esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Parsing and Utilization of System Log Features in Log Analysis: A Survey
## 2 Log Parsing
### Classification of Log Parsing
#### 2.1.3 Utility Itemset Mining  
Utility itemset mining is a technique commonly used in data mining, and it is used to solve specific problems related to identifying high value itemsets in transactional databases [28; 29]. In the field of log parsing, the utility itemset consists of frequently occurring log words, parameters, time, and other information, with the most critical being the frequent words that make up the log template. Therefore, more specifically, the practical itemset mining in the field of log parsing mainly adopts frequent pattern mining.  
The log parsing algorithm using frequent pattern mining is often based on the assumption that the words that appear frequently in the log are often part of the template. Similar to the early log parsing algorithms (SLCT [30], LFA [31]) that adopted frequent pattern mining, LogCluster [12] also relies on the frequency of words in log events. The difference is that LogCLuster allows variable length parameters, which combine multiple consecutive parameter tokens into one parameter token, so LogCLuster is better at handling log data with variable parameter lengths. When log statements containing the same parameters appear repeatedly within a period of time, LogCluster will include these frequently occurring parameters into the log template, which makes LogCluster inaccurate with some datasets. Unlike LogCluster, Logram [32] does not consider the number of occurrences of log tokens but instead considers the frequency of log n-grams. In Logram, log parsing is based on the assumption that frequent n-grams may be log templates. Logram first adopts a recursive method to continuously filter and extract _(n-1)-grams_ from _n-grams_ and then splices word pairs with a sequence relationship to form a log template. This kind of frequent pattern mining considering the context can avoid the problem of mis-parsing parameters. ULP [33] divides logs according to the length of log sentences and potential template tokens, pre-groups similar log events, and then performs word frequency analysis. Unlike Logram, ULP performs a separate word frequency analysis within each log group, rather than all log data, which makes it easier to distinguish log template parts. Prefix-Graph [34] automatically extracts frequent items of logs by constructing a prefix graph. In simple terms, this method first constructs a prefix tree for the log message collection, and each log message is represented as an edge from the root to the leaf node in the prefix tree. Then, its traverses the entire prefix tree and merges the same nodes of similar tree branches together to form a prefix graph until the prefix graph does not change. The common node sequence from the root to the leaf nodes is the log template. Based on the above process, Prefix-Graph shows excellent accuracy with various datasets. When dealing with datasets with many templates and long log messages, the prefix tree traversal and merging steps in the algorithm will bring a certain amount of time overhead.
## 3 Results and Discussion  
### Dataset Statistics  
The total number of soil samples was 188, which were split into a training set and a testing set by the Kennard-Stone (KS) method at the proportion of 7:3 [26], which is often used with VIS-NIR spectroscopy for model construction and model testing. The number of training sets was 131, and the number of testing datasets was 57. The data statistics are presented in Table 3. Table 3 also reveals that the data distribution of the available potassium content of the soil in the training set is similar to that in the testing set.  
The pretreatment method is indispensable during the training of a regression model and is also a key step for quantifying the analysis of the VIS-NIR spectrum. Information on the VIS-NIR spectrum of soil would be effective to filter noise and reduce the complexity of the pretreatment methods, but different methods have different effects on various regression models. Figure 3 shows the VIS-NIR spectrum with 29 pretreatment methods and a reflection spectrum (RS). Figure 3a is without the SG method, and Figure 3b is the SG method. The SG method reduces the noise of the spectrum and makes the curve smoother. The DT method creates pristine peaks in the spectrum, but the peak value of the original spectrum becomes zero. MC not only reduces the spectral offset but also weakens the characteristic points of the spectrum. The two scattering correction methods of SNV and MSC did not significantly change the spectral curve. FD, SD, and LG made great changes to the VIS-NIR.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Type** & **Number** & **Max/mg\(\cdot\)kg\({}^{-1}\)** & **Min/mg\(\cdot\)kg\({}^{-1}\)** & **Average/mg\(\cdot\)kg\({}^{-1}\)** & **Standard Deviation** \\ \hline Total & 188 & 670 & 60 & 190.6 & 133.1 \\ Train & 131 & 670 & 60 & 190.7 & 134.7 \\ Test & 57 & 600 & 60 & 188.2 & 128.0 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Soil-available potassium sample statistics.
## 2 Broadband System
### Wireless Broadband System
#### 2.2.1 Microwave Links  
Microwave link is a PtP LoS wireless transmission technique. The benefits of a microwave link can be felt in areas with high population densities or difficult terrains. In such scenarios, the cabled network installation costs are prohibitively high. Unlike the cabled network, the microwave links are simple to install and economical. Nevertheless, the link possesses comparatively limited bandwidth, which makes it to be less relevant for high-capacity network link applications. Furthermore, being an LoS solution, it offers a relatively shorter transmission distance. Microwave-based network capacity can be improved through the deployment of additional links. However, the relatively high expenditure involved in additional link installation may hinder the cost-effective benefit of the microwave link [16].
## 2 Study Area and Data
### Data  
Multisource remote-sensing data were used in this study, including the precipitation (PRE) [25], temperature (TEMP) [25], the NDVI [26], the digital elevation model (DEM) [27] and Landsat \(5/6/7\)[28]. The data used in this study are detailed in Table 1.
## 3 Results
### Experimental Data and Result Visualization
#### 3.4.3 Ablation Study  
Table 3 presents ablation experiments on the CEM, TEM, and LCM, validating the effectiveness of each component in our method. Although Transformers and CNNs learn complementary features from different perspectives, standalone Transformers perform worse than standalone CNNs in road extraction on both datasets. However, combining the two modules improved road IoU by over 1.64% on the German-Street dataset and 1.38% on the Massachusetts Roads dataset, along with an overall mIoU increase of over 0.71% and 0.66%, respectively. Incorporating the LCM, designed to capture road shape features, further improves road extraction performance, with a more significant improvement on the Massachusetts Roads dataset, where road IoU increases by 1.64%.  
\begin{table}
\begin{tabular}{l c c c c c c c c} \hline \multirow{2}{*}{**Model Name**} & \multicolumn{2}{c}{**Background**} & \multicolumn{2}{c}{**Road**} & \multicolumn{2}{c}{**Overall**} \\ \cline{2-9}  & **IoU (\%)** & **Recall (\%)** & **IoU (\%)** & **Recall (\%)** & **mIoU (\%)** & **mPA (\%)** & **Accuracy (\%)** & **f1-Score (\%)** \\ \hline PSPNet & 96.31 & 98.88 & 38.06 & 46.61 & 67.19 & 72.75 & 96.39 & 82.92 \\ PSANet & 96.07 & 98.34 & 39.65 & 52.86 & 67.86 & 75.6 & 96.17 & 84.65 \\ CCNet & 96.35 & 98.59 & 41.66 & 53.44 & 69 & 76.01 & 96.44 & 85.01 \\ Deeplabv3+ & 96.77 & 99.06 & 46.55 & 57.51 & 71.66 & 78.17 & 96.86 & 85.52 \\ Unet & 96.97 & 99.36 & 44.85 & 50.63 & 70.91 & 74.99 & 97.04 & 84.6 \\ Segformer & **97** & **99.48** & 44.19 & 48.76 & 70.59 & 74.12 & 97.07 & 84.06 \\ Our method & 96.83 & 98.58 & **49.71** & **63.85** & **73.27** & **81.21** & 96.93 & **88.38** \\ \hline \end{tabular} The bolded portions indicate the best method under the specific evaluation criterion.  
\end{table}
Table 2: Summary of the results on the Massachusetts Roads dataset.  
Figure 5: Visual comparison of various methods on the Massachusetts Roads dataset. Red boxes highlight areas where our method outperforms others in road extraction continuity.  
\begin{table}
\begin{tabular}{l c c c c c} \hline \multirow{2}{*}{**Dataset**} & **CEM** & **TEM** & **LCM** & **Road Iou** & **Overall Miou** \\ \hline \multirow{4}{*}{German-Street} & ✓ & & & 50.76 & 71.71 \\  & & ✓ & & 49.77 & 71.33 \\  & ✓ & ✓ & & 52.4 & 72.42 \\  & ✓ & ✓ & ✓ & 53.24 & 73.05 \\ \hline \multirow{4}{*}{Massachusetts} & ✓ & & & 46.69 & 71.86 \\  & & ✓ & & 45.98 & 71.27 \\ \cline{1-1}  & ✓ & ✓ & & 48.07 & 72.52 \\ \cline{1-1}  & ✓ & ✓ & ✓ & 49.71 & 73.27 \\ \hline \end{tabular} A \(\gamma\) symbol indicates that the module has been added.  
\end{table}
Table 3: Ablation experiment results of each module of our method on the German-Street dataset and Massachusetts Roads dataset.
## 2 Transient Characteristics of DC Forced Interruption  
### DC Interruption Scheme and Simulation Model  
The most well-known DC vacuum circuit-breaker scheme has a forced-commutation branch connected in parallel to a VI; the DC forced interruption schematic is shown in Figure 1. The forced-commutation branch contains a precharged commutating capacitor \(C_{1}\), a commutating inductor \(L_{1}\), and a thyristor \(VT_{1}\). The capacitor polarity is chosen such that the first capacitor discharge current wave \(I_{\mathrm{C}}\) is in the direction opposite to that of the arc current \(I_{\mathrm{A}}\) in the VI. If the amplitude of the commutating capacitor current \(I_{\mathrm{C}}\) exceeds that of the main circuit, \(I_{0}\), the arc current in the VI goes through zero, and the current is interrupted. After the interruption, the polarity of the commutating capacitor \(C_{1}\) is reversed to the primary polarity. The \(RC\) frequency-modulation circuit composed by \(R_{3}\) and \(C_{3}\) has two functions. First, the capacitor in the \(RC\) branch, whose capacitance is much higher than the stray capacitance of the VI, can uniformly distribute the transient recovery voltage across the break of each interruption. Second, the resistor in the \(RC\) branch can dampen the high-frequency oscillations in the transient recovery voltage, and can consequently suppress the rising rate of the transient recovery voltage [11].  
Based on the forced-commutation interruption, a simulation model is established using MATLAB/Simulink. The main circuit breaker VI is set as an ideal switch, and the contact resistance is ignored when the switch is closed. The ideal switch is closed in the DC-arcing stage, and different currents are obtained by adjusting the resistance \(R_{0}\). At the beginning of the simulation, the ideal switch is closed, and the thyristor \(VT_{1}\) is triggered in the forced-commutation branch after a delay of 1 ms. The current flowing through the main circuit breaker decreases, and when the current is less than a preset value, the ideal switch is opened. In the forced-commutation branch in the simulation model, \(L_{1}\) is set as 3.4 uH and \(C_{1}\) is 1620 uF. The frequency-modulation circuit consists of a resistor \(R_{3}\) and capacitor \(C_{3}\), with values of 0.1 \(\Omega\) and 0.1 uF, respectively. The \(L_{0}\), which has a value of 12 uH, is the stray inductance generated during accrual situations.
## 3 Results and Discussion
### Accuracy Assessment for the KPPP-Estimated OTL
The residual RMS was still significant, which indicates that these three tidal constituents are probably affected by the station-related error. Previous studies [13; 14] inferred that K1 and K2 may be affected by the satellite orbit error and multipath effect, because the period of the K1 coincides with that of the satellite constellation, and the period of the K2 is almost the same as the satellite orbital period, while P1 may be disturbed by residual GPS signal propagation errors or non-tidal daily cycles in the atmosphere because its period is very close to solar day. As shown in Figure 5, the total RMS of the S2 tidal constituent between the GPS estimates and the nine OTL models were between 0.5 to 0.6 mm. There are some not obvious systematic differences of 0.1 to 0.3 mm between the GPS estimates and the nine different OTL models, which may arise from the effect of the atmospheric S2 tide. The effect of the atmospheric S2 tide was analyzed using the GEOSFPTT atmospheric model provided by the International Mass Loading Service [8], with a resolution of 0.625\({}^{\circ}\)\(\times\) 0.5\({}^{\circ}\). The results show that the amplitude of the atmospheric tide displacement, as predicted by the GEOSFPTT model, is only 0.22 mm at Hong Kong. Figure 6 shows that, after correcting the atmospheric tide by the GEOSFPTT model, the RMS residual of the S2 tidal constituent between the GPS estimates and some OTL models slightly reduced. Overall there was still an obvious RMS residual of about 0.5 mm between the GPS estimates and the OTL models. Figure 5 shows that the residual RMS of the S2 tidal constituent were all nearly 0.5 mm. This indicates that there are some station-related errors of around 0.5 mm, which arise from GPS-related error. GPS signal propagation error or non-tidal daily cycles in the atmosphere may bias the estimate of S2 because the period of S2 is half of the solar day [13; 14]. This leads to the uncertainty of GPS-estimated S2 tidal constituent being at 0.5 mm level.
## 3 Results
### Optimization  
The optimal values of the factors and the predicted values of the responses are shown in Table 5.  
The results of the optimization revealed a recommended HMT regime for wheat flour of 87.56 \({}^{\circ}\)C temperature applied for 3 h at a moisture of 26.01%. GPF can be incorporated in wheat pasta at a level of 4.81% without major negative impact on quality parameters and with considerable nutritional and functional benefits.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Min** & **Max** & **Optimal** \\ \hline A—Temperature (\({}^{\circ}\)C) & 60.00 & 100.00 & 87.56 \\ B—Time (h) & 1.00 & 3.00 & 3.00 \\ C—Moisture (\%) & 14.00 & 30.00 & 26.01 \\ D—GPF level (\%) & 1.00 & 6.00 & 4.81 \\ G’ (Pa) & 59,690.00 & 586,600.00 & 355,471.01 \\ G’ (Pa) & 19,690.00 & 89,870.00 & 67,972.84 \\ Dough firmness (g) & 2058.00 & 6622.00 & 3355.70 \\ \(L^{*}\) & 63.06 & 77.93 & 71.90 \\ RS (\%) & 1.01 & 2.43 & 1.69 \\ TPC (mg GAE/100 g) & 6.90 & 14.85 & 11.36 \\ Pasta firmness (g) & 3028.00 & 5798.00 & 4473.80 \\ Pasta gumminess (g) & 2575.70 & 4649.91 & 3132.90 \\ DF (\%) & 0.02 & 3.10 & 2.80 \\ \hline \hline \end{tabular}  
* G—elastic modulus, G”—viscous modulus, \(L^{*}\)—luminosity, DF—dietary fiber, TPC—total polyphenols content, RS—resistant starch.  
\end{table}
Table 5: Minimum, maximum, and optimal values of the variables.  
Figure 9: Three-dimensional response surface plots presenting the synergistic effects of factors: GPF level–temperature (**a**) and moisture-time (**b**) on pasta resistant starch (RS).
# An Optimized CNN Model for Engagement Recognition in an E-Learning Environment
## 4 Method
### Model Evaluation
#### 4.5.2 F-Measure  
F-measure is a common metric for measuring the accuracy of a method and is often used to determine the accuracy of an algorithm. Precision and Recall are often mentioned separately in recognition and detection-related algorithms, and the F-measure is a balanced reflection of the accuracy of the algorithm by considering both of these values.  
Precision and Recall are defined as follows:  
\[Precision=\frac{TP}{TP+FP} \tag{3}\]  
\[Recall=\frac{TP}{TP+FN} \tag{4}\]  
In the above two formulas, some basic concepts of metric usage can be defined as follows:  
* **True Positive (TP):** Both the model-derived classification results and the actual classification results of the labels are engagement.
* **False Positive (FP):** The classification result obtained by the model is engagement, but the actual classification result of the label is not.
* **True Negative (TN):** Neither the model-derived classification results nor the actual classification results of the labels are engagement.
* **False Negative (FN):** The classification result obtained by the model is not engagement, but the actual classification result of the label is engagement.  
Generally, higher accuracy is better, but precision and recall sometimes conflict, and the F-measure is more balanced, which also means that the F-measure is a better metric of performance.  
The F-measure is defined as follows:  
\[F_{\beta}=(1+\beta)^{2}\frac{Precision\times Recall}{\beta^{2}(Precision+Recall)} \tag{5}\]  
where \(\beta\) is a user-defined parameter, and when \(\beta=1\), the F-measure is called F1-measure.
# Detection of Temporary Flooded Vegetation Using Sentinel-1 Time Series Data
## 5 Discussion
### Multi-Temporal Characteristics and Time Series Features
This ambivalence can probably be explained by different sensor characteristics and environmental conditions in the studies. The time series feature Z-Score VV/VH proved to be most suitable for the derivation of TFV. This can be explained by the different sensitivity of VV and VH for backscattering mechanisms [78], which causes the backscatter values to increase for VV and decrease for VH. In case of TFV, the increase of the backscatter values can usually be detected by VV polarization due to the double-bounce effect. The double-bounce effect cannot be detected due to the depolarizing property of VH polarization, and the increase of the backscatter values is not expected in VH. Because of the difference or ratio between these two polarisations, the increase in VV is intensified, allowing a more effective derivation of TFV compared to a simple use of VV polarisation. In the example of Namibia, VV seems to be influenced by the double-bounce-effect indicating the occurrence of TFV [60; 79], whereby VH is not influenced by the interaction between water surface and vegetation. Instead, VH is dominated by the specular reflection because of flooded soil in between the vegetation, which consequently results in a decrease in backscatter values. According to the difference in the backscatter between VV and VH for the analysed flood date, the time series feature Z-Score VV/VH was successfully used for the extraction of the TFV.
## 4 Results  
### Optical and SAR Image Composites  
Figure 8 shows the differences in quantiles and the interval mean for each class extracted from all of the training points. Water is easily recognized in the NIR, SWIR2, SWIR2, VV, and VH bands. The urban values in three VI bands and the red and SWIR2 bands were separated from other classes, while the forest values in SWIR1, SWIR2, and NDVI show a clear distinction from the crop values. For crops, corn was different from other crops in the NIR, SWIR1, SIWR2, LSWI, NDVI, VV, and VH bands. The EVI band shows a distinction between vegetation and non-vegetation (water and urban), but less differentiation between corn and other vegetation.  
Figure 8: Quantiles and interval mean values of six optical bands, three VI bands, and two SAR bands for five classes. Each line is the average with 95% confidence intervals estimated for each class from all of the training points.  
The VV and VH bands reveal the same ability to distinguish corn and other crops in quantiles and interval mean metrics, although the VV band can discriminate corn in August and September, which is exhibited in monthly composites (Figure 9). The value of other crops was always higher than corn in the VH band, and this pattern was enhanced in June and October. In the VV band, corn and other crops peaked in July, and then corn decreased below the other crops in September.
# A Modified Shuffled Frog Leaping Algorithm for the Topology Optimization of Electromagnet Devices
## 3 Normalized Gaussian Radial Basis Function Formulation  
In the TO process for the electromagnetic devices, a commonly practiced method when constructing the topology of the model is to divide the design space into a number of small elements, and assign material to each element based on the value of its topology function controlled by the design variables of the problem. Because the position vectors of the frogs can be any arbitrary numbers in the design space, the design variables in SFLA should be continuous. Therefore, in this paper, the normalized RBF modeling method [16] is employed to reconstruct the topology of the electromagnetic devices. In the normalized RBF method, the value of topology function at location \(x\) in the design space can be described as:  
\[s(x)=\frac{\sum\limits_{i=1}^{M}w_{i}\times\phi(\|x-x_{i}\|)}{\sum\limits_{i= 1}^{M}\phi(\|x-x_{i}\|)},w_{i}\in[0,1] \tag{6}\]  
where \(M\) is the total number of RBFs in the model, \(\|\cdot\|\) is the multidimensional norm, \(w_{i}\) is the weight of each RBFs and is the design parameter, and the function \(\phi\) is the predefined radial function. The value of \(s(x)\) determines the material type of the element \(x_{e}\) located at the position \(x\), which is either iron or air in this paper:  
\[\begin{array}{l}s(x)>0.5,\ Mat(x_{e})=Iron\\ s(x)<0.5,\ Mat(x_{e})=Air\end{array} \tag{7}\]  
where \(Mat(x_{e})\) defines the material type of element \(x_{e}\). Because only the weight of each RBF is controlled during the optimization process, the number of control parameters as well as the complexity of the problem can be significantly reduced. Moreover, the usage of RBFs helps to transfer the discrete topology matrix into function of continuous variables. The radial function used in this paper is the normalized Gaussian function with zero mean. For a RBF centered at \(x_{e}\), its value at any point \(x\) in the design space is:  
\[\phi(x)=\frac{1}{\sigma 2\pi}e^{-\frac{1}{2}\left(\frac{\mathrm{R}-x_{e}}{ \sigma}\right)^{2}} \tag{8}\]  
The radius of the RBFS is determined by its variance \(\sigma\). To ensure that the entire design domain can be reconstructed, the RBFs are set to be evenly distributed, and their radii are larger than the distance between any two consecutive RBFs. In the proposed TO process, both the position and the radius of RBFs are predefined to reduce the number of control variables with a less complicated reproduction process. The design parameter, which is the weight of each RBF, is set to be positive to avoid any possible conflict in the topology definition, and is bounded between [0,1] to reduce the design space. After the calculation of the topology function, the material type of the element is also determined and assigned in order to create a topology matrix accordingly. To further remove the checkerboard pattern in the topology, a 2D filter is used to refine the topology matrix. The actual FEM evaluation is then conducted using the created model.