esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Description of Driving Cycles  
Figure 7 presents the dynamic characteristics of eight most commonly used driving cycles, including US06, HWFET, NEDC, UDDS, JC08, NYCC, CCUR and CCEW. Among them, US06 and HWFET are both used to indicate high-speed driving conditions. It can be seen from Figure 7a,b that US06 has greater vehicle acceleration/deceleration and peak vehicle speed. However, HWFET can be considered as a relatively stable highway operating condition with a maximum speed of no more than 96 km/h [33].  
NEDC is a synthetic driving cycle that includes four repeating urban driving cycles and an extra-urban driving cycle. It is considered as the most robust driving cycle [33], i.e., vehicles based under the NEDC have the least degradation in performance than other driving cycles. However, the NEDC has relatively less dynamic changes compared with the other cycles described. In addition, in China's national standards GB/T 18386-2005 and GB/T 19233-2008, the NEDC is used to measure the mileage, emission level and fuel consumption of vehicles (including CV and EV).  
In the US, the UDDS standard cycle is commonly used to evaluate the performance of light vehicles in urban conditions [24]. The widely-known FTP-75 driving cycle comes from UDDS, by adding the first 505 s of UDDS to its end. The JC08 driving cycle is a new fuel consumption and emissions test evaluation standard cycle implemented in Japan in 2015. It mainly reflects the characteristics of  
Figure 7: The dynamic characteristics of driving cycles based on acceleration and speed for: (**a**) US06; (**b**) HWFET; (**c**) NEDC; (**d**) UDDS; (**e**) JC08; (**f**) NYCC; (**g**) CCUR; and (**h**) CCEW.  
crowded urban driving conditions. Compared with the previously used Japan 10-15 mode driving cycle, JC08 has longer mileage and more stringent acceleration/deceleration.  
NYCC represents the driving conditions in urban centers with frequent starts and stops. It can be found from Figure 7f that the peak speed is the lowest among all driving cycles, but the maximum acceleration/deceleration is large, only slightly smaller than that of US06. In contrast with HWFET, NYCC represents the low speed/high acceleration driving conditions, while HWFET cycle stands for the high speed/low acceleration driving conditions.  
Finally, this study also selected two urban circulation conditions with Chinese traffic characteristics, i.e., CCUR and CCEW. Expressway is defined as conditions with a vehicle speed of more than 60 km/h and no traffic lights, for instance the city ring of Beijing and Shanghai, etc. [25], while the speed on urban roads is significantly lower than that of expressways.
# A Surrogate Model Based Multi-Objective Optimization Method for Optical Imaging System
## 2 Methodology
### Process Flow  
Figure 1 shows the specific process steps of the method proposed in this paper, including:  
1. Decide on design parameters, including their ranges: the key design parameters that affect the performance of the optical system need to be decided first.
2. Experimental design: based on the number and range of parameters given in step 1, DOE needs to be carried out to decide the sample points in the design space and provide information including the number of samples and their distribution.
3. Sample points calculation: the ray-tracing-based program is then used to complete the calculation at each sample point and provide the interested targets required in the optimization.
4. Surrogate model training: the surrogate model can be trained using the output from the sample points in step 3. The accuracy of the trained model is estimated, and more sample points are required if the accuracy cannot meet the requirements.
5. Multi-objective optimization design: the multi-objective optimization algorithm is used at this step to optimize the design based on the prediction of the surrogate model and provide the final Pareto solution set as output.
6. Decision making: the final optimal design can then be chosen from the Pareto solution set depending on the desired design focus and strategy.
# Automatic Extraction of Material Defect Size by Infrared Image Sequence
## 5 Discussion and Analysis
### Influence of Size of Sensitive Region on Detection Results
#### 5.1.2 Influence of Size of Sensitive Region in the Secondary Detection  
From the above discussion, all the sizes of the sensitive region from \(3\times 3\) pixels to \(15\times 15\) pixels can be sampled to seek a suitable frame in the secondary detection when a relative correct result is acquired in the primary detection. To discuss the influence of size of the sensitive region in the secondary detection, 100 independent experiments were done as in the previous section.  
For uniform sampling, the size of each sensitive area sampling is the same, but the size is no longer fixed as the front. Instead, the size is randomly determined from \(3\times 3\) pixels to \(15\times 15\) pixels in 100 experiments. The results are shown in the scatter diagram shown in Figure 24. Compared with the results of Figure 20a obtained by a fixed size of \(15\times 10\), it can be seen that the diversity of results is greater than that of previous results. Especially for defect #1, it is no longer only one value for Frame 164, and more frames are selected in the interval (161, 173). Among the results, Frame 164 and Frame 170 are chosen with large probability.  
Figure 23: The 3D scatter diagram of selection frames with the sensitive region size variation: (**a**) The 3D scatter diagram of selection frames for #1; (**b**) The 3D scatter diagram of selection frames for #2.  
For random sampling, the parameters were set as the previous section except that the size of sensitive region variation range is enlarged from \(3\times 3\) pixels to \(15\times 15\) pixels instead of from \(10\times 10\) pixels to \(15\times 15\) pixels, and the results are shown in Figure 24b. Compared with the previous results (see Figure 20a), the range of frames found in the two statistical experiments was similar, except that defect #1 was slightly larger. This indicates that size has little influence on random sampling.
## 7 Antenna Qualification and Acceptance Test
### Physical Measurements of Unit Under Qualification  
In this step, the thicknesses of the plates forming the antenna stack-up are verified, as are the size and dimensions of the integrated antenna, the grounding, the conductivity of the connectors, and the interfaces with the satellite Leader-S. These verifications are conducted prior to the commencement of the functional tests. The antenna has a total height of 7 mm and a weight of 30 g.
# Dimensional Reduction-Based Moment Model for Probabilistic Slope Stability Analysis
## 5 Applications
### Example 1: Infinite Soil Slope  
A typical slice of the infinite slope was shown in Figure 2. The safety factor of the infinite slope is given by the following equation [42].  
\[\text{FOS}=\frac{\left(H\gamma cos^{2}\beta-u\right)tang^{\prime}+c^{\prime}}{H \gamma sin\beta cos\beta} \tag{17}\]  
where FOS is the safety factor of a slope, \(H\) is the depth of the soil layer to the potential failure surface, \(\beta\) is the slope inclination, \(\gamma\) is the total unit weight of soil, \(u\) is the pore pressure at the base of the slice, \(\varphi^{\prime}\) is the effective soil friction angle at the base of the slice and \(c^{\prime}\) is the effective cohesion at the base of the slice.  
To the infinite slope, there are six influence factors for the safety factor of slope. To illustrate the proposed method, two cases of slope stability were evaluated, i.e., frictional/cohesion soil (2 random variables) and frictional soil with pore pressures (4 random variables) to verify the proposed method.
# Common-Mode Voltage Analysis and Reduction for the Quasi-Z-Source Inverter with a Split Inductor
## 2 CMV Analysis in Different Operational States for the qZSI
### CMV during the Shoot-Through State  
Figure 2c shows the equivalent circuit of the qZSI during the ST state. In this case, the diode is reverse-biased and the capacitors transfer energy to the inductors. According to (1) and the equivalent circuit in Figure 2c, the CMV can be obtained as:  
\[v_{\rm cmv}^{st}=\frac{0+0+0}{3}=0 \tag{6}\]  
where \(v_{\rm cmv}^{st}\) is the CMV for all the ST vectors. During the ST state, all three phases are connected to the common reference.  
Figure 3: Switching vectors generated by the qZSI in the vector space.  
Figure 2: Equivalent circuits of the quasi-Z-source inverter during the (**a**) active state, (**b**) null state and (**c**) shoot-through state.  
During the ST state, the DC-link voltage is zero. However, during the active state and null state, the DC-link voltage reaches its peak value, which is equal to the sum of the two capacitor voltages in the qZSn. The relationship between the peak DC-link voltage and the two capacitor voltages can be described as:  
\[v_{\text{DC}}=v_{C_{1}}+v_{C_{2}} \tag{7}\]  
Assuming that the peak DC-link voltage and the capacitor voltages are constant during the operation and \(D\) is defined as the ST duty ratio in a switching cycle, in steady-state, the voltages of the two capacitors can be obtained as:  
\[V_{C_{1}}=(1-D)V_{\text{DC}} \tag{8}\]  
\[V_{C_{2}}=DV_{\text{DC}} \tag{9}\]  
The CMV values in different states can be obtained according to (7)-(9). Table 1 shows the switching vectors and the relevant CMV values. The CMV values in the qZSI are not constant in a switching cycle with the utilization of different vectors, as shown in Table 1. Thus, the CMV values vary depending on the modulation methods. The CMV fluctuation can lead to large leakage currents when the qZSI is applied to the PV system.
## 5 Limitations and Areas of Discussion  
Although the MSWT-DN model has achieved remarkable performance in rain removal tasks, its performance still has certain limitations under extreme rainfall conditions and complex backgrounds. In scenes with heavy rainfall or complex backgrounds, the model may have difficulty effectively distinguishing raindrop traces and background details, resulting in a decrease in the deraining effect. In addition, the high computational complexity of the model, especially in the case of multi-module design and large number of parameters, limits its real-time deployment in environments with limited computing resources, especially on devices with limited resources, such as edge devices or mobile platforms.  
These limitations make the model challenging to use in practical applications, especially in scenarios with severe weather and high computing requirements. In order to improve the wide applicability of MSWT-DN in practical applications, future research could focus on the following directions:  
* Enhance robustness under extreme rainfall conditions: the performance of the current model drops significantly under heavy rainfall conditions. In the future, the performance of the model under severe weather conditions can be improved by introducing more targeted rainfall features or designing more specialized modules. In addition, combining temporal information (such as the temporal model of video frames) may also effectively improve the performance of the model in dynamic and real-time applications.  
Figure 14: Qualitative analysis of several techniques for removing rain from the RealRain actual dataset. (**a**) represents the original rainy image, (**b**–**h**) show the results of the selected comparison algorithms, and (**i**) presents the result of the proposed algorithm.  
* Optimize computational efficiency: the computational overhead of the model is large, which limits its deployment in low-resource devices. To solve this problem, in the future, we could explore optimization techniques such as model pruning, quantization, and knowledge distillation to improve its efficiency in real-time applications by reducing model parameters and accelerating the inference process. Adaptive computing technology can also be used as an effective means to dynamically adjust the computational amount of the model according to the complexity of the input and reduce unnecessary computational consumption.
* Multimodal data fusion: future research could explore the fusion of multimodal data (such as depth maps, infrared images, etc.), which will provide more contextual information for the model and help to more accurately separate raindrop traces from background elements, especially in complex backgrounds and severe weather conditions, further improving the robustness of the model.
# Numerical Study on Cooling Performance of a Steam-Cooled Blade Based on Response Surface Method
###### Abstract  
In order to investigate the cooling mechanism of the turbine blade and to enrich and supplement the experimental study of the blade, a numerical study of a steam-cooled blade with five cooling channels was carried out based on the response surface model. The surface cooling efficiency and dimensionless temperature distribution of the steam-cooled blade were obtained with different mainstream inlet temperature, outlet pressure, pressure ratio of inlet to outlet, temperature ratio and flow ratio of steam to mainstream by using the flow-solid coupling numerical method. The influence of the working parameters on the cooling performance of air-cooled blade and steam-cooled blade, including the average cooling efficiency, temperature non-uniformity, and average dimensionless temperature, was comparatively investigated; the correlation equation of the working parameters on the cooling performance of the steam-cooled blade was obtained. The results show that the influence of mainstream inlet temperature and outlet pressure on the cooling performance of the steam-cooled blade is not significant; the cooling efficiency of the steam-cooled blade increases by 5.92%, 7.35% and 26.51% respectively as the mainstream inlet to outlet pressure ratio, the temperature ratio and the flow ratio of steam to mainstream increase; the dimensionless temperature increases by 3.74% as the temperature ratio increases and decreases by 0.93% and 4.09% as mainstream inlet to outlet pressure ratio and flow ratio increase; the temperature non-uniformity decreases by 4.09% and 30.08% respectively, as the mainstream inlet to outlet pressure ratio and temperature ratio increase and increases by 37.99% as the flow ratio increases; the effect of working parameters on air-cooled blade and steam-cooled blade is the same, but the steam-cooled blade has 14.06-17.81% higher cooling efficiency, 18.47-29.01% higher temperature non-uniformity and 1.86-2.58% lower dimensionless temperature compared to the air-cooled blade under the same working parameters; the correlation equation obtained by fitting the response surface model has higher accuracy.  
steam-cooled blade; cooling performance; response surface model; numerical study; empirical formulation +
Footnote †: journal: Applied sciences
## 2 Background Theory and Methods  
### Rear-Side Photothermal Deflection Method  
The photothermal effect describes the phenomenon by which optical energy is converted into thermal energy. The energy level of a material's surface is increased by collision with photons. The rear-side photothermal deflection method is configured to overcome the limitations of conventional technologies. As shown in Figure 2, the specimen consists of two layers: the thin light absorption film and the target materials. The probe beam was adapted to pass through an air layer at the rear part of the specimen to avoid mutual interference between the pump and probe beam. The light film sufficiently absorbs the energy of the pump beam regardless of the optical and thermal characteristics of the target materials. This was achieved by using copper, which is known to have an optical absorption coefficient of 6.1310 \(\times\) 10\({}^{5}\) cm\({}^{-1}\)[32]. Using Equation (1), the optical absorption length was determined to be as short as 16.31 nm, implying that the entire energy of the pump beam could be absorbed by the surface of the metal film. Furthermore, all the absorbed energy was converted to heat. The conduction causes a periodic temperature gradient in the target materials and in the layers of air at the top and bottom. When the probe beam passes through the temperature gradient of the bottom air layer of the specimen, deflection is generated accordingly. The deflection angle (\(\Phi\)) for the orientation of the irradiating probe beam can be expressed by Equation (2) below.  
\[L=\ \frac{1}{\lambda} \tag{1}\]  
\[\Phi=\int_{path}\frac{1}{n}\bigg{(}\frac{\partial n}{\partial T}\bigg{)}\
abla_ {n}T(x,y,z,t)ds \tag{2}\]  
Here, \(n\) denotes the refractive index, \(\partial n/\partial T\) the temperature coefficient of the refractive index, and \(\
abla_{n}T\) the temperature gradient perpendicular to the path of the probe beam.  
Since the method uses a pump beam modulated on a regular cycle, the temperature gradient of the bottom part of the specimen has repeating increases (heating) and decreases (cooling) according to the period of the pump beam. A temporal delay is generated between the bottom temperature gradient and the pump beam according to the thermophysical properties of the target materials. As a result, the deflection period of the probe beam through the bottom temperature gradient is delayed in comparison to the period of the pump beam, as illustrated in Figure 2. This is called a phase delay. The phase delay increases as the relative position between the pump beam and probe beam increases, which is dependent on the thermophysical properties of the materials. The thermal conductivity of the target materials can be derived by analyzing the phase delay according to the relative position between the pump beam and probe beam to the x-axis when using this principle.
## 4 Experiments and Results
### Algorithms  
The BERT, ALBERT, RoBERTa, DistilBERT, XLNet, and ELECTRA models are all based on transformer architecture, and are trained to perform natural language-processing tasks. However, there are some differences between them, i.e., in terms of the training data, architecture, etc.  
BERT was trained on a large corpus of text from Wikipedia and the BookCorpus dataset. The corpus was pre-processed by masking some of the tokens in the text, and the model was trained to predict the masked tokens, based on the surrounding context. ALBERT was also trained on the same corpus of text as BERT, but with a modified training objective that encouraged the model to share parameters across layers, as well as reduce the number of parameters. RoBERTa was trained on a larger corpus of text than BERT, including additional data from CommonCrawl and the WebText dataset. The training objectives were also modified, in order to improve the quality of the representations. DistilBERT was trained on a smaller corpus of text than BERT, but with a modified architecture that is faster and more memory-efficient. The training objective is also simplified compared to BERT. XLNet was trained on a large corpus of text from Wikipedia and the CommonCrawl dataset. The training objective was based on predicting the probability of each token in the text given its context, similar to language modeling. ELECTRA was trained using a pre-training method, where a generator network corrupts the input text, and a discriminator network is trained to distinguish between the original and the corrupted text. The model was trained on a large corpus of text from Wikipedia and the Common Crawl dataset.  
For deep learning, CNN is a powerful tool that can extract relevant features from input data, in this case, textual data, through convolutional layers. By capturing local patterns in the input text, CNNs can identify critical features and relationships between words, leading to better performances in sentiment analysis, topic classification, and language translation tasks. Similarly, logistic regression is a simple, yet robust, statistical model that  
Figure 1: Flowchart of the methodology presented in the article.  
can handle large volumes of data. Logistic regression is easy to interpret, and its coefficients can provide insight into the impact of each feature on the output.  
Hence, all these algorithms contribute to extracting different dimensions of hope and regret, which are later discussed in Section 5.
## 3 Materials and Methods
### Spline Filters and Wavelet Transforms  
Since bedforms can be interpreted as harmonic variations, they can be decomposed through mathematical analysis for waves or oscillatory systems. Available techniques include spectral analysis, moving average smoothing techniques, fractal theory and logistic regression, among others.  
In this work, a methodology based on the use of robust spline filters and continuous wavelet transforms to discriminate bedform geometry was applied [16]. This technique overcomes the limitations of the Fourier transform used in spectral analysis, as it can be applied to nonstationary, intermittent, aperiodic, discontinuous, nonlinear and three-dimensional processes, such as riverbed morphology.  
The algorithm of the spline filter uses the penalized least squares method and smoothes uniformly sampled data through the discrete cosine transform. The wavelet function used is the Morlet function, which can capture frequencies in the ripple scale (wavelength less than 60 cm), although the measurement resolution is lower.  
In this methodology, continuous wavelet analysis is performed on the original signal (bathymetric longitudinal profile: elevation or depth), generating a global wavelet spectrum that provides information about bedform wavelengths with greater frequency and intensity. Then, the spline filter is applied sequentially to the original signal and its derivatives, using the transformed wavelet analysis with the characteristic lengths obtained from the global spectrum to decompose and hierarchize the bedform profile into three categories.  
Usually, the first hierarchy corresponds to ripples or small dunes (wavelengths in the order of 5 m), the second hierarchy is associated with medium (10 m) or large (100 m) dunes and the third hierarchy comprises mega-dunes or bars. The sum of the three hierarchies' signals results in the original signal. More details about the methodology can be found in [PERSON] et al. [16; 39] and [PERSON] [40].  
Figure 5: Schematic diagram showing the systemic bias due to the not-computed scour volumes (adapted from [15]).  
Figure 6: River reach where the modified ISSDOTv2 method was applied (red area).