Resultados de investigación

This dataset includes data corresponding to the paper "Segmentation of Multiple Tree Leaves Pictures with Natural Backgrounds using Deep Learning for Image-Based Agriculture Applications" with DOI: 10.3390/app10010202

Stormwater management model (SWMM) software has recently become a modeling tool for the simulation of intermittent water supply systems. However, SWMM is not capable of accurately simulating the air behavior in the pipe-filling phase, missing therefore a relevant factor during pipe pressurization. This work proposes the integration of a conventional accumulator model in the existing SWMM hydraulic model to overcome this gap. SWMM source code was modified to calculate the air piezometric head inside the pipe based on the system boundary conditions, and the air piezometric head was incorporated in the SWMM flow rate pressure component. Experimental data were collected during the rapid filling of a pipe system for three possible configurations that are likely to occur in intermittent water supply pipe systems: no air release, small air release, and large air release. Results show that the improved SWMM better describes the effect of the air behavior using the extended transport (EXTRAN) surcharge method when compared to the original SWMM. Results also show that the SLOT method with predefined slot width is not suitable for this purpose; thus, further research is needed to assess if an adjusted slot width could provide better results.

Dataset of pomegranate tree (Punica granatum L. ‘Wonderful’) image times series. The pictures were collected by means of Raspberry Pi cameras with OV5647 sensor (5 MP, f2.9). Sensors were installed on fixed platforms for continuous measurement with zenithal orientation at a distance of approximately 1 metre from the canopy. Images were captured daily at 9 a.m. (GMT+2) from July to mid-October in 2021 and 2022. The resolution of the images is 640x480 pixels.

This dataset includes data corresponding to the paper "Intelligent thermal image-based sensor for affordable measurement of crop canopy temperature" with DOI: 10.1016/j.compag.2021.106319

The paper proposes a novel methodology to locate and quantify entrapped air pockets created during pipe-filling events often found in intermittent water supply systems. Different filling conditions were tested in an experimental pipe with a high point. Measurements were taken and video recordings were carried out to assess air pocket volumes for different air release conditions at the downstream end of the pipe. The stochastic nature of air pocket creation resulted in varying air volumes. A new numerical model capable of simulating the air pocket creation, dragging and entrainment has been proposed. The new model, AirSWMM, was implemented as an extension of the Stormwater Management Model (SWMM) with stochasticity of air pocket formation reproduced by simulations with different air entrainment rates. The obtained numerical results show that the proposed model, even though based on a single-phase one-dimensional flow, can accurately locate and approximately quantify the entrapped air pocket volumes

Data were collected using the autonomous underwater profilers called s-Nautilus and µ-Nautilus at the Real Club de Regatas de Cartagena, the La Isleta yacht club and at a point off Los Narejos beach. During these tests, the profilers monitored the depth for various depths using two thrusters or a ballast tank to adjust the buoyancy. These tests show the depth data recorded using both actuators for a given setpoint. - Thrusters_tests_Real_Club_de_Regatas.csv: control of the profiler s-Nautilus using thrusters at depths of 3, 2 and 1 metre holding each setpoint for 80 seconds at Real Club de Regatas. - VBS_tests_Real_Club_de_Regatas_1.csv: control of the profiler µ-Nautilus using ballast tank at depths of 4.5, 3.5, 2.5, and 1.5 metre holding each setpoint for 120 seconds at Real Club de Regatas. - VBS_tests_Real_Club_de_Regatas_2.csv: control of the profiler µ-Nautilus using ballast tank at depths of 4.5, 3.5, 2.5, and 1.5 metre holding each setpoint for 20 seconds once the profiler is within the established error depth (±20 cm) and speed (±2.5 cm s-1) limits at Real Club de Regatas. Disturbances are applied in a test when the profiler is set at each depth. - VBS_tests_La_Isleta.csv: control of the profiler µ-Nautilus using ballast tank at depths of 2.3, 1.9, 1.5 and 1.1 metre holding each setpoint for 20 seconds once the profiler is within the established error depth (±20 cm) and speed (±2.5 cm s-1) limits at La Isleta yacht club. - VBS_tests_Los_Narejos.csv: control of the profiler µ-Nautilus using ballast tank at depths of 3.1, 2.1 and 1.1 metre holding each setpoint for 20 seconds once the profiler is within the established error depth (±20 cm) and speed (±2.5 cm s-1) limits at the station off Los Narejos.

Additional file 2. Supplementary tables. Supplementary data and supplementary results.

This dataset contains Bluetooth Low Energy Separate Channel (SC BLE) Fingerprinting (FP) data recorded in an indoor facility. The dataset includes Received Signal Strength Information (RSSI) data collected from two independent location systems: one created with four BLE beacons connected to four traditional monopole antennas, and the other four beacons connected to two dual-port Frequency-Scanned Leaky Wave Antennas (FS LWA). Both systems are installed covering the same 7m x 5m area located in a basement zone. The dataset includes a reference radiomap file for each point equidistant 50cm to generate Fingerprinting techniques. The calibrated radiomap files are included in the Calibration_21112023 folder. In the name of each file, the {x, y} position where the data were recorded is included, with a total of 130 reference points. The data labelled as P1 and P2 are the RSSI recorded from beacons connected to FS LWA1, while P3 and P4 data corresponds to RSSI obtained from beacons of FS LWA2. Finally, P5, P6, P7 and P8 data sets belong to beacons connected to individual monopole antennas. Each calibration file contains 100 samples of RSSI received from the corresponding beacons at each one of the 130 reference points forming the calibration grid. The dataset also includes test samples for different days. These days are labelled as day 1, 8, 15, 22, 29, 51, 86 and 94 after the calibration day 0. This way, the variation of the different SC FP BLE antenna systems??performance over time, can be studied. This classification of the data as a function of time, is categorized in the folders with the names Test_day_XX_date. As done with the reference calibration information for day 0, a file with the RSSI collected in each reference point (within a total of 130 grid points) can be found in each folder. The name of the file indicates the reference point where the data were collected. Different to the reference calibration data of day 0 (where 100 RSSI samples were considered for each one of the 130 calibrated {x, y} positions), the test data obtained in different subsequent days is composed by ten samples of RSSI for each one of the 130 test point. Moreover, to test the performance of the systems when some modifications occur where the calibration was performed, some data tests are collected adding several offices furniture on the days 15, 22, 29, 51 and 94 after the calibration procedure performed at day 0. These data are stored in folders with the name Test_day_XX_with_furnitures_date. Similar to the previous test data, the name of the file includes 130 reference {x,y} points, with 10 RSSI samples each, and for the corresponding eight beacons (P1..P8), which are labelled for both monopole (P5, P6, P7 and P8) and FS LWA antenna systems (P1 and P2 for FS LWA1 and P3 and P4 for FS LWA2).

The dataset is organized into three main categories: STRESS, AEROBIC exercise, and ANAEROBIC exercise. Each category contains subfolders specific to individual subjects, where raw CSV files downloaded from Empatica E4 Connect are stored. Only the "tags" files have been cleaned to improve protocol understanding. These tags mark the beginning and end of protocol segments,which facilitates signal segmentation. In accordance with HIPAA Safe Harbor De-Identification guidelines, each participant is assigned a unique ID.The session dates and event marks during the protocols (in tags.csv) have been modified by a random number of days. Time samples have been shifted consistently across all records to maintain signal alignment. Participants from the first stage are labeled as "Sxx," while those from the second stage are labeled as "fxx". Each subject folder contains the raw signal .csv files provided by Empatica. These files follow this format: the first row is the initial time of the session expressed in UTC (Empatica provides time in Unix timestamp format, but files are already converted to UTC). The second row is the sample rate expressed in Hz. TEMP.csv: Data from temperature sensor expressed degrees on the Celsius (°C) scale. EDA.csv: Data from the electrodermal activity sensor expressed as microsiemens (µS). BVP.csv: Data from photoplethysmograph. ACC.csv: Data from 3-axis accelerometer sensor. The accelerometer is configured to measure acceleration in the range [-2g, 2g]. Therefore the unit in this file is 1/64g. Data from x, y, and z axis are respectively in first, second, and third column. IBI.csv:Time between individuals heart beats extracted from the BVP signal. No sample rate is needed for this file. The first column is the time (respect to the initial time) of the detected inter-beat interval expressed in seconds (s). The second column is the duration in seconds (s) of the detected inter-beat interval (i.e., the distance in seconds from the previous beat). HR.csv: Average heart rate extracted from the BVP signal.The first row is the initial time of the session expressed in UTC. The second row is the sample rate expressed in Hz. tags.csv: Event mark times. Each row corresponds to a physical button press on the device; the same time as the status LED is first illuminated. The time is expressed in UTC and it is synchronized with initial time of the session indicated in the related data files from the corresponding session Demographic data such as age, weight, and height are provided in the subject-info.csv file. Additionally, a file containing all self-reported stress levels for each stage is provided (Stress_level_v1.csv and Stress_level_v2.csv). Some participants experienced issues such as incorrect wristband placements, incomplete protocols, and connection problems. Details about these issues can be found in the data_constraints.txt file., This original dataset contains physiological signals collected during structured acute stress induction and aerobic and anaerobic exercise sessions using a wearable device. Blood volume pulse, motion-based activity, skin temperature, and electrodermal activity were recorded with the Empatica E4, a research-grade wearable. The stress induction protocol involved math and emotional tasks designed to provoke stress responses, interleaved with rest periods. Self-reported stress levels were also recorded during this procedure. For the exercise sessions, distinct routines on a stationary bike were created for aerobic and anaerobic activities. The dataset includes records from 36 healthy volunteers for stress sessions, 30 for aerobic exercise, and 31 for anaerobic exercise. By examining the variations in physiological signals, the effects of these activities can be analyzed. This dataset is a valuable resource for research on stress and exercise detection and classification. Methods: Measurement Device: The Empatica E4 wristband is a wearable wireless device designed for continuous, real-time data acquisition. It includes a PPG Sensor (sampling frequency: 64 Hz) that measures blood volume pulse (BVP), from which HR may be derived; a 3-axis Accelerometer(sampling frequency: 32 Hz) for motion-based activity;an Infrared Thermopile(sampling frequency: 4 Hz) to read peripheral ST and an EDA Sensor (sampling frequency: 4 Hz) to measure sympathetic nervous system arousal. The E4 was worn on the subject s non-dominant hand to reduce motion artifacts when performing the tests. Additionally, the Empatica button was used to mark events, which facilitates the identification and segmentation of each block of interest, enabling to easily identify and analyze different phases or sections of the protocol. Population: Enrollment in the study was managed through an online form. Exclusion criteria included individuals with chronic illnesses, a family history of sudden death during exercise, or those undergoing psychiatric treatment or taking medication that could impact physiological responses. Prior to participating in the tests, each participant signed an informed consent. Participants were males and females aged between 18 and 30 years. Experimental design and data acquisition: The data collection process was conducted in two stages. Initially, a cohort of 18 volunteers (V1) followed the protocol. A few months later, a second group of 18 volunteers (V2) participated using an updated protocol that incorporated improvements based on initial experience. A detailed pipeline for each activity is shown in the provided Jupyter Notebook. STRESS INDUCEMENT PROTOCOL: The original protocol began with a 3-minute baseline recording to serve as a reference. The first stress test was an adaptation of the widely used Stroop Test, created using PsyToolkit. Following this, participants had a 5-minute rest period. Next experiment was a modified version of the Trier Mental Challenge Test, obtained through Millisecond Software, LLC. This test involved a series of mathematical tasks within a 5 seconds time limit, accompanied by an annoying sound stimulus.Participants were also instructed to vocalize their responses, increasing the cognitive load and performance demands. Another 5-minute rest period preceded the final block, which comprised three tests, each with a time limit of 30 seconds. First, participants were asked to express their opinion about controversial topics and were then instructed to argue the opposite viewpoint, contrary to their true beliefs. Finally, participants counted backward from 1022 in decrements of 13, providing their answers aloud. Before and after each task and rest period, participants verbally reported their self-perception stress level on a scale from 1 to 10. AEROBIC PROTOCOL: The aerobic exercise test is an adaptation of the Storer-Davis Maximal Bicycle Protocol, involving continuous stationary cycling for approximately 35 minutes. Initially, we determined the maximum resistance for each participant by identifying the point at which they could no longer pedal with maximum effort. The protocol began with a 3-minute baseline recording during which the subject pedaled without resistance to warm up. Following the warm up, the subject pedaled in sync with a metronome, where each beat corresponded to one foot down (or one knee up),completing one revolution every two beats. The exercise started with low resistance (20\\% of maximum) and progressed through three 3-minute periods at increasing paces of 60, 70, and 75 rpm, with resistance gradually increasing to a medium level (30% of maximum). The protocol then included four additional periods: the first two lasting 3 minutes each and the second two lasting 2 minutes each. These periods featured paces of 80, 85, 90, and 95 rpm, with a gradual increase in resistance (5% per stage). The final phase implied a fixed medium-high resistance (50% of maximum) and consisted of three 2-minute periods at paces of 100, 105, and 110 rpm. This was followed by a 4-minute cooldown period with no resistance, and then 2 minutes of rest while remaining still. ANAEROBIC PROTOCOL: The anaerobic exercise test is an adaptation of the Wingate Anaerobic Test. The protocol began with a 3-minute baseline recording during which the subject pedaled without resistance to warm up. This was followed by three cycles, each consisting of 30 seconds of maximal effort, where the subject pedaled with utmost intensity against high resistance. Each cycle was succeeded by a 4-minute cooldown period with no resistance. Finally, a 2-minute recording was made while the subject remained still. Protocols improvements: The second version of the protocol incorporated several modifications based on previous experience. For stress induction,the Stroop Test was removed, and the second rest period was relocated between the opinion tasks and the subtraction test. Rest periods were extended and a relaxing video was shown . Additionally, the protocol was conducted remotely, which provided a more relaxed environment for rest periods. In the updated exercise protocols, participants attended in groups to a spinning room. The aerobic protocol was modified as follows: a baseline was introduced, followed by a 2:15-minute warm-up. This was succeeded by three 1:30-minute intervals at 70, 75, and 80 RPM, respectively. An 11:15-minute session at 85 RPM was conducted, leading into a final 4:30-minute period at 90/95 RPM (depending on the participant s condition). The session concluded with a 3-minute cooldown, followed by a rest period where participants sat on the bike without movement. For the anaerobic protocol, a fourth maximum power sprint was added, with the sprints extended to 45 seconds each, followed by a corresponding cooldown period.