esa-sceva/satcom-chunk-collection huggingface dataset:
# Beam Control in an Intracavity Frequency-Doubling Semiconductor Disk Laser
## 4 Conclusions  
In summary, we have demonstrated an intracavity frequency-doubled 516 nm green SDL using a type I phase-matched BBO as the non-linear crystal. The maximum output power is 1.3 W corresponding to a conversion efficiency of 27% with respect to the maximum fundamental power. The beam quality of the fundamental and SHG laser is investigated. By inserting an additional aperture in the laser cavity, we study the influence of the aperture size on the beam quality and beam profile of the output green laser. Finally, a nearly circular beam profile is realized with improved beam quality and brightness. This can provide a clear guide of how to further optimize the pump spot size and the distances for single fundamental mode operation.  
Data curation, G.H. and J.F.; Formal analysis, S.S. and S.T.; Funding acquisition, A.P. and B.S.; Investigation, G.H.; Supervision, C.T. and L.W. ([PERSON]); Validation, L.W. ([PERSON]) and H.L.; Writing--original draft, G.H.; Writing--review & editing, L.W. ([PERSON]).  
This work was supported by TRUMPF GmbH, in part by National Natural Science Foundation of China (Nos. 61790584, 61761136009 and 61774153).  
The authors declare no conflict of interest.
# End-of-Life Scenarios for Mass Timber: Assumptions, Limitations and Potentials--A Literature Review
## 3 Results
### Is Wood Always a Carbon Sink?  
Timber's carbon sequestration is usually reflected as a negative GWP number in stage A1 Raw Material Supply. This is because the raw material of wood itself gives a credit in terms of CO\({}_{2}\)e in GWP due to its biogenic carbon content. Trees absorb carbon dioxide during growth through photosynthesis, converting the carbon dioxide into biogenic carbon,which is stored within the wood material. This process allows wood to act as a carbon sink, sequestering CO\({}_{2}\) from the atmosphere and storing it in the biomass of the tree. However, the climate neutrality of wood products has been discussed and questioned within 29 papers. The controversy stems from the biogenic carbon stored in wood, which could be released back into the atmosphere through combustion or decomposition, depending on the chosen end-of-life (EoL) scenario [32; 36; 37; 38; 39]. The impacts in life cycle stage C are usually overlooked and not optimised from a holistic circular perspective. In order to accurately calculate the carbon sequestration, it is therefore essential to take into account the EoL of wood products. According to [PERSON] [40], the carbon benefits of wood should only be discussed along with its emissions when end-of-life values are included.  
According to [PERSON] et al. [30], although standards recognise the importance of temporary carbon storage in LCAs of wood products, currently there is still no agreed-upon method for how to measure it. Factors such as time, local forestry context and policy scenarios which determine the EoL options could lead to very different LCA results, as mentioned previously. [PERSON] et al. [27] have also acknowledged this gap and developed a state-of-the-art estimator (Wood Products Carbon Storage Estimator, WPsCS Estimator) to quantify the carbon stored in harvested wood products for a given region over a specific period. Factors such as service life, region-specific parameters, and time-dependent methods are considered. Although the tool is still limited in the product-based scale (e.g., relying on EPDs) instead of building or urban based scale, and only uses a constant service life for the entire simulation.
###### Abstract  
This study investigates the numerical modeling of a high-velocity circular free-water jet impinging into a plunge pool, focusing on the simulation and validation of mean and fluctuating dynamic pressures on the pool floor. Numerical simulations were performed using two different computation methods, two-phase volume-of-fluid and Euler-Euler, under conditions replicating experimental data obtained at a jet velocity of 7.4 m/s and plunge pool depth of 0.8 m. The models, based respectively on the Volume of Fluid (VoF) and Euler-Euler methods, were evaluated for accuracy in replicating experimentally measured pressures and air concentration values. The Euler-Euler solver, coupled with the k-Omega SST turbulence model, demonstrated mesh independence for mean dynamic pressures with a mesh resolution of 24 cells across the jet diameter. In contrast, two-phase volume-of-fluid exhibited mesh dependency, particularly near the jet stagnation point and pressure values higher than the experimental ones. While the Euler-Euler accurately captured mean pressures and air concentration in close agreement with experimental data, its Reynolds-Averaged Navier-Stokes (RANS) formulation limited its ability to simulate pressure fluctuations directly. To approximate these fluctuations, turbulent kinetic energy values were used to derive empirical estimates, yielding results consistent with experimental measurements. This study demonstrates the efficacy of the Euler-Euler method with the k-Omega SST model in accurately capturing key dynamic pressures and air entrainment in plunge pools while highlighting opportunities for future work on pressure fluctuation modeling across a broader range of jet conditions.  
refe-water jet diffusion; CFD; two-phase Euler; volume-of-fluid; experimental work; dynamic pressure Article
## 4 Analysis of the Effect of TBM Crossing Fault Fracture Zone Under Different Treatment Methods
### Analysis of Calculation Results
#### 4.3.4 Support Force Analysis  
To further analyze the stress on the supporting structure, axial force distribution maps of elements like cables and beams were examined. From Figure 17a, it can be seen that the initial support structure of the TBM in non-fault areas experiences a nearly uniform force with no concentration. However, at the fault location, the axial force on the pipe shed, conduit, and anchor rod increases significantly--by 52% to 68% compared to the non-fault area. For the cable unit, the axial force on the anchor rod in the surface pre-grouting zone ranges from 1.5 to 8.9 kN. In working conditions I-III, the axial force on the anchor rod increases by 77.9% to 83.8%, indicating that surface pre-grouting enhances the surrounding rock stability in the fault fracture zone.  
As shown in Figure 17b, for the beam unit, the stress on the arch frame within the surface pre-grouting range is about 56.7-81.2 kN. In working conditions I-III, the stress on the arch increases by 68.9-90.8%, indicating that surface pre-grouting significantly enhances the surrounding rock's bearing capacity, helping to share the arch's stress. In working conditions I-III, significant stress concentration occurs in the arch of the fault zone, suggesting that the support force required to reinforce fractured rock is much higher than for conventional geological sections.  
In conclusion, surface grouting reinforcement (working condition IV) effectively reduces rock fragmentation and increases surrounding rock-bearing capacity. Compared to the other methods, it provides the best reinforcement effect.
## 2 General Structure of the Methodology
### Fuzzy Fusion Estimation Module  
To enable the practical application of rail surface condition identification in adhesion control, this study proposes a multi-source information fusion mechanism that integrates train operation data with rail surface condition identification. This approach aims to characterize rail surface adhesion conditions more comprehensively and enhance the robustness of maximum adhesion coefficient estimation. Since the adhesion state between the wheels and rails during train operation is difficult to represent by an accurate mathematical model, fuzzy control is appropriate, and fuzzy control enables the effective integration of multiple types of information. The fuzzy control method is employed to fuse multimodal input information, selecting four representative rail surface states as standard conditions to encompass a wide range of operational scenarios. These standard rail surfaces are used to derive adhesion coefficient creep and slide speed curves. In real-world train operations, the actual adhesion characteristic curve does not precisely coincide with any single standard rail surface curve. Instead, it typically lies near one or multiple standard curves, indicating varying degrees of similarity between the actual rail surface and the predefined standard surfaces. To account for this, the adhesion coefficient estimated by the full-dimensional  
Figure 5: Flowchart of mixed-precision training.  
state observer and creep and slide speed are utilized as inputs, which are first fuzzified and subsequently compared against the four standard adhesion curves. Following defuzzification, four similarity scores are obtained, representing the degree of correlation between the current rail surface and each standard condition. These similarities are then used as weighting coefficients in a weighted average calculation to estimate the maximum adhesion coefficient, as expressed in Equation (6). To further refine this estimation, rail surface condition information is incorporated to adjust the four similarity scores. Through fuzzy logic control, correction values are computed and applied to the initial similarities. The adjusted similarity scores are then used as weighting coefficients in a second weighted average computation, yielding the final maximum adhesion coefficient after multi-source information fusion. The corresponding formula is presented in Equation (7).  
\[\mu_{max}=\frac{\sum_{i=1}^{4}x_{i}\mu_{maxi}}{\sum_{i=1}^{4}x_{i}} \tag{6}\]  
where \(\mu_{max}\) corresponds to the maximum adhesion coefficient of the four standardized rail surfaces and \(x_{i}\) corresponds to the similarity between the rail surfaces and the four standardized rail surfaces. \(\mu_{maxi}\) corresponds to the value of the maximum adhesion coefficient of the standard rail surface \(i\).  
\[\mu_{max}=\frac{\sum_{i=1}^{4}k_{i}x_{i}\mu_{maxi}}{\sum_{i=1}^{4}k_{i}x_{i}} \tag{7}\]  
where \(k_{i}\) corresponds to the rail surface condition correction factor.
# Underwater SLAM Meets Deep Learning: Challenges, Multi-Sensor Integration, and Future Directions
## 9 Research Gaps and Opportunities
### Opportunities for Future Research
#### 9.2.7 Exploring New Sensor Technologies  
The development of novel sensor technologies has the potential to significantly enhance underwater SLAM capabilities. Future research should focus on the following aspects:  
* **Event Cameras:** developing SLAM systems using neuromorphic event cameras that asynchronously detect brightness changes at microsecond resolution, enabling high dynamic range perception in low-light conditions with minimal power consumption [185];
* **Bio-Inspired and Quantum Sensors:** investigating bio-inspired [186] or quantum [187] sensors that can operate effectively underwater, offering new avenues for SLAM enhancement;
* **Sensor Miniaturization:** reducing the size and energy consumption of sensors to make them more suitable for small, battery-powered underwater vehicles [188];
* **Hyperspectral Imaging:** advancing hyperspectral imaging sensors to provide detailed spectral information, enhancing feature detection and scene segmentation in underwater SLAM;
* **Energy Harvesting:** incorporating energy-harvesting technologies, such as piezoelectric materials, to power onboard sensors through underwater vibrations or currents, extending operational duration;
* **Environmental Sensors:** integrating environmental sensors that monitor parameters such as salinity, temperature, and pH, provides valuable contextual data to improve the accuracy and reliability of SLAM systems.  
Continued research into these areas will be critical for overcoming the current limitations of underwater SLAM, enhancing collaboration among autonomous systems, integrating advanced technologies that enable more efficient and effective underwater exploration and navigation, and, so, revolutionizing several emerging applications, including under water archaeology, marine biology and ecology, offshore infrastructure inspection, disaster response and recovery, and deep-sea mining.
# A High-Speed Finger Vein Recognition Network with Multi-Scale Convolutional Attention
## 4 Experiments and Results  
### Dataset  
In this study, the proposed algorithm was evaluated on two publicly available finger vein datasets: the FV-USM dataset [18] and the SDUMLA-HMT dataset [39]. The FV-USM dataset provides extracted finger vein region-of-interest (ROI) images, and a total of 2952 images were used in this study. In contrast, the SDUMLA-HMT dataset does not include pre-extracted ROI images. To eliminate irrelevant backgrounds and noises while improving the recognition accuracy and efficiency, we performed ROI extraction on the dataset images, resulting in a total of 3816 extracted ROI images. First, Laplacian edge detection was applied to enhance the edge features of the image, followed by Gaussian filtering for smoothing to reduce noise interference. Subsequently, the least squares method was used to fit the upper and lower edges and the centerline of the finger, calculate its inclination angle, and perform image rotation correction to align the finger orientation. Next, based on the fitted edge lines, the largest inscribed rectangle of the finger was extracted to obtain a more standardized finger region. Since finger joints exhibit weaker absorption of near-infrared light, the horizontal light intensity distribution typically presents two peaks. We extracted the region between these two peaks as the finger vein region of interest (ROI) to ensure that the selected area contains the main vein information while reducing background interference. The ROI is the region in the finger vein image where vein information is most concentrated and informative. Using ROI images for recognition enhances the distinction between different vein regions while maintaining the stability and consistency of the same-class vein regions.  
To match the input requirements of deep learning models, all dataset ROI images underwent zero-padding and were normalized to \(224\times 224\) pixels to maintain a uniform input size. Both datasets were split into training, testing, and validation sets with a ratio of 4:1:1.
# Interference Mitigation Strategies in Beyond 5G Wireless Systems: A Review
## 3 Classification and Evolution of Interference Management Strategies in B5G Networks
### Background and Evolution of Interference Management Techniques  
The evolution of IM strategies in cellular networks has been closely tied to the generational advancements from 3G to B5G. In early mobile systems such as 3G, IMI primarily relied on static methods, including fixed frequency planning, orthogonal channel assignment, and rudimentary POC [43, 44]. These techniques were sufficient when user density was low and network topology relatively uniform.  
With the introduction of 4G and long-term evolution systems, more dynamic IM methods emerged. These included fractional FR, inter-cell interference coordination (ICIC), and semi-static scheduling techniques [45, 46, 47], which SE and partially addressed the growing complexity of network traffic. However, such strategies still depend heavily on predefined rules and centralized control, making them less responsive to RT variations in user behavior and NL [48].  
In the 5G era, and more critically in B5G networks, the limitations of these traditional approaches have become evident. The rise of ultra-dense networks (UDNs), mMTC, and HetNet architectures has introduced highly dynamic and interference-prone environments [49, 50]. Traditional rule-based systems lack the agility and scalability to operate effectively under such conditions.  
To overcome these challenges, AI-enabled and self-organizing IM strategies have gained traction in B5G research and standardization. Techniques such as reinforcement learning (RL), deep learning (DL), and FL allow for RT adaptation, proactive interference prediction (IPR), and decentralized decision-making [51, 52, 53]. These approaches enable the network to learn from historical data, dynamically allocate resources, and manage interference with greater precision across diverse deployment scenarios.  
Figure 3 provides a high-level visual summary of this evolution, illustrating the shift from static, rule-based techniques in 4G to adaptive, AI-driven methods in B5G.
## 2 Materials and Methods
### Robust Control Design
Figure 7: Assumed control setup: \(P(s)\): model of the controlled plant, \(k_{p},k_{i}\): parameters of the fixed-structure PI controller, \(S,T,S_{p},S_{c}\): closed-loop sensitivity functions, \(r\): setpoint reference, \(e\): tracking error, \(u\): manipulated variable, \(y\): controlled output. This boundary defines a set of admissible controllers. In case the set is nonempty, which means that there exists at least one controller of the given structure for the defined design constraint, one particular parameter combination has to be chosen. One of the possible choices is to select the controller with the highest integral gain, which is known to minimise the Integral error criterion  
\[IE=\int_{0}^{+\infty}e(t)\mathrm{d}t=\frac{1}{k_{i}}. \tag{35}\]  
The computation of the \(H_{\infty}\) region and its corresponding set of admissible controllers is a nontrivial task. However, as shown in [32], an explicit solution can be derived in the form of  
\[k_{i}(\omega) =F_{i}(\omega,x_{i},\gamma),\] \[k_{p}(\omega) =F_{p}(\omega,x_{i},\gamma,A,B,A_{1},B_{1},w,w_{1}),\] \[\omega \in[0,\infty), \tag{36}\]  
with the arguments of parametric curves \(F_{i},F_{p}\) defined as  
\[A \stackrel{{\Delta}}{{=}}Re(P(j\omega)),\ A_{1} \stackrel{{\Delta}}{{=}}\frac{\mathrm{d}A}{\mathrm{d}\omega},\] \[||H(s,k)||_{\infty}<\gamma;\;H(s,k)\stackrel{{\Delta}}{{=}}W(s) S_{*}(s), \tag{34}\]0 \[||H(s,k)||_{\infty}<\gamma;\;H(s,k)\stackrel{{\Delta}}{{=}}W(s) S_{*}(s), \tag{34}\]1  
and \(x_{l};\ l=\{1,2,3,4\}\) are the real roots of the \"companion\" polynomial  
\[||H(s,k)||_{\infty}<\gamma;\;H(s,k)\stackrel{{\Delta}}{{=}}W(s) S_{*}(s), \tag{34}\]2  
with the real coefficients \(a,b,c,d,e\) depending explicitly on \(\{\omega,x_{l},\gamma,A,B,A_{1},B_{1},w,w_{1}\}\) (see the reference [32] for the full derivation). The important result is that the computation of the \(H_{\infty}\) region always leads to a 4 th-order polynomial regardless of the order of the plant or the user-defined weighting functions. Therefore, analytic expression for its roots is available from the [PERSON]'s method and Cardano formulas. Their careful examination allows a separation of the real roots which lead to the solution of the \(H_{\infty}\) region boundary. Multiple design constraints may be formulated in the frequency domain for several weighted \(H_{\infty}\) norm of various closed-loop sensitivity functions. The resulting admissible region is then computed from the intersection of the individual regions corresponding to each design constraint. Computer software automating the derivation of the parametric regions has been recently developed; see [34]. A publicly available version will be released soon.
## 3 Materials and Methods
### Monitoring with the Hospital Network  
Location and temperature data can be collected using the hospital network. As shown in Figure 7, the location information of users and medical devices was obtained through the attached sensor, and temperature data were collected through the temperature sensor attached to the pharmaceutical refrigerator. Data were transmitted and stored. Stored data can be checked at any time through a dashboard. The dashboard monitoring system demonstrated in Figure 8 shows the flow of data collection with Gateway, AP and the server which gets displayed to the dashboard. The location of 31 infusion pumps and the temperature of 2 pharmaceutical refrigerators were monitored by the hospital network for 7 days.
## 4 Self-Healing Approaches
### Network Architecture
#### 4.2.3 Fast Decision Network  
In the context of self-healing, a fast decision network architecture can make quick, accurate decisions to enable rapid failure recovery. Speedy decision-making is critical to minimise downtime and maintain system availability in a self-healing system. A fast decision network can be implemented using ML algorithms to quickly analyse network data and make decisions based on real-time information. The algorithms can be trained to recognise network traffic patterns and behaviours and identify potential issues before they result in a system failure. For example, if a fast decision network detects that the CPU usage is high or that any other performance issues, it can quickly take action to mitigate the problem. This could involve diverting traffic to other devices, scaling up resources to handle the increased loads, or automatically triggering a self-healing mechanism to recover from the failure. A fast decision network is described by [1] as a representation of the amount of decision-making speed. It starts from the judging position and then quickly takes the local strategy to solve the grid risks for each node. The decision-making network will have a good or bad result after the configuration sub-strategy until the final node, and calculate the statistical probability of each corresponding node to obtain the overall probability. The process of a fast decision network does not consider the effect of local strategy configuration on global strategy. Fast decision network plays a significant role in enhancing the speed of fault resolution when an actual online operation is running. A fast decision network works more effectively when combined with a valuation network in a strategic configuration.