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Boilers: Theory of operation and Energy Efficiency المراجل البخارية: نظرية العمل وكفاءة الطاقة

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ArcGIS 10 Crack


This is the Complete Pre cracked License Manager.Tips to Install:1.Get the Cracked Permit Supervisor2. First Install the Cracked License Manager, If any Information respect Missing.dll documents Duplicate and Paste both of files ( Msvcp71.dll And Msvcr71.dll files) into Chemical:Windows.3. Replace the service.txt file fróm crack folder tó Install Listing.




ArcGIS 10 crack


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Arcgis 10.6 Break Desktop Total Free of charge Download LatestArcgis Break will be the greatest tool for geospatial analysis. It offers a full real-time survey and operating on the Macintosh and Windows operating program.ArcGIS cracked made by Esri is definitely a Geographical Info Program that can be used for making, dissecting and sharing maps and furthermore to create and gathering topographical details.


Through ArcGIS Online, you can explore maps from Esri and thousands of organizations worldwide and enrich them with your own data to create new maps, map layers and apps. Sign up for a free developer trial account at developers.arcgis.com.


Don't get stuck in finding the absolute optimal planning. The mathematical problem is a hard nut to crack. You'll probably be happy with a 95-99% good solution to start with, from my personal experience as a operations research consultant.


If you would like to give this a quick try, I recommend that you use a web browser to experiment. Start by downloading the Survey123 app into your desktop. You can download it for Windows or for your Mac. Once you have the app, manually launch it and login to download a couple of surveys. Then get the itemID of one of them to start and use it to adjust the itemID parameter: arcgis-survey123://?itemID=insert your own Form item ID here Then paste it into your web browser to launch Survey123 and open one of the surveys you downloaded. Continue experimenting by pre-populating survey questions.


PackageManager manager = mContext.getPackageManager(); Intent i = manager.getLaunchIntentForPackage("com.esri.survey123"); i.setData(Uri.parse("arcgis-survey123://?itemID=89bc8c7844e548e09baa3aad4695e78b")); startActivity(i);


First the *BUG*, after you save the web map, the "arcgis-survey123://" is replaced with "#" in the config. You must manually open up the config file and replace "#" with "arcgis-survey123://" AFTER you have saved the web map. This will happen every time you save the web map and only when dealing with custom pop-ups.


After you add the link to the web map, save, go back to my contents, open the web map back up. Check to see if arcgis-survey123:///?itemID=14b52d0cf8034975bc193751c67df72e&field:object_id=CODE was replaced with #?itemID=14b52d0cf8034975bc193751c67df72e&field:object_id=CODE.


Quick note: Currently, ArcGIS Online and Portal for ArcGIS will 'sanitize' urls added into popups that do not use the http or https protocol. In our case at hand here, arcgis-survey123:// is not using http/s, so it will get removed as all of us found out already. The sanity pass happens at the REST API level. There are good reasons for this clean-up to happen.


We are currently in discussions to see how we can best handle this situation. Our goal is to allow you to use arcgis-survey123:// invocations within a popup and of course support field-value variables myField as you have already pursued. Our plan is to have all of this addressed in our next update to ArcGIS Online (September) and in Portal for ArcGIS 10.5. In the meantime, there is really no way to make your Web Maps hold survey123 calls, except if you are lucky enough like Guido van der Kolk and can literally edit the JSON definition of the Web Map accessing the item OS directory in the machine where Portal for ArcGIS is installed. We need to get creative here for a little while.


Hi. I hate it when computers take things so literally. Change the the parameter name to itemID and you will have a better chance. The parameter is called itemID, not ID. This link should work for you: arcgis-survey123://?itemID=44fd0897c4f741f3adc8ad6e824d0fbf


Have a look at -started-with-public-surveys and pay particular attention to the Collaborate tab in survey123.arcgis.com The Collaborate tab includes the exact url scheme call you need to invoke to launch your public survey.


Sorry about that. We had to change owners of the survey to allow it to be shared publicly which may have changed the itemID. Try this arcgis-survey123://?itemID=21e1d9c90c974df7a0db0c7814c52af8 . I copied it from the collaborate tab.


I'm using custom URL's to populate fields with known values. However field values with special characters like '=' or ',' result in '%3D' or '%2C'. I've read that this has something to do with URL encoding but how can I fix this in an arcgis online webmap so the correct hyperlink is given without URL encoding?


For example: 'arcgis-survey123://?itemID=AAA&center=52.3780716382816,4.90005350773038' is converted into: 'arcgis-survey123://?itemID=AAA&center%3D52.3780716382816%2C4.90005350773038'


The mining of coal resources is necessary to support the smooth growth of the social economy, but doing so underground has resulted in significant ecological and environmental issues [1,2]. Surface cracks are one of the environmental issues brought on by coal mining in western China, particularly in the arid and semi-arid regions [3], which also results in building deformation, destruction of arable land, accelerated soil moisture evaporation, vegetation destruction, and soil erosion [4,5,6]. Additionally, it was discovered that cracks of various widths had a variety of noteworthy impacts on soil water content and soil respiration [7]. Therefore, in order to assess the degree of damage and crack development in the study area and to provide data support and assurance for land reclamation work, it is necessary to first obtain real-time, objective, and high-precision distribution information of surface cracks in the mining area [8]. This information must also be acquired and quantitatively described.


Surface crack extraction through UAV (unmanned aerial vehicle) images has achieved wide application [9]. UAVs have significant advantages such as a high resolution, flexibility and mobility, high efficiency and speed, and low operating costs [10], providing an ideal data source for information extraction of surface cracks in mining areas. The current methods for surface crack extraction through UAV images are mainly object-oriented [11,12], edge detection [13], threshold segmentation [14], manual visual interpretation [15], etc. Some scholars have also conducted experimental studies based on image processing and pattern recognition techniques to achieve crack measurement and statistical aspects with some results [16,17]. However, these studies are mainly based on image processing to extract information about cracks from UAV images. There is no mature method for evaluating the damage of cracks in coal mining subsidence areas, which would be useful for the data support of land reclamation and treatment plan design, so there is an urgent need to propose a more reliable method for evaluating the damage of cracks.


To solve the above problems, this article proposes a new surface crack damage evaluation method using the kernel density estimation method commonly used in geographic information analysis [18]. KDE (kernel density estimation) in two dimensions has been widely used in the field of geographic information analysis research and is an effective tool for spatial clustering analysis, hotspots, or risk point identification [19,20,21]. In this article, we use the kernel density estimation method to construct an evaluation method for surface crack damage caused by mining in the arid and semi-arid areas of Yulin city in northern Shaanxi Province, using the coal mining area as the study area. First, we obtain high-precision crack extraction results based on machine learning methods. Then, we calculate the surface crack nucleus density in the study area and take it as a grading index. Finally, combined with the results of the field investigation by crack management experts, the classification assessment of cracks is carried out. The damage degree of the study area is divided into three levels: light damage, moderate damage, and severe damage.


This article proposes a surface crack damage evaluation method based on nuclear density estimation for UAV images, and its flow chart is shown in Figure 4. Firstly, the UAV images were acquired and cracks were extracted. Secondly, the kernel density estimation method was used to calculate the density of the study cracks. Then, the kernel density of the surface crack was used as the basis, combined with the field survey results of the crack management experts, to determine the grading index. Finally, the damage degree of the study area was evaluated.


The geological environment of the mining area is complex, and the surface vegetation is overgrown; furthermore, the spectral color characteristics of the ground withered vegetation and surface cracks are similar, resulting in a low accuracy and efficiency in extracting cracks based on UAV images. In recent years, scholars have gradually applied artificial intelligence methods to image recognition and crack detection with good results. For the extraction of surface cracks in the mining area, Zhang Fan et al. [22] cut the complete UAV image into small sub-images for crack extraction through image cutting, which effectively avoided the interference of vegetation and obtained better results. Therefore, this article proposes a crack extraction method based on machine learning for UAV sub-images considering this method. First, MATLAB was used to convert the UAV image into sub-images with cut pixels of 50 50. Second, the sub-images containing cracks were identified by the support vector machine (SVM) machine learning method, the dimensionality reduction method via PCA (principal component analysis), and the image enhancement method via Laplace sharpening, and the crack extraction results of the sub-images were obtained using the threshold segmentation method. Third, the sub-images that do not contain cracks were image-processed to make their background black. Fourth, all processed images were restitched according to the original cut sequence number, obtaining the final UAV image crack extraction results. Fifth, the kappa coefficient method was used to evaluate the crack extraction accuracy, and 2000 sample points were randomly selected, with 1000 crack pixels and no-crack pixels each, and the manual visual interpretation results were used as the true values to verify the accuracy of the crack extraction results. The specific SVM, PCA, Laplace sharpening, and threshold segmentation methods are described in detail below.


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