esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Conclusions
In this regard, in addition to training with multi-year data, the introduction of new SAR satellite remote sensing data is also worth considering, since a large number of satellites are currently in service, such as the C-band GF-3 satellite series launched by China in 2016 [36], the C-band Radarsat-2 satellite launched by Canada in 2017 [7], the Russian Arktika-M-1 Arctic weather monitoring satellite [37], etc. In addition, new approaches can be considered, such as cryosphere detection using remote sensing data acquired via GNSS-R technology. This approach offers high temporal and spatial resolution, meaning it has broad applicationprospects. It can be used as a research object or data supplement and needs to be studied in the future. In the future, we will seek to use deep semi-supervised learning and unsupervised learning for Arctic sea ice mapping, as this will help to reduce the labor costs associated with sea ice mapping. We will also further refine the sea ice categories to provide more reliable ice data for Arctic navigation. Conceptualization, R.H. and C.W. ; methodology, R.H. and C.W. ; software, R.H.; writing--original draft preparation, R.H. and C.W. ; writing--review and editing, J.L. and Y.S. ; funding acquisition, C.W. and Y.S. All authors have read and agreed to the published version of the manuscript. This work was supported by the National Natural Science Foundation of China (No. 62172247). Data Availability StatementData presented in this study are available upon request from the corresponding author. The authors would like to thank the anonymous reviewers and members of the editorial team for their comments and contributions. The authors declare no conflict of interest. The following abbreviations are used in this manuscript: \begin{tabular}{l l} CIS & Canadian Ice Service \\ CBAM & Convolutional Block Attention Module \\ CNN & Convolutional Neural Network \\ ResNet & Residual Network \\ CRF & Conditional Random Field \\ AARI & Arctic and Antarctic Research Institute \\ HRNet & High-Resolution Network \\ U-HRNet & U-Shaped High-Resolution Network \\ U-Net & U-Shaped Convolutional Network \\ Grad-CAM & Gradient-weighted Class Activation Mapping \\ BD & Bhattacharyya Distance \\ SI & Separability Index \\ SIC & Sea Ice Concentration \\ ASPP & Atreus Spatial Pyramid Pooling \\ SAR & Synthetic Aperture Radar \\ HDC & Hybrid Dilated Convolution \\ IMO & International Maritime Organization \\ PCA & Principal Component Analysis \\ EW & Extra-Wide Swath \\ GLCM & Gray-Level Concurrence Matrix \\ SVM & Support Vector Machine \\ FFT & Fast Fourier Transform \\ \end{tabular}
# Survey on Quality of Experience Evaluation for Cloud-Based Interactive Applications
## 5 Strategies for QoE Measurement in CIAs
### Strategies Based on the Instrumentation of Programming Code
The time to first paint (TTFP) is the time it takes for the first pixel to appear on the user's browser screen after the first interaction with the web service. To calculate the TTFP, the researchers instrumented a CIA to obtain the time elapsed between the establishment of the first connection at the network level and the instant the first HTML element starts to be rendered. The TTFP is a metric the authors associated with QoE. Even if it does not measure the time to render the final response, it captures a scenario where the user becomes impatient, thinking that the CIA is stuck or not working properly if they do not see any kind of update on the screen [40]. The Page Load Time (PLT) represents the duration between the start of browsing and the entire web page loading. It is calculated as the time between the establishment of the first connection at the network level and when the page finishes loading and renders the last HTML element. The PLT was proposed by [PERSON] et al. as a measure of QoE. However, some HTML elements of the web page are not visible unless one scrolls to the non-visible part of the CIA. This is why Web View obtains the Time for Full Visible Rendering (TFVR) metric as an alternative. The TFVR represents the time it takes for the interactive web application to render the part directly visible to the user without scrolling. The instrumentation code automates the process of determining when each of the elements is rendered and their respective positions on the final web page. Some HTML elements, such as images, may be cached, and the instrumentation takes this phenomenon into account. Once the coordinates and dimensions occupied by each element of the DOM have been determined, the tool decides which elements and at what time the browser renders them in the visible part. Additionally, in the field of interactive web applications, there is the work by [PERSON] et al. in [44], which is similar. However, the work focuses on the speed index (SI) metric. This is a metric that evaluates the time it takes to render a change on the screen in web applications, but may also be applicable to CIAs. Specifically, the SI quantifies the speed with which screen updates are rendered. To do this, by instrumenting the CIA code in the client, it measures the duration between the rendering of two known graphical states characterised by the histogram of their pixels. Although the SI is not an approximate or proportional measure of interactivity time, it is sometimes used as a representative metric of QoE. For this purpose, [PERSON] et al. proposed a mathematical model to map different SI values to an MOS scale. Therefore, there are some common drawbacks of proposals based on the instrumentation of programming code. Primarily, it can be challenging to instrument applications, operating systems, or drivers. This implies that some of the proposals are less generalizable and, consequently, in certain cases, the measurements they provide are approximations.
## 3 Experimental Phenomena and Result Analysis
### Experimental Result Analysis
Before the loading reached 4%, the single-cycle energy dissipation of the TCSPSW-M-1 and TCSPSW-M-2 specimens was almost equal. As the loading continued, the single-cycle energy dissipation of the  
Figure 17: Comparison of skeleton curves of each specimen: (**a**) Skeleton curves of each specimen, (**b**) Skeleton curves of each specimen (except TSPSW-M-1). Figure 18: Comparison of stiffness degradation curves of each specimen: (**a**) Stiffness degradation curves of each specimen, (**b**) Stiffness degradation curves of each specimen (except TSPSW-M-1). TCSPSW-M-1 specimen began to be slightly higher than that of the TCSPSW-M-2 specimen. Throughout the entire loading process, the single-cycle energy dissipation of the TCSPSW-M-3 specimen was the lowest among the three specimens. This indicates that the flat steel plate shear wall has a stronger energy dissipation capacity in a single loading cycle. The TCSPSW-M-1 specimen has a higher energy dissipation capacity than the TCSPSW-M-2 specimen under higher loads, while the single-cycle energy dissipation of the TCSPSW-M-3 specimen is the lowest throughout the loading process, indicating its weaker energy dissipation capacity. Figure 19b presents the cumulative energy dissipation curves of the four specimens. After the loading ended, the cumulative energy dissipations of the four specimens were 175.97 kJ, 73.67 kJ, 63.96 kJ, and 38.56 kJ, respectively. The TSPSW-M-1 specimen had the highest cumulative energy dissipation, while the TCSPSW-M-3 specimen had the lowest. The highest cumulative energy dissipation of the TSPSW-M-1 specimen indicates that the flat steel plate shear wall has the strongest total energy dissipation capacity throughout the loading process. The total energy dissipation capacity of the corrugated steel plate shear walls is relatively weaker. However, the energy dissipation capacity of the TCSPSW-M-1 specimen is slightly better than that of the TCSPSW-M-2 specimen. The TCSPSW-M-3 specimen had the lowest cumulative energy dissipation, further confirming its weaker energy dissipation capacity.
## 2 Key Security Challenges in Traditional Supply Chain Management
### Data Security Issues in IoT Integration  
The IoT is a network of devices designed to collect and share data over the internet. This technology enables everyday objects to become smart and provides benefits across numerous sectors including industry, agriculture, and healthcare [9]. The IoT holds significant importance within supply chains, enhancing their performance through real-time data collection and monitoring capabilities. With IoT devices, a continuous data flow throughout supply chain stages is achieved via sensors [10]. Thus, the IoT offers the following advantages:  
* Enables real-time traceability, increasing transparency at every stage of the supply chain.
* Ensures continuous data flows, allowing more accurate inventory management and timely deliveries.
* Reduces human intervention through automated systems, accelerating processes and enhancing operational efficiency.
* Anticipates potential supply chain disruptions or deviations, thereby supporting risk management.  
However, as IoT devices generate large volumes of data and maintain continuous communication with servers and storage systems throughout the supply chain, they are exposed to various security risks, including data privacy concerns, unauthorized access, and network attacks [11]. Due to their dependence on centralized systems, they can become vulnerable to cyberattacks, potentially leading to data manipulation, traceability issues, and privacy risks [9]. At this point, blockchain technology holds promise for addressing IoT-related security issues. With its decentralized architecture, cryptographic methods, and immutable record features, blockchain enables secure data exchange among IoT devices, maintains data integrity, and reduces dependency on centralized servers, thus improving network resilience [12].
# Audio Features in Education: A Systematic Review of Computational Applications and Research Gaps
## 2 Methodology
### Identification of Research Works  
The literature data search was conducted on 27 November 2024. Scopus and Web of Science (WoS) were selected due to their broad coverage of peer-reviewed literature in education, technology, and computational sciences [15], making them appropriate for interdisciplinary reviews. To perform the search on both databases, we restricted the query to the title and abstract to balance precision and recall, as full-text searches produced an unmanageable number of irrelevant results. We used a structured query consisting of three conceptual blocks, along with a publication date restriction covering studies published after 2012. * Data source: This component targets studies where audio serves as a primary or derived source of data. The query includes terms such as audio and sound to capture explicit references to raw audio. To encompass studies that use features extracted from audio rather than the waveform itself, it also includes terms like speech transcript, dialogue, and discourse features. This increased the likelihood of including work analyzing linguistic or prosodic features even when the term \"audio\" is not explicitly mentioned. * Educational context: This block captures the environments in which learning interactions occur. Keywords such as collaborative learning, Group interaction, and teaching practice reflect classroom-based and peer-to-peer learning scenarios. Additionally, terms like meeting transcription are included to capture studies focusing on structured interactions in educational or academic contexts. This broader scope helps cover both formal classroom settings and informal learning environments such as workshops and seminars. * Techniques: This component targets the computational methods used to process audio and audio-derived data. The query includes terms related to core technologies such as machine learning, deep learning, and artificial intelligence, along with domain-specific methods like speech recognition, voice activity detection, and natural language processing (NLP). These terms ensure the inclusion of studies applying advanced analytical frameworks. The inclusion of learning analytics further ensures alignment with educational objectives, emphasizing the intersection between computational processing and pedagogical insight. The specific query terms used in this systematic review are presented in Figure 3. To account for terminological variation in a still-evolving research domain, we incorporated wildcard characters into search terms such as Speech Transcript$ and Speech Recogn*. The dollar sign ($) and asterisk (*) act as wildcard operators, allowing retrieval of multiple morphological variants of a base term.
# A Systematic Literature Review of Autonomous and Connected Vehicles in Traffic Management
## Abbreviations  
The following abbreviations are used in this review paper.  
ITS Intelligent Transportation System AV Autonomous Vehicles CV Connected Vehicles HV Hybrid Vehicle CAV Connected And Autonomous Vehicles SAE Society Of Automotive Engineers ACC Adoptive Cruise Control TPACC Three-Traffic-Phase Adaptive Cruise Control CACC Cooperative Adaptive Cruise Control SLR Systematic Literature Review IEEE Institute Of Electrical and Electronics Engineers IT Information Technologies V2V Vehicle-To-Vehicle V2I Vehicle To Infrastructure I2V Infrastructure-To-Vehicle GPS Global Positioning System LiDAR Light Detection and Ranging TdPN Temporal Delay Petri Net Based RTD Resistance Temperature Detector FCFS First Come, First Served (Technique) SRTF Shortest-Remaining-Time-First MARL Multi-Agent Reinforcement Learning ACVAS Advanced Cooperative Vehicle-Actuator System LCS Lane Control Signals VSL Variable Speed Limits SUMO Simulation Of Urban Mobility MPV Model Predictive Control ALADIN Augmented Lagrangian-Based Alternating Direction Inexact Newton TTC Time To Collision MIQP Mixed-Integer Quadratic Programming CISP Customized Synchronous Intersection Protocol BRIP Ballroom Intersection Protocol AREBIC Autonomous Reservation-Based Intersection Control RL Reinforcement Learning RAAL The Reserve Advance, Act Later KNN K-Nearest Neighbors CS Collision-Set CARA Collision-Aware Resource Allocation QoS Quality Of Service TP-AIM Trajectory Planning for Autonomous Intersection Management DCL-AIM Decentralized Coordination Learning of Autonomous Intersection Management VLC Visible Light Communication SICL Signal-Head-Free Intersection Control Logic CIC Cooperative Intersection Control SloV Social Internet of Vehicles ENN Elman Neural Network SAA Sparrow Search Algorithm IoV Internet of Vehicles OP Outage Probability
## 3 The Radiative Transfer Equation
### RTE Numerical Solutions  
The previous section shows that a full analytical solution to RTE (43) and even some simplified ones are expected to be complicated. Against such a background, numerical solutions are often preferred. [PERSON] [10] stated that there are four preferred numerical  
Figure 9: SSAA solution parameters.  
techniques for solving RTE: the discrete ordinate model, invariant-imbedding method, the stochastic model, and MCS. The primary difference between these schemes is the mathematical computational techniques implemented to solve the RTE or its derivatives and the potential implementation issues, which might lead to inaccurate results. All these numerical implementation methods require verification and validation. The accuracy of the implementation's output should be verified by comparing it with the available analytical solutions, benchmark problems, or experimental data. Furthermore, the results' accuracy and reliability should be validated against independent sources.  
Table 5 illustrates a focused synopsis of these numerical methods. The three subsections below provide brief details of implementing the discrete ordinate model, invariant-imbedding method, and stochastic model. For the MCS, the numerical approach will be discussed in a dedicated main section, which is the focus of this work.
## 4 Discussion
### Early Warning of P. glo Red Tide Outbreaks in Qinzhou Bay  
In summary, there is a close correlation between _P. glo_ red tides and the spatial distribution pattern of PC concentration. According to the PC statistical results in Figure 7, it is revealed that during the occurrence of _P. glo_ red tide, the area with PC < 0.9 \(\mu\)g/L increased exponentially, the area with undetected PC concentration (PC < 0.04 \(\mu\)g/L) increased by 4.97 km\({}^{2}\), and the area with undetected PC concentration aligns with the outbreak area of _P. glo_ red tide. This result indicates that the variation in PC concentration serves as an indicator for _P. glo_ red tide outbreaks. In the _P. glo_ red tide event, rainfall and temperature also play a contributing role, but rainfall is not a necessary condition for _P. glo_ outbreaks, as direct discharge of industrial wastewater can provide the necessary nutrients for _P. glo_ outbreaks. Based on these characteristics, with PC concentration variations as the main factor, and sea surface temperature as the secondary factor, a simple early warning threshold can be proposed for the _P. glo_ red tide outbreak in Qinzhou Bay: PC < 0.9 \(\mu\)g/L and temperature T < 20 \({}^{\circ}\)C. Such an early warning system could play a crucial role in mitigating the impact of harmful algal blooms on marine ecosystems, fisheries, and coastal areas.  
Building a system capable of directly forecasting _P. glo_ red tide outbreaks is a long-term vision for marine management. To enhance the accuracy of such forecasting, consideration still needs to be given to other factors that influence _P. glo_ outbreaks. For instance, heavy metals in seawater contribute to the synthesis of complexes that accelerate the formation of the cyst outer wall in _P. glo_. In a study conducted by [PERSON] et al. (2016), the concentrations of six heavy metals (Zn, Cd, Cr, Hg, As, Cu) in surface seawater and phytoplankton bodies in Qinzhou Bay were investigated. The results indicated that Cd levels exceeded standards at monitoring points near Sandu Harbor, which could also be one of the factors contributing to _P. glo_ outbreaks.  
In areas with low PC concentrations, optical substances like suspended matter and chlorophyll-a have a significant impact on PC concentration inversion. Future research should focus on eliminating the influence of other substances on PC spectral characteristics and developing inversion models better suited for lower PC concentrations. Further exploration is needed for _P. glo_ red tide warning methods, and future studies could investigate  
Figure 11: PC concentration of undetected zone.  
seawater salinity, dissolved oxygen levels, and heavy metal concentrations to enhance _P. glo_ red tide forecasting capabilities.
## 4 Results
### Cross-Comparison between Predicted Land Cover Maps and the CDL
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & \multirow{2}{*}{**Scenario**} & \multicolumn{3}{c}{**ARD**} & \multicolumn{3}{c}{**HLS**} \\ \cline{3-8}  & & **OA\%** & **k\_L** & **k\_Q** & **OA\%** & **k\_L** & **k\_Q** \\ \hline  & C1S & 80.8 & 0.820 & 0.878 & 77.9 & 0.782 & 0.875 \\
**Sample** & C1M & 80.4 & 0.819 & 0.868 & 77.4 & 0.777 & 0.871 \\
**Pool** & C2S & 80.4 & 0.819 & 0.867 & 77.4 & 0.779 & 0.866 \\  & C2M & 80.1 & 0.817 & 0.863 & 77.0 & 0.780 & 0.857 \\ \hline  & P01 & 78.9 & 0.808 & 0.853 & 75.5 & 0.776 & 0.828 \\
**Sample** & P05 & 80.1 & 0.817 & 0.866 & 77.2 & 0.776 & 0.868 \\
**Size** & P15 & 80.7 & 0.821 & 0.872 & 78.0 & 0.783 & 0.874 \\  & P25 & 81.0 & 0.823 & 0.876 & 78.3 & 0.786 & 0.876 \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Pixel-based comparison between 2016 predicted land cover maps and CDL, summarized by sample pools and sample sizes. Generally, SPL land cover maps had higher agreement with the CDL than the phenometrically-based maps (Table 12 and Table S16). Similar to model AA, the SPL RFC map clearly improved the accuracy regarding the barren/developed class but was not as accurate as CxQ and HPLM RFC maps when it came to wheat. The land cover map created from CMB RFC models had higher agreement with the CDL than those created with only spectrally-based or phenometrically-based RFC models. Among all ensemble maps, CMB RFC maps were consistently the most accurate. Compared to phenometrically-based and SPL, CMB maps improved PA and UA for both wheat and barren/developed classes. It is also important to note that the barren/developed areas estimated from the phenometrically-based maps were closer to CDL compared to values from the spectrally-based maps. However, both PA and UA of barren/developed from the phenometrically-based maps were lower, thus resulting in fewer correctly assigned pixels (Table 12, Figure 3).
## 2 2-D Phased Array Design
### Novel CORPSs Feeding System of 4 \(\times\) 9
The phase values required at the outputs of the feeding system can be determined by Equations (4) and (5) given previously. Then, each block of the 4 \(\times\) 9 feeding system generates nine output phases (as shown in Figure 11) by using only four phase values given at the input ports. The knowledge of the phase values (\(\psi_{1}\), \(\psi_{3}\), \(\psi_{7}\), and \(\psi_{9}\)) helps to build the other output phase states (\(\psi_{2}\), \(\psi_{4}\), \(\psi_{5}\), \(\psi_{6}\), and \(\psi_{8}\)) required at the antenna system. The feeding network works as a phase interpolator. The nine output phases are generated by using and knowing the value of the two extreme phases and two center phases. For instance, Tables 1-8 illustrate several example cases of the phase values used at the input ports to generate the output phase states for different beam-scanning angles. It is interesting to observe that the phase plane (output phases of the feeding network) can be retained by adjusting the phase values of the control or input ports. Figure 10: Behavior of the transmission and reflection coefficients for the ring-type node: (**a**) simulated and (**b**) measurements. Figure 9: Division or combination node (ring type): (**a**) prototype and (**b**) design in CST with dimensions. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Block** & \(\mathbf{\psi_{1}}\) & \(\mathbf{\psi_{7}}\) & \(\mathbf{\psi_{3}}\) & \(\mathbf{\psi_{9}}\) \\ \hline
1 & 0 & \(-187.54\) & \(-62.51\) & \(-250.05\) \\
2 & 0 & \(-187.54\) & \(-62.51\) & \(-250.05\) \\
3 & 0 & \(-187.54\) & \(-62.51\) & \(-250.05\) \\
4 & 0 & \(-187.54\) & \(-62.51\) & \(-250.05\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Phase values at the input ports of the feeding system for (\(\theta_{0}=10^{\circ}\) and \(\phi_{0}=0^{\circ}\)) (Conf. 2). Figure 11: Behavior of the phase through the 4 \(\times\) 9 CORPSs feeding system. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Block** & \(\mathbf{\psi_{1}}\) & \(\mathbf{\psi_{2}}\) & \(\mathbf{\psi_{3}}\) & \(\mathbf{\psi_{4}}\) & \(\mathbf{\psi_{5}}\) & \(\mathbf{\psi_{6}}\) & \(\mathbf{\psi_{7}}\) & \(\mathbf{\psi_{8}}\) & \(\mathbf{\psi_{9}}\) \\ \hline
5 & 0 & \(-31.25\) & \(