esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
Fluctuations in network bandwidth and the limited availability of processing and energy resources of consumer electronic devices demand video streaming solutions that adapt to changing network and device constraints. In this context, although solutions such as HTTP adaptive streaming consider the network bandwidth problem, adapting the transmitted video contents by considering both the network bandwidth and an individual device's energy constraints remains a compelling challenge.  
To this end, this paper presents an encoding algorithm that can generate HEVC-compliant bit streams with multiple arbitrary bit rate and decoding-complexity levels. The experimental results with respect to the simultaneous bit rate and decoding-complexity control suggest that the proposed algorithm achieves a target bit rate and a decoding-complexity level with 0.47% and 1.78% average errors, respectively. Furthermore, the proposed algorithm demonstrates an average 10.11 (%/dB) decoding-complexity reduction and up to 10.52 (%/dB) decoding energy reduction for 1 dB PSNR quality loss compared to HM 16.0 encoded bit streams in an Intel x86 CPU architecture--a significant improvement compared to the state-of-the-art techniques. In addition, the bit streams generated by the proposed algorithm demonstrate 20.36 (%/dB) average energy reduction per 1 dB quality loss for a RISC-based ARM CPU architecture. Finally, the future work will focus on developing the proposed model into an adaptive video streaming solution that considers the end-to-end network and device resource availability to determine coding parameters used to encode the streaming video.  
Conceptualization, T.M.; methodology, T.M.; software, T.M.; validation, T.M., D.T., and H.K.A.; formal analysis, D.T. and H.K.A.; investigation, T.M. and D.T.; resources, A.F. and C.H.; data curation, T.M.; writing--original draft preparation, T.M.; writing--review and editing, D.T., H.K.A., C.H., and A.F.; visualization, D.T. and H.K.A.; supervision, D.T., H.K.A., and A.F.; project administration, A.F. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors declare no conflict of interest.
###### Abstract  
The 2020 'Elucidating the role of clouds-circulation coupling in climate-Ocean-Atmosphere' (EUREC\({}^{4}\)A-OA) and the 'Atlantic Tradewind Ocean-Atmosphere Mesoscale Interaction Campaign' (ATOMIC) campaigns focused on improving our understanding of the interaction between clouds, convection and circulation and their function in our changing climate. The campaign utilized many data collection technologies, some of which are relatively new. In this study, we used saildrone uncrewed surface vehicles, one of the newer cutting edge technologies available for marine data collection, to validate Level 2 and Level 3 Soil Moisture Active Passive (SMAP) satellite and Hybrid Coordinate Ocean Model (HYCOM) sea surface salinity (SSS) products in the Western Tropical Atlantic. The saildrones observed fine-scale salinity variability not present in the lower-spatial resolution satellite and model products. In regions that lacked significant small-scale salinity variability, the satellite and model salinities performed well. However, SMAP Remote Sensing Systems (RSS) 70 km generally outperformed its counterparts outside of areas with submesoscale SSS variation, whereas RSS 40 km performed better within freshening events such as a fresh tongue. HYCOM failed to detect the fresh tongue. These results will allow researchers to make informed decisions regarding the most ideal product and its drawbacks for their applications in this region and aid in the improvement of mesoscale and submesoscale SSS products, which can lead to the refinement of numerical weather prediction (NWP) and climate models.  
saildrone; salinity; Soil Moisture Active Passive (SMAP); Hybrid Coordinate Ocean Model (HYCOM); EUREC\({}^{4}\)A; ATOMIC; physical oceanography; remote sensing; air-sea interactions 2022  
28 May 2022  
27 July 2022  
2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)]([https://creativecommons.org/licenses/by/](https://creativecommons.org/licenses/by/)) 4.0/).  
saildrone; salinity; Soil Moisture Active Passive (SMAP); Hybrid Coordinate Ocean Model (HYCOM); EUREC\({}^{4}\)A; ATOMIC; physical oceanography; remote sensing; air-sea interactions
## 1 Introduction
### CNN and Spatial Pyramid Pooling (SPP)-Net for Pattern Recognition  
In recent years, CNN algorithms have proven particularly effective in language and image recognition [17]. The network structure of the CNN algorithm includes many hidden layers, and it also has an unmatched feature-learning level compared with the traditional machine learning methods. A traditional classification method like SVM needs to conduct the feature extraction alone before feeding the data into the classifier. For example, [PERSON] and [PERSON] employed three feature extraction methods (i.e., DCT, continuous WT, and discrete WT) to realize the feature extraction before the classification [12]. It is noteworthy that the selection of the mother wavelet is very important to the feature selection. In addition, it is better to pre-compute the basic function of the DCT offline to improve the computational efficiency. As mentioned in Ref. [12], the selection of the best feature extraction method depends on the substantial value considered for the training time, and the training and testing performance. Therefore, the feature extraction is generated automatically, and such a feature extraction method has a better effect on classification for complex tasks. The CNN itself is a feature extractor, and its convolutional layer works as a series of filters that are deployed for feature extraction. Moreover, the other layers, such as the pooling layer and the fully-connected layer, are used to reduce the number of the parameters to be learned and retains the most useful information for a classifier.  
However, the existing CNNs mandate that the input data should have the same size and such a fixed-size constraint comes from the fully-connected layer and requires a fixed-length vector for the input. This artificial operation may result in loss of image information, which also affects the classification accuracy. A new structure of the CNNs called SPP-net [18] has solved these problems for pattern recognition by adding an SPP layer on top of the last convolutional layer. The SPP layer pools the features and generates fixed-length outputs, which are subsequently fed into the fully-connected layers (or other classifiers). The SPP-net allows CNN to accept inputs of any scale, which increases the scale invariance of the model, suppresses overfitting, and enables extraction of local features of the data at multiple scales [18; 19; 20]. The SPP-net is implemented by switching from one network size (\(224\times 224\)) to another (\(180\times 180\)) and training each full epoch on one network. After that, the network size should be switched to the other (while retaining all weights) for the next full epoch. Accordingly, most fixed-size pictures are trained on a single network, whereas different-sized pictures are trained on a separate network. The weights of different networks cannot be shared under such network switching.
# Opportunities and Challenges to Develop Digital Twins for Subsea Pipelines
## 1 Introduction  
Subsea pipelines are the flowlines connecting a subsea wellhead to a manifold or a platform, or the export lines (trunk lines) used as a long-distance transportation system for the oil and gas [1, 2]. Important considerations of the pipeline system are the efficient operation and the development of the system for future needs. Due to the low price of oil in recent years, oil and gas field operators are looking beyond the traditional operational maintenance strategy for the sake of reducing the downtime caused by the planned or sometimes unplanned preventive maintenance in the production field, thus reducing the operational cost (OPEX) [3, 4].  
In the digital solutions to subsea integrity management (SIM), the use and management of data bring benefits to the daily operations, e.g., the increased efficiency, optimization, reduction in cost, and safety [3]. New technologies and associated processes are required for increased safety and better monitoring of the subsea assets.  
Recent advancements in information and communication technologies, including cloud computing, high-performance processors, high dimensional visualization capabilities, internet of things (IoT), wearable technologies, additive manufacturing (AM), big data analytics (BDA), artificial intelligence (AI), autonomous robotic systems, submarine drones, and blockchain technology, have catalysed digital adoption across industries. These new technologies have facilitated the cyber-physical integration by which data can be collected, analysed, and visualized to make informed decisions and to serve as a basis for simulations to optimize operations [5]. The concept of cyber-physical interaction and associated simulation is referred to as a Digital Twin.  
The digital twin draws considerable interest as it can provide a cost-effective, reliable and intelligent maintenance strategy based on the machine learning (ML) algorithm [4] and assess extension life projects. The digital twin concept consists of three components: (1) the physical asset, (2) the virtual representation of the asset, and (3) the connection between the previous two components. The connection includes the information transferred from the physical asset to the digital twin and from the digital twin to the asset. A clear business purpose is a key principle in the development of digital twins to provide value [3].  
The concept of the digital twin has also been used in offshore structures as platforms and subsea pipelines throughout the stages of construction and operation life. In this paper, the vision of the digital twin and its evolution, and the applications of the digital twin in subsea pipelines in design, construction and service life are reviewed. Further discussions and comments are also addressed.
# Design of the Converter Prototype for Powering the Hydrogen Electrolyzer
## 1 Introduction
An analysis has dealt with the possibility of the maximization of the increase in the current density, while the voltage applied to the electrolyzer's terminals converges to the reversible voltage. This led to the usage of an inverter with a current-based output. However, for now, this is a prototype with lower power, which is the basis for further development and will be the subject of further research and measurements. Using a  
Figure 1: Principal diagram of alkaline electrolyzer. negative pulse opens possibilities for investigating the possible influence of this pulse on the release of gas bubbles from the electrode's surface and the change in ion concentration in the vicinity of the electrode. These phenomena can have a positive effect on the efficiency of the process. The results of this research and measurements will be the subject of future publications. This work aimed to design and construct an inverter for powering a hydrogen electrolyzer. The output signal of the inverter consists of rectangular current pulses. Amplitude, polarity, duty, and the number of consecutive pulses in one working cycle have been programmable. The whole inverter consists of three functional blocks. Firstly, the current source includes an interleaved buck converter that ensures a selectable and stable output current amplitude with a maximum range of 20 A. The second block is an inverter that shapes the current into the required shape. At the same time, the time between current pulses ensures a constant conductive current path for the output of the current source. In the end, the control block contained a microcontroller. The MCU offers complex control of the whole device; the program of the MCU can be easily modified to investigate the influence of control algorithm changes on inverter and electrolyzer behavior. The converter's block diagram is shown in Figure 2.  
This work presents the result of the diploma thesis, which is the basis for further development of a converter for the pulsed power supply of an electrolyzer with a power of approximately 5 kW. Further research will continue, and the goal will be to improve the parameters, increase the power, and evaluate various modes of operation. The goal of using a prototype with a lower power is to investigate the effect of current pulses, their combinations, and their times on the efficiency of water molecule decomposition in the electrolysis process. As this study focuses on a prototype, the efficiency of the device is not a primary concern at this stage. The main objective is to examine the influence of pulsed current waveforms on the electrolysis process. Future research will address the optimization of efficiency once the fundamental effects of pulse parameters are better understood.
# Soft X-ray Microscopy Techniques for Medical and Biological Imaging at TwinMic--[PERSON]
## 2 Materials and Methods
### TwinMic Imaging Modes
#### 2.1.1 Scanning Transmission X-ray Microscopy
A new SDD system consisting of 4 trapezoid chips, each with 8 square SDDs on it, is under development and has been already tested [54, 55, 56], and it is sketched in Figure 2. The new system will cover a larger solid angle (5 to 10 times higher) than the current one, allowing for faster measurements and, thus, reduced radiation damage. Once optimized, it will replace the current LEXRF system. While LEXRF mapping is a standard TwinMic acquisition mode, XANES spectroscopy is currently under development. It has already been performed on materials science-related topics [18,22,57], where metal concentrations were high enough to detect speciation changes; however, in order to become a standard TwinMic technique, it requires fine tuning and some changes in the beamline acquisition/control software. The scanning stage where the sample is located consists of two X and Y stepper motors, where a X-Y piezoelectric motor stage (with 80 \(\upmu\)m range of movement) is mounted. In the  
Figure 1: Schematic view of the TwinMic STXM mode setup with the microprobe forming zone plate (ZP) on the specimen (S), diffraction order-selecting aperture (OSA), transmission detection system with fast-read-out CCD camera (FRCCD) and visible light converting system (VLCS), and low-energy X-ray fluorescence (LEXRF) detector system based on 8 silicon drift detectors (SDDs) in backscattered configuration. The VLCS consists of a phosphor screen (PS), a lens (L) and a 45\({}^{\circ}\)-tilted mirror (M). Figure 2: Schematic view of the TwinMic current (**A**) and future (**B**) LEXRF system setup. first version of the sample stage, the scanning was performed exclusively by using the piezoelectric motors, after positioning the sample in the desired area by means of the stepper motors, allowing a movement range of 5 mm in both X and Y [7; 17]. A newer version of it was recently installed by replacing the mechanics and the stepper motors, allowing for a larger range of movement (12 mm \(\times\) 12 mm). This allows for larger samples and/or a larger number of samples to be mounted, avoiding several vacuum breaks and, therefore, reducing the measurements' deadtimes. Moreover, with the new sample stage, both STXM and XRF can be performed not only by using the piezoelectric motors, as originally designed, but also in a coarser way by using the stepper motors themselves. This allows for larger areas to be scanned, as it is shown in the results section.
# Circle-U-Net: An Efficient Architecture for Semantic Segmentation
## 3 Method
### Circle-U-Net
\[q_{att,i}^{l}=\varphi^{T}\Big{(}\sigma_{1}\Big{(}W_{x}^{T}x_{i}^{l}+W_{g}^{T}g +b_{xg}\Big{)}\Big{)}+b_{\varphi} \tag{1}\]  
\[\alpha^{l}=\sigma_{2}(q_{att}^{l}\ \Big{(}x^{l},g;\varnothing_{att}\ \Big{)} \tag{2}\]  
where \(\sigma_{1}(x)\rightarrow\) element-wise non-linearity, \(\sigma_{2}(\text{x})\rightarrow\) normalization function; \(x_{i}^{l}\rightarrow\) Activation map of a chosen layer i; g -> global feature vector provides information to AGs to point out irrelevant features in \(x_{i}^{l}\); \(W_{x},W_{g}\rightarrow\) Linear transformations; \(b_{xg}\), \(b_{\varphi}\rightarrow\) Bias terms. Loss function. We have tested our model with categorical cross-entropy loss function and Focal Tversky loss function, and it works better with both the type of loss functions. \begin{table}
\begin{tabular}{c c} \hline \hline
**Circle Connect Name** & **Connected Layers** \\ \hline cc1 & Conv2\_1 with conv 3\_4 \\ \hline cc2 & Conv 3\_1 with conv 4\_11 \\ \hline cc3 & Conv 4\_11 with conv 4\_23 \\ \hline cc4 & Conv 4\_1 with conv 5\_3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Circle connect name and layers. Figure 6: Circle connect Structure. Categorical cross-function is described as below. \[\text{Loss}=-\frac{1}{N}\sum\limits_{i=1}^{N}\sum\limits_{c=1}^{C}1_{y_{i}\in C _{y_{i}}}log\;P_{model}[y_{i}\in C_{c}] \tag{3}\]  
\(N\) is the total number of observations, \(i\) denotes specific observation, \(C\) denotes the total number of categories, and c denotes a specific category. \(\sum\limits_{i=1}^{N}\sum\limits_{c=1}^{C}\rightarrow\text{Observations of }^{ \prime}I\), till \(N\) and categories '\(c\)' till '\(C\)'; \(1_{y_{i}\in C_{y_{i}}}\rightarrow\text{ifth observation for }c\text{th category}\); \(P_{model}[y_{i}\in\;C_{c}]\rightarrow\text{probability predicted by the model for }i^{\text{th}}\) observation which belongs to \(c\)th category  
Focal Tversky loss function is described as below. \[Tl_{c}=\frac{\sum\limits_{i=1}^{N}p_{ic}g_{ic}+\in}{\sum\limits_{i=1}^{N}p_{ ic}g_{ic}+\alpha\sum\limits_{i=1}^{N}p_{ic}g_{ic}+\beta\sum\limits_{i=1}^{N}p_{ ic}g_{ic}+\in} \tag{4}\]  
where \(Tl\) is the Tversky index, \(\alpha+\beta=1;\;p_{ic}\rightarrow\text{probability that the pixel }i\) is of the lesion class \(c\); \(p_{i\overline{c}}\rightarrow\text{probability that the pixel }i\) is of the non-lesion class \(c\); \(g_{ic}\rightarrow\text{ground truth that the pixel }i\) is of the lesion class \(c\); \(g_{i\overline{c}}\rightarrow\text{ground truth that the pixel }i\) is of the non-lesion class \(c\);  
\[FTL_{c}=\sum\limits_{c}(1-Tl_{c})^{\frac{1}{\gamma}} \tag{5}\]  
where \(\gamma\) controls the non-linearity loss.We use the focal Tversky loss function. It solves class imbalance as FTL is robust to class imbalances.
# Comparison of ANN and ANFIS Models for AF Diagnosis Using RR Irregularities
## 3 Results and Discussion

Table 2: _Cont._The SAV parameter showed the highest ACC for classifying ECG recordings for both the ANN and ANFIS models at 85.33% and 89.33%, respectively. The ANN(100) showed SE:SP:PPR in NSR, PAF, and AF at 96.00%:100.00%:100.00%, 80.00%:89.09%:76.92%, and 80.00%:90.00%:80.00%, respectively. The ANFIS_trapmf were SE:SP:PPR in NSR, PAF, and AF at 100.00%:94.00%:89.29%, 88.00%:90.57%:81.48%, and 80.00%:96.00%:90.91%, respectively. The SAV is a comparison of RR intervals with the group median value. This research has improved this parameter from the MAD by magnifying the variation through squaring, so fluctuations and unstable signals can be clearly illustrated. Confusion matrixes of the SAV models for both the ANN(100) and ANFIS_trapmf models are shown in Figure 8. In the figure, the results illustrated that the ANFIS_trapmf was better than the ANN(100). In the ANFIS_trapmf, the chances of a wrong decision for NSR, PAF, and AF patients were 0%, 12%, and 20%, respectively. Regarding implementation based on these datasets, the ANFIS with trapezoidal membership functions is the most suitable model for AF diagnosis, and the SAV variable is  
Figure 8: The confusion matrices show (**a**) ANN(100) and (**b**) ANFIS_trapmf models with the SAV variable. Figure 7: The performance values of the best models of the ANN and ANFIS. the best indicator. It can be deployed as a decision-making tool in hospitals to assist cardiologists in diagnosing patients with AF. Although the CNN technique has gained popularity and was used widely in AF for heart arrhythmia diagnosis, ECG recordings in this research were not abundant to train and test the CNN models. Moreover, the time interval in this research was also larger than the existing research works that used CNN in arrhythmia classifications, so the detail of the signal image was large. CNN was tested with the datasets of this research, but it could not show the performance because of insufficient datasets.
# Design of Constant Speed Controller for Hydraulic Retarder Based on Robust Control
## 4 Simulation Results and Analysis
### Heavy-Duty Vehicle Simulation Conditions
By analysing Figure 10b, it can be seen that as the value of \(\varepsilon\) diminishes, the oscillation amplitude of the hydraulic retarder filling rate at the controller output increases, and an excessively low value of \(\varepsilon\) will result in high-frequency chatter in the control input. Considering the vehicle speed tracking performance requirements and the smoothness of the controller output, the control parameters of the high-frequency controller designed in this paper are \(k=1\) and \(\varepsilon=0.01\).
# Hash-Chain-Based Cross-Regional Safety Authentication for Space-Air-Ground Integrated VANETs +
## 5 Experiment
### Hash-Chain-Based Vehicle Cross-Regional Authentication Simulation
#### 5.2.1 Distributed Network Based on the Kafka Consensus  
We deployed the Kafka server and pre-fixed three virtual servers as ZooKeeper nodes to achieve the Apache ZooKeeper service, ZooKeeper coordinates each Kafka agent node and other Kafka agents. We use four virtual servers to form a Kafka cluster, and preset two organizations and issue different CA certificates to simulate multiple SMs from various sources in the actual process. Moreover, each organization joins a sorting node to communicate with Kafka nodes to achieve node consensus. Then we increase the number of nodes by two at a time, from 4 to 12. Among them, the maximum request delay for Kafka message packing is 2 s, the maximum number of packed messages is 100, the maximum capacity of blocks is 99 MB, and the maximum size of a message is 512 kb. According to the above settings, we conduct 100 simulation experiments for each different number of nodes and take the average value as the experimental result. Part of the delay data in milliseconds (_ms_) for a specific repeated experiment is shown in Table 2.  
The delay data in the above table is the delay required for every ten transactions. For example, the number of transactions equal to ten means the range from 0 to 10, and the number of transactions 100 means the range from 90 to 100 transactions. After the introduction of the Kafka consensus algorithm, when the number of transactions exceeds the maximum number of packaged messages (100) or the waiting time exceeds the maximum request delay of two seconds, the delay appears once a significant increase, while the other delays are about 26 ms. The time required when the number of transactions is an integer multiple of the maximum number of messages is the time it takes to generate blocks, such as the data in bold in Table 2. The delay of the non-integer maximum number of messages is the time it takes for the docker virtual machine to load after the network parameter restarts, which has nothing to do with the delay of generating blocks. In the actual application process, the network restart delay is not necessarily time. After repeating the experiment enough times, the average delay required to restart the network is about 26.5152 ms. As can be seen from the above table data, as the number of nodes increases, the delay of block generation also increases.