We introduce SARXarray and STMtools, two python libraries designed to support the exploitation of modern Interferometric Synthetic Aperture Radar (InSAR) data by enabling handling of larger-than memory datasets and the incorporation and fusion with relevant contextual information. The libraries are developed upon two innovative and well-established open-source Python libraries: Xarray [5] and Dask [6]. They are implemented as Xarray extensions. SARXarray is designed to manipulate and operate on larger-than-memory coregistered raster stacks such as Single-look Complex images (SLC) or interferograms, performing scatterer selection and producing STM objects. STMtools leverage the Space-Time Matrix (STM) concept [3], [4] and provides functionalities to process STM and perform enrichment/data fusion with other data sources. Both libraries are built on the Xarray library, providing support for a wide range of data formats, and utilize Dask for parallel computation, making them scalable for distributed computation infrastructures. By enabling InSAR data analysis incorporating contextual information, the two libraries enhance the potential to uncover underlying mechanisms driving deformation phenomena.

The increasing availability of large open datasets and computational resources offers unprecedented opportunities for new insight and discoveries. However, researchers are often faced with the challenge of running analyses at scale. We consider here an example from the domain of plant phenology and show how a Python-based deployable analysis environment leveraging Dask for parallel and distributed computing as well as Jupyter for interactive data exploration could be employed to run regression-based phenological models at high spatial resolution and continental scale.

Public Key Infrastructure (PKI) generates and distributes digital certificates to provide the root of trust for securing digital networking systems. To continue securing digital networking in the quantum era, PKI should transition to use quantum-resistant cryptographic algorithms. The cryptography community is developing quantum-resistant primitives/algorithms, studying, and analyzing them for cryptanalysis and improvements. National Institute of Standards and Technology (NIST) selected finalist algorithms for the post-quantum digital signature cipher standardization, which are Dilithium, Falcon, and Rainbow. We study and analyze the feasibility and the processing performance of these algorithms in memory/size and time/speed when used for PKI, including the key generation from the PKI end entities (e.g., a HTTPS/TLS server), the signing, and the certificate generation by the certificate authority within the PKI. The transition to post-quantum from the classical ciphers incur changes in the parameters in the PKI, for example, Rainbow I significantly increases the certificate size by 163 times when compared with RSA 3072. Nevertheless, we learn that the current X.509 supports the NIST post-quantum digital signature ciphers and that the ciphers can be modularly adapted for PKI. According to our empirical implementations-based study, the post-quantum ciphers can increase the certificate verification time cost compared to the current classical cipher and therefore the verification overheads require careful considerations when using the post-quantum-cipher-based certificates.