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Global Navigation Satellite System (GNSS) is pervasively used in position, navigation, and timing (PNT) applications. As a consequence, important assets have become vulnerable to intentional attacks on GNSS, where of particular relevance is spoofing transmissions that aim at superseding legitimate signals with forged ones in order to control a receiver’s PNT computations. Detecting such attacks is therefore crucial, and this article proposes to employ an algorithm based on deep learning to achieve the task. A data-driven classifier is considered that has two components: a deep learning model that leverages parallelization to reduce its computational complexity and a clustering algorithm that estimates the number and parameters of the spoofing signals. Based on the experimental results, it can be concluded that the proposed scheme exhibits superior performance compared to the existing solutions, especially under moderate-to-high signal-to-noise ratios.

 

Triggering lysosome‐regulated immunogenic cell death (ICD, e.g., pyroptosis and necroptosis) with nanomedicines is an emerging approach for turning an “immune‐cold” tumor “hot”—a key challenge faced by cancer immunotherapies. Proton sponge such as high‐molecular‐weight branched polyethylenimine (PEI) is excellent at rupturing lysosomes, but its therapeutic application is hindered by uncontrollable toxicity due to fixed charge density and poor understanding of resulted cell death mechanism. Here, a series of proton sponge nano‐assemblies (PSNAs) with self‐assembly controllable surface charge density and cell cytotoxicity are created. Such PSNAs are constructed via low‐molecular‐weight branched PEI covalently bound to self‐assembling peptides carrying tetraphenylethene pyridinium (PyTPE, an aggregation‐induced emission‐based luminogen). Assembly of PEI assisted by the self‐assembling peptide‐PyTPE leads to enhanced surface positive charges and cell cytotoxicity of PSNA. The self‐assembly tendency of PSNAs is further optimized by tuning hydrophilic and hydrophobic components within the peptide, thus resulting in the PSNA with the highest fluorescence, positive surface charge density, cell uptake, and cancer cell cytotoxicity. Systematic cell death mechanistic studies reveal that the lysosome rupturing‐regulated pyroptosis and necroptosis are at least two causes of cell death. Tumor cells undergoing PSNA‐triggered ICD activate immune cells, suggesting the great potential of PSNAs to trigger anticancer immunity.

 

Signal acquisition is a crucial step in Global Navigation Satellite System (GNSS) receivers, which is typically solved by maximizing the so-called Cross-Ambiguity Function (CAF) as a hypothesis testing problem. This article proposes to use deep learning models to perform such acquisition, whereby the CAF is fed to a data-driven classifier that outputs binary class posteriors. The class posteriors are used to compute a Bayesian hypothesis test to statistically decide the presence or absence of a GNSS signal. The versatility and computational affordability of the proposed method are addressed by splitting the CAF into smaller overlapping sections, which are fed to a bank of parallel classifiers whose probabilistic results are optimally fused to provide a so-called probability ratio map from which acquisition is decided. Additionally, the article shows how noncoherent integration schemes are enabled through optimal data fusion, with the goal of increasing the resulting classifier accuracy. The article provides simulation results showing that the proposed data-driven method outperforms current CAF maximization strategies, enabling enhanced acquisition at medium-to-high carrier-to-noise density ratios. 

This study compares macrophysical and microphysical properties of single‐layered, liquid‐dominant MBL clouds from the Measurements of Aerosols, Radiation, and Clouds over the Southern Ocean (MARCUS) (above 60°S) and the ARM East North Atlantic (ENA) site during the Aerosol and Cloud Experiments in Eastern North Atlantic (ACE‐ENA) field campaign. A total of 1,136 (16.5% of clouds) and 6,034 5‐min cloud samples are selected from MARCUS and ARM ENA in this study. MARCUS clouds have higher cloud‐top heights, thicker cloud layers, larger liquid water path, and colder cloud temperatures than ENA. Thinner, warmer MBL clouds at ENA can contain higher layer‐mean liquid water content due to higher cloud and ocean surface temperatures along with greater precipitable water vapor (PWV). MARCUS has a higher drizzle frequency rate (71.8%) than ENA (45.1%). Retrieved cloud and drizzle microphysical properties from each field campaign show key differences. MARCUS clouds feature smaller cloud droplets, whereas ENA clouds have larger cloud droplets, especially at the upper region of the cloud. From cloud top to cloud base, drizzle drop sizes increase while number concentrations decrease. Drizzle drop radius and number concentration decrease from cloud base to drizzle base due to net evaporation, and MARCUS' lower specific humidity leads to a higher drizzle base than ENA. The broader surface pressure and lower tropospheric stability (LTS) distributions during MARCUS have demonstrated that there are different synoptic patterns for selected cases during MARCUS with less PWV, while ENA is dominated by high pressure systems with nearly doubled PWV.

 

Abstract. Over the eastern North Atlantic (ENA) ocean, a total of 20 non-precipitating single-layer marine boundary layer (MBL) stratus and stratocumuluscloud cases are selected to investigate the impacts of the environmental variables on the aerosol–cloud interaction (ACIr) using theground-based measurements from the Department of Energy Atmospheric Radiation Measurement (ARM) facility at the ENA site during 2016–2018. TheACIr represents the relative change in cloud droplet effective radius re with respect to the relative change in cloudcondensation nuclei (CCN) number concentration at 0.2 % supersaturation (NCCN,0.2 %) in the stratified water vaporenvironment. The ACIr values vary from −0.01 to 0.22 with increasing sub-cloud boundary layer precipitable water vapor (PWVBL)conditions, indicating that re is more sensitive to the CCN loading under sufficient water vapor supply, owing to the combined effectof enhanced condensational growth and coalescence processes associated with higher Nc and PWVBL. The principal componentanalysis shows that the most pronounced pattern during the selected cases is the co-variations in the MBL conditions characterized by the verticalcomponent of turbulence kinetic energy (TKEw), the decoupling index (Di), and PWVBL. The environmental effects onACIr emerge after the data are stratified into different TKEw regimes. The ACIr values, under both lowerand higher PWVBL conditions, more than double from the low-TKEw to high-TKEw regime. This can be explained bythe fact that stronger boundary layer turbulence maintains a well-mixed MBL, strengthening the connection between cloud microphysical properties andthe below-cloud CCN and moisture sources. With sufficient water vapor and low CCN loading, the active coalescence process broadens the cloud dropletsize spectra and consequently results in an enlargement of re. The enhanced activation of CCN and the cloud droplet condensationalgrowth induced by the higher below-cloud CCN loading can effectively decrease re, which jointly presents as the increasedACIr. This study examines the importance of environmental effects on the ACIr assessments and provides observational constraintsto future model evaluations of aerosol–cloud interactions.