The Compact Muon Solenoid (CMS) Trigger of the Large Hadron Collider (LHC) particle accelerator at CERN selects potentially interesting particle collision data to process and archive for further study. The first stage of the trigger system, the hardware-based L1 Trigger, must sift through roughly 3 terabits/second of data describing the energy distribution of particles generated in the collisions, and reduce it to 100 megabits/second of event data that subsequent systems can handle. Without the CMS Trigger, the amount of experiment-generated data would quickly outstrip the archiving ability of the LHC system.Because of the sheer amount of input data and the rate at which it is generated, the hardware-based L1 Trigger is subject to stringent performance requirements. These will become even more severe as the LHC is upgraded over the next ten years, requiring a careful redesign of the L1 Trigger hardware. For example, future upgrades may introduce particle motiontracking data into the L1 Trigger, resulting in an increased input data rate of up to 40 terabits/second. The need to modify the design as the LHC system is upgraded, the low-volume cost advantages of FPGAs, and a desire for a flexible and adaptable system all point toward the use of FPGAs as a hardware implementation solution.In this paper, we present several different FPGA implementations of the electron/photon identification module, a key part of the new Clustering Algorithm for the upgraded L1 Trigger. We analyze the resource requirements and performance tradeoffs, and present a qualitative discussion of flexibility to meet the changing needs of the CMS experiment. Finally, we narrow potential design choices to the top candidates and use one in a full Clustering Algorithm implementation.

Abstract. Monitoring of the periglacial environment is relevant for many disciplines including glaciology, natural hazard management, geomorphology, and geodesy. Since October 2022, Rock Glacier Velocity (RGV) is a new Essential Climate Variable (ECV) product within the Global Climate Observing System (GCOS). However, geodetic surveys at high elevation remain very challenging due to environmental and logistical reasons. During the past decades, the introduction of low-cost global navigation satellite system (GNSS) technologies has allowed us to increase the accuracy and frequency of the observations. Today, permanent GNSS instruments enable continuous surface displacement observations at millimetre accuracy with a sub-daily resolution. In this paper, we describe decennial time series of GNSS observables as well as accompanying meteorological data. The observations comprise 54 positions located on different periglacial landforms (rock glaciers, landslides, and steep rock walls) at altitudes ranging from 2304 to 4003 ma.s.l. and spread across the Swiss Alps. The primary data products consist of raw GNSS observables in RINEX format, inclinometers, and weather station data. Additionally, cleaned and aggregated time series of the primary data products are provided, including daily GNSS positions derived through two independent processing tool chains. The observations documented here extend beyond the dataset presented in the paper and are currently continued with the intention of long-term monitoring. An annual update of the dataset, available at https://doi.org/10.1594/PANGAEA.948334 (Beutel et al., 2022),​​​​​​​ is planned. With its future continuation, the dataset holds potential for advancing fundamental process understanding and for the development of applied methods in support of e.g. natural hazard management.

Abstract. Permafrost warming is coinciding with accelerated mass movements, talking place especially in steep, mountainous topography. While this observation is backed up by evidence and analysis of both remote sensing as well as repeat terrestrial surveys undertaken since decades much knowledge is to be gained about the specific details, the variability and the processes governing these mass movements in the mountain cryosphere. This dataset collates data of continuously acquired kinematic observations obtained through in-situ Global Navigation Satellite Systems (GNSS) instruments that have been designed and implemented in a large-scale multi field-site monitoring campaign across the whole Swiss Alps. The landforms covered include rock glaciers, high-alpine steep bedrock bedrock as well as landslide sites, most of which are situated in permafrost areas. The dataset was acquired at 54 different stations situated at locations from 2304 to 4003 m a.s.l and comprises 209’948 daily positions derived through double-differential GNSS post-processing. Apart from these, the dataset contains down-sampled and cleaned time series of weather station and inclinometer data as well as the full set of GNSS observables in RINEX format. Furthermore the dataset is accompanied by tools for processing and data management in order to facilitate reuse, open alternate usage opportunities and support the life-long living data process with updates. To date this dataset has seen numerous use cases in research as well as natural-hazard mitigation and adaptation due to climate change.