FULL丨She was changing clothes, CEO accidentally came in and fell in love with her at first sight! from mrs president ran away Watch Video

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Series 1<br/><br/>Episode 1 00:00<br/>Taking Overs<br/><br/>Katrina Lyons was only visiting Cresdon Green, where she grew up, to borrow cash from her parents.<br/><br/>But when she tried to return to London, it turned out alien beings known as the Geonin had lowered a force field around the village - refusing to let anyone in or out.<br/><br/>Unfortunately almost everyone in the village is too cowardly, apathetic, polite or stupid to stand up to this alien menace, so Katrina has no choice but to start the resistance movement herself. In the cricket pavilion.<br/><br/>In this episode, the force field around the village is keeping out all the rain, so the villagers can no longer moan about the weather.<br/><br/>Uljabaan realises this means they're moaning about him instead. So he distracts villagers with a cricket match - aliens versus humans - whilst trying to build a device to simulate rain.<br/><br/>Katrina sees an opportunity to sabotage Uljabaan's computer - if she can keep the match going long enough...<br/><br/>Katrina Lyons ...... Hattie Morahan<br/>Field Commander Uljabaan ...... Julian Rhind-Tutt<br/>Margaret Lyons ...... Jan Francis<br/>Richard Lyons ...... Peter Davison<br/>Lucy Alexander ...... Hannah Murray<br/>Computer ...... John-Luke Roberts<br/><br/>Producer: Ed Morrish<br/><br/>First broadcast on BBC Radio 2 in March 2013.<br/><br/>Episode 2 28:49<br/>Minimum Volume<br/><br/>In this episode, Uljabaan is working on an education programme for after the invasion, creating a list of books suitable for children to read. His chosen advisor is Mrs Leeson from the charity shop.<br/><br/>Katrina is confident she could do a better job, and hopes she could use it for subversive purposes - so she volunteers. But can she sneak anything past Uljabaan? And can she convince Lucy she's not really being a traitor?<br/><br/>Katrina Lyons ...... Hattie Morahan<br/>Field Commander Uljabaan ...... Julian Rhind-Tutt<br/>Margaret Lyons ...... Jan Francis<br/>Richard Lyons ...... Peter Davison<br/>Lucy Alexander ...... Hannah Murray<br/>Computer ...... John-Luke Roberts<br/><br/>Episode 3 57:33<br/>Power Block<br/><br/>In this episode, Uljabaan is busy with a construction project and asks Margaret to help with some routine duties in the village - collecting data, checking up on the man they put down the old well, that sort of thing.<br/><br/>Katrina is dismayed - he's too busy to oppress the humans, so he's getting them to do it to each other.<br/><br/>But the construction project involves opening up the force-field that surrounds the village. Can Katrina and Lucy sneak a message into the outside world?<br/><br/>Katrina Lyons ...... Hattie Morahan<br/>Field Commander Uljabaan ...... Julian Rhind-Tutt<br/>Margaret Lyons ...... Jan Francis<br/>Richard Lyons ...... Peter Davison<br/>Lucy Alexander ...... Hannah Murray<br/>Computer ...... John-Luke Roberts<br/><br/>Episode 4 1:26:22<br/>Little Green Lights<br/><br/>Uljabaan's superior officer Gryvook is coming for an assessment visit. Uljabaan is hoping to get the go-ahead to invade the rest of Earth - but first he has to prepare a welcome banquet. Tradition dictates he should serve a human child...
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What is a phrasal verb? Phrasal verbs with Georgie<br/>A phrasal verb is a combination of a verb and one or more particles (typically prepositions or adverbs) that together create a meaning different from the original verb alone. The particles modify the meaning of the verb, often resulting in idiomatic expressions. Phrasal verbs are common in English and can be tricky for learners due to their sometimes unpredictable meanings.<br/><br/>**Examples of Phrasal Verbs:**<br/><br/>1. **Break down**: <br/> - Literal: \
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Hello and welcome to Session 16 of our Open RAN series! Today, we're diving into the fascinating world of machine learning and its impact on Open RAN networks. We'll be focusing on how machine learning can boost Open RAN performance, specifically in predicting throughput based on MCS coding schemes. This is a crucial aspect for optimizing network performance and resource allocation in Open RAN environments.<br/><br/>1. Introduction to Machine Learning in Open RAN:<br/>Machine learning plays a pivotal role in enhancing Open RAN networks by enabling predictive capabilities, particularly in throughput optimization. By leveraging machine learning models, Open RAN can predict throughput based on the Modulation and Coding Scheme (MCS) coding scheme. Throughput prediction is critical for optimizing network performance and efficiently allocating resources, ensuring a seamless user experience.<br/><br/>2. Developing Machine Learning Models for Throughput Prediction:<br/>Developing a machine learning model for throughput prediction in Open RAN requires several key considerations. Firstly, the model needs to be trained on a dataset that includes throughput data and corresponding MCS values. The model should be designed to handle the complex relationships between these variables and predict throughput accurately. Mathematical functions and algorithms such as regression and neural networks are commonly used for this purpose, as they can effectively capture the underlying patterns in the data.<br/><br/>3. Deployment of Machine Learning Models in Open RAN:<br/>The deployment of machine learning models in Open RAN involves several steps. Once the model is trained and validated, it is deployed to the network where it operates in real-time. The model continuously monitors network conditions and predicts throughput based on incoming data. This information is then used to dynamically allocate network resources, optimizing performance and ensuring efficient operation.<br/><br/>4. Training Data Acquisition Process:<br/>Acquiring training data for the machine learning model involves collecting throughput data and corresponding MCS values from the network. This data is then cleaned and formatted to remove any inconsistencies or errors. The cleaned data is used to train the model, ensuring that it can accurately predict throughput in various network conditions. The training data acquisition process is crucial as it directly impacts the accuracy and reliability of the machine learning model.<br/><br/>Subscribe to \
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