esa-sceva/satcom-chunk-collection huggingface dataset:
# Amplitude and Phase Information Interaction for Speech Enhancement Method
## 5 Result and Analysis  
### Comparison of Models Using Different Hyperparameters  
The sub-band width has a significant impact on feature extraction in the sub-band model. The sub-band width itself is a hyperparameter, determining the size of frequency bands into which the frequency axis is equally divided before extracting sub-band features. When the value of \(i\) is too small, it indicates that the frequency bands are centered around the corresponding frequency axis, resulting in a limited inclusion of adjacent frequencies. In this case, the sub-band can only capture correlated features within a narrow frequency range. When the value of \(i\) is too large, it means that a large number of adjacent frequencies are included, and the sub-band width gradually approaches the full-band, losing the ability to extract sub-frequency-domain correlations effectively. Only by setting an appropriate value for the sub-band width can it align with the frequency characteristics of human speech, allowing the model to learn sufficient and clear correlated features. Therefore, it is necessary to experiment with different sub-band widths in order to find the sub-band width value that approaches the optimal solution.  
The Lookahead value represents the number of milliseconds by which the amplitude spectrum and phase spectrum of the input speech are zero-padded at the end of the time axis. A larger Lookahead value indicates that the full-band model considers more future information corresponding to the current input speech, thereby emphasizing the correlation features between the current speech and the future input speech. In previous experiments, this value is commonly set to 32 milliseconds. As shown in Table 1, in the ablation experiments that incorporated the influence of echo, various combinations of Lookahead values and sub-band widths were explored, aiming to find the most suitable values while considering the correlation between these two hyperparameters. From experiments 1 to 8, it was found that when the number of Magphase units is 4, a closer Lookahead value to 2, and a sub-band width closer to 10 results in better model performance. By contrast, experiments 9 to 13 revealed that when the number of Magphase units is 3, a Lookahead value closer to 16 and a sub-band width closer to 20 led to better model performance. This conclusion demonstrates the complex interdependence among the hyperparameters of the proposed model, and selecting appropriate values for these hyperparameters can effectively improve the model's performance.
### Application of Wide Field-of-View (WFOV) Imagers for SSA  
Considering the latest technologies available for space optical sensors, we have focused our efforts on the use of space-based WFOV cameras, including star trackers and similar low-resolution imagers, for SSA applications. These commercial-grade cameras, such as the AURICAM(tm) star tracker by Sodern (Limeil-Brevannes, France) [5] and the PCO Complementary Metal Oxide Semiconductor (sCMOS) camera [6], are far less costly than dedicated imaging systems with custom telescopes. They are also readily available onboard satellites to provide attitude information. Additionally, WFOV cameras are capable of continuously monitoring large parts of the sky. These cameras can monitor multiple objects without actively tracking them, which allows for quick detection capabilities used in rapid capture, detection, timely identification, and tracking. Early warning of close approaches can also be implemented using full-sky coverage to respond to potential collisions and plan collision avoidance maneuvers if necessary. While the images obtained with WFOV cameras tend to have reduced resolution that lacks features for identification, simple detection in photometric analysis--with light curve extraction--provides sufficient information for astrometric analysis and object classification. In [7], light curve extraction has been performed on images captured from WFOV cameras, and in [8], object classification has been proven with light curves collected from ground-based telescopes.
###### Abstract
In recent studies, the design of alarm sounds is combined with vision and touch to improve drivers' driving safety based on user information feedback at different levels of automatic driving [20]. [PERSON] et al. 's study attempted to improve the takeover efficiency and safety of autonomous driving by applying visual, auditory, and haptic modalities under systematically classified takeover request scenarios in conditionally automated driving. The visual-auditory-haptic warning combination showed the best performance when considering all performance indices. It was also suggested that visual warnings performed worst, while hearing and touch were crucial for alarm interaction [21]. [PERSON] et al. [22] surveyed the fusion engines of multimodal input. In terms of auditory output, adaptive designs were proposed to adjust auditory feedback tones based on the driver's emotions [23] and driving style [24], and voice recognition was used to measure trust during interactions with AD vehicles [25]. The experimental results of [26] on autonomous driving alerts indicate that drivers exhibit higher alertness to auditory alerts under drowsy and distracted conditions and that gaze indicator changes are significantly greater under distracted conditions. [PERSON] et al. proposed a method for optimizing UAV flight paths using Bezier curves, offering insights for warning signal optimization [27]. Despite its importance, auditory feedback is rarely studied separately in ADASs. In addition, users' perception of sound information is affected by the environment they are in. Different experiences of users lead to bias in their cognition of sound, but users with similar experiences also have certain similarities in auditory cognition. [PERSON] [28] proposed the ternary interaction determinism, which holds that there are interactions and reactions among individual internal factors, the external environment, and behavior, among which individual internal factors include cognition, emotion, motivation, concept, and physiology. The occurrence of individual behavior is not only affected by environmental factors, but individual behavior will also affect reactions to the environment. Individual psychological and physiological factors are also important factors that trigger individual behavior. [PERSON] et al. (2019) and [PERSON] et al. (2020) also studied the influence of individual user factors and environmental factors on health information interaction behaviors, continuous use, and sharing behavior based on classic ternary interaction determinism. The meaning construction theory ([PERSON] et al., 2020) proposes that personal experience and internal cognitive structure directly affect the understanding and meaning construction of things, and drivers living in different environmental backgrounds have different personal experiences and cognitive structures. In essence, the process of users receiving a sound is people's cognition of auditory information. At present, the research on ADAS warnings has not achieved better information transmission efficiency, thus affecting reaction time and driving safety.
## 3 Results
### Evaluation of WIC and WIDs for Pilot Basins  
Figure 4 shows the WIC obtained for HWIP by combining observed flow data with simulation results from the Tank model, a widely used storage-type hydrological model for determining runoff processes. The WIC was calculated by subtracting the average water intake, which was estimated by analyzing the FDC for 7-year flow data from 2014 to 2020, using the simulated daily runoff of FRC data. In the case of HWIP, the DAR was not applied, and the number of water intake days was evaluated based on the average water intake (2421 m\({}^{3}\)/day) in 2018, yielding an average number of WIDs of 332 (90.9%) for the period 2014-2020. When the observed flow data and Tank simulation results were combined, the number of WIDs was 13.7 days (4.13%), higher than that obtained with the Tank model. In 2015, the Tank model simulation yielded 273 (74.8%) WIDs. The number of WIDs was 320 (47 additional days; 87.7%) when the observed flow data and the hydrological model were used. These results indicate that the simulated daily runoff data yielded an estimated average LF of 0.12 m\({}^{3}\)/s, whereas the estimated LF was 0.16 m\({}^{3}\)/s when a combination of observed flow and hydrologic model data was used, yielding 13.7 additional WIDs. The WIC calculated from the runoff data simulated by the Tank model is smaller than the actual situation. Nevertheless, based on the results above, in the absence of human intervention, people who rely on water collection at HWIP stations have an unreliable water supply for about 10% of the year during the period 2014-2020.  
Figure 5 shows the comparison results between the estimated WIC derived from observed flow data and actual water intake at the CWIP. In the case of CWIP, the average LF from 2007 to 2020 was estimated to be 0.65 m\({}^{3}\)/s based on the daily flow determined using the FDC. DAR was applied to the observed daily flow to determine the WIC and analyze the FDC for the 14-year data series, from 2007 to 2020, and the WIC was calculated by subtracting the average LF. By analyzing data from 2017 to 2020, which has actual operating data, for average WIDs based on a flow of 0.65 m\({}^{3}\)/s, it was estimated that water intake was possible on 306 days (83.8%) in 2017, 337 days (92.3%) in 2018, 353 days (96.7%) in 2019, and 343 days (93.7%) in 2020. Like the HWIP, the CWIP has had an average of 30 days per year without guaranteed water intake based on the low flow over the past four years.  
Figure 4: Water intake capacity (WIC) at the Hyunri water intake plant (HWIP) based on combining observed flow data with hydrologic simulations from 2014 to 2020.
## 2 Materials and Methods
### Linear and Non-Linear Modelling
The gain in the performance of the latter of the two models is so small that the conclusion about the inefficiency of the effort required by non-linear modelling in estimating the usable floor area of residential buildings remains valid. An alternative to regression models with a stepwise procedure for removing variables and cases is regularised models. Regularisation is a technique that aims to reduce the over-fitting of a model, and involves attaching a penalty function to the minimised sum of squares that results in either a desired reduction in the number of variables in the model or a reducing in the values of the model parameters, which in turn reduces the dependence of the estimate on random fluctuations in the explanatory. Linear models with both L1-type (LASSO, least absolute shrinkage and selection operator) [44; 45] and L2-type regularisation (ridge regression), as well as a hybrid of these, were used in an approach described in [45; 46]. Formally, we write the model with L1 regularisation in the form of a function minimisation task (finding the vector \(b\), that provides the smallest expression value):  
\[\sum_{i}\left(y_{i}-\beta_{0}-\sum_{j}\beta_{j}x_{ij}\right)^{2}+\lambda\sum_ {j}\left|\beta_{j}\right| \tag{3}\]  
where \(x_{ij}\) is a value of the \(j\)-th explanatory variable observed in the \(i\)-th object, \(\beta_{0}\) and \(\beta_{j}\) are model parameters, while \(\lambda\) is the regularisation parameter (penalty term). Similarly, the L2 regularisation can be written as minimising the expression:  
\[\sum_{i}\left(y_{i}-\beta_{0}-\sum_{j}\beta_{j}x_{ij}\right)^{2}+\lambda\sum _{j}\beta_{j}^{2} \tag{4}\]  
A hybrid of the two approaches, elastic net, in which the penalty function consists of both expressions, will instead be written as the following minimisation:  
\[\sum_{i}\left(y_{i}-\beta_{0}-\sum_{j}\beta_{j}x_{ij}\right)^{2}+\lambda_{1} \sum_{j}\left|\beta_{j}\right|+\lambda_{2}\sum_{j}\beta_{j}^{2} \tag{5}\]  
where parameters \(\lambda_{1},\lambda_{2}\) are oftentimes replaced by a pair \(\lambda,\ \alpha\) in a modified penalty term \(\lambda\left(\alpha\sum_{j}\left|\beta_{j}\right|+(1-\alpha)\sum_{j}\beta_{ j}^{2}\right)\), and the simulation and the simulation proceeds in two stages--for each value of \(\lambda\) from a certain set, a value of \(\alpha\) is found that minimises (5), and then the best of the resulting models is selected. Regularised models generally exhibit superior forecasting capabilities in comparison to the corresponding baseline linear regression models (containing the same ex planatory variables). Moreover, models derived through LASSO eliminate some of the explanatory variables.
# Pointer Meter Reading Recognition by Joint Detection and Segmentation
## 5 Conclusions  
For the task of automatic and accurate meter pointer reading, which is very desirable in intelligent factories, this work presents a deep learning-based method. The proposed method first detects the meter dial object, and its bounding box is output by our meter dial location network, which is a lightweight YOLO-like deep convolutional network. Next, the dial region image from the preceding stage is fed into a reference point segmentation network to segment the positions of the dial pointer and reference points. Then, we project the regular points onto uniformly aligned points along with a straight line and calculate  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Methods** & **mIoU** & **Time (ms)** \\ \hline FCN & 72.9 & 105 \\ SegNet & 71.0 & 67 \\ U-Net & 75.6 & 112 \\ RefineNet & 77.3 & 181 \\ Our method & 79.2 & 196 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Quantitative results of different methods for dial point segmentation.  
Figure 6: Some meter images and their corresponding reading results obtained by our method.  
the relative position between the pointer and aligned points to achieve the final scale value of the meter dial. Some quantitative evaluation results are provided to demonstrate the good recognition performance of our proposed method.  
In addition, this paper focuses on analog meter reading recognition, which is mainly built upon the object detection and segmentation techniques. Different from this task, digital meter reading recognition generally depends on the optical character recognition (OCR) technique. A generic reading approach is desirable to recognize both analog meters and digital ones. Another possible direction is to further compress the number of the model's parameters, resulting in a more lightweight detection model for platforms with low computational resources. In fact, pointer meters come in several specific shapes, e.g., a circular shape and a square shape. This is very important, and available geometric information is required for designing more efficient and effective pointer meter location models. All these studies could be future work.  
Conceptualization, Y.L.; methodology, Y.L. and X.L.; validation, Y.L.; formal analysis, C.Z.; investigation, X.L.; writing--original draft preparation, Y.L.; writing--review and editing, X.L.; supervision, C.Z.; funding acquisition, C.Z. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China, grant numbers U22A2033 and 62072281.  
Restrictions apply to the availability of these data. Data were obtained from two factories of a company in Shandong, China and are available by authors with the permission of the company.  
The authors declare no conflicts of interest.
# Thermal Failure Propagation in Lithium-Ion Battery Modules with Various Shapes
## 2 Experiment
### Apparatus and Experimental Design  
The schematic of experimental setup in this work is shown in Figure 1. Battery modules were placed vertically upon a supporting mesh made of iron wire in tests. A K-type thermocouple with a diameter of 1 mm was attached to the middle of each battery to measure its surface temperature and the temperature was recorded at the data acquisition equipment NI cDAQ-9174 (NI, Austin, TX, USA)) by a computer. The detailed arrangements and numbering of thermocouples are presented in Figure 2. The mass loss data was measured by an electronic balance Mettler Toledo XP10002S (Mettler, Zurich, Switzerland) with a resolution of 0.01 g and a maximum range of 9 kg. Discharging treatment was achieved by a charge/discharge cycle equipment NEWARE CT-3008 (NEWARE, Shenzhen, China) with a parameter of 5 V/6 A. Additionally, a cylindrical heater with a diameter of 18 mm, a height of 65 mm, and a power of 250 W was regarded as a thermally failed battery in the module to heat the other batteries. As shown in Figure 3, it was the typical temperature curve of the heater after powering on where the temperature increased at a rate of 4.25 \({}^{\circ}\)C/s firstly, and then the temperature rising rate declined gradually. Finally, it stabilized at 670 \({}^{\circ}\)C. The combustion gases in tests were extracted from the room with the help of an exhaust pump.  
Two groups of experiments were carried out to explore the influence of discharging treatment on the thermal failure propagation in battery modules with various shapes, including triangle, rectangle, parallelogram, line, hexagon, and square, where the batteries in modules were connected in parallel and the discharge rate for a single battery was 0.5 C. Discharging treatment would finish when the battery module failed. In each module, the heater was displaced in the corner. The detailed experiment configurations are further displayed in Figure 2 and Table 1.  
Figure 1: Schematic of the experimental setup.  
Figure 3: The temperature curve of the heater after powered on.  
Figure 2: Schematic of battery modules and thermocouple arrangements from the top view.
###### Abstract
In situ DLS setups have also been developed to complement SANS at several neutron beamlines so far, for example a SANS-I instrument at the SINQ spallation source (Paul-Scherrer Institute, PSI, Villigen, Switzerland) [11], D11 instrument at the Institute Laue-Langevin (ILL, Grenoble, France) [12; 13], and LOQ and ZOOM instruments at the ISIS Pulsed Neutron and Muon Source (Rutherford Appleton Laboratory, Didcot, UK) [14]. Therefore, the simultaneous combination of light scattering or absorption methods with SANS (or WANS) represents very powerful experimental approaches to be used in the microstructural characterization of soft and biological systems. In this paper we communicate recent upgrades to the sample environments available using the KWS-2, which is now equipped to perform in situ FTIR and dynamic light scattering (DLS). All of which can be performed simultaneously with standard or real-time SANS and enable in-beam monitoring of the sample quality or provide complementary information to neutrons.
## 4 Integration of the Roots Pump  
Based on the testing results on the test rig, the Roots pump developed was then separately integrated into three PEM fuel cell systems with outputs of 60, 80, and 110 kW, respectively. It is validated that the pump can deliver enough hydrogen for the fuel cell systems with different outputs under 100% loads. It is also proved that the Roots pump can operate steadily when the load of the fuel cell systems is down to 25% load. The schematic diagram of the test system and the integration photo of a fuel cell system with the Roots pump is shown in Figure 9. The improvement in the performance of the fuel cell system by integrating the Roots pump developed was observed in tests. The Roots pump operates stably in a wide range of rotation speeds. All the requirements of hydrogen recirculation for the fuel cell systems with outputs of 60, 80, and 110 kW were satisfied with the same Roots pump just by changing its rotation speed. Compared with the type of centrifugal pump such as the regenerative pump, the pressure difference and rotation speed are not coupled together in the Roots pump, which is greatly beneficial to the control of the fuel cell system. When compared with other types of positive displacement pumps such as claw pump and scroll pump, the advantages of the Roots pump include low cost, high efficiency, and robust reliability.  
The operating parameters of the Roots pump in PEM fuel cell systems under various loads are listed in Table 3. From the data in Table 3, it can be seen that the ratios of the pump power consumption to fuel cell system power are in the range of 0.77-0.89% with the fuel cell load of 100%, while that is 1.02-1.2% with the fuel cell load of 25%. Moreover, it is interesting to find that the Roots pump is  
Figure 9: The schematic diagram and the integration photo of a fuel cell system with the Roots pump: (**a**) The schematic diagram of the test system (**b**) Photo of the fuel cell stack and hydrogen pump (**c**) Photo of the air supply systemoperating with rotation speed in the range of 5400-8000 rpm under the pressure differences of 25-32 kPa. As shown in Figure 5a, there is no hydrogen delivered by the pump under the operating conditions with the combinations of the pressure differences and rotation speeds in Table 3. Comparing the data in Figure 5a and Table 3, it can be found that the performance of the Roots pump integrated into the fuel cell system is better than that measured with pure hydrogen on the test rig. This implies that the performance of the Roots pump is affected greatly by the impurities, such as water vapor and nitrogen, which exist inevitably in the anode circuit of the PEM fuel cell system. This might be because the water vapor and nitrogen are helpful for reducing the leakage within the Roots pump due to their higher mole mass and larger dynamic viscosity than that of the pure hydrogen. Thus, it is suggested that more in-depth research work should be carried out to investigate the anode purge strategy and the effects of the impurities on the performance of the Roots pump in the future.
# A Review on Computer Vision Technology for Physical Exercise Monitoring
## 3 Discussion about Articles
### Respiratory Rate
#### 3.6.1 Video-Based Image Processing  
The ventilation threshold is an important variable to investigate physical exertion. It is related to the anaerobic threshold, an event characterized by the increase in ventilation at a faster rate than what the body is capable of absorbing. In recent decades, many methods have been proposed to measure the respiratory rate during exercise using contactless technology. [PERSON] [73] proposed a technique to measure respiratory rate during pedal stroke using optical techniques. A dot matrix optical element was arranged in front of the participant's face and a laser was emitted to be captured by the camera CCD. After using low-pass digital filtering, a sinusoidal wave that vibrates at the respiratory frequency was calculated. The results showed high correlation when compared to data obtained using a gas analyzer.