esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Description
### The Outer Ring Expressway Dataset  
The Outer Ring Expressway Dataset is collected from a field investigation that was carried out at the merging areas of the Outer Ring Expressway in Shanghai, China. The location of this study field is shown in Figure 9. As illustrated in Figure 10, the data gathered and examined pertains to the westbound traffic flow at a merging section. This particular location comprises an on-ramp, an additional lane on the right, and the primary roadway. The video was captured using a DJI Mavic 2 Pro drone on 26 December 2020. Several prerequisites are taken into account for the data acquisition, such as overcast skies, adequate lighting, and wind speeds not exceeding 4 m per second. Furthermore, the drone was operated at an altitude of 250 m, covering a road stretch of 215 m in the recordings. The videos are shot in 4K resolution (4096 \(\times\) 2160 pixels) at a frame rate of 25 Hz, totaling 3 h, 38 min, and 6 s of footage. In this analysis, the vehicles are categorized into two groups: cars and trucks, with the latter being defined as vehicles exceeding 6 m in length [46].  
Figure 8: A sample video frame from the inD dataset.  
Figure 9: The location of the study area of the ORED.  
The vehicle trajectory processing, as outlined by [PERSON] et al. [47], encompasses three stages. Initially, vehicles are identified using a Canny-based ensemble detector. This is followed by the application of the kernelized correlation filter algorithm for tracking the vehicles. The process culminates with the implementation of wavelet transformation for the purpose of trajectory denoising. Figure 11 shows the study area, the coordinate system, and a schematic diagram of five lanes. The origin of the coordinates is \(O\). The horizontal direction on the video frame represents the direction parallel to the lanes, whereas the vertical direction on the video frame corresponds to the direction perpendicular to the lanes.  
Lane 1 includes the on-ramp and its merge with the mainline, and lanes 2 through 5 extend from the freeway's outermost part inward.
## 5 Results  
### Performance of the Snow Cover Mapping and Snow Line Altitude Detection Method  
The comparison of the automatically- and manually-derived SLAs for the four test glaciers reveals a good agreement with a coefficient of determination (r\({}^{2}\)) \(>\) 0.8 for all test glaciers (Figure 5). Inconsistencies occur when a glacier is covered by a thin fresh snow layer. On HEF in Figure 5a differences up to 380 m (arrow 1) and 220 m (arrow 2) can be detected, whereas for HJF in Figure 5d one difference of 220 m (arrow 3) and three times of more than 100 m are visible. In this case, there was no clear minimum detectable in the histogram and Otsu failed. Another reason for failure is related to small clouds and their shadows. In the case of HJF, this once caused a 400 m difference (Figure 5d, arrow 4) and for HEF a 260 m difference (Figure 5a, arrow 5). The extensive cloud shadow caused a large dark area on the glacier and the SLA detection found at least three elevation bins in a rowwith SC above it. Also cast shadows by topography caused differences. In four cases, this caused an SLA difference of more than 100 m. As a consequence, the r\({}^{2}\) of HJF (0.82) is the lowest one of all four glaciers.  
The analysis of the classification accuracy for the selected glaciers shown in Figure 3 is summarized in Table 4 and reveals an overall classification accuracy of 90.6%.
# Low-Resolution Target Detection with High-Frequency Information Preservation
## 6 Conclusions  
To enhance the performance of low-resolution target detection, we proposed a high-frequency information preservation block (HFIP) which can preserve scarce high-frequency observations during downsampling. Through decoupled high-frequency learning and dilated frequency-aware channel correlation, HFIP preserves task-relevant, high-frequency information in features and adds extra-high-frequency details during downsampling. Extensive experiments were carried out to confirm HFIP's significant improvement within existing CNN architectures.  
Conceptualization, L.C. and F.Z.; data curation, H.B., W.Y. and H.M.; formal analysis, F.Z. and Z.L.; funding acquisition, L.C. and F.Z.; investigation, F.Z., H.B. and W.Y.; methodology, L.C. and F.Z.; project administration, L.C.; resources, L.C. and F.Z.; software, F.Z. and H.B.; supervision, L.C. and F.Z.; validation, F.Z., H.B., W.Y., Z.L. and H.M.; visualization, F.Z.; writing--original draft, F.Z. and Z.L.; writing--review and editing, F.Z. and Z.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Natural Science Foundation of China (Grant No. 6220210586), Jiangsu Province Natural Science Foundation for Young Scholars (Grant No. BK20210586), NUPTSF (Grant No. NY221018), and Double-Innovation Doctor Program under Grant JSSCBS2020657.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors upon request.  
Figure 4: The qualitative results of the low-resolution human pose. The images have an original resolution of \(640\times 400\) in pixels and are enlarged here for better visualization.
# A Temperature Fiber Sensor Based on Tapered Fiber Bragg Grating Fabricated by Femtosecond Laser
## 3 Device Fabrication
During the inscription, both the objective and the fiber were immersed in the liquid, which can be observed from the faint halo as well as the bright reflection, as shown in Figure 4.  
Before the tapered FBG inscription, a FBG was fabricated 10 mm away from the taper in a non-tapered fiber region for comparison. The reference FBG is first-order and point-by-point written by femtosecond laser. The grating pitch is 550 nm, and the grating length is 1000 \(\upmu\)m.  
Figure 4: Femtosecond laser fabrication platform. Figure 3: Experimental setup for FBG inscription on tapered fiber by femtosecond laser. Then the tapered-FBG was inscribed by femtosecond laser in the tapered region using the point-by-point direct-writing method, and the inscription details in the thinnest waist are shown in Figure 5, which were directly obtained from the inscription objective. The grating pitch is 535 nm, and the grating length is 1000 \(\upmu\)m. From Figure 5, the taper profile, fiber core, fiber cladding, and inscription trace can be clearly noticed. The reflection spectra evolution before and after the tapered FBG fabrication were recorded by an OSA (AQ6370D, Yokogawa Test & Measurement Corporation, Tokyo, Japan) with a resolution of 0.5 nm, as shown in Figure 6. The characterized Bragg wavelength of the tapered FBG is chosen as peak A. The characterized Bragg wavelengths of the reference FBG before and after the tapered FBG are chosen as peak C and B, respectively. Relevant peak information is listed in Table 1.  
According to Figure 6 and Table 1, the comparison of the -3 dB fractional bandwidths between the tapered FBG and the reference FBG shows that due to the special geometry and mode propagation, the tapered-FBG has a wider bandwidth appearance. Also, it can be noticed that after the tapered FBG was fabricated, there is a minor fluctuation of the refection spectrum covering the whole C+L  
Figure 5: Inscription details of the proposed sensor using femtosecond laser in the taper region. Figure 6: Reflection spectra before and after the tapered FBG fabrication. bandwidth. Moreover, the Bragg wavelengths, the output powers and the \(-3\) dB fractional bandwidths of the reference FBG before and after the tapered-FBG inscription remain almost the same, which means that the reference FBG hardly suffers from the inscription interference.
# Performance Evaluation of Adaptive Tracking Techniques with Direct-State Kalman Filter +
## 2 Tracking Scheme of Direct-State Kalman Filter
### Equivalent Noise Bandwidth  
The digital one-sided equivalent noise bandwidth \(B_{\text{d}}\) is defined as [38; 39; 40]:  
\[2B_{\text{d}}\tau_{\text{int}}=\frac{1}{2\pi\text{j}}\oint_{|z|=1}H_{\text{c}} (z)H_{\text{c}}(z^{-1})z^{-1}\text{d}z \tag{35}\]  
Assuming that the integration time \(\tau_{\text{int}}\) tends to zero, the digital loop bandwidth is equivalent to the analog loop bandwidth \(B\)[38; 39; 40]. The relation between \(B\) and the coefficients of a third-order STL is expressed as:  
\[\lim_{\tau_{\text{int}}\to 0}B_{\text{d}}=B=\frac{\alpha_{2}^{2}\alpha_{1}- \alpha_{2}\alpha_{0}+\alpha_{1}^{2}}{4(\alpha_{2}\alpha_{1}-\alpha_{0})} \tag{36}\]  
Substituting Equations (27) and (34) in Equation (36), the relation between the steady-state equivalent noise bandwidth of a third-order DSKF, \(q\), and \(R\) is [5]:  
\[B_{\text{ss}}=\frac{5}{6}\,\frac{6}{R}\sqrt[]{\frac{q}{R}} \tag{37}\]  
Figure 6 shows the relation between \(q\) and \(B\) for different values of \(R\) based on Equation (37). This relationship eases the LBCA implementation in the DSKF since the loop bandwidth update can be related with the covariances.  
Finally, the relation between the \(B_{\text{ss}}\) and \(\mathbf{K}_{\text{ss}}\) is calculated based on Equations (34) and (37):  
\[\mathbf{K}_{\text{ss}}=\left[2\,\frac{6}{5}B_{\text{ss}}\,\tau_{\text{int}} \quad 2(\frac{6}{5}B_{\text{ss}})^{2}\tau_{\text{int}}\quad\left(\frac{6}{5}B_{ \text{ss}}\right)^{3}\tau_{\text{int}}\right]^{\text{T}} \tag{38}\]  
Figure 6: Relation between steady-state loop bandwidth \(B_{\text{ss}}\) and process error variance \(q\) in a third-order DSKF. ©IEEE. Adapted, with permission, from [20].
# ULYSSES: Automated FreqUentLY ASked QueStions for KnowlEdge GraphS
## 5 Experimental Evaluation
### Results  
The results are shown in Figures 4-6. Specifically, looking at the Rouge-1 (refer to Figure 4) chart, which assesses the ability to capture relevant words, BART achieved the highest precision score (0.42), followed by Gemini (0.38). Regarding recall, which measures the ability to identify positive cases, ChatGPT achieved the highest score (0.48), followed by Gemini (0.37). However, in terms of f-measure, which provides an overall assessment of performance, ChatGPT attained the highest score (0.37), followed by Gemini (0.31), whereas the rest followed.  
Moving on to the Rouge-2 chart (refer to Figure 5), which identifies relevant pairs of words, BART once again secured the highest score, in terms of precision (0.25), followed by T5 (0.20). In terms of recall, ChatGPT achieved the highest score (0.24), followed by Gemini (0.15). Subsequently, the f-measure's top scorer was ChatGPT (0.18), followed by Gemini (0.13).  
For Rouge-L (refer to Figure 6), which captures the structural coherence of the generated summaries, BART obtained the highest precision score (0.38), followed by ChatGPT (0.29). In terms of recall, ChatGPT again performed the best among the others (0.40), followed by Gemini (0.30). The highest f-measure score was achieved by ChatGPT (0.30), followed by Gemini (0.25).  
Overall, in the evaluation, which examined various NLP models in the task of query answering, ChatGPT demonstrated the best performance in recall and f-measure across metrics, followed by Gemini. Gemini showed a competitive but slightly inferior performance compared to ChatGPT. Subsequently, BART excelled in precision, thus ensuring the quality of the positive predictions. T5 generally performed the poorest among the four models, with lower precision, recall, and f-measure scores across all metrics. In general, BART seemed to be more effective for precise answers, while ChatGPT and Gemini emphasized  
Figure 4: Rouge-1 score.  
Figure 5: Rouge-2 score.  
Figure 6: Rouge-L score.  
capturing a wide range of relevant information and maintaining the general structure of the result.  
Although ChatGPT performed best, the result scores were considered moderate in the bibliography [34]. However, it achieved the best balance between relevance and capturing key points, as indicated by the f-measure scores compared to the other evaluated models. This shows that ChatGPT is the most suitable tool in constructing FAQ lists in our dataset. Furthermore, the quality of the responses from BART and T5 were inferior to those from ChatGPT and Gemini, which managed to build better human-readable sentences with high coherency - giving the impression of not being computer generated. T5, especially, produced sentences that seemed not to be coherent.
# Adaptive QoS-Aware Multi-Metrics Gateway Selection Scheme for Heterogenous Vehicular Network +
## 5 Results and Discussions
### Evaluation of Fixed Vehicle Applications versus Number of Vehicles
The AQAGS scheme determined how to deliver the FTP packets to the infrastructure network as a result of detecting an overloading condition on the LTE side. The gateway discovery process must be started by the IV when a condition of overloading arises, and the appropriate MGW for data transmission must be selected. The time it took to find vehicles increased as the number of vehicles increased. This procedure took some time, which increased the FTP delay. However, we evaluated the CPO encounter when FTP and VoIP were in use. In general, as shown in Figure 7b, the CPO rose as the number of vehicles did for the three tested schemes. However, the CPO for AQAGS in the VoIP application increased from 4.92% to 12.97% as the vehicle count rose from 50 to 500. With VoIP, there are not too many control packets sent between the vehicles as an MGW is not required for this application. Fewer control packets are required to measure the CQI for the attached LTE-eNB. Compared with the CMGM and Adroit schemes with respect to VoIP, AQAGS's CPO was lower by 19.95% and 16.21%, respectively. The highest CPO was expressed by CMGM, followed by Adroit, in the same variation of vehicle numbers. In contrast to Adroit, CMGM reported a 6.55% higher CPO when VoIP traffic was involved. This is because, for the cluster formation process of MGW selection, the CMGM algorithm involves the transmission of a significant number of control packets. In addition, when using FTP, our proposed AQAGS scheme showed the lowest CPO when compared with the other tested schemes. When compared with CMGM and Adroit in the FTP application, AQAGS's CPO measurement was 2.11% and 5.33% lower, respectively, for fewer vehicles. When the number of vehicles reached 500, the CPO observed was lower than that of CMGM and Adroit in FTP by 8.64% and 2.02%, respectively. Because all three mechanisms require FTP to pass through the MGW vehicle before transmitting to the LTE network, the CPO increases with the rise in vehicle numbers. During the process of determining the MGW, the IV and NV exchange numerous control packets. This process must be repeated whenever the IV and MGW's communication link fails. The CPO value for the FTP traffic increases as a result of the entire process.
## 3 Proposed Digital Canceler  
When considering the SI and RT channels, it is important to acknowledge that they will vary over time, as the environmental reflection paths are also time-variant. Therefore, we opted for adaptive filtering algorithms and incorporated a multi-layered structure, as shown in Figure 3, to enhance the RT estimation performance of the joint estimator.  
As depicted in Figure 3, the estimation of joint channel coefficients \(\mathbf{\hat{\epsilon}}[n]\) in the first layer is given by \(\mathbf{\hat{\epsilon}}_{(1)}^{T}[n]=[\mathbf{\hat{w}}_{(1)}^{T}[n],\mathbf{h}_{(1)}^{T }[n]]\); then, the residual signal \(y_{(2)}[n]\), obtained by subtracting the reconstructed SI signal from \(y[n]\), serves as the input for the second layer. Furthermore, we can obtain  
\[\begin{split} y_{(2)}[n]&=\mathbf{c}_{(1)}^{T}[n]\mathbf{u }[n]-\mathbf{\hat{w}}_{(1)}^{T}[n]\mathbf{x}[n]+v[n]\\ &=\mathbf{c}_{(2)}^{T}[n]\mathbf{u}[n]+v[n].\end{split} \tag{7}\]  
Figure 3: The multi-layered structure of the joint channel estimator.  
This procedure is consistently implemented across successive layers of the filter. In the \(m\)-th layer, this process is articulated as  
\[\begin{split} y_{(m+1)}[n]&=y_{(m)}[n]-\mathbf{\hat{w}}_{ (m)}^{T}\left[n\right]\mathbf{x}[n]+v[n]\\ &=\sigma_{(m+1)}^{T}[n]\mathbf{u}[n]+v[n]\end{split}, \tag{8}\]  
where \(y_{(m)}[n]\) is the input of the \(m\)-th layer and \(\mathbf{\hat{w}}_{(m)}[n]\) is the estimation of the SI channel, and we have \(\mathbf{\hat{c}}_{(m)}^{T}[n]=[\mathbf{\hat{w}}_{(m)}^{T}[n],\mathbf{\hat{h}}_{(m)}^{T}[n]]\) as the estimation of the joint channel.  
For each layer, while subtracting the residual from the received signal, an estimation of the RT channel \(\mathbf{\hat{h}}_{(m)}[n]\) is performed and the output of the last layer \(y_{(M+1)}[n]\) is regarded as the interest signal \(\hat{r}[n]\).
# Vehicle Speed Estimation and Forecasting Methods Based on Cellular Floating Vehicle Data
###### Abstract  
Traffic information estimation and forecasting methods based on cellular floating vehicle data (CFVD) are proposed to analyze the signals (e.g., handovers (HOs), call arrivals (CAs), normal location updates (NLUs) and periodic location updates (PLUs)) from cellular networks. For traffic information estimation, analytic models are proposed to estimate the traffic flow in accordance with the amounts of HOs and NLUs and to estimate the traffic density in accordance with the amounts of CAs and PLUs. Then, the vehicle speeds can be estimated in accordance with the estimated traffic flows and estimated traffic densities. For vehicle speed forecasting, a back-propagation neural network algorithm is considered to predict the future vehicle speed in accordance with the current traffic information (_i.e._, the estimated vehicle speeds from CFVD). In the experimental environment, this study adopted the practical traffic information (_i.e._, traffic flow and vehicle speed) from Taiwan Area National Freeway Bureau as the input characteristics of the traffic simulation program and referred to the mobile station (MS) communication behaviors from Chunghwa Telecom to simulate the traffic information and communication records. The experimental results illustrated that the average accuracy of the vehicle speed forecasting method is 95.72%. Therefore, the proposed methods based on CFVD are suitable for an intelligent transportation system.  
vehicle speed estimation; vehicle speed forecasting; cellular floating vehicle data; intelligent transportation system; cellular networks +
Footnote †: journal: Applied Sciences
# Design and Validation of a U-Net-Based Algorithm for Star Sensor Image Segmentation
## 3 Results
### BOSS Algorithm Comparison with Traditional Image Segmentation Algorithms  
Here, a comparison between BOSS and traditional algorithms is conducted. The F1 index (Equation (10)) is chosen to compare the performance of the algorithms. Every algorithm was tested against the same test set of 300 simulated images [31]. [PERSON]'s algorithm did not need to be configured, while LTA, WIT and LTS algorithms did. For these configurable algorithms, a tuning of their parameters was performed, and the F1 index was computed for every combination of their internal parameter.
## 2 Materials and Methods
### Main Machine Learning Algorithms
#### 2.2.2 K-Means
K-means has found various applications in the realm of atmospheric science, elucidating patterns and structures in meteorological and climatological datasets that were not immediately apparent. Within the domain of atmospheric science, this method has been used to categorize weather patterns, identify synoptic regimes, and even discern different types of air mass. For example, researchers might use the clustering of K-means in multiyear atmospheric profiles (comprising variables such as temperature, humidity, pressure, and wind speed) to discern predominant atmospheric states or patterns [44]. This categorization can provide invaluable information on typical meteorological conditions associated with specific regions and seasons. Moreover, the K-means algorithm has also been applied to satellite-derived datasets, such as aerosol optical depth or cloud cover, to identify spatial patterns or classify specific clouds or aerosol types based on their spectral properties [45]. The advantage of K-means in these applications is its ability to handle large datasets, often spanning multiple decades, allowing scientists to discern long-term climatological patterns. However, while the technique offers significant utility, its efficacy is based on appropriate preprocessing of data and the judicious selection of k, which remains a subject of ongoing research. As computational capacities grow and datasets become more extensive and intricate, the adoption of K-means clustering in atmospheric science is poised to expand, promising deeper insights into the complex dynamics of the atmosphere. For atmospheric studies and in general, variables with different units can introduce a significant challenge. The Euclidean distance, a fundamental metric in cluster space, may be adversely affected by these disparate units, potentially leading to skewed results. Normalizing the variables to align to a consistent scale diminishes the undue influence of varying units on the Euclidean distances. It is recommended to use feature scaling techniques that standardize variables to possess a mean of zero and unit variance. By adopting this approach, the dominance of variables with larger units in the distance computations is circumvented, thereby promoting an equitable consideration of all variables. This solution not only mitigates the challenges posed by different units but also improves the robustness and accuracy of K-means clustering outcomes.
# A Review of Abnormal Crowd Behavior Recognition Technology Based on Computer Vision
## 4 Research Status of Abnormal Crowd Behavior Recognition
### Deep Learning-Based Methods
#### 4.2.3 Methods Based on Generative Adversarial Networks
[74] proposed the first method that combines future frame prediction and reconstruction, which is implemented through an end-to-end network. The network consists of two continuously connected U-Net modules. The first module performs future frame prediction based on the input image sequence, and the second module reconstructs the future frame based on the predicted intermediate frame, making normal and abnormal behaviors easier to distinguish in the feature space. [PERSON] et al. [75] used the Bidirectional Multi-scale Aggregation Network (BMAN) for abnormal behavior recognition. By using bidirectional multi-scale aggregation and attention-based feature encoding, normal patterns including object scale changes and complex motions are learned. Based on the learned normal patterns, abnormal events are detected by simultaneously analyzing the appearance characteristics and motion characteristics of the scene through an appearance-motion joint detector. The enhanced reconstruction method combining the autoencoder embeds the adversarial training logic of GAN in the training framework of AE. The autoencoder combined with GAN can not only minimize the reconstruction error but also be linked with the discriminator in GAN to output a data distribution closer to the real one. The process is shown in Figure 12. A discriminator is integrated into the architecture of the autoencoder to compare and distinguish the reconstructed image of the autoencoder network and the original input image. The quality of the reconstructed image of the autoencoder network is enhanced through adversarial training. [PERSON] et al. [76] proposed a method of fast anomaly generative adversarial network (f-AnoGAN), training a Wasserstein GAN through normal samples and then training an encoder to map the image to the latent space to achieve fast inference and anomaly detection. During the anomaly detection process, the input image is reconstructed through the encoder and generator, and the combined score of the image reconstruction residual and the discriminator feature residual is used as a reliable indicator of anomaly.