esa-sceva/satcom-chunk-collection huggingface dataset:
# DNCL: Hybrid DOA Estimation and NMDS Cooperative Multi-Target Localization for RFID
## 4 System Design
### DOA Parameters Profile  
Using the antenna mobile scanning scheme, the reader interrogates the tags when the mobile antenna is at different positions. Based on recorded phase values, the corresponding angle of arrival of the tag at different positions of the antenna can be calculated. Following that, the linear model of DOA parameters was available to determine the position-related parameters of the tag.  
As shown in Figure 4, \(T\) denotes the position of the tag, \(x_{0}\) is the projection point of the tag on the linear track of the moving antenna, the x-coordinate of the tag, and \(d_{0}\) is the vertical distance of the tag to the x-axis. In the system, we make the antenna move linearly along the x-axis, and the position is marked as \(x\), where \(x=\frac{x_{4}-x_{1}}{2}\). The angle \(\theta\) formed by the straight line \(l_{Tx_{0}}\) and the straight line \(l_{Tx}\) is the direction of arrival corresponding to the tag when the antenna is located at each sampling point.  
According to the geometric relationship of the model shown in Figure 4, a model based on DOA parameters was constructed to compute the location characteristics of the tags. The specific steps are as follows: position of the antenna at \(x\), and the DOA of the tag is \(\theta\). Therefore, there exists the geometric relationship  
\[\tan\theta=\frac{x-x_{0}}{d_{0}} \tag{16}\]  
this is a linear equation between \(\tan\theta\) and \(x\). Obviously, the reciprocal of the slope of the equation is \(d\), and the intercept on the x-axis is \(x_{0}\).  
Based on the previous discussion, for each tag, we used \(X=\{x_{0},\cdots,x_{i}\}\) to denote the coordinates of the antenna at different locations. We can obtain its DOA sequence of \(B=\{(x_{0},\theta_{0}),\cdots,(x_{i},\theta_{i})\}\) relative to different locations. Further, the sequence \(\Gamma=\{(x_{0},\tan\theta_{0}),\cdots,(x_{i},\tan\theta_{i})\}\) can be obtained by replacing \(\theta\) with \(\tan\theta\). As shown in Figure 5, the \(d_{0}\) and \(x_{0}\) corresponding to the tag can be found from the straight lines fitted by the points in the sequence. Moreover, the distance to the tag when the antenna is in different positions can be obtained by  
\[d=\sqrt{d_{0}^{2}+\left(x-x_{0}\right)^{2}} \tag{17}\]  
Following that, the distance characteristic vector of the tag can be obtained. To more accurately describe the characteristics of the tags, we further incorporated their angle characteristics. Since the sampling points of the antenna are relatively large, we use the principal component analysis (PCA) method to degrade the dimensionality of the tag characteristics, and we can obtain the characteristic vector \(\xi=[d_{0},\cdots,d_{i},\theta_{0},\cdots,\theta_{i}]\) of the tag.
## 4 Experiments
### Parameter Settings  
We utilized Gehring and Homberger's extended VRPTW benchmark [28], comprising 200 customer instances, and modified and supplemented parameters. Firstly, in the original benchmark, all task service times are set to 90. To ensure task heterogeneity, we introduced a 0.5 disturbance to the task service times, where all task service times \(T_{\text{service}}\in\{t\,|\,90*(1-0.5)<t<90*(1+0.5)\}\). Secondly, we introduced a 0.3 disturbance to the vehicle capacities to create vehicular heterogeneity. Finally, we augmented the dataset by introducing carbon emission costs \(p\) for each route, where \(p\in\{0.8,\,1.0,\,1.2\}\), representing excellent, normal, and poor carbon emission levels for respective routes. To ensure fairness in experimentation, the overall evaluation is uniformly set to 10,000 assessments.  
Furthermore, we conducted tests using four different task distribution scenarios to evaluate the performance of the algorithm. The distribution of map locations is illustrated in Figure 6, where the red dots represent warehouses, and the blue dots represent points that require planning and allocation.  
Figure 6: The distribution of tasks. The red dots represent warehouses, and the blue dots represent points that require planning and allocation.
# Accelerometer-Based Pavement Classification for Vehicle Dynamics Analysis Using Neural Networks
###### Abstract  
This research examines the influence of various pavement types on vehicle dynamics, specifically concentrating on vertical acceleration and its implications for unsprung mass, including the wheels and suspension system. The objective of this project was to categorize pavement types with accelerometer data, enabling a deeper comprehension of the impact of road surface conditions on vehicle stability, comfort, and mechanical stress. Two categorization methods were utilized: a neural network and a multinomial logistic regression model. Accelerometer data were gathered while a car navigated diverse terrain types, such as grades, potholes, and cobbsstones. The neural network model exhibited exceptional performance, with 100% accuracy in categorizing all surface types, while the multinomial logistic regression model reached 97.14% accuracy. The neural network demonstrated exceptional efficacy in differentiating intricate surface types such as potholes and grates, surpassing the logistic regression model which had difficulties with these surfaces. These results underscore the neural network's effectiveness in the real-time categorization of road surfaces, enhancing the comprehension of vehicle dynamics influenced by pavement conditions. Future studies must tackle the difficulty of identifying analogous surfaces by enhancing methodologies or integrating more data attributes for greater precision.  
vehicle dynamics; pavement classification; accelerometer data; neural networks; road surface monitoring Article
This study would also point out that due to difficulty in weather forecasting, we consider as many climate elements as possible in the current work. Even when the forecasting is not reliable, the estimation of solar irradiance based on historical climate data could be useful in assessing and planning the solar production, often before the beginning of new construction or installation of solar panels on existing houses and buildings. This study proposes an estimation strategy to investigate impact factors estimating local solar irradiance based on local weather parameters. We also present a comparison of different methods to deduce solar irradiance including linear regression, SVM, MLNN, and LSTM. This study explores which of the local weather parameters could significantly impact the solar irradiance depending on seasonal changes and local environments if input data are from weather forecasting. The analysis through numerical estimation comprises the following aspects:  
* Collecting hourly nine year local weather data and solar irradiance values
* Estimation of one year solar irradiance using the data with the four different methods: linear regression, SVM, MLNN and LSTM
* Analysis of impact factors of each weather parameter
* Comparison of the results and validation of the four estimation methods
# The Combined ASTER MODIS Emissivity over Land (CAMEL) Part 2: Uncertainty and Validation
## 4 Validation with Laboratory Spectra
### RTTOV IASI Brightness Temperature Calculations  
#### 4.3.1 Methodology  
As an additional assessment of the CAMEL HSR product, IASI brightness temperatures (BT) are calculated using the RTTOV model and CAMEL HSR emissivity module for 616 selected channels (National Oceanic and Atmospheric Administration selection) for four focus days representing the four seasons including 15 January, 14 April, 15 July, and 29 September of 2008. Similar calculations are performed for a constant with wavenumber 0.98 emissivity spectrum to assess the impact of the difference between the CAMEL dataset and commonly used default 0.98 emissivity value. Both sets of calculated BTs are then compared to IASI observed radiances for a subset of cases over land (coastlines not included) and for clear sky conditions. The clear sky mask that is used is the MAIA cloud mask [17], which selects scenes with less than 95% cloud cover. Simplifications to the BT calculations include setting the satellite zenith and azimuth and sun zenith and azimuth angles to zero, not applying the snow fraction, and using no trace gas inputs besides water vapor. ECMWF 6 hourly, 0.25\({}^{\circ}\) spatial resolution, analyses are used as input into the RTTOV model. The model analyses are bilinearly-interpolated to the IASI observations, specifically the center of the IASI fields of view. To minimize the effect of rapid skin temperature variations on the comparisons, only nighttime cases (solar zenith angles greater than 85\({}^{\circ}\)) are used and a two-hour time restriction between IASI observations and ECMWF analyses time steps is applied.  
Figure 5: Difference of IASI minus CAMEL 8-year average emissivities at 0.25\({}^{\circ}\)\(\times\) 0.25\({}^{\circ}\) spatial resolution for (**a**) January 3.6 \(\upmu\)m; (**b**) July 3.6 \(\upmu\)m; (**c**) January 8.6 \(\upmu\)m; (**d**) July 8.6 \(\upmu\)m; (**e**) January 10.8 \(\upmu\)m; (**g**) January 12.1 \(\upmu\)m; (**h**) July 12.1 \(\upmu\)m. The years for which the 8-year averages are calculated are 2008–2015 for January and 2007–2014 for July. Colorbar limits change from channel to channel.
## 2 Experimental
### Settling Velocity and Effective Density Results  
Table 2 summarizes the measured settling velocities of 10 selected TM0 feed pellets. Using these values, corresponding Reynolds numbers and drag coefficients according to Equation (7) have been calculated. Shape factor \(\varphi=0.874\) (cylinder with equal height and diameter) was used in the correlation. Then, average value of the effective density can be evaluated from Equation (9), in this case it was 1054.4 kg/m\({}^{3}\) with confidence interval \(\pm\)1.1%. The fluid (water) used in our  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Char. Size (mm)** & **Time (s)** & **Velocity (cm/s)** & **Re** & \(\mathbf{C_{\rm d}}\) & **Density (kg/m\({}^{3}\))** \\ \hline
1 & 3.5 & 3.667 & 6.109 & 212.947 & 0.8845 & 1070.2 \\
2 & 3.9 & 3.466 & 6.463 & 251.044 & 0.8515 & 1067.8 \\
3 & 3.5 & 3.57 & 6.275 & 218.733 & 0.8786 & 1073.6 \\
4 & 3.2 & 7.1 & 3.155 & 100.556 & 1.1519 & 1025.5 \\
5 & 3.5 & 4.967 & 4.510 & 157.213 & 0.9679 & 1041.1 \\
6 & 3.8 & 4.566 & 4.906 & 185.679 & 0.9184 & 1042.6 \\
7 & 3.5 & 3.933 & 5.695 & 198.545 & 0.9011 & 1061.9 \\
8 & 3.4 & 4.56 & 4.912 & 166.352 & 0.9500 & 1049.7 \\
9 & 3.5 & 4.8 & 4.667 & 162.683 & 0.9569 & 1043.6 \\
10 & 3.2 & 4 & 5.600 & 178.486 & 0.9293 & 1067.7 \\ \hline average & 3.5 & 4.463 & 5.229 & 183.224 & 0.9390 & 1054.4 \(\pm\) 1.1\% \\ \hline \hline \end{tabular}
\end{table}
Table 2: Experimental results of settling velocity for TM0 pellets [17]. The resulting effective density of particles expressed from Equation (9) is 1054.4 kg/m\({}^{3}\)\(\pm\) 1.1%.  
Figure 4: **(left)** Illustrative figure of a feed pellet settling in a water column (tap water was used). (right) Photo of feed pellets TM75 (75% insect + 25% fish-based source of lipid and protein).  
calculations corresponded to temperature 20\({}^{\circ}\)C (density \(\varrho_{\rm f}=\)998.2 kg/m\({}^{3}\) and kinematic viscosity \(\
u=1.004\times 10^{-6}\) m\({}^{2}\)/s).  
The same experiments were done using TM75 feed pellets; see Table 3 for resulting settling velocities. According to Equation (9), the resulting effective density of these pellets was determined as 1072.9 kg/m\({}^{3}\)\(\pm\) 1.9%.
## 4 Discussion
#### Limitations  
This study is the first single-blind randomized clinical trial to reveal statistically significant results regarding the complementary therapeutic effect of MI on VO2 max and lactate levels in professional football players with Grade II ankle grains during the return-to-play period. However, the 4-week intervention period may be considered relatively short, potentially limiting the full effects of MI. Future research could explore the complementary role of MI in athletes from diverse sports backgrounds to compare its impact on sports performance across various mentalities and disciplines. Additionally, the absence of a control group performing only the physical therapy balance training protocol may have influenced the findings of this study. Another consideration is the inclusion of relaxation instructions in the MI content, which may have affected athletes' physical responses. This is particularly relevant given that VO2 max and lactate variables were measured using the Bruce Protocol, which requires maximal physical effort.
# Real-Time Webcam Heart-Rate and Variability Estimation with Clean Ground Truth for Evaluation +
## 2 Related Work
However, their results are only reported on their own private dataset (not publicly available), which makes direct comparison difficult. More recent works have shown and benchmarked video-based HRV measurement on publicly available datasets. [PERSON] and [PERSON] [32] introduce a wavelet transform and custom inter-beat-interval filtering rPPG algorithm and evaluate it on the publicly available PURE dataset [33]. They shown good correlation between time-domain ultra-short term HRV measurements from rPPG and PPG. Work by [PERSON] et al. [34] highlight the effectiveness of a clear signal for peak detection, and apply a slope sum function to create more pronounced peaks in the rPPG signal. Concurrent work by [PERSON] et al. [35] introduces one of the first deep learning based fully supervised techniques for HRV estimation, relying on a generative adversarial network to learn denoising of rPPG signals. Both these papers report their results on the UBFC-RPPG dataset [36], which is publicly available. Our method also estimates heart rate variability by obtaining precise temporal beat locations from the filtered BVP/rPPG signal, and we report our HRV results on a large number of public datasets. rPPG datasets. Due to a scarcity of rPPG datasets in the past, initial attempts at evaluating rPPG methods were on private self-recorded videos [23; 29; 37]. Some of the earliest publicly available datasets with heart rate annotations repurposed for rPPG research were introduced in [11; 12], both of which were originally recorded for the purpose of psychological studies. Although the lab setting and video compression makes some of these datasets less than ideal for rPPG, their public availability and large sample sizes provide a common platform for benchmarking rPPG methods. More recently, [21; 33; 38; 39] introduced datasets specifically recorded with the intention of being used for remote heart rate estimation research. These sets include variations in illumination, physical/physiological conditions, and camera types. In this paper, we introduce a new high frame-rate video dataset for rPPG evaluation designed with a focus on evaluating short-term heart rate variability estimation, which require longer observations. This dataset is made publicly available for research use (this dataset can be requested via email to the corresponding author(s)).
# Subwavelength Chiral Spiral Acoustic Metamaterials for a Robust Topological Acoustic Insulator
## 4 Conclusions  
In this paper, a chiral spiral acoustic topological insulator based on the Archimedean spiral scheme is proposed. The topological system does not depend on point group symmetry, and the spiral unit cell enjoys subwavelength characteristics which can effectively lower down the eigenfrequency of the system. The effect of geometric parameters of the spiral structure on the band gap is studied. It suggests that the rotation angle \(\theta\) only affects the third band gap and has little effect on the first and second band gaps. The frequency of the band gap decreases with the increase of spiral width \(w\), and the first and second band gaps gradually become narrow. The increase of the number of turns \(n\) will not only lower the frequency of the first resonant mode, but also introduce more eigenmodes, giving rise to new dispersion curves in band structure. According to the calculated Berry curvature, it is found that the first band gap is a topological trivial local resonant band gap, the second band gap is a topological non-trivial local resonant band gap, and the third band gap is a topological non-trivial Bragg scattering topological band gap. The interfaces composed of left-handed and right-handed spiral units confirm the existence of the edge state protected by chirality. Based on this, the straight and zigzag waveguide is constructed, and the edge states show strong robustness for the propagation at the topological interfaces. Our study reveals that the valley states do not strongly rely on the point group symmetry, since the chiral scheme can also generate a topological transition. This strategy lays a new pathway for the design of topological systems.  
Conceptualization, T.Y. and M.C.; methodology, T.Y., B.X., Y.L.; writing--original draft preparation, T.Y.; writing--review and editing, H.J.; supervision, Y.W.; All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Research Program of Beijing (Grant No Z161100002616034), the National Natural Science Foundation of China (Grant No 11972034), and the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No XDB22040301), and the Youth Innovation Promotion Association of the Chinese Academy of Science (Grant No 2020018).  
Not applicable.  
The data that support the findings of this study are available from the corresponding author upon reasonable request.  
The authors declare that they have no conflict of interest.
# Online Identification of Shearer Cutting State Using Infrared Thermal Images of Cutting Unit
## 3 Shearer Cutting Unit Tracking in Infrared Thermal Images
### Shearer Cutting Unit Tracking Based on Combination of Morphology and STC  
As per the analysis in Section 3.1, the STC algorithm was based on the relationship between the target and its surrounding space in the previous frame to find the location of the target in the next frame. Although the algorithm has good tracking ability for partial occlusion and deformation, the target is lost and cannot be found again if it is completely blocked, or its scale is changed significantly. In addition, the algorithm needs to locate the target ahead of time in order to achieve subsequent target tracking. Therefore, in order to improve the applicability of STC, this paper combines morphology and the STC algorithm to achieve the online tracking of a cutting unit during the shearer working process.  
The flowchart of the shearer cutting unit tracking method is shown in Figure 7. Firstly, the initial location of the shearer cutting unit in the \(t-1\) frame image is obtained by morphology, and then the STC is utilized to automatically track the cutting unit in the \(t\) frame image. The new morphological positioning is carried out again when the maximum likelihood estimation value of the confidence map is lower than the threshold. The new location of the cutting unit is determined again, and re-tracking can be achieved.
## 4 Discussion
[24] examined the effects of a double-J stent on an unobstructed ureter and found that the practical in vitro flow capabilities of stents did not seem to theoretically apply in vivo due to increased urine flow around the stent secondary to dilatation of the ureteral lumen in response to the stent and that the stenting could limit urinary drainage. This result means that the double-J stent itself could be one of the obstacles causing insufficient urinary drainage, even though the stent is widely used for relieving disturbances in urinary drainage due to a ureteral stenosis. Here, we performed the simulation with solid ureter and solid stent models differently from that of the human body. This study also has some limitations. First, we did not consider peristalsis or passive ureteral dilatation in the CFD analysis and thus did not perform the fluid structure interaction (FSI) simulation. Second, the tubular ureter model used in the study had a discrepancy with an uneven or undulated ureter in humans. Third, the results could be different if there had been more than a single site of stenosis. Further studies using the FSI simulation or an undilated ureter model based on the human anatomy could elaborate on the current study. Moreover, in vitro or in vivo studies could help to verify the findings of this study.
# Using Improved Brainstorm Optimization Algorithm for Hardware/Software Partitioning
## 1 Introduction
proposed a simple grouping method to accelerate the algorithm [26], [PERSON] et al. proposed the modified step-size to improve the search ability of BSO [27], [PERSON] and [PERSON] used the chaotic operation to improve the individual generation strategy [28], [PERSON] et al. utilised the incorporation of inter-and intra-cluster discussions to balance the global and local searching [29]. There are also some hybrid algorithms. For example, [PERSON] et al. hybridized BSO with simulated annealing algorithm [30], [PERSON] et al. hybridized BSO with the teaching-learning-based algorithm [31]. Because BSO performed well in many applications, it is applied to solve the hardware/software problem in this paper. In most of previous researches, BSO was usually applied to solve continuous optimization problems, but the hardware/software partitioning problem is a discrete optimization problem. Therefore, when the BSO is utilized to solve hardware/software partitioning problem, it has the shortage of premature convergence which will directly affect the quality of solutions. At the same time, the algorithm efficiency should be further improved. From what has been discussed above, this paper applies the BSO algorithm to hardware/software partitioning and improves its clustering method and updating strategy to generate the improved brainstorm optimization (IBSO) algorithm. Experimental results show that the IBSO algorithm performs well on hardware/ software partitioning. The rest of the paper is organized as follows. In Section 2, the related knowledge of hardware/software partitioning problem is described. Section 3 describes the process of BSO algorithm applied to hardware/software partitioning. In Section 4, IBSO algorithm is proposed. Finally, the empirical results and conclusion are elaborated in Sections 5 and 6 respectively.