esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Simulation Results and Discussion
### Effects of Volume Fraction  
In this subsection, the influence of particle volume fraction ranging between 15%, 25%, and 35% on the mechanical properties of MGMCs is studied under uniaxial compressive loading along the y-axis. Five different microstructural finite element models containing ellipse particles with random orientations and shapes are generated through the developed program for different particle volume fractions, respectively. It can be seen from Figure 8 that the volume fraction of the particle plays a small role on the stiffness of MGMCs, because the Ta particle and BMG have almost the same stiffness. When an MGMC containing 15% particles reaches the yield stress, the nominal stress decreases slowly for about 4% strain, and then loses its carrying capacity rapidly. In other words, the yield stress of an MGMC with 15% particles is also its strength. When an MGMC containing 35% particles reaches the yield stress, a hardening stage can be clearly seen with the increase of the loading. Shear bands intersect with each other and generate major and minor bands. The composites with 15 vol.% particles have the same shear band direction with pure BMGs due to the scale limitation of the particles in them. Nevertheless, the existing minor shear bands share plastic deformation with the major band. As a result, there will be no obvious difference between the different shear bands, which will improve the macroscopic ductility of the composites. Furthermore, with the increase of the particle volume fraction, shear bands cannot propagate in original direction because of the particle block. Ultimate shear bands are wavy, as shown in Figure 9.  
Figure 6: The influence of particle shape on the stress–strain curves of metallic glass matrix composites (MGMCs).  
Figure 7: The influence of particle shapes on normalized free volume in BMG: (**a**) \(r_{i}=1\); (**b**) \(r_{i}=2\) and (**c**) \(r_{i}=3\).
# Application of Label Correlation in Multi-Label Classification: A Survey
## 2 Overview of Label Correlation
### Introduction to Label Correlation
#### 2.2.1 Classification of Label Correlation Based on the Nature of Interrelationships  
In multi-label data, labels are not independent; there are correlations between them. Utilizing label correlations effectively is a key means of improving MLC methods [32]. Based on the nature of label relationships, label correlations can be divided into positive and negative correlations.  
(1) Positive Label Correlation  
Previous methods of utilizing label correlations have typically focused on the positive promotion between labels. Specifically, when an object is assigned a particular label, it often tends to simultaneously have other labels associated with that label. As shown in Figure 2a, an image labeled as \"ship\" is more likely to also be labeled as \"ocean\". Such mutually reinforcing label relationships are referred to as positive label correlations. When dealing with multi-label datasets, positive label correlations often manifest as label co-occurrence, where multiple related labels appear simultaneously in the same data instance.  
(2) Negative Label Correlation  
In practical applications, the presence of one label for an instance may indicate a lower likelihood of it having another label, or the absence of a label may increase the probability of the instance having a different label. This phenomenon is referred to as label negative correlation, and labels exhibiting strong negative correlations are known as mutually exclusive labels. For example, in the emotional labels of music, a sad song may evoke feelings of sadness and sorrow but is unlikely to bring joy to the listener. This example reflects the positive correlation between the labels \"sadness\" and \"sorrow\" as well as the mutually exclusive relationship between the labels \"sadness\" and \"joy\". Similarly, as shown in Figure 2b, the label \"bicycle\" is highly unlikely to appear in the same scene as the labels \"ocean\" and \"dining table\". Experiments have demonstrated that incorporating negative label correlations in multiple datasets positively impacts improving MLC performance [33].  
Figure 1: Classification of label correlations.
## 4 Measurement Results Analysis and Discussion
### Lateral Displacement  
Figure 11 shows the variation in the lateral soil displacement of the shaft with depth and construction schedule. The negative lateral displacement shows that the soil has moved out from the shaft. In Figure 11a, the distribution of the lateral displacement was triangular. The lateral displacement of the surface was the maximum displacement (8.4 mm). The minimum lateral displacement was at the bottom of the shaft. The lateral displacement of T2 was, overall, smaller than the displacement in the other two directions (see Figure 11b). The maximal displacement was 2.9 mm on the surface. This result indicates that the prefabricated supporting structure exhibits good performance with respect to deformation control. The lateral displacement of T3 was basically the same as that of T2 on the surface (see Figure 11c). The displacement increased sharply at a depth of 4 m, reaching 14.25 mm. Based on the observation of the stratum conditions on site, we found that it was the clayey silt with a high moisture content (25.9%) and a low compression modulus (8.9 MPa) that underwent a rather large deformation under prestress conditions.  
Figure 11: Measured lateral displacement: (**a**) T1; (**b**) T2; and (**c**) T3.  
In Figure 11, the following features of the lateral displacement can be concluded.  
The lateral displacement of all monitoring points moved toward the outside of the shaft within the excavation depth. This finding is consistent with the result of the measured earth pressure between the static earth pressure and the passive earth pressure. This result is obviously different from the soil displacement models of traditional passive supports [26].  
At the depth of each excavation step, the lateral displacements are larger than the last measured results after prestress support, and a displacement inflection develops. This inflection is caused by soil compaction under prestress conditions.  
The lateral displacement generally increased with increasing depth of excavation and supporting. The lateral displacement increase is a process of soil compaction, which causes prestress attenuation until equilibrium is reached. This phenomenon coincides with an attenuation in the earth pressure.  
The lateral displacement of the three directions was not equal. This displacement is related to the asymmetrical prestress. The lateral displacement of T2 is smaller than that of T1 and T3. This value indicates that the farther the distance from the shaft is, the smaller the lateral horizontal deformation will be.  
The stable value of the lateral displacement was larger than the value that was measured at the end of construction. This finding means that the soil is still slowly compacting under prestressing conditions after construction before finally stabilizing.  
The lateral displacement of the three directions shows the change trend of a large deformation at the top and a small deformation at the bottom. The reason for this variation is that the soil has poor compactness at the top and is more easily compacted by prestress.
# Dynamic Observer Modeling and Minimum-Variance Self-Tuning Control of EDM Interelectrode Gap
## 5 Test Verification  
The EDM equipment shown in Figure 1 was modified to meet the control requirements for the interelectrode gap in the machining. The improved EDM control system is shown in Figure 9. The no-load voltage of the pulse power supply was 100 V, the pulse width was 100 \(\upmu\)s, the pulse interval was 400 \(\upmu\)s, and the workpiece was p-type monocrystalline silicon. Since the no-load voltage between the electrodes was 100 V, the signal adjusting module was developed for linear transformations of the interelectrode output voltage. The output voltage range of the signal adjusting module was 0-10 V. Since the interelectrode gap model \(d\) was calculated based on the time constant \(\tau\) when the voltage rose to 0.63 U, the sampling frequency of the data acquisition card was relatively high. The high-speed data acquisition card PXle-5172 from NI (National instruments, Austin, TX, USA) was used for high-speed acquisition of the voltage signal and data acquisition. The bandwidth of the data acquisition card was 100 MHz, the sampling frequency was 250 MS/s, and the input voltage range was \(-\)40 to 40 V. The PXle-7342 motion controller of NI (National instruments, Austin, TX, USA) was used to control the motor movement, and the pulse output rate of the motion controller was up to 4 MHz. The motor was a Yaskawa SGM7J-04 AFC6S servo motor (National instruments, Austin, TX, USA) used in the position control mode. Through the calibration test, the positioning accuracy of the EDM system could reach 1 \(\upmu\)m. The position of the electrode was adjusted every 0.1 s during the test.
# Analysis of Sealing Characteristics of Lip Seal Rings for Deep-Sea Separable Pressure Vessels
## 3 Results and Analysis
### Lip seal Sealing Performance
As the water pressure increased, the peak contact stress shifted downstream, forming a pattern of two contact stress extreme points at a water depth of 10 m. Subsequently, as water pressure continued to increase, the peak contact stress downstream near the shoulder of the lip seal experienced a significant rise, surpassing the upstream extreme point. Figure 11d-f depicts the displacement vector of the lip seal deformation following installation by preload only, as well as at water depths of 10 m and 300 m underwater. At a water depth of 0 m, the upper surface of the lip seal experiences downward pressure exerted by the upper cover, while the bottom surface serves as a supportive base. The behavior of the upper part of the seal resembles that of a cantilever beam undergoing bending. The displacement direction remains consistent across all nodes. Notably, the surface of the seal's shoulder, delineated by the circled area in the figure, undergoes stretching, causing the previously rounded corners to become straightened. As the water depth increases from 0 m, the downward pressure on the upper part of the seal intensifies, resulting in an amplified bending deformation. Simultaneously, the inner cavity surface of the lip seal experiences a gradual increase in water pressure. Considering that the seal is composed of an incompressible super-elastic material, the water pressure acts as a compressive force on the seal. Consequently, the deformation of the seal under water pressure conforms to the  
Figure 11: Comparison of the distribution of contact stress and deformation displacement vectors at various points on the lip seal at different water depths. characteristic of maintaining a constant volume [20,21]. In conjunction with the geometric constraints imposed on the remaining surfaces of the seal, the seal material inevitably shifts towards the free surface, leading to displacement variations at each node, as depicted in Figure 11e,f. Notably, the center of the displacement vortex progressively migrates towards the upper right corner, in proximity to the seal shoulder. Upon closer examination of the direction of displacement within the seal shoulder material, it becomes evident that the seal undergoes continuous compression towards the seal shoulder as the water depth increases. This compression corresponds to the occurrence of downstream contact stress extremes. Conversely, the material upstream of the seal experiences a downstream compression due to the water pressure, leading to a gradual increase in contact stress. There is friction between the main sealing area on the upper surface of the seal and the upper spherical shell surface, and the squeezing effect at both ends of the lip seal cannot be transferred to the entire main sealing line, thus forming two local contact stress peaks and raising the peak contact stress.
## 4 Results
### Run-off Risk Assessment (FFPI)
#### 4.3.3 FFPI Results Validation  
As was presented in Section 3.3.5, the results validation and model's performance evaluation were done through the ROC curve and AUC. It can be observed that in terms of the success rate, both the MLP models obtained very good results. Thus, the AUC for MLP\({}_{1989}\) was equal to 0.895, while for MLP\({}_{2019}\), the AUC was equal to 0.874 (Figure 9a). Very good results were also achieved after the application of the prediction rate. Thus, the AUC for MLP\({}_{1989}\) was 0.864, while for MLP\({}_{2019}\), it was 0.837 (Figure 9b). These results highlight a high degree of accuracy associated with the FFPI maps. Therefore, the results obtained were used for further analysis.  
Figure 8: FFPI values: (**a**) 1989, (**b**) 2019, (**c**) 1989 (values/km\({}^{2}\)), and (**d**) 2019 (values/km\({}^{2}\)).
# Typhoon Loss Assessment in Rural Housing in Ningbo Based on Township-Level Resolution
###### Abstract
At present, emergency management departments around China mainly collect and summarize typhoon disaster data through grassroots disaster informants, and although the data time series is long, there are many human interference factors, the quality of historical data is not ideal, and it focuses on statistics of civil indicators such as the number of affected residents, casualties, and direct economic losses. In addition, the typhoon disaster data provided by meteorological and water conservancy departments have low spatial resolution and mainly focus on disaster-causing factor statistics, lacking disaster-bearing body statistics. Therefore, it is urgent to reconstruct the bottom-up data of disaster-bearing bodies from the county and township levels to provincial and municipal levels for historical typhoon disaster events in China. In recent years, the public catastrophe insurance work carried out by commercial insurance companies can better compensate for the above deficiencies in disaster statistics, the location of insurance targets can be precisely located to townships and villages, and the payout amount can accurately reflect the actual economic losses of the targets, which can be used to quantitatively assess typhoon loss risks. Therefore, this paper used rural housing as the target disaster-bearing body and proposed a new approach to establish a typhoon loss assessment model in rural housing based on township-level resolution by utilizing a RBF neural network with the help of policy insurance data, which can provide a scientific basis for the subsequent formulation of typhoon disaster insurance policies and typhoon emergency plans.
## 4 DA Experiments
### Experiment Characteristics
#### 4.1.2 DA Performance Evaluation  
The success of the assimilation in the experiments and Set-Ups is evaluated. Different measures for evaluating and comparing the results of various scenarios employed in this study are used as benchmark indicators. The velocity estimates obtained by Free-Run and Base-Test were compared with the drifter measurements in terms of RMSE at any station or location that observations were available.  
Root mean square error (RMSE) is estimated as:  
\[\text{RMSE}=\sqrt{\frac{1}{n}\ \sum_{i=1}^{n}(V_{simulated}-V_{observed})^{2}} \tag{8}\]  
To quantify the improvement gained by the DA, we defined percentage improvement as:  
\[\text{\% Improvement}=[1-[RMSE_{Free\ Run}/RMSE_{Estimate}]]\times 100 \tag{9}\]  
Absolute Accuracy Error is calculated as:  
\[\text{AAE}=|V_{simulated}-V_{Observed}| \tag{10}\]  
Mean Absolute Error is calculated as:  
\[\text{MAE}=\frac{1}{n}\sum_{i=1}^{n}|V_{simulated}-V_{Observed}| \tag{11}\]  
Deviations between simulated velocities before and after assimilation (i.e., Free-Run and DA experiments) were used to compare the performance of DA for both Eulerian and Lagrangian assimilations (i.e., Root Mean Square Deviation (RMSD)).  
The velocities in the model state are described in terms of north/east (\(V_{x}\) and \(V_{y}\)) components. The results here are presented in terms of the magnitude and direction of the currents because, operationally, these are the parameters of interest. The velocity magnitude and direction (\(\theta\)) are obtained as:  
\[V_{mag}=\sqrt{V_{x}{}^{2}+V_{y}{}^{2}}\ \ (\text{m/s}) \tag{12}\]  
\[\theta=\frac{\tan^{-1}\left(\frac{V_{y}}{V_{x}}\right)}{\pi}\times 180\ (\text{degrees}) \tag{13}\]
# Requirements for Crafting Virtual Network Packet Captures
## 3 Requirements
### Flexibility  
Packet captures of hardware-based networks provide mostly static information, as the number of changes inside this environment is very low. Connected systems such as servers or computers do not change in these environments, which results in a predictable network design. A major benefit of virtual networks and virtual environments is the flexibility and dynamic of the infrastructure. Typically, packet captures of virtual environments contain a huge number of changing network information such as IP addresses, protocols and involved hosts. This is a result of VMs that are started and stopped inside the environment, or suspended systems copied to different locations inside a user network. A packet generation process in this environment should provide the same flexibility as the environment; otherwise, the generation process is too static for a modern environment. As a result, the generator should be able to add additional systems into the capture file or change information of existing systems.
## 4 Results
### Acoustic and Occupancy Data in the NH Living Room  
The living room space under investigation had a volume of approximately 615 m\({}^{3}\) (h = 3 m, on average). The measured reverberation time prior to the acoustical retrofit intervention was 0.91 s and it represents the arithmetic average of the \(T_{20}\) values measured in the 0.5-2 kHz frequency range (\(T_{0.5\text{-}2\text{kHz}}\)). This choice was made to stay aligned with regional guidelines for the acoustic performance of such spaces: the recommended value for living rooms is 0.8 s [36], signifying that acoustic correction for the investigated space was desirable. The reverberation time measurements were repeated after the installation of the floating acoustic floor and absorbing ceilings and the new \(T_{0.5\text{-}2\text{kHz}}\) value was 0.39 s, showing a considerable improvement [13].  
Figure 2 shows occupancy and noise level data in the living room during the 12-h observation slot. People count is organized in three categories: staff, residents and visitors (the researcher carrying out the observation was excluded from this count). The 30-min slots were arranged to match the \(L_{\text{Aeq},30\text{min}}\) intervals. A pattern with two daily peaks for both sound levels and occupancy can be observed in the morning during breakfast, common activities time and early lunch, and in the afternoon with visitors' presence and dinner; between those peaks, levels go down as some residents return to their private bedrooms. \(L_{\text{Aeq},30\text{min}}\) generally range between 50 and 70 dB, showing that the acoustic environment of the living room was never particularly quiet. Sound level monitoring was repeated after the acoustical retrofit intervention and some differences were observed, but they are discussed in more detail in [13], as the topic is out of the scope of the present paper. In general, Figure 2 suggests that in the NH living room low ambient noise levels might also be associated with relatively higher occupancy (like in the slots 11:30-12:00 h or 17:00-17:30 h); in those cases, field notes confirmed that several residents were listening to a radio or watching TV (thus, constant noise and fewer people speaking). This supports the claims that NH living rooms exhibit different behavioural patterns if compared with other eating establishments.
# Study on Nanosecond Laser Ablation of 40 Cr13 Die Steel Based on ANOVA and BP Neural Network
###### Abstract  
Microstructured steel 40 Cr13, which is considered a hard-to-machine steel due to its high mechanical strength and hardness, has wide applications in the dies industry. This study investigates the influence of three process parameters of a 355 nm nanosecond pulse laser on the ablation results of 40 Cr13, based on analysis of variance (ANOVA) and back propagation (BP) neural network. The ANOVA results show that laser power has the greatest influence on the ablation depth, width, and material removal rate (MRR), with influence levels of 52.5%, 60.9%, and 70.4%, respectively. The scan speed affects the ablation depth and width to a certain extent, and the influence of the pulse frequency on the ablation depth and MRR is non-negligible. BP neural network models with 3-8-3, 3-10-3, and 3-12-3 structures were applied to predict the ablation results. The results show that the prediction accuracy is relatively high for the ablation width and MRR, with average prediction accuracies of 96.0% and 93.5%. The 3-8-3 network model has the highest prediction accuracy for the ablation width, and the 3-10-3 network model has the highest prediction accuracy for the ablation depth and MRR.  
nanosecond pulse laser: 40 Cr13; ANOVA; BP neural network +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Anonymization and Information Loss Estimation  
#### 2.2.1 K-Anonymity  
K-anonymity is the primary anonymization method that has been proposed for the prevention of identity disclosure in data publishing, limiting the probability of linking an individual to their records to \(1/k\)[9,31]. Of central importance to the k-anonymity concept is the quasi-identifier (\(QI\)) set, a set of seemingly innocuous dataset attributes whose linkage with external information can lead to the reidentification of individual records, as well as the equivalence class (\(EQ\)), a set of dataset records that are indistinguishable from each other with respect to the values of the QI set. A dataset satisfies the k-anonymity constraint if each record is identical to at least \(k-1\) records with respect to the \(QI\) set, which means that each equivalence class \(EQ\) consists of at least \(k\) records. K-anonymity can be enforced  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Original Dataset** & **Attributes after** & **Attribute Type** & **Values** & **Attribute Description** \\ \hline AGE & AGE & Numerical & 0–114 & Patient age \\ \hline SEX\_F & & & & \\ SEX\_M & & & & \\ \hline CURADM\_DAYS & CURADM\_DAYS & Numerical & 1–307 & Number of days during the current stay at the hospital \\ \hline OUTCOME\_H & & & & \\ \hline OUTCOME\_N & & & & \\ OUTCOME\_I & & & & \\ \hline OUTCOME\_D & & & & \\ \hline CURRICU\_FLAG & CURRICU\_FLAG & Categorical & [0, 1] & The patient had to be transferred to ICU during the current hospitalization \\ \hline PREVADM\_NO & PREVADM\_NO & Numerical & 0–170 & Number of previous admissions to the hospital \\ \hline PREVADM\_DAYS & PREVADM\_DAYS & Numerical & 0–627 & Cumulative number of days of previous hospital admissions \\ \hline PREVICU\_DAYS & PREVICU\_DAYS & Numerical & 0–315 & Cumulative days of ICU treatment during previous hospital admissions \\ \hline READMISSION & READMISSION & & & \\ \_30\_DAYS & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The attributes of the original dataset, the attributes after the one-hot decoding and their descriptions.  
using suppression and generalization techniques. Suppression is achieved by replacing some of the original attribute values with a specific value indicating its non-disclosure. Generalization, or re-coding, is achieved by replacing the attribute values with less specific but consistent values.