dataesr/scientific-paragraphs-categorization huggingface dataset:
The program can be remotely sent by the PLC via Modbus and stored in internal flash memory or controlled from the HMI for testing purposes by modifying sections of a Modbus stack. A real-time operating system (FreeRTOS) running on the ARM processor caters for the timely execution of the various components in the application (which is shown in Figure  8 ) and fosters reuse and maintainability of the developed code, which can be easily migrated to other platforms as well. We harness as well the capabilities of the Zynq heterogeneous platform for maintaining back compatibility with previous solutions by supporting legacy buses such as I2C, SPI, and RS-232, but the main advantages are the increased I/O capabilities and customization afforded by using the programmable section of the device. As can be seen in the left side of Figure  7 , the programmable logic of the heterogeneous platform has been used to move some of the functionalities that were missing in the BoxPC implementation, while overcoming the limitations of more constrained embedded devices (i.
Hardware/Software Architecture: RTOS and Custom IPs. In this section, we will briefly discuss how the real-time embedded application has been conceived and implemented in the ARM processor integrated in the heterogeneous all programmable Zynq platform. As described before, such platforms foster a processor-first approach, in which the ARM boots first, performing subsequently duties such as system initialization and configuration. Afterwards, the processor retrieves the configuration data for bootstrapping the programmable configurable logic, effectively fostering failsafe strategies and avoiding some of the pitfalls of reconfigurable devices in control systems. Once the device and peripherals are up and running, the ARM processor takes a more managerial role, federating the proper execution of the overall application, catered in this case by a real-time operating system (RTOS).
We have chosen FreeRTOS for a number of reasons: the code is open source and widely used and documented. Furthermore, the RTOS supports a wide range of microcontrollers and MPUs and has been especially designed for medium range devices, albeit consuming a very small memory footprint. The task diagram for the application as implemented using FreeRTOS is shown in Figure  8. The RTOS performs the following actions for a given deposition program. Task 1. The application can start by receiving a write request from the TCP/IP server (main PLC), in which case a task called Load Action Data retrieves the program data for the actuators, stored temporally into a Modbus table, using a tailored lightweight TCP/IP stack running on the ARM processor. Task 2. The deposition program can then move to nonvolatile memory (i.
e. , SD or flash) through an Actions Retrieval task. Once the program has been stored, the system waits for an initialization signal from the main PLC to start execution. The system can operate in two modes: in normal and in force/debug modes. The latter case is used for writing commands directly to a Modbus table, interacting directly with an external Human Machine Interface that enables testing arbitrary patterns monitoring any problems with the system. In the former case, the RTOS allocates portions of the program onto SDRAM memory using DMA for faster processing and uses the position and speed information from the system to execute these commands generated by CADFiber. Task 3. In normal operating mode, once the program execution is triggered by the main PLC, the commands are retrieved from the SDRAM memory (running the Actions Processing) software task in the ARM processor, which basically places the initial memory address for a given command and amount of actions to be performed.
This information is then sent as a burst to custom hardware accelerator, labeled Command Accelerator Module (CAM) in Figure  8. The CAM module is a hardware accelerated function wrapped using an AXI Intellectual Property Interface module, as depicted in Figure  9(a). This module resides in the programmable logic section of the device and has been implemented in such a manner that the number of actuators can be automatically selected by the application, enabling seamlessly modifying the external interface without undergoing any hardware modifications in the rest of the system. The module was implemented in VHDL using Xilinx ISE, functionally verified and subsequently wrapped with the AXI IPIF using the IP creation infrastructure provided by Vivado. This CAM module permits as well the interaction between the Actions Processing task at the RTOS level and the controlled plant via the HW/SW interface that translates the logical actions in the control program into electrical signals that drive the external electropneumatic controller that is in charge of actually triggering the pneumatic actuators in the robot's head, as depicted on the left side of Figure  7.
The module acts basically as a processor-configurable demultiplexer: depending on the head configuration, the module can control 16, 32, and 64 fibers, and, as such, the CAM controls how the data bursts are mapped from memory to the outputs, using a configuration register, a set of multiplexers, and a state machine that controls the shown datapath. Nonetheless, the capabilities of the Zynq platform foster backward compatibility through the use of QUAD SPI IPs that can be used with previous versions of the electropneumatic control system. This previous version, based on a microcontroller, used an SPI port and I/O expander to control up to 16 actuators; in the Zynq, several of these ports can be mapped in the PL section and control various sets of fiber bundles, but there is an associated timing penalty, as will be discussed in the next section.
Task 4. In order to trigger the actions at the correct time stamps, the processor subsystem has been extended with a Speed and Position Calculation Module (SPCM) (shown in Figure  9 (b)). This hardware accelerator, also implemented in the PL section of the Zynq, is responsible for calculating the position and speed of the robot using the signals from the quadrature encoder in tandem with the circuitry integrated in the AXI IP shown in the figure. As with the CAM module, the circuitry was described and functionally validated using Xilinx ISE and then exported to Vivado for creating a customized AXI-based hardware accelerator, which can be accessed by the processor via the HW/SW interface provided by the RTOS. In particular, the RTOS retrieves the next position at which a command is to be executed next and stores it into an internal register of the SPCM IP.
This value is compared with the position calculated by the SPCM and, depending upon the current speed, it can anticipate when the next command should be sent to the CAM IP, which is signaled to the RTOS via a message queue, triggered by an interrupt from the IP and captured by the Action Processing task, as depicted in Figure  8. On the other hand, the Action Processing task notifies the Actions Retrieval task whether it requires more data, so commands are always available for streaming. The processor can then buffer the data corresponding to the next section of the program onto the CAM internal FIFO, effectively eliminating unnecessary waiting times and thus increasing the attainable deposition throughput. It must be noted that these tasks are run cyclically by the FreeRTOS scheduler, which is generated automatically during the compilation of the operation, making the application easier to maintain and scale.
Implementation Results in the Zynq-7000 Platform. As described in the previous section, the CAM and SPCM HDL descriptions have been integrated as AXI-based hardware accelerators so they can be integrated into the proposed SoC platform, using Vivado IP integrator. Furthermore, the IPIF interface enables the application engineer to interact with the underlying hardware modules via the HW/SW interfaces via a simplified API and to exploit their functionalities by encapsulating them as FreeRTOS tasks. The SoC platform introduced in Section 5. 2 (and depicted in Figure  7 ) was thus created using Vivado, targeting the ZedBoard (which integrates the Zynq-7010 EPP, with a bus clock of 100 MHz), as depicted in Figure  10. The synthesis results showing the resource utilization for each of the modules are summarized in Table  1.
The table compares the resource utilization of the CAM and SPCM modules with that of a single instance of the QUAD SPI, the means of communication of the MCU-based implementation. On the other hand, the overall resource utilization of the modules in the PL region of the Zynq device is summarized in Table  2. The resource utilization, including other modules for putting the SoC together, accounts for 7. 4% (LUT) and   7. The platform description was then exported to SDK, where the application was put together using the FreeR-TOS Zynq port and associated files. The design was then programmed onto the device to carry out the tests, first in the mechatronic testbed to be presented in Section 6. 1 and, once validated, in the actual AFP robot in the ComposiTIC facilities.
A Mechatronic Platform for Fast-Prototyping Purposes. The main rationale for using a mechatronic testbed was to emulate the fiber deposition process (which is essentially performed in a single axis) by moving a deposition head with the aid of one-axis actuator. This one-axis mechatronic system is driven by SIMODRIVE POSMO A motor (from Siemens), which is controlled by SIMATIC Box PC (IPC 827C) running a real-time operating system (WinAC RTX) and enables the control of the motor via Profibus DP. The control architecture was created using the Total Integrated Architecture (TIA) software module by Siemens, as depicted in Figure  11(a). This software enables creating the control interface between the SIMATIC Box PC and The mechatronic testbed or fast-prototyping platform is depicted in Figure  12.
As mentioned previously, a TCP/IP server has been implemented using a lwIP stack running in the Zynq EPP 7000, which has been conceived in such a way that the actuators in the dummy robot head can be controlled using the Command Accelerator Module in the programmable logic section of the SoC FPGA device. For this purpose, a ZedBoard platform integrating a Zynq EPP 7000 device has been extended with a custom board to interface the IED with external signals, as depicted in Figure  13. For instance, the FMC connector in the board has been used to connect the processor subsystem in the device, via the customized hardware accelerator in the programmable logic section of the Zynq, with the power electronics in charge of driving an arrangement of Siemens electropneumatic valves. The latter subcomponent acts as the bridge between the electronic control systems and the actual mechanical action and represents thus the most important component of the system.
Furthermore, other components and sensors have been integrated in the mechatronic platform and interfaced with the IED. For instance, a quadrature pulse decoder has been integrated in the same axes of the Siemens SIMODRIVE POSMO A motor (through a mechanical coupling and the belt tensiometer) to provide information about the position of dummy deposition head, which is fed to the Speed and Position Calculation module in the programmable section of the SoC FPGA through an external port. It must be emphasized that the use of the Zynq EPP has enabled us to seamlessly move from an initial standalone prototype to a fully functional proof-of-concept mechatronic system and, finally, to its deployment in the actual AFP robot. Preliminary results obtained using the testbed as well as actual tests in the robot will be detailed in the next section.
Experimental Results and Performed Tests. For testing the increased processing capabilities of the proposed architecture, tailored test patterns were generated using CADFiber and tested using three different solutions for benchmarking purposes: the original BoxPC-based solution, a platform based on a Renesas microcontroller running FreeRTOS, and, finally, the SoC FPGA-based Zynq implementation, as depicted in Table  1. Several design patterns, customly designed to trigger the cutting sequences in the deposition program were first generated and tested in the mechatronic subsystem presented in Section 6. 1. These patterns needed to be tested over a distance of one meter, given the geometric limitations of the one-axis actuator; the required acceleration and nominal speed of the system were remotely controlled via Profibus DP using the SimoCom A software (as depicted in Figure  11 ).
Objective: Human body models have the potential to better describe the human anatomy and variability than dummies. However, data sets available to verify the human response to impact are typically limited in numbers, and they are not size or gender specific. The objective of this study was to investigate the use of model morphing methodologies within that context. Methods: In this study, a simple human model scaling methodology was developed to morph two detailed human models (Global Human Body Model Consortium models 50th male, M50, and 5th female, F05) to the dimensions of post mortem human surrogates (PMHS) used in published literature. The methodology was then successfully applied to 52 PMHS tested in 14 impact conditions loading the abdomen. The corresponding 104 simulations were compared to the responses of the PMHS and to the responses of the baseline models without scaling (28 simulations).
Models and simulation conditions The models used for the current study are the GHBMC models M50 v4. 3 (commercial version, Global Human Body Model Consortium, USA) and F05 v2. 2. The F05 model is a development version. Overviews of the models can be found in their respective manuals and in publications (e. g. ,  Vavalle et al. 2013; Davis et al. 2016). Both models are based on imaging data sets collected on human volunteers with dimensions close to the targeted percentiles (e. g. , statures of 1750 mm and 1499 mm for the M50 and the F05, respectively). Corresponding model masses are 77 kg for the M50 and 51 kg for the F05. The models have around 1. 3 million nodes for more than 2 million elements. They also share similar modeling principles: the same organs and structures are represented using similar numerical modeling assumptions (e.
Model morphing methodology Each model was then morphed to approximate the dimensions and masses of PMHS used in the reference studies from Table  1. As complete anthropometries are typically not available in the published studies, height, mass, and a relevant depth (e. g. , depth at location of impact) were used to estimate other anthropometric dimensions using the ANSUR anthropometric database (n = 3982,  Gordon et al. 1989 ). These three inputs were selected as these are consistently available in all reference studies, allowing use of the same methodology for all subjects. ANSUR subjects were selected first by gender and then by closest weight and depth (after isotropic scaling to the PMHS stature). When several subjects were within 2 mm for depth and 1 kg for weight of the PMHS (after stature scaling), their characteristics were averaged.
Data analysis The simulations (28 baselines, 104 scaled models) were run in Ls-Dyna Version 7. 1 MPP (LSTC, Livermore, CA). Output metrics included the force, the deflection, and, when possible, the abdominal compression (deflection divided by abdominal thickness) and the abdominal soft tissue compression (deflection divided by thickness between skin and spine at the level of impact). For the tests, as the soft tissue thickness is not available, it was taken as the average of the thickness measured on the corresponding scaled models. Outputs were compared using peak values and CORA scores (CORA software version 3. 61, Partnership for Dummies and Biomechanics, Germany). For test results, curves were used without scaling. For the CORA scores, corridor and cross-correlation methods were used for the baseline simulations (comparison between model and all curves).
Discussion and conclusions In this study, 66 pairs of simulations (52 scaled, 14 baselines) were run to study the abdominal response of two human body models designed to represent different percentiles of the population (50th male and 5th female). In the absence of suitable validation data sets specific to these percentiles, a methodology was developed to scale the models to estimated PMHS dimensions. It was successfully applied to 52 PMHS from 6 studies (times two models), enabling simulations without manual model corrections. The method requires only a few metrics that are almost always available in reference studies (stature, mass, depth of the impacted body region) and could be expanded to other regions. Based on these metrics, a plausible (in the sense of the ANSUR anthropometric database) set of anthropometric dimensions is predicted and then used to drive the model transformation using Kriging interpolation.
This approach is very simplified and many improvements should be considered in the future. First, the scaling uses the ANSUR database, in which the subjects are both younger and likely more fit than the population used in the experiment (muscle mass and distribution). Also, the lack of muscle tone in PMHS may have affected the abdominal geometry once in position (e. g. ,  Howes et al. 2013). The mass correction scheme (respecting the impact depth measured in the test position on the PMHS and modulating the circumferences outside this region of interest) may have helped, but using data sources more in line with the experimental population (elderly and PMHS) would better address this limitation. Also, instead of selecting the closest subjects in the ANSUR database, methods such as the one proposed by  Parkinson and Reed (2010)  could be used to predict the most likely set of dimensions or to sample a likely population respecting arbitrary constraints.
The linear dimensions and inertial characteristics of the body are important in locomotion and they change considerably during the ontogeny of animals, including humans. This longitudinal and ontogenetic study has produced the largest dataset to date of segmental morphometrics in a Catarrhini species, the olive baboon. The objectives of the study were to quantify the changes in body linear and inertial dimensions and to explore their (theoretical) mechanical significance for locomotion. We took full-body measurements of captive individuals at regular intervals. Altogether, 14 females and 16 males were followed over a 7-year period, i. e. from infancy to adulthood. Our results show that individual patterns of growth are very consistent and follow the general growth pattern previously described in olive baboons.
It has further been hypothesized that the divergence in limb mass distribution might reflect the need to maintain precise grasping abilities in the hands in order to forage successfully after infants become less reliant on gripping the mother with the feet for transport  (Raichlen, 2005b). In this context, we have produced the largest dataset of segmental morphometrics for a Catarrhini species to date and made it available for further exploration, e. g. in biomechanical studies such as (inverse) dynamic analyses of movement or musculoskeletal modelling. Based on this dataset and given that mass in newborn baboons is distally concentrated into the fore-and hindlimbs and that the body centre of mass has a more cranial position, we first hypothesized that, during early life, the morphology must adjust rapidly to meet the mechanical demands of developing the locomotor repertoire.
pers. obs. ) ; segment centre of mass (CoM), calculated from the proximal joint of the segment (from the hip joint for the trunk segment); segment mass moment of inertia in the sagittal (I x ) and coronal (I y ) planes; the third moment of inertia in the horizontal plane was not calculated for this study because of its low impact on locomotor mechanics (cf. Schoonaert et al. 2007). Note that the moment of inertia about the segment CoM can be find using the parallel axis theorem (see  Crompton et al. 1996; Raichlen, 2004). We calculated inertial parameters for the whole body and the whole forelimb and hindlimb. Using the mathematical and geometric software GEOGEBRA 5. 0, we designed free body diagrams of baboons at two extreme stages of their development, separately for males and females. We used a typical body posture (quadrupedal standing) observed in the wild with an average elbow joint angle of 155°and knee joint angle of 138°for all morphotypes (see  Patel et al.
e. when all longitudinal data are grouped together. After controlling for the normality of the sample of individual growth patterns  (Shapiro-Wilks test) , we applied the one sample t-test to assess whether the mean of the sample of individual growth patterns was equal to the mean of the general growth pattern. This analysis was done using R 2. 15 software (The R Foundation, Vienna, Austria, https://www. rproject. org/). To test differences between sexes, we used the slope values and the intercept values of the individual models. Comparisons were performed with permutation tests for independent samples (STAXACT 3. 1 software, Cytel, Inc. , Cambridge, MA, USA). Major age-related changes in segment lengths and masses were assessed using principal component analyses (PCA) following  Druelle et al.
The best fitting model is obtained when the coefficient of determination is considered high, i. e. R 2 > 0. 5, and higher degrees do not further improve the fit (note that the end of the curve is likely to be biased because of the end of the dataset, which is why we have indicated this part with a dashed line). To estimate the developmental period when the amount of variation changes, i. e. decrease or increase in the growth rate, we calculated the second derivative of each polynomial model. A change in the sign of the second derivative curve indicates a point of inflection. The period of growth rate decrease or increase was determined from the median of the previous period, or the median between two points of inflection (see Fig. 1  for an example). We also performed a PCA, including general body morphometrics, i.
Because the two pairs of limbs vary together with age, we modelled these relationships using the reduced major axis (RMA), which is an orthogonal regression (type 2 model). The general growth pattern was thus assessed using the identity line: if the slope of the latter falls inside the confidence interval (95%) of the slope of the RMA model, this indicates a similar (isometric) growth pattern between fore-and hindlimbs. As we did for the full dataset, we validated the general growth pattern using comparisons with the mean of the sample of individual patterns (one sample t-test after controlling for normality of the sample). In addition, we plotted the dimensionless CoM and the dimensionless RG (CoM and RG are normalized via a percentage of limb length) against age and used the polynomial method previously described in order to assess the transitions.
Furthermore, in females, there is no difference between morphotypes 4 and 5. According to this axis, the last two female morphotypes and the last male morphotype are very similar. PC2 represents relative limb length and total body mass. At all stages in their development, males are relatively heavier and have shorter relative limb lengths than females. In males, a significant increase in total body mass occurs in the period between morphotypes 4 and 5, which corresponds to the acceleration in mass gain described previously (Table  1 ); this was not found in the female growth pattern. Whole limb inertial properties: forelimb vs. hindlimb Judging by the high values of the coefficients of determination, the limb growth pattern is well estimated by RMA models (Fig. 7 ). There is no difference between the general pattern of limb growth from the full dataset and the individual pattern (Supporting Information Tables  S4  and S5 ).