esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Experimental Results and Discussion
### Experimental Result
#### 3.2.3 The Relation between the Z-Axis Height and the Induced Voltage  
The excitation coil produced a high-gradient magnetic field, which decreased as the height increased along the z-axis direction. Consequently, when wear particles enter the magnetic field at different heights, the induced voltage generated by the debris cutting through the magnetic field will also change. The debris speed was set to 2.12 m/s, the current was set to 0.5 A.  
Altering the height of the nylon thread can simulate the wear particles entering the magnetic field at different heights. The relationship between the z-axis height and the peak-to-peak values of the debris waveform are shown in Figure 12. The results show that the peak-to-peak values decreased as the z-axis height increased. From 1 mm to 4 mm, the peak-to-peak value attenuation was rapid, and from 5 mm to 9 mm, the attenuation was slow.  
In addition, when the z-axis height was 1 mm, the signal-to-noise ratio (SNR) was 13.57, and when the z-axis height increased to 8 mm, the signal-to-noise ratio was 3.41. These results indicate that the sensor can effectively distinguish the maximum height of the z-axis of the wear debris signal up to 8 mm. Figure 13 shows the z-axis magnetic field distribution of the inductive sensor. Clearly, the variation trend of the induced voltages from different heights is consistent with the z-axis magnetic field distribution of the inductive sensor.  
Figure 11: Variation in the output voltage with the velocity.  
Figure 12: Variation in the output voltage with the z-axis height.
## 4 6G and Dependability
### Dependability in 6G Networks
#### 4.2.3 Safety  
Safety and security, looking intertwined, are highly complicated in terms of defining their roles in communications networks. Safety, also defined similarly in [55], is a system's characteristic of preventing losses due to unintentional actions by normal, non-harmful actors. Security, on the other hand, relates to deliberate actions (mostly harmful) by deliberate actors. Safety in 6G communications networks can be achieved by taking several measures that are also related to security, which are discussed in the following security part. Aside from foolproof security, safety can be achieved by improving monitoring and response systems, increasing multiplicity or redundancy, and distributing important control functions throughout the network. EdgeAI thus plays a very important role in providing opportunity for redundant resources and distributing important network control functions. The concept of devolving control functions, with the help of miniaturizing edge to the extreme, as discussed in [56], can improve safety in terms of minimizing the impact of failures and delimiting the consequences. The same is true for communications links, using multiple access technologies to avoid blackout due to failure in one. Satellite communications [57; 58] present interesting solutions to be coupled with terrestrial networks for enabling safe operation in times of failures, as a redundant communication infrastructure. The key point in improving safety in 6G is enabling the system to function in the wake of uncertainty, failures in different perimeters and surroundings, and security vulnerabilities and attacks, which are discussed below.
# Algorithmic Techniques for GPU Scheduling: A Comprehensive Survey
## 3 Scheduling Algorithms and Their Performance
### Learning-Based Adaptive Algorithms
For example, if high-priority jobs begin arriving more frequently, the scheduler might increase the time-slice frequency to enhance multitasking capability or temporarily preempt low-priority jobs to make room. These techniques combine elements of prediction and feedback, aiming to dynamically stabilize key system metrics (e.g., response time, fairness) under workload fluctuations. Scalability and practical deployment of learning-based scheduling: Once trained, learning-based schedulers can make decisions in milliseconds--often accelerated further on GPUs--easily keeping pace with the few dozen scheduling events per second typical in production clusters. The principal overhead lies in training these models and in collecting representative data that captures the full variability of the target environment. A major scaling challenge is handling a dynamic and potentially very large job set: some designs address this by truncating inputs to a fixed-size \"window\" of the most urgent or largest jobs and summarizing the remainder, while others employ flexible architectures--such as transformers that natively accept variable-length sequences [170]--or iterative decision loops that process one job at a time. In clusters on the order of \(10,000\) GPUs, even these techniques can strain centralization; _multi-agent reinforcement learning_ (MARL), in which each agent oversees a rack or node group and coordinates via shared rewards or a meta-policy, offers one promising path toward decentralized, scalable control. Several RL-based prototypes have validated this approach in simulation. DeepRM and Decima showed that learned policies can surpass classical heuristics under complex constraints like DAG dependencies and heterogeneous job mixes [82; 127]. More recently, HeterPS employed an LSTM-based RL scheduler to allocate tasks across CPUs and GPUsin heterogeneous clusters, achieving faster end-to-end training times [171]. These studies demonstrate that, when properly trained, RL can internalize intricate scheduling dynamics and adapt to shifting workloads more effectively than static rules. Despite these advances, industrial uptake remains cautious. Scheduling is mission-critical, and missteps risk severe performance degradation or outages. Consequently, most production systems employ ML components in a hybrid fashion--using learned models for parameter tuning or runtime prediction, but retaining proven control logic as the primary decision maker--and incorporate extensive safety checks, fallback paths, and human-in-the-loop oversight before permitting fully autonomous operation.
# First Results from Sentinel-1A InSAR over Australia: Application to the Perth Basin
## 5 Results
### Multi-Temporal Analysis
#### 5.2.2 Adjacent Uplift and Subsidence  
South of Hillarys, contrasting, but adjacent regions of relative uplift and subsidence are identified in both the Sentinel-1A and TerraSAR-X deformation rate-maps and are shown by the rectangle labelled (b) in Figure 4A,B. Displacement profile y (Figures 4 and 5D,E) shows this transition from positive to negative displacement, recording maximum rates of \(\sim\)+15 mm/y and \(\sim\)\(-\)7 mm/y, respectively. This contrast between adjacent positive and negative displacements may be indicative of a barrier to flow in the groundwater system, such as a permeability contrast or unmapped fault [78]. Profile y also reveals a slight offset in the maximum displacement between the two datasets, which may be attributed to differences in the line of sight direction of each satellite or residual quasi-systematic atmospheric banding in the Sentinel-1A dataset in this part of the image (Figure 4A). Assessing the correlation between profile y from Sentinel-1A and TerraSAR-X shows that, whilst the results are largely similar, the magnitudes of displacements from TerraSAR-X tend to be \(\sim\)10% larger (Figure 5F).
# Machine Learning Enabled Performance Prediction Model for Massive-MIMO HetNet System
## 2 System Model
### Performance Metrics
#### 2.4.1 Coverage Probability (\(Cp\))  
\(Cp\) is the measure of the reliability of a typical transmission link, and is defined as the probability that a typical mobile user is able to achieve some threshold SINR (\(T\hbar\)), given by the following  
\[CP=Pr[SINR>T\hbar]. \tag{13}\]  
For successfully running any given application, the user needs to have an \(SINR\) value of more than a minimum value. If the \(SINR\) experienced by any user drops below the desired minimum value, the customer satisfaction would be compromised. Hence, a higher value of \(Cp\) implies a better quality of experience (QoE).
## 3 Results
### Spatial Variability of Annual and Seasonal Mean LST
#### 3.1.2 Daytime LST  
The annual mean of daytime LST over the studied major cities in India and surrounding countries during 2000-2023 ranged from 28.82 \({}^{\circ}\)C in Dhaka to 35.55 \({}^{\circ}\)C in Coimbatore. Similar to nighttime temperatures, the average daytime LST showed the variability of surface temperature across months and seasons, with the highest daytime temperature in all cities recorded during March, April, May, and June. Table 3 provides the seasonal and monthly breakdown of the average daytime LST. The average daytime LST over India and surrounding countries for AMJ months (Figure 1b) indicated that the Thar desert and Kutch region showed the highest warming (>50 \({}^{\circ}\)C) similar to the nighttime temperature pattern, and except for the Himalayan region, the average daytime LST over India ranged from 30 to 50 \({}^{\circ}\)C during AMJ. It is also noted that central India showed much higher warming during pre-monsoon to onset of monsoon, compared with other parts of the country. The highest and lowest average daytime temperature of major cities during pre-monsoon months was 41.49 \({}^{\circ}\)C recorded in Ahmedabad and 30.51 \({}^{\circ}\)C recorded in Dhaka, respectively. The lowest daytime temperatures were recorded during DJF/winter months, with the highest temperature recorded in Coimbatore at 32.39 \({}^{\circ}\)C and the lowest in Lahore at 19.64 \({}^{\circ}\)C. Similar to nighttime temperature, southern Indian cities such as Bengaluru, Chennai, Pune, Hyderabad, and Coimbatore showed the lowest daytime temperature in the month of December, while the other northern Indian cities showed the lowest daytime temperature during January. It is noted that there were conspicuous seasonal variations in the peaks of the daytime and nighttime temperatures over the cities, with daytime temperature peaking in the pre-monsoon summer months while the nighttime temperature of certain cities (Delhi, Dhaka, Kolkata, Lahore, Lucknow, Kanpur, and Dhanbad) peaked around the pre-monsoon months. A bar chart showing the seasonal average (a) nighttime and (b) daytime temperature is provided in Figure 5.
## 3 Nonlinear Mechanical Properties for the PRC with Interfacial Debonding
### Model Validation  
In order to verify the present micromechanical method, the experimental results by [PERSON] [36] for the elastic constant and uniaxial tensile stress-strain behaviors are used. By using the micromechanical method for the spherical PRC (the ratio of _a:b:c_ = 1:1:1 is as shown in Figure 1b), the elastic modulus is 94.4 GPa, which agrees well with the experimental results. Figure 2 shows the stress-strain behaviors of the spherical PRC. For comparing the interface properties effect on the macroscopic mechanical responses, composites with and without interfacial debonding are both considered. It can be found that the stress-strain responses in the linear elastic stage are unrelated with interfacial cohesion. As the loading strain increases, the theoretical results show higher stiffness behaviors than the experimental data for composites without consideration of interfacial debonding. The interfacial debonding influences on mechanical properties show a distinct difference when the true strain increases to 2.3%. Once the strain is greater than 2.3%, the interfacial debonding tends to decrease the stiffness behaviors sharply. In detail, compared with SiC/Al composites with interfacial debonding, the stress of composites with perfect interfaces provides a 27.9% increase when the true strain increases to 5%. Furthermore, compared with the experimental data [36], the theoretical relative error is less than 5% when the interfacial debonding is considered. Therefore, it should be denoted that the effective numerical method can be acquired only when the interfacial debonding is considered.
## 2 Experimental Work
### Sample Preparation
#### Mixer Selection  
Mechanical stirrer and high shear mixer (locally fabricated) were used for the dispersion of CNTs in bitumen. Researchers have utilized different mixing techniques for dispersion of nanomaterials in bitumen. The most commonly selected instruments are mechanical stirrer and high shear mixer. Mechanical stirrer uses simple propeller/fan blade configuration for mixing, while high shear mixer generates a vortex type movement within the material to ensure fast and aggressive mixing [29]. This study evaluated both types of mixers. One mixer (mechanical stirrer and high shear mixer) was selected based on dispersion of CNTs in asphalt binder. In many previous studies, mixing rate of around 1500 rpm with mechanical stirrer has been used for the dispersion of CNTs in bitumen [25; 28; 32] while a mixing speed of around 3000 rpm is generally selected for high shear mixer [29; 33]. These mixing rates were selected for this study as well. CNTs content, mixing temperature, and mixing time were kept constant for both mixers. One percent by mass of bitumen was taken as CNT content, while mixing temperature and mixing time were 158 \(\pm\) 5 \({}^{\circ}\)C and 45 min, respectively. SEM images were used to compare the dispersion of CNTs in bitumen for both mixers. Microscopy (SEM) specimen was examined point by point directly in a moving electron beam. Electrons reflected by the specimen were used to form a magnified, three-dimensional image on a television screen [34]. Bitumen is a petroleum product and contains volatile compounds, which is why, when subjected to focused electron beam during SEM test, the volatile compounds of bitumen evaporate due to high temperature, which not only contaminates the SEM chamber but also makes the scanning process difficult. To avoid this, sputtering of SEM samples was carried out. During the sputtering process, the bitumen sample was placed in a vacuumed reservoir and coated with thin layers of gold palladium. SEM analysis provided in Section 2.4.1 shows that mechanical stirrer was unable to break the agglomerates of the CNTs, while high shear mixer was able to disperse CNTs more uniformly. Hence, high-shear mixer was selected to disperse CNTs uniformly into the asphalt binder for sample preparation.
# MPFM-VC: A Voice Conversion Algorithm Based on Multi-Dimensional Perception Flow Matching
## 5 Results
### Baseline Models and Evaluation Metrics  
**(1) Baseline models**  
(1) Free-VC [24] (2022, ICASSP) is a voice conversion model that adopts the variational autoencoder architecture of VITS for high-quality waveform reconstruction; it is widely used in voice conversion tasks due to its efficient feature modeling capabilities.  
(2) Diff-VC [32] (2022, ICLR) is a diffusion model-based voice conversion method which can generate high-quality converted speech based on noise reconstruction; it is the representative diffusion model for VC tasks.  
(3) DDDM-VC [33] (2024, AAAI) is a newly proposed feature decoupling speech conversion method based on a diffusion model which improves the quality of converted speech and speaker consistency while maintaining the consistency of speech features.  
(4) Voiceflow [37] (2024, ICASSP) is a recent speech generation method based on flow matching that achieves high synthesis quality with a limited number of sampling steps.  
**(2) Evaluation index**  
(1) Mean Opinion Score (MOS): the naturalness of the synthesized speech was evaluated by 10 students with good Mandarin skills and sound sense as the audience.  
(2) Mel Cepstral Distortion (MCD): it measures the spectral distance between the converted speech and the target speech, where a lower value indicates higher-quality conversion.  
(3) Word Error Rate (WER): the intelligibility of the converted speech was evaluated by using automatic speech recognition, where a lower WER indicated higher speech intelligibility.  
(4) Similarity Mean Opinion Score (SMOS): listeners (10 students with good Mandarin skills and sound sense) scored the timbre similarity of the synthesized speech for us to measure the subjective similarity of the timbre after speech conversion.  
(5) Speaker Embedding Cosine Similarity (SECS): The cosine similarity between the original speech and the converted speech was calculated based on the speaker coding, which was used to objectively measure the degree of timbre preservation. The higher the value was, the closer the converted speech was to the target timbre.
## 5 Conclusions  
In this paper, we investigated the closed-form expression of the outage probability, average BER, and average channel capacity of the asymmetric dual-hop terrestrial-deep space hybrid RF/FSO system with a hybrid \(L\)-PPM-BPSK-SIM scheme. The simulation results illustrate that in deep space communication, the BER of the hybrid RF/FSO system is dozens of times lower than that of the FSO system. Hybrid modulation can improve the BER performance of the system, and the larger \(L\) is, the greater the impact will be. Under the premise of ensuring the communication rate, the BER performance can be improved by increasing \(L\). The small non-Kolmogorov spectral index, small solar wind density fluctuation factor, and large outer scale can reduce the BER of the system; reducing the wavelength is also a feasible method. In addition, we studied the effects of the scintillation index and pointing error on outage probability and average channel capacity. The larger the scintillation index and pointing error, the worse the system's channel capacity and outage probability performance.  
Considering the practical technology, using a hybrid RF/ FSO system, reducing optical wavelength, decreasing pointing errors, and excellent modulation technology are options to mitigate the impact of solar scintillation in coronal turbulence and ultimately improve deep space communications performance. In future research, if more influencing factors are considered, such as background noise and thermal noise, more optimization techniques are needed [42]. Coding technologies, such as LDPC code [43], and a high-performance detector, such as a superconducting nanowire single photon detector (SNSPD) [44], are attracting more attention at present. In addition, the development of a cognitive deep space optical communication network and adaptive optical systems provide strong support to further improve deep space exploration [45]. Therefore, this study may provide a theoretical foundation and design basis for the future hybrid RF/FSO system in deep space exploration.  
Conceptualization, methodology, M.J. and H.L.; software, validation, writing--original draft preparation, M.J.; formal analysis, investigation, data curation, R.W., Z.W. and D.D.; writing--review and editing, visualization, Y.H. and W.L.; supervision, project administration, funding acquisition, H.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported in part by the National Natural Science Foundation of China (61875057, 62175070, 61774062); the Science and Technology Program of Guangzhou (2019050001), and the Natural Science Foundation of Guangdong (2021A1515012652).  
Not applicable.  
The authors declare no conflict of interest.
## 6 Challenges
### Computational Challenges  
Computational challenges arise during smart system installation and integration. Common issues include network, battery, or communication failures, and malfunctioning built-in features requiring expert intervention. Additionally, updating the code can be user-unfriendly. Specific challenges in smart education include:  
* Installation and Integration: Involves complex algorithms and software configurations.
* Network, Battery, and Communication Issues: Frequent disruptions in the network, battery, and communication affect device performance.
* Malfunctioning Built-in Features: Core feature failures necessitate expert intervention.
* Code Updating: Users may face challenges in updating program changes.
* Internet Connectivity: Requires reliable internet access, limiting deployment in areas with poor connectivity.
* Privacy Issues: Sharing personal data can lead to privacy breaches, requiring robust data protection measures.
* Compatibility and Interoperability: Ensuring different devices and platforms work seamlessly is a complex task.
* Data Pollution: Managing excessive and irrelevant data is essential to maintain data quality.
* Artificial Intelligence: Implementing AI can be expensive and is still an emerging technology, which raises issues of trust.
# High-Speed Underwater Optical Wireless Communication with Advanced Signal Processing Methods Survey
###### Abstract  
Underwater wireless communication (UWC) technology has attracted widespread attention in the past few years. Compared with conventional acoustic underwater wireless communication technology, underwater optical wireless communication (UOWC) technology has promising potential to provide high data rate wireless connections due to the large license-free bandwidth. Building a high-performance and reliable UOWC system has become the target of researchers and various advanced and innovative technologies have been proposed and investigated. Among them, better hardware such as transmitters and receivers, as well as more advanced modulation and signal processing techniques, are key factors in improving UOWC system performance. In this paper, we review the recent development in UOWC systems. In particular, we provide a brief introduction to different types of UOWC systems based on channel configuration, and we focus on various recent studies on advanced signal processing methods in UOWC systems, including both traditional non-machine learning (NML) equalizers and machine learning (ML) schemes based on neural networks. In addition, we also discuss the key challenges in UOWC systems for future applications.  
underwater optical wireless communication (UOWC); digital signal; linear equalizer; nonlinear equalizer; supervised machine learning; reinforcement machine learning +
Footnote †: journal: _photonics_
###### Abstract  
This paper proposes a neural network (NN)-based control scheme in an Adaptive Actor-Critic (AAC) learning framework designed for output reference model tracking, as a representative deep-learning application. The control learning scheme is model-free with respect to the process model. AAC designs usually require an initial controller to start the learning process; however, systematic guidelines for choosing the initial controller are not offered in the literature, especially in a model-free manner. Virtual Reference Feedback Tuning (VRFT) is proposed for obtaining an initially stabilizing NN nonlinear state-feedback controller, designed from input-state-output data collected from the process in open-loop setting. The solution offers systematic design guidelines for initial controller design. The resulting suboptimal state-feedback controller is next improved under the AAC learning framework by online adaptation of a critic NN and a controller NN. The mixed VRFT-AAC approach is validated on a multi-input multi-output nonlinear constrained coupled vertical two-tank system. Discussions on the control system behavior are offered together with comparisons with similar approaches.  
adaptive actor-critic; model-free control; data-driven control; reinforcement learning; approximate dynamic programming; output reference model tracking; multi-input multi-output systems; vertical tank systems; Virtual Reference Feedback Tuning +
Footnote †: journal: Applied Informatics, Politehnica University of Timisoara, Timisoara 300006, Romania; [EMAIL_ADDRESS]