esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Filtering Recommendation Algorithm Based on TF-IDF and User Characteristics
## 4 Experiments  
### Dataset and Metrics  
To verify the effectiveness of the improved algorithm, this paper uses a dataset from the MovieLens recommender system [31]. The Movielens dataset is a public movie dataset released by the GroupLens Laboratory of the University of Minnesota. At present, there are eight versions with different sizes. The dataset mainly includes the following information: users ID, items ID, user's rating information of the items, and time stamp of the rating, etc. The Movielens-100k (ML-100K) data set and Movielens-1M (ML-1M) data set are used in this paper, and the basic information of the two datasets are shown in Table 1. In the experiment, the dataset is randomly divided into training set and testing set according to the ratio of 8:2 for comparative analysis.  
There are many evaluation indexes of recommender systems [32]. Because the ultimate goal of the improved collaborative filtering recommendation algorithm is to improve the accuracy of the recommendation results, this paper mainly considers the accuracy of the algorithm. To evaluate the recommendation accuracy of the improved recommendation algorithm, the root mean square error (RMSE) and the mean absolute error (MAE) are used to measure the effect of recommended systems [33]. MAE and RMSE are the measurement of the deviation of recommendations from their true user-specified. MAE and RMSE values can be obtained by calculating the rating deviation between the actual rating and the predicted rating between users. The lower the values of RMSE and MAE, the higher the accuracy of the algorithm recommended. The calculation methods for MAE and RMSE are defined as:  
\[MAE=\frac{\sum_{i=1}^{N}\mid p_{u,i}-r_{u,i}\mid}{N} \tag{12}\]  
\[RMSE=\sqrt{\frac{\sum_{i=1}^{N}\left(p_{u,i}-r_{u,i}\right)^{2}}{N}} \tag{13}\]  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset** & **User** & **Item** & **Number of Ratings** & **Rating Range** \\ \hline ML-100K & 943 & 1682 & 100,000 & 1–5 \\ ML-1M & 6040 & 3900 & 1,000,209 & 1–5 \\ \hline \hline \end{tabular}
\end{table}
Table 1: The information of the dataset.  
where \(N\) is the total number of rating forecast items in the testing set, \(r_{u,i}\) represents the actual rating of user \(u\) for the \(i\)-th item, and \(p_{u,i}\) is the prediction rating of user \(u\) for the \(i\)-th item.
## 2 System Model  
### Sc-FDE Signal  
Consider a pseudorandom noise-based (PN) single-carrier block transmission in which the modulated complex-value data streams are divided into blocks of size \(N-P\). We denote data block as \(\{x_{m}\}_{m=0}^{N-P-1}\). Each block is extended with a known PN sequence of length \(P\) to obtain blocks of size \(N\), which is expressed as  
\[\mathbf{s}=[x_{0},x_{1},\ldots,x_{N-P-1},c_{0},c_{1},\ldots,c_{P-1}]^{T}, \tag{1}\]  
where \(\{c_{n}\}_{n=0}^{P-1}\) is the PN sequence of length \(P\) and \((\cdot)^{T}\) represents matrix transpose. The same known PN sequence is inserted at the beginning of the transmission frame.  
The known PN sequence in the previous block can be viewed as the cyclic prefix (CP) of the current transmission block. Assuming that the maximum multipath delay spread is shorter than the PN symbol's duration, the received signal \(\mathbf{r}=[r_{0},r_{1},...r_{N-1}]^{T}\) is treated as the circular convolution between the transmission data block and the channel impulse response (CIR). Consequently, this transmission format implements a single carrier communication that allows us to exploit the benefits of the frequency domain equalization at the receiver without having the high PAPR of OFDM. A block SC-FDE diagram for the transmitter is shown in Figure 1a. First, an information bit sequence is encoded by a \(1/2\) rate convolution code (the industry standard convolution code (171, 133)). We then mapped the sequence into symbols using quadrature phase shift keying (QPSK). The SC-FDE system specifications are listed in Table 1. Each SC-FDE block contains 386 information bits or 772 convolution coded bits.
# Random Vibration Fatigue Analysis Using a Nonlinear Cumulative Damage Model
## 4 Fatigue Damage Accumulation Procedure
### Study Case  
We have a support that is subjected to a bending load (see Figure 5), and the support has the following features. It is cold drawn steel AISI 1025, with a modulus of elasticity E = 200 GPa, Poison's ratio \(\gamma\) = 0.29, yield strength Sy = 430 MPa, ultimate tensile strength Sut = 510 MPa, endurance limit Se = 255 MPa, density \(\rho\) = 7.9 g/cm\({}^{3}\), length L = 51 mm, Width W = 200 mm and a wall thickness of 3 mm. During its function, the component supports a load of 80 N, and its movement is free only in the vertical direction. Zone A (purple color) indicates that it is fixed, and zone B (red color) is the applied load. It is considered as a cantilevered beam and is submitted to an operating random vibration with an input PSD with the frequencies ranging from 10 to 55 Hz at an amplitude of 1.5 mm for a period of 2 h. The testing is carried out physically by using a vibration system.  
Thus, once the component has been submitted to the vibration fatigue by using the vibration system shaker with a vibration controller, it is required to calculate the bending stress.
## 6 Results  
### Usability Results  
This section presents the results of the application of the IBM CSUQ instrument; in addition, participants were invited to describe the application using the most representative word from a set of desirability tools [61]. As can be seen in Figure 4, the most frequent words that users attributed to NetAR were motivating, easy to use, and usable. Table 3 shows the mean, median, and standard deviation measures aggregated by the four CSUQ categories. Figure 5 shows the results of the IBM CSUQ questionnaire. A positive result is observed, given that the majority of the participants answered with the responses that have to do with \"I agree\" and \"I strongly agree\". Looking at the results, the study suggests that overall user satisfaction follows a positive trend, with the average result being \(\mu\) = 5.91 out of a maximum of 7 and a standard deviation of \(\sigma\) = 1.08. As for the usability of the system (SYSUSE), there is a similar trend, with an average value (\(\mu\) = 5.93) higher than the other factors of analysis both in the minimum value and in the average, showing that the usability is satisfactory for the users. The information quality (INFOQUAL) has the lowest results, with an average value (\(\mu\) = 5.84) indicating that it can be improved; therefore, we have considered that future improvements should aim at more accurate description of the scenes, more relevant models, and a detailed user manual. On the other hand, the interface quality (INTERQUAL) shows the highest values (\(\mu\) = 5.94), denoting that the interface was appreciated by the users and they consider the system to be easy to use. Figure 4: Words most frequently used by users to describe the NetAR application. Question P9 is the most negatively rated question (\(\mu=5.5625\), \(\sigma=1.311\), and \(M=6\)), which indicates that the system should improve when displaying error messages and how to solve them. The best rated questions are: question P2 (\(\mu=6.03\), \(\sigma=0.981\), and \(M=6\)) and question P12 (\(\mu=6.03\), \(\sigma=0.981\), and \(M=6\)), which indicates that the participants were able to effectively use the application because the information was easy to find. Regarding the usability of the system, question Q6 (\(\mu=5.96\), \(\sigma=1.130\), and \(M=6\)) and question Q7 (\(\mu=5.96\), \(\sigma=1.130\), and \(M=6\)) indicate that most users felt comfortable using the application and it took them little time to learn how to use it. Figure 5 shows the values obtained for each question of the IBM CSUQ. In summary, the results of the questionnaire were perceived as highly positive. INFOQUAL and SYSUSE
## 6 Discussion  
### Significance of the Proposed Method  
The spread-spectrum coding system proposed in this paper has a better BER performance than the underwater acoustic spread-spectrum communication system using classical coding methods, and the technique enables robust transmission of short-frame data. The performance of the spread-spectrum coding system combined with CCSDS short-block LDPC codes has been verified through simulations and pool experiments to outperform the spread-spectrum system combined with the classical coding method.  
The DSSS + LDPC spread-spectrum coding system has the best BER performance and is suitable for applications with limited transmission energies and high BER requirements, such as satellite communications. The soft spread-spectrum + LDPC spread-spectrum coding system offers improved band utilization, but is limited by the spread-spectrum method,  
Figure 19: BER of CSK spread-spectrum communication system under different coding methods.  
Figure 18: BER of M-ary spread-spectrum communication system under different coding methods.  
which reduces its BER performance. This approach is more suitable for communication scenarios with limited bandwidth resources.
## 3 Distributed PV Storage Virtual Synchronous Generator System
### Bidirectional PV Storage Converter
#### 3.4.3 Vsg  
The virtual synchronous generator (VSG) control adds the rotor motion equation of the synchronous generator to the control of the inverter so that the new energy inverter has a similar inertia characteristic of the synchronous machine. The VSG control block diagram was shown in Figure 14.  
where, \(P_{\mathrm{m}}\) is the input active power of VSG, which simulates the mechanical power in synchronous generator, \(P_{\mathrm{e}}\) is the active power output by the VSG, \(\omega_{\mathrm{N}}\) is the rated angular frequency (\(\omega_{\mathrm{N}}=2\pi f_{\mathrm{N}}\)), \(u_{\mathrm{VSG}}\) is the grid voltage, \(\omega_{\mathrm{g}}\) is the angular frequency of \(u_{\mathrm{VSG}}\), \(\omega\) is the virtual angular frequency, and \(\theta\) is the reference value of voltage phase angle. VSG rotor equation of motion can be expressed by the equation below.  
\[J\frac{\mathrm{d}\omega}{\mathrm{d}t}\ =\ \frac{P_{\mathrm{m}}-P_{\mathrm{e}}}{ \omega_{\mathrm{N}}}-D\Delta\omega \tag{26}\]  
where \(\Delta\omega\) is the difference between \(\omega\) and \(\omega_{\mathrm{g}}\), \(J\) is the virtual inertia, and \(D\) is the damping coefficient [22].  
In general, for a given synchronous generator, its moment of inertia and damping factor are almost constant. However, for the VSG, the moment of inertia and damping factor are only the control parameters, which can be adjusted in real time according to the needs and makes the VSG have effective dynamic characteristics.
# Dynamic Response of Dual-Disk Rotor System with Uncertainties Based on Chebyshev Convex Method
## 4 Numerical Analysis of the Rotor Dynamic Response Based on CCM
### Critical Speeds Uncertainty  
This subsection pays attention to the critical speeds influenced by uncertainty of the rotor system. The elastic modulus \(E\), material density \(\rho\), and support stiffness \(K_{2}\) are regarded as uncertain parameters. Among them, the elastic modulus \(E\) and material density \(\rho\) are related. The first three critical speeds influenced by different uncertain parameters are calculated when the uncertain degree is 10%. And the calculation results are recorded in Table 5.  
It can be concluded from Table 5 that the critical speeds of the rotor system are no longer fixed values, but change within an interval. Comparing the errors of critical speeds influenced by different uncertain parameters, it can be seen that errors effected by multi-parameters are greater than those caused by single parameters.  
The first three critical speeds influenced by uncertain multi-parameters are calculated when the uncertain degrees are 10% and 15% respectively. And the calculation results are recorded in Table 6. Comparing the critical speed interval errors under different uncertainty, the error on the critical speeds increases accordingly with the increase of uncertainty.
# PCONet: A Trainable Feedback Scheme of Precoder for the Uplink Multi-User MIMO Systems
## 5 Conclusions  
The proposed PCQNet has achieved considerable gains in BLER performance compared with the protocol codebook-based precoding scheme and the Lloyd-Max quantization scheme under the same CRs. The adaptability of trainable PCQNet architecture to different channel bandwidths is more competitive than the Lloyd-Max quantization scheme in bandwidth-limited scenarios. The PCQNet also provides better resilience to the mismatch between the trained SNRs and tested SNRs than the Lloyd-Max quantization scheme due to channel variations. Our experiments demonstrate that the application of the CNN-based  
Figure 8: The BLER performance comparison over the NAIE Channel with 16-QAM and K = 6.  
Figure 9: The BLER performance comparison over the NAIE Channel with 16-QAM and K = 8.  
PCQNet greatly improves the adaptability and the generality of the precoding matrix feedback in the uplink MU-MIMO systems. Importantly, it preserves only a slight degradation of BLER performance with high compression rate of the precoding matrix, making the compression architecture more attractive for the deployment of practical systems.  
Software, X.B.; validation, M.J. and W.F.; data curation, X.B.; Writing--original draft preparation, X.B.; writing--review and editing, M.J., and C.Z. All authors read and agreed to the published version of the manuscript.  
This work was supported in part by the National Key Research and Development Program of China under Grant 2020 YFB1807200 and the Jiangsu Province Basic Research Project under Grant BK20192002.  
Not applicable.  
The authors declare no conflict of interest.
## Appendix B Supporting Material: Detailed Models
### Degradation Models  
1. **Ageing**[9]: * Shunt resistance: \(R_{p}(t)=0.01826\,t^{2}-2.35239\,t+185.2\) * Series resistance: \(R_{s}(t)=0.00122\,t+0.53\)
2. **EVA Discoloration**[10]: * \(R_{s}=R_{s,\text{STC}}+9.9\times 10^{-3}\cdot\text{DYI}\) * \(R_{p}=R_{p,\text{STC}}-193\cdot\text{DYI}\) * \(\text{DYI}=5.68\times 10^{11}\exp\left(\frac{-90,000}{RT}\right)G\log\left(\frac{t}{360 0}\right)\)
3. **LID**[6]: * \(I_{\text{cc}}=I_{s,\text{STC}}+1.11\times 10^{-10}\cdot\frac{G}{1000}\exp \left(\frac{-43.268}{RT}\right)t\)
4. **Cracking**[12; 21; 22]: * \(R_{p}=\left(\frac{q}{RT}I_{s}\exp\left(\frac{qV}{RT}\right)\right)^{-1}\) * \(I_{s}=I_{s}^{\text{D}=0}(1-D)\), \(I_{\text{ph}}=I_{\text{ph}}^{\text{D}=0}(1-D)\), \(D=\frac{A_{\text{inactive}}}{A_{\text{active}}}\)
5. **Corrosion**[11]: * \(R_{s}=R_{s0}+\exp(R_{D}t-B)\) * \(R_{D}=A\cdot(\text{MM}_{RH})^{n}\cdot\exp\left(-\frac{E_{s}}{k_{B}T}\right)\), \(n=2\), \(E_{a}=1.32\,\text{eV}\)
6. **Potential Induced Degradation (PID)**[7]: * \(R_{p}=R_{p0}\cdot\exp(-\alpha\cdot V_{\text{sys}}\cdot t)\) * where \(R_{p0}\) is the initial shunt resistance, \(V_{\text{sys}}\) is the system voltage, \(t\) is time, and \(\alpha\) is a degradation rate constant.
# Assessing the Impact of an EV Battery Swapping Station on the Reliability of Distribution Systems
The risk of loss-of-load can be obtained according to the minimum path searching algorithm. In order to fill the above-mentioned gaps, this paper proposes a comprehensive methodological framework to investigate the potential role of BSSs with V2G capability in improving the reliability of supply in future distribution networks. For this aim, we first develop an empirical model with an explicit consideration of BSS characteristics for describing the energy demand of EVs and their resultant available generation capacity (AGC), which can be utilized for BSS operation. Then, on this basis, a quantitative method to quantify the effect of a grid-connected BSS on the distribution system reliability is proposed, which provides a practical tool to analyze the potential value of BSS resources in future distribution systems compared with most of the extant studies that only focus on an EV fast-charging paradigm. In order to explicitly capture the uncertainties associated with EV users' behaviors, Latin Hypercube Sampling (LHS) methods were utilized to obtain the time series of the BSS traffic flow and initial SOC of each EV battery, according to the probability distribution of corresponding uncertain factors. The resultant LHS-based sequential simulation improves the accuracy and convergence performance of the evaluation compared to traditional Sequential Monte Carlo Simulation (SMCS) based on simple random sampling (SRS). Compared with existing studies, the main contributions of this paper can be summarized as follows:  
* Proposing a comprehensive methodological framework to study the impact of BSSs on the reliability of distribution networks. * Analyzed and compared the influence of different charging strategy (peak-to-valley price-based charging and constant power charging) on the reliability of the distribution network. Using LHS to generate the time series of uncertain factors (including the fault status of the distribution network components, the traffic flow of the BSS, and initial State of Charge (SOC) of the replaced EV batteries), which improves the accuracy and convergence performance of the evaluation, as compared with traditional SMCS method. The rest of this paper is organized as follows. Section 2 provides an overview of the BSS conceptual framework. Then, the assessment of available generation capacity in BSS is described in Section 3. Section 4 presents the reliability modelling of system components. On these basis, Section 5 describes the procedures for conducting the reliability assessment of distribution networks. Then, Section 6 demonstrates the numerical studies. Finally, Section 7 concludes the paper.
# Influence of Girder Flaring on Load Effect in Girders of Composite Steel Bridges
## 5 Parametric Study
### Shear in Girders Due to Live Load
#### 5.2.2 Effect of Other Parameters  
All the other five parameters (slab thickness, cross-bracing spacing, girder stiffness, number of girders, and span length) were investigated through the analysis of the other ten bridges, B6-B15, as shown in Table 1. As shown in Figure 24, the refined analysis demonstrated that these parameters have very minimal influence on the shear GDF values, which is an expected finding. For the shear load effect in the interior girders, the difference between the lever rule and finite element outcome ranges between \(-4.9\%\) and \(9.5\%\). The corresponding difference between the lever rule and finite element analysis for the exterior girders ranges between \(-0.8\%\) and \(9.4\%\). Compared to the finite element findings, the overall results demonstrate that the lever rule gives much more sensible results than the AASHTO LRFD shear GDF expressions for the majority of the considered cases.
# Secure On-Off Transmission in UAV Relay-Assisted mmWave Networks
## 1 Introduction
### Contribution  
In this paper, we investigate the secure on-off transmission in UAV relay-assisted mmWave networks, and the secrecy performance is examined by considering the beam alignment errors. Our main contributions are summarized as follows:  
* Considering a ground mmWave communication networks aided by multiple UAV relays, where multiple eavesdroppers randomly distribute on the ground. More specifically, the locations of UAV relays and eavesdroppers are modeled as independent Poisson Point Processes (PPPs), and the opportunistic relay selection scheme is adopted. Then, the on-off transmission scheme is designed to enhance the secrecy performance of the considered networks, and we find that the lower SOP can be obtained by adopting the higher threshold of data transmission.
* With perfect beam alignment, the new closed-form expressions of transmit probability (TP) and SOP are derived in both the on-off and non-on-off transmission schemes by using the tools of stochastic geometry and Gauss-Chebyshev integration. It is revealed that the TP is not a monotonous function versus the altitude of UAV relays, which means that the best TP can be obtained by properly designing the UAV relays' altitude. In addition, the secrecy performance has remarkable positive correlations with the transmit power and the number of antenna elements.
* Taking into account the beam alignment error, a typical error model is adopted under the 3D antenna pattern, and the tractable expressions of the SOP are derived. Analyzing the numerical and simulation results, we find that the SOP will not have obvious deterioration with small beam alignment error, and we can enhance the secrecy performance by using the antenna array with large number of elements. However, when the beam alignment error is severe, the antenna array with smaller number of elements provides the lower SOP.  
The remainder of this article is organized as follows. The system model is presented in Section 2. In Section 3, we examine the TP and SOP in both the on-off and non-on-off transmission schemes, and the SOPs with beam alignment error are analyzed. Then, the simulation results are shown in Section 4. Finally, we summarize the conclusions in Section 5.
## 3 Results
### Experimental Setup  
In this study, two different real-world datasets were used to evaluate the effectiveness of the proposed hybrid underwater image enhancement method: Test-C60 and OceanDark. Both datasets consist of images reflecting difficult underwater conditions, without ground-truth references images, and containing distortions such as various color deviations and low contrast.  
The Test-C60 dataset is a subset of the Underwater Image Enhancement Benchmark (UIEB) [23] dataset and consists of 60 natural underwater images. These images represent difficult scene conditions such as yellowish, greenish, and bluish tones that cannot be fully corrected by existing enhancement methods. The OceanDark [29] dataset consists of 183 low-illumination underwater images with artificial lighting collected in deep sea conditions in the Northeastern Pacific Ocean. The images were taken between 389 m and 980 m depth and include different biological species and artificial objects.  
The MU-Fusion network used in the fusion phase of the proposed method was trained on 100 randomly selected raw underwater images from the UIEB dataset. During the training process, the learning rate of the model was determined as \(10^{-4}\), and training was performed for a total of 10 epochs. To properly configure the content loss, we set the two key parameters that control the activity level map generation: the feature map depth was set to **5** to leverage deep semantic information for identifying salient regions, while the local averaging window size was set to **3** to ensure the preservation of fine details. No special data augmentation method was used in the training process, and raw images were evaluated directly. Dataset distributions for MUFusion model training are given below in Table 1.  
Experimental studies were carried out on a computer with Intel i5-13600K processor, 32 GB RAM and NVIDIA RTX 4070 Ti GPU hardware. Various reference-free image quality metrics (UIQM, UICM, AG, IE and IL-NIQE) are used to evaluate the quantitative success of the proposed method, and Scale-Invariant Feature Transform (SIFT) [30]-based matching evaluations are performed for structural similarity analysis. The statistical significance of the obtained results is analyzed by the Wilcoxon signed-rank test.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Dataset** & **Number of Images** & **Purpose of Use** & **Explanation** \\ \hline UIEB(Training Subset) & 100 & Training & Divided into 19,375 patches of size 128 \(\times\) 128 \\ UIEB—TestC60 & 60 & Testing & Not used during training \\ OceanDark & 183 & Testing & Not used during training \\ \hline \end{tabular}
\end{table}
Table 1: Dataset distributions for MUFusion model training.