dataesr/scientific-paragraphs-categorization huggingface dataset:
Right after the end of the offline training session, their performance is assessed based on how well they approximate the training dataset D pod. Starting from the module that computes the z-coordinate for a {x, y}-pair ensuring a feasible target inside the robot's workspace, the computed error is [3. 5 ± 1. 4] (mm). Moreover, the module that approximates the inverse kinematics, by receiving the {x, y}pair along with the previously computed coordinate on the z-axis as input and outputs the corresponding actuation, results in [535 ± 316] (ticks) error measured for each motor. Taking into account that 32,768 ticks are equivalent to one full periodic rotation of the motor shaft, then the aforementioned error is translated to [2. 5 ± 1. 5] (mm) error in cable length. This corresponds to 5{%} of the robot's operational workspace, because the range of change for each cable is more than 50 mm.
This error is low considering the stochasticity induced by the mechanical structure of the soft robot. From now on, the learned models (ProMP library and I-SSGPR modules) are used as knowledge base by the planner in order to perform some desired tasks. On the one hand, the content of the ProMP library for both the actuation and the taskspace is fixed. Meanwhile, the I-SSGPR modules are constantly updated as new data are obtained in the course of the robot's execution. The experiments conducted on the real robot aim mainly at assessing both qualitatively and quantitatively the performance of the proposed extension, and subsequently at demonstrating its enhanced capabilities.
Characteristics of the geophones used in the study The geophones are of type GS11D 4000, with a sensitivity of 89. 2 V/ m/s, a natural frequency of 4. 5 Hz and a closing resistance of 47 kΩ. In the pavement, under the passage of each vehicle, the geophones deliver a voltage signal which is proportional to the vertical displacement velocity. In order to get the pavement vertical displacement (deflection), the signal needs to be integrated. However, previous studies have shown that a simple integration does not give realistic displacements. Fig. 2  (left) shows a comparison of the integrated geophone signal (with a simple integration), with the theoretical deflection signal under a 5-axle semi-trailer vehicle, calculated with the French pavement design software ALIZE-LCPC  [15]  and obtained with a reference laser sensor.
Geophones G1 and G2 were placed 1 m apart in the longitudinal direction, in order to determine vehicle speeds. The geophones were put in place during the pavement rehabilitation works. Geophones are placed after the construction of each layer. For this purpose, holes (slightly larger in diameter than the sensor) are made in the pavement. The geophone is placed in the hole and then covered with resin. A thin trench is then made to pass the cables and sealed by the same resin. The temperature probes are installed in the same way. All the sensors were connected to a remote data acquisition system located on the road side by cables, embedded in small trenches, cut in the pavement. The platform used for remote data acquisition on the Ax motorway, called PEGASE, was developed by Cam et al. [23, 24].
The PEGASE system is a modular wireless solution used for instrumentation purposes. It includes DSP (digital signal processing) and input output ports, a 32-bit low power processor optimized to route to RAM and FLASH memories; it also has a WIFI PCMCIA module for wireless communication. The PEGASE mother board is integrated with the Blackfin processor, a wireless communication module and a GPS module. The PEGASE card is Linux kernel based and configured with communication protocols, specified as the IP (internet protocol) and the global protocol; this allows the Wi-Fi network or 3 G/4 G connection to have remote sessions. The PEGASE platform can be remotely managed to program new data acquisition and processing algorithms, or modify existing ones. The PEGASE system is very compact, modular, and consumes little energy.
This allows to limit the number of recordings, by recording only deflections under heavy vehicles with a sufficiently high load, and not under passenger cars. The threshold of 5 mm/100 led to record approximately 100 to 160 heavy vehicle passages per day. On the other hand, the temperature measurements were recorded continuously (without any trigger), at a rate of one measurement every 15 min. The system started working in 2014 and after some preliminary tests in March, continuous measurements were carried out from October 2014 to December 2016. However, the data acquisition was interrupted from January to February 2015 and from January to March 2016 due to failure of some data acquisition boards. Such a remote monitoring system presents several advantages: • Remote control of instrumentation is particularly appropriate in the context of a heavy traffic motorway, due to difficulties of access to the site.
(  1 ): Δ i = f (t i+20 ) -f (t i ) t i+20 -t i (1) Fig. 2. Comparison of the signal obtained with the reference displacement sensor and the signal obtained with geophone GS11D before and after the treatment  [10, 17]. Where Δ i is the increment ratio of deflection versus time, in mm/s. Δ i is calculated by successive intervals of 20 points, to eliminate the effect of noise. Peak detection is based on the sign of increment Δ i : when Δ i changes from negative to positive, a downward peak, which is assumed to correspond to the passage of a wheel, is detected. A procedure based on this principle was developed using the Scilab software, to identify different vehicle configurations. The criterion used to identify each type of vehicle is defined in Table  2. Fig. 4  presents examples of signals corresponding to the different types of vehicles.
The results indicate a good agreement between the model and the measurements; for the load of 40 tons, confirming that the loads of the heaviest vehicles are close to 40 tons. In conclusion, it has been shown that if an initial model of the pavement is available (pavement layer moduli, layer thicknesses), the simulation of the in-situ geophone deflection signals, with the ALIZE software, can be used to estimate successively: • The subgrade modulus, using the deflection under the first axle, for which the load is supposed known, and equal to 6. 5 tons. • The vehicle loads, by adjusting the loads of the 4 other axles in the model, to fit the measured deflections Although the loads obtained with this simple approach seem realistic, additional test are planned, to compare the loads estimated with the geophones with reference measurements made with a weight in motion system, to verify the accuracy of the approach.
After defining a vehicle configuration, and initial axle load values, iterative calculations can be performed with the ALIZE software, by adjusting the axle loads, until a good match is obtained between the measured and calculated deflections. This approach has been illustrated in Section 6. 4, for a T2S3 truck. However, the accuracy of the load estimation still needs to be verified, by comparison with reference measurements made with a weight in motion system. It is planned to perform such comparisons in the near future. (4) Estimation of actual pavement damage and residual pavement life. Pavement design calculations are generally based on simplified assumptions concerning the traffic and climatic conditions. By monitoring continuously pavement layer moduli, pavement temperatures and traffic loads (as explained above), it becomes possible to calculate pavement damage using the real traffic and temperatures experienced by the pavement.
Conclusion and perspectives This article presents a new approach for monitoring deflections, on a test section of the Ax motorway, under real traffic, using geophones. The instrumentation included 4 geophones and 8 temperature probes, located at the base of each pavement layer. These sensors were connected to a data acquisition system based on PEGASE data acquisition boards, associated with a wireless transmission system, which sends the data via 3 G/4 G network to a remote database. The geophones measurements were treated with the data processing technique developed by  [13] , based on laboratory tests, and validated by full scale accelerated pavement tests. Deflection and temperature data recorded from March 2015 to November 2016 was used to analyse pavement performance. The geophones, installed at different lateral position, were used to determine the pavement deflections, but also to obtain additional information on the heavy vehicle traffic, like vehicle types (number of  axles), vehicle speeds and lateral positions.
This information was used to get statistics about the composition of the heavy traffic on the Ax motorway. Temperature data was used to evaluate the influence of temperature on pavement deflections. After correcting the effects of temperature, it was concluded that the pavement deflections presented no significant evolution during the two years of monitoring. Finally, simulation of the geophone signals was carried out. For that purpose, the geophone signals were sorted, and only signals corresponding to T2S3 trucks, and passing in the centre of the wheel path (close to geophone G1) were retained. To evaluate the simulation approach, one "representative" signal for each month was selected. These "representative" signals were compared with calculations with the linear elastic software ALIZE.
2) (ii) Modelling of the deflection signal with the Alize software (a) Definition of an initial pavement structure for the ALIZE calculations, for comparison with the representative measured signals. The initial pavement layer moduli were determined using results of Falling Weight Deflectometer (FWD) tests performed on the experimental section in October 2013. These tests were used mainly to back calculate the moduli of the cement treated layer, the former GB layer and the soil. Standard modulus values, obtained from the ALIZE database, were used for the bituminous layers (VTAC, HMBC and HMAC layer) (see the parameters of Table.
The study takes into account aspects such as data availability, cost, and quality, and uses Google Street View for observation and data collection. • Phase 2: Collect data using Google Street View: Data collection is carried out using the Google Street View tool to observe and record the selected variables. The observation is based on the relevant aspects of pedestrian mobility in a city  (Park, 2008) , such as the presence and condition of sidewalks, and pedestrian crossings facilities. The data is collected for each segment of the road network in the selected city. • Phase 3: Analyze the data: Descriptive statistics are used to analyze the data and create a road inventory that displays the characteristics of existing pedestrian infrastructure. Factor analysis is conducted to determine the interrelationships between variables and facilitate analysis of these patterns.
Cluster analysis is used to identify the groups of roadways generated in a city. This information is used to identify areas of improvement for the pedestrian infrastructure. • Phase 4: Conduct a case study: The methodology is applied to a emerging economy city in Latin America. A sample of different segments of the road network in this city is selected to apply the methodology. The data is collected for each segment and analyzed using the techniques described in Phase 3. • Phase 5: Compare infrastructure data from pre and post-investment: The study aims to compare the infrastructure data from 2014 (preinvestment) and 2022 (post-investment) to determine the impact of investment on urban pedestrian infrastructure. In conclusion, this study presents a comprehensive methodology for evaluating pedestrian infrastructure in urban cities, aiming to identify areas of improvement and address inequality in emerging economies.
Descriptive analysis This research focuses on the analysis of data, specifically the year 2014 and the year 2022, highlighting the significance of these specific time points rather than the data in between. By 2014, a total of 2196 road sections were analyzed, and the majority of the roads were found to be in fair condition. Sidewalks were in better condition than roads, with most being in fair condition due to differences in height, slope, obstructions, deficient width, or discontinuity. Type 1 roads, which have a higher vehicular flow, were found to have a greater number of lanes and separators, while type 2, 3, and 4 roads were expected to have fewer separators. Motorcycle lanes were lacking on more than 95% of type 2, 3, and 4 roads. Pedestrian infrastructure, including sidewalks and pedestrian traffic lights, was found to be lacking in a considerable number of roads, and ramps for easy mobility for people with disabilities were also found to be lacking in most roads.
Moreover, Table  1  shows a p-value of less than 0. 05 for the 2022 Bartlett's test for Sphericity. This suggests that the variables are related and that the factorial model is suitable to implement. Overall, both results suggest that the correlation between variables is high, and that the factorial model is adequate to explain the data, albeit with some differences in the KMO index values between the two years. In addition, a principal components analysis was made using SPSS. Table  2  shows that with four latent variables it is possible to explain more than 58% of the variability explained by the original variables from 2014 and 2022, due to the fact that the eigenvalues of these latent variables are greater than 1. Taking into account the number of components selected in Table  2 , it was possible to group each variables based on their highest load (Table  3 ).
2) A dense and globally optimized environment reconstruction algorithm, which can process millions of frames to calculate a highresolution image mosaic from multiple freely moving camera videos. 3) A unified and highly parallelized tracking framework combining the detection and reconstruction algorithm to generate full frame rate animal locations projected onto the very high-resolution environment reconstruction. 4) Integrated routines and graphical interfaces for user interactions to enable efficient corrections and manual annotations to contextualize the behavioral measurements. 5) The application of this framework to obtain a uniquely highresolution (time and position) dataset describing the entire foraging history of individual desert ants in their complex desertshrub habitat. Evaluations of our framework yield excellent spatial and temporal accuracy.
Data collection Our test scenario is a study of the foraging ontogeny of desert ants (Cataglyphis velox). For video recording, an off-the-shelf camcorder (Panasonic HDC-TM900) was used to capture uncompressed 1920 by 1080 video recordings at 50 frames per second. A custom-made camera rig with a 1. 5-m horizontal arm was used to capture video from directly above the ant (approximately 1 m) without the experimenter disturbing the forager (Fig. 1A ). To simplify the capture process, four standard red laser pointers were secured around the camera pointing downwards to create a visible light pattern on the ground to aid camera/ant alignment (Fig. 1, A  and B ). Given that ants blend in with the background and only cover approximately 30 by 6 pixels in the image (Fig. 1C ), they are barely visible for human observers and can only be identified in zoomed croppings (Fig.
Last, ant locations p i need to be projected onto the image mosaic via T i to embed the trajectories into the environment reconstructions (Fig. 2G ). The approaches are thus combined into a unified unsupervised tracking framework that yields high-accuracy paths and environmental maps without initialization or calibration routines. In contrast to nonvisual tracking methods [such as telemetry (41)], our resulting dataset preserves all visual environment information, allowing post hoc augmentations that both improve the data quality (e. g. , through human-in-the-loop tracker corrections) and allow emergent research questions to be tackled after data collection. In particular, labels can be added to individual frames (e. g. , to note behavioral changes such as acquisition of food) or specific locations tagged in the background image mosaics (e.
Tracking and reconstruction performance evaluation Our object detection and tracking algorithm is appearance agnostic and can be used to detect all kinds of animals and artificial objects in a variety of different environments and lighting conditions  (39). Here, we use this localization pipeline to successfully recover the position of ants in all 1. 8 million images of our ontogeny dataset. Our reconstruction pipeline registers background scenes to create high-resolution top-down image mosaics of the ant habitat on which the complete foraging paths of multiple ants can be plotted (Fig. 3A  and fig. S6 ). Previous evaluation has shown that our registration mechanism can achieve millimeter spatial accuracy and a median angular error of 3°  (40). The resultant data are particularly unique: The high-temporal resolution (50 Hz) allows observation of fine-scale behavior across the animals complete foraging path in their natural habitat (Fig.
6 cm. Integrating just 1% of these manual annotations into the globally optimized probabilistic inference task further improves the accuracy to 0. 31 cm median accuracy. In contrast, the results using linear interpolation needs more points to achieve similar median accuracy. We note that this is the accuracy of the true ant position rather than an estimate of position of the tracking device (e. g. , GPS unit) as a proxy for the animal position and that the behavioral statistics do not change substantially if manually specified corrections are included to the probabilistic inference task (cf. fg. S5). Performance is particularly robust for homing ants that travel quickly over open terrain while carrying a clearly visible food morsel. These results are in line with previous evaluations of the detection mechanisms in which a localization accuracy above 96% was achieved on the publicly available Small Targets within Natural Scenes dataset  (39).
" This lack of recent experience of the nest surrounds (all ants that had foraged in the previous 2 days were excluded) did not affect their homing ability either on their first homeward trip (Fig. 3C  and "Homing performance of ants without exploratory paths" section in Supplementary Text) or in their route displacement test for which they ranked second, third, and fifth of the 14. Nine of the ants made at least one initial exploratory path (returning without food) in the nest vicinity (explorations: mean = 2. 2, SD = 1. 9; see "The ant ontogeny dataset" section in Supplementary Text). However, unlike the usual characterization of learning walks  (33, 36, (44) (45) (46) , we saw no consistent growth in the duration (mean/SD, first: 27/23 s and last: 36/53 s) or distance covered (mean/SD, first: 0.
High spatiotemporal data yields mechanistic insights Our dataset provides the spatiotemporal resolution (50Hz positioning at centimeter accuracy) necessary for revealing the mechanisms that underpin guidance behavior in insects. For example, a host of competing hypotheses have been proposed regarding the encoding, storage location, and use of visual route memories in insects  (51, 53, 54). One way to tease apart these models is to reveal the precise circumstances under which navigating insects recognize their location (e. g. , the change in distance and orientation at which a view is recognized is likely to be specific to an individual model). Our data demonstrates local bursts of forward speed when the ant trajectory becomes aligned in a "familiar" direction (Fig. 6 , A and B), that is, along a route it has previously taken toward the nest.
It combines algorithms for small object detection  (39)  and full frame rate-optimized reconstruction (40) into a novel and fully automatic tracking framework, which is applicable to extremely challenging datasets. As a result, animal locations and habitat reconstructions from potentially multiple videos are incorporated in a global reference frame, enabling a wide range of behavioral quantifications and studies. We show its application to data from freely foraging ants in a complex desertshrub habitat, consisting of multiple videos from handheld cameras with more than 1. 8 million frames and a total of more than 2 km of insect tracks. The tracking module achieved centimeter-level precision without any manual correction, with tracks embedded in a shared reconstructed image mosaic. Note that this processing pipeline is designed to provide high-precision trajectories of individual animals followed by the camera.
BESS Charging/Discharging Behavior. In this section, we compare the response time of the studied BEESs with identical dimensions. Tese systems have a capacity of 48 Ah and 12 V for batteries and 500 Farad and 48 V for the supercapacitor and are subjected to a variable continuous current. Te charge/discharge response time of each type of BEES is simulated using MATLAB/Simulink software. From Figures  8  and 9 , it is shown that Li-ion batteries and SCs have the fastest charging time compared to other BEESs. For instance, at t � 90 s, the SOC of Li-ion batteries is approximately 12%, and they have a steeper recharge slope than the other BEESs. Te closest candidate is lead-acid batteries, with an SOC of approximately 10% under the same conditions and period. However, Ni-Mh and Ni-Cd have an SOC of less than 10% at this instant.
g. , R_int_nominal � 0. 01 ohms). Let R_int_fault be the increased internal resistance due to the fault (e. g. , R_int_fault � 0. 02 ohms). Let t be the time in seconds. Te voltage across the Ni-Mh battery with increased internal resistance can be expressed as V internal resistance nimh � V nominal -R int fault × 0. 1 × t. ( 29 ) Te developed MATLAB code provides a comprehensive battery modeling framework for simulating the behavior of diferent battery types, including Li-ion, lead-acid, supercapacitor, Ni-Cd, and Ni-Mh. It allows for the characterization of normal battery operation as well as the introduction of fault models, such as capacity fade and increased internal resistance. By specifying the battery model parameters and utilizing voltage equations, the code enables the analysis of battery voltage variations over time.
Battery Simulator Test. Te process of simulating battery behavior stands as a cornerstone in modern energy research and development. Within our comprehensive battery simulator test platform, we ofer a seamless solution for replicating the performance of a wide array of battery cells housed in our extensive database. By a simple selection from the dropdown menu, researchers gain access to an array of prestored cells, each ripe for simulation, as exemplifed in Figure  18. In a practical demonstration, we harnessed the power of controlled pulses, generating 50 ampere-hours (Ah) for charging and an equal and opposite -50 Ah for discharging, thus emulating the dynamic charging and discharging phases of an NCA lithium cell. Tis section ventures into the heart of battery simulator testing, exploring how it empowers researchers to scrutinize and fne-tune battery performance under diverse conditions.
While our prior research examined methods for large scale single-mediator hypotheses  31  , there is no such work for methods that can incorporate many potential mediators at once. Our study addresses this question first with an extensive simulation study, directly comparing the performance of sevesn different methods for mediation with high-dimensional mediators across a spectrum of settings. Along with metrics related to identification of key mediators and estimation of mediation effect, we include a computation time comparison to evaluate the scalability of the methods to large datasets. Next, to assess the utility of these methods on real data, we apply the same seven methods-plus two additional methods adapted from them-on the data from MESA to evaluate the mediating role of DNAm in the association between low education level and HbA1c.