esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 High Dynamic Infrared Image Enhancement Algorithm
### Algorithm Implementation  
In this study, by learning and analysing the DDE technique, wavelet transform is used to decompose the highly dynamic two-dimensional image into a low-frequency information layer and high-frequency information layer, and the image enhancement process is carried out. The applied image is captured in low light or at night without the help of light, offering all-weather characteristics and an advantage compared to ordinary images that need to be captured under light conditions. However, the captured infrared images, which are characterised by low brightness, low contrast and fuzzy details, also provide more trouble for the subsequent application processing, and the quality of the images is significantly improved after processing using this algorithm. The steps of the algorithm are as follows:  
1. Through the SWT2 wavelet transform, the image is decomposed into low-frequency information layer and high-frequency information layer images in three directions: horizontal, vertical and diagonal. After experiments, the low-frequency information layer of the primary decomposition is selected as the low-frequency information layer image for the subsequent processing, and the high-frequency information layer image of the secondary decomposition is selected as the high-frequency information layer image for the subsequent processing. The effect reaches the best state.
2. The low-frequency information layer image contains the background information of the image and has the disadvantage of low contrast. The CLAHE algorithm is used  
Figure 1: Image enhancement algorithm block diagram.  
for contrast enhancement processing. The CLAHE algorithm generates a mapping function by statistically calculating the grey values of the image, redistributes the grey values and applies bilinear interpolation to solve the block effect that occurs in the chunking process. The CLAHE algorithm also adopts contrast limitation to prevent the image from being over-enhanced, which provides the best processing results [15].
3. The high-frequency information layer contains the detailed texture and edge contour information of the image, and coefficient gain processing is carried out to highlight the effective information. The gain coefficients are determined by adaptive gamma transform, and the gamma \(\gamma\) values are calculated using variance \(\sigma\) and mean \(\mu\), which is an innovative way to improve the adaptability of the algorithm. The detailed information of the image is effectively enhanced to achieve the best image processing effect.
4. The processed high- and low-frequency information is synthesized using ISWT2 inverse wavelet transform, and the image is compressed for output.
# Muscle Activity and Biomechanics of Sprinting: A Meta-Analysis Review
## 3 Results
### Main Findings  
#### 3.2.1 Hamstring Activation Increases with Speed  
The biceps femoris long head showed the highest activation during the late swing phase, with reported values reaching -110% of the reference MVC in some studies e.g., [7]. This apparent exceedance is likely due to the normalization procedure used in those studies, where sprinting EMG signals were compared to submaximal or isometric reference contractions. Thus, the value reflects relatively greater activation during high-speed dynamic tasks, not a true physiological surpassing of maximal voluntary contraction. The semitendinos exhibited greater activation than the biceps femoris during the acceleration phase, suggesting a phase-dependent recruitment strategy for hamstring stabilization.
## 5 Results and Discussion
Figure 10: The level classification for accessing authentications. \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Authentications**} & \multicolumn{4}{c}{**Levels**} \\ \cline{2-5}  & **Unclassified** & **Confidential** & **Secret** & **Top Secret** \\ \hline Type1 & 7 & 6 & 7 & 6 \\ \hline Type2 & 6 & 7 & 6 & 5 \\ \hline Type3 & 6 & 6 & 8 & 5 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Classification levels of three specific authentication types. security and is acceptable based on our model. Type 3 is also suitable for adding restrictions and aiding developers in generating DBMS or other systems. Concerning elapsed time, Type 2 in RSA shows the highest average key generation time at 60,674.18 ms, compared to Type 1 and Type 3, which have key generation times of 91.63 ms and 151.97 ms, respectively. Similarly, RSA in Type 2 requires considerably more time than AES, where RSA takes 60,674.18 ms, and AES requires only 5.90 ms. The discussion and comparison of the results from Table 4 (with the generated model) and Table 5 (without the model) demonstrate significant improvements and optimizations. The generated model effectively reduces elapsed time and memory usage while enhancing security and accessibility for the selected algorithms. In addition, the results using MLS show significant improvements compared to those without using the MLS/DBMS model. Thus, for RSA, key generation time reduced by 7.01% (98.53 ms to 91.63 ms in Type 1) and memory usage decreased by 2.0% (from 97,816,986 bytes to 95,860,646 bytes in Type 1). Likewise, AES encryption and decryption times improved by 1.0-5.0% across all types, enhancing efficiency. The comparison shows that integrating MLS reduces elapsed time and memory usage, enhancing cryptographic efficiency and aligning with prior studies emphasizing MLS's optimization in secure systems. The comprehensive background review, as discussed in the review section, confirms that the evaluation of our model has demonstrated significant improvements, providing an optimal solution for the selected cryptographic algorithms. This aligns with the findings from prior research, highlighting the model's effectiveness in enhancing performance, security, and efficiency.
## 3 Proposed Protocol
### Data Forwarding Algorithm
The packet then is forwarded along the Euclidean routing path until reaching a node (let us call this node as \(t^{\prime}\)) that has the hole information of \(\mathcal{H}(t)\). Please note that since the last vertex of the Euclidean routing path is a vertex of \(\mathcal{H}(t)\), such a node \(t^{\prime}\) always exists. With the hole information of \(\mathcal{H}(t)\), \(t^{\prime}\) determines a so-called _sub-base path_ which is the shortest Euclidean path from \(t^{\prime}\) to \(t\), which bypasses \(\mathcal{H}(t^{\prime})\), \(\mathcal{H}(t)\) and \(\mathbb{C}(\overline{t},\overline{t^{\prime}})\). This sub-base path then is magnified by a transformation  
Figure 2: Data forwarding. The blue/green lines are the base path/sub-base path, the red/orange paths are the Euclidean routing path/sub-Euclidean routing path. The orange polygons are the items of \(\mathbb{C}(\overline{s},\overline{t})\). (**a**) Case 1: \(s\) is a \(t\)-aware node; (**b**) Case 2: \(s\) is a \(t\)-blind node. to obtain a so-called _sub-Euclidean routing path_. The packet then is greedily forwarded along the sub-Euclidean routing path until reaching \(t\). In the following, we first present the algorithm to determine the base path in Section 3.3.1. Then, we describe how to magnify the base path to obtain the Euclidean routing path in Section 3.3.2.
## 2 Related Work
### Hybrid Fuzzy Logic and Reinforcement Learning  
Combining fuzzy logic with reinforcement learning has emerged as a promising direction for improved broadcast decision-making in MANETs and other network protocols [22; 23]. In these hybrid approaches, fuzzy inference systems handle uncertainties by mapping real-time network measurements to linguistic variables and intermediate decisions, dynamic membership functions, and adapting the ranges that are assigned to those linguistic variables. Meanwhile, reinforcement learning refines decision-making process ratios, such as BC/DNB variables. Furthermore, the role of RL can be changed to adjust either the fuzzy rule sets or the membership functions over time based on observed outcomes in the network, depending on need.  
We discovered that one advantage of this synergy is that RL can systematically tune the parameters of the BC/DNB ratio to account for further improving packet duplication by reducing the ratio of nodes taking part in broadcasting if the network coverage is already at maximum, meaning that lesser nodes could broadcast the message while keeping the broadcasting intact. Temporal variations in network metrics, such as node, RSSI, and Connection Time, can be accounted for and adapted to using a dynamic membership function. Hybrid systems thus maintain the interpretability and human-readable nature of fuzzy logic while adding a learning loop that continuously updates the decision policy. However, these methods can become more complex in terms of computational requirements, as they integrate both fuzzy inference overhead and the iterative updates of RL agents; therefore, using them in less network-constrained environments could prove to be more appropriate. Moreover, similar to the pure RL approach, finding the right balance between exploration (trying new fuzzy membership configurations or rules) and exploitation (using the learned best practice) remains a non-trivial challenge; furthermore, new approaches where the initial parameters selected for dynamic membership are changeable could open a way to a method where sets of parameters could be used depending on the given network statistics.
## 2 Related Works
### Routing Strategies in Pub/Sub Systems for DTN  
Extending Pub/Sub systems in DTNs introduces new challenges because of the networks' intermittent connectivity. To the best of our knowledge, the only work on Pub/Sub communication in DTNs is discussed in [17, 18]. The authors in [17] exploit distributed community detection from human traces and propose a Socio-Aware Overlay over detected communities for Pub/Sub communication. In that paper, centrality nodes are defined, with which an overlay is created. Since each node here needs to detect its own local community for the duration of the movement, the communication overhead is very high. This is particularly true for the centrality node. However, the sensors' energy is limited and, therefore, that strategy is not suitable for DTSNs.  
In [18], the authors aim at Pub/Sub-based content distribution in DTNs, which achieves a more efficient utilization of network resources. Content is identified using a channel-based subscription system: interested users subscribe to channels and senders publish content by sending it as DTN bundles to the channel. Per-bundle utility calculation is deployed for local replication decisions and each node individually applies prioritization to control bundle processing. Finally, self-organized local prioritization can lead to a better overall performance in the network.
## 3 Technical Challenges and Recent Efforts to Address Them  
Despite their great potential aforementioned, MC devices have their inherent constraints, such as membrane wetting and fouling, which limits their applicability in practical operations. More importantly, although membrane wetting and fouling seem to be two separate issues that can be possibly dealt with one by one, those two are mostly related to each other. For example, highly hydrophobic membranes that are capable of preventing pore wetting are generally vulnerable to membrane fouling [13]. Thus, many researchers have studied to mitigate or eliminate such problems to increase the overall productivity in MCs for the biomethane recovery. In this section, we enumerate recent progress on anti-wetting and anti-fouling modifications on the hollow fiber MCs.
# AI-Driven Pilot Overhead Reduction in 5G mmWaveMassive MIMO Systems
## 2 System Model
### AI-Driven Pilot Placement  
Recent developments in AI and machine learning provide robust capabilities for enhancing different facets of wireless communication systems, such as pilot placement, allocation, and processing, in order to reduce the pilot overhead size while maintaining good signal characteristics. Machine learning algorithms can analyze historical channel data and user mobility patterns to intelligently place pilots in areas where channel variations are significant or where users are densely located. Through the optimization of pilot positions guided by learned patterns, AI can effectively minimize pilot overhead without compromising the accuracy of channel estimation. The simplest form of utilizing machine learning in pilot placement is shown in the equation below:  
\[p_{k}=f(x_{k}) \tag{7}\]  
where \(x_{k}\) represents a feature vector associated with the \(kth\) pilot symbol, which may include parameters such as channel quality, user density, interference levels, etc. [6]. The function is learned using machine learning techniques to predict the optimal pilot positions based on the input features. In supervised learning, the process involves acquiring knowledge from data that are labeled in order to formulate predictions or decisions. Conversely, unsupervised learning involves working with data that lack labels to unveil patterns within the dataset. In this paper, methods of each category are used to reduce pilot overhead. The k-clustering technique is used as an unsupervised machine learning method; as for the supervised method, the linear regression and random forest regression methods are applied. Moreover, SVD is integrated with the supervised methods in order to further improve system performance.
# Research Status and Progress of All-Position Narrow-Gap GMAW for Pipelines
## 2 Current Status of Research on Narrow-Gap All-Position GMAW
### Droplet Transition Behavior in Narrow-Gap GMAW
This is because, with a larger gap, the arc can reach the root of the weld seam, causing most of the molten metal in the molten pool to fill the gap. At this point, droplets are pulled apart by an asymmetric magnetic field before they grow to the transfer size, forming what is known as \"arc break transfer\". In laser-arc hybrid welding, the laser-induced plasma alters the conductive path and forces acting on the molten droplet [16]. In laser-arc hybrid welding, pulse-induced laser [17; 18] and the phase difference between the two wires in dual-wire P-GMAW [19] also affect the droplet transfer behavior, which in turn influences the weld bead formation.
## 2 Literature Review
### Remote Sensing for Air Quality Monitoring  
A satellite is an object placed in an outer space orbit to collect data faster than instruments on the ground [21]. It has a variety of uses for weather forecasting, GPS navigation, scientific research, broadcasting, and Earth observation. Copernicus is the European Union's Earth monitoring program, including the Sentinel satellite, which was previously known as Global Monitoring Environment and Security (GMES). The program uses satellite data to provide a comprehensive set of terrestrial, atmospheric, and oceanic parameters [22]. The Copernicus program relies on its space segment of observational satellites and measurements using airborne sensors. It uses data collected by these sensors to generate the time series for its research. The name \"Sentinel\" refers to the Copernicus satellite constellation. The satellites and observers belong to six families. Sentinel-1 maintains the continuity of the radar data collected by Envisat and ERS. Sentinel-2 and -3 monitor oceans and land masses. Sentinel-6 ensures operational continuity for Jason altimetry missions, while Sentinel-4 and -5 are intended for meteorological and climate logical missions to ensure the availability of atmospheric data [22]. The field of meteorology is the study of phenomena and the effects of the atmosphere on weather and climate. The analysis of the atmosphere focuses on weather forecasting processes. Weather phenomena are something that meteorologists can monitor and interpret [23]. The power of deep learning can be used with satellite data to make suitable forecasts and provide experts with information about weather, climate, and the state of the planet. This allows them to prepare for storms, understand the impact of climate change, and develop plans to improve the use of natural resources and protect vulnerable populations.
# Multi-Object Tracking with Grayscale Spatial-Temporal Features
## 1 Introduction
Although the optical flow estimation algorithm has high computational efficiency, it is sensitive to illumination changes and is not suitable for target occlusion or large-scale target tracking. Therefore, we do not need many computation resources and data to train. Its accuracy is comparable to some current deep learning methods on MOT16 benchmarks [7]. In summary, this paper proposed the following four contributions:  
* A way to quickly extract spatial-temporal features just by traditional methods. The spatial-temporal features can deliver information perfectly between two frames. This way can quickly obtain the spatial-temporal features of the target. This method does not require the use of deep learning methods. * A method to reduce ID matching times. This method reduces matching times to less than three times for most targets by using the spatial-temporal features. * A method joins feature extraction and matching to reduce unnecessary feature extraction and matching. Target matching is performed in three stages. Different strategies are adopted for matching and feature extraction according to each stage. * A better way to deal with occlusion problems. Based on the spatial-temporal features, we can infer the region where the occluded target disappears. After it is re-detected, matching is performed in this region, which reduces the interference of similar targets outside the area.
# Error Performance of Amplitude Shift Keying-Type Asymmetric Quantum Communication Systems
## 3 Model of an ASK-Type AQC System
### Description of Received Quantum State
#### Error Probability Determined by Optimum Quantum Measurement  
The error probability is an important performance evaluation index for communication systems. In this paper, it is assumed that Bob has no information about the _a priori_ probabilities \(\xi_{\mathsf{b}}\in\{\xi_{\mathsf{0}},\xi_{\mathsf{1}}\}\) that correspond to the binary information \(\mathsf{b}\in\{0,1\}\). If the _a priori_ probabilities of the quantum states are unknown, it is known that use of the Bayes decision criterion with equal _a priori_ probabilities under the quantum minimax criterion is the optimum approach from quantum detection theory [22]. If we suppose that the received quantum states are \(\rho^{(0)}_{\mathrm{SA}}\) and \(\rho^{(1)}_{\mathrm{SA}}\), and that the corresponding _a priori_ probabilities are equal, i.e., \(\xi_{\mathsf{0}}=\xi_{\mathsf{1}}=1/2\), then the error probability given by the optimum quantum measurement [23] is  
\[P_{\mathsf{e}}=\frac{1}{2}\Bigg{(}1-\sum_{\lambda_{i}>0}\lambda_{i}\Bigg{)}, \tag{22}\]  
where \(\{\lambda_{i}\}\) are the eigenvalues of \(\rho^{(0)}_{\mathrm{SA}}-\rho^{(1)}_{\mathrm{SA}}\).
###### Abstract  
Actuator failure and joint flexibility will dramatically impact space robot system control. In this paper, free-floating flexible-joint space-manipulator dynamic-modeling is studied and a state-observer-based robust backstepping fault-tolerant control is proposed for the system joint actuator failure. Based on the flexible-joint simplified model, the system's rigid-flexible coupled-dynamic equations are established according to momentum conservation, angular momentum conservation, and the Lagrange equation. Then the system is decoupled based on the singular perturbation method. For the slow subsystem, a robust backstepping fault-tolerant controller base on a state observer is designed to eliminate the angle error, compensate for the uncertain parameter and the external disturbance, and achieve the joint-trajectory asymptotic-tracking. The use of a speed filter makes it inappropriate to measure and provide feedback about the system's velocity signals, so the controller is simpler and more precise. For the fast subsystem, a velocity differential-feedback control is adopted to suppress the system vibration caused by the flexible joint, to ensure the stability of the system. Finally, the feasibility and effectiveness of the model and control method are proved by some simulations. The simulation results indicate that the proposed fault-tolerant control method can make the free-floating flexible-joint space manipulator system track the desired trajectory accurately and steadily, regardless of whether the actuator fails or not.  
free-floating space manipulator; flexible joint; robust; backstepping; singular perturbation
# Multimodal Video Analysis for Crowd Anomaly Detection Using Open Access Tourism Cameras
## 2 Related Work
The system employs a real-time object detection algorithm, a key frame selection algorithm to identify significant scene changes, and a super-resolution algorithm to improve object resolution and visual quality. The proposed super-resolution network combines pixel and feature spaces, using an asymmetric, recursive deep back-projection approach to efficiently reconstruct high-resolution images. Experiments on videos from various scenarios show significant improvements in object detection accuracy, key frame selection, and super-resolution quality, contributing to more efficient video analysis tools for public safety. In [13], the use of YOLO models for real-time person detection to monitor occupancy in indoor spaces, a key measure during the COVID-19 pandemic, is explored. It proposes an algorithm that estimates the area of a region in square meters using YOLO-generated bounding boxes, assuming each person occupies 0.66 square meters. The maximum occupancy is calculated based on a density of one person per square meter. The performance of various YOLO models (v3, v4, v5s, v3-Tiny, v4-Tiny) was evaluated in terms of accuracy, FPS, and processing time. YOLO v3 showed the highest accuracy, while YOLO v5s had the highest FPS. The study highlights the algorithm's ability to adapt to different camera resolutions, but notes potential inaccuracies in low-resolution videos. The findings offer insights into developing intelligent surveillance systems for occupancy monitoring and social distancing compliance.
## 3 Determination of Key Parameters
### The Influence Mechanism of Hole Spacing on the Plastic Zone of Round Hole  
After determining the aperture, it is necessary to select the appropriate borehole spacing. The surrounding rock near the roof cutting borehole will be damaged by plastic deformation due to mining. The radius \(R_{0}\) of the plastic zone around the circular hole can be calculated by Equation (33). The minimum distance of the plastic zone interaction between adjacent boreholes is the hole spacing \(D=2R_{0}\).  
\[R_{0}=R\left[\frac{(p_{0}+C_{0}\cot\phi)(1-\sin\phi)}{C_{0}\cot\phi}\right]^{ \frac{1-\sin\phi}{2\sin\phi}} \tag{33}\]  
where \(R\) is the aperture (mm); \(p_{0}\) is hydrostatic pressure (MPa); \(C_{0}\) is the cohesion of rock (MPa); and \(\phi\) is the internal friction angle (\({}^{\circ}\)).  
According to the People's Republic of China Coal Industry Standard MT/T 984-2006 [30], the main sizes of the drill bit with a diameter greater than 40 mm are 48 mm, 55 mm, 65 mm, 75 mm and 94 mm in the coal or semi-coal rock strata with a uniaxial compressive strength of less than 50 MPa. Substituting into Equation (33), the relationship between \(D\) and the radius of plastic zone \(R_{0}\) and the diameter of borehole \(2R\) is shown in Table 2.  
Considering the ease of the construction of the roadway space, a roof cutting hole diameter of 48 mm is selected, and the hole spacing is limited to no more than 166 mm.