Low-cost Equipment Research Articles

Res-UNet Ensemble Learning for Semantic Segmentation of Mineral Optical Microscopy Images

In geology and mineralogy, optical microscopic images have become a primary research focus for intelligent mineral recognition due to their low equipment cost, ease of use, and distinct mineral characteristics in imaging. However, due to their close reflectivity or transparency, some minerals are not easily distinguished from other minerals or background. Secondly, the number of background pixels often vastly exceeds the number of pixels for individual mineral particles, and the number of pixels of different mineral particles in the image also varies significantly. These have led to the issue of data imbalance. This imbalance results in lower recognition accuracy for categories with fewer samples. To address these issues, a flexible ensemble learning for semantic segmentation based on multiple optimized Res-UNet models is proposed, introducing dice loss and focal loss functions and incorporating a pre-positioned spatial transformer networks block. Twelve optimized Res-UNet models were used to construct multiple Res-UNet ensemble learnings using heterogeneous ensemble strategies. The results demonstrate that the system integrated with five learners using the weighted voting fusion method (RUEL-5-WV) achieved the best performance with a mean Intersection over Union (mIOU) of 91.65 across all nine categories and an IOU of 84.33 for the transparent mineral (gangue). The results indicate that this ensemble learning scheme outperforms individual optimized Res-UNet models. Compared to the classical Deeplabv3 and PSPNet, this scheme also exhibits significant advantages.

Fused filament fabrication of polyetheretherketone in vacuum: the influence of high vacuum on layer adhesion in z-orientation

AbstractThe application of fused filament fabrication (FFF) in vacuum changes the heat transfer of the process. This work investigates the influence of the working ambient pressure conditions in FFF-based 3D printing of polyetheretherketone (PEEK) specimens, and its impact on the resulting part strength. Layer adhesion drastically improves with decreasing pressure, maximum layer adhesion is reached for ambient pressure below $$10^{-3}$$ 10 - 3 mbar. We show that simple and low-cost vacuum equipment is sufficient to achieve such pressure conditions, making this process interesting for the general processing of high-temperature polymers using FFF.

A few-shot diabetes foot ulcer image classification method based on deep ResNet and transfer learning

Diabetes foot ulcer (DFU) is one of the common complications of diabetes patients, which may lead to infection, necrosis and even amputation. Therefore, early diagnosis, classification of severity and related treatment are crucial for the patients. Current DFU classification methods often require experienced doctors to manually classify the severity, which is time-consuming and low accuracy. The objective of the study is to propose a few-shot DFU image classification method based on deep residual neural network and transfer learning. Considering the difficulty in obtaining clinical DFU images, it is a few-shot problem. Therefore, the methods include: (1) Data augmentation of the original DFU images by using geometric transformations and random noise; (2) Deep ResNet models selection based on different convolutional layers comparative experiments; (3) DFU classification model training with transfer learning by using the selected pre-trained ResNet model and fine tuning. To verify the proposed classification method, the experiments were performed with the original and augmented datasets by separating three classifications: zero grade, mild grade, severe grade. (1) The datasets were augmented from the original 146 to 3000 image datasets and the corresponding DFU classification’s average accuracy from 0.9167 to 0.9867; (2) Comparative experiments were conducted with ResNet18, ResNet34, ResNet50, ResNet101, ResNet152 by using 3000 image datasets, and the average accuracy/loss is 0.9325/0.2927, 0.9276/0.3234, 0.9901/0.1356, 0.9865/0.1427, 0.9790/0.1583 respectively; (3) Based on the augmented 3000 image datasets, it was achieved 0.9867 average accuracy with the DFU classification model, which was trained by the pre-trained ResNet50 and hyper-parameters. The experimental results indicated that the proposed few-shot DFU image classification method based on deep ResNet and transfer learning got very high accuracy, and it is expected to be suitable for low-cost and low-computational terminal equipment, aiming at helping clinical DFU classification efficiently and auxiliary diagnosis.

BIM-based optimization approach to reduce life cycle costs by focusing on the integration of construction and operation phases in office-commercial buildings

This study investigates the application of Building Information Modeling (BIM) to reduce life cycle costs (LCC) by addressing critical nonconformances encountered during the construction and operational phases of buildings. The primary aim is to identify nonconformances that escalate operational costs and to evaluate how BIM can effectively reduce these costs. The research begins with a comprehensive review of existing literature and utilizes the Delphi method to identify four key nonconformances that most significantly impact operational expenses. To further analyze these issues, semi-structured interviews and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) are employed to determine the cost ranges of components associated with these nonconformances in both the construction and operational phases. The study finds that BIM-based optimization can lead to substantial cost reductions, although there are limitations due to the lack of precise data on the operational phase of low-cost equipment. The key contribution of this research is the development of a framework that helps designers create more effective BIM models, enhancing facilities management (FM) and reducing LCC. The originality of this study lies in its integrated approach, combining BIM and optimization techniques to directly address operational cost issues, which provides a novel perspective on improving life cycle cost management in buildings. Future research should expand the dataset and explore a broader range of nonconformances to refine and extend the framework's applicability.

MR and Ultrasound Elastography for Fibrosis Assessment in Children: Practical Implementation and Supporting Evidence-AJR Expert Panel Narrative Review.

Quantitative MRI and ultrasound biomarkers of liver fibrosis have become important tools in the diagnosis and clinical management of children with chronic liver disease (CLD). In particular, MR elastography is now routinely performed in clinical practice to evaluate the liver for fibrosis. Ultrasound shear-wave elastography has also become widely performed for this purpose, especially in young children. These noninvasive methods are increasingly used to replace liver biopsy for the diagnosis, quantitative staging, and treatment monitoring of patients with CLD. Although ultrasound has the advantages of portability and lower equipment cost than MRI, available evidence indicates that MRI may have greater reliability and accuracy in liver fibrosis evaluation. In this AJR Expert Panel Narrative Review, we describe how, why, and when to use MRI- and ultrasound-based elastography methods for liver fibrosis assessment in children. Practical approaches are discussed for adapting and optimizing these methods in children, with consideration of clinical indications, patient preparation, equipment requirements, and acquisition technique, as well as pitfalls and confounding factors. Guidance is provided for interpretation and reporting, and representative case examples are presented.

Multimodal Data-Driven Segmentation of Bone Metastasis Lesions in SPECT Bone Scans Using Deep Learning.

Patients with malignant tumors often develop bone metastases. SPECT bone scintigraphy is an effective tool for surveying bone metastases due to its high sensitivity, low-cost equipment, and radiopharmaceutical. However, the low spatial resolution of SPECT scans significantly hinders manual analysis by nuclear medicine physicians. Deep learning, a promising technique for automated image analysis, can extract hierarchal patterns from images without human intervention. To enhance the performance of deep learning-based segmentation models, we integrate textual data from diagnostic reports with SPECT bone scans, aiming to develop an automated analysis method that outperforms purely unimodal data-driven segmentation models. We propose a dual-path segmentation framework to extract features from bone scan images and diagnostic reports separately. In the first path, an encoder-decoder network is employed to learn hierarchical representations of features from SPECT bone scan images. In the second path, the Chinese version of the MacBERT model is utilized to develop a text encoder for extracting features from diagnostic reports. The extracted textual features are then fused with image features during the decoding stage in the first path, enhancing the overall segmentation performance. Experimental evaluation conducted on real-world clinical data demonstrated the superior performance of the proposed segmentation model. Our model achieved a 0.0209 increase in the DSC (Dice Similarity Coefficient) score compared to the well-known U-Net model. The proposed multimodal data-driven method effectively identifies and isolates metastasis lesions in SPECT bone scans, outperforming existing classical deep learning models. This study demonstrates the value of incorporating textual data in the deep learning-based segmentation of lowresolution SPECT bone scans.

Enfoque multitareas implementado en una minicomputadora para el control y monitoreo de un motor a pasos industrial

This paper presents the implementation of the multitasking approach on a Raspberry Pi single board minicomputer, with experimental results, of the control and monitoring of an industrial type Nema 34 stepper motor through a user interface that allows the configuration of the control operation and data visualization to interact with the physical devices. One of the tasks consists of generating a pulse width modulation that is sent to the DQ860HA driver to rotate the stepper motor considering 400 pulses per revolution, while the second task simultaneously counts the number of pulses produced by an encoder with a resolution of 100 pulses per revolution, which is coupled to the motor shaft. The use of low-cost computer equipment and open-source software makes the presented development can be considered as a frugal technology application for the control and monitoring of an industrial system, with experimental results that validate the correct operation of two tasks that must be executed simultaneously.

Implementation of the diagnostic capabilities of the CMOS sensor in the NIR environment, using 1070 nm interference filter and a conventional IR-pass filters set

NIR reflectography with silicon sensors (CMOS) is commonly acquired with 780 nm band-pass filters that allow the acquisition of clear images and high shutter speeds, while maintaining a low equipment cost. In this way, however, acquisition between 1000 nm and 1150nm-where the silicon sensor is still formally infrared sensitive-is in fact overhelmed by the stronger sensitivity in the infrared spectrum portion between 780 nm and 980 nm. Coupling a 1070 nm (± 5 nm) interferencial filter to an 87C nm IR pass filter (FHWH 850 nm) acquisitions in this portion of the NIR spectrum were carried out, witnessing a outstanding increase in visibility of underdrawings and pentimenti. A practical test of the effectiveness of the filters system mounted on the Nikon D800 IRUV was made, comparing the results with those obtained by the “Osiris” InGaAs detector by Opus Instruments. The comparison was performed on the “Deposition” (oil on panel) by Antonio Semino (1485–1555) in the collection of the Accademia Ligustica of Genoa, highlighting a significant qualitative proximity between the results obtained with the interference system and those with InGaAs detector, compared to the conventional acquisition with single IR long-pass filter.In addition, the 1070nm+87C filter system was used to increase the recognition capability of azurite pigments. This procedure widens the possibilities of first-impact diagnostics by means of low-cost and market available imaging systems based on commercial CMOS IRUV cameras..

Investigation of Arc Stability in Wire Arc Additive Manufacturing of 2319 Aluminum Alloy.

Wire Arc Additive Manufacturing (WAAM) technology, known for its low equipment and material costs, high material utilization, and high production efficiency, has found extensive applications in the fabrication of key components for the aerospace and aviation industries. The stability of the arc is crucial for the WAAM process as it directly affects the forming of the parts. In this study, the monitoring data of electrical signals and arc morphology during the WAAM process of 2319 aluminum alloy were investigated using a high-speed camera system and current/voltage acquisition system. By analyzing the current and voltage signals, as well as the arc imaging results, the influence of arc stability on the forming of the cladding layer was studied. The experimental results indicated that when both current and voltage exhibit regular periodic fluctuations, this manifests as a stable short-circuit droplet transition form, while sudden changes in these signals represent abnormal droplet transition forms. The adaptability of the process directly influenced the arc shape, thereby affecting the forming of the cladding layer. Under the process parameters of welding speed of 240 cm/min and wire feeding speed of 6.5 m/min, it was observed that the current signal exhibited a tight state and the variance of the arc width was minimized. This indicated that at a higher wire feeding speed, the droplet transfer frequency was increased. Under these process parameters, the arc output was more stable, resulting in a uniform metal coating.

Collision-free tracking for a mobile robot based on purely geometric planning

A purely geometric planning method for a mobile robot in unknown environments is proposed to ensure collision avoidance with obstacles within the safety time interval while moving toward the goal. The robot initially detects a point cloud of obstacles using a 2D LiDAR. Euclidean clustering is employed to classify the point cloud into distinct point classes. Each point class is then identified as a directed closed-loop rectangle representing the obstacle. A relative orientation kd-tree is designed to store the vertices of the obstacles and determine which obstacles should be considered in the obstacle avoidance algorithm. A velocity divider is introduced to obtain a linear convex area of possible obstacle avoidance velocities. Linear planning is then used to calculate the optimal obstacle avoidance velocity for control. A virtual reference point method is proposed to address the problem of an unreachable goal in a singular configuration. Experimental results show that the directed closed-loop rectangle and relative orientation kd-tree facilitate rapid updates of required obstacle points with low-cost sensor equipment. A deterministic path for a given scenario is demonstrated, confirming the reliability of the geometric planning method for the obstacle avoidance velocity region and the optimal obstacle avoidance velocity. The proposed algorithm is further validated in scenarios with multiple obstacles and dynamic environments involving moving obstacles.

Development and dissemination of a series of surgical skills and procedures video tutorials using a novel, low-cost, and sustainable simulation kit (GlobalSurgBox)

IntroductionSurgical simulation and video-based learning are limited in lower-resource settings. We sought to develop and assess a series of surgical tutorials using a low-cost simulator. MethodsWe created 8 surgical skills and procedures videos using low-cost equipment. We assessed video quality using the DISCERN scale and the Global Quality Scale (GQS). ResultsVideos ranged from surgical techniques to complex procedures. We uploaded these to Youtube and included them in the curriculum of a medical school in Rwanda. Excluding the cost of the kit (25 USD), production costs ranged from 2 to 5 USD. All videos scored a mean DISCERN of 2.44 ​± ​1.05 and GQS of 3.06 ​± ​0.90. Generally, these lacked points on providing additional sources of information and addressing areas of uncertainty. ConclusionsThis study addresses the demand for accessible surgical education resources. Using low-cost, standardized materials ensures consistency, democratization of training, and feasibility.

The Study of Removal of Polyvinyl Chloride (PVC) Particles from Wastewater through Electrocoagulation

Plastic was produced massively, especially using polyvinyl chloride (PVC) as a raw material. Unfortunately, this condition cause affects the environment, which creates a new pollutant issue. It is essential to study the removal of PVC microplastics in current water treatment processes. The study of wastewater treatment can be achieved using electrocoagulation, which has several benefits, including low-cost, simple chemicals, and accessible equipment operation. This research investigated the study of the removal of PVC microplastics from wastewater by electrocoagulation. The new potential of the electrocoagulation technique using Al-Al electrodes was studied systematically at various variations, i.e., electrolysis time, electrolyte concentration, initial pH, coagulation speed, and electrolyte type. The results showed that the PVC microplastics removal efficiency reached 100% after electrolysis for 60 min, electrolyte concentration of 0.01 mol/L, initial pH of 7, coagulation speed of 500 rpm, the type of electrolyte used was NaCl at a flocculation speed. These optimum conditions also reduced the value of turbidity of wastewater samples from 1.39 ± 0.02 to 1.10 ± 0.05 NTU. The results of this study provide an engineering perspective in optimizing operational parameters for removing PVC microplastics in aquatic environments.

Comprehensive analysis of arc methods of 3D printing of metal products: assessment of the efficiency and prospects of using TIG as a heat source

In recent years, additive technologies have become increasingly important for the production of parts with complex geometries, enabling the rapid and efficient creation of objects with different shapes and configurations in industrial sectors such as medicine, aerospace and construction. The focus of the study was the analysis of arc processes for additive manufacturing, particularly through non-consumable electrode welding in an inert gas environment (TIG) and its applications as a heat source. Modern methods of 3D printing metal products, such as SLM (Selective Laser Melting), EBM (Electron Beam Melting), and LMD (Laser Metal Deposition), allow the production of parts with good quality indicators: accuracy, surface roughness, mechanical properties, and others. However, these methods are expensive due to the technological complexity of the equipment, and a weakness of these methods is their low productivity compared to arc methods. The results presented in this article show that the productivity of arc methods in additive manufacturing is several times higher than that of SLM, EBM, and LMD, and arc methods are more cost-effective due to lower equipment costs and reduced energy consumption. The article presents the schemes of arc methods of additive manufacturing. One of the promising directions in the development of hybrid technology, namely the use of the TIG heat source for sintering metal powders, offers an effective way to reduce the cost of additive manufacturing by replacing the laser as a heat source, while allowing the continued use of various types of metal powders, reinforcing materials, and metal-ceramic blends.

Ensemble Fusion Models Using Various Strategies and Machine Learning for EEG Classification.

Electroencephalography (EEG) helps to assess the electrical activities of the brain so that the neuronal activities of the brain are captured effectively. EEG is used to analyze many neurological disorders, as it serves as a low-cost equipment. To diagnose and treat every neurological disorder, lengthy EEG signals are needed, and different machine learning and deep learning techniques have been developed so that the EEG signals could be classified automatically. In this work, five ensemble models are proposed for EEG signal classification, and the main neurological disorder analyzed in this paper is epilepsy. The first proposed ensemble technique utilizes an equidistant assessment and ranking determination mode with the proposed Enhance the Sum of Connection and Distance (ESCD)-based feature selection technique for the classification of EEG signals; the second proposed ensemble technique utilizes the concept of Infinite Independent Component Analysis (I-ICA) and multiple classifiers with majority voting concept; the third proposed ensemble technique utilizes the concept of Genetic Algorithm (GA)-based feature selection technique and bagging Support Vector Machine (SVM)-based classification model. The fourth proposed ensemble technique utilizes the concept of Hilbert Huang Transform (HHT) and multiple classifiers with GA-based multiparameter optimization, and the fifth proposed ensemble technique utilizes the concept of Factor analysis with Ensemble layer K nearest neighbor (KNN) classifier. The best results are obtained when the Ensemble hybrid model using the equidistant assessment and ranking determination method with the proposed ESCD-based feature selection technique and Support Vector Machine (SVM) classifier is utilized, achieving a classification accuracy of 89.98%.

RAPiD: a rapid and accurate plant pathogen identification pipeline for on-site nanopore sequencing.

Nanopore sequencing technology has enabled the rapid, on-site taxonomic identification of samples from anything and anywhere. However, sequencing errors, inadequate databases, as well as the need for bioinformatic expertise and powerful computing resources, have hampered the widespread use of the technology for pathogen identification in the agricultural sector. Here we present RAPiD, a lightweight and accurate real-time taxonomic profiling pipeline. Compared to other metagenomic profilers, RAPiD had a higher classification precision achieved through the use of a curated, non-redundant database of common agricultural pathogens and extensive quality filtering of alignments. On a fungal, bacterial and mixed mock community RAPiD was the only pipeline to detect all members of the communities. We also present a protocol for in-field sample processing enabling pathogen identification from plant sample to sequence within 3 h using low-cost equipment. With sequencing costs continuing to decrease and more high-quality reference genomes becoming available, nanopore sequencing provides a viable method for rapid and accurate pathogen identification in the field. A web implementation of the RAPiD pipeline for real-time analysis is available at https://agrifuture.senckenberg.de.

Application of microbubble air flotation to harvest Microcystis sp. from agriculture wastewater: The regulation and mechanisms.

The harvesting of microalgae is the main bottleneck of its large-scale biomass production, and seeking an efficient, green, and low-cost microalgae harvesting technology is one of the urgent problems to be solved. Microbubble air flotation has been proven to be an effective measure, but the mechanisms of microbubbles-algal cell attachment are still unclear. In this study, microbubble air flotation was used as a harvesting method for Microcystis cultured in agricultural wastewater. The process mechanism of microbubble air flotation harvesting microalgae in wastewater was fully revealed from three aspects (the design of bubble formation, the adhesion law, and the recovery rate of microalgae under different working conditions). The results show that the length of the release pipe is the main factor affecting the proportion of microbubbles with a particle size of less than 50 μm. In the process of adhesion, when the particle size of microbubbles is 0.6-1.7 times the size of Microcystis, the adhesion efficiency of microbubbles to Microcystis is the highest. Under the conditions of pressure 0.45 MPa, gas-liquid ratio 5%, and release pipe length 100 cm, the harvesting performance of Microcystis was the best. Microbubble air flotation has better harvesting performance (63.5%, collection rate) of Microcystis with higher density. By understanding the mechanism of microbubble flotation, the technical parameters of microbubble flotation for harvesting energy microalgae are optimized to provide support for the development of efficient and low-cost devices and equipment for collecting microalgae.

Quantifying non-transferrin-bound iron (NTBI) in human plasma: incorporating BODIPY-pyridylhydrazone (BODIPY-PH) within a thin green film linked to a portable fluorescence-based device.

Free iron in human serum or non-transferrin-bound iron (NTBI) can generate free radicals and lead to oxidative damage. Moreover, it is highly toxic to various tissues and a vital biomarker related to the iron-loading status of thalassemia and Alzheimer's patients. In NTBI in healthy individuals, NTBI levels are typically less than 1µM; current NTBI analysis usually requires advanced instrumentation and many-step sample pretreatment. To address this issue, we employed our invented BODIPY derivative, BODIPY-PH, as a fluorescence probe and trapped it onto the microcentrifuge tube lid using tapioca starch. The fluorescence intensity of BODIPY-PH increased with increasing NTBI concentration (turn-on). The developed portable reaction chamber facilitates rapid analysis (∼5min) using small sample volumes (10 μL sample in a total volume of 600 μL). Under optimum conditions, using the sample-developed portable fluorescence device and fluorescence spectrometer, we achieved impressive limits of detection (LOD) of 0.003 and 0.0015μM, respectively. Furthermore, the developed sensors show relatively high selectivity toward Fe3+ over other metal ions and biomolecules (i.e., Fe2+, Cr3+, Cu2+, and glucose). The sensor performance in serum samples of thalassemia patients exhibited no significant difference compared to the labeled value (obtained from standard methods). Overall, the developed fluorescence sensor is suitable for determining NTBI and offers high sensitivity, high selectivity, and a short incubation time (5min). Moreover, the method requires a limited number of reagents, is simple to use, and uses low-cost equipment to determine NTBI in human serum samples.

Investigating the effect of path planning strategies on 3D metallic structure deposited using robotic WAAM

Wire Arc Additive Manufacturing (WAAM) is an advanced technique for producing medium-to-large-sized components, offering high deposition rates and low equipment costs. Path planning strategies are critical in determining grain growth direction and achieving isotropic properties in the components. This study introduces a novel path planning strategy, the switchback mode, to mitigate unidirectional grain growth and enhance the mechanical properties of WAAM-deposited components. ER-4043 aluminium alloy walls were deposited using the switchback mode and compared with the conventional bi-directional path planning strategy. The findings reveal that different path planning strategies result in variations in layer size, surface morphology, microstructure, residual stress, microhardness, wear resistance, and tensile properties. Notably, the switchback mode demonstrated superior mechanical strength compared to the bi-directional mode, with yield strength (YS) increasing from 90.2 MPa to 113.9 MPa, ultimate tensile strength (UTS) from 148.8 MPa to 178.7 MPa, and elongation percentages from 18.4 % to 21.3 %. Furthermore, the residual stress changed from tensile to compressive when switching from bi-directional to switchback mode.

Enhanced Point-of-Care SARS-CoV-2 Detection: Integrating RT-LAMP with Microscanning.

The COVID-19 pandemic has highlighted the urgent need for rapid and accurate diagnostic methods for various infectious diseases, including SARS-CoV-2. Traditional RT-PCR methods, while highly sensitive and specific, require complex equipment and skilled personnel. In response, we developed an integrated RT-LAMP-MS assay, which combines rapid reverse transcription loop-mediated isothermal amplification (RT-LAMP) with microscanning (MS) technology for detecting SARS-CoV-2. The assay uses magnesium pyrophosphate formed during LAMP amplification as a visual marker, allowing direct observation via microscopy without the need for additional chemical indicators or probes. For the SARS-CoV-2/IC RT-LAMP-MS assay, the sample-LAMP reagent mixture was added to a microchip with SARS-CoV-2 primers and internal controls, then incubated at 62 °C for 30 min in a heat block, followed by amplification analysis using a microscanner. In clinical tests, the RT-LAMP-MS assay showed 99% sensitivity and 100% specificity, which is identical to the RT-LAMP results and comparable to the commercial AllplexTM SARS-CoV-2 assay results. Additionally, the limit of detection (LOD) was determined to be 10-1 PFU mL-1 (dynamic range: 103~10-1 PFU mL-1). The assay delivers results in 30 min, uses low-cost equipment, and demonstrates 100% reproducibility in repeated tests, making it suitable for point-of-care use in resource-limited settings.

W-band photonics-aided OFDM system integrating sensing and communication with phase noise suppression scheme

Photonic-aided technology offers many advantages in millimeter-wave (MMW) communication and sensing, such as low equipment cost and wide bandwidth. The combination of photonics-aided systems with orthogonal frequency division multiplexing (OFDM) waveforms enables high-performance integration of sensing and communication. In this paper, we propose and experimentally demonstrate a W-band photonic-aided OFDM system integrating sensing and communication (ISAC). To address the destruction of signal coherence by phase noise of the system, this paper proposes an OFDM sensing scheme combined with frequency-domain filtering to reduce the sidelobe interference caused by phase noise. Combined with offline digital signal processing algorithms, the system achieves high-precision ranging-Doppler imaging with a ranging resolution of 0.96 cm at a detection distance of 2 m. In terms of communication, after 10 m of wireless transmission, the net rate reaches 47.06 Gbit/s. The proposed system makes full use of the system's resources by using OFDM signals as both the sensing and communication signals. This may be beneficial for some potential applications of 6G in the future.