esa-sceva/satcom-chunk-collection huggingface dataset:
# A New Path Tracking Method Based on Multilayer Model Predictive Control
## 3 Simulation
### Performance with Positioning Error  
The purpose of this group is to verify the performance of the controller with positioning errors.  
At present, positioning devices such as BDM670 of Beijing BDStar Navigation Co. Ltd can provide centimeter-level positioning. Consequently, we added a random error within \(\pm\) 0.01 m to the horizontal and vertical coordinates of the articulated vehicle model. Since the judgment of Equation (30) is based on errors, positioning errors will affect the function. To avoid the articulated vehicle always traveling at the minimum of the reference velocity, we increased \(\mu_{1}\) and \(\mu_{2}\). In this group, \(\mu_{1}\) was 4 and \(\mu_{2}\) was 2.  
Figures 18-24 show the results of this group of simulations. The trend of each variable with positioning error was the same as the trend of the ideal variable. Therefore, we believe that multilayer MPC can be used in a working environment with small positioning errors.  
Figure 16: Heading error of the comparison between calculation processes.  
Figure 17: The computation time of the comparison between calculation processes.  
Figure 19: The longitudinal velocity of the simulation with positioning errors.  
Figure 20: The articulated angle of the simulation with positioning errors.  
In addition, multilayer MPC still faces some challenges. As Figure 21, The articulation angle speed is not smooth due to the influence of the positioning error. Of course, this is a phenomenon that cannot be avoided by any controller, but we think there is still the possibility of improvement here. We believe that nonlinear MPC is more robust than linear MPC. If the linear MPC-based path tracking controller in multilayer MPC is replaced with the nonlinear MPC controller, the amplitude of the articulated angle speed may be reduced. However, the real-time performance of nonlinear MPC is suboptimal, requiring further work in the future solve this problem.  
Figure 21: Articulated angle speed of the simulation with positioning errors.  
Figure 22: Displacement error of the simulation with positioning errors.
# GIS-Based Evaluation Method for Accessibility of Social Infrastructure Facilities
## 3 Methodology  
### Social Infrastructure Planning  
This paper deals with social infrastructure planning issues. Social infrastructure planning authorities want to know where the social infrastructure is vulnerable because  
Figure 1: Comprehensive accessibility evaluation of social infrastructure.  
they have a limited budget and need to prioritize their investment. They want to determine where to place the social infrastructure facilities and the capacity of those facilities. As previously mentioned, there have been two approaches to social infrastructure planning: (1) population-based approach and (2) access-based approach. The main problem with the population-based approach is that new suburbs are not serviced with social infrastructure until a residential population gets large enough. This is a problem in suburban areas with low population densities, promoting deprivation amplification [18]. There is no indication of maximum distances to social infrastructure services or the size of catchment areas in a population-based approach.  
The access-based approach is a relatively recent concept. Accessibility is also one factor determining a suitable location for green infrastructure and defining future scenarios of public space in cities [19; 20]. The so-called '20-min neighborhood' concept gives people the ability to fulfill most of their day-to-day life within 20 min of walking from home, and access to safe biking and local transportation options [21]. These communities are planned using strategic planning guidelines that incorporate maximum distances or maximum time as the basis for social infrastructure provision. The planning strategy ensures that urban areas have good access to social services.  
However, it is not easy to find explicit methods for implementing such strategies. Social infrastructure planning authorities need to know where to place social infrastructure facilities. To this end, authorities first need to know where social infrastructure is vulnerable. Therefore, this study suggests a method to comprehensively evaluate neighborhoods or cities regarding accessibility and identify blind spots in social infrastructure.
## 4 Experimental Results  
### Setup  
We perform speech enhancement using the CHiME4 [14] data corpus, other than using the TIMII data corpus and noise to constitute the noisy speech, because there is no real noisy data for the constituted simulated noisy speech in TIMII data corpus. The corpus consists of real and simulated audio data taken from the 5k WSJ0-Corpus with four different types of noise, i.e., bus (BUS), cafe (CAF), pedestrian area (PED), and street junction (STR). There are totally 8738 utterances for training, 3280 utterances for validation, and 2640 utterances for testing. Besides, the corpus is recorded by a six-channel microphone array, although we only use the single-channel subset, because this paper only studies single-channel speech enhancement algorithm.  
The setting for the network and the training parameters are as follows. The generator G uses an auto-encoder architecture with skip connections from the encoder to the decoder. The encoder is composed of 22 one-dimensional convolutional layers of filter width 31 and strides 2, the decoder is a mirroring of the encoder with the same parameters set. The discriminator D follows the same one-dimensional convolutional structure as the encoder of G. The weights of all layers are initialized by a Xavier initializer, and all the biases are initialized with zeros. An RMSprop optimizer with a fixed learning rate sets to 0.0002 is adopted to train the models. In order to minimize the distance between the generated speech and the real clean speech, \(L_{1}\) term is used as a regularization [9], and the weight parameter \(\lambda\) is set to 100. We carry out the training and evaluation procedures on a workstation with Intel Xeon E5-2630 CPU and two GTX 1080 ti GPUs.
## 2 Thermal Error Modeling  
### Selection of Temperature-Sensitive Points  
To ensure the precision of the thermal error forecasting model and address the issue of collinearity among temperature measurement points, it is imperative to carry out the selection of points that are sensitive to temperature. Herein we use hierarchical clustering combined with the K-medoids algorithm and [PERSON] s correlation coefficient method (H-K-P) to conduct the task of selecting temperature-sensitive locations. The K-medoids algorithm is more robust, more easily interpretable, more widely applicable, and more computationally efficient than the K-means method [38]. Hierarchical clustering can reflect well the categories from the K-medoids principle. Then, the correlation scores among all temperature data points and thermal errors within each cluster are computed according to [PERSON] s correlation coefficient method, and the points with the strongest correlation to thermal errors in each grouping are identified as the key points in their respective clusters. In this way, K temperature-sensitive points are selected.
# Influence of Thoracolumbar Mobility on Running Performance: A Comparative Study
## 4 Discussion
### Relationship Between Thoracolumbar Mobility and Fluid Amount  
Runners with two years of experience consume significantly more water in the last 12 h compared to those with six months of running experience or sedentary individuals. While muscle tone is higher in runners, fluid intake was not directly linked to tissue stiffness or elasticity. Studies suggest that proper hydration may help reduce injury risk by improving tissue flexibility. The interaction between muscle contraction proteins and water, along with their tendency to form regular structures, emphasizes the connection between hydration and muscle function. Fluid flow may alter muscle mechanics and performance. Moreover, intracellular water content in lean mass (including the muscle) may influence muscle strength, elasticity, functional capacity and frailty. However, further studies are needed to confirm this hypothesis. Further research is needed to investigate the effects of hydration on muscle function and performance [49; 50].  
Thoracolumbar mobility may decrease in highly trained runners (with two years of experience) due to the intensity of their training regimens. Similarly, sedentary individuals also exhibit reduced TL mobility. Interestingly, TL mobility appears to be optimal in runners with six months of experience, who typically train less intensely. These findings suggest that both intensive training and sedentary lifestyles can adversely affect lumbar mobility, while moderate training levels may be beneficial. Furthermore, trunk extension and lumbar extensor flexibility were better in the two-year running group, indicating the influence of training intensity and years of running experience on trunk ROM.  
Based on these observations, we recommend diversifying exercise programs to include stretching, mobility, and muscular mechanical properties. This approach may help mitigate the increased muscle tone, reduced lumbar mobility, and decreased trunk flexion ROM observed in the two-year running group. Such interventions could not only improve performance but also reduce the risk of musculoskeletal issues associated with running mechanics.
## 3 Experimental Results, Analysis and Discussion
### Identification of the Failing Joint
Computing this feature for data in Tables 2 and 3 reveals that data from FJ2-B tests show lower values than those obtained from FJ1-B tests, as exhibited in bar plots of Figure 6. For data sets produced with the exciter driven at 80 Hz and 600 mVpp, all values for \(\omega n/\zeta\) are clearly (and consistently) lower for FJ2-B tests in mode I (Figure 6a) than those obtained for FJ1-B tests. This trend remains, albeit not with the same consistency, for values of \(\omega n/\zeta\) in mode II (Figure 6b). The case of data sets produced with the exciter driven at 80 Hz and 800 mVpp is a bit different due to the value of \(\omega n/\zeta\) for test FJ1-B #1 being abnormally low and comparable to values obtained for FJ2-B test data in mode I (Figure 6a). But, even then all values of \(\omega n/\zeta\) for FJ2-B test data are consistently low. In mode II, values for \(\omega n/\zeta\) are lower for FJ2-B tests, as for the case of the exciter being driven at 600 mVpp (Figure 6b). Hence, identifying the joint that is failing is a task which is possible to be based on: (i) performing tests during the structure's inspection phase; (ii) approximating damping factors of specific modes by means of the second-order approximation (in Section 2.3); (iii) computing values of feature \(\omega n/\zeta\), and comparing with past results. Note that results are obtained without dismantling the tested structures and/or separately examining each joint by means of dedicated equipment [10; 11; 12; 13]. Consequently, the currently obtained results describe whether a joint is fit for purpose or not, and do not provide explanation on the origins of failure (as is the case for [10; 11; 12; 13]). The current framework operates by estimating spectral characteristics of the structure's response to testing via simple algorithmic procedures (i.e., the second order approximation) and specific features (\(\omega n/\zeta\)). In that sense, the proposed framework operates in a similar manner to vibration-testing based techniques [18; 19] and achieves similar results but without the associated cost in terms of equipment (accelerometers, amplifiers [18]) or modelling expertise required for the user [19].
## 4 Model Validations
The predicted power under the \(Exp\) and \(Gaussian\) waveforms also both agree well with experimental data, except that the predicted power under the \(Exp\) waveform oscillates during the fall phase in the negative pulse (10\(\sim\)33 ns). The discharge energy predicted by both waveforms is nearly equivalent during the first negative pulse (10\(\sim\)45 ns). The peaks of \(Q_{A\_ui}\) in the \(Exp\) and \(Gaussian\) waveforms at 33 ns are 3.23 mJ and 3.09 mJ, which are 3% and 7% lower than the experimental value 3.33 mJ. During the positive pulse, the two calculated energies are also nearly the same as each other and both are higher than the experimental results. The peaks of \(Q_{A\_ui}\) in the \(Exp\) and \(Gaussian\) waveforms at 55 ns are 3.30 mJ and 3.42 mJ, which are 5% and 9% larger than the experimental value 3.13 mJ. The discharge energy under the \(Exp\) waveform oscillates after 75 ns, but the result of the \(Gaussian\) waveform rapidly reaches to a constant without any oscillations. Although \(Q_{A\_ui}\) in the simulations are very different from the experimental value, the agreement of the discharge  
Figure 7: The comparison of the \(P_{A\_ui}\) (**a**) and \(Q_{A\_ui}\) (**b**) predicted in \(R99\) and the ones in the referred experiment [27]. energy during the pulse is well. It is concluded that the numerical model in this work calculates good discharge energy. Because the discharge power and energy predicted by the \(Exp\) and \(Gaussian\) waveforms are almost identical to each other during the high voltage pulse and the power after the pulse is very small, the \(Gaussian\) waveform is used subsequently for simplification. To investigate the effects of grid resolution, a finer mesh with half the size in the whole computational domain is used for comparison. \(Q_{A\_ui}\) predicted by \(R99\) in both resolutions are shown in Figure 8. The predicted results are nearly the same, indicating that the grid convergences. Therefore, the coarse mesh is used subsequently.
## 4 Proposed Methodology
### Searching for New Solutions  
The proposed SA searches for new solutions by moving a sequence of tasks to another workstation. In order to search for a better solution, two constraints should be observed. First, sequences allocated to a workstation with a longer cycle time should be more likely to be selected to be reallocated to the adjacent workstation. Second, there must be no interference between operators in the new solution. The procedure for searching new solutions is as follows:  
Step 0: Set \(A_{w}\) for \(w=1\)-\(W_{max}\). (\(A_{w}\) is the set of sequence numbers whose corresponding tasks are assigned to workstation \(w\)).  
Step 1: Select a workstation \(w\) at random, based on the probability  
\(P_{w}=\frac{\sum_{i=1}^{l}\sum_{j=1}^{l}T_{i}\times X_{ij}\times Y_{iw}}{\sum_ {i=1}^{l}T_{i}}\).  
Step 2: If only one task is assigned to workstation \(w\) (\(\sum_{j=1}^{l}Y_{jw}\)=1), go to step 1. Otherwise go to step 3.  
Step 3: Select a sequence number \(j\) at random from \(A_{w^{\prime}}\).  
Step 4: If \(\left\{\begin{array}{c}j=2J-1,\text{and}\ Y_{j+1,w}=Y_{j-1,w}=1\\ j=1,\text{and}\ Y_{j+1,w}=1\\ j=J,\text{and}\ Y_{j-1,w}=1\end{array}\right.\), go to step 3. Otherwise go to step 5.  
Step 5: Calculate \(B_{j}\), the workstation that task with sequence number \(j\) is assigned to (\(B_{j}=\sum_{w=1}^{W_{max}}w\times Y_{jiw}\) for \(j=1\)-\(J\), and \(B_{0}=B_{j+1}=1\)).  
Step 6: Randomly select another workstation \(w^{\prime}\) among \(B_{j-1}\) to \(B_{j+1}\), and let \(Y_{jw}=0\) and \(Y_{jw^{\prime}}=1\).  
For example, if Figure 6a is the current solution, and all \(Y_{jw}\) can be represented by the matrix in Figure 8, the set \(A_{w}\) are in the right hand side of the matrix, and \(B_{j}\) are shown in the bottom of the matrix. Initially, the probability of workstations being selected to have one task removed are (\(13/30\), \(10/30\), \(7/30\)). Assume that Workstation 2 is selected and then task with sequence number 6 selected. As \(Y_{52}=0\), and \(B_{5}=3\), \(B_{7}=2\), then the task with sequence number 6 can be moved to either Workstation 3 or Workstation 2. However, the task with sequence number 6 was originally assigned to Workstation 2, so it will be moved to Workstation 3. The result is shown in Figure 9. The purpose of Step 6 is to comply with the second constraint. In the example, if the task with sequence number 6 is assigned to Workstation 1, interference occurs between operators in Workstations 1 and 2, as shown in Figure 10.  
In Step 4, the purpose is to prevent the selection of a task that cannot be allocated to another workstation. If the task with sequence number \(j\) is selected, but both sequence number \(j-1\) and \(j+1\) are allocated in the same workstation. \(B_{j-1}\), \(B_{j}\) and \(B_{j+1}\) are the same, and no other workstations can be selected in Step 6.  
Figure 8: An example for set \(A_{w}\) and \(B_{j}\) calculation.
# Portable Sensor System for Registration, Processing and Mathematical Analysis of PPG Signals
## 2 Portable Sensor System for Registration, Processing and Analysis of PPG Signals
### Preprocessing and Mathematical Methods for PRV Analysis
#### 2.3.1 Module for Preprocessing  
PPG technology is a modern, non-invasive method for measuring the activity of the cardiovascular system, based on the examination of blood volume that is caused by cardiac pulsations by the detection and analysis of optical radiation passing through human skin. The input data are obtained while using an integrated sensor directly in digital form or by using a discrete sensor and subsequent analog-to-digital conversion.  
Cardiac function testing by determining the mathematical parameters while using the non-invasive integrated PPG-based method requires the use of pure biomedical signals. The extracted PPG data must undergo preprocessing (Figure 3) to remove the artifacts (such as Powerline Interference from the power frequency, artifacts caused by the recording system, the signal amplifiers used, etc.) in order to perform correct measurements and analyzes; interference with input data caused by poor contact with the sensor, involuntary movement of the body and others. To these external factors are added the location of the sensor, the presence of ambient lights and electrical sources. The individual characteristics of the subjects studied [23], such as the properties of the skin and the temperature of the human body, have significant influence.  
The permanent constituent contained in the input data is removed by a software DC (Direct Current) filter (DC component is the constant voltage added to a pure AC waveform). Subsequent is the reduction of noise components while using averaging filter, elimination of single large deviations using a median filter (triggered by motion, for example), the removal of high frequency components with a low pass filter (4 Hz cutoff frequency), and low frequency artifacts with a high pass filter (0.4 cutoff frequency). These cut-off frequencies are chosen to correspond to the frequency range within which the physiological human heart rate is approximately located. The frequency is for the final data--if there is a frequency of individual peaks greater than 4 Hz or less than 0.4 Hz, then they are filtered. We assume a heart rate greater than 240 beats/min. (4 Hz) and less than 24 beats/min. (0.4 Hz) is due to some error in the signals or data.  
Studies have been conducted that have found that the excessively fast movement (such as running, sports involving running, or fast movement) can cause artifacts to investigate the occurrence of artifacts  
Figure 3: Photoplethysmographic (PPG) preprocessing.  
in PPG due to movement. In these cases, no analysis data are taken. In addition, the input signal processing program includes procedures that check the quality of the input signals and, if the signals are not of good quality, they are not analyzed.
## 1 Introduction  
Wind-blown pool fires, which occur as a result of the spill and ignition of hydrocarbon fuels in ventilated environments, such as aircraft engines [1], shafts [2], subways [3; 4; 5; 6], etc., present serious risks to people and properties. They are a crucial foundational topic in the research area of flames. Wind tunnel fire experiments have been the most widely used method for studying the phenomena associated with the development of wind-blown fires. [PERSON] and [PERSON] [7] experimented on the burning rate of a 1 m-diameter aviation fuel pool fire in a wind tunnel and reported that when the wind speed increased from 0.57 to 2 m/s, the combustion rate decreased by nearly 25%. [PERSON] [8] studied the influence of the HRR (heat release rate) of a pool fire in a wind tunnel, and it was found that the HRR of large-scale pools continuously increased with increasing wind velocity, while the HRR of small-scale pools increased first and then decreased. [PERSON] et al. [9] investigated the effect of 0.55 to 2.2 m/s cross airflow on fires by changing the length-width ratio of a rectangular pan, and an empirical flame tilt angle prediction model was suggested. [PERSON] [10] conducted an experimental investigation on heptane and ethanol fires with square pool sizes of 0.1 to 0.25 m in wind speeds of 0 to 2.5 m/s and found that the flame length elongated as the wind speed increased.  
Previous research has primarily concentrated on crosswind influences on fire development, and the effect of blockage has been ignored. In practice, congested vehicles in tunnels, clutters in aircraft engines, and obstructions in ship engine rooms can be considered as blockages in fire accidents. Besides crosswinds, these blockages in a ventilated environment have a significant influence on fire development, which will affect the airflow field and thus the flame shape [11]. [PERSON] [12] studied the influence of baffle height and wind speed on the stability of an aviation kerosene pool fire, and it was shown that the most stable flame was obtained with a 2.54 cm-high baffle in a wind tunnel when the wind speed was 3.96 m/s. [PERSON] [13] experimentally investigated the effect of a 10-40 cm-high baffle and 0-3 m/s crosswind on an 8 cm pool fire outside the wind tunnel, and an empirical model for predicting flame height and inclination was established.  
In general, reports on the condition of combining factors of baffles and crosswinds on pool fire behavior are rare, and researchers have mainly focused on pool fires at 0-3 m/s wind speeds. In this paper, wind tunnel fire experiments were carried out to obtain the flame morphology characteristics of pool fires under the combined influence of obstacles and crosswinds. Numerical simulations were performed to obtain an airflow field together with the HRR of an N-heptane pool fire under a 2 to 10 cm-high baffle and a 1 to 5 m/s crosswind.
###### Abstract  
The results of studying the waveforms of longitudinal and transverse photocurrent pulses generated in thin, semitransparent CuSe/Se films as a function of the angle of incidence (\(\alpha\)) of a femtosecond laser beam at linear and circular polarizations are presented. It has been established that the durations of unipolar longitudinal photocurrent pulses at linear and circular polarizations of laser pumping do not depend on the angle \(\alpha\). It is shown that the evolution of the temporal profile of the helicity-sensitive transverse photocurrent with a change in \(\alpha\) strongly depends on polarization. At linear polarization, the shape of the unipolar pulses remains virtually constant; however, at circular polarization, the generation of unipolar and bipolar pulses is possible, with the waveforms strongly depending on the angle \(\alpha\). The influence of the incidence angle on the waveforms of transverse photocurrent pulses is explained by the transformation of linear and circular polarization into an elliptical upon the refraction of light at the air/semitransparent film interface and by the interplay of photocurrents arising due to linear and circular surface photogalvanic effects in the film. The presented findings can be utilized to develop polarization and incidence angle-sensitive photovoltaic devices.  
circular photocurrent; polarization; helicity; bipolar photovoltage; waveforms; femtosecond excitation; surface photogalvanic effect; thin films Article