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The spatial impulse response (IR) interpolation problem is of general interest, e.g. for imaging of subsurface structures based on seismic waves, rendering of audio and radar IRs, as well as for numerous spatial audio applications. A commonly used model represents the occurring reflections as equivalent source positions, often being determined using a sparse re-construction framework employing spa
We have implemented and evaluated a secondary organic aerosol scheme within the chemistry transport model TM5-MP in this work. In earlier versions of TM5-MP the secondary organic aerosol (SOA) was emitted as Aitken-sized particle mass emulating the condensation. In the current scheme we simulate the formation of secondary organic aerosol from oxidation of isoprene and monoterpenes by ozone and hyd
Meta-analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or network models. Differences among included studies depend on variations in target populations (ie, heterogeneity) and variations in study quality due to study d
Side-channel resilience is a crucial feature when assessing whether a postquantum cryptographic proposal is sufficiently mature to be deployed. In this paper, we propose a generic and efficient adaptive approach to improve the sample complexity (i.e., the required number of traces) of plaintext-checking (PC) oracle-based sidechannel attacks (SCAs), a major class of key recovery chosen-ciphertext S
The Internet of Things (IoT) is a large network of connected devices. In IoT, devices can communicate with each other or back-end systems to transfer data or perform assigned tasks. Communication protocols used in IoT depend on target applications but usually require low bandwidth. On the other hand, IoT devices are constrained, having limited resources, including memory, power, and computational
Data analytics is regarded as an important function of 5G networks. The Network Data Analytics Function (NWDAF) is standardized in 3GPP to enhance 5G network performance by analyzing data from network functions and user equipment. Abnormal behavior detection, which is part of the NWDAF framework, has the potential to be a powerful tool to improve 5G network security. Despite this, only limited res
This work proposes a novel design for secure cloud storage systems using a third party to meet three seemingly opposing demands: reduce storage requirements on the cloud, protect against erasures (data loss), and maintain confidentiality of the data. More specifically, we achieve storage cost reductions using data deduplication without requiring system users to trust that the cloud operates honest
Extreme environmental events such as Heat Waves (HWs), cold waves, and droughts intensified by climate change are increasingly associated with adverse health outcomes. In this study, investigation of the extreme temperature across Uttar Pradesh (U. P.), one of India’s largest and densely populate states has been done. Using high-resolution climate data from the Providing REgional Climates for Impa
Urban meteorology, the study of atmospheric phenomena in urban areas, is vital for understanding the dynamic interactions between rapid urbanization and environmental changes. Over the past two decades (2004–2024), urban heat islands (UHIs), extreme rainfall, pollution, and temperature anomalies have intensified, adversely affecting human health, infrastructure, and ecosystems worldwide. The prese
Serum electrophoresis (SPEP) is a method used to analyze the distribution of the most important proteins in the blood. The major clinical question is the presence of monoclonal fraction(s) of antibodies (M-protein/paraprotein), which is essential for the diagnosis and follow-up of hematological diseases, such as multiple myeloma. Recent studies have shown that machine learning can be used to asses
The rapid development of containerization technology comes with remarkable benefits for developers and operation teams. Container solutions allow building very flexible software infrastructures. Although lots of efforts have been devoted to enhancing containerization security, containerized environments still have a huge attack surface. Completely avoiding severe security issues have so far not be
Globally, solar projects are being rapidly built or planned, particularly in high solar potential regions with high energy demand. However, their energy generation potential is highly related to the weather condition. Here we use state-of-the-art Earth system model simulations to investigate how large photovoltaic solar farms in the Sahara Desert could impact the global cloud cover and solar gener
A distinctive representation of image patches in form of features is a key component of many computer vision and robotics tasks, such as image matching, image retrieval, and visual localization. State-of-the-art descriptors, from hand-crafted descriptors such as SIFT to learned ones such as HardNet, are usually high-dimensional; 128 dimensions or even more. The higher the dimensionality, the large
We uncover a critical side-channel vulnerability in the Hamming Quasi-Cyclic (HQC) round 4 optimized implementation arising due to the use of the modulo operator. In some cases, compilers optimize uses of the modulo operator with compiletime known divisors into constant-time Barrett reductions. However, this optimization is not guaranteed: for example, when a modulo operation is used in a loop the
Beyond 5G/6G communication systems promise to significantly impact the development of a New Generation of CCAM. Reconfigurable Intelligent Metasurfaces (RIM) are by now established as a key enabling technology for 6G Systems. They have been extensively investigated the last few years, as they possess exotic properties allowing for precise control over any aspect of an impinging wave. As such, they
The ability to accurately estimate room impulse responses (RIRs) is integral to many applications of spatial audio processing. Regrettably, estimating the RIR using ambient signals, such as speech or music, remains a challenging problem due to, e.g., low signal-to-noise ratios, finite sample lengths, and poor spectral excitation. Commonly, in order to improve the conditioning of the estimation pro
In this work, we introduce a spatio-temporal kernel for Gaussian process (GP) regression-based sound field estimation. Notably, GPs have the attractive property that the sound field is a linear function of the measurements, allowing the field to be estimated efficiently from distributed microphone measurements. However, to ensure analytical tractability, most existing kernels for sound field estim
