The present dataset contains three years of continuous, high-frequency air-quality monitoring of particulate matter (PM2.5 and PM10) in the metropolitan area of Asunción and the Western Region of Paraguay. Data were collected from eleven monitoring stations: nine in urban Asunción, one at the National University of Asunción campus in San Lorenzo, and one in the semi-rural Cerrito, Paraguayan Chaco. The monitoring period covers from April 2019 to December 2021. The monitoring network was established to record spatio-temporal variations in particulate matter concentrations throughout urban and semi-rural environments. Key meteorological variables, including temperature, relative humidity, atmospheric pressure, and wind direction, were continuously recorded to enable analysis of dispersion trends. Particulate matter concentrations were measured at five-minute intervals using Optical Particle Counters (OPC-N2), with data automatically transmitted to a central server every thirty minutes for quality control, storage, and processing. This dataset allows direct comparison with national air-quality standards established by the National Resolution No. 259/2015 of the Ministry of Environment and Sustainable Development. Its full coverage supports policy evaluation, cross-sector assessments, and studies on environmental and public health impacts. Full technical documentation is included to secure transparency, traceability, and reproducibility, providing the basis for management and scientific research in Paraguay.

The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) aims to detect the coherent scattering (CE nu NS) of reactor antineutrinos off silicon nuclei using thick fully depleted high-resistivity silicon CCDs. Two Skipper-CCD sensors with subelectron readout noise capability were installed at the experiment next to the Angra-2 reactor in 2021, making CONNIE the first experiment to employ Skipper-CCDs for reactor neutrino detection. We report on the performance of the Skipper-CCDs, the new data processing, data quality, and event selection for CE nu NS interactions, which enable CONNIE to reach a record low detection threshold of 15 eV. The data were collected over 300 days in 2021-2022 and correspond to exposures of 14.9 g-days with the reactor-on and 3.5 g-days with the reactor-off. The difference between the reactor-on and off event rates shows no excess and yields upper limits for the neutrino interaction rates, comparable with previous CONNIE limits from standard CCDs and higher exposures. Searches for new neutrino interactions beyond the Standard Model improve the previous CONNIE limit on a simplified model with light vector mediators. A first dark matter (DM) search by diurnal modulation by CONNIE obtains the best limits on the DM-electron scattering cross section by a surface-level experiment. These promising results, obtained using a very small-mass sensor, illustrate the potential of Skipper-CCDs to probe rare neutrino interactions and motivate the plans to increase the detector mass in the near future.

The South Atlantic Anomaly (SAA) is a region where the Earth's magnetic field weakens significantly, allowing high-energy particles to enter. The Faculty of Engineering of the Universidad Nacional de Asunción (FIUNA) developed the South Anomaly Monitoring Unit (MUA) mission for the GuaraniSat-2 nanosatellite to monitor the flow of these particles. This work presents the design and implementation of the Detection and Conditioning Unit, in charge of capturing the passage of energetic particles This operates with low voltage SiPM (24.4 V average) with adjustable polarization voltage, producing a 500 nanosecond 3.3 V pulse for the detected events, which will be read by the mission's Control Unit. The complete assembly (Detection and Control Units) has a reduced size of $80 \times 83 ~\text{mm}$ (PCB) with a maximum height of 20 mm, and operates with a power consumption of 0.6 W average, below the maximum of 1 W assigned to the detection unit.

Millicharged particles, proposed by various extensions of the standard model, can be created in pairs by high-energy photons within nuclear reactors and can interact electromagnetically with electrons in matter. Recently, the existence of a plasmon peak in the interaction cross section with silicon in the eV range was highlighted as a promising approach to enhance low-energy sensitivities. The CONNIE and Atucha-II reactor neutrino experiments utilize Skipper-CCD sensors, which enable the detection of interactions in the eV range. We present world-leading limits on the charge of millicharged particles within a mass range spanning 6 orders of magnitude, derived through a comprehensive analysis and the combination of data from both experiments.

Dengue fever remains a persistent and growing public health concern in many tropical and subtropical regions, where warm climates and urbanization create ideal conditions for mosquito proliferation, such as in Asunción, Paraguay, our focal city. Epidemic control strategies targeting the control of mosquito populations, particularly those of the Aedes aegypti (AE), are essential for mitigating outbreaks and protecting vulnerable communities. This work aims to tackle this problem by introducing a computer vision system that utilizes the You Only Look Once (YOLO) architecture for counting mosquitoes and classifying their sex in real-time, while also addressing issues related to out-of-focus and incomplete specimens. The distilled YOLOv8-nano model reached an mean Average Precision (mAP)50 of 88.7% for female mosquitoes and 94.1% for male mosquitoes, with inference times around 4.3 milliseconds on Graphics Processing Unit (GPU) and 1 second on a Raspberry Pi 5. The results indicate that the model can be integrated into embedded systems for automated vector surveillance.

We present the construction and validation of a low-cost muon detector in Paraguay, located at the center of the South Atlantic Magnetic Anomaly (SAMA), where the geomagnetic cutoff rigidity is 9.63 GV. The detector consists of plastic scintillator plates coupled with silicon photomultipliers for light detection. To verify its performance, we measured the average muon flux rate and investigated its correlation with geomagnetic activity, particularly the disturbance storm time (Dst) index, during the May and October 2024 Forbush Decrease events. Using the Truncated Time-Shift test-a recent statistical method for comparing time-series-we found a strong correlation, indicating our detector reliably measures the muon flux over time. Our measurements also allowed us to resolve the detailed morphology of the Forbush decreases, by comparison with local magnetic field fluctuations. These initial results represent a step forward in ground-level radiation monitoring within the SAMA, and highlight the potential of economical muon detectors as components of early-stage diagnostic systems for space weather forecasting.

This paper presents a low-cost autonomous station for real-time monitoring of particulate matter ($\mathbf{P M}_{1}, \mathbf{P M}_{2.5}$, $\mathbf{P M}_{4}, \mathbf{P M}_{10}$) and meteorological variables. The system integrates an SPS30 sensor, an ESP32-S3 with LTE, and a solar supply with Li-ion batteries. Data are uploaded via HTTP to a cloud server, with an SD-card buffer for outages. A semi-urban onemonth deployment with 5 min sampling and 1 h uploads showed stable operation, energy autonomy, and PM levels consistent with regional air quality. The design is replicable with locally available components and supports scalable networks in lowinfrastructure regions. This work contributes an open, energyaware architecture suitable for expanding indicative air-quality monitoring and evidence-based decision making.

Abstract Oscura is a planned light-dark matter search experiment using Skipper-CCDs with a total active mass of 10 kg. As part of the detector development, the collaboration plans to build the Oscura Integration Test (OIT), an engineering test with 10% of the total mass. Here we discuss the early science opportunities with the OIT to search for millicharged particles (mCPs) using the NuMI beam at Fermilab. mCPs would be produced at low energies through photon-mediated processes from decays of scalar, pseudoscalar, and vector mesons, or direct Drell-Yan productions. Estimates show that the OIT would be a world-leading probe for mCPs in the ∼MeV mass range.

In the CONNIE experiment, 14 charge-coupled device (CCD) sensors record particle interactions near a nuclear reactor in Brazil. Muons, originating from cosmic rays, create background noise that can hinder the detection of neutrino interactions. This study proposes an intelligent system for re-constructing muon traces within the sensor data. To achieve this, a Convolutional Neural Network (CNN) model based on YOLOv8 was developed. Two datasets containing real experimental images were prepared: one for training the model to identify and classify muonic events, and another for calibrating an algorithm to predict muon trajectories based on the impact characteristics in the detectors. The system achieved a detection success rate exceeding 83 % for single muons in real data, demonstrating its potential for mitigating noise and enhancing particle tracking in physics experiments.

This study introduces an innovative muon tracking algorithm designed for the Coherent Neutrino-Nucleus Interaction Experiment (CONNIE). In this experiment, a cluster of twelve charge-coupled device (CCD) sensors is strategically positioned in close proximity to the Angra II nuclear reactor. The primary objective of the experiment is the detection of antineutrinos produced by the reactor, serving as a gateway to investigating non-standard neutrino interactions through Coherent Neutrino-Nucleus Scattering (CEvNS). However, the images acquired by these sensors reveal an abundance of muonic particles, originating from the collision of cosmic rays with the Earth's atmosphere. This study is fundamentally focused on the advancement of a muon tracking system that will allow to trace the trajectories of the muons or any other particle with high energy. The GEANT4 toolkit has been used to create synthetic images that will be used to validate the algorithm, that will be use in the analysis of images collected by the CCDs of the experiment. In this work, we evaluated the algorithm's performance using synthetic images, achieving an efficiency of 98.78%. This result underscores the algorithm's robustness and reliability in reconstructing muon trajectories.

Solar radio bursts (SRB) play a crucial role in understanding solar activity and its influence on systems across the world. The classification of SRB into distinct types based on morphology and frequency drift requires vast data and poses significant challenges for automated detection and classification. In this paper, we introduce a deep learning-based approach to address this challenge by leveraging a curated dataset from the CALLISTO network and a ground-based radio astronomy station. A convolutional neural network is trained to identify and classify SRB, despite the low signal-to-noise ratios, dynamic solar atmosphere and limited training data. This work contributes to the automation of SRB analysis and showcases the potential of deep learning in decoding complex astrophysical phenomena. Preliminary results demonstrate the efficacy of our approach, paving the way for a revolutionary advancement in the field of SRB analysis.

Oscura is a proposed multi-kg skipper-CCD experiment designed for a dark matter (DM) direct detection search that will reach unprecedented sensitivity to sub-GeV DM-electron interactions with its 10 kg detector array. Oscura is planning to operate at SNOLAB with 2070 m overburden, and aims to reach a background goal of less than one event in each electron bin in the 2-10 electron ionization-signal region for the full 30 kg-year exposure, with a radiation background rate of 0.01 dru. In order to achieve this goal, Oscura must address each potential source of background events, including instrumental backgrounds. In this work, we discuss the main instrumental background sources and the strategy to control them, establishing a set of constraints on the sensors' performance parameters. We present results from the tests of the first fabricated Oscura prototype sensors, evaluate their performance in the context of the established constraints and estimate the Oscura instrumental background based on these results.

Small radio telescopes make it possible to observe the hydrogen emission line of 21 cm at 1420 MHz and to demonstrate the presence of dark matter by measuring the galactic rotation curve of the Milky Way.This paper presents the design, integration and analysis of data carried out to assemble a radio telescope using (COTS, Commercial off-the-shelf).The design uses inexpensive and commercially available materials.The receiver system consists of low-noise amplifiers, band-pass filters, and a software-defined radio USB receiver that provides digitized samples for spectral processing on a computer.The experimental results obtained through several tests carried out to adjust the software settings are presented, to finally estimate the velocity dispersion of the neutral hydrogen of the arms of the Milky Way and compare the results with other observations.

Short-term electricity demand forecasting represents a fundamental tool for decision-making by entities engaged in electricity management since it allows the development of strategies to meet variations in electricity demand in short periods.The accuracy of predictive models is an important factor for energy operations and the scheduling of energy generation sources to meet the demand at each instant.Intelligent models based on Recurrent Neural Networks (RNN) require hyperparameter adjustment.These models have several hyperparameters that substantially affect their performance.Our paper implements a Long-Short Term Memory (LSTM) model and four search methods to adjust its hyperparameters.First, we select the length of historical window and the hidden state size of LSTM cells for optimization.Second, we draw comparisons between the grid search, random search, a Bayesian scheme, and a genetic algorithm.The data set used for training and validation of the model includes hourly electricity consumption and meteorological variables recorded in Paraguay from 2015 to 2021.The proposed model was evaluated through numerical experiments with classical error measures such as the root mean square error (RMSE), the correlation, the runtime, and the mean absolute percentage error (MAPE).Our comparative study shows that grid search and genetic algorithm give the optimal hyperparameters with high validation accuracy on the test dataset.However, it is important to note that grid search may require much more evaluations and computational resources.

In recent decades, the world has experienced a health crisis due to increased infectious disease cases such as COVID-19, Dengue, Zika, etc. Dengue is a neglected tropical disease transmitted by mosquito vectors, mainly by the Aedes Aegypti.This work is focused on Paraguay, where the virus has surpassed 16,000 notifications so far, this 2022.This disease has an incidence throughout the country, which results in a large amount of available data.The time series clustering can find a subjacent structure within a large amount of data, simplifying analysis and interpretation of it.This article contrasts two different clustering methods (Shape-based and Feature-based), followed by a feature selection procedure.Initially, both methods are tested, getting the highest Silhouettes scores with the feature-based approach.Subsequently, one feature is removed in each experiment and the results are ranked, getting higher silhouette scores by eliminating the least important feature.Results show that better clustering is obtained by performing an adequate feature selection through the ranking procedure.

One mandatory requirement for validating new electronic devices and machines is electromagnetic compatibility (EMC) tests because any new design can emit or receive unwanted electrical interference, affecting the operation of other systems or devices in their environment.This requirement becomes critical in the case of real-time control systems, electromedical devices, and outer space operations.The Gigahertz Transverse Electromagnetic Cell (GTEM) is a tool for performing preliminary electromagnetic compatibility tests on new products.It generates a standardized and uniform electromagnetic field in a shielded environment, integrating pyramidal electromagnetic absorbers with large bandwidth.Compared to other environments, such as an anechoic chamber, its reduced dimensions and cost make it a suitable test laboratory environment for EMC measurements.This article describes the steps to design and build a Radio frequency isolated chamber (GTEM) with dimensions 4 m long, 2.2 m wide, and 1.5 m high.A literature review of the recommendations is summarized.The design of the mechanical parts and construction techniques are described to generate a construction guide for future interested parties.Its critical dimension was analyzed with simulations using finite elements method to match the characteristic impedance of the exciter impedance and guarantee to minimize reflections.

. Dengue fever is an endemic disease, present in tropical and subtropical regions, transmitted by the Aedes Aegypti mosquito vector. It has recently appeared in non-tropical regions with dry weather. This represents a setback for advanced temperature-based reference models, since mosquitos reproductive cycle does not necessarily match with the outbreaks. This situation indicates that other variables are also involved in epidemic outbreaks. In this work we propose to include a component that capture this process, whether entomological, environmental or related to population mobility, and include it to the reference model by adding a Gaussian function to the formulation of humans ( β h ) and vectors ( β v ) transmission rate. The parameters to be adjusted for this function were evaluated by a probabilistic model selection experiment. The parameters for this function are u , σ and k . The results indicate that, our model outperforms the reference model, and that additional information about outbreaks can be obtained from the new parameters. .

A bstract The Coherent Neutrino-Nucleus Interaction Experiment (CONNIE) is taking data at the Angra 2 nuclear reactor with the aim of detecting the coherent elastic scattering of reactor antineutrinos with silicon nuclei using charge-coupled devices (CCDs). In 2019 the experiment operated with a hardware binning applied to the readout stage, leading to lower levels of readout noise and improving the detection threshold down to 50 eV. The results of the analysis of 2019 data are reported here, corresponding to the detector array of 8 CCDs with a fiducial mass of 36.2 g and a total exposure of 2.2 kg-days. The difference between the reactor-on and reactor-off spectra shows no excess at low energies and yields upper limits at 95% confidence level for the neutrino interaction rates. In the lowest-energy range, 50 − 180 eV, the expected limit stands at 34 (39) times the standard model prediction, while the observed limit is 66 (75) times the standard model prediction with Sarkis (Chavarria) quenching factors.

PytuTester is an open-source ventilator tester developed to help bio-engineers in the design and verification of new ventilator prototypes. A ventilator tester allows measuring the flow, pressure, volume, and oxygen concentration provided to the patient. During the global pandemic COVID-19, several open-source ventilators prototypes were developed; however, due to high cost and demand testers, they were not available. In this context, a low-cost tester was developed using a Raspberry Pi and medical-grade sensors for the test ventilators prototypes. This paper presents the design files, software interface, and validations tests. Our results indicate that the tester has good accuracy to evaluate the efficacy and performance of new prototypes. When tested on two ventilator designs developed in Paraguay, PytuTester reported flow profiles that were concordant with the industry-standard VT650 Gas Flow Analyzer. PytuTester was then field deployed to test several DIY ventilator designs in low-resource areas.