esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Method  
### Nighttime Vehicle Detection System  
#### 3.1.1 Object Detection and Feature Extractor  
The Faster R-CNN [5] was selected as the fundamental objection detection algorithm for our nighttime vehicle detection system. Different pretrained CNN architectures were also used as feature extractors in the Faster R-CNN [5]. The hyperparameters were carefully selected according to a deep analysis [10] of the Faster R-CNN [5] and optimized training process. A typical Faster R-CNN [5] was used in our system. Feature extraction was performed with a CNN; objects were proposed by RPNs; ROI pooling was performed with images of different sizes; and classification was performed by fully connected layers.  
We had approximately 9003 labeled nighttime images, which were insufficient for training the entire system. The system included numerous parameters that had to be trained with a large amount of data to handle the model's capacity. By using the transfer learning technique, which is a widely used method of image recognition and object detection, we could train the model without using an excessively large data set. Therefore, we used VGG16 [1] and ResNet101 [2] with parameters pretrained on ILSVRC-2012-CLS [11] to deal with the problem of an insufficient amount of training data. As shown in Figure 5, for VGG16 [1], we fixed the parameters of the first four convolutional layers with the following max pooling layers (represented in the brown part) and freed the other parts of the neural networks (as denoted in the blue part). For ResNet101 [2], we fixed the parameters of the first convolutional layer with its corresponding max pooling layers and the following single residual block (three sets of bottleneck structures), as displayed in the brown part of Figure 5. The other parameters were freed for training, as denoted in the blue part of Figure 5.  
Although the ROI pooling in the Faster R-CNN [5] can cope with different sizes of feature maps, we fixed the input size to \(500\times 375\) pixels for training and inference. If the input size was larger than \(500\times 375\) pixels, we performed scaling. The reason for performing scaling but not allowing free size input is stated in the experimental results and discussion part. The reason is related to the challenge of objects that occupy only a few pixels. We set the number of proposed boxes before and after non-max suppression [5] during inference phase to be 2500 and 250, respectively, because a reasonably high  
Figure 5: The usage of pre-trained convolutional neural networks (CNNs).  
number of proposed boxes can help increase the recall of the system [10]. Although the accuracy is diminished when increasing the number of proposals [10], localization is vital in our nighttime vehicle detection task.
## 2 Materials and Methods
### Experimental Design  
Response surface methodology (RSM) is a systematic experimental mathematical model for predicting sorption behavior and evaluating the relative importance and interaction of each parameter (TB concentration, sugar concentration, and fermentation time). To obtain a high-quality yogurt, RSM based on a three-factor and three-level Box-Behnken design (BBD) was employed to study the optimum fermentation conditions for TB-containing yogurt. The value of each factor was obtained based on the results from the previous single-factor experiment, and the score for sensory evaluation of the yogurt samples was the response value. This experimental design consisted of 17 runs in total, including 12 factor points and 5 replications at the center point to estimate the experimental error. The experimental design matrix and its results are shown in Table 1. The response factors were monitored with a second-order polynomial equation (Equation (1)) to figure out the predicted response (Y).  
\[\mathrm{Y}=\beta_{0}+\beta_{1}\mathrm{A}+\beta_{2}\mathrm{B}+\beta_{3} \mathrm{C}+\beta_{12}\mathrm{AB}+\beta_{13}\mathrm{AC}+\beta_{23}\mathrm{BC}+ \beta_{11}\mathrm{A}^{2}+\beta_{22}\mathrm{B}^{2}+\beta_{33}\mathrm{C}^{2} \tag{1}\]  
where Y is the response variable (sensory score), \(\beta_{0}\) is a constant, \(\beta_{1}\) and \(\beta_{2}\) are the linear coefficients, \(\beta_{11}\), \(\beta_{22}\), and \(\beta_{33}\) are the quadratic coefficients, and \(\beta_{12}\), \(\beta_{13}\), and \(\beta_{23}\) are the interaction coefficients. A, B, and C represent TB concentration, sugar concentration, and fermentation time, respectively. The BBD output also contains the 3D surface response plot, which indicates the interaction between the responses and independent variables.
## 3 Experimental Tests
### Storage Stability  
The storage stability test, which is crucial for evaluating the uniform dispersion of nanomaterial modifiers such as NT, NA, and NS, follows the [46] guidelines. This assessment aims to identify any potential sedimentation of nanomaterials within an asphalt binder during storage by measuring the difference in softening points (\(\Delta\)T) between the top and bottom samples extracted from the test tube. This measurement indicates the presence of any separation or settling of nanomaterials within the binder. The conditioning procedure includes placing samples into cylindrical molds measuring 32 mm in diameter and 160 mm in height, which are then heated in an oven at 163 \({}^{\circ}\)C for 72 h before being allowed to cool at room temperature. The effectiveness of the mixing conditions was confirmed, as evidenced by the \(\Delta\)T values, which remained below the acceptable limit of 2.2 \({}^{\circ}\)C, thus indicating that the nanomaterials were evenly distributed without significant separation.
## 5 Classification Performed by Machine Learning Algorithms
### Convolutional Neural Network
#### 5.3.1 Spectrograms extraction  
Drawing inspiration from the work of [PERSON] et al. [27], four spectrograms were extracted from each binaural audio recording. The first two spectrograms were calculated from the left (\(l\)) and right (\(r\)) channel signals, respectively. The remaining two spectrograms were computed accordingly from the sum (\(m=l+r\)) and difference signals (\(s=l-r\)). While it could be argued that the latter two spectrograms are redundant as they do not introduce any new information, they might facilitate the network training process and hence improve its performance, as indicated in our pilot study [14].  
Mel-frequency spectrograms are commonly exploited in the deep learning algorithms used for speech recognition, acoustic scene classification or audio event recognition [27; 31; 43; 44]. While Mel-frequency spectrograms offer a better resolution at low frequencies, it is unclear whether such a type of spectrogram is still superior in terms of \"spatial\" audio scene classification. According to the results of our pilot experiment (omitted here due to space limitations), CNN fed with linear-frequency spectrograms yielded slightly better results compared to those obtained with Mel-frequency spectrograms. Therefore, only linear-frequency spectrograms were used in this study.  
The temporal resolution of the spectrograms was set to 40 ms with a 50% overlap. Hence, for the recordings of 7 s in duration used in this study, each spectrogram contained 349 time frames. A Hamming window was applied to the signals in each frame. The number of frequency bands in the spectrograms was set to 150 since according to the outcomes of our pilot experiment [14], such a number of frequency bands provides a reasonable trade-off between the classification accuracy and the computational load imposed on the network. The low- and high-frequency limits of both types of spectrograms were set to 700 Hz and 16 kHz, respectively (extending the frequency range beyond those limits brought little benefit in the CNN performance). The range of the spectrogram values was limited (clipped) to 90 dB relative to the peak value.  
The image resolution of the spectrograms was equal to \(150\times 349\) pixels (number of frequency-bands \(\times\) number of time-frames). The spectrograms were combined into three-dimensional tensors and then fed to the network input. The size of each tensor was equal to \(150\times 349\times N_{\mathrm{ch}}\), where \(N_{\mathrm{ch}}\) represented the number of channels retained in a tensor. In this study, the number of channels \(N_{\mathrm{ch}}\) was equal to four, since each binaural recording was represented by four spectrograms.  
In summary, every audio recording was converted to four spectrograms, with the resolution of \(150\times 349\) pixels each. According to the typical practice in the area of machine learning [27; 44], the spectrograms were standardized prior to their use by the convolutional network. The spectrograms were calculated in MATLAB using a VOICEBOX toolbox [45].
## 3 Results
### The Proposed Mapping Accuracies for the SYNE-ANEW and NAWL-NAWL Datasets  
For evaluation of the new mapping matrices, a 10-fold cross-validation procedure was performed, as described in Section 3.3 for the two datasets SYNE-ANEW and NAWL-NAWL independently. Cross-validation results for the datasets are presented in Tables 8 and 9.  
The proposed mapping matrix validation results on the SYNE-ANEW dataset are relatively high. For the dimension of valence, over 33% accuracy was obtained for the 10% precision threshold, while, for the 20% and SD-based precision thresholds, the accuracy was around 60%. For the dimension of arousal and dominance, over 58% accuracy was obtained for the 10% precision threshold, while, for the 20% and SD-based precision thresholds, the accuracy exceeded 90%. Single-dimension results are very promising. However, the joint dimension accuracies are lower. Setting a 10% precision threshold resulted in 17.8% accuracy, while the 20% and SD-based thresholds resulted in 54.9% and 59.9% accuracies, respectively. The observed accuracies are higher than for the reference mapping for the same dataset; however, these might still be insufficient for high precision requirements.  
The proposed mapping matrix validation results on the NAWL-NAWL dataset are very high. For the dimension of valence, over 90% accuracy was obtained for all precision thresholds. For the dimension of arousal, over 74% accuracy was obtained for the 10% precision threshold, while, for the 20% and SD-based precision thresholds, the accuracy exceeded 97%. Single-dimension results are satisfactory. The joint dimension accuracies are slightly lower, but still exceed 97% for the 20% and SD-based precision thresholds. The observed accuracies are higher than for the reference mapping for the same dataset.
# Prediction of Ground Surface Settlements Induced by EPB Shield Tunneling in Water-Rich Soft Strata
## Case Study in Changzhou
### Comparison between Predicted and Monitored Results  
By superimposing all the displacement components in Figures 9-12, the total ground surface deformation profile of case A can be obtained. Then the predicted total deformation was tested against the field monitored results, and the comparison results are shown in Figure 13. In general, the comparison between them shows a good agreement. As presented in Figure 13, the ground surface first rises ahead of the shield machine and then collapses behind it. A maximum positive displacement of about 2 mm is observed in the range of \(0<\mathrm{Y}<40\), and a maximum negative displacement of about 6 mm is observed in the range of \(-40<\mathrm{Y}<0\). As shown in Figure 14, three columns of monitoring points, i.e., the SD3, SD4, and SD5 in Figure 13b, are selected for the comparison of longitudinal settlement in case A, of which the SD3 monitoring point is located directly above the tunnel centerline. It can be seen that in the range of the \(-20<\mathrm{Y}<40\), the prediction results show a great agreement with the monitoring results. However, in the range of \(-100<\mathrm{Y}<-20\), the predicted value is lower than the monitored value. This can be attributed to the consolidation and settlement of the water-rich soft strata in the Changzhou area. After the soil layer is disturbed by shield tunneling, the excess pore water pressure gradually dissipates, resulting in long-term settlement. The empirical method proposed in Section 3 can be used to predict this long-term settlement.  
Figure 11: Ground movements induced by shield shell friction.  
Figure 12: Ground movements induced by cutter head rotation.  
Figure 14: Comparison of predicted and measured longitudinal ground displacement in case A.  
Figure 13: Comparison of predicted and monitored ground surface displacement in case A. (**a**) predicted displacement; (**b**) monitored displacement.  
The comparison of predicted and monitored ground surface displacement in case B was obtained using the same superposition method as in case A, as shown in Figure 15. It can be seen that the comparison results show a good agreement. The ground surface first presents an uplift ahead of the shield machine and then subsides behind it. Positive displacement was observed within the region of \(-10<x<10\) and \(0<y<10\), whereas negative displacement was observed within the region of \(-20<x<20\) and \(y<0\). Figure 16 shows the comparison results of longitudinal ground displacement in case B. A substantial displacement change was observed within the region of \(-10<y<10\). Compared with Figure 15, Figure 16 shows a sharper deformation shift and a larger maximum settlement, which can be attributed to the shallower burial depth of Case B. This indicated that the influence of shield working loads on the surface deformation is more obvious when buried to a shallow depth.  
Figure 15: Comparison of predicted and monitored ground surface displacement in case B. (**a**) predicted displacement; (**b**) monitored displacement.
# Analysis of Cavity Corner Geometry Effect on Recirculation Zone Structure
## 3 Results and Discussion
### Investigation of Flow Parameters Corresponding to Flow Stability  
Skin friction coefficient \(C_{f}\) distribution on the cavity bottom wall is given in Figure 14. Increased \(C_{f}\) is associated with increased static pressure along the cavity. The minimum \(C_{f}\) is observed behind the leading cavity wall, where small secondary vortices or smooth streamlines are present depending on corner geometry. \(C_{f}\) abruptly increases with the beginning of the recirculation zone and reaches a plateau in Ch1 and Ch2 in the middle of the cavity at the recirculation zone centre. The Ch3 recirculation zone is not fully formed; therefore, Cf values are lower, and a plateau is not observed as the recirculation zone size decreases along the cavity, contrary to Ch1 and Ch2. A slight decrease in \(C_{f}\) is observed at \(x/L\sim\)0.7 in Ch1 and Ch2 as the recirculation zone weakens along the cavity. In Ch3, \(C_{f}\) reduction is more pronounced due to shear layer reattachment to the cavity bottom. As the trailing wall of the cavity approaches, the favourable pressure gradient rapidly increases, leading to increased \(C_{f}\). In Ch1 and Ch2, \(C_{f}\) distribution along the cavity is similar because of analogous recirculation zone dynamics. However, in Ch3, the adverse pressure gradient is lower at the inlet of the cavity; therefore, a weaker recirculation zone is formed, leading to lower flow resistance.  
Figure 12: Velocity \(u\) profiles at the x–z plane height of \(y/h_{1}=0.5\).  
Figure 13: Turbulence intensity profiles along the cavity at \(\mathrm{Re}=1000\).
# Feasibility Analysis on the Use of Ultrasonic Communications for Body Sensor Networks
###### Abstract  
Ultrasonic waves have good propagation in the human body and have been widely applied in biomedical device design without any reported side effects. Therefore, ultrasonic waves can provide an alternative method as an information carrier for body sensor networks (BSNs). This paper presents a novel wireless communication method that uses ultrasonic sound waves as a medium for healthcare systems. We investigated the feasibility of our proposal by testing it in a real digital communication experimental setup. To find an acceptable modulation method, the functionality of the proposed ultrasound-based digital communication approach was tested involving three principal modulation methods: amplitude shift keying (ASK), frequency shift keying (FSK), and phase shift keying (PSK). The modulated digital signals obtained from the experiments were compared with the simulated signals. Analysis of the results shows that ultrasonic waves are feasible and can be used for digital communication.  
ultrasonic communication; body sensor network (BSN); healthcare; wireless communication 2018 18 4496 doi:10.3390/s18124496
# Finsformer: A Novel Approach to Detecting Financial Attacks Using Transformer and Cluster-Attention
## 3 Materials and Methods
### Dataset Annotation  
Dataset annotation is a crucial step in ensuring the effectiveness of model training. For financial attack detection datasets, the annotation process involves categorizing transaction records as \"normal\" or \"attack\".  
1. Annotation criteria: A series of criteria based on transaction characteristics, such as transaction frequency, amount, and historical behavior of the parties involved, were established. For instance, frequent large transactions might be flagged as suspicious attacks.
2. Expert review: The annotation process involved the participation of experts in the financial field. They conducted preliminary annotations based on their experience and industry knowledge, especially for complex or ambiguous cases.
3. Algorithmic assistance: To enhance efficiency, simple machine learning algorithms were used for pre-annotation, followed by manual expert review. This method combines the efficiency of algorithms with the accuracy of human expert judgment.
4. Iterative optimization: The annotation process is iterative. After initial training on pre-annotated data, the model's predictions are used to guide further manual annotations, forming a feedback loop.  
A key mathematical principle in the annotation process is [PERSON] theorem, which can be used to calculate the probability of a transaction being an attack given certain specific transaction features. [PERSON] theorem [44] is expressed as  
\[P(A|B)=\frac{P(B|A)\times P(A)}{P(B)} \tag{6}\]  
where \(P(A|B)\) is the probability of event \(A\) occurring under condition \(B\), \(P(B|A)\) is the probability of condition \(B\) given that event \(A\) has occurred, and \(P(A)\) and \(P(B)\) are themarginal probabilities of event \(A\) and condition \(B\), respectively. Applying this principle allows for a more accurate assessment of the correlation between various transaction features and potential attack behaviors, thereby improving the accuracy of annotations and the performance of the model. Overall, the data collection and annotation process in this study aims to ensure the representativeness, authenticity, and privacy protection of the dataset while enhancing annotation efficiency and accuracy through the combination of expert knowledge and algorithmic analysis. This process is vital for developing an efficient and accurate financial attack detection model.
# Study of RF Stray Currents in ITER Neutral Beam Test Facilities
## 4 Interpretation of Rf Stray Currents in Spider
### Cvd at Primary Side  
When only CVDI is installed, its capacitance becomes transparent to the oscillator circuit, since it is in parallel with significantly larger capacitors as \(C_{V},C_{1}\) and \(C_{2}\) (see Figure 2). In addition, when the tetrode is in conduction, one of the branches formed by one CVD leg, \(C_{V},C_{1}\) and \(C_{2}\) is short circuited. In this configuration, a common mode current circulates through the output filters of the power supplies (see a possible path in Figure 5), due to the stray capacitance between primary and secondary windings of the RF transformer. The common mode current magnitude, in this case, is mainly defined by the impedance of the transformer stray capacitance and the voltage between primary and secondary winding (i.e., the transformer ratio).  
Figure 4: RF stray current paths with CVD installed at RF transformer primary side. (**a**) Common mode current related to the connection of one CVD leg to the outer conductor of the RF coaxial line. (**b**) Differential mode current related to the connection of one CVD leg to the inner conductor of the RF coaxial line.
# Study on Multi-Span Tension Coupling Relationship of Gravure Printed Electronic Equipment
[PERSON] studied the model of the winding tension subsystem and established mathematical models for components in the rewinding  
Figure 1: Diagram of a roll-to-roll gravure printed electronic machine. tension subsystem, such as the winding mechanism and swing roller mechanism [25]. [PERSON] and [PERSON] conducted modeling analysis on the commonly used swing roller mechanism in tension systems [8; 26]. [PERSON] studied the characteristics of the discontinuous unwinding tension subsystem and established a model of the discontinuous unwinding tension subsystem [27]. It can be seen that the above studies mainly focus on the unwinding tension subsystem or rewinding tension subsystem, while research on the printing tension subsystem is relatively scarce. In these studies, there is almost no research on the tension coupling relationship. In printed electronic equipment, higher requirements are placed on the tension system for product accuracy; therefore, it is necessary to conduct more in-depth research on the factors that affect tension, such as the coupling relationship in multi-span tension systems. The tension system of gravure printed electronic equipment is a multi-span system composed of multiple functional units connected by the web. A multi-span tension system is not simply a combination of multiple single-span tension systems; rather, there is a coupling relationship between each single-span tension system. To obtain the characteristics of the multi-span tension system, it is necessary to study the multi-span tension coupling model. Based on the research on single-span tension systems, this article conducts in-depth research on the coupling relationship of multi-span tension systems. Firstly, according to the specific structural characteristics of the printing tension subsystem, the mathematical model of the printing tension subsystem including drying factors is established, and then aiming at the multi-input and multi-output physical quantities of the system, through simulation and experiment methods, the multi-physical quantity coupling law of multi-span tension system is analyzed and verified.