esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Burst morphology is a crucial indicator for evaluating the effectiveness of blasting, as it directly reflects the actual state of the blasting results. The results of rock displacement following blasting partially reflect the effectiveness of throw blasting, while the rock ejection process serves as the macroscopic manifestation of the blasting method. To accurately assess the impact of different delay times on burst formation, this study addressed the issues of rock movement and ejection in underground blasting. Using three-dimensional modeling, we constructed a FEM-SPH model and utilized LS-DYNA numerical simulation software to investigate the movement patterns of rock in precise delayed blasting scenarios underground. This study explored the spatiotemporal evolution characteristics of rock movement post-blasting. Digital electronic detonators were used to set precise inter-row delay times of 25 ms, 50 ms, and 75 ms. The results revealed that the ejection distance of blasted rock in underground mining increased with longer inter-row delay times, while the slope angle of the blasted muck pile decreased as the delay time increased. Furthermore, at a micro level, the study found that a 75 ms delay created new free surfaces, providing effective compensation space for subsequent blasts, thereby improving blasting outcomes. Analysis of the 25 ms and 50 ms delay periods indicated a clamping effect on rock movement. Field comparisons of blasting results were conducted to validate the influence of precise delay times on the movement patterns and spatiotemporal evolution characteristics of blasted rock.  
FEM-SPH; cast blasting; burst morphology; precision delay +
Footnote †: journal: Applied sciences
# A Smart Rehabilitation Glove Based on Shape-Memory Alloys for Stroke Recovery
## 5 Conclusions  
This research harnessed the electro-thermomechanical coupling behavior of shape-memory alloys (SMAs) to develop a smart rehabilitation glove prototype for post-stroke hand-dysfunction recovery. The glove integrates an amplification mechanism, achieving compact dimensions and ultra-lightweight construction. Operating voltage requirements and actuation forces are governed by the phase-transformation kinetics of SMA wires. A companion mobile application enables wireless control with functionalities for patient health monitoring and dynamic adjustment of training intensity modes, facilitating personalized rehabilitation protocols and real-time clinician consultation. Experimental validation encompassed SMA mechanical characterization, wireless-module performance, and rehabilitation efficacy across intensity modes.  
This smart rehabilitation glove features the following characteristics: (1) Superior portability: Unlike conventional motor-driven or pneumatic alternatives, the SMA-based system offers exceptional lightweight properties and wearability, enabling home-based and ambulatory rehabilitation. (2) Integrated telehealth: The mobile platform combines personalized health reporting, remote clinician consultation, and adjustable training modes, significantly reducing operational complexity and healthcare expenditures, particularly for elderly users. (3) Adaptive rehabilitation: Multi-level intensity modes (low/medium/high) permit patient-specific regimen adjustment according to clinical assessment, transforming monotonous exercise into engaging, individualized therapy.
# Floating Step-Down Converter with a Novel Lossless Snubber
## 3 Power Component Design
### Design of Output Capacitor \(C_{o}\)  
By analyzing the operating principle of the proposed converter in Section 2.2, it can be seen that when the switch is turned off, according to the KCL law,  
\[i_{Co}=i_{Lo}-I_{o} \tag{31}\]  
Since the DC component of the output inductor current \(I_{Lo}\) is approximately equal to the output current \(I_{o}\), the maximum current that can be discharged from the output capacitor is half of the output inductor current ripple. Incidentally, the maximum voltage ripple \(\Delta v_{Co}\) of the output capacitor \(C_{o}\) is set to be within 0.05% of the output voltage, so the following equation can be obtained:  
\[\Delta v_{Co}=\frac{\frac{1}{2}\times\frac{\Delta i_{Lo}}{2}\times\frac{T_{s}} {2}}{C_{o}}\leq V_{o}\times 0.05\text{ }\% \tag{32}\]  
By rearranging (32), (33) can be obtained as follows:  
\[\begin{array}{l}C_{o}\ \ \ \geq\frac{\Delta i_{Lo}}{8\times V_{o}\times 0.05 \text{ }\%\times f_{s}}\\ \ \ \ \ =\frac{(V_{in}-V_{o})\times D}{8\times V_{o}\times 0.05\text{ }\%\times L_{o}\times f_{s}^{2}}=73.33\text{ }\mu\text{F}\end{array} \tag{33}\]  
\begin{table}
\begin{tabular}{c c} \hline \hline
**System Operation Mode** & **CCM** \\ \hline Nominal input voltage (\(V_{in}\)) & 300 V \\ \hline Nominal output voltage (\(V_{o}\)) & 135 V \\ \hline Nominal output current (\(I_{o,total}\)) & 2 A \\ \hline Minimum output current (\(I_{o,min}\)) & 0.4 A \\ \hline System switching frequency (\(f_{s}\)) & 100 kHz \\ \hline \hline \end{tabular}
\end{table}
Table 1: System specifications of the proposed circuit.  
Therefore, a 100 uF Rubycon electrolytic capacitor is selected as the output capacitor \(C_{o}\).
# Electro-Optic Sensor for Measuring Electrostatic Fields in the Frequency Domain
## 3 Conclusions  
We have presented a novel electro-optic sensor optimized for measurements of DC electric fields in a harsh environment, such as a particle accelerator. The sensor is composed of a chain of two EO crystals; one crystal placed close to the field to be measured, and another crystal exploited to continuously tune the sensor with a bias voltage, and to apply a modulation voltage allowing DC measurement to be performed in the frequency domain.  
Theoretical analysis and experimental results confirm the feasibility of the proposed detection technique. The method can by applied to a wide range of electric field intensities by scaling the EO crystals' dimensions.  
Further improvements are under investigation. In particular, a custom acquisition chain based on a VFC acquisition card developed at CERN [23] and a 24-bit ADC/DAC module is under development to improve the sensitivity of the system.  
We aim to enhance the dynamic range on the first harmonic power evaluation from the current 40 dB. Our analysis indicated that an improved acquisition chain will extend the dynamic range to more than 80 dB, leading to an improvement of over four orders  
Figure 23: Shift of the transfer function due to decreasing temperature.  
Figure 22: Shift of the transfer function due to rising temperature.  
of magnitude. The increased resolution and speed of the system will also open the possibility of studying in detail the impact of other parasitic effects experience by EO crystals, as temperature drift and space charge, on the measurement quality.  
Conceptualization, M.K. and A.C.; formal analysis, A.C.; Visualization, A.C.; writing--original draft preparation, A.C.; writing--review and editing, M.K. and R.H.; supervision, M.K. and R.H.; funding acquisition, M.K. All authors have read and agreed to the published version of the manuscript.  
This project has received funding from the ATTRACT project funded by the EC under Grant Agreement 777222.  
The authors express their gratitude to [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] from the Beam Instrumentation Group at CERN, and [PERSON] from Royal Holloway University of London, for their help with the design and construction of the EO test bench and for the commissioning of the laser lab.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
EO Electro-optic  
DC Direct current  
BPM Beam position monitor  
SHiP Search for Hidden Particles  
WP Working point
# Research on Tbps and Kilometer-Range Transmission of Terahertz Signals
###### Abstract  
THz communication stands as a pivotal technology for 6G networks, designed to address the critical challenge of data demands surpassing current microwave and millimeter-wave (mmWave) capabilities. However, realizing Tbps and kilometer-range transmission confronts the \"dual attenuation dilemma\" comprising severe free-space path loss (FSPL) (\(>\)120 dB/km) and atmospheric absorption. This review comprehensively summarizes our group's advancements in overcoming fundamental challenges of long-distance THz communication. Through systematic photonic-electronic co-optimization, we report key enabling technologies including photonically assisted THz signal generation, polarization-multiplexed multiple-input multiple-output (MIMO) systems with maximal ratio combining (MRC), high-gain antenna-lens configurations, and InP amplifier systems for complex weather resilience. Critical experimental milestones encompass record-breaking 1.0488 Tbps throughput using probabilistically shaped 64 QAM (PS-64 QAM) in the 330-500 GHz band; 30.2 km D-band transmission (18 Gbps with 543.6 Gbps\(\cdot\)km capacity-distance product); a 3 km fog-penetrating link at 312 GHz; and high-sensitivity SIMO-validated 100 Gbps satellite-terrestrial communication beyond 36,000 km. These findings demonstrate THz communication's viability for 6G networks requiring extreme-capacity backhaul and ultra-long-haul connectivity.  
t  
terahertz communication; long-distance transmission; atmospheric absorption; photonic-assisted; polarization multiplexing; maximal ratio combining; high-gain antennas; power amplifiers; coherent detection; satellite-ground communication
# Telepresence Social Robotics towards Co-Presence: A Review
## 3 From Telepresence to Co-Presence Design
### Co-Presence Design
#### 3.1.2 Action Capabilities  
Advances in robot software and hardware, lighter and stronger materials, component miniaturization, and lighter and more powerful batteries have broadened robots' capabilities. Robot mobility has improved significantly, enabling robust navigation in an unstructured environment and in rough terrain [139; 140], and they can climb stairs, walk fast, and run, such as the Boston Dynamics ATLAS robot [141] or Honda ASIMO Robot [142]. Advances in humanoid mobility and equilibrium are remarkable, including compliant interactions and variable speed [143]. Having arms, hands, and fingers with more degrees of freedom (DOFs) enabled new types of interactions such as high-fidelity gestures, grasping objects smoothly [144], or even open doors and pass though them [141; 145]. Whole-body expressive movements [146], facial features to support expression synthesis [147], and speech synthesis technologies are enabling better HRIs.
## 2 System and Signal Characterization
### Impulsive Noise  
Impulsive noise [11; 12; 13] is typically experienced in low frequencies, consisting of short-duration noisy pulses. In the underwater environment, this is caused by sea life activity, which can be as high as 40 dB above the AWGN.  
Impulsive noise can be acceptable in analog communications, being, however, disruptive in digital communications, originating a burst of errors. Therefore, the AWGN model is not well suited to model an environment characterized by impulsive noise. The Markov chain is an important model to characterize the impulsive noise, which is closer to the real environment [13]. The Markov chain is a model with memory because the next state depends on the actual state [13]. The Markov chain has four different models, known as binary state model, Markov-Middleton model, partitioned Markov chain, and second-level Markov chain [13]. As can be seen from Figure 2, the binary state model has two states \((0,1)\), which best characterize the states of impulsive noise. The state \(S_{1}\) represents the channel free of impulsive noise (good channel), while the \(S_{0}\) represents the opposite (bad channel). The probability of transitions from \(S_{i}\) to is given by the values \(p_{i,j},\ i,j\in\ \{0,1\}\)[13].
###### Abstract  
The evidence for a correlation between landscape patterns and surface water quality is still weak. We chose the Yi River watershed in China as a study area. We selected and determined the chemical oxygen demand, ammonia nitrogen, total phosphorus, dissolved oxygen, and electric conductivity to represent the surface water quality. We analyzed the spatial distribution of the surface water quality. Buffer zones with five different radii were built around each sampling site to analyze landscape patterns on different scales. A correlation analysis was completed to examine the influencing rules and the response mechanisms between landscape patterns and surface water quality indicators. The results show that: (1) Different landscape composition types impact the surface water quality differently and increasing the area of forest land can effectively reduce non-point source pollution, (2) an increase in urban area may threaten the surface water quality, and (3) landscape compositional change has a greater influence on surface water quality compared to landscape configurational change. This study provides a scientific foundation for the spatial development of watersheds and outlines a strategy for improving the sustainability of surface water quality and the surrounding environment.  
surface water quality; landscape pattern; correlating relationship; Yi River watershed
# Underwater Image Mosaic Algorithm Based on Improved Image Registration
## 2 Materials and Methods
### Method of Image Enhancement
#### 2.1.2 Sigma Filtering for Original Image Denoising  
Digital images are usually distorted by noise, which is usually classified as Gaussian noise and impulse noise. The common method to reduce Gaussian noise is to replace the brightness of pixel \(P\) with the average value of brightness near pixel \(P\). Average filtering replaces the center pixel value by obtaining the average value of all pixels in the sliding window. When a pixel is obviously different from its neighbor, it is easy to appear a rectangular frame, resulting in unnecessary image distortion. This situation can be avoided when Gaussian filtering is used. According to the two-dimensional Gaussian law, the Gaussian filtering adds a gray value that attenuates with the distance from the center of the window, which can produce a better noise suppression effect. However, both kindsof filters will blur the image. The median filter replaces the center pixel value by taking the median of all the pixels in the sliding window [29; 30]. This filter is highly effective at suppressing salt and pepper noise, but it has a fatal drawback. It severely distorts the image and removes some rectangular stripes [23].  
Sigma filtering is effective for noise cancellation, which removes the noise while keeping the edge sharp. In two-dimensional image space, it is assumed that the center pixel is \(I(x_{0},y_{0})\), and the size of the sliding window is \(n\times n\). In the sliding window, the pixel values other than the center point are \(I(x,y)\), \(x\) and \(y\) are not more than \(n\). The center pixel transformed by Sigma filtering can be described as:  
\[\delta(x,y)=\left\{\begin{array}{ll}0,&|I(x,y)-I(x_{0},y_{0})|>t\\ 1,&|I(x,y)-I(x_{0},y_{0})|\leq t\end{array}\right. \tag{3}\]  
\[I^{\prime}(x_{0},y_{0})=\frac{\sum\limits_{k=1}^{n}\sum\limits_{l=1}^{n} \delta(k,l)\cdot I(k,l)}{\sum\limits_{k=1}^{n}\sum\limits_{l=1}^{n}\delta(k,l)} \tag{4}\]  
In Equations (3) and (4), \(\delta(x,y)\) is used to count the pixel positions in the window where the difference in pixel intensity from the center point does not exceed a fixed tolerance \(t\), and pixels with a large difference are not allowed to participate in the average effect. Choosing the appropriate \(t\) value can remove the isolated point noise without damaging the image structure.
# A Comprehensive Survey on Knowledge-Defined Networking
## 2 Overview of Knowledge-Defined Networking (KDN)
### KDN vs. SDN vs. Conventional Network
#### 2.3.9 Security Features  
Traditional networks have limited security features, which are basically implemented using firewalls and intrusion detection systems. SDN provides better security compared to traditional networks due to the centralization of security policies and easier deployment of security protocols, even though SDN is known for different attacks such as denial of service attacks, malware attacks, spoofing attacks, data modification attacks, etc. KDN provides even more security than SDN by using ML to detect threats in real time using the knowledge plane and mitigate the threats using the control plane.  
Table 1 summarizes a comparison among KDN, SDN, and traditional networking.
# A Novel Real-Time Match-Moving Method with HoloLens
## 2 Background Theory
### Related Works  
Similar to our study, several studies have been conducted to extract the external parameter of an RGB camera and use it for match-moving. One such method proposed by [PERSON] et al. was used with a low-pass filter to smoothly correct the tracking data that generated the virtual camera's transform \(x\), \(y\), and \(z\) values of every frame from recorded images [15]. In addition, [PERSON] et al. mounted the main camera and the secondary camera firmly using and calculated the relative position value between the two cameras using [PERSON]'s correction method [16]. Thereafter, the two cameras record images to extract the external parameters, and that recorded images from each camera are optimized to generate a stable camera trajectory. When feature point loss occurs due to dynamical camera movement, it can connect the trajectory by calculating the cross-correlation between the feature point of the two camera images through the missing feature point trajectory connection algorithm [17].  
Similarly, [PERSON] et al. proposed a method of estimating the camera's external parameters using the feature point from the recorded images and the GPS position data as match-moving. It used the structure from motion (SfM) algorithm for feature point tracking and used GPS data-based correction to estimate errors caused by various factors [18].  
Recently, [PERSON] et al. proposed a match-moving method and real-time 3D preview visualization system using the Kinect Fusion algorithm. They used structure light Kinect1 and two RGB cameras to extract a depth map and track the camera pose and trajectory. However, this method was limited in terms of precision. When the surrounding environment or objects moved, noise was generated, which lowered tracking accuracy. Therefore, there is a disadvantage that the surrounding environment must be fixed or an additional noise removing step is required [19].  
Figure 2: Common reasons for match-moving errors in traditional methods. (**a**) occlusion and (**b**) motion-blur cause problems in feature tracking.  
Similarly, our previous work reported on a real-time optimized mapping environment for real-time spatial mapping of HoloLens [20] and studied the building of a virtual studio system for broadcasting. Existing Microsoft Spectator View [21] causes network delay and frame breaks due to the number of polygons (or vertices) in the 3D modeling file. In order to use it as a broadcasting studio, we proposed a method of using only the head transform of HoloLens [22]. In this paper, we propose a stabilized match-moving system and a pre-visualization system using HoloLens based on these previous studies.
## 5 Performance Evaluation
### Experimental Results
#### 5.2.1 Scenario 1: Task Scheduling in Fog Environment  
In the first setup, we defined a Fog layer consisting of 10 Fog nodes, which connected to each other to satisfy user requirements in a geographical area. Four algorithms were deployed to produce schedules for bags of tasks, which included from 40 to 500 tasks to be executed in Fog environment. The average results were obtained by running four algorithms in each dataset over 30 times with 500 generations (or iterations) and the coefficient balance \(\alpha=0.5\). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameter** & **BLA** & **TCaS** & **MPSO** \\ \hline Running times & 30 & 30 & 30 \\ \hline \multirow{3}{*}{Population size (\(\mathbb{N}\))} & queen & \(1\) & \\ \cline{2-4}  & drones (\(D\)) & 30 & 100 \\ \cline{2-4}  & workers (\(W\)) & 69 & \\ \hline Crossover rate (\(\alpha\)) & 90\% & 90\% & \(c_{1}=c_{2}=1.5\) \\ \hline Mutation rate (\(\gamma\)) & 1\% & 1\% & \(w=0.9\to 0.1\) \\ \hline Number of generations & 500 & 500 & 500 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: BLA, TCaS and MPSO parameters. \begin{table}
\begin{tabular}{c c} \hline \hline System & Intel® CoreTM i7-4710 MQ, CPU 2.50 GHz \\ Memory & 8 GB \\ Simulator & iFogSim \\ Operating system & Windows 10 Professional \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Simulation setup. Figure 5 shows the comparison of time-cost trade-off of four algorithms. It was obvious that our proposed algorithm, TCaS, dominated the first position in every dataset with high average fitness value of 0.945, which was better than MPSO, BLA and RR by one average 7.97%, 8.17% and 44.08%, respectively. Experiencing on small datasets, from 40 to 200, BLA gave better solutions than MPSO. However, when the dataset was bigger, i.e., the problem became more complex, the fitness value BLA reached recorded a gradual decrease with the increase in dataset size; meanwhile, MPSO maintained its stability with its fitness value of around 0.87. Consequently, MPSO algorithm produced better solutions than BLA by an average of 1.89% on datasets with size from 250 to 500. The simple RR algorithm gave the worse results of the three algorithms with fitness value of around 0.65. Table 7 presents the scheduling length and processing cost of the four algorithms. The fitness function was contributed by two main components: makespan and total costs. Thus, with high fitness value, TCaS algorithm demonstrated superiority on almost datasets in both time and cost aspects. Our proposed method overtook all other algorithms in execution time; the schedule of TCaS algorithm consumed less time than that of MPSO by 16.15% and BLA by 16.77%, and saved on average 2.32 times over the whole 11 datasets compared to that of RR algorithm. Considering the monetary cost, TCaS required the least cost of the four algorithms on almost all datasets, except on the smallest dataset of 40, where BLA won. TCaS saved 0.48%, 0.53% and 2.15% of processing cost compared to BLA, MPSO and RR algorithm, respectively.
## 5 Experiment and Discussion
### Experiment Result and Discussion
#### 5.3.3 The Effect of Attention Balance Parameter \(\beta\)  
In order to further illustrate the role of the attention balance mechanism, the experiment verifies the effect with the attention balance mechanism (MRDE) and without the attention balance mechanism (MRDE_Null) on the IMDB dataset, and the results are shown in Figure 3.  
As can be seen from Figure 3, MRDE get the best results on the MKM meta-path, which means it mainly focuses on the MKM relationship; on the contrary, MRDE_Null mainly focuses on the MDM and MAM relationship. It is not difficult to understand that MKM builds relationships through the same keywords between movies, with the commonality of most keywords, the movie nodes are more closely connected, and their representations become more similar, which makes MKM gain greater attention.  
After adding the attention balance mechanism, MRDE can not only maintain a high attention weight for MDM and MAM but also assign a certain weight to MKM instead of ignoring it directly. In this way, MRDE can aggregate features of nodes that cannot be contracted in MDM and MAM.