esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Results
### Compare the Model Performance with Recent Researches  
In the second experiment, we used AUC values to compare the performance of CGFace, IF-CGFace with VGG and its performance on DSCG1 and DSCG2 computer-generated dataset [1].  
For the VGG-16 implementation, we used the default configuration, i.e., image size of 224 \(\times\) 244, learning rate 0.01, and 30 epochs. For the work in [1], The first dataset (DSCG1) composed of 30 extremely realistic CG images of faces collected from different websites and 30 PG images. The 30 CG images depict 15 male and 15 female faces with image resolutions varying from 389 \(\times\) 600 to 557 \(\times\) 600 pixels. PG images have resolutions varying from 387 \(\times\) 600 to 594 \(\times\) 600 pixels. The second dataset (DSCG2) composed of 160 images (80 CG and 80 PG) with the resolution from 236 \(\times\) 308 to 1569 \(\times\) 2000 for CG images and from 194 \(\times\) 259 to 5616 \(\times\) 3744 for PG images. The author in [1] proposed a new CG image detection method that took advantage of the inconsistencies occurring in eye's region of CG images containing people. Given an image of a person, they extracted features from eye's region with and without removing specular highlights, using a deep convolutional neural network model based on VGG19 and transfer learning approach.  
As shown in Figure 10, our ADA-CGFace outperforms VGG as well as the model proposed by [1] on DSCG1 and DSCG2 dataset. The highest AUC achieved on ADA-CGFace is on DSCG1 dataset, at about 92%, followed by XGB-CGFace at 85% on DSCG2 dataset. On DSCG1 and DSCG2, the reported AUC [1] were 84% and 88%, respectively, whereas our model achieved an AUC of 92% for DSCG1 and 89% for DSCG2.  
Figure 9: A comparison regarding AUC for various methods through 4 folds on the imbalanced environment (No. CG images / No. real images = 1/10).
## 1 Introduction
### Channel Interference Effect  
For the proposed technique, due to the digital filtering operation in the transmitters, spectral guard bands are not used between adjacent ONUs to improve the spectral utilization efficiency, as seen in Figure 4d. In addition, in the considered PONs, to effectively mitigate the channel fading effects, low-frequency ONU1 and high-frequency ONU2 employ optical double sideband (ODSB) and OSSB signals, respectively. As such, it is easy to understand that channel interferences between different ONUs occur mainly due to SSBI arising from directly detecting ONU2's OSSB signals and the imperfect digital filter-induced power leakages.  
To investigate the channel interference effects between different ONUs and further evaluate the resultant transmission performance degradations, based on the experimental parameters used in obtaining Figure 5, the power penalties caused by the channel interference effects between the considered two ONUs are measured and illustrated in Figure 6, which presents the receiver sensitivity variations of each channel of an ONU with the other ONU's 4 channels being switched on and off [16]. For ONU2, the channel interference effects can only lead to negligible power penalties of <0.6 dB for both the upstream and direct inter-ONU communications. This indicates that for the considered digital filter length of 32, the imperfect digital filter-induced channel interference effects are negligible, which agrees with the results obtained in [24]. However, in comparison with ONU2, ONU1 suffers extra power penalties of <1.8 dB (2.35 dB-0.57 dB = 1.8 dB). This is mainly caused by the ONU2-produced SSBI effects, which locate at the baseband spectral regions after direct detection, and are spectrally overlapped with ONU1' signals [29].  
For practical implementation, to further mitigate the SSBI-induced channel interference effects, conventional SSBI estimation and cancellation techniques [30; 31] may be employed in the receiver DSPs to jointly cancel the SSBI effect. On the other hand, other channel fading mitigation techniques/solutions may also be applied to replace the adopted optical filter-based OSSB signal generation techniques, including IMDD system linearization techniques [32], modified OSSB techniques with reduced SSBI [33], dispersion compensating fibers/devices [34], chirp-managed lasers [35], and O-band optical transmissions [36].  
In addition, as seen in Figure 6, it can also be found that for each ONU, CH3 suffers a relatively large power penalty in comparison with all other channels of the same ONU. Such a relatively high power penalty suffered by CH3 is a result of the interplay between the channel interference effects and the transceiver-embedded cascaded IFFT/FFT operations [24].
## 7 Conclusions  
This paper proposes a PSL-SUE model to solve the passenger assignment problem under the integrated operation mode of cross-line and skip-stop between state and sub-urban railways. An improved MSA algorithm considering train capacity is designed to solve the model. The passenger travel utility in the PSL-SUE model is represented by the path's generalized travel cost, which is divided into waiting time cost, in-vehicle time cost, transfer time cost, and fare cost. Furthermore, the in-vehicle time cost takes into account the train congestion and incorporates a congestion penalty function. The different fare policies of the two rail systems are also represented by the functions and added into the generalized travel cost. Besides, the schedule-based network is also established to describe all possible paths and the impedance of the links. This network is used to calculate the components of the path's generalized travel cost.  
A numerical example is designed to verify the validity and feasibility of the proposed model and algorithm. The results show that the model and algorithm can be applied to the passenger assignment problem in the practical cases of cross-line and skip-stop operation between state and suburban railway systems. The proposed PSL-SUE model has superiority over the traditional MNL-SUE model. Furthermore, the sensitivity of three main parameters is analyzed. First, passengers' higher degree of familiarity with network operation information, the more accurate the assignment results, but with more iteration times and slower convergence speed. Secondly, the number of passengers on the state train and the probability of passengers choosing the state train decreases with higher state railway fares. In contrast, the opposite is true for the hourly wage. The passenger number and probability increase with the higher hourly wage. When the state railway fare is lower than 0.5 CNY/km or the hourly wage is higher than 50 CNY/h, the generalized travel cost of the paths that include the state train is lower, and the state train becomes more attractive.  
With the interconnection between rail systems, the proposed model and solution procedure can be used for solving passenger assignment problem under integrated operation mode between different railway systems. It can provide ideas for network operation research and foundations for operators to develop operation plans. For further research, there are still some aspects can be explained. Some improved models based on extended logit could be used (e.g., C-logit models, nested logit models, paired combinational logit model) to avert the IIA property of MNL models. Meanwhile, with the development of big data and telematics, the passenger path choice behavior supported by real-time network information deserves further research.  
Both authors took part in the discussion of the work described in this paper. Writing--original draft preparation, X.H.; methodology, X.H., X.W.; writing--review and editing, Q.L.; data curation, S.L., K.H. All authors have read and agreed to the published version of the manuscript. This research received no external funding.
## 4 Experimental Results
### Comparison of the HSAN with Other Models on Other Public Datasets  
In this section, we attempt to evaluate our proposed model, which learns the classification task of the English language. There are four public datasets with the English language  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Model** & **Hierarchical** & **Attention** & \(F1_{macro}\) & \(F1_{weighted}\) & \(F1_{overall}\) \\ \hline GRU & No & No & 30.94\% & 40.89\% & 35.92\% \\ GRU(Soft) & No & Soft & 53.50\% & 58.92\% & 56.21\% \\ GRU(Squashing) & No & Squashing & 61.36\% & 62.70\% & 62.03\% \\ HAN & Yes & Soft & 43.56\% & 57.62\% & 50.59\% \\ HSAN & Yes & Squashing & 61.70\% & 63.22\% & 62.46\% \\ \hline \hline \end{tabular}
\end{table}
Table 7: Evaluation of the hierarchical and nonhierarchical structures.  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Model** & _ACC_ & \(F1_{macro}\) & \(F1_{weighted}\) & \(F1_{overall}\) \\ \hline SVM & 54.50\% & 52.89\% & 52.10\% & 52.50\% \\ RandomForest & 46.40\% & 47.61\% & 38.68\% & 43.15\% \\ TextCNN & 60.00\% & 58.39\% & 58.75\% & 58.57\% \\ TextRCNN & 58.60\% & 58.60\% & 58.46\% & 58.53\% \\ DPCNN & 60.30\% & 57.16\% & 58.98\% & 58.07\% \\ Transformer & 55.40\% & 47.34\% & 53.80\% & 50.57\% \\ CapsuleNet & 45.25\% & 30.60\% & 41.47\% & 36.04\% \\ BERT (_bert-base-chinese_) & 57.40\% & 53.21\% & 51.48\% & 52.35\% \\ BERT (_distilbert-base-multilingual-cased_) & 55.80\% & 53.43\% & 54.58\% & 54.00\% \\ BERT (_allenai/longformer-base-4096_) & 49.00\% & 43.94\% & 45.03\% & 44.49\% \\ HAN & 58.70\% & 54.15\% & 55.77\% & 54.96\% \\ HSAN & 62.30\% & 56.12\% & 61.76\% & 58.94\% \\ \hline \hline \end{tabular}
\end{table}
Table 8: Performance of HSAN with other popular models.  
to evaluate our proposed HSAN performances. All datasets use the under-sampling to reduce samples with random sample 10% data. The IMDB Review dataset was released by [PERSON] et al. (2019), and it belongs to a binary classification task that has 25000 training samples, 25000 testing samples, and balanced data. The Yahoo Answer dataset was released by [PERSON] et al. (2019); it belongs to a ten-class classification task that has 1,400,000 training samples, 60,000 testing samples, and balanced data. The Yelp Review Polarity dataset was released by [PERSON] et al. (2019), and it belongs to a binary classification task that has 560,000 training samples, 38,000 testing samples, and balanced data. The Amazon Review Polarity dataset was released by [PERSON] et al. (2019), and it belongs to a binary classification task that has 3,600,000 training samples, 400,000 testing samples, and balanced data. Table 9 presents the evaluation results for HAN and HSAN classification models in the test set. Overall, the HSAN model was superior to the HAN model on four datasets. Therefore, the proposed squashing-attention mechanism also can improve the classification performance in the English language.
# Object Detection in Remote Sensing Images by Combining Feature Enhancement and Hybrid Attention
## 4 Results and Analysis
### Experimental Comparison and Analyses  
#### 4.3.1 Experimental Comparison for FEFN  
In order to make full use of the features in different layers and enhance the expression capability of multi-scale features, the FEFN is added to the feature extraction network. Experiments are conducted before and after adding the FEFN respectively, and the detection results are shown in Table 1. The mAP is improved by 1.51% after adding the FEFN.  
The visualization comparison results are shown in Figure 6. Without FEFN, some small vehicles (SV) and the ground track field (GTF) are missed, which is shown in Figure 6a. For good observation, we use the red arrows to indicate the missed objects. In contrast, after adding FEFN, these missed objects are detected accurately, which is shown in Figure 6b.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Method** & **mAP(\%)** \\ \hline Without FEFN & 72.21 \\ With FEFN & 73.72 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Performance of FEFN.  
At the same time, the soccerball field labelled by the yellow bounding box is located more accurately. FEFN makes full use of the features in different layers to enhance the expression capability of multi-scale features, and thus obtains better results.
## 2 Materials and Methods
### Moving Object Detection Based on Robust Feature Threshold Segmentation
After determining the robust feature points, the moving object foreground area is segmented according to the optical flow value of the robust feature points, and the moving object area is retrieved. The maximum optical flow value of the robust feature points was used as the optical flow threshold for moving object extraction. The maximum grey value constant in the background feature points is used as a threshold, and the binary image of the foreground region of the moving target is extracted. (3) Image morphological processing of the target foreground region  
The rough extraction result of the moving object foreground area is established following the threshold processing of the image. The generated binary images contain many problems, such as salt and pepper noise, the discontinuity of the moving object extraction area, and an image hole in the foreground area due to the image noise and the optical flow estimation errors. Such concerns can be addressed by corrosion, expansion, open operation, closed operation, and other operations in image morphological processing. Figure 5 shows the moving object extraction results. Figure 5a illustrates the normalization grey image of the image optical flow vector field. Figure 5b shows that the rough extraction result of the moving target is based on the optical flow's grey value with the Harris robust feature points as the threshold. Figure 5c presents the morphological image processing results; the edge of the foreground region after morphological processing is smoother. Figure 5d shows the mask display for the extraction result. This paper's moving target detection algorithm can accurately extract the moving target region. Figure 4: Comparison of different feature points extraction and their corresponding robust feature points results. (**a**) Current image, (**b**) next image, (**c**) FAST feature points, (**d**) robust and FAST feature points, (**e**) SURF feature points, (**f**) robust SURF feature points, (**g**) Harris feature points, and (**h**) robust Harris feature points. The green points in the figure are matching points, and the red dots in figures (**d,f**) are the error detection points. The pseudo-code of moving object detection is as Algorithm 2:  
```
Input: Image \(I_{t}\), \(I_{t+1}\) and optical flow estimation \(\omega\) at time \(t\) Output: moving objects area \(R\)  Calculating optical flow field \(\overset{\cdot}{\omega}(x,y)=[uv]\) to optical flow grey value \(V(x,y)\)  Normalized grey image \(V_{norm}\)  Extracting [PERSON] feature points of image \(I_{t}\)  Calculating corresponding points in image \(I_{t+1}\): \(I^{B}+\omega=I_{t+1}{}^{B}\)  Calculating affine transformation matrix of background model: \(M_{t\to t+1}*I_{t}{}^{B}=I_{t}{}^{B}+\omega\)  Calculating optical flow threshold of background robust feature points  Extracting binary image of moving objects region  Morphological processing
```  
**Algorithm 2** Moving object detection
# _Sci_
## 2 Materials and Methods
### History of Laser Employment as Link Source  
Relevant laser applications have a long, interesting pedigree that harks back to the beginning of the space era.  
_\"The registered history of laser technologies for space application starts with the first laser echoes reflected off the Moon in 1962. Since then, photonic technologies have become very prominent in most technical development. Their presence has also dramatically increased in space applications thanks to the many advantages they present over traditional equivalent devices, such as the immunity against electromagnetic interference, as well as their efficiency and low power consumption. Lasers are one of the key components in most of those applications.\"_[4; 5]  
The application of lasers for communications across great distances has an even longer pedigree, although the notions were not actualized until the last century.
## 5 Unbalanced PSI-CA Protocol Based on Cuckoo Filter
### Experimental Analysis  
For this protocol, experiments were conducted, and the runtime was recorded for various combinations of data volumes, as shown in Table 2. The table also compares the runtime of [PERSON]'s PSI-CA protocol constructed based on the DH key-exchange mechanism [35]. Since preprocessing can be completed offline, the runtime of the unbalanced PSI-CA protocol based on Cuckoo filter refers to the total time of the the cardinality calculation process. The original protocol refers to the PSI-CA protocol constructed based on the DH key-exchange mechanism, and the new protocol refers to the unbalanced PSI protocol based on Cuckoo filter.  
Through the experimental data, this paper can observe several key phenomena. First, when the cardinality of the smaller dataset (number of elements) remains constant while the number of elements in the larger dataset increases rapidly, it is observed that the runtime of the protocol does not change much, remaining consistent. This indicates that, although the size of the large dataset increases dramatically, the efficiency of the protocol is not significantly affected, thereby proving that the design of this protocol can effectively mitigate the negative impact of dataset size imbalance on protocol efficiency. In particular, the overall runtime of the protocol is more related to the cardinality of the smaller dataset and has very low relevance to the cardinality of the larger dataset.  
At the same time, this experiment also found that, under balanced dataset conditions, the runtime of the PSI-CA protocol based on the DH key-exchange mechanism and the unbalanced PSI-CA protocol based on Cuckoo filter does not differ significantly. This indicates that, when the sizes of the sets are similar, both protocols can exhibit comparable performance, providing an efficient solution.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \begin{tabular}{c} **Cardinality of** \\ **Dataset** \\ **from Participant One** \\ \end{tabular} & \begin{tabular}{c} **Cardinality of** \\ **Dataset** \\ **from Participant Two** \\ \end{tabular} & \
\begin{tabular}{c} **Original Protocol** \\ **Runtime (Seconds)** \\ \end{tabular} &
\begin{tabular}{c} **New Protocol** \\ **Runtime (Seconds)** \\ \end{tabular} \\ \hline \(2^{10}\) & \(2^{15}\) & 1.8095 & 0.1685 \\ \(2^{10}\) & \(2^{17}\) & 7.3003 & 0.1663 \\ \(2^{10}\) & \(2^{20}\) & 56.1277 & 0.1641 \\ \(2^{10}\) & \(2^{25}\) & 1859.9520 & 0.1840 \\ \(2^{15}\) & \(2^{15}\) & 5.2207 & 5.2725 \\ \(2^{15}\) & \(2^{17}\) & 10.0672 & 5.5104 \\ \(2^{15}\) & \(2^{20}\) & 63.4835 & 5.2807 \\ \(2^{15}\) & \(2^{25}\) & 1886.3966 & 5.5166 \\ \(2^{17}\) & \(2^{17}\) & 20.7044 & 21.1474 \\ \(2^{17}\) & \(2^{20}\) & 71.9252 & 21.2865 \\ \(2^{17}\) & \(2^{25}\) & 1977.5657 & 21.9713 \\ \(2^{20}\) & \(2^{20}\) & 170.3074 & 171.5354 \\ \(2^{20}\) & \(2^{25}\) & 2054.2694 & 186.9644 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of running time of PSI-CA protocol based on DH key-exchange mechanism and Cuckoo Filter based.
# Flexible Simulation Platform for Generating Realistic Waveforms with Voltage Notches
## 4 Analysis of Key Parameter Dependencies, Limitations, and Impacts
### Impact of Key Parameters on Voltage Notch Waveforms
#### 4.2.2 Scenario 1: RC-Snubbers  
This scenario examines the impact of RC-snubber circuits on commutation oscillations by varying both the snubber capacitor (\(C_{sn}\)) and the snubber resistor (\(R_{sn}\)).  
* Simulations vary \(C_{sn}\) at 0.2-\(C_{base}\), 0.5-\(C_{base}\), \(C_{base}\), and 2-\(C_{base}\).  
Figure 8: Impact of (**a**) \(S_{sc}\), (**b**) \(S_{r}\), (**c**) \(X_{com}\), and (**d**) \(\alpha\) on voltage notch waveforms (**top**) and input line currents (**bottom**).  
* Fixed values include \(S_{sc}\) = 5 MVA, \(S_{r}\) = 25 kVA, \(X_{com}\) = 37.3 m\(\Omega\), \(\alpha\) = 30\({}^{\circ}\), \(R_{D}\) = 10.45 \(\Omega\), \(U_{rm}\) = 1400 V, and \(U_{rm}\) = 1120 V.
* Increasing \(C_{sn}\) decreases the peak and frequency of commutation oscillations. The corresponding \(R_{sn}\) values are selected as optimal for each \(C_{sn}\). Line currents increase in both peak magnitude and duration with increasing \(C_{sn}\).
* Simulations vary \(R_{sn}\) within a range derived from the design curve (\(R_{sn}/R_{base}\) from 1.25 to 6).
- Fixed values are \(S_{sc}\) = 5 MVA, \(S_{r}\) = 25 kVA, \(X_{com}\) = 37.3 m\(\Omega\), \(\alpha\) = 30\({}^{\circ}\), \(R_{D}\) = 10.45 \(\Omega\), and optimal \(C_{sn}\) = 0.70 uF.
- \(R_{sn}\) provides damping for commutation oscillations. Higher \(R_{sn}\) values reduce the snubber circuit's efficiency, limiting its significant impact within the suitable range. Current waveforms show slight damping.
###### Abstract  
A comprehensive numerical investigation of the uncoupled chemical, thermal, and transport effects of CO\({}_{2}\) on the temperature of CH\({}_{4}\)/O\({}_{2}\) counterflow diffusion flame under high pressure up to 5 atm was conducted. Three pairs of artificial species were introduced to distinguish the chemical effect, thermal effect, and the transport effect of CO\({}_{2}\) on the flame temperature. The numerical results showed that both the chemical effect and the thermal effect of the CO\({}_{2}\) dilution in the oxidizer side can decrease the flame temperature significantly, while the transport effect of CO\({}_{2}\) can only slightly increase the flame temperature and can even be ignored. The reduction value of the temperature caused by the chemical effect of CO\({}_{2}\) grows linearly, while that caused by the thermal effect increases exponentially. The \(RP_{chem}\) and \(RP_{thermal}\) are defined to explain the temperature reduction percentage due to the chemical effect and the thermal effect of CO\({}_{2}\) in the total temperature reduction caused by CO\({}_{2}\) dilution, respectively. The \(RP_{chem}\) decreases with the increase of the pressure, the strain rate, and the CO\({}_{2}\) dilution ratio, while the \(RP_{thermal}\) behaves in the opposite manner. In the above conditions, the chemical effect plays a dominant role on the flame temperature reduction.  
metane; CO\({}_{2}\) dilution; chemical effect; thermal effect; counterflow flame +
Footnote †: journal: Applied Sciences
## 5 Discussion  
In this paper, a multi-source integrated navigation system based on factor graph optimization is proposed. Aiming to reduce the impact of interfered sensors under a challenging urban environment, the interactive multiple models algorithm based on factor graph optimization is applied, and the multi-stage fault detection, isolation, and recovery function provides flexible navigation frameworks for different conditions. The vehicle experiment shows that the proposed approach has a good navigation performance in urban environments, and the system can effectively identify and isolate faulty sensors. The proposed IMMFGO and MSFDIR algorithms provide a new idea for multi-source information fusion, but there still exists some shortcomings for further exploration, summarized as follows:  
* The IMMFGO algorithm can be applied to the navigation system in the case of multiple sub-models. We only verify the performance of the algorithm for two sub-models, and more situations need to be considered later;
* The MSFDIR algorithm provides a feasible solution for the detection, isolation, and recovery of faulty sensors, and the performance of other solutions should be studied on this basis;
* We select a representative urban environment experiment to validate the proposed method. It is fascinating to explore more urban data and experiments to verify the universality of the proposed scheme.