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77 results for “Local networks”

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zenodo44/100

Seismic monitoring of Hans glacier (Svalbard) using dedicated local network

Seismic dataset registered during monitoring of Hans glacier (Svalbard) using dedicated local network in Hornsund 10/2017-04/2018 carried by Wojciech Gajek and coworkers financed by an internal grant of Institute of Geophysics Polish Academy of Sciences. Dataset can be used for analyzing the glacier seismicity. More on that topic in Svalbard can be find in Seismology chapter of SESS 2019 report <a href="https://sios-svalbard.org/SESS_Issue2">https://sios-svalbard.org/SESS_Issue2</a> Project log in ResearchGate: <a href="https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network">https://www.researchgate.net/project/Seismic-monitoring-of-Hans-glacier-Svalbard-using-dedicated-local-network</a>   The data includes seismic records (3C) from the temporary seismic network. It is advised to take into the processing also the permanent station HSPB. Data is packed as a zip archive. Its structure is SDS, compatible with ObsPy query system. The structure includes HSPB but HSPB data is not there due to limited file space here (its publicly available eg in Orpheus).   Other files are: coordinates, map data availability chart my presentation from ESC Malta with preliminary results photos from field installation data conditioning report Have fun. You can contact me via researchgate: <a href="https://www.researchgate.net/profile/Wojciech_Gajek">https://www.researchgate.net/profile/Wojciech_Gajek</a>

opencc-by-4.0Dec 2018View details →

zenodo44/100

Local survey network and monitoring data of VLBI telescope at Metsähovi

Observations of local surveying network and monitorin at Metsähovi. More information is in file Data_description.pdf.

opencc-by-4.0Apr 2022View details →

zenodo44/100

Dataset of "Comparison of Localization Methods for Internet of Things in 5G Cellular Networks: A Wide-scale Assessment"

As the 3rd generation partnership project (3GPP) organization pushes out new releases, positioning in heterogeneous mobile networks enables the achievement of the accuracy required in the majority of industrial applications without dependence on global navigation satellite systems (GNSS). This study presents the results gathered during an extensive measurement campaign related to the practical applicability of localization in next-generation heterogeneous networks. We present an accuracy comparison of basic timing advance (TA) localization with the k-nearest neighbor (KNN), decision tree-based random forest (RF), extreme gradient boosting (XGBoost), and long short-term memory (LSTM) recurrent neural network. Our results demonstrate that TA cannot be considered an optimal solution from the perspective of localization accuracy because the error roughly corresponds to the average separation distance from the base station (BS) to the end device (ED). In addition, we found that the LSTM approach is not optimal for the outdoor localization of moving ED because of the combination of multiple factors, with sparse deployment being the most important. The median value of the location error of the LSTM was more than 200m higher than that of the TA for the self-validation dataset. However, a simple KNN regression shows solid results for 5G New Radio (NR) operating in the non-standalone (NSA) mode. KNN provided the most accurate results of all methods, with median error values of approximately 12 (k=3) and 82 (k=5) m for the self-validated and cross-validated datasets, respectively.

embargoedcc-by-4.0May 2024View details →

zenodo40/100

Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks"

Local optima network metrics from the IEEE CEC 2024 paper "Information flow and Laplacian dynamics on local optima networks".  There are two CSV files: one for each of the two iterated local search confgurations used to construct the networks (low or high). In each file, a row contains information about one QAPLIB instance. Easch row contains all the metrics computed for the associated LON and also algorithm performance data on the instance.

opencc-by-4.0Mar 2024View details →

zenodo40/100

Data of "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step."

Data related to =========== title = "Recurrent Neural Networks (RNNs) with dimensionality reduction and break down in computational mechanics; application to multi-scale localization step.", journal = "Computer Methods in Applied Mechanics and Engineering", volume ="390", year = "2022", doi = "https://doi.org/<a href="http://dx.doi.org/10.1016/j.cma.2021.114476">10.1016/j.cma.2021.114476</a> ", pages = "114476 ", author = "Wu, Ling and Noels, Ludovic" We would be grateful if you could cite the paper in the case in which you are using the data   The files replace version 1 whose zip was corrupted.

opencc-by-4.0Nov 2021View details →

dryad40/100

Data from: Domain-specific neural networks improve automated bird sound recognition already with small amount of local data

An automatic bird sound recognition system is a useful tool for collecting data of different bird species for ecological analysis. Together with autonomous recording units (ARUs), such a system provides a possibility to collect bird observations on a scale that no human observer could ever match. During the last decades progress has been made in the field of automatic bird sound recognition, but recognizing bird species from untargeted soundscape recordings remains a challenge. In this article we demonstrate the workflow for building a global identification model and adjusting it to perform well on the data of autonomous recorders from a specific region. We show how data augmentation and a combination of global and local data can be used to train a convolutional neural network to classify vocalizations of 101 bird species. We construct a model and train it with a global data set to obtain a base model. The base model is then fine-tuned with local data from Southern Finland in order to adapt it to the sound environment of a specific location and tested with two data sets: one originating from the same Southern Finnish region and another originating from a different region in German Alps. Our results suggest that fine-tuning with local data significantly improves the network performance. Classification accuracy was improved for test recordings from the same area as the local training data (Southern Finland) but not for recordings from a different region (German Alps). Data augmentation enables training with a limited number of training data and even with few local data samples significant improvement over the base model can be achieved. Our model outperforms the current state-of-the-art tool for automatic bird sound classification. Using local data to adjust the recognition model for the target domain leads to improvement over general non-tailored solutions. The process introduced in this article can be applied to build a fine-tuned bird sound classification model for a specific environment.

opencc-zeroSep 2022View details →

zenodo40/100

3D Printing of Double Network Granular Elastomers with Locally Varying Mechanical Properties

Dataset for the article entitled "3D Printing of Double Network Granular Elastomers with Locally Varying Mechanical Properties", by Eva Baur, Benjamin Tiberghien and Esther Amstad published in Advanced Materials.

opencc-by-nc-4.0Mar 2024View details →

zenodo40/100

Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach

20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.

opencc-by-4.0Oct 2022View details →

zenodo40/100

Local Optima Network Analysis of Multi-attribute Vehicle Routing Problem

Multi-Attribute Vehicle Routing Problems (MAVRP) are variants of Vehicle Routing Problems (VRP) in which, besides the original constraint on vehicle capacity present in Capacitated Vehicle Routing Problem (CVRP), there are other restrictions that model diverse real-life system attributes. Among the most common attributes studied in the literature are the vehicle capacity and the maximum route length constraints. The impact of these restrictions on the overall structure of the problem and on the performance of local search algorithms used to solve it is not well known. This paper aims to explain how constraints impact different variants of VRP by altering the structure of the underlying search space. We focus on the analysis of Local Optima Networks (LON) for multiple Traveling Salesman Problem (m-TSP), and VRP with capacity (CVRP), distance (DVRP), and both (DCVRP) constraints. We present results that indicate that metrics obtained for a sample of local optima provide valuable information on the behavior of the landscape under modifications in the constraints of the problem.  The dataset contains the data extracted from the local optima network for a set of variants belonging to the family of vehicle routing problems.

opencc-by-4.0May 2022View details →

zenodo40/100

Operator-Software Impact in Local Tie Networks: Case study at Geodetic Observatory Wettzell (Data set)

The operator-software impact describes the differences between results introduced by different operators using identical software packages but applying different analysis strategies to the same data. This contribution studies the operator-software impact in the framework of local tie determination, and compares two different analysis approaches. Both approaches are used in present local tie determinations and mainly differ in the consideration of the vertical deflection within the network adjustment. However, no comparison study has yet been made so far. Selecting a suitable analysis approach is interpreted as a model selection problem, which is addressed by information criteria within this investigation. A suitable model is indicated by a sufficient goodness of fit and an adequate number of model parameters. Moreover, the stiffness of the networks is evaluated by means of principle component analysis. Based on the date of a measurement campaign performed at the Geodetic Observatory Wettzell in 2021, the impact of the analysis approach on local ties is investigated. For that purpose, an innovated procedure is introduced to obtain reference points of space geodetic techniques defining the local ties. Within the procedure, the reference points are defined independently of the used reference frame, and are based on geometrical conditions. Thus, the results depend only on the estimates of the performed network adjustment and, hence, the applied network analysis approach. The comparison of the horizontal coordinates of the determined reference points shows a high agreement. The differences are less than 0.2 mm. However, the vertical components differ by more than 1 mm, and exceed the coverage of the estimated standard deviations. The main reasons for these large discrepancies are a network tilting and a network bending, which is confirmed by a residual analysis.

opencc-by-4.0Sep 2022View details →

zenodo40/100

Uncovering local aggregated air quality index with smartphone captured images leveraging efficient deep convolutional neural network

Short Description: In this research, we vigorously analyze the difficulties of predicting location-specific PM2.5 concentration from photos captured by smartphone cameras. Here, we particularly focus on Dhaka, the capital of Bangladesh, considering its very high level of air pollution exposure to a huge number of its dwellers. In our research, we develop a Deep Convolutional Neural Network (DCNN) and train it using more than a thousand outdoor photos captured and labeled by us. We capture the photos at various locations in Dhaka, Bangladesh, and label them based on PM2.5 concentration data extracted from the local US consulate as computed by the NowCast algorithm. During training with the dataset, our model learns a correlation index through supervised learning, which improves the model's ability to act as a Picture-based Predictor of PM2.5 Concentration (PPPC) making it capable of detecting comparable daily aggregated AQI index from a photo captured by a smartphone. Code and More Details: https://github.com/lepotatoguy/aqi

opencc-by-4.0Apr 2023View details →

dryad40/100