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The camera equipment carried on the Apollo-11 flightwas comprehensive. In addition to the usual TV and small-film camerason board, there was a special camera for near-distance stereoscopicshots of the moon. And, of course, there were also the cameras which,for this article, are the most important, viz., three Hasselblad500ELs.






Two of the 500ELs were identical to the ones carried onthe Apollo-8, -9 and -10 flights. Each had its own Zeiss Planarf-2.8/80 mm lens. A Zeiss Sonnar f-5.6/250 mm telephoto lens was alsocarried. One of the conventional 500ELs, along with the telephotolens and two extra magazines, was in the Apollo-11 Command Modulethroughout the flight. The other conventional 500EL, and two extramagazines as well, were placed in the lunar module. Also in the lunarmodule - and making its first journey in space - was a Hasselblad500EL Data Camera, which was the one to be used on the moon'ssurface.




(4) The Data Camera was modified to preventaccumulation of static electricity. When film is wound in a camera,static electricity is generated on the film surface. Normally, thiselectricity is dispersed by the metal rims and rollers that guide thefilm, and by the humidity of the air. In a camera fitted with aReseau plate, however, the film is guided by the raised edges of theplate. As glass is a non-conductor, the electric charge that buildsup at the glass surface can become so heavy that sparks can occurbetween plate and film - especially if the camera is used in a verydry environment or in vacuum. Sparks cause unpleasant patterns toappear on the film and can be a hazard if the camera is used in anatmosphere of pure oxygen. To conduct the static electricity awayfrom the Reseau plate in the Data Camera, the side of the platefacing the film is coated with an extremely thin conductive layerwhich is led to the metallic parts of the camera body by two contactsprings. Contact is effected by two projecting silver deposits on theconductive layer. The Reseau plate, or register glass, is not a newdevelopment in photography. What is most remarkable, however, is thatthe group of Hasselblad staff working on NASA camera projects incollaboration with Carl Zeiss was successful in applying the idea toa small camera - like the Hasselblad 500EL Data Camera. This camerais not only useful in space photography, it is particularly suitablefor all kinds of aerial photography. The special cameras produced inthe past for aerial photography were large and intended for a largenegative-format - frequently meaning high prices. The Hasselblad500EL Data Camera with its Reseau plate produced a small andcomparatively low-cost camera which gave satisfactory results inaerial photographic work.



To see how much astronauts would be risking with exposure, Wang and his team measured just how much charging and arcing (when a gas has an electric breakdown and sends an electric current through the air) affected both clean and dusty suits made of Gore-tex. The latter were covered in a lunar dust simulant. Sure enough, the clean suits were not prone to shock, but the more dust there was on the suit, the higher the chance of getting zapped.


Miraculously, a watermelon finally bounced off the trampoline and onto the Chrysler 300 in the parking lot. The melon reportedly dentedthe roof and broke the electric moon roof, which cost the productionteam $6,500 to fix.


ElectroStatisticsJapanese Name:エレクトロRomanized Name:
Erekutoro
Official English Name:ElectroDebut:Chapter 804; Episode 753[1]User(s):Mink TribeFighting Style Focus:Channeling electricity
Electro
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Users of Electro can channel electricity through parts of their bodies in order to shock anything they touch.[1][3][4] This power can also be channeled through weapons.[1][5][6] This techniq

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It's pretty tough to "Poke" someone -- digitally, anyway -- with a straight face. But toss a sophisticated smartphone into that equation, and... well, it's still just as tough. For those who'd like to make awkward family gatherings even more awkward way less awkward this holiday season, hit up the source links to get the (free) Facebook Poke app for iPhone, iPad and iPod touch. With the Poke app, you can poke or send a message, photo, or video to Facebook friends "to share what you're up to in a lightweight way," with each message expiring after 1, 3, 5 or 10 seconds. If you ever see something you're uncomfortable with, you can click the gear menu and report it. Not that you have any friends that would weird you out or anything.


If your kid is too young for an iPhone but might like a device that still sends iMessages, places FaceTime calls, streams music and can play games, you might want to consider Apple's new iPod touch, which is available now and starts at $199.


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Results: 
 A total of 13,142 patients and 407,888 patients underwent isolated meniscectomy in the Humana and Medicare databases, respectively. Of the patients, 395 (3.01%) and 3,770 patients (0.92%) underwent revision arthroscopy, and 629 patients (4.79%) and 38,630 patients (9.47%) underwent TKA within 1 year of meniscectomy in the Humana and Medicare databases, respectively. Obesity (Humana: OR = 1.33, 
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The IEEE 57-bus test case represents a simple approximation of the American Electric Power system (in the U.S. Midwest) as it was in the early 1960s. The data were provided by Iraj Dabbagchi of AEP and converted into the IEEE Common Data Format by Rich Christie at the University of Washington in August 1993 [1],[2].


Thanks for sending the data. Although the dynamic response has improved, but it still does not give me perfect response. I mean I still receive the error of "Initial condition suspect" for just one state and the value of DSTATE is huge (-0.63092).


It may be counterintuitive. But some argue that the key to training AI systems that must work in messy real-world environments, such as self-driving cars and warehouse robots, is not, in fact, real-world data. Instead, some say, synthetic data is what will unlock the true potential of AI. Synthetic data is generated instead of collected, and the consultancy Gartner has estimated that 60 percent of data used to train AI systems will be synthetic. But its use is controversial, as questions remain about whether synthetic data can accurately mirror real-world data and prepare AI systems for real-world situations. 




For an autonomous vehicle company, an advantage of generating synthetic data is that it could train its vehicles on dangerous conditions, right? It can put in snow and ice, hard turns, that kind of thing?




It seems like synthetic data could help with the big problem of algorithmic bias, since one of the sources of algorithmic bias is bias in data sets used to train AI systems. Can we use synthetic data to train AIs in the unbiased world that we would prefer to live in, as opposed to the world we actually live in?



While the use of synthetic data may be of utility in some relatively simple data sets, it is still very immature in many areas of more complex data, such as precision medicine. Attempting to create synthetic data across a person's electronic health record, multi-omic data (genomic, transcriptomic, proteomic, etc), wearables (e.g., Fitbit, etc), and other sources has a very long way to go. And most forms of synthetic data would need to be refreshed frequently anyway due to concept drift and data drifts.


In this test case archive you can find a number of load flow test cases and dynamic test cases. The solved power flow data are generally provided in the IEEE Common Data Format [1]. An documentation of the format can also be found in the test case archive of the university.


The above power system test cases and additional information about the data format as well as the test systems themselves can be found on the website of the Power Systems Test Archive of the University of Washington.





A group of researchers at the Center for Intelligent Systems & Networks (KIOS) has extended a number of the IEEE Test Systems with dynamic model data appropriate for carrying out time-domain simulation. The researchers derived the model parameters based on typical model data described in [4]. The dynamic model data are freely available on their website and are provided in d

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Wenwu soon attacked Ta Lo with the might of the Ten Rings organization, and he progressed to the sealed door. As Ying Li, the Dweller-in-Darkness implored him to free it, crying out in Ying Li's voice. Using the power of his rings, Wenwu damaged the door, causing the Dweller's Soul Eaters to escape and attack the village, stealing souls to bring back to the Dweller.


Once its Soul Eaters had harnessed enough souls to restore his strength, the Dweller broke free, much to the horror of both Shang-Chi and Wenwu. The Dweller then grabbed Wenwu and consumed his soul as well, before flying toward the village, his army of Soul Eaters by his side. The people of Ta Lo and the Ten Rings joined forces to fight the Dweller's forces, and the Dweller was soon attacked by the Great Protector. They then fought against each other but the Dweller gained the upper hand as his minions provided more souls to boost his strength, grabbed the Great Protector and began consuming her soul, however Katy Chen fired her last Dragon Scale Arrow right into the Dweller's throat, hurting him and freeing the Great Protector from his grasp, the Great Protector then began heading towards the water with the Dweller following closely. The Great Protector began to trap him once more as Shang-Chi and Xialing attacked him. Using his rings to push the Dweller deeper into the water, Shang-Chi pushed the rings deeper into the Dweller, causing him to explode.[1]


The Dweller-in-Darkness was intelligent and manipulative. He used voice mimicry to lure people to his prison, using the voices of their loved ones to get them to free him. The beast knew how best to manipulate his unknowing saviors, and was able to trick Xu Wenwu, the leader of the Ten Rings. In addition, the Dweller was incredibly destructive, seeking to consume all the souls he could to gain more power.


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This section addresses different combinations of hyper-parameters used by deep learning approaches for empirical investigations. Key configuration elements of the experimental platform, to train the deep learning models, were as follows: Python 3.7.9 version, NVIDIA Graphical Processing Unit (GPU) driver version 460.32.03, Compute Unified Device Architecture (CUDA) 11.2 and Tesla K80 GPU with 12 GB memory.


Iterative applications of the Grid-Search scheme enabled the identification of the optimal hyper-parameters that yielded SOTA weighted F1-scores, on the two code-mixed data sets. The finally selected hyper-parameters were the ones that exhibited best performance for the deep learning models. All the hyper-parameters were tuned on training and development data. On the sensible selection of optimal hyper-parameters, those parameters were exercised for evaluation on the test data. Table 5 displays a overview of the optimal hyper-parameter values for the best performing models.


This section summarizes the results and analysis of simple deep learning models, bidirectional models, hybrid models, and transformers. Efficacy of the proposed system was evaluated in terms of the F1-score,Footnote 13 on a held-out test data, using the SklearnFootnote 14 machine learning tool.


Figures 4 and 5 present the accuracy and loss curves respectively of the training data set. Initially, the accuracy curve shows that as the number of epochs increases, the accuracy curve grows fast; at some point, all the models converge into a single line. Hence, with the rise in the number of epochs, accuracy of the models increases, reaching saturation after a few additional epochs. The loss curves, notably high at the get-go, follow steep descents after a few epochs, reaching a plateau with increased number of epochs. However, neither oscillations nor further decrease in loss was observed with additional increase in the number of epochs. As a rule, smaller the value of the loss (closer to 0) the better the performance of the deep learning models on the test data.


Data summarized in Table 9 reaffirm the augmented F1-score of SA of the proposed system as being comparable to the extant benchmark criteria. F1-scores for SA, as compared to OLI tasks, are relatively low, limited by data availability. GRU gave impressive results among all the experimented models. GRU works better than CNN as the latter does not consider long term dependencies in the sentences, which is important in text analytics. GRUs are simpler in nature due to the presence of additional update and forget gates in LSTM. Unlike LSTM, GRUs are able to avoid being overfitted, as reflected in the F1-scores of the proposed deep learning models. The lowest misclassification rate of 0.236 which can be ascribed to GRUs having the highest F1-score.


The paper addresses the problem of part-of-speech (POS) tagging for Malayalam tweets. The conversational style of posts/tweets/text in social media data poses a challenge in using general POS tagset for tagging the text. For the current work, a tagset was designed that contains 17 coarse tags and 9915 tweets were tagged manually for experiment and evaluation. The tagged data were evaluated using sequential deep learning methods like recurrent neural network (RNN), gated recurrent units (GRU), long short-term memory (LSTM), and bidirectional LSTM (BLSTM). The training of the model was performed on the tagged tweets, at word level and character level. The experiments were evaluated using measures like precision, recall, f1-measure, and accuracy. During the experiment, it was found that the GRU-based deep learning sequential model at word level gave the highest f1-measure of 0.9254; at character-level, the BLSTM-based deep learning sequential model gave the highest f1-measure of 0.8739. To choose the suitable number of hidden states, we varied it as 4

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