esa-sceva/satcom-chunk-collection huggingface dataset:
# Metallurgical Aspects in the Welding of Clad Pipelines--A Global Outlook
## 3 Microstructure Evolution
### Weld Metal Microstructures  
The WM microstructure in clad pipes is complex as it is a mixture of two different materials (mixed zone) for the root pass (see Figure 11a), the filler wire, and the clad material. For the hot pass (see Figure 11b), the situation can be even more complicated as a third material, the carbon steel, is also incorporated. The use of the term \"hot pass\" is due to the weld being deposited after the root pass within limited timing. The Schaeffler diagram [56], DeLong [57] diagram, and WRC-1992 [58] diagram have limited use in the prediction of the final microstructure of the WM for Ni-based superalloys due to very high Ni\({}_{\text{eq}}\) values (\(>\)55-60 wt %) and low dilution with the carbon steel. The final microstructure will be the Ni-rich \(\gamma\)-phase. In addition to the chemical composition of the WM, the cooling rate must be considered. It should be noted that the CMT process is often used for root pass welding [59] as a low-heat-input welding process. It tends to provide high cooling rates in the WM, i.e., with \(\Delta\)f8/5 values even below 2.5 s at atmospheric pressure, which tends to decrease further (to 0.6 s) with increasing pressure [60]. Such fast cooling rates may promote martensite formation.  
Figure 11: (**a**) Root pass with double-material mixing and (**b**) hot pass with triple-material mixing in welding.  
Figure 10: Precipitation kinetics of Cr\({}_{23}\)C\({}_{6}\) carbide as a function of carbon content [55]. The dotted red arrow indicates more rapid kinetics due to carbon diffusion from the carbon steel to the clad.
# Smart Viniculture: Applying Artificial Intelligence for Improved Winemaking and Risk Management
## 7 Conclusions  
AI has brought significant advancements to the winemaking industry, enhancing product consistency, operational efficiency, and safety. By leveraging AI-driven technologies, winemakers can achieve precise control over various aspects of production, from vineyard management to bottling. AI systems enable real-time monitoring of critical parameters, predictive modeling for fermentation, and advanced quality control during bottling, ensuring that each stage of the process meets high standards of quality and efficiency.  
AI's ability to integrate data from IoT devices and employ machine learning algorithms for predictive analytics has revolutionized traceability and food safety in the wine industry. By detecting and managing microbiological risks, such as mycotoxins and bacterial contamination, AI ensures the production of safe and high-quality wines. The use of AI in enhancing traceability from vineyard to consumer also builds consumer trust and meets regulatory requirements.  
Despite its many benefits, AI adoption in winemaking faces several challenges, including data integration issues, high implementation costs, and the need for transparent AI models that can be trusted by winemakers. Addressing these obstacles will require collaboration among wineries, technology providers, and research institutions to develop cost-effective, standardized AI solutions.  
Looking forward, the future of AI in enology looks promising. Potential advancements include more sophisticated sensory analysis tools, enhanced integration with global supply chain systems, and personalized wine recommendations. Furthermore, AI-driven sustainability practices, such as optimized resource use and integrated pest management, will further promote environmental stewardship in the industry.  
To ensure the responsible deployment of AI, ethical considerations and regulatory frameworks must be established. Issues such as data privacy, labor impacts, and sustainability need to be addressed to create a fair and equitable integration of AI technologies.  
In conclusion, AI is set to transform winemaking, driving improvements in efficiency, quality, and sustainability. By embracing AI, the industry can overcome existing challenges and continue to innovate, paving the way for a future of precision winemaking that meets the evolving demands of consumers and the global market.  
The authors collaborated closely to develop this review: Conceptualization, I.I.-B. and C.G.; methodology, I.I.-B., J.M., V.E.G.-R. and C.G.; investigation, I.I.-B., V.E.G.-R. and J.M.; resources, M.C., I.I.-B. and J.M.; writing--original draft preparation, I.I.-B., V.E.G.-R., J.M. and C.G.; writing--review and editing, M.C., J.M.C., V.E.G.-R. and C.G.; supervision, J.M.C., V.E.G.-R. and C.G.; project administration, J.M.C. and C.G.; funding acquisition, J.M.C. and C.G. All authors have read and agreed to the published version of the manuscript.  
This research was supported by a grant from PID2021-1228990-B-C22 funded by MCNN/AEI/ 10.13039/501100011033 and by ERDF/EU.
# Study on Shock Initiation Randomness of Energetic Materials on a Macroscopic Scale
## 4 Applied Example
### Numerical Model  
The example [41] took Composition B as the test explosives, the 5 mm-thick steel plates as the shield plates, and the 20 mm-long cylindrical steel with a diameter of 10 mm as the fragments. The test results showed that, with a shock velocity of fragments lower than 1003.5 m/s, Composition B was not initiated, while with a shock velocity of fragments higher than 1049.8 m/s, Composition B was initiated. It can, thus, be seen that the critical shock initiation velocity of Composition B should be between 1000 m/s and 1050 m/s under the above working conditions.  
In the repeated numerical simulations of the randomness study on this example, the numerical model of two-phase reactive flow worked as the theoretical model, the generalized interpolation material point method (GIMPM) [42] worked as the numerical simulation method, and the calculation program was the self-compiled GIMPM program in the Fortran language. The numerical model constructed by this GIMPM program is shown in Figure 3, and the coordinates of the three gauges in this model are shown in Table 1. In addition, the deterministic parameter values of Composition B in the Lee-Tarver equation of reaction rate and the JWL equation of state were taken from the literature [43], and these deterministic parameter values are shown in Tables 2 and 3.  
Figure 3: The numerical model of the example.  
Figure 5: The color contour plots of the pressure when the \(\triangledown\)s are 1003.5 m/s and 1049.8 m/s, respectively. (**a**) The contour plot when \(t=22\) μs, \(v=1049.8\) m/s. (**b**) The contour plot when \(t=22\) μs, \(v=1003.5\) m/s. (**c**) The contour plot when \(t=25\) μs, \(v=1049.8\) m/s. (**d**) The contour plot when \(t=25\) μs, \(v=1003.5\) m/s. (**e**) The contour plot when \(t=28\) μs, \(v=1049.8\) m/s. (**f**) The contour plot when \(t=28\) μs, \(v=1003.5\) m/s.  
Figures 4 and 5 show that when the fragment shock velocity is 1049.8 m/s, all the pressure peaks of the three gauges are over 20 GPa, and detonation waves are also generated inside Composition B, suggesting that Composition B has been initiated by the shock load. What can also be concluded is that when the fragment shock velocity is 1003.5 m/s, the pressure curves of the three gauges are all gentle, with a pressure peak lower than 2.5 GPa, and stress waves with low pressure, not detonation waves, are generated inside Composition B, suggesting that Composition B has not been initiated by the shock load. These numerical simulation results are consistent with the test results. Thus, this program can accurately simulate the shock initiation of Composition B, and the training sample sets and test sample sets constructed by the repeated numerical simulations via this program can also be identified as accurate and qualified to meet the needs of training and testing the GABPNNs.
## 2 Materials and Methods
### Feature Wavebands Selection Algorithms  
Since hyperspectral data contain a large number of wavelength variables, and many wavelengths may not provide useful information for model construction, screening effective characteristic wavelengths is crucial for constructing robust and efficient prediction models. Aiming at the problems of low modeling efficiency, high computational complexity and low model accuracy caused by redundant wavelengths, this study adopted a phased wavelength screening strategy. Firstly, the uninformative variables elimination (UVE) algorithm is used to extract the effective characteristic wavelengths from the preprocessed spectral data, and then the wavelength subset was further optimized by the successive projections algorithm (SPA). This hierarchical screening strategy effectively balances the detection accuracy and computational efficiency, and can satisfy the fast and efficient detection needs in practical applications.
## 2 Materials and Methods  
### Study Area  
This study was conducted at a regional scale between 12\({}^{\circ}\)S and 23\({}^{\circ}\)S and 128\({}^{\circ}\)E and 138\({}^{\circ}\)E, which is known as the Northern Australian Tropical Transect (Figure 1). This region, particularly the northern NATT, has a classic monsoon climate pattern, which receives more than 80% of its annual precipitation during November to April [4]. From the northern mesic tropics to xeric central Australia, mean annual rainfall ranges from 1700 mm to approximately 300 mm [4].  
Correspondingly, the vegetation follows a wet-dry gradient that shifts from Eucalyptus-dominated forests, open forests, and woodlands in the coastal northern areas to Acacia-dominated open woodlands, scattered shrubs, and Hummock grassland into the vast inland [4]. More detailed descriptions with respect to ecosystems, climate, and soils of the entire study region can be found in [PERSON] et al. (2013) and [PERSON] et al. (2011) [4; 36].  
To contrast satellite observation with ground-based evidence, we selected five representative flux tower sites across the extensive study area: Howard Springs (AU-How (131.15\({}^{\circ}\)E, 12.495\({}^{\circ}\)S)), Dry River (AU-Dry (132.371\({}^{\circ}\)E, 15.259\({}^{\circ}\)S)), Sturt Plains (AU-Stp (133.3502\({}^{\circ}\)E, 17.1507\({}^{\circ}\)S)), Alice Springs Mulga (AU-ASM, (133.2493\({}^{\circ}\)E, 22.2828\({}^{\circ}\)S)), and Ti Tree East (AU-TTE (133.64\({}^{\circ}\)E, 22.287\({}^{\circ}\)S)) [37]. The major vegetation types over the five sites, respectively, are Eucalyptus woodland (AU-How), Eucalyptus open forest (AU-Dry), Tussock grassland (AU-Stp), Mulga open woodland (AU-ASM), and Corymbia savanna (AU-TTE) ([[http://www.ozflux.org.au/](http://www.ozflux.org.au/)]([http://www.ozflux.org.au/](http://www.ozflux.org.au/)), accessed on 1 October 2020).
## 4 Results
### Experimental Results
#### 4.2.4 Optimal Combination of Selected Beneficial Sources
However, a 26.5% increase in CA is observed when EMC-MMFT is applied and compared to DTE, which achieves a mean CA of 36.5% and outperforms both ROD and LSA-MMFT. The superior classification performances achieved in this instance are attributed to the selected optimal combination of high beneficial domains after low-beneficial domains have been discarded at once, when EMC-MMFT is employed. In this case, high beneficial SDs are mainly selected to minimize the effect of NT and utilized to enhance the prediction rate across individual TDs. Hence, superior classification performances are observed when EMC-MMFT is employed, while inferior performances are attributed to the remaining non-related low-beneficial domains when a single low-beneficial domain is removed at a time, when ROD, DTE, and LSA-MMFT are employed. These results further validate the fact that removing a single low-beneficial domain in each iteration poses severe implications for the classification performance across individual TDs, while selecting only the best combination of closely related high beneficial sources, denoted by superior classification performances, can significantly enhance the prediction rate across individual TDs.
# Adaptive Learned Belief Propagation for Decoding Error-Correcting Codes
## 4 Weighted Belief Propagation
### WBP over \(\mathbb{F}_{q}\)  
The construction and decoding of the PB QC-LDPC binary codes can be extended to codes over a finite field \(\mathbb{F}_{q}\)[80; 81]. Here, there are \(q-1\) LLR messages sent from each node, defined in Equation (1) [81]. The update equations of the BP are similar to (9)-(10), and presented in [81] for the extended min-sum (EMS) and in [82] for the weighted EMS (WEMS) decoder.  
Figure 2: Tanner graph unrolled to an RNN.  
The parameter sharing for the four-dimensional ragged array \(\{\gamma^{(t)}_{\tau_{c,d^{\prime}}}\}_{t,t\in\mathbb{C}_{\sigma},c\in V_{c,d^{ \prime}}\in\mathbb{F}_{q}}\) is defined in Section 4.1. In the check-node update of the WEMS algorithm, it is possible to assign a distinct weight to each coefficient \(q^{\prime}\in\mathbb{F}_{q}\) for every variable node. For instance, in the Type \(T_{c}CQ\) scheme, \(\gamma^{(t)}_{\tau_{c,d^{\prime}}}=\gamma_{\tau}\) and \(\beta^{(t)}_{c,\sigma,d^{\prime}}=\beta_{\
u}\), so there is one weight per variable and one per check node \(\forall\ t\in T\). In the case of Type \(T_{b}VCQ\), there is only one weight per variable, iteration, and coefficient. In this case, if BP is based on the non-binary EMS algorithm, the decoder is called the neural normalized EMS (NNEMS).  
**Remark 1**.: _The EMS decoder has a truncation factor in \(\{1,2,\cdots,q\}\) that provides a trade-off between complexity and accuracy. In this paper, it is set to \(q\) to investigate the maximum performance._
## 4 The Proposed Methodology
```
1:Initialize birds' positions \(P_{i}(i=1,2,...,n)\) with \(n\) birds, the birds' speed \(S_{i}(i=1,2,...,n)\), the objective function \(f_{n}\), iterations \(T_{max}\), the parameters of \(t=1,r_{1}\), \(r_{2}\), \(R\), \(K_{1}\), \(K_{2}\), \(K_{3}\), \(K_{4}\), \(K_{5}\)
2:Calculate\(f_{n}\) for each bird \(P_{i}\)
3:Find the best bird position \(P_{best}\)
4:while\(t\leq Tmax\)do
5:for(\(i=1:i<n+1\))do
6:if(\(R<0.5\))then
7: Update the current swimming bird's position as: \(P(i+1)=P_{best}(i)-K_{1}\).\(|K_{2}\).\(P_{best}(i)-P(i)|\)
8:else
9: Update the current flying bird's velocity as: \(S(i+1)=K_{3}V(i)+K_{4}r_{1}(P_{best}(i)-P(i))+K_{5}r_{2}(P_{Gbest}-P(i))\)
10: Update the current flying bird's position as: \(P(i+1)=P(i)+S(i+1)\)
11:endif
12: Apply the stochastic fractal search diffusion as: \(P=Gaussian(\mu_{P},\sigma+\beta\times P)-\beta^{\prime}\times S\)
13:endfor
14: Calculate\(f_{n}\) for each bird \(P_{i}\)
15: Set \(t=t+1\)
16: Update \(R\), \(K_{2}\), \(K_{1}\)
17: Find the best position \(P_{best}\)
18: Set \(P_{Gbest}=P_{best}\)
19:endwhile
20:Return the best solution \(P_{Gbest}\)
```  
**Algorithm 1** The Proposed DTOSFS Algorithm
# A Review of Recent Advances in Roll Stability Control in On-Road and Off-Road Vehicles
## 2 Research Method  
This study adopts a systematic literature review approach, consisting of three steps--retrieval, screening, and evaluation--to systematically reveal the knowledge framework and mainstream research perspectives in the field of vehicle roll stability control. First, the relevant literature was retrieved from major academic databases, such as Web of Science, IEEE Xplore, ScienceDirect, Scopus, and Sage, to ensure the broadness and authority of the sources. During the retrieval process, core keywords, such as \"Roll Stability Control\", \"Vehicle Rollover Prevention\", \"Lateral Stability Control\", \"Active Roll Control\", and \"Anti-Roll System\", were combined with vehicle-type-related keywords, including \"Road Vehicle\", \"Highway Vehicle\", \"Off-Road Vehicle\", \"Heavy-Duty Vehicle\", \"Mining Truck\", and \"Agricultural Vehicle\". A preliminary selection of approximately 600 papers published between 2000 and 2025 was made, with duplicate papers removed. Subsequently, a second round of screening was conducted, focusing on topics such as roll stability control, rollover prevention strategies, and stability enhancement technologies for road and off-road vehicles. The selected papers were then evaluated in depth to identify representative research outcomes and technological evolution pathways. Finally, a subset of the most representative papers was analyzed to summarize the current state of technological development in roll stability control and to predict future research trends.  
Figure 2a shows the annual publication trend of research on vehicle roll stability control. It is evident that research in this field has significantly increased over the past decade, likely driven by advancements in control technologies and the growing demand for safety, reflecting scholars' continuous focus on roll safety and rollover prevention. Additionally, Figure 2b illustrates the keyword co-occurrence network in the field of RSC. It is clear that rollover prevention research is highly correlated with topics such as rollover risk estimation, active suspension, yaw motion control, and MPC. This indicates that achieving comprehensive improvements in vehicle roll safety and dynamic performance relies on multi-system collaboration and integrated control strategies, aimed at enhancing vehicle stability under complex operating conditions.
# Channel Access in Wireless Smart Grid Networks Operating under ETSI Frame-Based Equipment Rules
## 3 Channel Access
### FBE Variants
#### 3.2.4 Deterministic-Backoff FBE  
Deterministic-backoff FBE (DB FBE) is our second proposed variant based on the deterministic-backoff algorithm [38]. Instead of muting for a number of FFPs after a data transmission, DB FBE nodes refrain from accessing the channel (i.e., backoff) for several FFPs before a transmission. This backoff procedure is governed by the variable \(b\), which indicates the number of FFPs during which a node cannot access the channel. Initially, \(b\) is set to an initial backoff value \(a\). During the backoff periods, the node monitors the channel and decrements \(b\) after each empty FFP. An FFP is determined to be empty on the basis of a single CCA, which is conducted at the beginning of the FFP. If the node detects a busy channel, then an interrupt counter \(i\) is incremented. Once \(b\) reaches zero, the node can start transmitting. If a collision occurs, the node increments a retransmission counter \(r\), otherwise \(r\) is set to zero. After each transmission, a new backoff value is selected. If the value of \(r\) modulo \(r_{max}\) (maximum number of retransmissions) is less than a threshold of \(\beta\), the node sets \(b=\alpha+i\) and resets \(i\), in the other case \(b\) is randomly selected from the range of \([1,r_{max}-1]\). Figure 5 represents the flowchart of the entire DB FBE algorithm.  
Figure 4: Operation of floating FBE.
### Works Related to General Scatterer Distribution Models  
Several works related to scattering distribution models applied to general communication schemes are presented in [13; 14; 15; 16; 17]. In [13; 14], a uniform distribution model that locates the scatterers in a 2D disk region centered on the optical receiver is presented. For the system modeling, the authors consider a mobile station as a receiver. Although the focus of the model presented is not VLC systems, this proposal could be applied to any type of technology based on wireless communication, which includes dust particles that generate scattering. In [15], the authors present a statistical analysis in terms of arrival time and arrival direction of a geometric channel model that considers a mobile transmitting station and scatterers distributed in a hemispheric area around a receiving base station. The results show that this scatterer distribution can be implemented in any multi-path wireless communication system. The manuscript presented in [16] introduces to the state of the art a Gaussian model of scatterer distribution. The authors mention that this model is applicable to multi-path wireless communication systems due to its spatial and temporal properties. The demonstrated statistical results are presented in terms of arrival angle and arrival time. Finally, the work in [17] proposes a general channel model for any communication system that includes scattering particles. This three-dimensional model is based on stochastic geometry, where a Gaussian distribution of scatterers located around an arbitrary point within a spheroid is assumed. Furthermore, the transmitter and receiver are considered to be located at the focal points of this spheroid. Statistics in terms of the arrival angle and the arrival time of the model are obtained. Being a generalized channel model, this proposal could be extrapolated to wireless communication systems in indoor environments, in order to evaluate their performance.  
As we can see in the analyzed literature, a large part of the proposed scatterer distribution models can be implemented in any type of multi-path wireless communication system, both indoors and outdoors. This advantage implies that they can also be extrapolated to VLC systems applied to underground mines.
## 3 Aim, Materials, and Methods
### Multi-Enhancement Command and Scheduling Model for Communication Network
#### 4.2.1 Early Warning and Monitoring  
In the early warning and monitoring stage, advanced technologies such as satellite remote sensing, unmanned aerial vehicle remote sensing, global positioning system (GPS), Beidou positioning system, geographic information system (GIS), and Internet of Things  
Figure 3: Multi-enhancement command and scheduling model for communication network. Source: Own processing according to the literature.  
sensors are employed to accurately measure and comprehensively observe various regions of the Earth's surface from multiple perspectives. Satellite remote-sensing and unmanned aerial vehicle remote-sensing technologies offer a wide range of geospatial information facilitating the rapid assessment of overall area conditions; the global positioning system (GPS) and Beidou positioning system ensure the precise positioning of monitoring targets for data accuracy; the geographic information system (GIS) integrates and analyzes these vast datasets to provide valuable reference information. Furthermore, Internet of Things sensors enable the real-time monitoring of environmental changes in specific areas such as temperature, humidity, and pressure for more accurate prediction of the weather fluctuations and natural disaster risks. Following the collection of this extensive information, the command system employs data processing and analysis techniques to extract valuable insights that serve as a scientific foundation for subsequent early warnings and decision-making.