esa的Proba-3将在今年9月由ISRO的PSLV-XL发射，目标轨道为600*60530km，59°的HEO轨道。Proba-3由两个部分组成——CSC（Coronagraph Spacecraft，日冕航天器）和OSC（Occulter Spacecraft，掩星航天器），两者总重550kg（其中CSC 300kg，OSC 250kg）。在运行期间，OSC将遮挡住CSC，两者保持相对静止，从而遮挡住来自太阳的强烈光亮，让CSC更加清晰和准确地观测太阳日冕。OSC需要以毫米级的精度保持位置。为此，OSC装配了冷气推进器，从而实现精准的位置控制。但由于轨道的特殊性（HEO只能在远地点附近“相对容易”地维持两器的相对静止，因为远地点附近的两器速度都相对很小，更容易进行控制），Proba-3将只能维持6小时的连续观测时长。
注：
【1】PSLV-XL（Polar Satellite Launch Vehicle-XL）是ISRO开发和使用的一种运载火箭。它是PSLV家族中的一员，是一种可靠的多用途运载火箭。PSLV-XL采用四级固体模块和一个液体模块组成。高44米，直径2.8米，质量320吨。它具备将卫星送入低地球轨道（LEO，3.8t）、太阳同步轨道（SSO，1.75t）和地球同步转移轨道（GTO，1.425t）的能力。PSLV-XL曾经成功地发射了许多印度国内和国际的卫星，是印度航天研究领域的重要成就之一。
【2】HEO（Highly Elliptical Orbit）指高椭圆轨道，轨道离心率较高。HEO轨道通常具有较长的周期，常用于一些特殊的卫星任务，包括观测卫星和通信卫星等。由于HEO轨道的高度变化较大，卫星可以在轨道的远地点处停留更长时间，以观察地球的不同区域或提供持续的通信服务。
【3】ISRO是指印度空间研究组织（Indian Space Research Organisation）。它是印度的国家航天机构，负责印度的太空研究和发展。ISRO成立于1969年，总部位于印度班加罗尔。ISRO的主要任务包括开展卫星通信、导航、气象、地球观测等领域的研究和发射任务，以促进印度在空间科学和技术方面的发展。ISRO已经成功发射了多种类型的卫星，并且在近年来取得了一系列重要的成就，例如成功发射月球探测器、火星探测器等。
注：
【1】PSLV-XL（Polar Satellite Launch Vehicle-XL）是ISRO开发和使用的一种运载火箭。它是PSLV家族中的一员，是一种可靠的多用途运载火箭。PSLV-XL采用四级固体模块和一个液体模块组成。高44米，直径2.8米，质量320吨。它具备将卫星送入低地球轨道（LEO，3.8t）、太阳同步轨道（SSO，1.75t）和地球同步转移轨道（GTO，1.425t）的能力。PSLV-XL曾经成功地发射了许多印度国内和国际的卫星，是印度航天研究领域的重要成就之一。
【2】HEO（Highly Elliptical Orbit）指高椭圆轨道，轨道离心率较高。HEO轨道通常具有较长的周期，常用于一些特殊的卫星任务，包括观测卫星和通信卫星等。由于HEO轨道的高度变化较大，卫星可以在轨道的远地点处停留更长时间，以观察地球的不同区域或提供持续的通信服务。
【3】ISRO是指印度空间研究组织（Indian Space Research Organisation）。它是印度的国家航天机构，负责印度的太空研究和发展。ISRO成立于1969年，总部位于印度班加罗尔。ISRO的主要任务包括开展卫星通信、导航、气象、地球观测等领域的研究和发射任务，以促进印度在空间科学和技术方面的发展。ISRO已经成功发射了多种类型的卫星，并且在近年来取得了一系列重要的成就，例如成功发射月球探测器、火星探测器等。
九个典型算法，学会了不仅可以提高效率，对认知也有帮助。#ai科普#
1. Linear Regression: Used for predicting a continuous value. It's simple yet effective for various problems.
2. Logistic Regression: Despite its name, it's used for classification tasks, particularly binary classification. And I also use class probabilities (class proba), which is the probability of the class label.
3. Decision Trees: Used for both classification and regression tasks. They split data into branches to form a tree structure.
4. Gradient Boosting Machines (GBM): An ensemble technique that builds predictive models in a stage-wise fashion, often yielding high-quality predictions. I use these frequently for high accuracy and performance.
5. Random Forests: An ensemble method that uses a collection of decision trees to improve prediction accuracy and avoid overfitting.
6. Support Vector Machines (SVM): Primarily used for classification tasks, SVMs are effective in high-dimensional spaces.
7. K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm used for classification and regression.
8. Naive Bayes: A group of simple, probabilistic classifiers based on applying Bayes' theorem with strong independence assumptions.
9. Neural Networks: Versatile and powerful, used for a wide range of tasks including classification, regression, and unsupervised learning. Deep learning models, a subset of neural networks, are particularly notable for their performance in complex tasks like image and speech recognition.
1. Linear Regression: Used for predicting a continuous value. It's simple yet effective for various problems.
2. Logistic Regression: Despite its name, it's used for classification tasks, particularly binary classification. And I also use class probabilities (class proba), which is the probability of the class label.
3. Decision Trees: Used for both classification and regression tasks. They split data into branches to form a tree structure.
4. Gradient Boosting Machines (GBM): An ensemble technique that builds predictive models in a stage-wise fashion, often yielding high-quality predictions. I use these frequently for high accuracy and performance.
5. Random Forests: An ensemble method that uses a collection of decision trees to improve prediction accuracy and avoid overfitting.
6. Support Vector Machines (SVM): Primarily used for classification tasks, SVMs are effective in high-dimensional spaces.
7. K-Nearest Neighbors (KNN): A simple, instance-based learning algorithm used for classification and regression.
8. Naive Bayes: A group of simple, probabilistic classifiers based on applying Bayes' theorem with strong independence assumptions.
9. Neural Networks: Versatile and powerful, used for a wide range of tasks including classification, regression, and unsupervised learning. Deep learning models, a subset of neural networks, are particularly notable for their performance in complex tasks like image and speech recognition.
Bianca Mei-Roșu și Eduard Ionescu au triumfat pe tabloul principal pentru a fi campioni naționali, în proba de simplu, după cele două zile de concurs de la Izvorani! #servimpentruistorie #hairomania #tabletennis
Cele mai palpitante meciuri s-au încheiat, iar rezultatele din fazele finale ale competiției au stabilit câștigătorii de la această ediție! În timp ce Bianca Mei-Roșu a cucerit aurul după victoria în fața Ioanei Sîngeorzan, scor 4-2, finala la masculin a fost adjudecată de Eduard Ionescu, scor 4-0 cu Horia Ursuț.
Toate rezultatele sunt pe site-ul FRTM.
⚬Podium simplu feminin
1. Bianca Mei-Roșu (LPS NR Constanța)
2. Ioana Sîngeorzan (CS Gloria 2018 Bistrița-Năsăud)
3. Camelia Mitrofan (CSM Constanța)
3. Alesia Sferlea (CSM Arad)
⚬Podium simplu masculin
1. Eduard Ionescu (CSA Steaua București)
2. Horia Ursuț (CS Rapid București)
3. Darius Movileanu (CSA Steaua București)
3. Andrei Tomică – (CSM Focșani)
Cele mai palpitante meciuri s-au încheiat, iar rezultatele din fazele finale ale competiției au stabilit câștigătorii de la această ediție! În timp ce Bianca Mei-Roșu a cucerit aurul după victoria în fața Ioanei Sîngeorzan, scor 4-2, finala la masculin a fost adjudecată de Eduard Ionescu, scor 4-0 cu Horia Ursuț.
Toate rezultatele sunt pe site-ul FRTM.
⚬Podium simplu feminin
1. Bianca Mei-Roșu (LPS NR Constanța)
2. Ioana Sîngeorzan (CS Gloria 2018 Bistrița-Năsăud)
3. Camelia Mitrofan (CSM Constanța)
3. Alesia Sferlea (CSM Arad)
⚬Podium simplu masculin
1. Eduard Ionescu (CSA Steaua București)
2. Horia Ursuț (CS Rapid București)
3. Darius Movileanu (CSA Steaua București)
3. Andrei Tomică – (CSM Focșani)
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