Liquid rocket booster integration study. Volume 5, part 1: Appendices

The impacts of introducing liquid rocket booster engines (LRB) into the Space Transportation System (STS)/Kennedy Space Center (KSC) launch environment are identified and evaluated. Proposed ground systems configurations are presented along with a launch site requirements summary. Prelaunch processing scenarios are described and the required facility modifications and new facility requirements are analyzed. Flight vehicle design recommendations to enhance launch processing are discussed. Processing approaches to integrate LRB with existing STS launch operations are evaluated. The key features and significance of launch site transition to a new STS configuration in parallel with ongoing launch activities are enumerated. This volume is the appendices of the five volume series

Case-based approach for designing graphics from locative and temporal data

Thesis (M.S.V.S.)--Massachusetts Institute of Technology, Dept. of Architecture, 1993.Includes bibliographical references (leaves 57-58).by Craig Michael Kanarick.M.S.V.S

Shallow Buried Improvised Explosive Device Detection Via Convolutional Neural Networks

The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.The issue of detecting improvised explosive devices, henceforth IEDs, in rural or built-up urban environments is a persistent and serious concern for governments in the developing world. In many cases, such devices are plastic, or varied metallic objects containing rudimentary explosives, which are not visible to the naked eye and are difficult to detect autonomously. The most effective strategy for detecting land mines also happens to be the most dangerous. This paper intends to leverage the use of a Convolutional Neural Network (CNN) to aid in the discovery of such IEDs. As part of a related project, an autonomous sensor array was used to detect the devices in terrains too hazardous for a human to survey. This paper presents a CNN and its training methodology, suitable to make use of the sensor system. This convolutional neural network can accurately distinguish between a potential IED and surrounding undergrowth and natural features of the environment in real-time. The training methodology enabled the CNN to successfully recognise the IEDs with an accuracy of 98.7%, in well-lit conditions. The results are evaluated against other convolutional neural systems as well as against a deterministic algorithm, showing that the proposed CNN outperforms its competitors including the deterministic method

Towards Autocomplete Strategies for Visualization Construction

Constructive visualization uses physical data units - tokens - to enable non-experts to create personalized visualizations engagingly. However, its physical nature limits efficiency and scalability. One potential solution to address this issue is autocomplete. By providing automated suggestions while still allowing for manual intervention, autocomplete can expedite visualization construction while maintaining expressivity. We conduct a speculative design study to examine how people would like to interact with a visualization authoring system that supports autocomplete. Our study identifies three types of autocomplete strategies and gains insights for designing future visualization authoring tools with autocomplete functionality. A free copy of this paper and all supplemental materials are available on our online repository https://osf.io/nu4z3/?view_only=594baee54d114a99ab381886fb32a126Comment: 5 pages, 4 figure

APISENS- Sentiment Scoring Tool for APIs with Crowd-Knowledge

Utilizing pre-existing software artifacts, such as libraries and Application Programming Interfaces (APIs), is crucial for software development efficiency. However, the abundance of artifacts that provide similar functionality can lead to confusion among developers, resulting in a challenge for proper selection and implementation. Through our preliminary investigation, we found that utilizing the collective knowledge of a crowd can greatly assist developers in acquiring a thorough and complete understanding of the complexities involved in the software development process. Especially as emotions are an inseparable part of human nature, it influences developers' activities. In this regard, we attempt to build a tool that can retrieve sentiment information for software APIs so that developers can determine APIs to utilize for their tasks. We employ the dataset from the most popular platforms (i.e., Twitter and YouTube) to build our research prototype. The source code, tool, and demo video are available on GitHub at \url{https://github.com/FalconLK/APISens}

Automated Rule-Based Selection and Instantiation of Layout Templates for Widget-Based Microsites

Veebi avatud arhitektuuron loonud soodsa pinnase veebisolevate andmete kasutamiseks nii keerulisemates kui lihtsamates veebirakendustes. Andmete kogumise ja visualiseerimise lihtsustamiseks lihtsates veebirakendustes on loodud hulganisti tööriistu, mille seas on ka mashup'ide loomise tööriistad. Olemasolevate tööriistadega kõrge kasutatavusega mashup veebilehe loomine võib aga paraku olla keerukas, kuna nõuab erinevate tehnoloogiate ning programmeerimiskeelte tundmist, rääkimata kasutatavuse juhtnööridega kursisolemist. Kuigi osad mashup'ide platvormid, a'la OpenAjax Hub, lihtsustavad olemasolevate komponentide kombineerimist, on lahendamata probleemiks siiani nende rakenduste kasutatavus. Käesolev magistritöö kirjeldab reeglipõhist lahendust andmete visualiseerimise vidinate jaoks sobiva veebilehe malli automaatseks valimiseks vastavalt enimlevinud veebilehtede kasutatavuse juhtnööridele. Selleks laetakse vidinate ning struktuurimallide kirjeldused koos kasutatavuse juhtnööridest saadud reeglitega reeglimootorisse ning kasutatakse reeglimootorit ekspertsüsteemina, mis soovitab sobivamaid malle vastavalt etteantud vidinate komplektile. Lahenduse reeglipõhine ülesehitus võimaldab uute vidinate ning mallide lisandumisel või juhtnööride muutumisel operatiivselt reageerida nendele muutustele reeglibaasi täiendamise kaudu. Väljapakutud lahendus realiseeriti käesoleva töö raames Auto Microsite rakendusena, mis koosneb serveri- ning kliendipoolsest osast. Serveri poolel toimub reeglite abil vidinate komplekti visualiseerimiseks sobiva malli valimine kasutades OO jDREW RuleML reeglimootorit ning rakenduse paketeerimiseks koodi genereerimine. Kliendi poolel kasutatakse OpenAjax Hub raamistikkuvidinate turvaliseks eraldamiseks ning omavahel suhtlemapanemisel. Samuti on kliendi poolel lahendatud genereeritud veebilehe vastavusse viimine brauseri võimalustega. Katsetamaks Auto Microsite rakendust praktikas loodi seda kasutades realisatsioonid kahele lihtsale stsenaariumile. Esimesel juhul viusaliseeriti Euroopa 1997-2008 tööjõukulude (Hourly labour costs in Euros (European Union 1997-2008) ing. k.) andmeid kaardi, tabeli, kokkuvõtte ja menüü vidinatega. Teisel juhul kasutati lisaks andmete visualiseerimise vidinatele ka väliseid andmeallikaid, mis olid realiseeritud mittevisuaalsete vidinatena. Saadud andmed visualiseeriti kahe tabeli ning ühe kaardi vidinaga. Näidisveebilehtede loomise tulemusena järeldub, et rakendus sobib lihtsate veebilehtede loomiseks. Lisaks on võimalik lahendust täiendada keerukamate veebirakenduste automaatseks loomiseks läbi vastavate mallide ning reeglite lisamise.This thesis proposes a rule-based widget and layout template matchmaking solution for widget-based microsites. The solution takes as an input a set of widget descriptions and a set of layout templates with widget placeholders and returns a microsite, where the most suitable template has been instantiated with corresponding widgets. Matchmaking is based on applying a rule engine to metadata of widgets and placeholders about their content categories and dimensions,. Additional usability rules are used to further improve the results with respect to commonly accepted usability guidelines. Such a solution makes it possible to modularly enhance the usability results in the future simply by adding new usability rules and layout templates. Furthermore, the solution can be applied in mashup creation tools for layout selection. The proposed solution has been implemented and is called Auto Microsite in this thesis. The system consists of a server-side and a client-side component. The server-side component matches widgets with layout template placeholders according to the given rules by using the OO jDREW RuleML engine. The client-side is responsible for presenting the mashup appropriately for the client device. The latter is based on OpenAjax Hub 2.0 framework, which enables secure sandboxing and communication of widgets in the generated microsite. Furthermore, OpenAjax Metadata 1.0 specification is used in this thesis to package the widgets such that they could be easily reused. In order to evaluate the Auto Microsite system in practice two proof of concept (PoC) scenarios were implemented. The first scenario visualized "Hourly labour costs in Euros (European Union 1997-2008)" data using widgets for a map, a table and a summary. In the second scenario, also data was queried through a SOAP service and a Web site. In the scenario data was visualized using two table widgets and a map widget. The SOAP service and queries to the Web site were packaged as non-visual widgets to fit the framework. The POCs demonstrate that the Auto Microsite system is able to construct widget-based microsites. Furthermore, the framework is capable of constructing also more complex Web applications, with several pages and more content widgets, by adding new rules and templates