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State-of-the-art space science missions increasingly rely on automation due to spacecraft complexity and the costs of human oversight. The high volume of data, including scientific and telemetry data, makes manual inspection challenging. Machine learning offers significant potential to meet these demands. The Euclid space telescope, in its survey phase since February 2024, exemplifies this shift. Euclid's success depends on accurate monitoring and interpretation of housekeeping telemetry and science-derived data. Thousands of telemetry parameters, monitored as time series, may or may not impact the quality of scientific data. These parameters have complex interdependencies, often due to physical relationships (e.g., proximity of temperature sensors). Optimising science operations requires careful anomaly detection and identification of hidden parameter states. Moreover, understanding the interactions between known anomalies and physical quantities is crucial yet complex, as related parameters may display anomalies with varied timing and intensity. We address these challenges by analysing temperature anomalies in Euclid's telemetry from February to August 2024, focusing on eleven temperature parameters and 35 covariates. We use a predictive XGBoost model to forecast temperatures based on historical values, detecting anomalies as deviations from predictions. A second XGBoost model predicts anomalies from covariates, capturing their relationships to temperature anomalies. We identify the top three anomalies per parameter and analyse their interactions with covariates using SHAP (Shapley Additive Explanations), enabling rapid, automated analysis of complex parameter relationships. Our method demonstrates how machine learning can enhance telemetry monitoring, offering scalable solutions for other missions with similar data challenges., Comment: Presented at IAC 2024

Sustainable sanitation and resource recovery technologies are needed to address rapid environmental and socioeconomic changes. Research prioritization is critical to expedite the development and deployment of such technologies across their vast system space (e.g., technology choices, design and operating decisions). In this study, we introduce QSDsan - an open-source tool written in Python (under the object-oriented programming paradigm) and developed for the quantitative sustainable design (QSD) of sanitation and resource recovery systems. As an integrated platform for system design, process modeling and simulation, techno-economic analysis (TEA), and life cycle assessment (LCA), QSDsan can be used to enumerate and investigate the opportunity space for emerging technologies under uncertainty, while considering contextual parameters that are critical to technology deployment. We illustrate the core capabilities of QSDsan through two distinct examples: (i) evaluation of a complete sanitation value chain that compares three alternative systems; and (ii) dynamic simulation of the wastewater treatment plant described in the benchmark simulation model no. 1 (BSM1). Through these examples, we show the utility of QSDsan to automate design, enable flexible process modeling, achieve rapid and reproducible simulations, and to perform advanced statistical analyses with integrated visualization. We strive to make QSDsan a community-led platform with online documentation, tutorials (explanatory notes, executable scripts, and video demonstrations), and a growing ecosystem of supporting packages (e.g., DMsan for decision-making). This platform can be freely accessed, used, and expanded by researchers, practitioners, and the public alike, ultimately contributing to the advancement of safe and affordable sanitation technologies around the globe.

Permutation decoding is a technique which involves finding a subset $S$, called PD-set, of the permutation automorphism group of a code $C$ in order to assist in decoding. An explicit construction of $\left \lfloor{\frac{2^m-m-1}{1+m}} \right \rfloor$-PD-sets of minimum size $\left \lfloor{\frac{2^m-m-1}{1+m}} \right \rfloor + 1$ for partial permutation decoding for binary linear Hadamard codes $H_m$ of length $2^m$, for all $m\geq 4$, is described. Moreover, a recursive construction to obtain $s$-PD-sets of size $l$ for $H_{m+1}$ of length $2^{m+1}$, from a given $s$-PD-set of the same size for $H_m$, is also established. These results are generalized to find $s$-PD-sets for (nonlinear) binary Hadamard codes of length $2^m$, called $\mathbb{Z}_4$-linear Hadamard codes, which are obtained as the Gray map image of quaternary linear codes of length $2^{m-1}$.

Abhyankar and Moh in their fundamental paper on the embeddings of the line in the plane proved an important inequality which can be stated in terms of the semigroup associated with the branch at infinity of a plane algebraic curve. In this note we study the semigroups of integers satisfying the Abhyankar-Moh inequality and give a simple proof of the Abhyankar-Moh embedding theorem., Comment: 5 pages. This short note is conceived as an appendix to arXiv:1208.0913

DSPOBJ is a FORTRAN subroutine to control the display of three-dimensional line networks on a stand-alone, general-purpose, interactive computer graphics system. The program controls the creation and manipulation of transformation matrices for the display and control of multiple sets of line networks. It provides advanced graphics features such as independent and global scaling, rotation and translation, cross-sectioning, reflection, and simultaneous display of four views.

The XIO system is a set of subroutines that provide generalized data management capability for FORTRAN programs using a direct access file. Arrays of integer, real, double precision, and character data may be stored, each logical group of data identified by a unique matrix number. A matrix may be organized and stored as batches to reduce core requirements. Batches may be accessed randomly or sequentially. The file may be checkpointed and retained, allowing for restarts with stored values. The XIO subroutines operate on either IBM 360-370/OS/VS or DEC PDP-11/RSX computing systems.

Convective heat-transfer tests were made on a 5-inch-diameter hemisphere to determine the variation of Stanton number with the ratio of wall temperature to total temperature. The tests were made at a nominal Mach number of 2 for stagnation temperatures of 760 deg R, 1,030 deg R, and 1,380 deg R. The model was constructed so that radiation effects and also streamwise conduction effects within the model skin were minimized. The results of the tests verified that these effects were small. Tests which were made with different masses of air inside the model to check for conduction effects to the internal air cavity showed these effects to be negligible. For laminar flow on the hemisphere, the Stanton number remained essentially constant as the ratio of wall temperature to total temperature increased. However, for fully established turbulent flow, the Stanton number at some stations decreased on the order of 50 percent as the ratio of wall temperature to total temperature increased. A theory which agreed fairly well with the trend of this decrease is shown for comparison.

A heat-transfer investigation has been made on a blunt cone-cylinder model at a Mach number of 1.98 at yaw angles from 0 deg to 9 deg. The results indicate that, except for the hemispherical nose, the heat-transfer coefficient increased on the windward side and decreased on the leeward side as yaw angle was increased. In general, the increase in heat transfer on the windward side was higher than the corresponding decrease on the leeward side. A comparison with theory (NACA Technical Note 4208) yielded agreement which was, in general, within 10 percent on the cone at all test conditions and on the cylinder at an angle of yaw of 0 deg.