Intelligent Communication

A corpus of parallel text in 21 European languages from the proceedings of the European Parliament. The Europarl parallel corpus is extracted from the proceedings of the European Parliament (1996-2011). It includes versions in 21 European languages: Romanic (French, Italian, Spanish, Portuguese, Romanian), Germanic (English, Dutch, German, Danish, Swedish), Slavik (Bulgarian, Czech, Polish, Slovak, Slovene), Finni-Ugric (Finnish, Hungarian, Estonian), Baltic (Latvian, Lithuanian), and Greek. Parallel sentence counts are in the range 400K-2M, depending on the language combination. The goal of the extraction and processing was to generate sentence aligned text for statistical machine translation systems. For this purpose we extracted matching items and labeled them with corresponding document IDs. Using a preprocessor we identified sentence boundaries. We sentence aligned the data using a tool based on the Church and Gale algorithm. The Europarl corpus was collected mainly to aid research in statistical machine translation (training, evaluation), but it has been used for many other natural language problems: word sense disambiguation, anaphora resolution, information extraction, etc. Monolingual datasets are also available for 9 languages. These are supersets of the parallel versions. Monolingual word counts are in the range 7M-54M, depending on the language. Test Sets: Several test sets have been released for the Europarl corpus. In general, the Q4/2000 portion of the data (2000-10 to 2000-12) should be reserved for testing. All released test sets have been selected from this quarter. Source: Europarl website

The Berlin V2X dataset offers high-resolution GPS-located wireless measurements across diverse urban environments in the city of Berlin for both cellular and sidelink radio access technologies, acquired with up to 4 cars over 3 days. The data enables thus a variety of different ML studies towards vehicle-to-anything (V2X) communication. The data includes information on * physical layer parameters (such as signal strength and signal quality) * cellular radio resource management like cell identity, carrier aggregation and assigned resource blocks * wireless Quality of Service (QoS) like delay and throughput (for cellular) or packet error rate (for sidelink) * positioning information. The datasets are labelled and pre-filtered for a fast on-boarding and applicability. The measurement methodology pursues an application to Machine Learning (ML) for tasks such as QoS prediction, transfer learning, proactive radio resource allocation or link selection, among others.

Dataset containing channel state information (CSI) alongside ground truth data (position tags, timestamps) of a massive MIMO-OFDM system measured with the DICHASUS channel sounder. Measurement parameters and machine-readable file format descriptions are provided in a JSON file (spec.json). Distributed antenna setup with line-of-sight (LoS) and non-line-of-sight (NLoS) channels, measured in the ARENA2036 research factory campus environment.

This dataset provides wireless measurements from two industrial testbeds: iV2V (industrial Vehicle-to-Vehicle) and iV2I+ (industrial Vehicular-to-Infrastructure plus sensor). iV2V covers 10h of sidelink communication scenarios between 3 Automated Guided Vehicles (AGVs), while iV2I+ was conducted for around 16h at an industrial site where an autonomous cleaning robot is connected to a private cellular network. The data includes information on physical layer parameters (such as signal strength and signal quality), wireless Quality of Service (QoS) like delay and throughput, and positioning information. The datasets are labelled and pre-filtered for a fast on-boarding and applicability. The common measurement methodology to both datasets pursues an application to Machine Learning (ML) for tasks such as fingerprinting, line-of-sight detection, QoS prediction or link selection, among others.

MVX incorporates realistic physical world simulation with a differentiable accurate ray tracing wireless simulation that includes multi-agent and multimodal datasets for AI-driven digital twin applications in vehicular communication systems.

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