esa-sceva/satcom-chunk-collection huggingface dataset:
# Integrated Earthquake Catalog III: Gakkel Ridge, Knipovich Ridge, and Svalbard Archipelago
## 3 Results
### Merging Catalogs
**(a)** Comparison of the metric distribution ARC_SHB/ARKH pairs (blue histogram) and the same metric for ARKH/ARKH earthquakes (red histogram); **(b)** Threshold optimization: the red line shows the probability of missing a duplicate, the blue line shows the probability of a false duplicate, and the black line shows the total probability of the first and the second kind errors. The threshold value \(Ro=12\), which minimizes the total number of errors, approximately 0.7%, is shown by a dashed line; **(c)** Distribution of normalized \(DT\) and \(DR\), and the contour line of metric (1). Colored dots represent ARC_SHB/ARKH pairs (pink dots are duplicates, blue dots are unique events), and black dots are distances between ARKH/ARKH events in metric (1). The values of the metric for earthquakes in the ARKH catalog are significantly larger than for ARC_SHB/ARKH duplicates (pink dots). The contour line of the metric (1) \(Ro=12\) provides close to optimal separation of duplicates and naturally grouped events. Absolute duplicates are not shown. Figure 3:1 Stage 3. Determination of numerical parameters of the metric (1) for merging the ISC and RUS catalogs. Distributions of variables \(DT\), \(DX\), \(DY\), and the dependence of dispersion and mean values on time and magnitude of events. Figure 3:2 Stage 3: Merge of catalogs ISC and RUS. (**a**) Comparison of the metric distribution ISC/RUS pairs (blue histogram) and the same metric for RUS/RUS earthquakes (red histogram); (**b**) Threshold optimization: the red line shows the probability of missing a duplicate, the blue line shows the probability of a false duplicate, and the black line shows the total probability of the first and the second kind errors. The threshold value \(Ro=12\), which minimizes the total number of errors, approximately 0.7%, is shown by a dashed line; (**c**) Distribution of normalized \(DT\) and \(DR\), and the contour line of metric (1). Colored dots represent ISC/RUS pairs (pink dots are duplicates, blue dots are unique events), and black dots are distances between RUS/RUS events in metric (1). The values of the metric for earthquakes in the RUS catalog are significantly larger than for ISC/RUS duplicates (pink dots). The contour line of the metric (1) \(Ro=12\) provides close to optimal separation of duplicates and naturally grouped events. Absolute duplicates are not shown.
# Lossless and High-Throughput Congestion Control in Satellite-Based Cloud Platforms +
## 3 Integrated System and Algorithm Design
### Data Forwarding Scheduling
To achieve this balance, Data packets from the flow with the fewest sent bytes are selected first. The scheduling algorithm is described as follows:  
Figure 7: Data forwarding scheduling in CCLF. After sending one Data packet, retrieve the database \(Q\). Select the non-empty queue \(q_{\max}\) with the highest priority \(p_{\max}\) from \(Q\). 2. If there is only one sub-queue \(s_{i}\) in \(q_{\max}\), remove the first element \(l\) from \(s_{i}\) and insert the corresponding Data packet into the output queue of the interface. Update the sent byte count for \(s_{i}\) from \(byte(s_{i})\) to \(byte(s_{i})+byte(l)\). 3. If there are multiple sub-queues \(s_{j}\) in \(q_{\max}\), select the sub-queue with the least sent bytes: \(s_{\text{opt}}=\arg\min(byte(s_{j}))\). Remove the first element \(l\) from \(s_{\text{opt}}\), insert the associated Data into the output queue, and update the byte count for \(s_{\text{opt}}\) from \(byte(s_{\text{opt}})\) to \(byte(s_{\text{opt}})+byte(l)\). 4. If the sub-queue \(s_{i}\) or \(s_{\text{opt}}\) becomes empty after removing the first element \(l\), erase it to prevent selecting an empty sub-queue in subsequent steps. 5. Repeat step (1) until the termination condition is met. We track the sent byte count for each flow and use this information to schedule the forwarding of Data packets. In the above scenario, we can ensure a balanced distribution of bandwidth among multiple data streams with equal priority and other factors. Notably, the CCLF method can handle dynamic changes in traffic priority. It uses a router-driven scheduling mechanism where routers adaptively adjust forwarding rates based on updated priorities, ensuring all flows are allocated appropriate rates. Additionally, in satellite communication, high-priority flows are reserved for critical real-time data, including information related to satellite control, and occupy only a small proportion of network traffic. This results in low-priority flows occupying a significant proportion of the network traffic, thus ensuring they are not starved or blocked. The design ensures balanced resource allocation while supporting both real-time and general data transmission. In summary, the CCLF method leverages the inherent CS storage to provide an effective solution for heavy traffic transmission. It enables lossless forwarding by optimizing packet scheduling, and reduces end-to-end data acquisition delay in LEO satellite networks. Moreover, it ensures high bandwidth utilization, thereby enhancing overall throughput.
# Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series
## 6 Discussion
### Discussion of Proposed Methodology  
Cryptocurrency prediction is considered a very challenging forecasting problem, since the historical prices follow a random walk process, characterized by large variations in the volatility, although a few hidden patterns may probably exist [48; 49]. Therefore, the investigation and the development of a powerful forecasting model for assisting decision making and investment policies is considered essential. In this work, we incorporated advanced deep learning models as base learners into three of the most popular and widely used ensembles methods, namely averaging, bagging and stacking for forecasting cryptocurrency hourly prices.  
The motivation behind our approach is to exploit the advantages of ensemble learning and advanced deep learning techniques. More specifically, we aim to exploit the effectiveness of ensemble learning for reducing the bias or variance of error by exploiting multiple learners and the ability of deep learning models to learn the internal representation of the cryptocurrency data. It is worth mentioning that since the component deep learning learners are initialized with different weight states, this leads to the development of deep learning models each of which focuses on different identified patterns. Therefore, the combination of these learners via an ensemble learning strategy may lead to stable and robust prediction model.  
In general, deep learning neural networks are powerful prediction models in terms of accuracy, but are usually unstable in sense that variations in their training set or in their weight initialization may significantly affect their performance. Bagging strategy constitutes an effective way of building efficient and stable prediction models, utilizing unstable and diverse base learners [50; 51], aiming to reduce variance and avoid overfitting. In other words, bagging stabilizes the unstable deep learning base learners and exploits their prediction accuracy focusing on building an accurate and robust final prediction model. However, the main problem of this approach is that since bagging averages the predictions of all models, redundant and non-informative models may add too much noise on the final prediction result and therefore, possible identified patterns, by some informative and valuable models, may disappear.  
Figure 8: Autocorrelation of residuals for ETH dataset of ensemble models using CNN-BiLSTM as base learner.  
Figure 9: Autocorrelation of residuals for XRP dataset of ensemble models using CNN-BiLSTM as base learner.  
On the other hand, stacking ensemble learning utilizes a meta-learner in order to learn the prediction behavior of the base learners, with respect to the final target output. Therefore, it is able to identify the redundant and informative base models and \"weight them\" in a nonlinear and more intelligent way in order to filter out useless and non-informative base models. As a result, the selection of the meta-learner is of high significance for the effectiveness and efficiency of this ensemble strategy.
# Performance of Ship-Based QKD Under the Influence of Sea-Surface Atmospheric Turbulence
## 1 Introduction  
Quantum communication technology is divided into fiber-optic quantum communication and free-space quantum communication due to different transmission channels. Currently, it is difficult to further expand the transmission distance of fiber optic quantum communication due to its inherent transmission loss. Free-space quantum communication is considered the best choice for realizing a global quantum communication network due to its advantages, such as low channel loss and the possibility of relaying through space platforms. Among them, quantum key distribution (QKD) has been widely studied and applied because of its high security [1, 2, 3, 4, 5]. Despite these advancements, maritime QKD applications remain under-explored. This is mainly because optical signals transmitted at the sea-surface are susceptible to refractive index fluctuations induced by ocean turbulence, leading to light intensity scintillation, beam drift and wave-front distortion, which degrade communication performance [6, 7, 8]. In wireless optical communications under ocean turbulence, the bit error rate (BER) performances of M-ary phase shift keying, subcarrier intensity modulation [9], M-ary pulse position modulation [10], average BER and interruption probability [11], and double-headed pulse interval modulation [12] have been investigated. However, the specific impacts on QKD systems operating in maritime environments remain poorly understood. In addressing this gap, our study quantifies the performance degradation of ship-based QKD under atmospheric turbulence of different intensities, providing favorable support for building a global quantum network [13, 14].
## 2 Fault Analysis and Feature Extraction
### Single-Phase Ground Fault Simulation and Analysis
#### 2.1.1 High Resistance Ground Fault
The transition process occurs through a parallel connection between the zero-sequence capacitance and arc suppression inductance. Additionally, the transition resistance is inversely proportional to the zero-sequence current at the fault point and directly proportional to the transient resonant frequency. The maximum resonance frequency only slightly exceeds the power frequency and in this case, the resonance frequency approaches the power frequency, the waveform of the zero-sequence current exhibits beat frequency phenomena. As an example, consider a high resistance ground fault with a ground resistance of 1000 \(\Omega\) that occurs 1 km from phase A of fault line 3 at 0.01 s. The zero-sequence current of the fault line and non-fault phase is shown in Figure 2.  
Simulated results indicate that in the high-resistance earth fault state, the first half wave phase of the fault phase is the opposite, and the wave's amplitude differs greatly from that of the non-fault phase. A high transition resistance results in a resonance frequency that is virtually the same as a working frequency, and a zero-sequence current oscillation frequency that is certain, but there is a phenomenon known as the beat frequency at the outer edge of each wave peak, resulting in a waveform with a longer wavelength. Figure 2: Zero-sequence current of High-resistance ground fault line and non-fault line.
# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 3 Designs and Developments
### Remote Cockpit and 5G Communication System
All data from sensors, microcontrollers, and external software are received, processed, and routed through ROS topics that enable communication across system components. The communication between the prototype vehicle and control station is conducted over a 5G wireless network, ensuring rapid and reliable transmission. Modules like Logitech signal transmission, mode switching, and latency monitoring manage their respective data streams and interface with ROS for seamless integration. These components ensure that the system remains adaptable, responsive, and safe across various operational conditions. To enhance safety and control flexibility, the system includes five operating modes: (1) vehicle emergency mode, activated by the onboard surveillance system in response to high latency, obstacle detection, or manual emergency activation; (2) vehicle manual mode, which disengages all electric actuators to allow onboard human control; (3) cockpit emergency mode, triggered only by the remote operator; (4) autonomous mode, where the vehicle operates independently; and (5) remote control mode, enabling teleoperation via the control station. The transition between these modes is managed by the control mode switcher module, which makes decisions based on real-time diagnostics. For example, if the system detects high latency during autonomous operation, it prevents switching to remote-control mode until latency levels fall within safe limits. This constraint ensures that control is not transferred under unsafe conditions, reinforcing operational safety. The system's design prioritizes responsiveness and robustness, providing both manual and automated fail-safes to handle abnormal events efficiently.
# Fall Detection Based on Continuous Wave Radar Sensor Using Binarized Neural Networks
###### Abstract  
Acidents caused by falls among the elderly have become a significant social issue, making fall detection systems increasingly needed. Fall detection systems such as internet of things (IoT) devices must be affordable and compact because they must be installed in various locations around the house, such as bedrooms, living rooms, and bathrooms. In this study, we propose a lightweight fall detection method using a continuous-wave (CW) radar sensor and a binarized neural network (BNN) to meet these requirements. We used a CW radar sensor, which is more affordable than other types of radar sensors, and employed a BNN with binarized features and parameters to reduce memory usage and make the system lighter. The proposed method distinguishes movements using micro-Doppler signatures, and spectrogram is binarized as an input to the BNN. The proposed method achieved 93.1% accuracy in binary classification of five fall actions and six non-fall actions. The memory requirements for storing parameters were reduced to 11.9 KB, representing a reduction of up to 99.9% compared with previous studies.  
Keywords:fall detection; internet of things; lightweight; continuous wave radar; short-time Fourier transform; micro-Doppler signature; spectrogram; binarization; binarized neural network +
Footnote †: journal: Applied sciences
## 2 JTAG Background  
JTAG [4], commonly referred to as boundary-scan and defined by the Institute of Electrical and Electronic Engineers (IEEE) 1149.1, was developed for validating proper assembly of components on PCB boards in the 1990s. JTAG is mainly used by IC manufacturer at the developing and testing stage as well as giving access to the internal subsystem of IC, or for breakdown examination and debugging. When this standard became popular for the first time, it became necessary to differentiate between JTAG compatible devices and normal devices. Thus, the committee came up with a standard hardware description language called \"Boundary Scan Description Language (BSDL)\", which unveils the internal resources of an IC such as logical port description, scan port identification, package pin mapping, entity declaration, boundary register description, etc. which can be unique for each IC manufacturer. JTAG consists of  
* _Test Access Port (TAP)_: This port gives easy access to test functions built into a component. It has four input ports, i.e., Test Clock (TCK), Test Mode Select (TMS), Test Data Input (TDI), Test Reset (TRST) (optional), and one output port, i.e., Test Data Output (TDO). Here, TCK, TMS and optional TRST are broadcast signals, whereas TDI builds serial chain, in which test signals are injected and get test output signals at TDO port. Only these four pins are required to inject test signals on PCB regardless of the number of components on PCB.
* _TAP Controller_: The TAP controller is a finite state machine which is responsible for the behavior of JTAG boundary scan logic in the chip. The TAP controller generates signals to control the operation of the test data registers, instruction registers, and associated circuitry. The TAP controller finite machine has two main paths for setting and retrieving information from either the instruction register or the data register.
* _Instruction Register_: The \"Instruction register\" decides on the operation mode of the Boundary Scan IC. The IEEE standard has three main instructions (modes): BYPASS, SAMPLE/PRELOAD, and EXTEST.
* _One or more data register(s)_: These registers are used to read-out information in the component. There are a minimum of two mandatory registers described in this standard, i.e., Boundary Scan and bypass registers. There are data registers which contain \"device ID\" and are known as \"idcode\" register.  
JTAG specification enables testing of all components which are not time critical such as resistors, crystals, driverICs, logic gates, reset ICs and even RAM ICs or Flash ICs (parallel as well as serial). This specification offers a low cost, less time requirement for the testing operation, a simplified connection architecture and hence widely accepted and followed in the industry.
###### Abstract
It then analyzes the subsidence-monitoring capabilities of LT-1 and Sentinel-1 data across four dimensions: subsidence-monitoring results, interferometric phase, scattering intensity, and interferometric coherence. This paper also explores the appropriateness of different band sensors for subsidence monitoring in this area, thus offering valuable insights and a theoretical basis for future data selection in surface subsidence monitoring.
## 2 Data Description
The normalization approach shown in Figure 5 provides representation of the data by a histogram graph, enabling a detailed examination of each variable's distribution. The distribution of DC shows a pronounced central peak, suggesting limited variability and a strong clustering of values around the mean. Conversely, the salinity parameter exhibits a multimodal distribution with broader variability, indicative of distinct subsets or measurement inconsistencies within the dataset. The pressure and temperature parameters reveal asymmetrical distributions, pointing to potential skewness or non-uniform sampling across experimental conditions. The boxplots embedded within each violin highlight the interquartile range, median, and potential outliers, offering complementary insights into central tendency and spread. Such detailed exploratory data analysis is indispensable for understanding the dataset's underlying structure and ensuring informed decisions in subsequent analytical or modeling workflows. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Statistic** & **P (MPa)** & **T (K)** & **Salinity (mol/L)** & **DC (10\({}^{-9}\) m\({}^{2}\)/s)** \\ \hline Count & 176 & 176 & 176 & 176 \\ Mean & 12.15 & 320.06 & 2.35 & 2.07 \\ Std Dev & 7.99 & 26.35 & 2.42 & 0.97 \\ Min & 0.10 & 286.15 & 0.00 & 0.13 \\
25\% & 5.66 & 300.15 & 0.51 & 1.47 \\ Median & 10.00 & 313.00 & 1.00 & 1.81 \\
75\% & 19.79 & 341.15 & 4.00 & 2.73 \\ Max & 30.00 & 398.00 & 6.76 & 4.50 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Descriptive statistics for the employed dataset of all models. Figure 4: Violin plots depicting the distributions of (**a**) pressure (P) in MPa\({}_{a}\), (**b**) temperature (T) in K, (**c**) salinity in mol/L, and (**d**) diffusion coefficient. Figure 3: Correlation heatmap illustrating relationships among P, T, salinity, and Diffusion Coefficient (DC).
###### Abstract  
The coupling of renewable energy sources with electrolyzers under stand-alone conditions significantly enhances the operational efficiency and improves the cost-effectiveness of electrolyzers as a technologically viable and sustainable solution for green hydrogen production. To address the configuration optimization challenge in hybrid electrolyzer systems integrating alkaline water electrolysis (AWE) and proton exchange membrane electrolysis (PEME), this study proposes an innovative methodology leveraging the morphological analysis of Pareto frontiers to determine the optimal solutions under multi-objective functions including the hydrogen production cost and efficiency. Then, the complementary advantages of AWE and PEME are explored. The proposed methodology demonstrated significant performance improvements compared with the single-objective optimization function. When contrasted with the economic optimization function, the hybrid system achieved a 1.00% reduction in hydrogen production costs while enhancing the utilization efficiency by 21.71%. Conversely, relative to the efficiency-focused optimization function, the proposed method maintained a marginal 5.22% reduction in utilization efficiency while achieving a 6.46% improvement in economic performance. These comparative results empirically validate that the proposed hybrid electrolyzer configuration, through the implementation of the novel optimization framework, successfully establishes an optimal balance between the economy and efficiency of hydrogen production. Additionally, a discussion on the key factors affecting the rated power and mixing ratio of the hybrid electrolyzer in this research topic is provided.  
hybrid electrolyzer; multi-objective optimization problem; Pareto front; hydrogen production efficiency; hydrogen production economy
###### Abstract  
Risk estimation holds significant importance in the selection of risk reduction measures and ensuring machinery safety. However, subjective influences of assessors lead to an inconsistent understanding of risk among relevant stakeholders, hindering the achievement of safety. As similarities exists in product updates or applications in engineering practice, the historical risk information of similar products or applications has essential application value. A novel deep learning approach was established to estimate risks based on historical risk information. To address the issue of overfitting caused by a limited dataset, a data augmentation technique was employed. Our experiment was conducted on the raw, 2\(\times\), and 6\(\times\) hazard event dataset of an industrial robot, demonstrating a substantial improvement in both accuracy and stability. On the validation dataset, there was an increase in median accuracy from 55.56% to 96.92%, with a decrease in standard deviation from 0.118 to 0.015. On the new dataset, the trained network also showed near-perfect performance on similar hazard events and trustworthiness on completely different ones. In cases of risk deviations, approximately 80% of them were small deviations (\(\{R|_{Mediation}\mid\leq 2\}\)) without a noticeable bias (\(R|_{dis}\) is close to 1). The LSTM-based deep learning network makes risk estimation \"black-boxed\" and \"digitized\". Assessors just need to focus on hazard identification with risk being determined by the trained network, mitigating the impact of individual factors. Moreover, the historical risk estimation information can be transformed into a trained network, facilitating the development of a standardized benchmark within project teams, enterprises, and relevant stakeholders to promote coordinated safety measures.  
risk estimation; LSTM; deep learning; subjective factor; machinery safety +
Footnote †: journal: Applied sciences