Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
1. IntroductionSince 1978 a new type of remote sensing has been developed - radar remote sensing, but it was not until 1990 when the applications for this type of remote sensing started to be tested. The present paper tries to present the applications for Interferometric Synthetic Aperture Radar (InSAR) by expounding some cogent papers and their results. Even though there is plenty literature for the methodological approach (Adam et al., 2009, Blanco-Sanchez et al., 2008, Poneos and Dana, 2008, Scheuchl et al., 2009, Simonetto and Follin, 2012, Wegmuller et al., 2010, Zhu et al., 2009, Zhu and Bamler, 2010) in this paper we will focus only on the practical approach for this technique.SAR (Synthetic Aperture Radar) technology involves the existence of a certain type of radar, capable of sending and receiving a long wave signal, based on the move registered by the radar between the antenna mounted on a spacecraft or aircraft, and the object. This type of radar can provide remote images with high resolution. The applications of this technology go beyond the atmosphere into the extraterrestrial environment. An example of extraterrestrial application is the Magellan mission (Campbell, 1995) that studied Venus. The satellite was launched in June 1989 and its mission was to map Venus's surface. The mission lasted for three years and the satellite mapped 98% of the surface with a spatial resolution of the images of about 100 m.The principle behind this technology is to cast a radar signal towards an object, which is reflected and reaches back to the antenna. Depending on the time and the properties of the reflected beam, the signal is used to generate images. A major advantage of radar images, compared to optic ones, is that they can be acquired during the night and also they have a high spatial resolution of about 1 m.SAR images started to be acquired in 1978, when the first SAR satellite (SEASAT) was launched but became popular only after the 1990s as technology improved.After the 1990s many radar satellites were launched: ERS-1 (launched in 1991 by ESA), ERS-2 (launched in 1995), JERS-1 (1992, Japan), RADARSAT (1995, Canada), ALOS PALSAR (2005, Japan). Two of the newest radar satellites are TerraSAR-X (2007) and TanDEM-X (2010), both launched by DLR (German Spatial Agency). TanDEM's mission is to second TerraSAR and to map the entire terrestrial surface to obtain a global digital elevation model with a 1 m resolution.Closely related with the SAR technology, InSAR (Interferometric Synthetic Aperture Radar) uses at least two SAR images (the product obtained is called interferogram) to track the temporal changes of different objects of interest, to map the vertical movements or the texture changes from certain areas or to obtain a very high resolution DEM.The InSAR technique is used frequently in earth sciences because it can measure the finest vertical movements, thus it is being used in many domains, especially in monitoring natural hazards. Also, InSAR reveal better results in urban areas because of scatter elements, so this paper focuses mostly on InSAR applications in urban areas.2. Application of InSARThe applications for this technology ranges from geology - tectonics and neotectonics, earthquakes, volcanology (Perlock et al., 2009) to geomorphology - elevations and subsidence, sometimes under 1 mm, caused by groundwater over exploitation or by old mines, to glaciology (Bamber et al., 1999, Rott et al., 2007) and to land use - forest monitoring and land use changes (Amarsaikhan et al., 2007, Del Frate et al., 2008), but also in urban applications (Dell'Acqua et al., 2011, Dell'Acqua and Gamba, 2003), building extraction and measuring (Bennett and Blacknell, 2003, Dong et al., 2011), and change detection (Schmitt et al., 2011, Thonfeld and Menz, 2011).This technique is successfully applied in risk studies, from prevention to mitigation. In these cases InSAR is useful for monitoring the elements at risk, early response and rapid assessment of damages (Brunner et al. …
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it