esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Physical Layer-Based Full-Duplex Underwater Acoustic Communication Systems
### Comparison of Different FD-Physical Layer-Based UWAC Systems
#### 2.5.1 Spectral Efficiency  
In comparison to A-DTWR in [19], the OFDM-based FD cooperative system AUA-CCS in [40] greatly improved the performance of time-varying channels and achieved maximum spectral efficiency by inserting a much shorter and more manageable CP at the source. The IBFD communication channel is used in OFDM-based IBFD-UWAC systems such as ML-SC-IBFD [23], A-IBFD [24], HSIC systems [41], and TRDC [22] to improve the system spectral efficiency. In comparison to ML-SC-IBFD [23], A-IBFD [24] and TRDC [22], the HSIC system in [41] demonstrated substantial performance in terms of high efficiency.  
Furthermore, the SDFE-based TRDC [22] has a moderate performance in terms of low efficiency as compared to the other systems ML-SC-IBFD [23], A-IBFD [24] and A-SIC [25]. Owing to the fact that the SDFE-based TRDC [22] system employs a hybrid self-interference cancellation scheme, while HSIC [41] employs analog interference cancellation to suppress the interference, and A-SIC [25] employs the NLMS algorithm to suppress the SI signal for the increase in spectral efficiency. The FSK based FD-UWAC systems [29], RLS-adaptive [25] and ANE [60] adopts the FD-UWAC channel, and significantly enhancement the spectral efficiency. However, the FSK based UWAC system in [29] has shown high spectral efficiency compared to other digital schemes such as RLS-adaptive [25] and ANE [60]. The FSK-based UWAC system [29] sends data at a rate of 21 bits per second over a bandwidth of 60 kHz, which improves spectral efficiency. By deploying full-duplex operation between the FD-UWAC modems, the spread spectrum (SS) based Hybrid-SS system [33] achieves higher spectral performance.
## 4 Results and Discussion
### Experimental Tests for Effect of Cutting Parameters on Surface Roughness  
We apply the turning (dry cutting) and single-point cutting as our experimental test. Figure 19 shows the experimental instruments. The cutting tool used carbide inserts and a tool holder. We applied three different carbide inserts and a different tool holder. These inserts have the same nose radius 0.8 mm and edge radius 20 \(\upmu\)m. After installation, the effective rake which is a combination of the tool holders angle of inclination and the rake built into the insert, are about 0\({}^{\circ}\), 3\({}^{\circ}\) and 7\({}^{\circ}\). Figures 20-22 are the comparisons of surface roughness between the simulation results and experimental results (cutting depth in turning is set 0.1 mm). From these figures, we can conclude that although there are differences between the surface roughness of the SPH cutting model calculated by SPM and the measured surface roughness from  
roughness value when the cutting parameters are changed. Therefore, the cutting parameters can be investigated and optimized by the SPH model. It should be known that the real cutting process is a highly complex process. All the existing models which simulate the cutting process simplify several factors that affect the cutting process, such as machine tool vibration and tool wear. Therefore, the simulation is not as accurate as experiments in the real world and it is hard to simulate the cutting model to predict the surface roughness accurately. We simulated the cutting process to predict the variance trend of surface roughness with different cutting parameters and then compared the simulation results with the real experimental results. From another perspective, it might add more errors if we compare the simulated results with another simulation (there will be an accumulation of errors which can lead to more inaccurate results). After serious consideration, we compared our simulated results with real experimental results to analyze the variation trend of surface roughness with different cutting parameters. Although we ignore a lot of factors (cutting edge angle, the vibration in actual cutting process et al.), we only consider the cutting parameters which have significant influences on surface roughness and can also be controlled by the 2D SPH cutting model. In the experiment, all the parameters remain the same except the three parameters (cutting speed, rake angle and feed).
# Multi-NETVIS: Visual Analytics for Multivariate Network
## 2 Related Work and Design Requirements
### Multivariate Network Visual Analysis  
With the diversification of data, a network abstracted from entity correlation is a complex network with multiple attributes rather than a simple network. For a multivariate network, research [14] has shown that while the node-link diagram is the most frequent network expression, it is limited in terms of its encoding attributes. Although a table-based network representation is more conducive to presenting the network attributes, it is not able to visually present the topology.  
Visual analytics research into multivariate networks has provided a variety of methods to reconcile these deficiencies. [PERSON] et al. [15] implemented an interactive system called g-Miner to mine group correlation structures in multivariate network data. Through the integration of a mining algorithm, cross-level exploration, a multi-structure group view, and other visual design elements, the network group was iteratively mined and user feedback was integrated into the loop analysis. [PERSON] et al. [16] designed a visualization technology called ClusterVis to support the exploration of clustered or user-defined subgraph node attributes. In another study [17], nodes were allowed to be aggregated based on node attributes, topology, or a combination of the two, providing multiple patterns for exploration of multivariate networks. For interaction analysis, [PERSON] et al. [18] proposed the analysis method HyBridVis and established an interactive layout to present a features swarm of one or more attributes at different scales. In order to improve the analysis and understanding of pure topology structures, [PERSON] et al. [19] proposed an interactive and linear dimensionality reduction network layout and interactive analysis method. [PERSON] et al. [20] presented an evaluation that investigated the effects of background stories on graph perception.  
In conclusion, the current work mainly focuses on the exploration of structure and multivariate data, neither of which is convenient for structural or multivariate analysis. In addition, few of the works mentioned above provide users with a simplified overview.
# An Evaluation of Machine Learning Models for Forecasting Short-Term U.S. Treasury Yields
###### Abstract  
This study explores the historical evolution and short-term predictive modeling of the U.S. 10-year Treasury bond yield, a critical indicator in global financial markets. Recognizing its sensitivity to macroeconomic conditions, the research integrates economic variables, including the federal funds rate, core Consumer Price Index (CPI), real Gross Domestic Product (GDP) growth rate, and the U.S. federal debt growth rate, to assess their influence on yield movements. Four forecasting models are employed for comparative analysis: linear regression (LR), decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural networks. Using historical data from the Federal Reserve Economic Data (FRED), this study finds that the RF model offers the most accurate short-term predictions, achieving the lowest mean squared error (MSE) and mean absolute error (MAE), with an R\({}^{2}\) value of 0.5760. The results highlight the superiority of ensemble-based nonlinear models in capturing complex interactions between economic indicators and yield dynamics. This research not only provides empirical support for using machine learning in economic forecasting but also offers practical implications for bond traders, system developers, and financial institutions aiming to enhance predictive accuracy and risk management.  
r 2020 April 202511 June 202  
While these models offer interpretability and theoretical grounding, they often struggle to capture the nonlinear and high-dimensional nature of modern financial data. In recent years, machine learning (ML) methods have emerged as powerful tools in financial forecasting due to their ability to model complex, nonlinear relationships and adapt to evolving patterns in large datasets [6; 7].  
This study aims to investigate the short-term forecasting performance of four different models, linear regression (LR), decision tree (DT), random forest (RF), and multilayer perceptron (MLP) neural networks, on the U.S. 10-year Treasury yield. By incorporating key macroeconomic indicators as input variables and comparing model performance based on statistical accuracy metrics, this research seeks to determine which modeling approach provides the most reliable forecasts. The emphasis on short-term prediction (1 to 3 months ahead) is aligned with practical decision-making needs in trading and risk management settings, where timing and precision are crucial.  
Additionally, this research contributes to the growing body of literature on applying machine learning in macro-finance by focusing specifically on a single, influential yield rather than the entire term structure. Through this approach, this study offers actionable insights for both academic researchers and financial practitioners aiming to enhance predictive capabilities using data-driven methods.
# Fine-Grained Detection of Pavement Distress Based on Integrated Data Using Digital Twin
## 3 Methodology
### Digital Twin-Based Generation of Integrated Dataset for Pavement Distress
#### 3.1.2 Virtual Distress Rendering Based on a Lightweight Physics Engine  
Based on the inspection images collected by the UAV, a Pix4D Mapper is used to perform image stitching, air triangulation, point-cloud creation and polygon-network generation, obtaining a digital orthophoto image of the inspection area (pavement background model). The generated pavement background model (.obj format) was further imported into the UE4.26 physics engine, and a renderer capable of accurately modelling  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \multicolumn{6}{c}{**Parameter Setting**} \\ \hline \multirow{5}{*}{Camera parameters} & Image sensors & Maximum resolution & \multirow{2}{*}{Photo format} & \multirow{2}{*}{Shutter speed} & \multirow{2}{*}{Focal length} & \multirow{2}{*}{ISO range} \\  & 1-inch CMOS; & & & & & 100–3200 \\  & 20 million & & & & (Automatic) \\  & effective pixels & (20.48 million & & (32) & & (100–6400 \\  & total pixels) & & & & (manual) \\ \cline{2-6} \multirow{5}{*}{Flight parameters} & Flight height & \multirow{2}{*}{(m)} & Flight speed & Positive & Lateral & \multirow{2}{*}{Shooting angles} & \multirow{2}{*}{Output resolution} \\  & (m) & (m/s) & overlap rate & overlap rate & & & \(5472\times 3648\) \\ \cline{1-1}  & 25 & 2 & 80\% & 75\% & \(-90^{\circ}\) & (3:2) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of high-precision cameras used in UAV.  
Figure 3: Schematic diagram of UAV-based image acquisition of asphalt pavement.  
asphalt materials is used to create virtual blueprints of five types of disease (including transverse cracks, longitudinal cracks, cross cracks, alligator cracks, and potholes) based on real pavement distress. These are randomly deployed in the pavement background model, as shown in Figure 4. In addition, complex working scenarios such as shadows, occlusions and blurs are simulated using light and environment renderers in UE 4.26, which are shown in Figure 5. These scenarios are deployed randomly to the pavement background model to generate a digital twin model capable of simulating the damage state of the asphalt pavement.
# AUV-Aided Optical--Acoustic Hybrid Data Collection Based on Deep Reinforcement Learning
## 3 Network Model
### Acoustic Data Collection Link  
When the AUV traverses near the node, it is necessary to construct a communication link for data collection. As for the acoustic link, the acoustic wave is affected by the absorption of medium and the scattering of impurities in water. The path loss of underwater acoustic channels is related to frequency \(f\) and distance \(d_{ac}\). To this end, the total attenuation is given as follows [34].  
\[A(d_{ac},f)=d_{ac}^{k}a(f)^{d_{ac}}, \tag{1}\]  
where \(k=1.5\) represents propagation loss, and \(a(f)\) is the absorption coefficient in dB/km given by the Thorp formula [35]  
\[\begin{split} 10\log a(f)=0.11\frac{f^{2}}{1+f^{2}}+44\frac{f^{2}}{4 100+f^{2}}\\ +2.75\times 10^{-4}f^{2}+0.003.\end{split} \tag{2}\]  
Consequently, given the acoustic signal transmit power \(P_{trans}^{ac}\) and frequency \(f\), the signal-to-noise ratio (SNR) can be expressed as [36]  
\[SNR_{ac}(d_{ac},f)=\frac{P_{trans}^{ac}/A(d_{ac},f)}{N(f)\Delta f}, \tag{3}\]  
where \(N(f)\) and \(\Delta(f)\) represent the total noise level including four kinds of interference noise and the bandwidth of the receiver, respectively. Therefore, the transmission power of acoustic communication satisfying the minimum \(SNR_{\text{min}}^{ac}\) is expressed as  
\[P_{trans}^{ac}=SNR_{\text{min}}^{ac}A(d_{ac},f)N(f)\Delta f. \tag{4}\]
###### Abstract  
Wireless and power line communications (PLC) are important components of distribution network communication, and have a broad application prospect in the fields of intelligent power consumption and home Internet of Things (IoT). This study mainly analyzes the performance of a dual-hop wireless/power line hybrid fading system employing an amplify-and-forward (AF) relay in terms of outage probability and average bit error rate (BER). The Nakagami-m distribution captures the wireless channel fading; whereas the PLC channel gain is characterized by the Log-normal (LogN) distribution. Moreover, the Bernoulli-Gaussian noise model is used on the noise attached to the PLC channel. Owing to the similarity between LogN and Gamma distributions, the key parameters of probability density function (PDF) with approximate distribution are determined by using moment generating function (MGF) equations, joint optimization of \(s\) vectors, and approximation of LogN variable sum. The MGF of the harmonic mean of the dual Gamma distribution variables is derived to evaluate the system performance suitable for any fading parameter \(m\) value. Finally, Monte Carlo simulation is used to verify the versatility and accuracy of the proposed method, and the influence of the hybrid fading channel and multidimensional impulse noise parameters on system performance is analyzed.  
hybrid fading; harmonic mean; Internet of Things (IoT); moment generating function (MGF); mutual approximation +
Footnote †: journal: Sensors
# Self-Supervised Remote Sensing Image Dehazing Network Based on Zero-Shot Learning
## 4 Experiments and Discussions
### Results for Synthetic Non-Uniform RS Hazy Images
Furthermore, the average performance of our proposed method across the three evaluation metrics exceeds that of all dehazing methods in the traditional category and outperforms most methods in the learning-based category. These results highlight the competitive non-uniform haze removal ability of our proposed dehazing approach.
## 4 Resource Allocation for Downlink and Uplink  
The proposed system is divided into two sections, namely, downlink and uplink. In this section, the resource allocation scheme used for each is discussed. Before discussing the proposed system, let us briefly discuss the resource allocation scheme used in traditional systems, traditional Wi-Fi and hybrid VLC-Wi-Fi that we will compare the proposed system with. The resource allocation scheme used for traditional Wi-Fi is based on the latest IEEE 802.11 ac Standard [33]. The hybrid VLC-Wi-Fi system uses a cooperative load balancing scheme, which was recently introduced in [34]. Their system is divided into two sections: downlink and uplink. The downlink section is based on a hybrid VLC and Wi-Fi system while the uplink section is based on the traditional Wi-Fi system. Cooperative load balancing that achieves proportional fairness is implemented by using a dual decomposition method. The detailed architecture of the two mentioned systems is discussed in Section 5. In Sections 4.1 and 4.2, the resource allocation scheme for the downlink section and the uplink section is described respectively.
# A Framework to Quantify the Quality of Source Code Obfuscation
## 6 Experiment
### Cost Measurement Results  
Figure 6 displays the results of using three indicators to measure Cost. In Figure 6, the time overhead is measured in milliseconds, while space overhead and file size are measured in kilobytes. In our experiments, the Stunnix C/C++ Obfuscator and the Semantic Designs C-GCC4 Obfuscator had minimal impact on all Cost measurement indicators. Conversely, Tigress Obfuscator and Obfuscator LLVM substantially affected space overhead and file size. Specifically, the addition of Tigress Obfuscator options can be observed to increase numbers gradually, but the obfuscation tools did not significantly affect time overhead. We attribute this to the experiment conducted in a multi-core environment with a high-performance CPU, the test code being less than 200 lines long, and the applied obfuscation  
Figure 5: Resilience measurement results.  
techniques not generating sufficient load to affect program performance markedly. Table 6 details the quantification results for obfuscation Cost.  
The experiments confirmed that control flow obfuscation techniques are more effective than layout obfuscation and data obfuscation, which is consistent with our expectations.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Tools**} & \multicolumn{4}{c}{**Cost (ms, Kilo-Byte)**} \\ \cline{2-5}  & \multicolumn{2}{c}{**Time Overhead**} & \multicolumn{1}{c}{**Space Overhead**} & \multicolumn{1}{c}{**File Size**} \\ \hline Baseline & Average & 0.768 & 2.29 & 15.78 \\ \hline Stunnix C/C++ Obfuscator & Average & 0.770 & 2.29 & 15.78 \\  & Growth Rate & 0.30\% & 0\% & 0\% \\ \hline Semantic Designs C Obfuscator & Average & 0.778 & 2.29 & 15.77 \\  & Growth Rate & 1.23\% & 0\% & \(-0.07\%\) \\ \hline Obfuscator LLVM & Average & 0.767 & 10.73 & 23.30 \\  & Growth Rate & \(-0.13\%\) & 376.01\% & 47.94\% \\ \hline Tigress C Obfuscator (Level 1) & Average & 0.782 & 2.50 & 15.88 \\  & Growth Rate & 1.83\% & 9.31\% & 0.69\% \\ \hline Tigress C Obfuscator (Level 2) & Average & 0.761 & 3.88 & 16.99 \\  & Growth Rate & \(-0.92\%\) & 69.91\% & 7.69\% \\ \hline Tigress C Obfuscator (Level 3) & Average & 0.776 & 5.33 & 18.09 \\  & Growth Rate & \(1.07\%\) & 133.43\% & 14.63\% \\ \hline \hline \end{tabular}
\end{table}
Table 6: Results of measurement for quantifying obfuscation quality—Cost.  
Figure 6: Cost measurement results.  
Furthermore, we verified that different obfuscation techniques impact various measurement indicators, underscoring the necessity of using a range of methods, as we do in our approach, to evaluate obfuscation quality effectively.
# MEMS-Based Vibration Acquisition for Modal Parameter Identification of Substation Frame
## 3 Analysis of Experimental Data Characteristics
### Wind Speed Time History  
During the test, anemometers were installed on the busbar truss, specifically at the second truss level, to monitor variations in wind speed at that location. It should be noted that while the wind direction and speed varied randomly, the wind loads acting on the structure primarily occurred along the parallel truss and vertical truss directions. Therefore, wind speed was monitored specifically in these two directions. Traditionally, anemometers are placed at a height of 10 m, and the average wind speed over a 10-min period at this height is calculated to compare the operational wind speed of the structure with the design wind speed. In this study, the anemometers were positioned at the busbar truss at a height of 15.7 m to monitor wind speed variations at the truss location, as shown in Figure 7. The recorded wind speeds in both directions during the test are below 4 m/s. According to  
Figure 5: Time–frequency domain response gathered by MEMS: (**a**) time-domain response; (**b**) frequency-domain response.  
Figure 6: Time–frequency domain response gathered by 941b: (**a**) time-domain response; (**b**) frequency-domain response.  
wind speed statistics, the average wind speed is 0.68 m/s in the parallel truss direction and 0.80 m/s in the vertical truss direction. From an experimental perspective, wind loads act as partial excitations during tests. The wind speed fluctuates randomly within a small range, suggesting that the wind load provides the structure with a near-stationary random excitation. Additionally, the recorded wind speed and temperature variations during the tests can serve as valuable references for future finite element analysis of the structure.  
After obtaining the average wind speed at the truss, the Davenport wind speed theory can be applied to calculate the equivalent wind speed at different heights, as shown in the following equation:  
\[U(z)=U(z_{ref})(\frac{z}{z_{ref}})^{\alpha} \tag{1}\]  
where \(U(z)\) is the equivalent wind speed at height \(z\), \(U(z_{ref})\) is the wind speed at the reference height \(z_{ref}\), and \(\alpha\) is the surface roughness coefficient, typically ranging from 0.1 to 0.3. In this study, \(\alpha\) was set to 0.2.  
By calculation, the equivalent wind speeds at different heights of the structure are obtained, as illustrated in the wind profile diagram in Figure 8. It can be seen that, at the same height, the average wind speed in the vertical truss direction is consistently higher than in the parallel truss direction; the average wind speeds at the top of the substation frame are 0.80 m/s and 0.96 m/s, respectively. The calculated wind profile results provide a valuable reference for the wind-resistant design of the substation frame from an operational perspective.  
Figure 7: Wind speed and temperature variations during the test: (**a**) parallel truss direction; (**b**) vertical truss direction.
# Congestion Control Mechanism Based on Backpressure Feedback in Data Center Networks
## 4 Bfcc
### Rate Reduction Module  
The module is implemented with the help of two collaborative parts, the control at the ingress pipeline and the control at the egress pipeline, which are divided into two speed reductions. The admission control at the ingress pipeline enables the management of the classification of congested and non-congested flows, while the egress control in the egress pipeline decides whether packets continue to be forwarded or not.