esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Engineering Example Application
### Algorithmic Sorting
#### 4.6.2 Nemenyi Test  
The [PERSON] ranking test, by itself, does not suffice to determine the superiority or inferiority of algorithms. To conduct a more comprehensive assessment, the Nemenyi  
Figure 12: Comparison of [PERSON] ranking test algorithms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Variable Name** & **Median** &
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} & **P** & **[PERSON]’s f-Value** \\ \hline LSSVM & 16.791 & 6.495 & 0.043 & 0.13 \\ SSA-LSSVM & 17.089 & 5.16 & & \\ GWO-LSSVM & 17.763 & 4.858 & & \\ IESSA-LSSVM & 20.046 & 3.186 & & \\ \hline \hline \end{tabular}
\end{table}
Table 5: The results of [PERSON]’s test analysis.  
test emerges as a crucial tool for post hoc two-by-two comparisons [23]. The statistical formula employed for calculating the test volume for the comparison between the ith and jth samples is as follows:  
\[\chi^{2}_{i,j}=\frac{\left(\overline{R_{i}^{-}\overline{R_{j}}}\right)^{2}}{ \frac{N(N+1)}{12}(\frac{1}{n_{i}}+\frac{1}{n_{j}})C} \tag{23}\]  
\[C=\text{q}\sqrt{\frac{N(N+1)}{6K}} \tag{24}\]  
where c is the corrected statistic, \(p\) is the uncorrected statistic, \(N\) is the total number of sample observations, \(K\) is the number of overall rank orders, \(\overline{R_{i}}\) is the mean rank of group i, \(\overline{R_{j}}\) is the mean rank of group j, \(n_{i}\) is the number of sample instances in group i, \(n_{j}\) is the number of sample instances in group j, \(t_{i}\) is the i-th number of same-ranked individuals.  
The statistic \(\chi\) obeys an x-distribution with degrees of freedom (number of groups \(-\) 1), from which the \(p\)-value can be obtained and the conclusion drawn.  
[PERSON]'s method is a significant approach used for conducting pairwise comparisons. In this context, the algorithms GWO-LSSVM (A), LSSVM (B), SSA-LSSVM (C), and IESSA-LSSVM (D) were subjected to comparison to ascertain the presence of noteworthy differences in their relative standings. The resulting findings have been summarized in Table 6, provided below:  
From Table 6 above, it can be observed that the significance of IESSA-LSSVM, in comparison to the other algorithms, is below 0.05. This suggests a significant distinction between the proposed algorithm and the other three. Notably, GWO-LSSVM and SSA-LSSVM exhibit comparable outcomes in the test. Conversely, the LSSVM algorithm's performance ranks at the lowest.  
Considering factors such as convergence accuracy and speed, a comprehensive ranking of the four algorithms has been formulated and presented in the accompanying table. In the [PERSON] ranking test, the IESSA-LSSVM algorithm showcased distinctiveness from the remaining algorithms, with a figure exceeding 20 and a standard deviation of 3.18, which is the smallest. In the Nemenyi test, where pairwise comparisons were conducted, the significance of IESSA-LSSVM becomes even more pronounced. When incorporating convergence speed and accuracy into the assessment, the algorithms can be ranked as shown in Table 7.
## 3 Results
### Evaluation of Trained Models
#### 3.2.5 Artificial Neural Networks  
The Artificial Neural Networks algorithm showed superior results compared to the previous algorithms, achieving the highest overall precision values. This is evident in the confusion matrix shown in Figure 8, where a decrease in the number of false positives is observed for both the balanced and imbalanced datasets. The F1-Score value supports this claim, with a value of 0.9012 and 0.9533 for the imbalanced and balanced datasets, respectively (See Table 13). Its ability to capture complex relationships among the data and adapt to different patterns positions it as a favorable option, particularly in cases of imbalanced datasets.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Unbalanced Dataset** & **Balanced Dataset** \\ \hline Precision & 0.8848 & 0.9286 \\ Recall & 0.8515 & 0.9283 \\ F1-Score & 0.8666 & 0.9283 \\ Kappa & 0.8158 & 0.8925 \\ \hline \hline \end{tabular}
\end{table}
Table 11: Metrics obtained from the Gradient Boosting algorithm.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Metrics** & **Unbalanced Dataset** & **Balanced Dataset** \\ \hline Precision & 0.7800 & 0.8052 \\ Recall & 0.8307 & 0.8000 \\ F1-Score & 0.7877 & 0.8005 \\ Kappa & 0.7380 & 0.7000 \\ \hline \hline \end{tabular}
\end{table}
Table 12: Metrics obtained from the Naive Bayes algorithm.  
Figure 6: Confusion matrix resulting from the execution of the Gradient Boosting algorithm. On the left are the results derived with the unbalanced dataset and on the right with the balanced dataset.  
Figure 7: Confusion matrix resulting from the execution of the Naive Bayes algorithm. On the left are the results derived with the unbalanced dataset and on the right with the balanced dataset.
# Hybrid LSTM-Attention and CNN Model for Enhanced Speech Emotion Recognition
###### Abstract  
Emotion recognition is crucial for enhancing human-machine interactions by establishing a foundation for AI systems that integrate cognitive and emotional understanding, bridging the gap between machine functions and human emotions. Even though deep learning algorithms are actively used in this field, the study of sequence modeling that accounts for the shifts in emotions over time has not been thoroughly explored. In this research, we present a comprehensive speech emotion-recognition framework that amalgamates the ZCR, RMS, and MFCC feature sets. Our approach employs both CNN and LSTM networks, complemented by an attention model, for enhanced emotion prediction. Specifically, the LSTM model addresses the challenges of long-term dependencies, enabling the system to factor in historical emotional experiences alongside current ones. We also incorporate the psychological \"peak-end rule\", suggesting that preceding emotional states significantly influence the present emotion. The CNN plays a pivotal role in restructuring input dimensions, facilitating nuanced feature processing. We rigorously evaluated the proposed model utilizing two distinct datasets, namely TESS and RAVDESS. The empirical outcomes highlighted the model's superior performance, with accuracy rates reaching 99.8% for TESS and 95.7% for RAVDESS. These results are a notable advancement, showcasing our system's precision and innovative contributions to emotion recognition.  
CNN; speech emotion recognition; LSTM; attention mechanism +
Footnote †: journal: Applied sciences
# Adaptive Sensor Array Error Calibration Based Impact Localization on Composite Structure
## 2 Signal Modal of Imprecise Sensor Array
The eigenvalue decomposition of \(\mathbf{R}_{x}\) is:  
\[\mathbf{R}_{x}=\mathbf{U}_{\mathrm{S}}{\sum}_{\mathrm{S}}\mathbf{U}_{\mathrm{ S}}{}^{\mathrm{H}}+\mathbf{U}_{\mathrm{N}}{\sum}_{\mathrm{N}}\mathbf{U}_{ \mathrm{N}}{}^{\mathrm{H}} \tag{10}\]  
Here, \(\mathbf{U}_{\mathrm{S}}\) denotes the signal subspace corresponding to the largest eigenvalue \({\sum}_{\mathrm{S}}\).\(\mathbf{U}_{\mathrm{N}}\) denotes the noise subspace corresponding to those small eigenvalues \({\sum}_{\mathrm{N}}\). To describe the orthogonal properties, the spatial spectrum of imprecise sensor array can be calculated by  
\[P_{2D-MUSIC}=\mathbf{A}(r,\theta)^{H}\mathbf{\Gamma}^{H}U_{N}U_{N}^{H} \mathbf{\Gamma}\mathbf{A}(r,\theta)=\left\|U_{\mathrm{N}}^{H}\mathbf{\Gamma} \mathbf{A}(r,\theta)\right\|^{2} \tag{11}\]  
Construct a cost function \(J\) as  
\[J=\mathbf{\gamma}^{H}\|\mathbf{\tilde{A}}(r,\theta)^{H}U_{N}U_{N}^{H}\mathbf{ \tilde{A}}(r,\theta)\|\mathbf{\gamma} \tag{12}\]  
where \(\mathbf{\gamma}=[\Gamma_{-M},\cdots,\Gamma_{M}]^{T}\). Then, reasonable estimates of \((r,\theta)\) and \(\mathbf{\Gamma}(r,\theta)\) could be obtained by minimizing the cost function, that is  
\[\{\mathbf{\Gamma}(r,\theta),r,\theta\}=\mathrm{argmin}(\mathbf{\gamma}^{H}\| \mathbf{\tilde{A}}(r,\theta)^{H}U_{N}U_{N}^{H}\mathbf{\tilde{A}}(r,\theta)\| \mathbf{\gamma}) \tag{13}\]  
Minimize (13) with respect to \(\mathbf{\gamma}\) under the constraint \(\mathbf{\gamma}^{H}\mathbf{\omega}=1\) where \(\mathbf{\omega}=[1,0,\cdots,0]^{T}\). The result of this minimization problem is well known and is given by  
\[\mathbf{\gamma}^{(k+1)}=WW_{k}{}^{-1}\mathbf{\omega}/(\mathbf{\omega}^{T}WW_{ k}{}^{-1}\mathbf{\omega}) \tag{14}\]  
where \(WW_{k}=diag\{\overline{a}(r_{k},\theta_{k})^{H}\mathbf{U}_{N}\mathbf{U}_{N}^{H }\overline{a}(r_{k},\theta_{k})\}\). Given a preset threshold, the algorithm performs the iterations until \(J\) converges, and the process seen as Figure 2.
## 4 Experiment and Analysis  
### LOS Deformation Rate from Stacking-InSAR  
The LOS deformation rate of Weihe Faults was derived from stacking-InSAR technology using the collected Sentinel-1A images, as shown in Figure 4. All measurement values are relative to a common reference point (the red cross in Figure 4) that is considered to be stable over the entire time period. The positive and negative values indicated that the ground object was approaching and away from the satellite along the LOS direction, respectively. It is observed that the deformation presents a clear spatial pattern: the south of the Weihe Fault is mainly dominated by negative values, while the north is mainly dominated by the positive values. The maximum deformation is about 6.8 mm/yr, which is located at the north-west of Figure 4. The minimum deformation is about \(-8.2\) mm/yr, which is located at the center-south of Figure 4. The LOS deformation is a combination of east-west, north-south and vertical components. It is possible to separate these components from LOS deformation if multi-platform SAR images with different orbital directions are collected, such as ALOS-2, TerraSAR-X and COSMO-SkyMed. However, it is difficult to separate these components in this case since only Sentinal-1A ascending images were involved in the deformation estimation. As indicated in a previous study, the Weihe Fault is dominated by the vertical deformation and slight strike-slip shear movement. Therefore,  
Figure 3: Displacement diagram of dip-slip fault.  
it is speculated that the LOS deformation rate estimated by stacking-InSAR in Figure 4 is mainly from the vertical component due to the fault and human activities.
## 4 Experimental Test-Bed
### Dataset Vibration Signal Acquisition  
Three accelerometers denoted A1, A2 and A3 (model PCB 603C01) were vertically placed on the compressor case. A1 is located close of the discharge valve, A2 is located close of the intake valve of the second stage. A3 is located close to the B1 roller bearing. A plot showing the location of each accelerometer is presented in Figure 13. Although other sensors are located in the test-bed for recording signals such as current, sound, acoustic emission and pressure, they are not considered in this paper.  
The analog signals were transferred using a coaxial-cable to a National Instruments model NI-9234 compact data acquisition card (cDAQ) connected to the NI-9188 chassis to stream the digital signals to a laptop computer through a 100 Mbps Ethernet Link. The acquisition process is controlled using a LabView software running on a laptop for performing the A/D conversion and storing the data in a hard-disk for off-line processing. The measurements were taken under a 57.7 Hz constant motor rotation frequency, resulting in a 12.8 Hz crankshaft rotation frequency. There are two crankshaft cycles per compression cycle, in consequence, the total compression cycle takes approximately 0.156 s. The tank pressure was kept constant at 3 bar. A set of 15 vibration signals for each sensor and for each machinery condition was collected. A total of \(15\times 13=195\) signals of 10 s each were digitized at sampling frequency of 50 kHz.
## 1 Introduction
However, the RGB information on the image was used as the only matching metric in the above methods, there were still distortion and warping in the boundary, especially in the images that accompany with stronger structural information [35]. Moreover, selecting a new texture block in a random way for image synthesis is time-consuming. In this study, an image synthesis algorithm was proposed, which only needs to collect a small number of images in a real scene, and can synthesize a large number of images as needed. Subsequently, a label automatic annotation algorithm is presented to provide the label images for training the CNN model that is designed to detect the lateral distance between the vehicle and lane strip. Due to external load, suspension, and tire during high-speed driving, a large inclination angle would be produced. For this reason, a big recognition error of LatDisLanes may be caused. Therefore, this study proposes a dynamic correction model to eliminate the effect of inclination angle on recognition accuracy. The main contributions of this work are as follows. 1. An algorithm for image synthesis is proposed using only a few images from the real scene and a large number of high-quality images can be obtained. 2. A label automatic annotation algorithm that provides the label images for training the CNN model based on the synthetic images is presented. 3. A CNN model, LatDisLanes, is designed to recognize the lateral distance. Furthermore, to reduce the influence of inclination angle on model recognition accuracy, this study proposes a dynamic correction model. This manuscript is structured as follows: In Section 2, a recognition model that is based on CNN is proposed. In order to train the model, an approach for generating label images is presented and the proposed dynamic correction model aiming to reduce the recognition error that is caused by inclination angle is proposed. The evaluation of proposed approach is given in Section 3 and concluding remarks are presented in Section 4.
# LTU Attacker for Membership Inference
## 6 Results
### Black-Box Attacker
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**QMNIST** & \multirow{2}{*}{Orig. order + Seeded} & \multirow{2}{*}{Rand. order + Seeded} & \multirow{2}{*}{Not Seeded} \\ Utility \(|\) Privacy & & & & \\ \hline Logistic lbfgs & 0.92 & 0.00 & 0.91 & 0.00 & 0.91 & 0.00 \\ Bayesian ridge & 0.92 & 0.00 & 0.92 & 0.00 & 0.89 & 0.00 \\ Naive Bayes & 0.70 & 0.00 & 0.70 & 0.00 & 0.70 & 0.00 \\ SVC & 0.91 & 0.00 & 0.91 & 0.00 & 0.88 & 0.00 \\ KNN* & 0.86 & 0.27 & 0.86 & 0.27 & 0.83 & 0.18 \\ LinearSVC & 0.92 & 0.00 & 0.92 & 0.69 & 0.91 & 0.63 \\ SGD SVC & 0.90 & 0.03 & 0.92 & 1.00 & 0.89 & 1.00 \\ MLP & 0.90 & 0.00 & 0.90 & 0.97 & 0.88 & 0.93 \\ Perceptron & 0.90 & 0.04 & 0.91 & 1.00 & 0.92 & 1.00 \\ Random Forest & 0.88 & 0.00 & 0.88 & 0.99 & 0.85 & 1.00 \\ \hline
**CIFAR-10** & \multirow{2}{*}{Orig. order + Seeded} & \multirow{2}{*}{Rand. order + Seeded} & \multirow{2}{*}{Not Seeded} \\ Utility \(|\) Privacy & & & & \\ \hline Logistic lbfgs & 0.95 & 0.00 & 0.95 & 0.00 & 0.95 & 0.00 \\ Bayesian ridge & 0.91 & 0.00 & 0.90 & 0.00 & 0.90 & 0.00 \\ Naive Bayes & 0.89 & 0.00 & 0.89 & 0.01 & 0.89 & 0.00 \\ SVC & 0.95 & 0.00 & 0.94 & 0.00 & 0.95 & 0.00 \\ KNN* & 0.92 & 0.44 & 0.91 & 0.49 & 0.92 & 0.49 \\ LinearSVC & 0.95 & 0.00 & 0.95 & 0.26 & 0.95 & 0.22 \\ SGD SVC & 0.94 & 0.32 & 0.94 & 0.98 & 0.93 & 0.99 \\ MLP & 0.95 & 0.00 & 0.94 & 0.98 & 0.95 & 0.97 \\ Perceptron & 0.94 & 0.26 & 0.94 & 1.00 & 0.93 & 0.96 \\ Random Forest & 0.92 & 0.00 & 0.93 & 0.99 & 0.91 & 0.92 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Utility and privacy of QMNIST and CIFAR-10 of different scikit-learn models with three levels of randomness: original sample order + fixed random seed (no randomness); random sample order + fixed random seed; random sample order + random seed. The Defender data and Reserved data have both 1600 examples. All numbers shown in the table have _at least_ two significant digits (standard error lower than 0.004). For model implementations, we use scikit-learn (version 0.24.2) with default values. Shaded in gray: fully deterministic models with Privacy \(\equiv 0\). Figure 3: **Left: Histograms of predicted probabilities of membership IN the Defender set, for the Defender set and the Reserve set, each of size 1000. Right: Scatter plot comparing the actual proportions of being IN the Defender set with the predicted probabilities of membership IN the Defender set, for groups of 40 samples with similar predictions (with error bars for proportion). Many Defender samples are correctly predicted members with high confidence. **
## 5 Discussion  
Applicability of the SWAT Model and InVEST Model in Calculating Groundwater Reserves and Surface Water Production in the Study Area  
This work comprehensively addresses the unique characteristics of the study area, incorporates pertinent elements affecting winter snowfall and melting on groundwater reserves, and selects appropriate parameters consistent with regional attributes. The SWAT model is deemed appropriate for calculating groundwater reserves in the cold temperate zone. The evolution of groundwater resources in cold climates primarily involves precipitation, evaporation, infiltration, runoff, freeze-thaw cycles, and groundwater movement.  
Figure 6: Groundwater permeability efficiency between 2010 and 2018.  
Figure 7: Distribution of the water production at scale, 2010–2018.  
The SWAT model can simulate and visualize the aforementioned hydrological cycle's process. A solitary SWAT model may solely mitigate mistakes in the freeze-thaw cycle's process through the adjustment of pertinent parameters. Nonetheless, within the entire hydrological cycle, the impact of the freeze-thaw cycle is quite minor, and the inaccuracy can be rectified through parameter rate timing [34]. The advancements in remote sensing technology have enhanced the accuracy of meteorological, soil type, land use, and other data necessary for the SWAT model, hence increasing the correlation between the model's final results and the observed values [35]. In conclusion, the SWAT model demonstrates strong adaptability in calculating groundwater reserves in cold regions.  
The study area features relatively unobstructed terrain and climate characteristics, with data such as digital elevation models (DEMs), precipitation, and temperature being readily accessible for collection. These constitute the fundamental data required for operating the InVEST model's water production module, and the principles of water production assessment are also notably consistent [36]. The water yield module of the InVEST model computes the water yield based on the water balance principle, taking into account precipitation, evaporation, and additional parameters. Despite the prolonged winter and frigid environment in the study location, precipitation and evaporation remain significant factors influencing the water yield. This model's principle aligns with the hydrological processes in frigid regions. It can somewhat indicate the water production in the studied area [37]. Water resources in cold regions are essential for ecosystems and human activities. The InVEST model's water production module can evaluate alterations in water production in these areas under varying land use patterns and climate change scenarios, offering a scientific foundation for the planning, management, and protection of water resources in cold regions. In conclusion, the InVEST model demonstrates strong adaptability within the studied domain.
# Experimental Analyses of Temporal Activity-Sequencing Anomalies in Process Mining
## 3 Temporal Activity-Sequencing Anomaly Detection Approach
### Detection and Removal of the Temporal Activity-Sequencing Anomalies and Noises
```
0: Process enactment event logs (\(\omega\epsilon\)), Threshold \(T\)
0: Non-noised temporal activity-sequencing graph
1:procedureMain
2: Initialize Graph G = \(\emptyset\), activityMap = \(\emptyset\)
3: Open the process enactment event log dataset file (\(\omega\epsilon\))
4: G = GetGraphFromLog((\((\omega\epsilon)\)) (*)
5: n = (\(\omega\epsilon\)).GetNumberOfTraces
6:for(\(\forall_{node}\in(G)\)) do
7: new(tempActivityInfo)
8:for(\(\forall_{outGoingEdge}\in(node)\)) do
9: beginNode = outGoingEdge.GetStartNode()
10: endNode = outGoingEdge.GetEndNode()
11:\(w\) = outGoingEdge.GetValue()
12: tempActivityInfo.add(beginNode,endNode,w)
13: activityMap.add(tempActivityInfo)
14:endfor
15:endfor
16:for(\(\forall_{pair}\in(activityPairMap)\)) do
17: ratioValue = pair.\(w_{2}\)/ pair.\(w_{1}\)
18:if (ratioValue\(>\)Threshold-\(T\)) then
19: G.RemoveEdge(pair.Start, pair.End)
20: G.RemoveEdge(pair.End,pair.Start)
21:endif
22:endfor
23: Close the enactment log file (\(\omega\epsilon\))
24: Return Graph G
25:endprocedure
```  
**Algorithm 1** The temporal activity-sequencing anomaly detection and removal algorithm
## 3 Results and Discussion
### Obtained Evaluation  
An investigation was conducted using an experimental design to study the impact of cutting parameters like spindle speed, feed rate, and depth of cut on surface roughness. Measurements were recorded after the completion of the milling operation. With the help of experimental data, both the ANN and MRA models were developed to forecast surface roughness. Table 3 shows the measured roughness values according to the values  
Figure 11 shows comparison diagrams of the measured \(R_{a}\) values and predicted values for the MRA models. The RA-2 model predicted the highest roughness (3.478 \(\upmu\)m) in the case of a speed of 3000 rev/min, a step of 1000 mm/min, and a depth of cut of 1 mm. The lowest roughness (2394 \(\upmu\)m), i.e., the best surface quality after machining, was predicted with 6000 rev/min, 600 mm/min, and 0.6 mm. The RA-3 model had the highest roughness (3434 \(\upmu\)m) in the case of 4000 rev/min, 1000 mm/min, and 1 mm. The lowest roughness (2140 \(\upmu\)m), i.e., the best surface quality after machining, was predicted with 6000 rev/min, 600 mm/min, and 0.6 mm.  
The highest degree of correlation was achieved by the 5-S-R model, followed by 10-S-R, 5 \(\times\) 2-S-R, and 5-S-T, while models RA-3 and RA-2 achieved a lower correlation. Figure 12 represents the tabular relationship of the determination coefficient \(R^{2}\) for all prediction models.  
Figure 10: Comparison of predicted and measured results of \(R_{a}\) with 5 \(\times\) 2-S-R model.  
Figure 9: Comparison of predicted and measured results of \(R_{a}\) with 5-S-T model.