esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Related Work
### Encrypted Traffic Datasets and Construction Methods
#### 2.1.3 Dataset Construction and Improvement Methods  
Due to the small and unbalanced size of the existing open datasets, some researchers focus on improving the datasets, not the data collection stage.  
[PERSON] et al. [23] proposed a semi-supervised learning method using a Generative Adversarial Network (GAN) combined with deep learning to solve an unbalanced dataset problem, called DCGAN. Unlike DCGAN, [PERSON] et al. [24] directly used GAN to fill unbalanced encrypted traffic samples. Then, [PERSON] et al. [25] proposed PacketCGAN, based on the conditional GAN. In addition, [PERSON] et al. [26] proposed a method through a \"hallucinator\" neural network for the problem of unknown datasets.  
GAN can fill the encrypted traffic datasets, but it cannot reach the new encryption sample with different types. It also cannot add other characteristics within the exact category characterization of the samples.  
In addition to GAN, there are currently several traffic generation tools, such as AppScanner [27], Lashkari [28], and ExperimenTor [29]. The above tools need to run in the experimental environment, which can only ensure that the protocol features of traffic are similar to that of the actual traffic. The lack of user behavior and environment characteristics makes the samples lack authenticity and reliability.
## 4 Results and Analysis
### The Beijing Urban Scene
The recall of the building detection was 87.1%, and went down to 81.4% when correct height-classification was added. The precision and recall of high and middle level buildings detection and classification were over 95%. However, the recall of the low level buildings was only 66.8%, while the precision remained very high, at over 90%. There were 61 undetected buildings, in which 57 undetected buildings belonged to low level. The recall of total building detection rose to 95.4%, and reached 90.7% when considering correct height classification. The number of false alarms was maintained in the single digits, and erroneous height judgment occurred for less than 6% of the total buildings. Therefore, shadow information is valuable and effective in building detection and height-classification, although it is not reliable enough for detecting low-level buildings.
## 3 Results  
### Unit Activation  
Once the unit is assembled and ready to operate, the user must activate the connection between the reader and the four hubs. Communication with the reader was achieved using a web browser (Internet Explorer) and typing the reader's address, which is composed of the reader's name and serial number printed on the reader (http://speedwayr-XX-XX-XX-local) (Figure 4a). In our case, the reader's serial number was (13-b0-bc). On the reader's main page, connectivity can be visualized by pressing \"Show connections\" (Figure 4a) followed by \"Check connections\" (Figure 4b). Proper connection of all hubs is indicated by a green checkmark (Figure 4c).
# Cluster Analysis of Pedestrian Mobile Channels in Measurements and Simulations
## 2 Cluster Tracking in Mobile Measurements
### Clustering and Tracking Results  
The spatial distributions of clusters in LoS and NLoS conditions are shown in Figure 5a,b, respectively. In the LoS condition, one cluster survives during the whole movement near the main path on 0\({}^{\circ}\). In the NLoS condition, on the other hand, no cluster can keep surviving during the whole movement. Some clusters with a shorter lifetime can be observed. Their angles and power smoothly change with the movement. It is noted that those clusters with less than 5% normalized power were removed in the figures to make the main contributed clusters clearer for observation. Ignored clusters' contributions to the channels are slight, and most of them have a short lifetime that only exists only in 1 snapshot. However, in the following statistical works, all of them are considered.  
Figure 5: Clustering and tracking profiles of measurement clusters. (**a**) Cluster-tracking profiles in LoS condition. (**b**) Cluster-tracking profiles in NLoS condition. (**c**) Birth and death histogram of clusters per snapshot. (**d**) cumulative density functions (CDF) of cluster lifetime distances (m).  
From Figure 5c, we see the birth and death histograms of clusters per snapshot that were calculated, which show the birth and death rate of clusters of all snapshots during movement. The total number of newly appeared clusters per snapshot is slightly larger than that of newly disappeared clusters per snapshot. Obviously, the change of 1 or 2 clusters per snapshot takes more probabilities. The polynomial fitting of both birth and death rate is also given in the figure.  
The cumulative density functions (CDFs) of cluster lifetime distances are shown in Figure 5d. In our former work, Reference [9], the sizes of visibility regions (VRs) are calculated from the results by KPM algorithm, which are shown in blue and green curves in this figure. VR is a concept proposed in COST 2100 [20], which is an indicator of where corresponding clusters are active. In linear mobile measurements, VR diameters are the same as the lifetime distances of clusters. From this figure, we can see the effect of the newly introduced KF algorithm. Especially in LoS, most clusters show shorter lifetime distances, less than 1 m. In addition, the corresponding log-normal fit is given.
## 4 Tree-Based Protocols
### Manchester Coding
#### 4.4.4 Collision Window Tree Protocol  
The Collision window Tree protocol (CwT) is the second proposed window-based protocol that applies a dynamic window to CT [49; 50]. This protocol adopts two techniques: bit tracking and bit windowing. The bit tracking uses Manchester coding in order to identify the colliding bit in the tags' responses. This technique avoids using the CRC, which QwT used in order to identify the type of slot. This protocol does not remove idle slots as CT does, but instead decreases the total amount of bits transmitted by all the tags.  
The reader interrogates tags by transmitting a query [\(q_{1}\)...\(q_{L}\)] of length \(L\), attached to the \(ws\) of length [\(\log_{2}\)\(ws\)] + 1 bits. The bit-string \(ws\) informs the tags of the number of bits that they must send in their reply. The variable \(ws\) is computed in every slot and is transmitted together with the query. Only matching tags transmit \(ws\) to the last query bit received, [\(t_{L}+1...t_{ws}+L\)] of their ID [\(b_{1}\), \(b_{2}\)...\(b_{k}\)]. When the reader transmits a query, three possible slot statuses can take place after a tag's response:  
* A collision slot occurs when at least one colliding bit is found. Then the reader creates two additional queries [\(q_{1}\), \(q_{2}\)...\(q_{L}\), \(w_{1}\)...\(w_{col}\)\(-\)1, 0] and [\(q_{1}\), \(q_{2}\)...\(q_{L}\), \(w_{1}\)...\(w_{col}\)\(-\)1, 1], using bit tracking. The supplementary queries are made from the last transmitted query [\(q_{1}\)...\(q_{L}\)] appended with the received bits [\(q_{L}\), \(w_{1}\)...\(w_{col}\)\(-\)1] indicating the first colliding bit.
* A go-on slot occurs when at least one tag responds and the expression \(L+ws<\ k\) is met. Then, the reader creates a new query based on the former one and the received window from the last slot. The next \(ws\) is calculated using the heuristic function in Equation (1).
* A success slot occurs when the reader checks the expression \(L+ws=k\) and if it matches, the tag is successfully identified.  
A CwT flow chart is presented in Figure 20. The reader transmits the first query (0) with \(ws=1\). The matching tags respond with \(ws\) bits and the reader looks for a colliding bit. After slot identification, the reader creates a new query and calculates the value of \(ws\), depending on the type of slot.  
The CwT provides a significant decrease in the number of tag-transmitted bits, but this benefit comes with a certain decrease in the number of slots and the reader-transmitted bits. This protocol achieves important energy savings due to the reduction in the time required by the tag transmission process [50].  
Table 5 compares some of the tree-based protocols.
## 3 Results
### Effect of Heat Treatment on the SiO\({}_{2}\)/C Composite
Figure 8: SEM images (500\(\times\) and 10,000\(\times\) magnification) of graphite (**a**,**b**) and 10% SiO\({}_{2}\)/C composite with heat treatment at (**c**,**d**) 400 \({}^{\circ}\)C, (**e**,**f**) 500 \({}^{\circ}\)C, and (**g**,**h**) 600 \({}^{\circ}\)C.  
The initial charge/discharge curve of the samples at different heat treatment temperature of SiO2/C is shown in Figure 9a. Based on the cyclability and rate ability of the cell, 500 \({}^{\circ}\)C is the optimum temperature to obtain the SiO2/C composite. It is predicted that at higher temperature, the SiO2/C composite is agglomerated into larger sizes, which lowers the diffusion kinetics of Li-ion during the charge-discharge process. At low temperature, the composite process did not completely occur. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Heat Treatment Temperature** & **Initial Specific Capacity** & **Columbic Efficiency** \\ (\({}^{\circ}\)**C**) & **(mAh g\({}^{-1}\))** & **(\%)** \\ \hline
400 & 506 & 74 \\
500 & 586 & 76 \\
600 & 705 & 82 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Initial specific capacity of the SiO2/C composite depending on the heat treatment temperature. Figure 9: (a) Initial charge/discharge curve of the SiO2/C composite at 0.1 C; (b) cycling performance of the SiO2/C composite; (c) rate performance of the SiO2/C composite at various temperatures. The rate performance of the SiO2/C composite depending on heat treatment variation is shown in Figure 9c. The samples were tested at the charge and discharge current of 0.1, 0.5, and 1 C. It is clearly observed that the samples treated at various temperatures have a high specific capacity of approximately 410 mAh g\({}^{-1}\) at the current rate of 1 C. Figure 9c also shows that the reversible capacity increases from 581 mAh g\({}^{-1}\) to 595 mAh g\({}^{-1}\) after two cycles at the heat treatment temperature of 500 \({}^{\circ}\)C. The latest research explains that an increase in reversible capacity owing to SiO2 is not fully active at the beginning of cycling and will be active during next cycling [14; 16; 17]. The obtained results confirm that the SiO2/C composite heat-treated at 500 \({}^{\circ}\)C produces the best results, owing to its excellent specific capacity, good cyclability, and high performance as anodes for LIBs [18; 32; 33]. Based on Table 4, the overall result is considered good since the starting material is far more inexpensive and the process is simple compared to previous reports.
## 1 Introduction  
We can find the origins of continuous [PERSON]'s method in the seminal paper of [PERSON] [1]. Actually, it appears in the study of the basins of attraction related with the relaxed [PERSON]'s method for solving a complex equation \(p(z)=0\)  
\[z_{n+1}=z_{n}-h\frac{p(z_{n})}{p^{\prime}(z_{n})},\quad n\geq 0,\quad h\in\mathbb{C} \tag{1}\]  
It is well known, since the works of [PERSON] and [PERSON] at the end of the 19 th century, and [PERSON] and [PERSON] in the early 20 th century, that the basins of attraction of [PERSON]'s method (obtained for \(h=1\) in Equation (1)) have an intricate fractal structure. [PERSON], as well other authors ([2,3]), has realized that this fractal structure shrinks away when \(h\to 0\), as we can appreciate in Figure 1.  
We can identify iterative method Equation (1) as an [PERSON] approximation of the differential equation  
\[z(0)=z_{0},\quad z^{\prime}(t)=-\frac{p(z(t))}{p^{\prime}(z(t))} \tag{2}\]  
with step size \(h\).  
Initial value problem Equation (2), or its improved version of finding a function \(z:[0,\infty)\to\mathbb{C}\) such that  
\[z(0)=z_{0},\quad p(z)^{\prime}(t)=-p(z(t)) \tag{3}\]  
for a given \(z_{0}\in\mathbb{C}\), where \(p\) is a non-constant complex polynomial is known as continuous [PERSON]'s method. We refer to [1] for the theoretical basis of continuous [PERSON]'s method. In particular, it is shown that solutions \(z(t)\) of Equation (2) (or Equation (3)) flow to a zero of \(p\) while keeping the argument of \(p(z(t))\) constant at \(\arg(p(z_{0}))\).  
For instance, as it was pointed out by [PERSON] _et al._[3], if \(p(z)=z^{3}-1\) in Equation (2), the explicit solution is  
\[z(t)=\sqrt[3]{(z_{0}^{3}-1)e^{-t}+1}\]  
The choice of the appropriate branch of the cube root, defined by the rays \(\theta=\pi/3\), \(\theta=\pi\) and \(\theta=-\pi/3\), determines the ternary division of the complex plane that can be seen in the three graphics of Figure 1. According to these authors, the fractal boundaries in the basins of attraction of the roots can be originated by numerical errors inherent to the discretization of initial value problem Equation (2).  
This work is two fold. In Section 2, we consider other different strategies (not only [PERSON]'s method) for solving initial value problem Equation (2). The efficiency of the iterative processes obtained in this way is analyzed.  
Figure 1: Basins of attraction of the relaxed [PERSON]’s method given in Equation (1) applied to the polynomial \(p(z)=z^{3}-1\) for \(h=1\), \(h=2/3\) and \(h=1/3\), respectively.
# Study on Corrosion Behavior of Waterborne Polyurethane Coating with High Thermal Conductivity
## 3 Results and Discussion
### SRP Analysis  
Figure 9 shows the surface Kelvin potential mapping of the coating at different stages of the salt spray test. Apparently, the surface potential of the scratch is lower than that of the nearby coating before the salt spray tests (Figure 9a,b). This is related to the release of electrons process. The coating displays an insulator characterization; thus, the electron escape process is very difficult and shows a higher surface potential. Moreover, Figure 9a shows that the potential difference between the coating and scratch is around 0.13 V for the sample without composite filler; while the waterborne polyurethane coating with 2 wt % filler shows a smaller value: 0.09 V (Figure 9b). It is mainly attributed to the enhancement of the electric conductivity due to the addition of carbon materials. According to the previously published literatures [32; 33; 34], the surface Kelvin potential has a linear relationship with the corrosion potential of the sample; thus, the change of the Kelvin potential can be used to examine the change law of the corrosion potential. Consequently, it can be speculated that the substrate metal of the waterborne polyurethane coating sample will suffer from more severe galvanic corrosion at the initial stage due to the higher potential difference [35]. The addition of a composites filler will improve the corrosion protection performance to some extent.  
Figure 9c,d is the surface potential distribution after 3 d of salt spray tests. It can be found that the surface potential of the scratch area increases. Especially for the sample without composite filler, the surface potential of the coating and scratches reverse. This is because under the salt spray environment, the substrate metal is corroded, and a lot of corrosion products are piled on the scratch area. In this situation, the electron release process at the scratch area becomes difficult, and thus, the surface potential rises. Compared with the coating with composite filler, the coating without filler undergoes more serious corrosion damage after 3 d of the salt spray experiment due to the higher corrosion potential difference; thus, the surface potential of the scratch area significantly enhances and caused the potential reversal phenomenon. In fact, this surface potential change further confirms the advantages of the composites filler for the improvement of corrosion protection performance, which is consistent with the EIS results. As the salt spray time extends, the corrosion extent of the metal at the scratch area is aggravated. In the meantime, the amount of the corrosion products increases and becomes compact, which hinders the release process of electrons and causes the surface potential of the scratch area to further increase (Figure 9e,f).  
Figure 8: The change trend of coating porosity with the salt spray time.
## 5 Results and Discussion
### Comparison of Training Processes and Evaluation Indexes
Ultimately, the models with various attention mechanisms maintain stable accuracy rates of approximately 98.5% with losses remaining relatively constant at 0.05. Table 4 shows the model's evaluation metrics. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Models** & **Accuracy/\%** & **Loss/\(1\times 10^{-3}\)** & **F1-Score/\%** & **Parameters** & **Model Size/MB** & **FLOPs** \\ \hline ResNet18 & 100.00 & 1.12 & 100.00 & 11,179,077 & 42.70 & 2.38 G \\ ResNet50 & 98.50 & 0.29 & 98.50 & 23,518,277 & 90.00 & 5.38 G \\ VGG16 & 98.50 & 44.73 & 98.49 & 138,357,544 & 527.81 & 20.21 G \\ Inception-v3 & 98.50 & 1.23 & 98.50 & 21,795,813 & 93.29 & 3.86 G \\ DenseNet121 & 100.00 & 0.83 & 100.00 & 6,958,981 & 27.17 & 3.76 G \\ Dense121-DSC & 100.00 & 1.75 & 100.00 & 5,134,538 & 20.30 & 2.38 G \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics of common models. Figure 15: Curves of accuracy and loss during training of models with different attention mechanisms. Table 4 reveals that after the model is modified, its performance deteriorates due to the reduced ability to learn key information. The implemented attention mechanism, furthermore, showed a 2% improvement in accuracy under the addition of the CAM, a 0.5% improvement under the addition of the SAM, and a 1.5% improvement under the addition of the SE, revealing the potential for attention modules to enhance the model's learning ability. The Dense-DSC-CBAM model has only 3.6% of the parameter quantity of the DenseNet121 model, yet the accuracy is only 0.5% lower. Additionally, the FLOPs and model size have greatly reduced, being only 1/10 and 4.2%, respectively. Moreover, in comparison to other lightweight models, the Dense-DSC-CBAM model has similar FLOPs but fewer parameters and a smaller model size. The evaluation indexes such as model accuracy, loss, and F1-score remain nearly unchanged, making the Dense-DSC-CBAM model a more favorable option. Due to these advantages, the model could be readily deployed on hardware terminal devices with weaker performance. The comparison with other models indicates that the performance of the model designed in this paper is superior to traditional machine learning algorithms, indicating that the feature value constructed by deep learning is better than traditional machine learning algorithms.
## 4 Experimental Verification
### Case 1
The extracted IMF component energy ratio feature and the time-frequency domain feature are collected as feature vectors and input into PNN model. Then, 70% of the 300 sets of gear fault data are randomly selected as training set data and the remaining 30% as test set data. Using the rule of thumb to determine the size of the spread parameter the following is carried out: first select the initial value of spread = 0.1, and then gradually increase the size of the parameter, and finally in spread = 1.5 when the final diagnostic effect is the best, then select spread = 1.5. After the fault diagnosis, the confusion matrices for the training and test sets of the gear fault data are shown in Figure 12. At the model training level, label 1 in the training set has a total of 70 data and there are two misclassifications (both sample 7 and sample 16 classify label 1 as label 2) with an accuracy of 97.14%. Label 2 has a total of 70 data with one misclassification (sample 19 classifies label 2 as label 1) with an accuracy of 98.57%. Label 3 has 70 data and has an accuracy rate of 100%. Label 1 with 30 data in the test set is misclassified by one with an accuracy of 96.67%. Label 2 has 30 data, one misclassification and 96.67% accuracy. Label 3 has 30 data with 100% accuracy. The misclassification that occurred is that Sample 27 predicted label 1 as label 2 and sample 7 predicted label 2 as label 1, with some confusion between label 1 and label 2. Figure 11: Energy ratio of IMF components for gear data in different conditions. Figure 12: Gear fault diagnosis confusion matrix. The PSO-VMD feature extraction algorithm is compared with the optimized IPSO-VMD, EMD and compared with PNN, support vector machines using extreme learning machine (ELM) [38]. The diagnostic results for the gear fault samples are shown in Table 4. It can be seen that for the gear data, the IPSO-VMD-PNN algorithm is the most effective with an overall accuracy of 97.78%.
# Unmanned Aerial Vehicle Depth Inversion to Monitor River-Mouth Bar Dynamics
## 3 Depth Estimation Method
### Estimation of Wave Frequency  
The representative wave frequency \(f_{r}\) is also determined from coherent signals along the circumference. Since the propagation time results from the cross-correlation analysis, the wave period can be evaluated consistently as a weighted average frequency by the cross-spectrum of intensity signals at \(A\) and \(B_{n}\). As such, \(f_{r}\) can be expressed as:  
\[f_{r}=\frac{1}{N}\sum_{n=1}^{N}\frac{\int_{f_{1}}^{f_{2}}|C_{AB,n}(f)|df}{\int_{ f_{1}}^{f_{2}}|C_{AB,n}(f)|df} \tag{8}\]  
where \(C_{AB,n}(f)\) is the cross-spectrum of intensity signals at \(A\) and \(B_{n}\), and \(f_{1}\) and \(f_{2}\) are the lower and upper frequencies of the bandpass filter, respectively (\(f_{1}=0.05\) Hz and \(f_{2}=0.2\) Hz at present). This wave frequency represents one of the coherent intensity signals over the circumference.  
Figure 8a shows the distribution of the representative period \(T_{r}\) (\(\equiv\)1/\(f_{r}\)) for Case 3, which corresponds to the wave-celerity vector field in Figure 7. The wave period slightly varies over the domain due to the spatial variation of signal coherence. The value tends to be relatively lower when the swell signal is weak. Figure 8b shows the distribution of the mean cross-correlation coefficient, which measures local signal coherence in the frequency band of 0.05-0.2 Hz. The value tends to be higher around the river-mouth bar, where the swell develops a steeper front due to wave shoaling and refraction in shallow water. We estimate the water depth from local values of \(c\) and \(T_{r}\) based on Equation (1). The estimated water depth is then converted to the seabed level using the mean sea level as a datum after the tide correction.
## 2 Methodology
### NuSTAR PSF  
The PSF of _NuSTAR_ describes how the telescope and detector system respond to point X-ray sources, effectively characterizing the blurring introduced by the instrument's optical components. It is effectively determined by its focusing optics, which consist of multilayer-coated, grazing-incidence mirrors designed to reflect high-energy X-rays. The on-axis PSF has a full width at half maximum of approximately 18\({}^{\prime\prime}\) and an HPD of about 58\({}^{\prime\prime}\)[10]. This relatively broad PSF stops _NuSTAR_ from achieving high spatial resolution in the hard X-ray band (3-79 keV).  
However, the PSF varies with off-axis angle due to optical aberrations inherent in the telescope's design (Figure 1). As sources appear offset from the optical axis (the image center), the PSF broadens and becomes more asymmetric, resulting in decreased spatial resolution and increased overlap between nearby sources. Additionally, the PSF exhibits a slight energy dependence because of reflectivity variations in the multilayer mirror coatings. Accurately modeling the _NuSTAR_ PSF--including its dependence on energy and off-axis angle--is crucial for image deconvolution techniques aiming to resolve sources in close proximity, such as SXP 15.3 and SXP 305 [28, 29]. In this study, we utilized the calibrated PSF models stored in CALDB and accounted for the specific offsets and energy ranges that pertain to the particular FPMA and FPMB data sets.