This paper demonstrates Pyneapple-R, an open-source library for scalable and expressive regionalization. Re-gionalization algorithms, also known as the ‘spatially-constrained clustering algorithms', have been widely adopted in spatial analysis tasks and now evolving towards a more large-scale and fine-scale direction. Through collaborations with social scientists and domain experts, we have identified emerging challenges in existing regionalization techniques, particularly regarding scalability and expressiveness. As data volumes continue to grow and regionalization algorithms become increasingly crucial to decision-making across various fields, enhancing these aspects can significantly impact the quality and effectiveness of re-search and applications. To address these challenges, Pyneapple-R provides novel algorithms for regionalization queries including the expressive p-regions algorithm, the scalable max-p regions algorithm, and the expressive max-p regions problem. To show-case Pyneapple-R, we have developed frontend web applications that enable users to interact with the algorithms by selecting constraints or simply engaging in conversation with the system to issue queries with the help of popular AI models. Interactive notebooks, designed to demonstrate the superiority and simplicity of Pyneapple-R, provide varying levels of detail to help social scientists and developers explore its full potential.

This paper introduces a generalized spatial regionalization problem, namely, PRUC ( P -Regions with User-defined Constraint) that partitions spatial areas into homogeneous regions. PRUC accounts for user-defined constraints imposed over aggregate region properties. We show that PRUC is an NP-Hard problem. To solve PRUC, we introduce GSLO (Global Search with Local Optimization), a parallel stochastic regionalization algorithm. GSLO is composed of two phases: (1) Global Search that initially partitions areas into regions that satisfy a user-defined constraint, and (2) Local Optimization that further improves the quality of the partitioning with respect to intra-region similarity. We conduct an extensive experimental study using real datasets to evaluate the performance of GSLO. Experimental results show that GSLO is up to 100× faster than the state-of-the-art algorithms. GSLO provides partitioning that is up to 6× better with respect to intra-region similarity. Furthermore, GSLO is able to handle 4× larger datasets than the state-of-the-art algorithms.

Waves of misery is a phenomenon where spikes of many node splits occur over short periods of time in tree indexes. Waves of misery negatively affect the performance of tree indexes in insertion-heavy workloads. Waves of misery have been first observed in the context of the B-tree, where these waves cause unpredictable index performance. In particular, the performance of search and index-update operations deteriorate when a wave of misery takes place, but is more predictable between the waves. This paper investigates the presence or lack of waves of misery in several R-tree variants, and studies the extent of which these waves impact the performance of each variant. Interestingly, although having poorer query performance, the Linear and Quadratic R-trees are found to be more resilient to waves of misery than both the Hilbert and R*-trees. This paper presents several techniques to reduce the impact in performance of the waves of misery for the Hilbert and R*-trees. One way to eliminate waves of misery is to force node splits to take place at regular times before nodes become full to achieve deterministic performance. The other way is that upon splitting a node, do not split it evenly but rather at different node utilization factors. This allows leaf nodes not to fill at the same pace. We study the impact of two new techniques to mitigate waves of misery after the tree index has been constructed, namely Regular Elective Splits (RES, for short) and Unequal Random Splits (URS, for short). Our experimental investigation highlights the trade-offs in performance of the introduced techniques and the pros and cons of each technique.

This paper analyzes various receiver schemes for orthogonal frequency division multiplexing (OFDM) transmission in impulsive noise. We consider Rayleigh block-fading and model the noise process by additive white symmetric α-stable noise (AWSαSN). We begin by discussing maximum-likelihood (ML) detection performance of baseband OFDM. Though optimal, the computational cost increases exponentially with the number of carriers. Alternatively, one may evaluate soft-estimates of the transmitted symbol block and employ carrier-wise detection to lower computational complexity. We analyze such schemes under the frameworks of M-estimation and compressed sensing theory. Moving on, we highlight important rules that ensure the passband AWSαSN process is converted to a baseband form suitable for the discussed schemes. Finally, it is shown that linear passband-to-baseband conversion actually reduces the signal-to-noise ratio (SNR) at the receiver and that all these rules may be avoided by applying an estimation scheme directly on the passband samples.

In this paper we analyze design aspects of a single-carrier digital communications receiver in the presence of impulsive noise. We use the additive white symmetric α-stable noise (AWSαSN) to model the channel noise. By introducing passband sampling, efficient constellations and suitable baseband detectors, we show that the uncoded error performance of the conventional (linear) receiver can be enhanced given the real and imaginary components of the transmitted symbol are decoded separately. The performance may be improved further by sacrificing the linearity of the system. Various non-linear estimation and joint-detection schemes are discussed and their error performance analyzed. It is shown that if the receiver bandwidth is large enough, impulsive noise may be effectively countered in a single-carrier communications system.

Impulsive noise is a non-Gaussian heavy-tailed random process that is encountered in various communication scenarios. If not catered for, a single impulse will corrupt several symbols in an OFDM block. In this paper we analyze the maximum-likelihood (ML) detection error performance of uncoded OFDM. Results are presented for two different models with emphasis on binary and quadrature phase-shift keying (BPSK/QPSK) constellations. As the number of carriers increases, the error performance actually tends towards the Gaussian noise error curve irrespective of the noise impulsiveness. These results provide benchmarks to validate error performance of various schemes in impulsive noise.

The performance of conventional digital communication schemes in the presence of additive white Gaussian noise (AWGN) has been widely studied and optimized. The efficiency of these systems, however, is severely hampered if the channel noise is impulsive. Impulsive noise is non-Gaussian in nature and is modeled well by random processes based on heavy-tailed symmetric α-stable (SαS) distributions. If the noise samples are identical and mutually independent, the additive white SαS noise (AWSαSN) model is used to simulate the channel. As performance is conventionally analyzed at the baseband level, we investigate characteristics of complex baseband noise derived from passband AWSαSN. The baseband noise samples are shown to be mutually independent with identical distribution. The bivariate distribution of each complex noise sample takes on a star-like geometrical configuration. We also investigate the baseband scale parameter as a function of the noise impulsiveness and system parameters.

The conventional additive white Gaussian noise (AWGN) model adequately simulates many noisy environments that hamper the performance of practical digital communication systems. However if the channel noise is impulsive, the approximation this model provides reduces significantly. The AWGN channel may then be replaced by the more general additive white symmetric α-stable noise (AWSαSN) model. When converted to its complex baseband form, the resulting noise for the non-Gaussian AWSαSN case is radically different from its Gaussian counterpart. In this paper we investigate the properties of baseband noise for the general AWSαSN case using conventional passband-to-baseband conversion schemes. The converted noise is generally not isotropic and furthermore the real and imaginary components may be dependent. By varying certain physical parameters we may attain different non-isotropic distributions. Using the variable geometry offered by these distributions, efficient placement of signal points on the constellation map for the quadrature phase-shift keying (QPSK) scheme is proposed. It is shown that efficient placement of signal points significantly improve the uncoded error performance of the system. We plot the bit error rate (BER) and symbol error rate (SER) curves against a signal-to-noise ratio (SNR) measure for a few selected rotated versions of the QPSK scheme.

RADARSAT-1 baseline data acquisition planning has been performed for the past nine years under the Canadian Space Agency's Background Mission. Background Mission is about building uniform, global archives in support of RADARSAT-1 Program objectives of time- and site-specific data collections. Most of these objectives were met in the first five years of the nominal mission duration. An extended phase of Background Mission is under way, the most important element of which is a continuous four-season coverage of the Arctic Basin. This paper reports on the progress of this coverage campaign, which started in mid-2003, and has since been implemented uninterruptedly for summer, fall, winter and spring snapshots of the northern polar cap and a large area around it. It is expected that the systematic, sustained RADARSAT Background Mission coverage of the Arctic would result into valuable temporal records of a part of the world that is particularly sensitive to global climate change.

RADARSAT-1 Background Mission signifies baseline acquisitions of the satellite for building various site, time and application-specific data archives. The archives are to be considered benchmarks for global change studies and can be used in such applications as terrain mapping with radargrammetry and interferometry. These acquisitions were part of several coverage types that were undertaken over the past six years of the satellite operations. The variety of coverage is the result of the satellite's variable imaging modes, such as viewing angle, ground resolution, swath width, etc. Early ScanSAR beam coverage provided snapshots of large surfaces of the Earth. A RADARSAT beam-pair stereo coverage of the world's landmass is nearing completion. Remote oceanic islands were imaged successively to build multi-seasonal and multi-annual data sets for change detection. RADARSAT Fine beam resolution data were collected over most of the major cities of the world. Data acquisitions during the left-looking mode of operations of the satellite focused on the regions considered important in terms of geoscience applications. In the fall of 2000, the Canadian Space Agency improved RADARSAT-1 orbit maintenance for Fine beam interferometry over Antarctica. The opportunity was availed to collect similar data over Canada, Greenland, Iceland and some other ice capped regions of the world. These data acquisition campaigns are referred to in the following account on the status of RADARSAT-1 Background Mission.

The International Charter 'Space and Major Disasters' is an initiative on the part of the European, French, Canadian and Indian space agencies as well as the U.S. National Oceanic and Atmospheric Administration (NOAA) to pool together their space and ground resources in response to natural and technological disasters. Since the start of the Charter operations on November 1, 2000, it has been invoked several times to cover such events as a landslide in Slovenia, earthquakes (and resulting landslides) in El Salvador and India; oil spills off the coasts of Galapagos, Lebanon and Denmark; flooding in France, Germany, eastern Siberia and Indonesia; and volcanic eruptions in Italy and Congo. In all these cases, multi-satellite data and products were delivered to users to assist them in their relief efforts. The following is an account of the Charter operational procedure and brief descriptions of the various disasters for which satellite data was requested under the Charter.