esa-sceva/satcom-chunk-collection huggingface dataset:
# Real-Time Stress Field Prediction of Umbilical Based on PyEf-U-Net Convolutional Neural Network
## 5 Experimental Results and Discussion  
### Experimental Parameters and Details  
The dataset consists of 386 umbilical component layout results obtained with the DE-GLM algorithm. Since the prediction accuracy of the deep learning model depends largely on the training dataset samples, the selected data samples are representative of the umbilical component layout results, so that the PyEf-U-Net model can have a better generalization ability, which facilitates obtaining a high-performance deep learning model and acquiring the best model training parameters so that it can gain high-accuracy prediction results. A total of 193 data samples are selected from the original dataset as the test dataset, and the remaining original data samples are expanded via data augmentation methods. The expanded training dataset consists of 1544 data samples, and the validation dataset consists of 193 data samples. To clearly illustrate the idea of dataset division, part of the training dataset, validation dataset, and testing dataset are divided as shown in Figure 13.  
The PyEf-U-Net network model is trained with Pytorch framework, [PERSON] is chosen as the optimizer, the learning rate is set to 0.0001, and the Batch size is set to 4. The above parameters are the optimal hyperparameters for network training.  
The loss function refers to the function of the cost required to map one event to another event associated with it, connecting the learning problem to the training network by minimizing the loss function, which achieves the purpose of training the network parameters. The trained network model has the best performance and the final prediction map of the stress field of the umbilical is closest to the sample labels. Multi-class cross entropy loss function is chosen in this paper to train the proposed PyEf-U-Net network, and the equation is shown below:  
\[Loss(\text{CrossEntropyLoss})=-\frac{1}{N}\sum_{i=1}^{N}\sum_{j=1}^{K}y_{i,j} \log\hat{y}_{i,j} \tag{21}\]  
where \(y_{i,j}\) denotes the data label value, \(\hat{y}_{i,j}\) refers to the model output value, \(N\) refers to the number of samples, and \(K\) indicates the label value.  
Figure 13: Division of the dataset for the network model.  
To evaluate the reasonability of the prediction results of the network model, the calculation results of the DE-GLM algorithm are compared and analyzed with the prediction results of the network model, with the formula shown below:  
\[Accuracy=\frac{TP+TN}{TP+FP+FN+TN} \tag{22}\]  
\[F_{1}=2\times\frac{Precision\times Recall}{Precision+Recall} \tag{23}\]  
\[Precision=\frac{TP}{TP+FP} \tag{24}\]  
\[Recall=\frac{TP}{TP+FN} \tag{25}\]  
The classification confusion matrix is shown in Table 5.
# The Vibration Isolation Design of a Re-Entrant Negative Poisson's Ratio Metamaterial
## 5 Simulation of Transfer Performance and Experimental Verification  
In this section, we use simulation and experimental methods to validate the vibrational suppression capability of the proposed N-NPRM and compare the simulations with the experimental results to verify the accuracy of the simulation method. The results show that the simulation agrees well and can effectively guide the design of N-NPRM.  
An experimental test of VTL was carried out, as shown in Figure 11a. The sampling frequency of the experiment was 10,000 Hz, and the sampling time was 10 s. A vibration shaker was used to excite the sample. The white noise generated by a sweep signal generator was used as an excitation signal to control the vibration shaker, and the sweeping frequency range was from 1 Hz to 5000 Hz. Two acceleration sensors were used to collect the vibration signal, and the sensitivities were 1.075 \(\mathrm{mv/(m\cdot s^{2})}\) and 1.067 \(\mathrm{mv/(m\cdot s^{2})}\), respectively. The ultimate testing range of the acceleration sensor was 10,000 Hz.  
The results of the simulations and experiments are shown in Figure 12. As can be seen in Figure 12a,b, the frequency consistency corresponding to the structural mode peaks obtained by the simulation and experiment is good (peaks in yellow box), which  
Figure 11: The testing of vibration transmission loss. (**a**) The testing system of vibration transmission loss, (**b**) The metamaterial samples with metal pins and particle damping.  
indicates that the simulation method was effective. Figure 12c shows that the simplified method for N-NPRM with particle damping is feasible, because the frequency consistency corresponding to the simulation and experimental structural mode peaks is good (yellow box), indicating that it was feasible for the stiffness and quality simplification of the particle damping. However, because the action mode of the particle damping was different from the structural damping, the suppression effect of the structural vibration was quite different. Therefore, Figure 12d is an experimental comparison of the vibration suppression effect of N-NPRM with steel pins and N-NPRM with particle damping, and the results show that the particle damping can achieve a very good vibration suppression effect in the low- and middle-frequency bands (peaks in yellow boxes). It is worth noting that particle damping has advantages in the application of the vibration suppression of space equipment due to its good environmental adaptability and high reliability.
# Compression of Multibeam Echosounders Bathymetry and Water Column Data
## 4 FAPEC Tailoring for the KMALL Data Format
### Additional Remarks  
Before proceeding to test the proposed algorithm, we shall remark on its robustness in front of unexpected data. The main purpose of the mechanisms here described is to avoid any kind of loss. It is useful in two cases: corrupted KMALL files and non-KMALL files. In practice, both cases are the same, as distinguishing a corrupted datagram from a non-KMALL file would require a continuous checking of data.  
The approach we follow makes use of the header information in a KMALL datagram. In the format specification [13], it is established that the first 4 bytes are the datagram size in bytes, the following 4 bytes are the datagram type and the last 4 bytes of the datagram are also the size. This information allows us to perform two checks: first, we validate the datagram type. That is, the first byte must be a hash sign #, and the following three bytes must be capital letters from the Latin alphabet. If this condition is not fulfilled, we can assume that the input is not a KMALL datagram. If this test is successful, we perform a second one, which mainly consists in comparing the sizes given in the header and in the last bytes with the chunk size. If the result is bigger (as an absolute value), we can assume that those fields are not datagram sizes; hence, the input data are non-KMALL. These checks are summarized in Algorithm 1.  
If the tests fail and we assume non-KMALL data, we apply a fallback algorithm, which consists in predicting a sample with the previous one for the whole chunk. Note that the advantage of chunking appears again: if a KMALL chunk is corrupted but the next one is correct, then the algorithm proposed in the previous sections will be applied.  
```
1:\(dgmType\gets datagram\_header\_dgmType\)
2:\(dgmBytes\gets datagram\_header\_dgmBytes\)
3:if\(\text{dgmType[0]}\) = # && dgmType[1:4] = [A-Z]+ then
4:if\(|\text{dgmBytes}|>\text{chunkSize}\)then
5:\(compressCallback()\) {Apply a simple differential pre-processing}
6:else
7:\(compressKmall()\) {Apply algorithm described in previous sections}
8:endif
9:else
10:\(compressCallback()\)
11:endif
```  
**Algorithm 1** KMALL datagrams consistency check algorithm.
# Situation Assessment of Air Traffic Based on Complex Network Theory and Ensemble Learning
## 4 Experimental Setup and Details  
All experiments used the same configuration of computer (CPU Intel i7-7700, RAM 16 GB) and experimental platform, Python 3.7.1, Windows10-64 bit. Air traffic operation data in East China in 2019 were provided by the Air Traffic Management Bureau of the CAAC.
## 3 Results and Discussion
### Numerical Simulation of the Cup Test  
Results of the cup test evaluated as the relationships of the experimentally-measured drawing forces versus blankholding forces are shown in Figure 8. The slopes of the dependent drawing force to the blankolding force express the values of friction coefficients \(\mathrm{f_{m}}\), \(\mathrm{f_{c}}\) as is shown in Table 5.  
Comparing friction coefficients \(\mathrm{f_{1,2}}\) determined by the friction simulator (Table 4) and the friction coefficient values \(\mathrm{f_{m}}\) determined by the cup test (Table 5) resulted in good conformity for material DC 05 with a difference \(\Delta\mathrm{f}\) = 0.008. However, there is a higher difference \(\Delta\mathrm{f}\) = 0.03 for material AISI 304, even though it is within the error of measurement.  
When comparing the results of the cup test and numerical simulations of the cup test, there is good conformity for material DC 05 with difference \(\Delta\mathrm{f}\) = 0.016, but the difference for material AISI 304 is higher \(\Delta\mathrm{f}\) = 0.031 (Table 6). Considering it is the austenitic stainless  
Figure 8: Drawing forces to blankholding forces dependence in the cup test.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Material** & \(\mathbf{F_{N}}\) **(kN)** & **2** & **5** & **10** & **20** & **30** & **Linear Regression** & \(\mathbf{f_{m}}\) & \(\mathbf{f_{c}}\) \\ \hline \multirow{4}{*}{DC 05} & \(\mathrm{F_{pm}}\) (kN) & 19.08 & 19.91 & 21.08 & 23.2 & 24.8 & 18.88 + 0.204 \(\times\mathrm{F_{N}}\) & 0.102 & - \\  & \(\mathrm{F_{pc}}\) (kN) & 17.5 & 18.41 & 19.23 & 20.92 & 22.45 & 17.40 + 0.172 \(\times\mathrm{F_{N}}\) & - & 0.086 \\  & \(\Delta\mathrm{F_{pm(m<)}}\) (kN) & 1.58 & 1.5 & 1.85 & 2.28 & 2.35 & & & \\  & \(\Delta\mathrm{F_{pm(m-)}}\) (\%) & 8.28 & 7.53 & 8.78 & 9.83 & 9.48 & & & \\ \hline \multirow{4}{*}{AISI 304} & \(\mathrm{F_{pm}}\) (kN) & 28.56 & 29.21 & 31.4 & 35.22 & 39.54 & 27.46 + 0.397 \(\times\mathrm{F_{N}}\) & 0.199 & - \\  & \(\mathrm{F_{pc}}\) (kN) & 24.55 & 25.7 & 26.8 & 30.33 & 34.02 & 23.79 + 0.335 \(\times\mathrm{F_{N}}\) & & 0.168 \\ \cline{1-1}  & \(\Delta\mathrm{F_{pm(m<)}}\) (kN) & 4.01 & 3.51 & 4.6 & 4.89 & 5.52 & & & \\ \cline{1-1}  & \(\Delta\mathrm{F_{pm(m-)}}\) (\%) & 14.04 & 12.02 & 14.65 & 13.88 & 13.96 & & & \\ \hline \end{tabular}
\end{table}
Table 5: Comparison of experimentally measured \(\mathrm{F_{pm}}\) and calculated \(\mathrm{F_{pc}}\) values for drawing forces.  
Figure 7: Dependence of friction coefficient \(\mathrm{f_{1,2}}\) on the blankholder pressure.  
steel, different hardening models should be used in the numerical simulation because they affect the maximum force in the cup test [25; 26].
## 4 Application of Moisture Identification and Algorithm Comparison
### Moisture Identification Result  
Only the Energy, Duration, Counts Number, and Peak Amplitude parameters were retained after the two-step selection; thus, the four features were used to conduct the classification of rock moisture using the support vector machine (SVM) classifier. The SVM is a widely used supervised classifier that transforms non-separable samples into high-dimensional feature spaces by a non-linear mapping algorithm. There were 350 samples in the training group, with 70 samples for each moisture content. The test group consisted of 150 samples, with 30 samples for each moisture content. An identification model was obtained by training, and the identification results are shown in Figure 9a. According to the results, there were eight errors in the total of 150 test data samples, and the overall identification rate was 94.7%.  
Figure 9: Output of comparative experiment using the support vector machine (SVM), where (**a**) is the result of the two-step-method, (**b**) is the result of principal component analysis (PCA), and (**c**) is the Pearson coefficient method.
## 6 Comparison of the NOP Query with Related Technologies and Possible Applications
### Information Retrieval \(\times\) NOP Query  
Information retrieval aims to find relevant documents in a large collection, minimizing manual effort [70]. Meanwhile, NOP Query also performs data/event retrieval following a different method from the existing literature, especially for applications where the entries are regular, as the start of the notification chain depends on the change in the values of _Attribute_(s). The NOP query can also support information retrieval offline, e.g., without regular entries, but requires further experimentation to adapt to this context.
# Detecting Composite Functional Module in miRNA Regulation and mRNA Interaction Network
## 4 Experiments and Results
### Comparison of Overall Performance  
In order to compare overall performance among the three methods, we test MBCFM, Mirsynergy and SNMNMF on the ovarian cancer dataset, respectively. In accordance with the MBCFM algorithm steps, we firstly detected 13,050 maximal bicliques by using maximal biclique enumeration algorithm. We next computed the density of each biclique and got rid of the bicliques whose _I-density_ value equaled to zero, and 273 R-pair miRNA-mRNA clusters were retained. We then expanded the modules by adding neighbor mRNAs into the R-pair miRNA-mRNA clusters. Here, we set _R-density_ threshold \(\theta\) = 0.5, we furthermore added related miRNAs to the modules to prevent the omission of regulatory miRNAs. After removing the miRNA clusters with the number of miRNAs or mRNAs are less than two, 94 composite miRNA regulatory modules were finally identified by MBCFM, which have 5.42 miRNAs and 40.3 mRNAs per module on average (see Table 1). For Mirsynergy and SNMNMF, they identified 84 and 49 modules respectively, and the respective averaged number of miRNAs (mRNAs) per module is 4.17 (9.92) and 4.04 (81.88). The maximum number of miRNAs in a single module detected by MBCFM is 18. In contrast, the largest number of miRNA in Mirsynergy is 12, and in SNMNMF is 9.  
We further evaluated the density of the connection in the identified modules. The averaged density values of MBCFM, Mirsynergy and SNMNMF are 0.312, 0.063, and 0.004, respectively. Notably, the modules identified by MBCFM are more densely connected as compared to other two methods (\(p<1.744\times 10^{-10}\), one-sided Wilcoxon signed rank test). In contrast to Mirsynergy and SNMNMF, the relatively dense modules identified by MBCFM are in better agreement with the cooperative regulation.  
Besides, MBCFM proved to be much more computationally efficient than Mirsynergy and SNMNMF in running time. Tested on the same data ovarian cancer dataset and same platform Windows 7 system with 4 GB RAM, it took MBCFM less than one hour, while it took Mirsynergy more than one hour and SNMNMF more than 24 hours to finish the computation.
# Research on Non-Contact and Non-Fixed Cable Force Measurement Based on Smartphone
## 2 The Proposed Method for Cable Force Determination
### Cable Force Determination
#### 2.3.2 Cable Force Calculation  
At this stage, the main vibration frequency \(F_{h}\) and the order of frequency \(n\) have been obtained, based on which the cable force can be calculated by using Equation (7).  
\[T=4 ml^{2}\frac{F_{h}{}^{2}}{n^{2}}-\frac{EI}{l^{2}}(n\pi)^{2} \tag{7}\]  
In this equation, \(T\) is the cable force, \(m\) is the cable mass per unit length and \(l\) is the length of cable. \(F_{h}\) corresponds to the \(n\)-th natural frequency of the cable. \(E\) and \(I\) represents the elastic modulus and the inertia of moment of the cable, respectively. Considering that the impacts of flexural stiffness on cable force is less than 3% and would further decrease  
Figure 8: Spectrum analysis results.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Order of Frequency Represented** & **2 nd** & **3 rd** & **4 th** & **5 th** \\ \hline
**Possibility** & 37.5\% & 70.8\% & 54.2\% & 50\% \\ \hline \hline \end{tabular}
\end{table}
Table 3: Determination of order of frequency.  
when the cable length increases, the equation can be further simplified as follows, i.e., Equation (8).  
\[T=4 ml^{2}\frac{{F_{h}}^{2}}{n^{2}} \tag{8}\]  
In this manner, the cable force can be efficiently estimated by substituting the peak frequency and corresponding order of frequency obtained in the previous steps.
## 4 Implementation Procedure
### Validation
#### 4.3.4 Optimized Site or Node for Data Allocation  
The Genetic Algorithm (GA) is employed to allocate the fragmented data to different nodes. The GA optimizes the allocation process based on a fitness function, which is thetotal cost of allocation to ensure that the data fragments are distributed efficiently across the nodes, enhancing the overall system performance. The total cost of the allocation provides insights into the efficiency of the allocation process.
## 5 Conclusions  
### Main Contributions  
This study contributes to the advancement of medical image analysis by applying DL to the classification of DFUs, a medical condition with severe complications that can drastically affect a patient's life, such as infection, amputation and low levels of quality of life. An application was presented, demonstrating how artificial intelligence can contribute to clinical decision-making and patient outcomes through the early and accurate diagnosis of ulcers in an automated way.  
One of the main contributions of this work lies in demonstrating that strong classification performance for diabetic foot ulcers can be achieved even with limited medical data, through a comparative analysis of three well-established CNN architectures (MobileNetV2, VGG16 and VGG19) adapted using transfer learning, data augmentation and fine-tuning. This analysis highlights the viability of applying DL in real-world clinical contexts, where data availability is often constrained and does not present the highest quality. Moreover, the comparative results underscore the practical trade-offs between the models: while MobileNetV2 offers a lightweight design suitable for deployment on devices with limited resources, the very high performance of VGG16 and VGG19 suggests that they could also be suitable for clinical applications where diagnostic accuracy is the priority.  
Furthermore, the results further emphasise the importance of architectural decisions and fine-tuning techniques in optimising model performance. MobileNetV2, although providing advantages of efficiency and deployability, lacks sensitivity, and so there must be compromises to be made in clinical practice. On the other hand, VGG16's and VGG19's improved performance, especially inclusion of dropout and Batch Normalisation layers, indicates the necessity for precision model management to optimise diagnostic accuracy.  
A further contribution of this study is the fairness analysis based on skin tone, aimed at identifying potential disparities in model performance across different skin types. The results revealed lower reliability for individuals with \"Light\" and possibly \"Intermediate\" skin tones, a limitation that could lead to unequal diagnostic outcomes in practice. These findings contribute to the field by exposing fairness gaps that are often overlooked in diabetic foot ulcer classification research. Furthermore, it reinforces the need for more inclusive datasets and evaluation protocols to ensure that AI-based diagnostic systems serve all patient populations equitably.
# A Review of Fault Diagnosis Methods for Key Systems of the High-Speed Train
## 6 Discussion and Conclusions  
While enjoying the convenience and comfort of high-speed trains, increasing attention has been paid to their safety during service. In addition, as a piece of large and complex equipment, the performance degradation and early failure of various key systems inevitably occur during the service of high-speed trains, thus affecting the safe operation of trains. Therefore, condition monitoring and fault diagnosis of key train systems are of great value and are increasingly becoming a difficult challenge.  
Through the previous review, it was known that the fault diagnosis of key systems of high-speed trains is realized by feature extraction and pattern recognition, but it is mainly carried out by the traditional manual inspection, test bench, and video monitoring, which still has many shortcomings. Firstly, existing fault diagnosis methods do not work online on a large scale. Secondly, the monitoring data collected is not comprehensive and objective enough, which restricts the in-depth research on the fault diagnosis of key train systems. Third, the practicality of the existing methods needs to be further improved to be applied to the fault diagnosis of high-speed trains. Therefore, this review has the following prospects:  
1. Developing online monitoring technology can ensure the reliability and safety of high-speed trains. This can not only quickly identify early failures of key systems but also predict performance degradation, thus establishing a long-term warning mechanism.
2. By installing various sensors (voltage, current, vibration acceleration, displacement, and pressure) in the key system of the train, a large number of real train operation data and corresponding historical data can be obtained, which provides data support for train fault diagnosis based on the combination of big data and experience knowledge.
3. With the advantages of machine learning and deep learning, a big data-driven condition monitoring and fault diagnosis platform for key systems of high-speed trains is being developed. This can shift from traditional planned repairs to condition repairs and forecast repairs, thereby reducing annual maintenance costs and preventing potential safety accidents.  
Supervision, S.X.; funding acquisition, S.X.; methodology, H.T.; writing--original draft, H.T.; visualization, H.T.; formal analysis, H.T.; validation, H.T.; resources, C.Y.; investigation, H.Y. All authors have read and agreed to the published version of the manuscript.  
This research was undertaken at the Key Laboratory of Traffic Safety on Track (Central South University). The authors gratefully acknowledge the support from the National Key R&D Program of China (2022 YFB4300300). This paper is also supported by the National Natural Science Foundation of China (52202455) and the Fundamental Research Funds for the Central Universities of Central South University (Grant No. 2021 zzts0161).  
Not applicable.  
The authors declare no conflict of interest.
# Amphishea: A Novel Persistent Buffer Management Strategy to Improve SMR Disk Performance
###### Abstract  
The explosive growth of massive data makes shingled magnetic recording (SMR) disks a promising candidate for balancing capacity and cost. SMR disks are typically configured with a persistent buffer to reduce the read-modify-write (RMW) overhead introduced by non-sequential writes. Traditional SMR zones-based persistent buffers are subject to sequential-write constraints, and frequent cleanups cause disk performance degradation. Conventional magnetic recording (CMR) zones with in-place update capabilities enable less frequent cleanups and are gradually being used to construct persistent buffers in certain SMR disks. However, existing CMR zones-based persistent buffer designs fail to accurately capture hot blocks with long update periods and are limited by an inflexible data layout, resulting in inefficient cleanups. To address the above issues, we propose a strategy called Amphisheaen. First, a two-phase data block classification method is proposed to capture frequently updated blocks. Then, a locality-aware buffer space management scheme is developed to dynamically manage blocks with different update frequencies. Finally, a latency-sensitive garbage collection policy based on the above is designed to mitigate the impact of cleanup on user requests. Experimental results show that Amphisheaen reduces latency by an average of 29.9% and the number of RMWs by an average of 37% compared to current state-of-the-art strategies.  
shipled magnetic recording; RMW; in-place update; garbage collection; persistent buffer +
Footnote †: journal: Applied Sciences