Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions,leading to significant residual errors in geospatial applications.Here,we propose a residual-based neural correc...Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions,leading to significant residual errors in geospatial applications.Here,we propose a residual-based neural correction(RBNC)strategy,in which a neural network learns to model only the systematic distortions left by an initial geometric transformation.By focusing solely on residual patterns,RBNC reduces model complexity and improves performance,particularly in scenarios with sparse or structured control point configurations.We evaluate the method using both simulated datasets(with varying distortion intensities and sampling strategies)and real-world image georeferencing tasks.Compared with direct neural network coordinate converters and classical transformation models,RBNC delivers more accurate and stable results under challenging conditions,while maintaining comparable performance in ideal cases.These findings demonstrate the effectiveness of residual modelling as a light-weight and robust alternative for improving coordinate transformation accuracy.展开更多
We investigate the decoy state quantum key distribution via the atmosphere channels. We consider the efficient decoy state method with one-signal state and two-decoy states. Our results show that the decoy state metho...We investigate the decoy state quantum key distribution via the atmosphere channels. We consider the efficient decoy state method with one-signal state and two-decoy states. Our results show that the decoy state method works even in the channels with fluctuating transmittance. Nevertheless, the key generation rate will be dra-matically decreased by atmosphere turbulence, which sheds more light on the characterization of atmosphere turbulence in realistic free-space based quantum key distributions.展开更多
To prevent,detect,and protect against forest fires,forest personnel need to define rules for determining forest fire risk.In Portugal,all municipalities must annually produce forest fire risk(FFR)maps.To produce more ...To prevent,detect,and protect against forest fires,forest personnel need to define rules for determining forest fire risk.In Portugal,all municipalities must annually produce forest fire risk(FFR)maps.To produce more reliable FFR maps more easily,we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour.The model provides all the maps involved in the FFR model(susceptibility map,hazard map,vulnerability map,economic value map,and potential loss map)and was produced according to Portuguese Forest Authority's(AFN,Autoridade Florestal Nacional)rules for determining the FFR.This model was tested for the Portuguese municipality Santa Maria da Feira,where 40%of the total municipality area falls in the category"very high"or"high"fire risk.The"very high"fire risk area is mainly classified as broad-leaved forest and has the steepest slopes(〉15%).The distance of burned areas to roads was also analyzed;the proportion of burned areas increased with increasing distance to the main roads.In addition,92.6%of the"high"and"very high"risk zones were located in areas with lower elevation.These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors.The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS pro-prietary software.Although the results were obviously the same,the model developed here presents several advan-tages over the other two approaches.Besides being faster,it is easy to change the model parameters according to user needs(i.e.,to the rules of different countries),and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena(e.g.,floods,earthquakes,landslides).The model is easy to use and to create risk and hazard maps rapidly in a free,open source environment that does not require any programming knowledge.展开更多
Optical gains of type-Ⅱ In Ga As/Ga As Bi quantum wells（QWs） with W, N, and M shapes are analyzed theoretically for near-infrared laser applications. The bandgap and wave functions are calculated using the self-con...Optical gains of type-Ⅱ In Ga As/Ga As Bi quantum wells（QWs） with W, N, and M shapes are analyzed theoretically for near-infrared laser applications. The bandgap and wave functions are calculated using the self-consistent k·p Hamiltonian, taking into account valence band mixing and the strain effect. Our calculations show that the M-shaped type-Ⅱ QWs are a promising structure for making 1.3 um lasers at room temperature because they can easily be used to obtain 1.3 um for photoluminescence with a proper thickness and have large wave-function overlap for high optical gain.展开更多
基金National Council for Scientific and Technological Development,Grant No.421278/2023-4,No.309248/2025-6。
文摘Coordinate transformation models often fail to account for nonlinear and spatially dependent distortions,leading to significant residual errors in geospatial applications.Here,we propose a residual-based neural correction(RBNC)strategy,in which a neural network learns to model only the systematic distortions left by an initial geometric transformation.By focusing solely on residual patterns,RBNC reduces model complexity and improves performance,particularly in scenarios with sparse or structured control point configurations.We evaluate the method using both simulated datasets(with varying distortion intensities and sampling strategies)and real-world image georeferencing tasks.Compared with direct neural network coordinate converters and classical transformation models,RBNC delivers more accurate and stable results under challenging conditions,while maintaining comparable performance in ideal cases.These findings demonstrate the effectiveness of residual modelling as a light-weight and robust alternative for improving coordinate transformation accuracy.
基金Supported by the National Natural Science Foundation of China under Grant Nos 11574400,U1304613,11204197,11204379 and 11074244the National Basic Research Program of China under Grant No 2011CBA00200the Doctor Foundation of the Ministry of Education of China under Grant No 20113402110059
文摘We investigate the decoy state quantum key distribution via the atmosphere channels. We consider the efficient decoy state method with one-signal state and two-decoy states. Our results show that the decoy state method works even in the channels with fluctuating transmittance. Nevertheless, the key generation rate will be dra-matically decreased by atmosphere turbulence, which sheds more light on the characterization of atmosphere turbulence in realistic free-space based quantum key distributions.
文摘To prevent,detect,and protect against forest fires,forest personnel need to define rules for determining forest fire risk.In Portugal,all municipalities must annually produce forest fire risk(FFR)maps.To produce more reliable FFR maps more easily,we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour.The model provides all the maps involved in the FFR model(susceptibility map,hazard map,vulnerability map,economic value map,and potential loss map)and was produced according to Portuguese Forest Authority's(AFN,Autoridade Florestal Nacional)rules for determining the FFR.This model was tested for the Portuguese municipality Santa Maria da Feira,where 40%of the total municipality area falls in the category"very high"or"high"fire risk.The"very high"fire risk area is mainly classified as broad-leaved forest and has the steepest slopes(〉15%).The distance of burned areas to roads was also analyzed;the proportion of burned areas increased with increasing distance to the main roads.In addition,92.6%of the"high"and"very high"risk zones were located in areas with lower elevation.These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors.The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS pro-prietary software.Although the results were obviously the same,the model developed here presents several advan-tages over the other two approaches.Besides being faster,it is easy to change the model parameters according to user needs(i.e.,to the rules of different countries),and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena(e.g.,floods,earthquakes,landslides).The model is easy to use and to create risk and hazard maps rapidly in a free,open source environment that does not require any programming knowledge.
基金Supported by the National Basic Research Program of China under Grant No 2014CB643902the Key Program of Natural Science Foundation of China under Grant No 61334004+3 种基金the National Natural Science Foundation of China under Grant No 61404152the Strategic Priority Research Program of the Chinese Academy of Sciences under Grant No XDA5-1the Foundation of National Laboratory for Infrared Physics,the Key Research Program of the Chinese Academy of Sciences under Grant No KGZDEW-804the Creative Research Group Project of Natural Science Foundation of China under Grant No 61321492
文摘Optical gains of type-Ⅱ In Ga As/Ga As Bi quantum wells（QWs） with W, N, and M shapes are analyzed theoretically for near-infrared laser applications. The bandgap and wave functions are calculated using the self-consistent k·p Hamiltonian, taking into account valence band mixing and the strain effect. Our calculations show that the M-shaped type-Ⅱ QWs are a promising structure for making 1.3 um lasers at room temperature because they can easily be used to obtain 1.3 um for photoluminescence with a proper thickness and have large wave-function overlap for high optical gain.
