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  • 1
    Electronic Resource
    Electronic Resource
    Springer
    Distributed computing 5 (1992), S. 175-186 
    ISSN: 1432-0452
    Keywords: Fault-tolerance ; Agreement ; Distributed system
    Source: Springer Online Journal Archives 1860-2000
    Topics: Computer Science
    Notes: Summary The binary Byzantine Agreement problem requiresn−1 receivers to agree on the binary value broadcast by a sender even when some of thesen processes may be faulty. We investigate the message complexity of protocols that solve this problem in the case of crash failures. In particular, we derive matching upper and lower bounds on the total, worst and average case number of meassages needed in the failure-free executions of such protocols. More specifically, we prove that any protocol that tolerates up tot faulty processes requires a total of at leastn+t−1 messages in its failure-free executions —and, therefore, at least [(n+t−1)/2] messages in the worst case and min (P 0,P 1)·(n+t−1) meassages in the average case, whereP v is the probability that the value of the bit that the sender wants to broadcast isv. We also give protocols that solve the problem using only the minimum number of meassages for these three complexity measures. These protocols can be implemented by using 1-bit messages. Since a lower bound on the number of messages is also a lower bound on the number of meassage bits, this means that the above tight bounds on the number of messages are also tight bounds on the number of meassage bits.
    Type of Medium: Electronic Resource
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  • 2
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    Unknown
    PANGAEA
    In:  Supplement to: Weber, Samuel; Beutel, Jan; Da Forno, Reto; Geiger, Alain; Gruber, Stephan; Gsell, Tonio; Hasler, Andreas; Keller, Matthias; Lim, Roman; Limpach, Philippe; Meyer, Matthias; Talzi, Igor; Thiele, Lothar; Tschudin, Christian; Vieli, Andreas; Vonder Mühll, Daniel; Yücel, Mustafa (2019): A decade of detailed observations (2008-2018) in steep bedrock permafrost at Matterhorn Hörnligrat (Zermatt, CH). Earth System Science Data, 11, 1203-1237, https://doi.org/10.5194/essd-11-1203-2019
    Publication Date: 2023-03-08
    Description: The data presented is a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland at 3500 m a.s.l. during the time period 2008-2018 by the PermaSense project. This data set constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over the past decade. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models (code for generating, processing and validating this data set is published on Zenodo https://doi.org/10.5281/zenodo.2542715, 2019). This data set provides primary data products as well as derived data products: GNSS raw data: GNSS observables in the form of daily RINEX 2.11 files GNSS derived data products: Daily positions computed using double-differencing GNSS processing Timelapse images: High-resolution visible light images Timeseries data raw: Per-year and location files or raw sampled data: Weather station, ground temperature, ground resistivity, fracture displacement and inclinometer data Timeseries derived data products: Cleaned and aggregated hourly values of the above Timeseries sanity plots: Standardized plots to obtain a visual overview and check data All data contained in this data set including updates to newer data can also be retrieved using the toolset available at https://doi.org/10.5281/zenodo.2542715, 2019 from the online PermaSense data repository at http://data.permasense.ch.
    Keywords: File content; File format; File name; File size; Long-term monitoring; Matterhorn_Hoernligrat; Matterhorn, Switzerland; Mountain Permafrost; MULT; Multiple investigations; Natural hazards; PermaSense; Uniform resource locator/link to file; Wireless sensors
    Type: Dataset
    Format: text/tab-separated-values, 40 data points
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  • 3
    Publication Date: 2023-07-19
    Description: 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 meter 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.
    Keywords: Binary Object; Binary Object (File Size); Cryosphere; File content; GNSS; kinematics; Landslides; mass movements; MULT; Multiple investigations; Natural hazards; Permafrost; Rockfall; Swiss_Alps
    Type: Dataset
    Format: text/tab-separated-values, 20 data points
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  • 4
    Publication Date: 2023-07-19
    Description: 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 meter a.s.l and comprises 229'669 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. All data contained in this data set including updates to newer data can also be retrieved using the toolset available at https://git.uibk.ac.at/informatik/neslab/public/permasense/permasense_datamgr from the online PermaSense data repository at http://data.permasense.ch.
    Keywords: Binary Object; Binary Object (File Size); Cryosphere; File content; GNSS; kinematics; Landslides; mass movements; MULT; Multiple investigations; Natural hazards; Permafrost; Rockfall; Swiss_Alps
    Type: Dataset
    Format: text/tab-separated-values, 22 data points
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  • 5
    Publication Date: 2023-11-21
    Description: The data presented is a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland at 3500 m a.s.l. during the time period 2008-2019 by the PermaSense project. This data set constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 114.5 million data points captured over the past decade. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. This data set provides primary data products as well as derived data products: GNSS raw data: GNSS observables in the form of daily RINEX 2.11 files GNSS derived data products: Daily positions computed using double-differencing GNSS processing Timelapse images: High-resolution visible light images Timeseries data raw: Per-year and location files or raw sampled data: Weather station, ground temperature, ground resistivity, fracture displacement and inclinometer data Timeseries derived data products: Cleaned and aggregated hourly values of the above Timeseries sanity plots: Standardized plots to obtain a visual overview and check data All data contained in this data set including updates to newer data can also be retrieved using the toolset available at https://gitlab.ethz.ch/tec/public/permasense/permasense_datamgr from the online PermaSense data repository at http://data.permasense.ch. The version/tag used for the 2020 edition of the Matterhorn data is https://gitlab.ethz.ch/tec/public/permasense/permasense_datamgr/tree/matterhorn_data_2020.
    Keywords: File content; File format; File name; File size; Long-term monitoring; Matterhorn_Hoernligrat; Matterhorn, Switzerland; Mountain Permafrost; MULT; Multiple investigations; Natural hazards; Uniform resource locator/link to file; Wireless sensors
    Type: Dataset
    Format: text/tab-separated-values, 85 data points
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  • 6
    Publication Date: 2023-11-21
    Description: The data presented is a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland at 3500 m a.s.l. during the time period 2008-2020 by the PermaSense project. This data set constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 154.1 million data points captured over the past decade. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. This data set provides primary data products as well as derived data products: GNSS raw data: GNSS observables in the form of daily RINEX 2.11 files GNSS derived data products: Daily positions computed using double-differencing GNSS processing Timelapse images: High-resolution visible light images Timeseries data raw: Per-year and location files or raw sampled data: Weather station, ground temperature, ground resistivity, fracture displacement and inclinometer data Timeseries derived data products: Cleaned and aggregated hourly values of the above Timeseries sanity plots: Standardized plots to obtain a visual overview and check data. All data contained in this data set including updates to newer data can also be retrieved using the toolset available at https://gitlab.ethz.ch/tec/public/permasense/permasense_datamgr from the online PermaSense data repository at http://data.permasense.ch. The version/tag used for the 2021 edition of the Matterhorn data is https://gitlab.ethz.ch/tec/public/permasense/permasense_datamgr/tree/matterhorn_data_2021.
    Keywords: Binary Object; File content; Long-term monitoring; Matterhorn_Hoernligrat; Matterhorn, Switzerland; Mountain Permafrost; MULT; Multiple investigations; Natural hazards; Wireless sensors
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
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  • 7
    Publication Date: 2023-11-21
    Description: The data presented is a unique ten+ year data record obtained from in-situ measurements in steep bedrock permafrost in an Alpine environment on the Matterhorn Hörnligrat, Zermatt, Switzerland at 3500 m a.s.l. during the time period 2008-2021 by the PermaSense project. This data set constitutes the longest, densest and most diverse data record in the history of mountain permafrost research worldwide with 17 different sensor types used at 29 distinct sensor locations consisting of over 154.1 million data points captured over the past decade. By documenting and sharing this data in this form we contribute to making our past research reproducible and facilitate future research based on this data e.g. in the area of analysis methodology, comparative studies, assessment of change in the environment, natural hazard warning and the development of process models. This data set provides primary data products as well as derived data products: GNSS raw data: GNSS observables in the form of daily RINEX 2.11 files GNSS derived data products: Daily positions computed using double-differencing GNSS processing Timelapse images: High-resolution visible light images Timeseries data raw: Per-year and location files or raw sampled data: Weather station, ground temperature, ground resistivity, fracture displacement and inclinometer data Timeseries derived data products: Cleaned and aggregated hourly values of the above Timeseries sanity plots: Standardized plots to obtain a visual overview and check data. All data contained in this data set including updates to newer data can also be retrieved using the toolset available at https://gitlab.ethz.ch/tec/public/permasense/permasense_datamgr from the online PermaSense data repository at http://data.permasense.ch. The version/tag used for the 2022 edition of the Matterhorn data is https://gitlab.ethz.ch/tec/public/permasense/permasense_datamgr/tree/matterhorn_data_2022.
    Keywords: Binary Object; File content; Long-term monitoring; Matterhorn_Hoernligrat; Matterhorn, Switzerland; MULT; Multiple investigations; Natural hazards; Wireless sensors
    Type: Dataset
    Format: text/tab-separated-values, 36 data points
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  • 8
    Publication Date: 2021-07-21
    Description: Lahars are among the most hazardous mass flow processes on earth and have caused up to 23 000 casualties in single events in the recent past. The Cotopaxi volcano, 60 km southeast of Quito, has a well‐documented history of massively destructive lahars and is a hotspot for future lahars due to (i) its ~10 km2 glacier cap, (ii) its 117–147‐year return period of (Sub)‐Plinian eruptions, and (iii) the densely populated potential inundation zones (300 000 inhabitants). Previous mechanical lahar models often do not (i) capture the steep initial lahar trajectory, (ii) reproduce multiple flow paths including bifurcation and confluence, and (iii) generate appropriate key parameters like flow speed and pressure at the base as a measure of erosion capacity. Here, we back‐calculate the well‐documented 1877 lahar using the RAMMS debris flow model with an implemented entrainment algorithm, covering the entire lahar path from the volcano edifice to an extent of ~70 km from the source. To evaluate the sensitivity and to constrain the model input range, we systematically explore input parameter values, especially the Voellmy–Salm friction coefficients μ and ξ. Objective selection of the most likely parameter combinations enables a realistic and robust lahar hazard representation. Detailed historic records for flow height, flow velocity, peak discharge, travel time and inundation limits match best with a very low Coulomb‐type friction μ (0.0025–0.005) and a high turbulent friction ξ (1000–1400 m/s2). Finally, we apply the calibrated model to future eruption scenarios (Volcanic Explosivity Index = 2–3, 3–4, 〉4) at Cotopaxi and accordingly scaled lahars. For the first time, we anticipate a potential volume growth of 50–400% due to lahar erosivity on steep volcano flanks. Here we develop a generic Voellmy–Salm approach across different scales of high‐magnitude lahars and show how it can be used to anticipate future syneruptive lahars.
    Description: A generic model approach is developed to simulate massive syneruptive lahars at Cotopaxi from initiation on the steep volcano flanks to distal reaches. Evaluation of 14 calibration constraints shows that the Voellmy–Salm model reliably reproduces bulk behaviour of syneruptive lahars. Estimations of lahar erosivity on the volcano flanks anticipate an erosion‐related volume increase for future Cotopaxi lahars between 50 and 400%.
    Description: Bundesministerium für Bildung und Forschung http://dx.doi.org/10.13039/501100002347
    Keywords: 551 ; debris flow erosion ; lahar ; model calibration ; numerical model ; predictive modelling
    Type: article
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  • 9
    Publication Date: 2019-02-10
    Description: A lasting legacy of the International Polar Year (IPY) 2007–2008 was the promotion of the Permafrost Young Researchers Network (PYRN), initially an IPY outreach and education activity by the International Permafrost Association (IPA). With the momentum of IPY, PYRN developed into a thriving network that still connects young permafrost scientists, engineers, and researchers from other disciplines. This research note summarises (1) PYRN’s development since 2005 and the IPY’s role, (2) the first 2015 PYRN census and survey results, and (3) PYRN’s future plans to improve international and interdisciplinary exchange between young researchers. The review concludes that PYRN is an established network within the polar research community that has continually developed since 2005. PYRN’s successful activities were largely fostered by IPY. With 〉200 of the 1200 registered members active and engaged, PYRN is capitalising on the availability of social media tools and rising to meet environmental challenges while maintaining its role as a successful network honouring the legacy of IPY.
    Repository Name: EPIC Alfred Wegener Institut
    Type: Article , isiRev
    Format: application/pdf
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  • 10
    Publication Date: 2018-06-19
    Print ISSN: 2169-9003
    Electronic ISSN: 2169-9011
    Topics: Geosciences , Physics
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