esa-sceva/satcom-chunk-collection huggingface dataset:
# Tracking Dynamic Northern Surface Water Changes with High-Frequency Planet CubeSat Imagery
## 5 Discussion  
### Assessment of Planet Imagery  
#### 5.1.1 Utility of Planet CubeSat Imagery for Tracking Surface Water Dynamics  
This study demonstrates that Planet imagery is suitable for mapping and tracking changing inundation extent of hundreds of small, heterogeneous water bodies in a complex, dynamic wetland region. We find the surface areas of small water bodies can be reliably and automatically extracted from both RapidEye and PlanetScope imagery, with a mean NRMSE of \(\sim\)11%. While the nominal \(\sim\)10 m geolocation error of Planet imagery [33] will likely cause difficulties for comparing Planet imagery to other fine-scale map products and/or field measurements, our treatment of water bodies as fuzzy-geolocation objects mitigates this error for time-series analysis purposes, and a geolocation offset of \(<\)10 m is sufficient for analysis of lakes \(>\)2500 m\({}^{2}\). Furthermore, this geolocation error is a systematic offset (though with inconsistent directions between images) and would thus be correctable through a simple transformation. The strong correlation between Yukon River effective width and discharge (Figure 6), further highlights Planet's utility for obtaining useful sub-seasonal hydrologic measurements from space.
# Low-Power, High Data-Rate Digital Capsule Endoscopy Using Human Body Communication
## 3 Techniques to Improve Performance in the CEHBC System
### Frequency Selective Digital Transmission
The High-rate FS spreader will only output a signal with at least two identical values with the majority selector block among the three input values received from the Sub-FS SpreaderH1, Sub-FS SpreaderH2, and Sub-FS SpreaderH3. The output value of the majority selector is the same two bit value as that presented in Equation (2). \[TX\_OUT=A\cdot B+B\cdot C+C\cdot A \tag{2}\]  
By applying FSDT techniques with Walsh code, which has fundamental frequencies from DC to half the frequency of the main clock, the CEHBC system can reduce the influence of signal attenuation to be the greatest near DC when using the human body as the medium. There is also an advantage if a user selects a specific Walsh code: the CEHBC system could shift the spread signal to a specific band without the RF frequency conversion portion. In addition, the FSDT technique performs the function of multiplying the baseband signal by the Walsh code, so that it generates a spread spectrum effect on the signal. Therefore, it is possible to restore the signal that was to be transmitted originally in the dispreading process, even if the spreading signal has been damaged by interference or noise components generated in the channel. In addition to the FSDT method, there is a differential Manchester coding (DMC) method that uses a specific frequency band without using baseband signals near DC. The Figure 6 shows the BER performance comparison between FSDT and DMC in the additive white gaussian noise (AWGN) environment. The DMC transmits data at half the rate of the operation clock, and the FSDT proposed in this paper can transmit data with 3/16 times of the operation clock. When transmitting the 6 Mbps signal using these two modulation methods, the FSDT method shows the carrier power to noise power ratio (CNR) improvement of about 8.4 dB compared with the DMC method (at BER = 10\({}^{-6}\)).
## 2 Materials and Methods
### Simplified Model for the Loosening Torque  
Based on these works by [PERSON] et al. [31; 32] and [PERSON] et al. [33], the present work proposes a simpler analytical formula, obtained by curve-fitting, to predict self-loosening. The complete development of the model is detailed in the Supplementary Materials section. As a final result, the following equation that expresses the loosening torque \(T_{L}\) and therefore predicts self-loosening in a simple and straightforward way was obtained:  
\[T_{L}=\mu_{h}\cdot F_{a}\cdot r_{h}\cdot\left(1-\frac{F_{e}}{\mu_{h}F_{a}} \right)^{1/2}+\mu_{t}\cdot F_{a}\frac{r_{t}}{\gamma\cdot cos\alpha}\cdot\left( 1-\gamma\cdot\delta\cdot\frac{F_{e}}{\mu_{t}\cdot F_{a}}\right)^{1/2}-\left(F_ {a}\cdot\frac{tan\beta}{\gamma}\cdot r_{t}\right) \tag{8}\]  
Being:  
\[\delta=\frac{1+cos\alpha}{2} \tag{9}\]  
Figure 2: Geometric parameters of the screwed joint of a dental implant assembly.  
where, \(F_{e}\) is the transverse load acting on the screw and \(F_{a}\) is its axial load. Note that \(F_{a}\) is not necessary equal to \(F_{p}\) in (3)-(4), since the force that introduces a transverse force \(F_{e}\) may also change the axial force in the screw, making it different to \(F_{p}\). Additionally, Equation (8) can be considered as a generalization of the classical torque-preload equation with the first term being \(T_{h}\), the second one \(T_{t}\) and the third one \(T_{p}\), when the transverse load \(F_{e}\) is set to zero. As \(F_{e}\) helps reducing the loosening torque, this variable appears in the first and second terms reducing the underhead and thread frictional torques. In this sense, an important observation must be made regarding \(T_{h}\) and \(T_{t}\): once any of these two resisting torques is completely overcome, i.e., once the expression between Macaulay brackets becomes negative, the term is put to zero in Equation (8).  
Equation (8) predicts the screw self-loosening for a combination of an axial and transverse load \(F_{e}\) that makes the loosening torque \(T_{L}\) becomes zero. Its main advantage is its torque-preload-like form, which makes it quite simple to use and intuitive to understandable while it quantifies the influence of the parameters involved in the phenomenon of self-loosening. As a particular application, the model has been successfully implemented in a new step-by-step methodology to study and predict screw self-loosening in dental implants that is presented in the following section.
## 1 Introduction
Furthermore, convolutional neural networks (CNNs) are greatly developed with the excellent characteristics of parameter sharing and spatial pooling, which make it more advantageous in computing speed and accuracy [19]. However, all these ML methods have a limitation in situations that involve a time-series input. Then recurrent neural networks (RNNs) are proposed with adding a twist where the output from previous time step is fed as input to the current step. The most important feature of RNN is that the hidden state can remember all information calculated from the previous sequence [20]. Thus, it can generate output through prior input (the past memory) and learning in training. It is common to complete the parameter learning of recurrent neural networks by learning over back-propagation, wherein the error is passed forward step by step in the reverse order of time. In [21], a learning-based method is applied to improve the RNN training process while the number of prediction time-steps increases. However, RNNs still suffer from the long-term dependencies, and long short-term memory (LSTM) fills the gap by setting a gate control unit that can choose and keep some useful information in the long-term sequential data. Being different from the traditional RNN, the model is trained by both the stored information of last time step and new input of the current moment, which enhances the prediction accuracy and stability greatly [22]. However, for the practical application in CSD, it is equally significant to analyze the interrelated influencing factors as the productivity prediction. LSTM lacks effective processing for high-dimensional characteristics of large-scale data; it should be used integrating with other methods. Principal component analysis (PCA) is one of the most widely used algorithms for feature reduction, which reconstructs the main k-dimensional feature based on the original n-dimensional feature by deep learning. While PCA is a pure data-driven method that cannot consider the casual relationship and correlation between variables, the procedure of variable analysis based on a working mechanism and human experience is necessary. [PERSON] et al. described a HEPCA model, which made variables supplement based on expert knowledge after the PCA process and generate a more accurate input for the predictive model [23]. Therefore, combining the advantages and characteristics of the different methods described above, this paper presents the long short-term memory integrating principal component analysis model (PCA-LSTM) to predict productivity using the monitoring sensors data. The PCA-LSTM is strutted into four phases. In the first phase, monitoring sensors are analyzed to select related variables according to the working mechanism and knowledge. In the second phase, PCA method is applied to extract the deep feature from the high-dimension dataset and to obtain the correlation of variables. In the third phase, a prediction model is built and trained by the LSTM network. Finally, cross validation and comparative analysis are conducted with a generated model from \"Chang Shi 10\" in China.
# U-Net with Asymmetric Convolution Blocks for Road Traffic Noise Attenuation in Seismic Data
## 1 Introduction
Unlike traditional methods, the deep learning method can be trained to extract the potential features of wave data through many hyperparameters without manual adjustment [23], which has more effective noise attenuation performances for real data. [PERSON] et al. used the trained deep CNN model to decompose an input waveform into a signal of interest and noise; test results suggest that most of the recovered signal waveforms have high similarity to the target waveforms and suffer little distortion [24]. [PERSON] and [PERSON] proposed a deep-denoising autoencoder (DDAE) to attenuate random noise, and the DDAE encodes the input data to multiple levels of abstraction. Then, it decodes those levels to reconstruct the signal without noise [25]. [PERSON] and [PERSON] trained a deep CNN with residual learning for denoising to avoid artifacts generated in the results by transform-based denoising methods [26]. However, the deep learning method has yet to apply to traffic noise attenuation. In the seismic or acoustic record, traffic noise shows as vertical stripes with higher amplitudes, which arise earlier than the first arrival and grow stronger with time. These features can be identified and utilized by using the deep learning method. We introduce the UNet to attenuate the traffic noise. The network improves the working of convolutional neural networks significantly by combining the down-sampling path and up-sampling path with skip connections. In recent years, the UNet has been widely used in seismic or acoustic data processing and interpretation [27; 28; 29; 30; 31; 32; 33; 34]. Although the UNet method is superior to the traditional method, its feature extraction ability still has room for improvement. [PERSON] et al. (2019) proposed ACBs to replace the original square convolution blocks in the convolution network to enhance the performance of CNN [35]. To improve the ability of the UNet to separate traffic noise and effective signals, we introduce ACBs and propose an improved model termed UNet with ACBs (ACB-UNet). The denoising ability of the network is closely related to the quality of training data. To train the network more effectively and efficiently, a large amount of synthetic data and real data are used to construct the training data. This paper introduces the ACB-UNet to suppress road traffic high amplitude noise. First, we introduce and analyze the acquired data set and traffic noise and generate the training data of the network. Then, we present the architecture of the ACB-UNet and related theories. Finally, the ACB-UNet is applied to synthetic and real data, and the test shows that the road traffic noise can be attenuated very well.
# Integrating Blockchains with the IoT: A Review of Architectures and Marine Use Cases
## 4 Applications and Challenges of Blockchains in MIoT and IoUT
### Blockchain Integration and Applications in MIoT/IoIT
In this context, blockchain technology can safeguard, for example, underwater acoustic communications, ensuring reliable information transmission that is crucial for timely disaster prediction and responsive actions. Additionally, the decentralized nature of blockchains addresses privacy concerns in the localization of underwater devices, ensuring secure data processing and validation without the need for third-party interference [20]. In naval defense operations, the IoUT is crucial in supporting challenging operations such as surveillance, mine detection, and recovery missions. More specifically, the blockchain's immutability and traceability features, in particular, help safeguard the transmission and storage of sensitive data, which is essential for maintaining confidentiality and operational integrity. On a broader scale, these same benefits also extend to underwater exploration, where they help protect marine environments from mismanagement and guarantee that the data collected are both accurate and reliable. Finally, blockchain technology is becoming increasingly useful in industries such as shipping and fisheries, offering a reliable way of tracking goods throughout the supply chain. This enables companies and consumers to verify the origin of products, thus reducing the risks of tampering or fraud [36]. In the fishing industry, for instance, a blockchain supports the ethical sourcing of seafood, helping to combat illegal fishing and promote the sustainable management of marine resources. A notable example is given by Fishcoin [48], an initiative that facilitates data sharing across the seafood supply chain, utilizing digital tokens to incentivize transparent communication among all of the network's stakeholders. While the blockchain has many other potential applications in MIoT/IoUT systems, this section has focused on some examples. These use cases illustrate how the blockchain can enhance various aspects of marine industries, from efficient supply chain management and sustainable fishing practices to advanced environmental monitoring and disaster response strategies. For instance, blockchains could be integrated into IoUT applicationssuch as underwater pipeline monitoring and harbor surveillance, providing secure data exchange and system transparency [17].
## 5 Simulation and Evaluation Results
### Privacy Protection Effectiveness Evaluation
#### 5.2.2 Evaluation Metrics  
According to [43], the packet characteristics used for traffic identification are mainly the various statistical values of packet size and packet jitter. That is, the adversary can analyze the usage pattern privacy of each terminal based on its captured task size, and task arrival interval over a period of time during task offloading. To this end, we characterize privacy protection effectiveness in terms of the following two metrics:  
* Task size: Task size assesses the variability in computational task size transmitted from STs to the satellite before and after adding the redundancy package. Intuitively, a larger change in task size means a more significant difference, which is more effective in confusing the adversary and therefore better for privacy protection.
* Task jitter: Task jitter assesses the variability in the task arrival interval received by the satellite before and after adding the redundancy package. Similarly, a larger change in task jitter is more effective in confusing the adversary and better for privacy protection.
## 4 Discussion  
It can be seen from the data in Figure 4 that there is significant scatter of the data points around the fit owing to thermal, background and system noise. The nature and in particular the symmetry of this noise is such that it is not detrimental to the fit and is included as a baseline offset. Furthermore, the ability to collect a number of spin echoes per experiment without impacting on the repetition time of the experiment allows for a sufficiently high density of data points to allow for a good level of confidence in the fit parameters. The homogeneity of the system yields a prolate ellipsoid with its major axis along the gap between the magnets, which is to be expected. The transmit efficiency is the limiting factor in the sensitive volume, which may, however, be an advantage for practical uses of the system yielding a small sensitive spot that is well visualised in concurrent modalities such as ultrasound imaging. At present, the system is only capable of reaching anterior placentae although there is evidence in the literature to suggest that there is an increased risk of placental disease with such placement [19]. In order to reach a posterior placenta, the additional thickness presented by the spine must be overcome. This will require greater spacing between the pair of magnet arrays, which will produce a corresponding drop of field strength that will require additional averaging or more efficient RF coil designs. It may be desirable in such a case to have variable spacing between the pair of magnet arrays, such that the depth can be optimised for each measurement (although this brings with it the complication of needing to vary the tuning between experiments).
## 5 Conclusions  
It was shown that it was possible to estimate the age of a subject based on their low-resolution image obtained from a DXA scan, although the convolutional neural network focused on spatial features in the area of the whole body. As it was experimentally verified, for low-resolution images the multispectral features did not improve the final results. It is worth noting that both the usage of the depthwise convolution layer and data augmentation improved results. Future work should focus on extraction information correlated dual-energy X-ray absorptiometry, by taking into account the different parameters (e.g., bone mineral density) and additional metadata (e.g., gender), coupled with image segmentation. We also plan to expand the conducted study, by investigating different regression models. It is necessary to point out that all data used in this study were collected with the same densitometer. To properly validate the proposed solution, the DXA data from other sources should be obtained and tested. Lastly, the additional experiments should be conducted using higher resolution images (e.g., DXA scans of body parts).  
**Author Contributions:** Conceptualization, J.R., K.J. and T.N.; methodology, J.R. and K.J.; software, K.J.; validation, J.R. and T.N.; formal analysis, J.R. and P.W.; investigation, K.J. and J.R.; resources, T.N. and P.W.; writing--original draft preparation, K.J. and N.G.; writing--review and editing, K.J., P.W., J.R., T.N. and N.G.; visualization, K.J. and T.N.; supervision, J.R.; project administration, J.R.; data acquisition, P.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
This study was approved by the Independent Bioethics Committee for Scientific Research at the Medical University of Gdarsk (Approval No. NKBBN/124/2022).  
Not applicable.  
The DXA dataset is not released because data samples contain patients personal data. Publicly available RSNA 2017 dataset was also analyzed in this study. This data can be found here: [[https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pediatric-bone-age-challenge-2017](https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pediatric-bone-age-challenge-2017)]([https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pediatric-bone-age-challenge-2017](https://www.rsna.org/education/ai-resources-and-training/ai-image-challenge/rsna-pediatric-bone-age-challenge-2017)), accessed on 17 May 2022.  
This work has been partially supported by Gdansk University of Technology, the Faculty of Electronics, Telecommunications, and Informatics.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} DXA & dual-energy X-ray absorptiometry; \\ MAE & mean absolute error; \\ CNN & convolutional neural network; \\ ROI & regions of interest \\ BMD & bone mineral density \\ BMC & bone mineral content \\ RSNA & Radiological Society of North America \\ \end{tabular}
# A Novel Transfer Learning-Based OFDM Receiver Design for Enhanced Underwater Acoustic Communication
## 5 Simulation and Experimental Results
### Average Fade Rating Analysis of Watermark Channels and Impact on OFDM Receiver
At low SNRs, the model struggles, but at higher SNRs, it effectively adapts to the channel's inherent fluctuations. Figure 16: ROC curve of DNN for different SNR, for the channel NOF1. Figure 17: ROC curve of DNN for different SNR, for the channel BCH1. Figure 15: Confusion metrics of DNN at SNR 15 (dB), when trained and tested on a same channel across different channels. Figure 18 reveals that the DNN consistently outperforms the FCNN in key performance metrics, including accuracy, precision, and MCC, across all channels. Specifically, at 15 dB SNR, the DNN achieves near perfect scores in these metrics, showing a substantial advantage over the FCNN, which has slightly lower performance, especially in more complex channels like KAU1 and KAU2. This solidifies the DNN's superiority in generalizing to diverse underwater environments. In Figure 19 in BCH1 channel, at SNR = 0 dB, the DNN significantly outperforms OMP, with a 50% reduction in BER (0.25 vs. 0.6), illustrating its ability to handle moderate noise levels more effectively. While MMSE performs slightly better than LS at this SNR, DNN maintains a clear advantage, showcasing its superior ability to handle noise and enhance classification accuracy. As the SNR increases to 10 dB, the performance gap widens further. DNN reaches a BER close to 0.05, while LS and MMSE struggle to bring BER below 0.1. FCNN also shows improvement over LS but remains far behind DNN, demonstrating the deep neural network's advantage in adapting to UWA conditions. In Figure 20 in the NOF1 channel, the performance trend remains consistent. The DNN shows better robustness in more stable channels like NOF1, achieving a BER of approximately 0.05 at 15 dB, which is significantly lower than that of OMP, MMSE and LS. While MMSE and FCNN improve over LS, they still lag behind DNN, particularly in challenging real-world underwater environments. This reinforces the DNN's superiority, particularly at higher SNRs, where it is able to achieve nearly optimal classification performance despite varying channel conditions. However, while the DNN performs exceptionally well when trained and tested on the same channel, real-world underwater communication systems often face scenarios where the training and testing environments are mismatched, leading to performance degradation. This highlights the importance of transfer learning, which emerges as a promising direction. Transfer learning allow the model, trained in one environment, to adapt effectively to a different, potentially unseen environment. This involve fine-tuning the pre-trained model using a limited amount of data from the new channel, helping maintain high performance even when environmental conditions change.