esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Approach for Software Defect prediction Based on the Power Law Function
## 2 Fundamental Characteristics of Power Law Function and Software Defects
### Approach for Software Defect Prediction Based on Power Law Function  
In this section, we present the approach for detecting software defects based on the power law function. The general algorithm of the proposed model is expressed in Algorithm 1:  
```
Input D \(\leftarrow\) Original unlabeled datasets Inst. \(\leftarrow\) Instances in datasets M \(\leftarrow\) Each metric in dataset D MV \(\leftarrow\) Each value of the metric in dataset D
1 for M in All M do
2 for i = 1 to number of M do
3 Building power-law function for each M
4 According to Equation (14), compute transformation value in maximal curvature point T
5 if MVi. Insti > T
6 MVi. Insti is higher value, MVi. Insti = 1
7 else
8 MVi. Insti is normal value, MVi. Insti = 0
9 end if
10 end for
11 for each Insti in Inst.
12 Calculate the total number K of each Insti in the D with MVi. Insti = 1
13 Sort values of K, clustering into two groups, a top half and a bottom half.
14 for i = 1 to number of Inst. do
15 compare each K value in Inst.
16 if K values in top half
17 Inst.i is defect-proneness
18 else
19 Inst.i is defect-free tendency
20 end if
21 end for
22 Output Dataset with defect label
```  
**Algorithm 1** Software defect prediction based on power-law function  
Figure 4 shows the overall process of this approach. The kernel of our approach is to use the power law function to describe the metrics distribution and set the transformation value of the maximal curvature point as the threshold value to label the metrics.  
Figure 4: The overall process of the proposed unsupervised approach in software defect prediction.  
The process is stated in detail as follows.
## 4 Numerical Analysis
### Numerical Analysis Verification  
This section validates the numerical model's accuracy by comparing the experimental and simulated data.  
Figure 9 compares the TC\({}_{\text{A}}\) temperature distribution in the numerical analysis results to the TC\({}_{\text{A}}\) temperature distribution in the model test results, and Figure 9 compares the TS\({}_{\text{A}}\) thermal strain distribution in the numerical analysis results to the TS\({}_{\text{A}}\) thermal strain distribution in the model test results. Combined with a method of verifying the accuracy of the numerical model given in the literature [30], Table 3 shows the root mean square error (RMSE) analysis of the numerical analysis results of the temperature and strain of the pavement at different depths after 6 h of heating compared with the experimental results. The maximum root mean square errors of the pavement temperature and thermal strain were 0.7 \({}^{\circ}\)C and 23.82 \(\mu\varepsilon\), respectively, which were within the error range.  
The accuracy of the numerical analysis model was confirmed by the findings of this error analysis. Instrumental measurement error in the model test, as well as heat loss after energizing the carbon fiber heating wire, can create modest differences between the simulation and test findings.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Index** & **Unit** & **Concrete Pavement** & **Carbon Fiber Heating Wire** \\ \hline [PERSON]’s modulus (E) & MPa & \(3.25\times 10^{4}\) & \(1.2\times 10^{6}\) \\ Poisson’s ratio (\(\upmu\)) & – & 0.2 & 0.2 \\ Density (\(\uprho\)) & kg/m\({}^{3}\) & 2440 & 1800 \\ Thermal conductivity (\(\lambda\)) & W/(m\({}^{\circ}\)C) & 2.2 & 0.09 \\ Heat capacity (c) & J/(kg\({}^{\circ}\)C) & 1100 & 900 \\ Coefficient of linear expansion (\(\upalpha\)) & \(1/^{\circ}\)C & \(10^{-6}\) & \(3.5\times 10^{-5}\) \\ Compressive strength (\(\sigma_{\text{c}}\)) & MPa & 45 & \(1.2\times 10^{3}\) \\ Tensile strength (\(\sigma_{\text{b}}\)) & MPa & 5.0 & \(1.7\times 10^{3}\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Material parameters of concrete pavement and carbon fiber heating wire.  
Figure 8: Schematic diagram of numerical model meshing.
# Fusion Classification Method Based on Audiovisual Information Processing
## 4 Experimental Result and Analysis
### Comparative Test
#### 4.3.3 Ablation Test
Specifically, the experiment focused on the key modules of M-AVFA algorithm and observed their impact on model performance by replacing or removing specific feature fusion one by one, as shown in Table 9  
The experimental results show that different feature fusion methods have significant effects on the performance of multimodal target recognition. Among them, self-attention mechanism fusion is superior to splicing feature fusion and weighted feature fusion in accuracy, recall rate, accuracy rate and F1 score, indicating that it can more effectively capture the complementary information among multimodal features, thus improving the model's adaptability to complex scenes and the accuracy of target recognition. This result provides an important basis for optimizing the feature fusion module of M-AVFA algorithm and verifies the superiority of self-attention mechanism in multimodal target recognition task.
## 3 Three-Dimensional Numerical Model  
### Finite Element Calculation Model  
In this paper, a 3D geologic model is created by Midas GTS NX 2022 software developed for the geotechnical field. In order to minimize the negative influence of the boundary constraints on the calculation results in the finite element model, the left and right outer boundaries are taken to be 3-5 times the width of the excavation area, and the upper and lower boundaries are taken to be 3-5 times the height of the excavation area. The length (X direction) \(\times\) width (Y direction) \(\times\) height (Z direction) of the finite element model is 600 m \(\times\) 316 m \(\times\) 749.6 m, and it can be considered to be unaffected by construction factors, such as excavation, etc., outside the taken range.  
The boundary constraints imposed during the calculation are the following: the ground surface is free constraint; the front and rear boundaries of the model are constrained by displacement in the Y-axis direction and the left and right boundaries are constrained by displacement in the X-axis direction due to the presence of extrusion in the subsurface geotechnical body; and the lower boundaries of the model are fixed constraints, which limit the vertical and horizontal displacements and rotations.  
The 3D solid model building process is shown in Figure 4. Firstly, according to the contour data of the actual geological topographic map, the TGM (terrain generator) in GTS NX is used to generate the actual free ground surface, the solid model is generated according to the dimensions, and finally, the solid model is generated by combining the stratigraphic section, the tunnel dimensions, and the spatial relationship.  
This paper uses isotropic three-dimensional tetrahedral structural units to simulate mudstone and malmstone with the extended Drucker-Prager yield criterion.  
The traditional Drucker-Prager criterion overcomes the weaknesses of the Mohr-Coulomb criterion by considering the effect of intermediate principal stresses on yielding and damage, and the yield surface is smooth and without edges. However, the disadvantage is that it does not consider the nonlinear characteristics of yielding and damage and the characteristics of geotechnical materials with unequal compressive strengths on the flat surface, and the approximation of the actual damage conditions is poor. The Midas GTS NX 2022 finite element software used in this paper extends the classical Drucker-Prager model, and the shape of the yield surface in the meridional plane can be simulated by a linear function, hyperbolic function, or exponential function model.  
The surrounding rocks around the tunnel structure are symmetrically seeded with linear gradients to divide the fine cells and improve the calculation accuracy, with a grid size of 2-10 m, and the 3D grid model is generated with 689,200 cells after giving the rock parameters and the intrinsic model.
# Discovering Trends in the Digitalization of Shipping:
## 2 Current State of the Maritime Industry: An Industry 4.0 Perspective
### Cybersecurity in the Digital Era  
With the maritime industry's increasing reliance on digital systems, cybersecurity has emerged as a paramount concern. The transition to digital technologies exposes the sector to cyber threats, necessitating robust security measures [3].  
Representative of this is the significant incident in Australia in 2023, which saw a cyberattack paralyze several major ports for days. This attack targeted DP World's ports, handling around 40% of Australia's freight trade, causing widespread disruptions. This event is part of a broader trend of cyber threats facing the maritime industry globally, including ransomware attacks on Japan's busiest port, Nagoya, and disruptions at oil terminals in western Europe due to cyberattacks. Furthermore, the \"NotPetya\" malware in 2017 also severely impacted the global shipping giant Maersk, along with cyber incidents at major ports in the Netherlands, Canada, India, South Africa, and the United States [14; 15].  
In response to these growing threats, measures and guidelines are being developed and implemented. For example, the U.S. issued Presidential Executive Order 14028 in May 2021, focusing on enhancing cybersecurity across government agencies, critical infrastructure, and private companies. Moreover, the International Maritime Organization (IMO) is actively working to address cyber risks by revising guidelines for maritime cyber risk management [16].
## 4 Numerical Results
### Bifurcation Diagrams and the Corresponding Maximal Lyapunov Exponent  
Figure 9a,b depict the numerically obtained bifurcation diagrams of the state-variables \(y_{1}\) and \(y_{2}\), respectively. The corresponding maximal Lyapunov exponent of the system is shown in Figure 9c. The Lyapunov exponent corresponding to the forcing phase was ignored here, since it is always equal to zero. During this calculation we have used the classical algorithm [30], where the re-normalization period was assumed to be equal to period of external excitation. For each value of the excitation angular frequency, the exponent was computed over time equal to 1000 normalization periods, after ignoring 100 initial excitation periods of transient motion.. We have chosen the bifurcation parameter value \(\omega_{\rm nd}\) in the range between 3.89 and 4.12. When the parameter value is increased up to 3.905 from a low value, we get only the fixed point attractor at \(y_{i}^{*}=0\). When \(\omega_{\rm nd}\) is varied more in the positive direction, the fixed point becomes unstable, and a period-1 orbit emerges by a supercritical Hopf bifurcation. While \(\omega_{\rm nd}\) is increased more, the period-1 orbit persists up to the parameter value 4.023. The corresponding Lyapunov exponent is the negative value, which is shown in the Figure 9c. At the point of 4.023, a Neimark-Sacker bifurcation occurs, and the period-1 orbit loses its stability, and a quasi-periodic trajectory emerges. The maximal Lyapunov exponent reaches zero value at the bifurcation point 4.023. The quasi-periodicity of the system persists up to the parameter value 4.09. If one can notice carefully in the bifurcation diagram, there is a small chaotic window in-between the quasi-periodic orbit region in the parameter space between 4.05 to 4.09. The value of the \(\omega_{\rm nd}\) corresponding to the chaotic regime is 4.074. The maximal Lyapunov exponent shows the positive value where the system shows chaos. When the \(\omega_{\rm nd}\) value is increased further from 4.09, there is also a range of chaotic attractors with small periodic windows. The periodic window occurs at 4.094. The fixed point solution starts to exist from the value of \(\omega_{\rm nd}=4.10\). So, there is an interplay between quasi-periodicity and the chaotic orbits and chaotic orbits with periodic windows in the state variables in the bifurcation diagram. Due to the symmetric nature, the system has two bifurcation diagrams--one in the positive \(y\)-axis and another in the negative \(y\)-axis. We have chosen the initial condition as \([0.333,0,0,0]\).
## 2 Related Research
At Mobile World Congress 2019, China Mobile, together with Lenovo, Intel and Baicells, exhibited 4G/5G O-RAN dual-modecloud-based white-box small station solutions, and CertusNet released 5G independent grouping network base station products that conform to the O-RAN architecture. In the investigation of the underlying virtualization technology for 5G base stations, the Institute of Computing, Chinese Academy of Sciences, has proposed a super base station design to address the issue of excessive energy consumption of centralized baseband processing pools. The design is capable of allocating processing resources in accordance with the actual service load, demand, and other realistic scenarios, as well as running tasks on various hardware processing devices in accordance with the real-time requirements of the base station processing tasks. The virtualization of 5G access networks has been partially studied by the terrestrial mobile communication sector, but more research needs to be performed on RF digitization, baseband virtualization, and integrated resource scheduling of network computing and storage. Additionally, because of its architecture's focus on terrestrial mobile communications, it is still unable to handle multi-protocol system reconfigurability and multi-star and multi-station resource multiplexing for high- and low-orbit, wide- and narrow-band convergent satellite internet systems. At present, most of the research is focused on the design of virtual station architectures in the field of terrestrial mobile communication. The design of satellite communication systems has not been considered. Moreover, satellite-borne architecture designs have no research foundation. In the field of satellite communications, little research has been conducted in China on satellite ground stations oriented to rapid functional reconfiguration and on algorithms for fast migration of virtual functions, and there are no mature products related to virtualized satellite measurement, operation and control stations and communication base stations. In this paper, we provide an architecture for a horizontal shared virtual network that converges computing, communication, and storage facing to the complex and dynamic environment on the satellite. In order to realize quick migration of network functions, reduce the real-time calculation overhead of network migration, and achieve balanced network loads, we offer a fast adaptive migration algorithm for network functions based on multi-dimensional environment awareness.
## 5 Results and Discussion
### Validation  
The calibrated surrogate model was used for simulating the experiment Case R20, see Table 3, with a similar setup but different water vapor pressure (\(270\times 10^{5}\) Pa) and air volume flux in the HTF channel (\(47.22\) kg m\({}^{-2}\) s\({}^{-1}\)). The lower water pressure results in a lower equilibrium temperature and, despite a larger heat-transfer flux, in a slower conversion. The experimental data exceed 100% conversion against the statement of Reference [31] that conversions of maximum 95% were reached. We attribute this to measurement uncertainty. Figure 6 shows the simulation results of the surrogate model.  
Figure 5: Simulation results of the calibrated surrogate model with its 95% confidence interval (CI); the surrogate model is based on 200 simulation runs of the numerical model plotted in gray. The experimental reference data are plotted in black including error-bars representing the uncertainties.  
As outlined in the previous section, the simulation does not reach complete conversion due to the high value of the parameter \(\text{k}_{R,2}\) that phenomenologically represents the changed reaction dynamics once agglomerates have formed. As the experiment reaches 100% in 6000 s, possibly no agglomerates are present yet. This would be the case, if this experimental run was one of the first in a row of the altogether 33 experiments performed with the same reactor filling [32]. The chronological order of the experiments is not mentioned in References [31; 32] or Reference [20]. The parameter \(\text{k}_{R,2}\) limits the reaction rate at progressed conversion and the temperature profiles unfold accordingly. Experimental data and simulation results of the temperatures T1, T3, and T7 are shown in Figure 5(a)-c. The profiles show a good agreement with the experimental temperature curves. Thus, we tend to attribute this to a measurement bias in the experimental data regarding conversion.  
The model results overestimate the equilibrium temperature of the experiment. The equilibrium condition for pressure and temperature of Reference [14], which was used for the calibration case, is not adapted to the pressure conditions of the reaction conditions. The simulated temperatures, however, are still in the range of measurement uncertainty. Despite that, the temperature profiles of this experimental setup are reproduced with an accuracy comparable to the calibration case. The decrease in temperature is slightly steeper in the model results, but the velocity of the reaction front matches the model results. The final temperatures for each measurement lay within the range of measurement uncertainty.  
Figure 6: Simulation results (blue) for model validation according to the experimental data of Case R20.
# A Data-Driven State Estimation Based on Sample Migration for Low-Observable Distribution Networks
## 2 Fundamental Principles of Deep Transfer Learning  
### Principles of Deep Learning  
Deep learning (DL) has recently revolutionized machine learning (ML) across various domains, including state estimation. DL learns the intrinsic mapping rules from sample data by leveraging its powerful nonlinear fitting capabilities. Through multi-layer processing, low-level feature representations are progressively transformed into high-level abstractions, enabling the completion of complex regression and classification tasks using relatively simple models. Unlike traditional shallow learning methods, DL offers several key advantages: (1) DL adopts a multi-layer network structure, incorporating multiple hidden layers. (2) Feature learning is the core of DL, by transforming raw feature representations into a new feature space through layer-by-layer processing. This process simplifies classification and prediction tasks. Compared to manually crafted feature construction, DL-based feature learning better captures the rich intrinsic information embedded in the data.  
In this paper, a DL model is employed as the state estimator. A multi-layer architecture is designed, where distribution network measurements serve as inputs, and node voltages are the outputs. The network parameters are iteratively learned and optimized to model the nonlinear mapping between the measurement data and node voltages. Once training is completed, the model can be deployed for online SE.
## 5 Conclusions  
A numerical simulation was conducted to analyze the horizontal wind speed and sand volume fraction around and in a railway culvert. Then we proposed some new culverts to reduce sand deposition. And comparisons of the sand sedimentation level between the original and proposed culverts were made.  
The horizontal velocity had obvious stratification at different cross-sections of the culvert body, and the maximum velocity was reached in the middle. The curve of the horizontal wind speed along culvert axis had a W shape. The wind speed increased inside the original culvert, indicating a strong acceleration effect. The sand particles at the wing walls accumulated at the culvert inlet, increasing the sand volume fraction inside the culvert, demonstrating the deflection by the wing walls. As the inflow velocity increased, the sand accumulation potential decreased markedly. The sand volume fraction in the original culvert was the highest at \(v=10\) m/s.  
It was found that the wing walls, subgrade blocking, and a low inflow are important reasons for sediment accumulation in the railway culvert through the analysis of formation mechanism of sediment accumulation. Based on this, we tried to improve the culvert structure and two culvert designs to reduce sand deposition were proposed. The sand volume fraction was significantly lower in the proposed culverts than in the original culvert, and the straight culvert provided the best performance.  
The inflow direction may cause differences in the sand accumulation state at the entrance and inside of the culvert. And the wind tunnel experiments on the sand sedimentation mechanism around railway culvert could better verify and supplement the results of this paper. So they deserve our attention and are important research works we will be carried out in the future.  
Conceptualization, J.X.; methodology, J.X. and N.H.; software, J.X.; validation, H.D.; formal analysis, J.X.; investigation, G.S. and X.Z.; resources, N.H.; data curation, J.X.; writing--original draft preparation, J.X.; writing--review and editing, J.X. and H.D.; supervision, G.S. and X.Z.; project administration, N.H.; funding acquisition, N.H. and X.Z. All authors have read and agreed to the published version of the manuscript.  
This work was funded by the State Key Program of National Natural Science Foundation of China (41931179), Key Research and Development Program of Gansu Province (22Y7 FA019), Science and Technology Research Project of China National Railway Group Co. LTD (N2022G068), Scientific and Technological Research and Development Plan of China Railway Urumqi Bureau Group Co., Ltd. (WLMQ-KGHZGS-HRTLGGB-2020-0031), Natural Science Foundation of Gansu Province (22 JR5 RA344), and Youth Doctoral Foundation of Gansu Provincial Department of Education (2022 QB-063).  
This study did not involve humans or animals.  
This study did not involve humans.  
This study did not report any data.  
The authors declare no conflict of interest.
## 4 Lunar Antenna Types
### Wire Antennas
#### 4.4.3 Bow-Tie Antennas  
The bow-tie antenna is constructed from a bi-triangular sheet of metal, with the feed located at its vertex. Bow-tie antennas fed by coaxial line, stripline, and coplanar waveguides offer several benefits as UWB antennas, including low profiles, excellent radiation efficiency, ultra-broad impedance bands, and ease of construction. Because of their benefits, they are widely employed in a range of applications, including ground penetrating radars, mobile stations, and pulse antennas [76,77]. Detailed geometry and impedance calculations are in [78].  
The previous iteration of the LPR utilized a CH2 transceiver antenna consisting of a series of bow-tie antennas. CH2 operates in the 250-750 MHz range and has a depth resolution of less than 30 cm. To achieve an ultra-wideband capability, this set of antennas was equipped with resistors that absorbed the current reflected by the set's end. Additionally, a shallow, rectangular conducting cavity was incorporated into the bow-tie antenna design to enhance radiation performance and prevent interference from the surrounding environment. The cavity was positioned behind the antennas to increase radiation directed toward the ground and improve the forward-to-backward ratio. The CH2 transceiver antenna was installed at the bottom of the lunar rover, approximately 30 cm above the lunar surface. Figure 15 provides a detailed construction schematic of the CH2 antenna. Each element has a size of 336 mm \(\times\) 120 mm, with a spacing of approximately 160 mm between them. FEKO software and FDTD methods were used. A shallow, rectangular conducting cavity was used in the design to reduce interference from the surrounding environment.  
Figure 14: (**a**) The location of the LPR antennas on the lunar rover. (**b**) The folding of the CH1 antennas on the lunar rover. Adopted from Lunar Penetrating Radar onboard the Chang’e-3 mission by [PERSON] et al., Research in astronomy and astrophysics (2014) [75]. Reproduced by permission of RAA. All rights reserved.