esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Data Collection and Analysis
### Recording Vocalization and Observation  
The dog's vocalization recordings were made by positioning the dog in the house and exposing it to the different scenarios. A H4N handy recorder was used for recording the sounds of the dogs. For the scenarios of dog sounds during play, walking, and stranger running, recordings were made in an open environment.  
A total of 6171 bark sounds were recorded from 16 different, individual dogs, from the breeds of Rajapalayam and Kombai Hound. Each of the barks contained a continuous 3-4 barks, from which individual barks were manually separated and extracted. A total of 5200 barking sounds were taken for analysis, which includes 325 barking sounds from each dog based on age range and species using a systematic sampling procedure. Sounds were recorded from young dogs of age 1-3 years and older dogs of 6-10 years. The Dog's behavior and its barking sounds in different situations, which included playing, walking, stranger's presence, and owner presence, were recorded.  
There were significant variations in the dog sounds when an unknown person approached the dogs. The dogs were aggressive when the unknown person approaching distance was less than 3 m, and they were comparatively less aggressive when the distance increased. The recordings were made manually and in real-time using a microphone connected to H4N handy recorder. The microphone was positioned at a distance of 3-5 m while recording the dog sounds. Recordings were collected by placing the dog inside the house, making the dog roam in the grounds, and placing it alone. Table 1 consists of dog sounds recorded in different contexts.
## 4 Field Scale Test
### Experimental Monitoring Plan  
Nine monitoring holes are set up in the anchorage and surrounding rock during the experiment, and the detailed locations of each monitoring hole are shown in Figures 8 and 9. The inclinometer hole employs a high-precision digital inclinometer (Shaanxi Right M&C Technology Co., Ltd., Xi'an, China) with an accuracy of \(\pm\)0.02 mm/m, the sliding micrometer utilizes an SMP-100 model sliding micrometer with \(\pm\)0.003 mm precision, and the multi-point borehole extensometers adopt vibrating wire-type displacement sensors (Durham Geo Slope Indicator, Seattle, WA, USA) with \(\pm\)0.1% FS accuracy.  
C1 and C2 inclinometer holes are vertically arranged in the surrounding rock between the double anchors, with C1 located on the front anchor surface and C2 located on the rear anchor surface. Multiple displacement monitoring holes, numbered D1, D2, and D3, are arranged parallel to the axis direction of the anchor and double anchor centerlines. On the upper surrounding rock of the double anchor and on both sides of the double anchor, parallel to the anchor axis direction, sliding micrometer monitoring holes are arranged with a spacing of 0.5 m, and the monitoring hole numbers are H1, H2, H3, and H4, respectively.  
Figure 7: Scaled model. (**a**) Plane view; (**b**) sectional view (unit: cm).
# Co-Seismic Magnetic Field Perturbations Detected by Swarm Three-Satellite Constellation
## 3 Results of the Analysis
For the earthquake that occurred on 2014-02-18, only the tracks of Bravo and Charlie (Figures 3 and 4) are provided as Swarm Alpha was not in nominal conditions during the passage over the epicentral area. Most of the data present a discrepancy between ASM and VFM measurements (Flag_F = 16), and the attitude information was interpolated with the nearest points for lack of data from two or all three star-cameras. These conditions were probably due to the early stage of the mission that we analyzed. An earthquake happened offshore Chile on 2015-11-11 at a depth of 10 km between the Nazca and South-American plates. In this case, a perturbation was visible just north of the epicentre and we cannot exclude that this could be an electromagnetic phenomenon coming directly from the fault. There was a certain symmetry of the signal with respect to the epicentre latitude. The ionospheric disturbance was possibly associated with the high magnitude earthquake (Mw 6.9) considered in this case (Figure 6).
# Trajectory Planning of a Semi-Trailer Train Based on Constrained Iterative LQR
## 2 Constructing Trajectory Planning Problems  
Vehicles are essentially dynamic systems, and establishing vehicle kinematic equations can directly and accurately describe the motion characteristics of vehicles. The trajectory planning problem of a self-driving semi-trailer train is defined as an optimal control problem. Planning the optimal trajectory translates into solving for a series of optimal values. The optimal control problem for solving the trajectory can be expressed as [28]:  
\[\begin{split}& Minimize=J(X(t),U(t))\\ & s.t.x_{t+1}=f(x_{t},u_{t})\\ & h_{box}(u(t))\leq 0,\\ & h_{collision}(x(t))\leq 0,\\ & x(0)=x_{init},\;u(0)=u_{init},\\ & g(x(t_{f}),\;u(t_{f}))\leq 0.\end{split} \tag{1}\]  
where \(X(t)=[x(t),y(t),v(t),\theta_{1}(t),\theta_{2}(t)]^{T}\) contains the state profiles. \(x(t)\) and \(y(t)\) are the coordinates of the location of the vehicle reference point in the geodetic coordinate system; \(v(t)\) is the velocity at the reference point of the vehicle; \(\theta_{1}(t)\) and \(\theta_{2}(t)\) are the yaw angles of the tractor and semi-trailer, respectively. \(U(t)=[jerk(t),\omega(t)]^{T}\) contains the control profiles. \(jerk(t)\) is the derivative of the vehicle acceleration, and \(\omega(t)\) is the angular velocity of the front wheel turning angle of the tractor. \(x_{t+1}=f(x_{t},u_{t})\) represents the vehicle kinematics equations. \(h_{box}(\cdot)\leq 0\;\) is the inequality constraint that needs to be satisfied when the vehicle is moving. \(h_{collision}(\cdot)\leq 0\) is the obstacle avoidance constraint that must be satisfied when the vehicle moves. \(x(0)=x_{init}\;\) represents the initial conditions of the state profiles, and \(u(0)=u_{init}\) represents the initial conditions of the control profiles. \(g(x(t_{f}),u(t_{f}))\leq 0\) represents the implicit boundary condition when \(t=t_{f}\).
## 3 System Model
### Initial CH Selection  
The major objective functions used for the selection process of the CH and CM are velocity, link capacity, latency, and residual energy of the node. The expression for the calculation of  
\[RE_{CH-CM}^{energy}=\frac{\sum_{i=1}^{n}OF_{CH}^{energy}(rem)-OF_{CH}^{energy }(initial)}{\sum_{i=1}^{n}OF_{CM}^{energy}(rem)-OF_{CM}^{energy}(initial)} \tag{1}\]  
where, \(OF_{CH}^{energy}(rem)\) represents residual energy of the CH, \(OF_{CH}^{energy}(initial)\) represents initial energy of CH, \(OF_{CM}^{energy}(rem)\) represents remaining energy of the CM, \(OF_{CM}^{energy}(initial)\) represents initial energy of CM.  
\[OF_{CH}^{energy}(rem)-OF_{CH}^{energy}(initial)=\sum_{i=1}^{N}RE\big{(}CH_{j} \big{)}-\sum_{i=1}^{N}IE\big{(}CH_{j}\big{)} \tag{2}\]  
\[OF_{CM}^{energy}(rem)-OF_{CM}^{energy}(initial)=\sum_{i=1}^{N}RE\big{(}CM_{j} \big{)}-\sum_{i=1}^{N}IE\big{(}CM_{j}\big{)} \tag{3}\]  
where, N is the number of nodes inside the cluster, \(RE\big{(}CH_{j}\big{)}\) represents the remaining energy of the CH inside the cluster, \(IE\big{(}CH_{j}\big{)}\) represents
## 7 Conclusions and Future Work  
In this work, a set of classification systems of persons at risk of suffering from overweight/obesity have been developed. Four different FS strategies have been employed for the two experiment datasets, one FS from the literature, two evolutionary FS methods, and no FS. For this work, ten machine-learning models have been employed. For the application of the feature-selection methods, we chose three of these 10 methods, based on reasons of performance and possible application to medical clinical practice. Thus, the FS methods were successfully applied in Gradient Boosting, XGBoost, and Decision Tree algorithms. The most important conclusions of this work are:  
* Although not a surprising finding, we have found GA to be a very competent tool to perform feature selection and thus improve the training of classification models.
* The _Stud_ selection, which uses an elitist set that is always part of the crossover process, achieves promising results.
* If we look at the fitness of the best individual in each of the final populations, they all maintain a similar standard deviation, perhaps indicating that we might expect to obtain similar results in the future with new data sets.
* About the variables related to pathologies, it is necessary to identify what is the cause and what is the effect. A good example would be sleep apnea. On several occasions, models have been based on apnea when classifying, but this disorder is caused by overweight or obesity in many cases. A person may have apnea due to being overweight or obese but will not necessarily be overweight or obese due to suffering from apnea.
* Finally, significant differences were found among the algorithms, with Gradient Boosting with feature selection being the one obtaining the best results.  
The models developed in this work will be the basis of a recommendation system. This system will be able to warn people about behavioral tendencies that will end up producing overweight and obesity and recommend healthy habits to replace them.
# Heuristic Techniques for the Design of Steel-Concrete Composite Pedestrian Bridges
## 5 Conclusions  
Three heuristic algorithms were applied to a steel-concrete composite pedestrian bridge to find an efficient design. DLS, SA, and GSO are used to automatically find optimum solutions. All procedures are useful in the automated design of steel-concrete composite pedestrian bridges. Furthermore, a parametric study was carried out. The conclusions are as follows:  
* The GSO optimization algorithm obtained worse results than DLS and SA, but if we apply the DLS to the best GSO solutions, then this combination of heuristic techniques reaches the lowest cost solution;
* The results show the potential of the application of heuristic techniques to reach automatic designs of composite pedestrian bridges. The reduction in costs exceeds 20%. It is important to note that the current approach eliminates the need for experience-based design rules;
* The CO\({}_{2}\) emission comparison showed that the reduction between the original structure and the GSO with DLS combination optimized structure reduced the CO\({}_{2}\) emission by 21.21%. Thus, an improvement of 1 \(\upmu\)m\({}^{2}\) produced a reduction of 1.74 kg CO\({}_{2}\)/m\({}^{2}\). Therefore, the solutions that are acceptable in terms of CO\({}_{2}\) emissions are also viable in terms of cost, and vice versa;
* The results indicate that cost optimization is a good approach to environmentally friendly design, as long as cost and CO\({}_{2}\) emission criteria reduce material consumption;
* The parametric study showed that there is a good correlation between span length and cost, amount of material, and geometry. This relationship could be useful for designers, to have a guide to the day-to-day design of steel-concrete composite pedestrian bridges. However, the tendencies of the thickness of the flanges and webs of the steel beam are not clear;
* The heuristic techniques look for lower amounts of materials, which allows the reduction of the self-weight of the structure. In addition, the optimization algorithms look for an increase of the depth of the section to improve their mechanical characteristics. For an optimized pedestrian bridge, the relationship between the steel beam depth and span length takes a value of 1/27.  
**Author Contributions:** This paper represents a result of teamwork. The authors jointly designed the research. [PERSON] drafted the manuscript. [PERSON], [PERSON], [PERSON], and [PERSON] edited and improved the manuscript until all authors are satisfied with the final version.  
**Acknowledgments:** The authors acknowledge the financial support of the Spanish Ministry of Economy and Business, along with FEDER funding (DIMALIFE Project: BIA2017-85098-R).  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 1 Introduction  
In modern infrastructures, structural health monitoring is essential to assess and diagnose the current situation before retrofit and repair commences. The common building material used is reinforced concrete. Reinforced concrete can be assessed on the basis of factors such as time, cost, and idle period. For practical reasons, researchers focus on developing non-destructive tests for the fast and economical assessment of a structure with better sensitivity to internal damage against load.  
Reinforced concrete comprises two main materials that, when combined together, perform well against aging and mechanical damage. The first material is concrete, which is very good in compression and consists of water, cement, sand, aggregates, and sometimes admixtures. This material is very complex in itself because different mixtures, curing methods, sizes of aggregates, and methods of placements can cause inconsistent voids, segregation of aggregates, variable interfacial transition zones, and varying compressive strengths. The sensitivity to internal damage using a nondestructive test such as acoustic emission was explored in plain concrete materials [1; 2]. Sensitivity to damage of a nondestructive test parameter is present when the parameter's values are widely spread during low to high application of load. On the other hand, an insensitive parameter is characterized by an abrupt change, showing sudden failure of the materials. This situation is not ideal for structural health monitoring since there is no sign before failure occurs. The second material in reinforced concrete is the addition of reinforced steel bars to increase the tensile capacity of the structure, especially when bending occurs. There are still limited studies on the sensitivity of nondestructive tests in reinforced concrete structures [3; 4].  
In this paper, an ultrasonic test was done on beams with varying reinforcements. The analysis of the ultrasonic test results against load is very complicated since there is a nonlinear relationship of parameters that influence the damage resulting from the applied load. In addition, the neutral axis was recorded using strain gauges in the midpoint of both faces at the top and bottom. Furthermore, feed-forward backpropagation artificial neural network (ANN) was used as a tool to establish a model using available datasets for the prediction of the damage represented by the load applied. These ANN models were used to simulate and investigate the sensitivity of each parameter, classified as short- and long-range sensitivity. A similar study was done in plain concrete cubes, and it was found that the second harmonic amplitude and strain greatly influences the stress level of plain concrete cubes [5]. Limited research explored the ultrasonic test, and especially the nonlinear ultrasonic test, applied to reinforced concrete beams. This paper illustrates the ability of ANN to obtain a good simulation in order to define a guide to select ultrasonic parameters based on sensitivity, useful when assessing damage for concrete with low or high water/cement ratio (WC) and varying reinforcement content.
# The Influence of Track Irregularity in Front of the Turnout on the Dynamic Performance of Vehicles
## 4 Result Analysis
### Influence of Different Irregularities
#### 4.2.1 Analysis of the Maximum in Dynamic Indicators  
The effects of different irregularities on the maximum value of dynamic indicators at different wavelengths for 100 groups of random parameter samples are shown in Figure 11. The error bar represents the standard error of the mean maximum value. As a comparison, the black bars in Figure 11 represent the extreme values of the dynamic responses without any irregularities in front of turnout. The AL, LL, and CL in front of the turnout have different degrees of influence on the vehicle dynamics compared to the results without irregularity. The AL mainly affects the wheelset lateral force and lateral acceleration of the vehicle body; the LL has a significant effect on the wheel-rail vertical force, wheel unloading rate, and vertical acceleration of the vehicle body, and the CL has the greatest influence on the wheel unloading rate and lateral acceleration of the vehicle body.  
Figure 11: _Cont._  
The change in maximum values of dynamic indicators varies for different wavelengths of irregularities. The LL and CL at wavelengths of 3 m and 5 m cause significant increases in wheel-rail vertical forces and wheel unloading rate. The vertical force at 3 m wavelength of the LL is close to the limit of 170 kN, and the wheel unloading rate at 3 m wavelength of the LL and CL is nearly 1.0, which is much higher than the limit of 0.8. The AL at wavelengths of 10 m and above has a significant effect on wheelset lateral force. The LL in each wavelength band has a significant effect on the vertical acceleration of the vehicle body, among which the wavelength of 3 m has the greatest influence. The AL and CL in the whole wavelength section have a significant effect on the lateral acceleration of the vehicle body. The lateral acceleration under the AL is close to or greater than the limit of 0.6 m/s\({}^{2}\), with the maximum value occurring at 40 m wavelength; the maximum value of the lateral acceleration under the CL occurs at the wavelength of 3 m.
## 4 A Brief Discussion of the Literature Review Assessment
### Genetic Programming  
GP methods have also been recognised recently as one of the more advanced tools for GWL predictive modelling. With its high flexibility and accuracy, this method enables the modelling of complex and nonlinear relationships in groundwater data. Research shows that GP and GEP are powerful tools. Combining these with optimisation methods and uncertainty analysis can enhance the accuracy and reliability of predictions. Some key findings that highlight the strengths and challenges of using GP in GWL modelling are:  
* GEP and precise modelling--[4] has shown that the GEP, with its three-gene structure, is highly effective for GWL modelling. This method can accurately capture the inherent variations and complexities in the groundwater data using a time series dataset, providing precise and reliable predictions;
* Importance of weather station locations--[2] demonstrate that the precise placement of weather stations significantly impacts the accuracy of GP predictions. This finding is significant for optimising weather station networks;
* Comparison of GP with other methods--various studies, such as the research by [70], have shown that GPR outperforms GP in predicting groundwater salinity. Ref. [72] also revealed that GP performs better than the ANFIS in monthly GWL predictive modelling;
* Combining GP with optimisation and simulation methods--[73] highlight the high efficiency of combining GP with Continuous Ant Colony Optimisation and Monte Carlo Simulation;
* Uncertainty analysis in GP methods--[71] emphasise the importance of integrating uncertainty analysis into GP-based models. This integration improves the reliability and robustness of the models;* Application of GP in identifying pollution sources--[68] show that the GP-based method for Bayesian experimental design is highly effective in identifying groundwater contaminant sources. This method, using MCMC, enhances the accuracy and efficiency of environmental monitoring efforts;
* Seasonal models--[74] indicates that linear regression approaches are suitable for predicting summer GWL, whereas ANNs are more effective for winter predictions.
# Spatiotemporal Heterogeneous Effects of Built Environment and Taxi Demand on Ride-Hailing Ridership
## 4 Results  
### Model Comparison  
Table 4 presents the model fitness of the OLS, GWR, and MGWR models. Three indexes, namely the residual sum of squares (RSS), Akaike information criteria (AICc), and adjusted R\({}^{2}\), are used to evaluate the performance of these models. The results confirm that the OLS model shows a lower adjusted R\({}^{2}\) than the two local regression models.  
Additionally, the OLS model yields higher RSS and AICc values. Hence, the global OLS model demonstrates inferior performance in explaining the link between ride-hailing ridership and its influencing factors. Moreover, owing to the capability to handle multiscale bandwidths, MGWR is determined to be preferable to GWR in estimating the links between ride-hailing ridership and its influencing factors at different locations.  
In addition, we further calculate the optimal bandwidths for factors in both GWR and MGWR, presented in Table 5. The results suggest that the bandwidths of most exogenous variables differ from each other, suggesting that employing universal bandwidth in GWR may lead to unreliable estimations. In general, spatial heterogeneity can be assessed by examining the scales that influence exogenous variables. Smaller bandwidth values indicate less spatial heterogeneity, with the variable's bandwidth value approaching the number of grids. The MGWR results reveal that each exogenous variable requires different optimal bandwidths to effectively capture the influencing scales of the spatial process. In contrast, GWR employs universal bandwidths for all three periods (morning peak, evening peak, and off-peak), with values of 110 (7.1%), 121 (7.8%), and 128 (8.2%), respectively. However, MGWR demonstrates various optimal bandwidths for the exogenous variables. For instance, the bandwidths for taxi ridership, metro station density, distance to CBD, road density, and bike lane density range between 43 (2.8%) and 791 (50.9%), indicating significant spatial heterogeneity in the relationships between these variables and ride-hailing ridership. On the other hand, the bandwidths for bus stop density, distance to the nearest bus stop, and distance to the nearest metro station range between 1324 (85.14%) and 1554 (99.99%), suggesting that the relationships between these variables and ride-hailing ridership do not vary significantly across grids. Thus, these variables should be treated as global variables. Furthermore, the bandwidths for population density and land use mix differ across the three periods. The results reveal that population density is a global variable during the two peaks and a local variable during the off-peak period. Conversely, land use mix is considered a global variable during the off-peak period and a local variable during the two peaks. These findings obtained from MGWR are discussed in greater detail in later sections of this paper.
## 3 Results and Discussion
### Two factors central composite design
#### 3.1.4 Other Factors: SPME Fiber, Stirring, and Salting Out  
Coatings of SPME fiber were selected among PDMS (100 \(\upmu\)m film thickness), carboxen/PDMS (75 \(\upmu\)m film thickness) and PDMS/DVB (65 \(\upmu\)m film thickness). The PDMS/DVB fiber was chosen because better recoveries of TCE were achieved (Figure 5A). Responses for TCE in non-stirred samples was about 50% of those obtained in stirred samples (Figure 5B), so stirring was used. Increasing the ionic strength by adding 3 g NaCl or Na\({}_{2}\)SO\({}_{4}\) did not influence the efficiency of the extraction (Figure 5C), therefore the addition of salt was not considered in the experiments.  
Figure 4: Effect of dispersive solvents (**A**), volume of acetonitrile (**B**) and extraction time (**C**) on LLME–SPME extraction efficiency (_n_ of 3). Hex: hexane; ACN: acetonitrile; MeOH: methanol. Note that in Figure 4B, volume of acetonitrile refers to additional volume of acetonitrile as dispersive solvents added to the solution, and the acetonitrile in TCE standard solution was not counted.