cement to fabricate the coal based paste fill material. The utilization of raw coal as a filler is selected to capitalize on resource efficiency and preclude wastage. Furthermore, when coal particles are employed as the principal component, procurement expenditures are diminished, particularly in mining operations where gangue is deficient. Moreover, the coal utilized in the fill process is reclaimed through secondary mining post-fill, thereby eliminating resource wastage. The properties of coal are listed in Tables 2 and 3.  
The data in Table 4 present the relative contents of various main minerals in coal samples. The analysis of these elements is crucial for comprehending the coal grains' material characteristics and their subsequent application in industrial production.