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Description
Hytera BL2010 2000mAh High-Capacity Lithium-Ion Battery (Anti-Counterfeit)Hytera BL2010 2000mAh High Capacity Lithium Ion Battery (Anti Counterfeit) Secure and Intelligent Power for PD4, PD5, and PD6 Series Radios The Hytera BL2010 is a high performance, 2000mAh Lithium Ion battery engineered to provide reliable, long lasting power for Hyteras professional digital portable radios. In mission critical sectors like security, manufacturing, and logistics, battery failure or the use of substandard third party power cells can
Hytera BL2010
2000mAh High-Capacity Lithium-Ion Battery (Anti-Counterfeit)
Secure and Intelligent Power for PD4, PD5, and PD6 Series Radios
The Hytera BL2010 is a high-performance, 2000mAh Lithium-Ion battery engineered to provide reliable, long-lasting power for Hytera’s professional digital portable radios. In mission-critical sectors like security, manufacturing, and logistics, battery failure or the use of substandard third-party power cells can lead to communication blackouts and hardware damage. This battery solves those issues by integrating an advanced anti-counterfeiting IC chip that communicates directly with the radio, allowing users to instantly verify the battery's authenticity and ensuring peak operational safety.
Designed for heavy-duty professional use, the Hytera BL2010 features a robust, IP67-rated construction, making it completely dust-tight and capable of withstanding immersion in water. This high-capacity cell is specifically optimized for radios running Firmware R7.0 or above, providing a stable voltage supply that maximizes the efficiency of the radio's transmitter. By choosing the genuine BL2010, you protect your equipment from the risks of non-original accessories while gaining the extended runtime necessary to power through full work shifts in any environmental condition.
Key Features
- Anti-Counterfeiting Technology: Features a built-in authentication IC that allows the radio to verify the battery as a genuine Hytera product, protecting your hardware from damage.
- 2000mAh High Capacity: Offers extended talk time and standby life, ensuring your team stays connected without needing mid-day recharges.
- IP67 Waterproof Protection: Fully sealed against dust and water ingress, allowing the radio to be submerged in up to 1 meter of water for 30 minutes without power loss.
- Lithium-Ion Efficiency: Delivers a high energy-to-weight ratio with no "memory effect," supporting up to 500 charge cycles while maintaining consistent performance.
- Optimized for Modern Firmware: Specifically designed for PD4i, PD5i, and PD6i series radios utilizing Firmware R7.0 or higher for enhanced power management.
Compatibility
| PD402i | PD412i | PD482i | PD502i |
| PD562i | PD602i | PD662i | PD682i |
Specifications
- Part Number: BL2010
- Chemistry: Lithium-Ion (Li-Ion)
- Capacity: 2000 mAh
- Voltage: 7.4 V
- IP Rating: IP67 (Submersible)
- Weight: 102 g
- Dimensions: 84.2 x 52.6 x 18.2 mm
Benefits
- Guaranteed Equipment Safety: The authentication chip prevents the use of dangerous, uncertified batteries that could overheat or leak.
- Superior Environmental Resilience: The IP67 rating ensures that communication remains active even in heavy rain or industrial wash-down areas.
- Lightweight Ergonomics: Provides high energy density in a compact 102g package, keeping the radio easy to carry on a belt for long durations.
- Reduced Total Cost of Ownership: A long cycle life of 500+ charges means fewer battery replacements over the lifespan of your radio fleet.
Warranty
1-year manufacturer's warranty.
Shipping Notes
- Free Standard Shipping on $100+ Orders to the USA.
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Exchange/Return Notes
- We offer a 30-day return/exchange service after receiving.
- Final sale items are not eligible for returns or exchanges.
- To process your return/exchange, please contact us at [email protected]
- Please click here for more details>>> Return & Exchange Policy
4.1 ★★★★★
Based on 9 reviews
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Product Reviews
★★★★★ 5
Excellent book on ML
Format: Paperback
This is a great book on machine learning. Topics covered are extensive - from beginner level to advanced topics including math behind different algorithms. However, not "all" algorithms are covered. Please go through the table of contents.
The first part - 11 chapters - covers machine learning concepts and second part covers advanced topics with Pytorch. There are lots of excellent code and they work!!
The quality of the book I received is excellent. I have gone through all 742 pages, and it has held up very well!!
I used Jupyter notebook to run all examples. I created a new notebook and copied and pasted the code and ran them. This approach worked very well for me. At the same time, I could experiment with my take on the code snippets and definitely added to my knowledge.
Only issue I have is on the second part of the book discussing PyTorch: (1) Some packages are a bit older version: e.g., transformer 4.9.1 whereas current version is 4.48+. It took some tweaking/recoding to get the examples working. (2) There is not much discussion on why certain architecture was chosen - e.g., number of layers, is there a rule of thumb on how to improve performance by changing these parameters? Even with CUDA the code run for a long time. Therefore, experimenting with different values of parameters become too time consuming. (3) On the same note, if I can achieve test accuracy of 90%+ using logistic regression and almost the same (perhaps one or two percent better with PyTorch with IMDB movie review dataset and that two much faster why should I use PyTorch for this dataset? Obviously, PyTorch is for certain types of problems. Discussions can be included by not adding to the exhaustive (and apt) contents.
Personally I was disappointed by lack of any example on time series.
Must have for ML practitioner as a reference and guide.
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Reviewed in the United States on December 20, 2024
★★★★★ 5
Excellent Textbook for Hands-On Learning of ML
Format: Kindle
This textbook is for the serious life-long learners of machine learning. There are at least two ways to ‘consume’ this book.
For the expert in ML, this is a textbook to study as a clear comprehensive ML overview and then to dive into sections of interest or ignorance. The concepts are grounded in code examples and are well cited (with links) to sources. Further, this textbook is appropriate if you are TensorFlow-centric and want to broaden into cutting-edge ML models/tools coded in PyTorch.
For a new learner to ML, this is a textbook to DO (not just READ) with hands-on and brain-engaged. If you realize that ML is a key life-long skill for your career, consider this textbook as part of a daily learning habit (10-30 min).
From personal experience, my advice to the new learner is as follows… First, clone the GitHub repository, setup your Python environment, and study the textbook, while working through the notebooks. Go on tangents and break the code. Do this methodically as part of your daily learning habit, but do not hesitate to jump ahead several chapters to prepare for tomorrow’s meeting. There is enough excellent material here for a full year of ML adventures.
I did a similar strategy with Raschka’s first textbook. About four years ago, I had finished Andrew Ng’s Deep Learning Specialization as a student in his first cohort. I knew the concepts well but could not do the actual application coding. I was surprised how my Python coding improved by following Raschka’s clean and elegant style. And Raschka’s code examples were meaty enough to be springboards into working applications.
Several textbook editions later, what is different about this new edition?
First, it moves you through scikit-Learn (a firm foundation) to PyTorch, instead of TensorFlow. PyTorch is a better stepping-stone, both conceptually and practically. With PyTorch, you will go further with less energy, while being able to convert your efforts into TensorFlow as needed. In addition, most of the cutting-edge ML/AI/DL research is in PyTorch. It is nice to read a recent arXiv paper, clone their repository, click on the Colab tutorial, and replicate their experiments, along with picking up a ton of new coding tricks & tips. I am excited to work through these PyTorch sections to hone my skills.
Second, there is a clear recognition of model tracking and tuning practices. This is often a gap in other ML textbooks and courses. Once you progress beyond the simple demo examples in a lecture, you realize that the real work is experiments, more experiments, and still more experiments, so that you must understand what the model architecture and hyperparameters are doing to your dataset. There is good coverage of scikit-Learn pipeline, grid search, model performance, and the like.
Third, ML/AI/DL practice is rapidly evolving. Every week new ML packages/services become available that could save much grief on your current project. What is refreshing about Raschka’s textbook series is that he constantly adding cutting-edge topics because he likes to stay current and to help us stay current. Hence, this edition contains recent ML treats as: transformers, self-supervised learning, autoencoders-to-GAN, graph neural networks, DBSCAN, t-SNE (with brief mention of UMAP), and PyTorch-Lightning.
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Reviewed in the United States on February 26, 2022
★★★★★ 4
Just learning it
Format: Paperback
Nice learning book just have to finish it
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Reviewed in the United States on December 10, 2025
★★★★★ 5
Very useful book
Format: Paperback
I use it for the machine learning class I teach.
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Reviewed in the United States on May 3, 2026
★★★★★ 5
Cover many areas in detail and recommendations for more to read for what's outside
Format: Paperback
Good book!
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Reviewed in the United States on May 4, 2026
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