Pancreatic cancer has one of the highest cancer mortality rates, as it often detected in late stages, when unresectable tumours are present. Researchers have identified a biomarker associated with the early detection of pancreatic cancer, called Carbohydrate Antigen 19-9 (CA19-9), and have recommended it for pancreatic cancer screening, and for the monitoring of the efficacy of pancreatic cancer treatments. The development of a biosensor for the detection of CA19-9 is discussed in this paper. The biosensor uses capacitive spectroscopy on gold interdigitated electrodes. This electrochemical transducer mechanism was selected as appropriate due to its increased popularity in point-of-care applications. Mouse monoclonal anti-CA19-9 antibodies were covalently bound to the gold surface using cysteamine hydrochloride and glutaraldehyde, and immobilization was verified with a Zeiss AxioObserver fluorescence microscope. Next, the antigen was prepared in different concentrations, and added to the prepared electrodes for 20 minutes. Spectroscopy was run using the PalmSens4 Electrochemical Interface, and five different concentrations of CA19-9 were successfully detected in this process. The concentrations ranged from 10 U/ml to 300 U/ml, which includes the threshold concentration of CA19-9 for the detection of pancreatic cancer, of 37 U/ml. This biosensor is therefore suited to detect the CA19-9 concentrations needed for pancreatic cancer screening.

Low-cost, disposable, and high stretchability strain sensors attract considerable attention. In this paper, we conduct a numerical study to prove the concept of a low-cost and disposable microfluidic-based flexible sensor capable of detecting the arterial pulse waveform even if the sensor is under an axial strain of up to 160%. The strain sensor is comprised of an electrolyte-enabled long winding microchannel integrated with a pair of interconnects and silicone-based packaging. Two theoretical models and one finite element model(FEM) are established to evaluate the effect of the microchannel’s key design factors on the sensor performance. The key design factors of the microchannel, include the single winding width and length of the primary microchannel(PM), the width ratio ( secondary microchannel (SM) width to PM width), the number of the grid line, and the electrolyte material. Three models have a similar resistance change trend when under the same axial strain and same design parameters. However, the obtained resistance values from theoretical model 1 are much larger than those obtained from the FEM and theoretical model 2. The sensor is more sensitive when using ionic electrolytes as compared with liquid metal.

Concentric ring electrodes are showing promise in noninvasive electrophysiological measurement but electrode design criteria are rarely detailed and justified. Toward that goal, the use of realistic finite dimensions model of concentric ring electrode in this study was two-fold. First, it was used to optimize the surface Laplacian estimate coefficients for tripolar electrode configuration with dimensions approximating the commercially available t-Lead electrodes manufactured by CREmedical. Two differential signals representing differences between potentials on the middle ring and on the central disc as well as on the outer ring and on the central disc are combined linearly into the Laplacian estimate with aforementioned coefficients representing the weights of differential signals. Second, it was used to directly compare said tripolar configuration to the optimal tripolar concentric ring electrode configuration of the same size via finite element method modeling based computation of relative and normalized maximum errors of Laplacian estimation. Obtained results suggest the optimal coefficients for Laplacian estimate based on the approximation of the t-Lead dimensions to be (6, -1) as opposed to (16, -1) widely used with this electrode in the past. Moreover, compared to the optimal tripolar concentric ring electrode configuration, commercially available tripolar electrode of the same size leads to a median increase in Laplacian estimation errors of over 4 times. These results are consistent with previously obtained results based on both negligible and finite dimensions models but further investigation on real life phantom and human data via physical concentric ring electrode prototypes is needed for conclusive proof.

Intelligent signal processing holds great importance for the future of resilient, adaptable communications networks. The unique qualities of deep space require an interplanetary Internet to be highly autonomous, efficient, and adaptable to varying Quality of Service (QoS). Deep learning has shown great promise in the field of signal processing for being computationally efficient, capable of handling errors from nonlinear effects (e.g., hardware impairments), and handling low signal-to-noise ratios. A recent survey by Pham, Q.V., et al. notes that none of the papers studied the improvements in classification in the high-order modulation regime. Additionally, these papers did not explore performance of their models in resource limited environments. A hierarchical interplanetary Internet that imposes a variety of constraints on its nodes offers a unique opportunity to explore realistic tradeoffs in model performance. This paper seeks to leverage the processing, storage, and data transmission capabilities of each level of the interplanetary Internet through federated learning. This will reduce data redundancy between nodes and minimize overhead transmission costs on the network. The goals of this project are the following: (i) Detail possible insights into future channel estimation techniques applied to noisy, nonlinear models. (ii) Explore application of deep learning models for high-order modulation schemes. (iii) Quantify the resource-demand reduction resulting from the use of a deep neural network for intelligent signal processing. (iv) Analyze the adaptability of an interdependent system of deep neural networks in the context of a centralized/decentralized federated learning network.

The latest satellite infrastructure for data processing, transmission and reception can certainly be improved by upgrading tools used to deal with very large amounts of data from every different sensor incorporated within the space missions, in order to develop a better technique to process data, in this paper we will take an insight into multimodal data fusion using machine learning algorithms. The planned introduction of several current and future missions such as Lunar Flashlight and Lunar IceCube, EQUULEUS CubeSat (JAXA), Luna 25 (ROSCOSMOS), Chang’e 7 (CNSA) in the lunar environment will greatly benefit from cooperative data structures. The Lunar Gateway (current paradigm). This paper will discuss how machine learning models are used to recreate environments from heterogeneous, multi-modal data sets. The current lunar data environment consists of archived data from Lunar Prospector, SMART 1, LADEE, and others. In particular, for those models based on neural networks, the most important difficulty is the vast number of training objects of the connected neural network based on Convolutional Neural Networks (CNN) to avoid overfitting and underfitting of the models. The CNN is a fully connected deep neural network with architectures for multimodal deep learning fusion models, but these architectures cannot deal with high-dimensional data, so we can discuss their strengths and weaknesses to create a similar neural network but using other architectures to improve the data transmission and reception.

Establishing control over a mission to explore space is still one of the most difficult tasks. In order to achieve such mission control, we need communications into space through the transmission and reception of radio signals. To improve communication conditions, we propose a tracking system to locate space gadgets and transmit signals at minimum distances to reduce free space attenuation. We propose the case of a satellite sent off to the Moon or Mars to points where tracking devices can no longer reach them. In the paper, we discuss the methods and strategies to carry on this idea. The fingerprint of magnetic and gravitational fields can give us information to differentiate the quantity of electromagnetic waves that are received at a point in space in three dimensions. Each planet has specific characteristics, a field around the planet, whether magnetic, electrical, or otherwise, that protects its surface. The use of a spectrometer of masses allows us to identify the neighboring magnetic field, as well as the composition of celestial bodies, and is a clear solution for the observation and monitoring of the planet. Also, the use of an oscillator is proposed to enhance the spectrometer. In conjunction with the use of a magnetometer, we can get an accurate measurement of the field of celestial bodies, magnetic or not and its composition. Also, with the integration of an accelerometer the altitude will be transformed into speed data, and to analyze its variation, we turn this data into gravitational force and define if the satellite is closer to the atmosphere of the celestial body. Attached to the sensing stage, a network of SatComs will be used to amplify the received signal to reach the ground station. Two SatComs per orbit will be positioned into specific Lagrange points of the celestial body.

The solar system is still uncommunicated and unknown for humankind. To acquire more knowledge about the solar system, we send satellites and rovers to explore those planets, however it is costly and takes a lot of effort. Soil retains information about the environment of celestial bodies, and we can process that information to make decisions about future infrastructure settlements that could provide advantages for the interplanetary Internet. The interplanetary internet communications must be scalable, interoperable, secure, and easy for data transmission. But before thinking about carrying out soil analysis through surface exploration, we can see that the first step is to analyze it using sensing satellites studying the structure of their data collection orbits through intelligent vision. In this paper we propose the use of cameras mounted on sensing satellites for the soil analysis during orbit. The satellite will be equipped with cameras (high-resolution, infrared, spectral, optical) for general scanning of surface elements with AI post-processing, and mass spectrometer for spectroscopy. This equipment will be used to analyze the chemical composition of the surface of the bodies, the magnetic field lines, the material radiation, detecting rocks and gas elements, and identify the surface characteristics, among others. In this paper, we discuss how to develop the architecture of an interplanetary internet physical platform with space-to-ground observations and measurements. A satellite orbiting a celestial body will become a sensor node with physical layers designed with relays and a modular setup, as well as a data transport method and location estimation sensing system, as a basis for the interplanetary Internet system. The design of the interplanetary Internet must consider the information from analysis and observation of celestial bodies variables and parameters, as a fundamental flow of information that must be transported through the network to be further analyzed and used.

Celestial bodies of our solar system remain as a major unexplored and unexploited reserve of natural resources available to humans. Furthermore, those constitute a valuable source of information about the origins and evolution of the solar system and an alternative to establish human settlements in the future. Observation and understanding of the land conditions of those celestial bodies is vital to learn more about those celestial bodies, to generate accurate maps of them, to look for natural resources of interest, and to evaluate the feasibility and help in the preparation of future land missions. A satellite constellation constitutes an important infrastructure element to observe those celestial bodies and to transmit the retrieved information back to Earth. Nonetheless, the operation of sensing satellites in other planets needs understanding of the requirements to perform such observations. In this paper we discuss those sensing requirements from the point of view of orbits and payload requirements for one of our closest neighbors of the solar system (Moon, Mars). To analyze the orbit of the sensing satellite, we discuss the required altitude to facilitate ground observation, the orbit’s conditions (such as radiation levels and orbital perturbations, among others), suitable orbit configurations, required number of satellites, and ways to estimate the required time to perform full observation of the celestial body. To evaluate suitable payloads, we discuss available information in the literature (such as known atmospheric and land conditions) to determine the best observation frequencies and determine the best kind of payload (such as sensors, a camera, or a lower frequency observation payload) to study that celestial body. Finally, we discuss some important considerations such as the requirements of satellite communication link to transmit the retrieved information back to Earth.

Common food contaminants include pathogens, toxins, pesticides, veterinary drugs and
illegal additives, while common allergens are mainly proteins [1]. Food induces different
hypersensitivity reactions in allergic people when humans are exposed to harmful allergens.
Generally, patients with food allergies undergo prophylactic practices as there is no medical
treatment. Food allergy affects 1-10 % of the population and reports show that its prevalence
in children increased by 50% between 1997 and 2011 [2]. Symptoms can occur from minutes
to hours after exposure, and may include difficulty in breathing, low blood pressure, itchy
rash, swelling of the tongue, and life-threatening systemic reaction called anaphylaxis.
Ara h1, 2, 3 and 6 are considered to be major allergens found in peanuts which trigger to an
immunological response in more than 50% of the allergic population representing the first
leading cause of anaphylactic fatalities worldwide [3]. ELISA is the most commonly used
method for determining low levels of food allergens in food (ingredients, processed foods
and beverages) [4], but lacks of simplicity and reduced costs and oftentimes leads to delays
in results acquisition. Since functional foods and new food manufacturing technologies are
emerging, there is an ongoing demand for analytical strategies for on-site sensitive detection
of food allergens.
This poster presents a strategy for the determination of the presence of allergens in food
products by enabling an electrochemical approach based on DNA strands. The affinity
reaction between the DNA strands and Ara h1, followed by conformational and structural
changes in the recognition layer, was triggered by means of electrochemical mediators
labelled at the DNA sequence. Several strategies were addressed to diminish the protein
fouling at the electrode surface and obtain the optimal sensing parameters. Each step of
electrode modification was characterized by electrochemical techniques such as cyclic and
differential pulse voltammetry, and electrochemical impedance spectroscopy. Applications
on food samples will be presented.

References
[1] Melinte G, Hosu O, Cristea C, Marrazza G. TrAC Trends in Anal. Chem. 2022;154,
116679.
[2] Facts and statistics|food allergy research. www.foodallergy.org/life-with-foodallergies/
food-allergy-101/facts-and-statistics.
[3] Mueller GA, Maleki SJ, Pedersen LC. Curr Allergy Asthma Rep. 2014;14(5):429.
[4] Hosu O, Selvolini G, Marrazza G. Curr Opin Electrochem. 2018;10:149–56.

The recent development of the Global Positioning System (GPS) and the increasing availability of sensor technologies to monitor and record behavioral activities provide a real opportunity to extend the database and to understand the grazing behavior of animals. The purpose of this study was therefore to investigate the seasonal variations in the grazing activities of dairy goats browsing in forest rangeland using accelerometers and GPS. Eight experimental goats were fitted with GPS collars and leg sensors to monitor their grazing activities. A trial was conducted in order to use the data from the GPS collars and leg sensors to estimate times spent grazing/eating, as well as other grazing activities. The calibration study involved the visual observation of eight goats equipped with GPS collars and sensors over a three-day period. Measurements were undertaken during the three main grazing seasons (spring, summer, and fall). Goats spent most of their daytime foraging budget grazing during spring and fall (p< 0,001). The goats prolonged their lying time in summer (p< 0,001) at the expense of standing duration. The number of steps was numerically greater in both seasons of summer and fall (>6500 steps). The horizontal and vertical distances traveled by goats were significantly higher in fall and summer. Goats spent 59% of their feeding duration on grazing (eating) during the spring in contrast to the summer (36%), and fall (45%) seasons. The combination of GPS collars and accelerometers contributed to a better understanding of the grazing activities of dairy goats in the studied forest rangeland.