esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Monitoring Results and Analysis
### Contact Pressure between the Surrounding Rock and the Primary Supports  
Figure 15 has shown the duration curve of the contact pressures between the surrounding rock and the primary supports for section DK68+295. It can be seen that for each newly installed monitoring element, the monitored values of the pressures increased rapidly in the first 30 days. In general, the increase of the pressures were obviously related to the stage of the excavation. After the excavation of UB, MB and LB, the monitoring values of the installed monitoring points reached 46.8-70.0%, 23.1-85.7% and 82.4-96.1% of the corresponding final values, respectively. The excavation of the upper part of the tunnel can be considered to be the main element in the formation of the pressures. In addition, it can be seen that the pressures were basically stable when the second supports were installed.  
The final distribution of the contact pressure between the surrounding rock and the primary supports has been shown in Figure 16. It can be seen that there were obvious spatial differences in the distribution of the pressures. Specifically speaking, the pressures of the crown, spandrel and haunch were obviously greater than that of the arch springing and the inverted arch. The maximum pressure occurred in the left spandrel which displayed a value of 408.9 kPa. In addition, the monitored values of the inverted arch did not exceed 100 kPa. It can be seen that the loads acting on the shotcrete were relatively small.  
Figure 16: The final distribution of the contact pressure between the surrounding rock and the primary supports.  
Figure 15: Curves of the contact pressure between the surrounding rock and the primary supports over time.
## 3 Results and Discussion
### Dosimetric Characterization
#### 3.2.4 Dose Response  
A linear dose response over a wide dose range is desirable for a dosimeter to be considered for multiple dosimetric applications. Figure 10 shows the dose response of MgB4O7:Zn glass irradiated with \(\gamma\)-rays; each data point represents the averaged measurement value of three samples. The dose response was observed to be linear throughout the tested dose range. The dose response is comparable with previously reported studies [9; 16; 37].
## 1 Introduction
In the UV-Visible spectral range, tropospheric trace gases are routinely monitored using the Multi-AXis DOAS (MAX-DOAS) technique [7; 8]. The quality of the retrieved products of NDACC-affiliated instruments is assessed through participation in regular intercomparison exercises, e.g., the CINDI [9] and CINDI-2 [10] campaigns. Protocols and calibration procedures have also been established to ensure network consistency and long-term stability of the generated data records. However, the UV-Vis NDACC MAX-DOAS network has been lackingharmonisation in data retrieval approaches, and the traceability of its procedures could be improved. Therefore, to further enhance NDACC UV-Vis capabilities towards meeting FRM requirements, the ESA initiated the FRM4 DOAS project, which aims at (1) specifying calibration procedures and best practices for instrument operation and characterisation, (2) improving and harmonising retrieval algorithms applied to UV-Vis ground-based DOAS spectrometers and (3) operationally delivering consistent data products as needed for the validation of current and future atmospheric composition satellite missions. This paper describes the concept of the FRM4 DOAS system and its main realisations. This includes a round-robin selection of retrieval algorithms and their integration in a centralised data processing system developed in support of the NDACC and operationally reporting NO2, ozone and HCHO data products with a typical latency of 1 day. Section 2 describes the status of the measurement network and the associated requirements. It also briefly documents the retrieval algorithms used in the system and provides a detailed description of the processing system. Section 3 addresses validation activities as well as the first results from the system operated in a demonstrational mode. Finally, Section 4 concludes with a discussion of the main outcome of the project and future challenges.
## 4 Experiments
### Real-Image Denoising  
In real-world applications, the distribution of real noise is often unknown. Therefore, the performance of state-of-the-art denoising methods degrades sharply when denoising noisy real-world images. To further demonstrate the effectiveness of our proposed method, we tested it on real-world noisy images and compared its denoised results with those of CBM3D [1], LIME [34], FFDNet, Restormer [12], VDNet [33], Yu2022 [32], and DIP. Figure 10 shows one low-light image from the SCIE [35] dataset and its denoised versions generated using different methods. As shown in Figure 10, most denoising methods enhance low-light images with a large amount of noise remaining at the edge of the text. However, the proposed method can preserve the texture details of the edge while removing noise. In Figure 11, we compare the denoised results of a RAW camera image from the Nam [36] dataset. Clearly, the PSNR of the proposed method is 3.41 dB higher than that of the DIP method. Compared with VDNet, which was developed for real-world noisy image denoising, our proposed method achieves a 2.83 gain over VDNet. Moreover, the PSNR of the supervised Restormer method is significantly lower than that of our proposed method. This indicates that our proposed method integrates the advantages of both internal and external priors and demonstrates the robustness and generalization capability of our proposed method. Digital X-ray imaging is widely used in clinical diagnoses [37]. To ensure the safety of patients, the radiation dose in computed tomography (CT) clinical practice is essentially reduced, which often introduces noise and artifacts to the image. As the distribution of noise and artifacts in low-dose CT images is extremely irregular and closely related to the position of human tissues, employing an unsupervised denoising method for low-dose CT-image denoising is a better choice. As shown in Figure 12, we additionally tested our proposed method for low-dose CT-image denoising. A full-dose CT image was used as a clean image to compare the denoising results. Evidently, the denoised image generated by the DIP method loses detailed information, and denoised images generated by the supervised methods VDNet and Restormer still contain noise and artifacts. The proposed method can effectively reconstruct the structure and texture information and achieve the best denoising effect.  
Figure 10: Visual comparison of the denoised images generated from different methods on the low-light image.
# Real-Time State of Charge Estimation for Each Cell of Lithium Battery Pack Using Neural Networks
## 5 Result and Discussion
### SOC Estimation of MNN and LSTM  
#### 5.2.1 SOC Estimation Performance Comparison According to the Number of Inputs  
In this paper, the SOC error rates of the lithium battery packs were compared according to the difference in the number of inputs using 2, 6, and 8 voltages as the input values. Only pure voltage data were utilized for the comparison. After charging and discharging at both room temperature and high temperature, the collected data was evaluated using the MNN and LSTM to estimate the SOC of each cell. Tables 2 and 3 compare the error rates of Cell 1 to Cell 4, using the MNN and LSTM for 2, 6, and 8 voltages, at room temperature, and high temperature, respectively. Each error rate was calculated using the Mean Absolute Error (MAE). The equation for obtaining the MAE is as follows,  
\[MAE=\frac{1}{n}\sum_{i=1}^{n}\abs{y_{i}-y} \tag{14}\]  
where, \(n\) is the number; \(y\) is the actual SOC value; and \(\hat{y}\) is the estimated SOC value.  
As a result of the experiment, it was confirmed that with the MNN model, the error rate was lower when using 6 voltage data as input values than when using 2 or 8 voltage data. With the LSTM model, when 6 and 8 voltage data were used as input values, the error rate was lower than when using 2 voltage data as input values. When using 6 and 8 inputs, there was a slight difference in error rate. However, it can be inferred that it is more efficient to use 6 voltage data as input values, because the more data there is, the more computation time required and learning efficiency decreases.
# Research on Sentiment Classification of Online Travel Review Text
###### Abstract  
In recent years, the number of review texts on online travel review sites has increased dramatically, which has provided a novel source of data for travel research. Sentiment analysis is a process that can extract tourists' sentiments regarding travel destinations from online travel review texts. The results of sentiment analysis form an important basis for tourism decision making. Thus far, there has been minimal concern as to how sentiment analysis methods can be effectively applied to improve the effect of sentiment analysis. However, online travel review texts are largely short texts characterized by uneven sentiment distribution, which makes it difficult to obtain accurate sentiment analysis results. Accordingly, in order to improve the sentiment classification accuracy of online travel review texts, this study transformed sentiment analysis into a multi-classification problem based on machine learning methods, and further designed a keyword semantic expansion method based on a knowledge graph. Our proposed method extracts keywords from online travel review texts and obtains the concept list of keywords through Microsoft Knowledge Graph. This list is then added to the review text to facilitate the construction of semantically expanded classification data. Our proposed method increases the number of classification features used for short text by employing the huge corpus of information associated with the knowledge graph. In addition, this article introduces online travel review text preprocessing, keyword extraction, text representation, sampling, establishment classification labeling, and the selection and application of machine learning-based sentiment classification methods in order to build an effective sentiment classification model for online travel review text. Experiments were implemented and evaluated based on the English review texts of four famous attractions in four countries on the TripAdvisor website. Our experimental results demonstrate that the method proposed in this paper can be used to effectively improve the accuracy of the sentiment classification of online travel review texts. Our research attempts to emphasize and improve the methodological relevance and applicability of sentiment analysis for future travel research.  
user generated content; sentiment analysis; classification; keyword extraction; text representation; sampling; machine learning; TripAdvisor +
Footnote †: journal:
# Fast Spectral Approximation of Structured Graphs with Applications to Graph Filtering
## 3 Topological Graph Theory
### Graph Separators
An important result, linking vertex and edge separators, is that a good edge separator for a graph \(\mathcal{G}\) automatically provides a good vertex separator for \(\mathcal{G}\) given that the restriction on the balance of the sets is not very strict. Although the converse of this statement is not true in general, for bounded degree graphs (as is the case for most graphs encountered in real applications) it can be shown that the definition of an edge separator and vertex separator is equivalent up to a constant. Therefore, good vertex separators can be obtained from good edge separators (and vice versa) for such types of graphs. As in this work we are interested in partitioning a graph into disjoint sets, we mostly focus on edge separators. However, unless it is explicitly stated, throughout this work when we refer to graph separators we do not further make the distinction between edge or vertex separators.
# A Microscopic On-Ramp Model Based on Macroscopic Network Flows
## 2 Methods  
### Trajectory Data  
For the analysis, we used real vehicle trajectories recorded by drone photography from the German freeway A565 near the interchange Bonn-Beuel in May 2019; see Figure 2 (top) [34]. Vehicle types and trajectories were obtained using image processing, after which the raw data points were interpolated using third-order polynomials. A stretch of approximately 270 m covering (including the entry lane) 4 unidirectional lanes was recorded. A total of 31 datasets were obtained from recordings over 4 days that took place either in the morning or in the afternoon, each one covering the traffic over periods of approximately 5 min. The number of entering vehicles varied with the time of day; on average, 44 vehicles passed the junction per minute throughout the data collection period. The macroscopic model was trained using the datasets 1, 4, 10, 11, 16, 19, 20, and 30. For the calibration of the new model, we used datasets 1-3, whereas for the comparison study, we used datasets 4-7. The latter groups were recorded at similar hours of the day with similar heavy vehicle percentage; see also Table A1 (Appendix A).  
Figure 1: Flow chart connecting the modeling, validation, and simulation techniques used in this work.  
Figure 2: Snapshot of the freeway A565 from a drone photograph (**top**) and our SUMO model (**bottom**). The on-ramp is situated on the topmost lane with travel direction from right to left. The segmentation of the on-ramp is indicated.
# Management of Obstructive Sleep Apnea in Hospitalized Patients
## 4 Treatment Options in Hospitalized Patients
### Supplemental Oxygen and High-Flow Nasal Cannula  
Supplemental oxygen has been shown to improve hypoxemia in patients with OSA, although it can increase the duration of apnea-hypopnea events [69]. In a trial comparing CPAP to supplemental O2 for sleep apnea treatment, the two groups had similar reductions in nocturnal hypoxemia; however, patients who received CPAP significantly improved blood pressure [70]. This difference was present even in patients whose blood pressure was well controlled on medications. Similarly, in a later trial comparing CPAP to supplemental O2, oxygen reduced intermittent hypoxia and improved blood pressure but did not affect arousal or sleepiness [71]. This data suggest that supplemental oxygen can be used for the short-term treatment of OSA in hospitalized patients.  
High-flow nasal cannula (HFNC) using humidified room air (20-70 L) mixed with oxygen (FiO2 21-100%) has been successfully used as a treatment of hypoxemic and hypercapnic respiratory failure in place of a positive pressure device such as CPAP or BIPAP [72; 73]. HFNC has been proposed as a treatment for OSA, and the use of oxygen at 20 L per minute has been shown to reduce both the AHI and the arousal index [74]. In a few small studies, HFNC has been successfully used to treat OSA in patients unsuitable for CPAP [75; 76]. In a small study of stroke patients who could not use CPAP, HFNC was successfully used to reduce AHI [75]. Similarly, in a small study of surgical patients at high risk of OSA, HFNC between 20 and 40 L was better tolerated by the patient than CPAP. It also resulted in a reduced number of desaturation events [76]. In summary, based on current evidence, patients who cannot tolerate a PAP device due to claustrophobia, delirium, or the risk of aspiration during acute illness can be managed with HFNC while inpatient [59; 76].
# Efficient Feature Selection Using Weighted Superposition Attraction Optimization Algorithm
## 3 Results and Discussion
### Performance of the Metaheuristic on the Real-World Datasets
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Dataset** & **DE** & **GA (R)** & **GA (T)** & **PSO** & **FPA** & **SOS** & **MPA** & **MRFO** & **WSA** \\ \hline Heart & 0.9333 & 0.9167 & 0.9167 & **0.9500** & 0.9167 & 0.8500 & 0.9000 & 0.9167 & 0.9167 \\ Ionosphere & 0.9429 & **0.9857** & 0.9571 & 0.9714 & 0.9429 & 0.9000 & 0.9714 & 0.9714 & **0.9857** \\ German & 0.7950 & 0.8200 & 0.8200 & 0.8150 & 0.8050 & 0.7700 & 0.8100 & 0.8000 & **0.8350** \\ Breast & 0.9928 & 0.9928 & **1.0000** & 0.9928 & 0.9856 & 0.9928 & 0.9928 & 0.9856 & 0.9928 \\ Sonar & **1.0000** & **1.0000** & **1.0000** & **1.0000** & 0.9512 & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ Ovarian & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** & **1.0000** \\ Australian & 0.9130 & 0.9058 & **0.9203** & 0.8913 & 0.9130 & 0.8406 & 0.8913 & 0.9130 & 0.9958 \\ Colon & **1.0000** & **1.0000** & **1.0000** & 0.9167 & **1.0000** & 0.9167 & **1.0000** & **1.0000** & **1.0000** \\ Diabetes & 0.7974 & 0.7974 & 0.7974 & 0.7778 & 0.8105 & 0.7909 & 0.8105 & 0.8039 & **0.8170** \\ \hline Average rank & 4.94 & 4.28 & 3.89 & 5.33 & 5.61 & 7.39 & 5.00 & 5.00 & 3.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Best classification accuracy achieved by each algorithm for each dataset. The computational time is one of the key factors to be considered while identifying the appropriate algorithms for a given task. This is because in real-life conditions, the number of instances may run in millions, indicating a huge computational burden. The best computational time for all the algorithms is reported in Table 7, which indicates the ranking of the considered algorithms is FPA > DE > WSA > PSO > MRFO > MPA > GA (T) > GA (R) > SOS. Similarly, the mean and standard deviation values of the best computational time of 20 independent trials are provided in Table 8, which arrange these algorithms as FPA > DE > PSO > WSA > MRFO > MPA > GA (T) > GA (R) > SOS. The fastest algorithm (FPA) is noticed to be on average 3.2 times faster than the slowest algorithm (SOS). This further reiterates the need for computationally efficient algorithms. So far, with respect to optimal solutions and classification accuracy, WSA is noticed to outrank the other considered algorithms. However, in terms of average computational time, it is ranked fourth and is observed to be about 2.7 times faster than SOS, but would take approximately 19% more time than FPA.
## 3 Method
### Training Method
#### 3.3.3 Optimizer  
In the NN training, the loss computed in the previous section is used to iteratively update the network parameters to minimize the loss. The optimizer is the algorithm responsible for parameter optimization. In this study, we employed the Adam optimizer [24], which is defined in Equations (3)-(5):  
\[m_{t,i}=\beta_{1}m_{t-1,i}+(1-\beta_{1})g_{t,i} \tag{3}\]  
\[v_{t,i}=\beta_{2}v_{t-1,i}+(1-\beta_{2})g_{t,i}^{2} \tag{4}\]  
\[\Delta w_{t,i}=-\frac{\epsilon}{\sqrt{v_{t,i}}}m_{t,i} \tag{5}\]  
Adam is an optimization algorithm that combines the advantages of two previous methods: AdaGrad and RMSProp. It is widely used in NN training because of its efficiency and stability. Here, _g_[t,i]_ denotes the gradient of the loss function, _m_[t,i]_ represents the exponential moving average of the gradients, and _v_[t,i]_ represents the exponential moving average of the squared gradients. The term \(\Delta\)_w_[t,i]_ indicates the update applied to the network parameter. The subscript refers to the current time step, and \(i\) denotes the index of the parameter being optimized. The symbols \(\beta\) and \(\epsilon\) are hyperparameters that control the behavior of the optimizer.
# Numerical Simulation of Heat Transfer Performance of a Dimpled Tubular Heat Exchanger
## 4 Calculation and Analysis
### Heat Transfer Performance of Dimpled Tubes with Different Dimple Sizes  
In order to facilitate the analysis, with the same structural parameters and numerical calculation method, the numerical simulation of heat transfer performance with smooth wall heat exchange tube is calculated and compared with those of the dimpled heat exchange tube.
# Seismic Design of Steel Moment-Resisting Frames with Damping Systems in Accordance with KBC 2016
## 5 Conclusions  
This study proposed an efficient seismic design procedure for building structures with damping systems subjected to requirements of the revised Korean building code, KBC 2016, using nonlinear response history analysis. The proposed design procedure was validated by two design examples of steel moment-resisting frame with metallic yielding dampers and viscoelastic dampers, respectively. The conclusions from this study are summarized as follows.  
* The proposed design procedure makes use of nonlinear response history analysis, but does not repeat time-consuming nonlinear response history analysis until convergence of design solution. Instead, design parameters of damping devices are determined using the response reduction curve prescribed by a limited number of response history analyses. Only five times of response history analysis is sufficient for practical application.
* The proposed design procedure can predict seismic response of nonlinear structures with considerable accuracy because basic response reduction factors are obtained through nonlinear response history although equivalent linearization technique is used partially to estimate effects of damping devices with limited computational efforts.
* The proposed design procedure does not require an optimization procedure and can be conducted using commercial structural analysis software. However, the proposed design procedure provides a systematic process to update the design parameters of damping devices and converges to a final design meeting design goals.
* The proposed design procedure for structures with damping systems can reduce structural materials of seismic-force-resisting systems efficiently by 30 to 50% compared to those without damping systems as illustrated by design examples for steel moment-resisting frames.  
**Author Contributions:** Formal analysis, S.-H.J.; writing--original draft, J.-H.P.; writing--review & editing, T.-W.H.  
**Acknowledgments:** This work was supported by Incheon National University Research Grant in 2016.  
**Conflicts of Interest:** The authors declare no conflict of interest.