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5G cellular networks are now a reality and promise to improve key performance indicators (KPIs), such as Gbps data rates and latencies in the order of milliseconds. While some of these KPIs are achievable in urban scenarios, rural areas often face challenging connectivity conditions due to the lack of terrestrial network (TN) infrastructure. To solve this problem, non-terrestrial networks (NTNs) such as satellite-based solutions, have been introduced to provide coverage in remote regions. Therefore, a multi-connectivity approach can be integrated to simultaneously serve an end-user by merging satellite and cellular links in a joint approach. This study explores, using experimental data, the benefits of both TN-TN and TN-NTN multi-connectivity in rural environments. The results obtained demonstrate that a traditional single-connectivity approach may not be sufficient to provide service to rural environments due to the KPIs requirements given several use cases within these rural areas. The multi-connectivity strategy, which jointly integrates 5G and satellite networks, meets the network availability requirements for latency, downlink throughput, and uplink throughput KPIs at least 98%, 99%, and 95% of the time, respectively, for several use cases, such as precision agriculture, livestock monitoring, and forest management. These include applications like microclimate monitoring, remote operational support, early pest detection, and real-time tracking of livestock transport., Comment: 12 pages, 9 figures

Structural breaks occur in timeseries data across a broad range of fields, from economics to nanosciences. For measurements of single-molecule break junctions, structural breaks in conductance versus displacement data occur when the molecular junction ruptures. This moment is significant because the molecule is likely in its most extended geometry, and therefore resembles most closely the geometry used in theoretical predictions. Conventional single-molecule break junction data analysis, on the other hand, typically uses the entire molecular plateau to estimate the single-molecule conductance, which skews the estimate when the plateau is sloped. Borrowing from econometrics, where the study of structural breaks is well established, we present change point detection (CPD) as a tool to search for junction rupture in single-molecule break junction data, and improve estimates in single-molecule conductance. We demonstrate that using CPD instead of the conventional 1D conductance histogram to determine the mean molecular conductance yields a standard deviation in the estimate of typically half that of the conventional approach, greatly improving accuracy. We apply CPD to three separate data sets, two on 4,4'-bipyridine and one on a silane, two at room temperature and one at 4 K, two in one lab, one in another, to show the wide applicability of even the simplest of CPD algorithms: the Chow test. This versatility and better accuracy will propagate into more accurate theoretical simulations. These improved metrics, in turn, will further improve any downstream analyses, including all emerging machine learning approaches., Comment: 33 pages and 11 figures and supporting material of 8 pages and 3 figures

From administrative registers of last names in Santiago, Chile, we create a surname affinity network that encodes socioeconomic data. This network is a multi-relational graph with nodes representing surnames and edges representing the prevalence of interactions between surnames by socioeconomic decile. We model the prediction of links as a knowledge base completion problem, and find that sharing neighbors is highly predictive of the formation of new links. Importantly, We distinguish between grounded neighbors and neighbors in the embedding space, and find that the latter is more predictive of tie formation. The paper discusses the implications of this finding in explaining the high levels of elite endogamy in Santiago.

Based on a geocoded registry of more than four million residents of Santiago, Chile, we build two surname-based networks that reveal the city's population structure. The first network is formed from paternal and maternal surname pairs. The second network is formed from the isonymic distances between the city's neighborhoods. These networks uncover the city's main ethnic groups and their spatial distribution. We match the networks to a socioeconomic index, and find that surnames of high socioeconomic status tend to cluster, be more diverse, and occupy a well-defined quarter of the city. The results are suggestive of a high degree of urban segregation in Santiago.

The Canonical Polyadic (CP) tensor decomposition is frequently used as a model in applications in a variety of different fields. Using jackknife resampling to estimate parameter uncertainties is often desirable but results in an increase of the already high computational cost. Upon observation that the resampled tensors, though different, are nearly identical, we show that it is possible to extend the recently proposed Concurrent ALS (CALS) technique to a jackknife resampling scenario. This extension gives access to the computational efficiency advantage of CALS for the price of a modest increase (typically a few percent) in the number of floating point operations. Numerical experiments on both synthetic and real-world datasets demonstrate that the new workflow based on a CALS extension can be several times faster than a straightforward workflow where the jackknife submodels are processed individually.