esa-sceva/satcom-chunk-collection huggingface dataset:
# Public Key-Based Authentication and En-Route Filtering Scheme in Wireless Sensor Networks
## 2 Related Works
However, ECDSA produces a 40-byte signature, resulting in large message payload. An ECPVS signature scheme [21; 22] can offer smaller signature size than ECDSA. Besides, the proposed scheme is vulnerable to report disruption attacks and selective forwarding attacks. [PERSON] et al. [1] proposed an enhanced and efficient cluster-based authentication protocol using digital signature rather than message authentication codes (MACs), called DSEDA. DSEDA can avoid false information at intermediate nodes, and can guarantee end-to-end data authentication with the aid of a digital signature. Besides, a report collection mechanism was proposed to filter out bogus reports as soon as possible, and a mechanism to compensate lack of legitimate secret shares was proposed to enhance data availability. However, if a CH node is compromised, it can generate false reports with legitimate signatures, which can pass the en-route filtering. [PERSON] et al. [18] presented an energy-aware routing and filtering node (ERF) selection scheme for commutative cipher-based en-route filtering (CCEF). First, the keys are disseminated to only some intermediate nodes using a probabilistic method. Then, a path from the BS to the selected CH is established with consideration of distance and energy in all sensor nodes. When a CH receives a query from the BS, it creates the final report which contains all the MACs from the participating nodes, and transmits the report to the BS. The filtering nodes, which are selected based on attack information, presence of witness key and current state of a node, verify the MACs using the property of commutative ciphers. The proposed approach can significantly prolong the network lifetime by balancing network energy consumption. However, it is vulnerable to report disruption attacks and selective forwarding attacks. In summary, the existing PKC-based schemes have two major drawbacks: one is that they are vulnerable to report disruption attacks or selective forwarding attacks; the other is that they do not provide any measure to detect and punish the malicious nodes. To overcome these drawbacks, a public key-based authentication and en-route filtering scheme (PKAEF) is proposed in this paper, which can resist multiple types of attack such as false data injection attack, report disruption attack, and selective forwarding attack. Moreover, a trust value-based mechanism for detecting and punishing the malicious nodes is proposed, which can mitigate the impact of malicious nodes.
# Entity Relationship Extraction Based on a Multi-Neural Network Cooperation Model
## 3 Proposed Method
### Loss Function  
The loss function was used to calculate the difference between the predicted result and the real value. The model should use the cross-entropy function to determine the proximity between the actual output and the expected output. Its calculation formula is as follows, where p is the result of the model output and q is the label corresponding to the data.  
\[\mathrm{loss(p,q)}=-\log(\frac{\exp(\mathrm{p[q]})}{\sum_{i}\exp(\mathrm{p[q ]})}) \tag{11}\]
###### Abstract  
Recently, an orthogonal frequency division multiplexing (OFDM) technique for the infinite impulse response (IIR) channel (IIR-OFDM) was proposed, which carries the dedicated guard interval to maintain the circular convolution of the received signal and channel coefficients. Therefore, the loop of an IIR channel can be converted to the frequency domain, and single-tap equalization can still be used to equalize loop interference, like classical OFDM. In this paper, we describe how to build the IIR system based on the channel with loops and derive the properties of the dedicated guard interval for a general multi-order IIR channel, which is different from the classical cyclic prefix (CP) obtained by replicating the samples at the tail end of the signal. In particular, we address two special models for first-order and delay IIR channels. It is demonstrated that the guard interval composition and power characteristics of the two special models are similar. Moreover, the complexity of the guard interval depends not only on the maximum delay of the loop, but also on the number of loops. Finally, we simulate the IIR-OFDM performance under different IIR channels.  
orthogonal frequency division multiplexing (OFDM); loop interference; single-tap equalization; infinite impulse response (IIR) 202413390/electronics31224519
## 3 Results and Discussion
### Experimental Validation on the Predicted Optimal Setting  
Equation (8) was used to predict the GRG before the validation experiment conducting under the aforementioned processing parameters:  
\[\text{GRG}_{\text{prediction}}=\overline{\text{GRG}_{\text{total}}}+\sum_{\text{ j=1}}^{\text{q}}\left(\overline{\text{GRG}_{\text{j}}}-\overline{\text{GRG}_{\text{total}}}\right) \tag{8}\]  
where \(\text{GRG}_{\text{prediction}}\) is the predicted GRG; \(\overline{\text{GRG}_{\text{total}}}\) stands for the mean of total GRG; \(q\) is the total number of processing parameters, which is four in this study; \(\overline{\text{GRG}_{\text{j}}}\) is the average GRG value at the desired level for the \(j\)th processing parameter. After calculation, the predicted GRG at the desired level of processing parameters is found to be 1.  
Afterwards, the validation experiment was conducted under the predicted optimal set point. The derived micro-hardness, wear volume and GRG are shown in Table 13. Compared to the best experiment setup in the original L16 Taguchi orthogonal design (6 th run), the micro-hardness was improved from 66.3 HRC to 68.9 HRC, which was 3.92% improvement. Similarly, the wear volume was reduced from 0.0003904 mm\({}^{3}\) to 0.0003121 mm\({}^{3}\), which was 25.09% improvement. The error rate between predicted GRG and experimental validated GRG was 5.3%. This result indicates the processing parameter set obtained from grey relation analysis bring the improvement on both responses of micro-hardness and wear resistance. In addition, the prediction of grey relation grade also displays favorable accuracy.
## 2 Materials and Methods
### Evaluation  
SSIM (Structural Similarity Index Measure) and PSNR (Peak Signal-to-Noise Ratio) were used to objectively evaluate image quality in rectangular and polar-transformed spectrograms. SSIM assesses image similarity by evaluating luminance, contrast, and structural similarity between two images [40], with values closer to 1.0 indicating greater similarity. PSNR calculates the mean square error between two images [40], with higher PSNR values indicating better image quality. For calculating SSIM and PSNR for images sized 128 \(\times\) 128 and 96 \(\times\) 96, we used the 224 \(\times\) 224 images as reference images. A two-sample unpaired \(t\) test was performed to determine if the rectangular and polar SSIM or PSNR values are significantly different.  
Deep learning training was conducted on a Windows PC (12 th Gen Intel(r) Core(tm) i9-12900K, 32 GB RAM (Intel, Santa Clara, CA, USA), and NVIDIA GeForce RTX 3080 with 10 GB memory (Nvidia, Santa Clara, CA, USA)). For each deep learning model, we compared two schemes: (A) rectangular spectrograms and (B) polar-transformed spectrograms, each processed by the P-T method. We tested three prediction methods: MobileNet, ResNet50, and DenseNet121, with final predictions based on soft-voting across five-fold cross-validation.  
The Scikit-learn library was used to calculate \(F1\)-score, precision, recall, accuracy, and confusion matrices [41]. For a given class \(c\), precision (\(P_{c}\)), recall (\(R_{c}\)), and \(F1\)-score (\(F1_{c}\)) are defined as follows:  
\[P_{c}=\frac{TP_{c}}{TP_{c}+FP_{c}} \tag{1}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Class** & **Model Development** & **Test** \\ \hline Atrial fibrillation (Afib) & 409 & 90 \\ Normal sinus rhythm & 2924 & 754 \\ Other rhythm & 1352 & 323 \\ Noise & 96 & 29 \\ \hline Total & 4781 & 1196 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Number of recordings for model development and test.  
\[R_{c}=\frac{TP_{c}}{TP_{c}+FN_{c}} \tag{2}\]  
\[F1_{c}=\frac{2\times P_{c}\times R_{c}}{P_{c}+R_{c}} \tag{3}\]  
\(TP_{c}\), \(FN_{c}\), and \(FP_{c}\) represent the number of true positives, false negatives, and false positives for class \(c\), respectively.  
Since our study deals with a multi-class classification problem, we adopted macro-average calculations for the evaluation. The noise class was excluded for the calculation of \(F1\)-score according to the PhysioNet/CinC Challenge 2017 guidelines. Thus, we calculated the macro \(F1\)-score as follows:  
\[F1=\frac{F1_{N}+F1_{A}+F1_{O}}{3}. \tag{4}\]  
The accuracy score was calculated as:  
\[\text{Acc}=\frac{\text{Number of correct predictions}}{\text{Total number of samples}}. \tag{5}\]
# Prediction of Carbonate Reservoir Porosity Based on CNN-BiLSTM-Transformer
## 4 Discussion
### Model Application Evaluation  
In this study, Well M149 was selected as an example to analyze the practical application of the CNN-BiLSTM-Transformer model and compare its performance with other models (Figure 10). As shown in Figure 10. the figure consists of nine tracks. The first four tracks display depth and well logging curves, while tracks five to nine present the porosity predictions of different models alongside core physical analysis results. The CNN-BiLSTM-Transformer model effectively integrates neighborhood information and curve features to comprehensively predict porosity. As a result, its prediction error remains the most stable and exhibits the least fluctuation across all depth intervals, indicating that the CNN-BiLSTM-Transformer achieves the best prediction performance and the strongest generalization ability among the compared models. The results demonstrate that the CNN-BiLSTM-Transformer achieves the lowest prediction error and the most stable porosity estimates across different depth intervals. By leveraging neighborhood information and temporal features from well logging curves, this model enables accurate porosity prediction across the entire well section.  
Figure 10: Prediction effect comparison histogram.
# Packet-Forwarding Algorithm in DTN Based on the Pheromone of Destination Node
###### Abstract
The remainder of this paper is organized as follows: Section 2 discusses related works; Section 3 describes the proposed network model and its analysis; Section 4 provides the designed packet-forwarding algorithm based on community structure; Section 5 presents the experimental results and discussion; and, finally, Section 6 concludes this study.
# Multi-Objective Optimization Study of Annular Fluid Flow Structure in Cordless Core Drilling Tools
## 2 Structural Design and Principles of Hydraulic Lifting Cordless Coring Drilling Tool
### Numerical Simulation Model of Spearhead and Spool Flow  
In the positive circulation process, the mud flows out through the mud pump, flows into the inlet of the spearhead through the internal channel of the drill pipe, and then enters the inlet of the spearhead to form the back pressure through the internal flow channel, so that the spearhead moves to form the path. In this process, the loss along the way and the internal energy loss of the spearhead are taken into account. Ansys Fluent can accurately simulate laminar flow, turbulence, chemical reactions, multiphase flow, and other complex flow phenomena because of its rich model, powerful processing capability, and software compatibility. The establishment of the spearhead and spool fluid simulation model is shown in Figure 2.  
By using the FLUENT Meshing pre-processing tool, the extracted flow channel is meshed. In numerical simulation, the mesh not only serves as the carrier of the finite element volume method but also as the geometric representation of the analytical computational fluid dynamics model. The mesh can be divided into two types. One is unstructured mesh, and the other is structured mesh. As the flow rules of the internal flow field of the flow channel are chaotic and dominated by turbulent flow, the mesh is mainly divided into unstructured meshes in this simulation. In finite element calculation, the quality of mesh division has a significant influence on the accuracy of the calculation results and the  
Figure 2: Fluid simulation model of spearhead and spool valve. (**a**) Spearhead and spool assembly diagram annular liquid flow structure. (**b**) Mesh delineation model of the flow channel of an annular liquid flow structure.  
efficiency of the calculation. A triangular face mesh is used to mesh the flow channel. The boundary expansion is added, and the number of layers is 5. To complete the mesh division of the flow channel model, the triangular face mesh structure is divided into a total of 83,193. The steady-state solution is selected, and the k-epsilon double equation model (k-\(\varepsilon\) model) is chosen as the turbulence model, where \(k\) is the turbulent pulsation kinetic energy. The larger the k, the higher the length and time scale of the turbulent pulsation. The energy dissipation rate of the pulsation is expressed in terms of the energy diffusion coefficient \(\varepsilon\), with a larger \(\varepsilon\) indicating a smaller length and time scale of the turbulent pulsation.
# State Parameter Fusion Estimation for Intelligent Vehicles Based on IMM-MCCKF
## Abbreviations  
\begin{tabular}{l l} CKF & Cubature Kalman filter \\ EKF & Extended Kalman filter \\ MCC & Maximum correntropy criterion \\ MCCKF & Maximum correntropy criterion Kalman filter \\ UKF & Unscented Kalman filter \\ \(F_{xij}\) & Tire longitudinal force \\ \(F_{yji}\) & Tire lateral force \\ \(t_{w}\) & Track width \\ \(m\) & Curb weight \\ \(V_{x}\) & Longitudinal speeds \\ \(V_{y}\) & Lateral speeds \\ \(\lambda_{ij}\) & Longitudinal slip ratio \\ \(\delta\) & Front wheel angle \\ \(\mu\) & Road adhesion coefficient \\ \(v_{\alpha ij}\) & Wheel center velocity \\ \(C\) & Tire stiffness \\ \(\alpha_{ij}\) & Tire slip angle \\ \(B_{fr}\) & Wheelbase \\ \(x(k)\) & The state vector \\ \(z(k)\) & The measurement vector \\ \(V(P,Q)\) & Correntropy \\ \(\theta(P,Q)\) & Kernel function \\ \(\omega_{i}\) & Weight factor \\ \(Q_{(k-1)}\) & Process noise covariance matrix \\ \(R_{k}\) & Measurement noise covariance matrix \\ \(\chi_{k}\) & Error term \\ \(W_{k}\) & Weight matrix \\ \(H_{k}\) & Observation matrix \\ \(G_{\tau}\) & Kernel function \\ \(\overline{c}_{j}\) & Normalization factor \\ \(p_{ij}\) & Transition probability \\ \(\varepsilon\) & Threshold \\ \(\tau\) & kernel bandwidth \\ \end{tabular}
## 2 Principles and Methods  
### Beam Misalignment Principle of the Optomechanical System  
The misalignment principle diagram of the spatial filter and its supporting truss coupling Optomechanical system is shown in Figure 1. Figure 1a shows the torsion posture of the integrated support ANSYS. When the response of the Optomechanical structure is inconsistent under the excitation or the rigid center of the supporting structure does not coincide with the mass center, the optical axis of the spatial filter under the integrated support will deviate. Figure 1b shows the posture of the split support torsion pendulum. When the lens offset of the two support trusses in the \(x\) direction is different, the optical axis of the spatial filter under the split support will also deviate.  
To obtain the minimum amplitude of the output light angle, the ideal situation is to install the spatial filter on the integrated support truss, as shown in Figure 1a. When the spatial filter is long, torsion will also appear when it is placed on the integrated support truss. It is also reasonable to install the spatial filter on two identical split support trusses, as shown in Figure 1b, considering the factors of duty cycle and rationality of experimental layout. When installing a long optical path spatial filter in the laboratory, two identical trusses are usually used to support a confocal lens spatial filter system.  
When the excitation amplitude, frequency, or phases of the two support trusses are different, the translational modal response of the space filter support trusses in \(x\) direction is different. The system optical axis deviates from the static optical axis, and the system optical axis and the static optical axis are distributed at \(\theta\) angle, which will cause the beam transmission maladjustment. Therefore, it is significant to study and analyze the convergence of beam pointing offset response caused by different amplitude, frequency, or phase excitation when the spatial filter system is placed on two identical support trusses.
### SEVIRI's Daily Omission Rate  
Omission rates presented in Table 2 indicate the ability of SEVIRI to detect active fires in the same location and at the exact same time as MODIS. A similar routine was used here instead to assess the ability of SEVIRI to detect fires at the same location and on the same day as MODIS. Re-evaluating SEVIRI's active fire detection performance on a daily basis (_i.e._, using all 96 SEVIRI observations and all four MODIS observations per day) revealed that SEVIRI's daily omission rate in ROIs 1, 2, 3, and 4 decreases to 3%, 12%, 31% and 41%, respectively. These results quantitatively affirm that although SEVIRI may fail to detect a fire concurrent with a MODIS overpass, at some time during the day the observation conditions may improve or the same fire may become powerful enough to trigger the FTA detection algorithm.
# Resource Allocation to Massive Internet of Things in LoRaWANs
## 3 Link-Level Model
### Link Performance Model  
The link performance model targets abstraction of the real operation of the physical layer. More precisely, the model summarizes the performance of the Semtech SX1301 [44], SX1272 [45] chips commonly used in GW and EDs, respectively. The link performance model emulates their parallel decoding, sensitivity, and interference resistance performances.
## 2 Finite Element Model
### Seismic Wave Delay and Amplification  
As a large-span structure, researchers should consider the impact of traveling wave effects when studying the original structure. Therefore, it is necessary to set a reasonable time difference for the seismic wave input at different supports to simulate the traveling wave effect when calculating the seismic response. Based on the data obtained from the geological survey of the building site, the equivalent shear wave velocity of the original structure building site was calculated as \(V_{se}=339\) m/s through the weighted average value. Then, the time difference between the seismic wave reaching each support was calculated (Figure 5). According to the calculation results, the time difference in seismic waves can be taken as 0.4 s. In detail, the support of the steel truss structure at the hillside is delayed by 0.4 s compared to the bottom support of the shear wall structure to input seismic waves when calculating the seismic response.  
Figure 4: Perspective view of the structure.  
Figure 5: Structural supports. The red box represents a group of supports at the same elevation.  
Since the original structure is built against a mountain and the slope and height of the mountain where the steel truss structure is supported are substantial, researchers should consider the amplification effect of the mountain on seismic waves. According to the relevant regulations in the Code for Seismic Design of Building (GB50011-2010) [24] and engineering experience, the amplification factor of seismic wave acceleration could be determined as 1.6. In other words, the peak value of acceleration is amplified by 1.6 times after a series of actions when transmitted to the slope top by the seismic wave input at the slope bottom.
# Machine Learning in Beyond 5G/6G Networks--State-of-the-Art and Future Trends
###### Abstract  
Artificial Intelligence (AI) and especially Machine Learning (ML) can play a very important role in realizing and optimizing 6G network applications. In this paper, we present a brief summary of ML methods, as well as an up-to-date review of ML approaches in 6G wireless communication systems. These methods include supervised, unsupervised and reinforcement techniques. Additionally, we discuss open issues in the field of ML for 6G networks and wireless communications in general, as well as some potential future trends to motivate further research into this area.  
6G; wireless communications; artificial intelligence; machine learning +
Footnote †: journal: _electronics_  
0000-0001-6000-0000][PERSON]  
1,* *  
communication in higher frequency bands (THz and mm-wave) [5]. IRS emerges as a key technology in future 6G networks. IRS receives a signal from the base station (BS), and reflects the signal with induced phase changes, which are adjusted by a controller. The reflected signal can be added coherently with the signal from the BS to either boost or attenuate the overall signal at the receiver. IRS may not amplify the signal power without power but has minimal power requirement for the operation of the controller and reconfiguration of the elements to have full control over the reflection signal.  
IRS is energy and cost efficient, by inducing smart radio environments, and is free from self-interference, so can be used as other related wireless technologies such as, conventional relaying, backscatter communication (BackCom), and mMIMO relaying. IRS can be a solution for energy and spectral-efficient issue in 6G systems [6]. IRS will play a crucial role in 6G communication networks, similar to that of massive MIMO in 5G networks. Thus, IRS can be used to help achieve massive MIMO 2.0 in 6G networks [7].  
6G networks will enhance and expand 5G applications and will meet the following requirements [8; 9]:  
* Achieve higher data rate per user/device (10-100 times greater than 5G);
* Support wider coverage;
* Support larger number of connected devices;
* Integrate low latency communications;
* Reduce the energy consumption;
* Support massive Internet of Things (IoT) and integrate virtual reality (VR) and augmented reality (AR) into one extended reality (XR);
* Generate large amounts of data through the Internet of Everything (IoE);
* Support distributed massive MIMO;
* Support high and reliable connectivity;
* Support real-time dynamic analysis and self-awareness;
* Support trust and security mechanisms for safer integration.  
Application and feature description of 5G and 6G networks [9; 10; 11; 12] are presented in Table 1.
# Miniaturized PIFA for 5G Communication Networks
## 4 Simulation  
For the simulations, High Frequency Structure Simulator (HFSS) program ver. 11, which is based on the Finite Integration Method (FINM), is used to analyze the designed multi-band internal antenna for mobile communication. The radiation pattern of the multi-band internal antenna is simulated at H-plane, E1-plane, and E2-plane respectively, and the simulated radiation patterns are as shown in Figures 3-5.  
As mentioned above, this paper suggests the multi-band internal antenna for WCDMA800, WCDMA850, GSM900, DCS, PCS, WCDMA2100, LTE2100 mobile communication services, and 5G communication networks. Figures 4-6 show the radiation pattern of the internal antenna at 824 MHz  
Figure 4: Radiation Pattern (E1-Plane).  
Figure 5: Radiation Pattern (E2-Plane).  
\(-2.17\) GHz frequency band. To prove the antenna characteristics of the designed multi-band internal antenna in numerical value, Tables 1 and 2 show the voltage standing wave ratio (VSWR) and the peak gain of the antenna at each frequency band, and the radiation pattern of the antenna is almost omni-directional, as shown in Figures 3-5. In the simulation of the radiation pattern, the impedance bandwidth is set from 824 MHz to 2.17 GHz, and the insertion loss is the minimum at 960 MHz. Although the simulated radiation patterns at H-plane and E-plane are not symmetric patterns, they show forward and backward patterns. Moreover, the radiation patterns are the maximum at the perpendicular direction to the antenna side.