esa-sceva/satcom-chunk-collection huggingface dataset:
# Study of the Mechanism of a Stable Deposited Height During GMAW-Based Additive Manufacturing
## 4 Conclusions  
Based on the experimental and simulation results, the following conclusions can be drawn:  
1. The predefined layer height influences the actual height of the welding bead during GMAW-based additive manufacturing. If the predefined layer height is set to be larger than that of the ideal value, the height of the welding bead increases, and the width decreases. Conversely, if the predefined layer height is set to be smaller, the height of welding bead decreases and the width increases.
2. When the value of the predefined layer height changes within a certain range, other manufacturing parameters of the GMAW-based multilayer deposition process, including the welding speed, wire feed speed, welding current and voltage, shield gas flow rate, and temperature of the substrate and ambient remain constant; this situation ensures that the total height of the thin wall is almost equal to the predefined layer height multiplied by the number of layers. This ensures that the distance the substrate moved along the negative z direction during the building process is equal to the total height of the manufactured part. The welding process is continuous, and the ideal predefined layer height is almost equal to the stable deposited height.
3. During the GMAW process, the changes in the distance between the torch and the top surface of the part cause the dry extensions of the electrode to vary; furthermore, this situation leads to an increase or decrease in the heat input into the molten pool. Therefore, the dry extension of the electrode is the key factor that influences the geometry of the welding bead, especially the layer height; it has a compensating effect that makes the actual layer height close to the predefined value, so the deposited layer height can maintain a stable state.  
[PERSON] developed the idea for the study. [PERSON] performed the research, analyzed data and wrote the manuscript. [PERSON], [PERSON] and [PERSON] discussed the results and revised the manuscript. All authors have read and agree to the published version of the manuscript.  
Figure 16: The manufactured models with the predefined layer height set to 0.85 mm, and other manufacturing parameters match this value: (**a**) a cylindrical model; (**b**) a hexagonal prism model; (**c**) a columnar model of heterosexual cross section.  
**Funding:** This research was funded by the National Nature Science Foundation of China (No. 51975518), the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (No. 51821093), the Key Research and Development Plan of Zhejiang Province (No. 2018C01073), the Ningbo Science and Technology Plan Project (No. 2019B10072), and the Fundamental Research Funds for the Central Universities (No. 2019 QNA4004).  
**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.
###### Abstract  
Carbon dioxide (CO\({}_{2}\))-cured concrete is a novel material that can effectively reduce CO\({}_{2}\) emissions in the atmosphere. However, limited research has been found to investigate the corrosion behavior of CO\({}_{2}\)-cured reinforced concrete. In this paper, the corrosion resistance of reinforced cement mortar is investigated. The mortars were cured in CO\({}_{2}\) for 1 day-28 days. Water-cement ratios (w/c) of 0.3, 0.4 and 0.5 were designed. The corrosion resistance of inner steel bars was researched by the methods of ultrasonic velocity, electrical parameters (AC electrical resistance, Tafel curve method and AC impedance spectroscopy). Moreover, scanning electron microscope was selected for observing the micro-morphology of CO\({}_{2}\)-curing mortar. X-ray diffraction spectrum was used to characterize components of steel bars' passive films. The results show that CO\({}_{2}\) can effectively increase electrical resistivity and ultrasonic velocity, thus improving the corrosion resistance of reinforced cement mortar. The enhancement of carbon dioxide curing increases with the increasing w/c. The mass-loss rate, the electrical resistivity and the decreasing rate of ultrasonic velocity increase with the increasing sodium chloride freeze-thaw cycles, indicating the continuous increase in the corrosion degree of reinforcement. The corrosion deterioration degree of steel bars decreases with the increasing CO\({}_{2}\)-curing time. Specimens with w/c of 0.3 and 0.4 show the highest and lowest corrosion deterioration resistances after sodium chloride freeze-thaw cycles. Microscopic characterization found that CO\({}_{2}\) curing could increase the corrosion resistance of the inner steel bars by improving the compactness of the cement matrix. Moreover, the iron oxides on the surface of the passivation film decreased after CO\({}_{2}\) curing.  
carbon dioxide; corrosion resistance; water-cement ratios; NaCl freeze-thaw cycles; electrical parameters; microscopic experiments +
Footnote †: journal: Applied Sciences
# Boosting Safety Protocol Compliance with Eye-Tracking in Chemical Plants
## 4 Discussion
Future research should explore expanding this system into diverse industrial contexts, including healthcare emergency simulations and logistics safety operations, to evaluate generalizability. The integration of multi-modal biometric sensors--such as EEG, heart rate monitors, or galvanic skin response--may offer even richer feedback loops to assess cognitive load and stress response. Furthermore, longitudinal studies are needed to determine how gaze-guided training affects long-term skill retention and transferability to real-world environments. Finally, industrial deployment at scale will require validating the system's robustness in live settings, including noisy environments, variable lighting, and team-based operational tasks. While the results are promising, the study has several limitations. First, the sample size (\(n=59\)) may not fully capture the variability across a broader industrial workforce, and future studies should aim to include larger and more diverse participant pools. Second, the training was conducted in a controlled VR simulation rather than in a live operational setting, which may limit ecological validity. Third, the use of monetary incentives and self-selection into the study may introduce participant motivation bias. Finally, our analysis focused on short-term task performance, and long-term retention effects remain to be evaluated.
# A Review of Experimental and Numerical Studies of Lithium Ion Battery Fires
## 4 Thermal Runaway
### Mitigating the Risk of Thermal Runaway and Fire
#### 4.3.3 Battery Management System  
LIBs are required to operate in a variety of conditions such as high and low temperatures. As past research has suggested, temperature impacts the performance of LIBs considerably. The battery management system (BMS) monitors battery parameters and provides warnings and initiates shutdowns in a system when hazardous conditions are imminent. Therefore, effective BMS that allow regulation of the battery temperature is critical in reducing the occurrence of thermal hazards. BMS are traditionally categorized as internal thermal management (ITM) and external thermal management (ETM). These two categories can be broken down further into the following subcategories:  
* Internal management system (IMS)--direct current (DC)\(\rightarrow\) alternating current (AC) method and component optimization;
* External management system (EMS)--air cooling system (ACS), liquid cooling system (LCS), and phase-change material-based cooling system (PCM-CS).  
Early warning provisions for single batteries, such as real-time monitoring of voltage, current, resistance, and other data are expected to be able to provide an indication and subsequent alarm when an abnormal result within the aforementioned variables appears. For battery packs, management monitoring observes and analyses changes in voltage, current, resistance, and other electrical parameters that occur during normal operation of the battery pack--temperature variations and gases that may be escaping. Particular attention focuses on monitoring escaping gas due to the flammable nature of the organic materials; research suggests that sensors used to detect these gases may be able to provide sufficient warning to mitigate the risk of a thermal event [64]. A comparison of EMS is presented in Table 8.
# Design and Depth Control of a Buoyancy-Driven Profiling Float
## 4 Results and Analysis
### Thermocline Determination  
The thermocline is one of the most common phenomena in the upper layer of water. Nowadays, there are two most commonly used methods for determining the thermocline: one is based on the average temperature gradient [4], and the other is based on the peak temperature gradient [3; 5].  
In the depth control of the three locations in Figure 9, the maximum depth was 60 m (red marker), followed by 40 m (green marker) and 10 m (yellow marker). These three depths are very close to the highest local water depth. The temperature data during the diving process of the float were processed through the average temperature gradient method, and the thermocline conditions of the three places were obtained. The final measurement results are shown in Figure 9. The color bar represents the variation of temperature with depth. The thermocline at a water depth of 40 m accounted for the largest proportion of the total depth, as shown in Table 2. It was found that the range of the thermocline at water depths of 40 m and 60 m was very similar, and the temperature of the thermocline changed dramatically in the upper and lower portions, while the temperature in the middle portions changed relatively little. In the area over 40 m, the water temperature changed slowly. In the area with water depth of 10 m or so, the temperature changes in thermocline were more evident and uniform.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Measured Depth** & **Top Boundary** & **Bottom Boundary** & **Thermocline Thickness Ratio** \\ \hline
60 m & 4.2 m & 34.68 m & 50.8\% \\
40 m & 1.94 m & 32.96 m & 77.55\% \\
10 m & 2.55 m & 7.7 m & 51.5\% \\ \hline \end{tabular}
\end{table}
Table 2: Thermocline properties.  
Figure 8: Field test of velocity tracking during the float descent to a depth of 60 m.
###### Abstract  
High-resolution and wide-swath (HRWS) synthetic aperture radar (SAR) is a promising technique for applications such as maritime surveillance. In the maritime environment, normally only a few targets such as ships are interested. However, before detecting them, considerable receiving resources and computation time are required to receive the echoes of the whole scene and process them to obtain imaging results. This is a heavy burden for online monitoring on platforms with limited resources. To address these issues, different from the concept of whole-scene-oriented imaging, we propose a target-oriented imaging concept, which is implemented by an adaptive receiving-processing-decision feedback framework with feedback connection. (1) To reduce receiving resource consumption, we propose a two-dimensional adaptive receiving module. It receives sub-band echoes of targets only through dechriping and subaperture decomposition in the range and azimuth directions, respectively. (2) To reduce computation time, we propose a target-oriented processing module. It processes subarea echoes of targets only through parallelly conducting inverse fast Fourier transform (IFFT) and back projection (BP) in the range and azimuth directions, respectively. (3) To allocate resources reasonably, we propose a decision module. It decides the necessary receiving window, bandwidth, and image resolution through constant false alarm rate (CFAR) detection. (4) To allocate resources adaptively, we connect three modules with a closed loop to enable feedback. This enables progressive target imaging and detection from rough to fine. Experimental results verify the feasibility of the proposed framework. Compared with the current one, for a typical scenario, at least 30% of the system's resources and 99% of computation time are saved.  
target-oriented imaging; high resolution and wide swath (HRWS); azimuth multichannel SAR; dechirping; variable resolution; back projection algorithm; target detection Article
## 3 Results
### Biophysical Opportunities for Low ILUC Risk Crops
#### 3.1.4. Crops and Yielding Potentials in Marginal Land  
Growing biomass crops on marginal land, especially perennial grasses such as switchgrass and miscanthus, is expected to benefit the environment by increasing carbon sequestration [15; 16; 17; 19] and reducing nonpoint source pollution compared with those from traditional crop land.  
Researchers concluded that producing perennial grasses on marginal Mediterranean land is feasible and, if appropriately managed, will have relatively few non-desired environmental side effects. In Mediterranean areas, growing perennial crops can have lower negative environmental impact than wheat farming, and it also provides benefits regarding soil properties and erodibility [16; 19]. The main reasons for this are lower levels of mechanisation (and thus soil disturbance) and year-round soil coverage of perennial crops, and deeper and well-branched rooting [19]. According [PERSON] et al. (2016) [55], an additional reason for miscanthus' ability to build up carbon in the soil is that it allocates high proportions of the assimilated carbon below ground as a carbon reservoir for growth in the spring. This was confirmed by [PERSON] et al. (2016) [55] and in field trials for miscanthus, switchgrass, giant reed, and three woody crops (willow, poplar, and black locust) that, compared to grain maize, on average built up 45% more SOC in the root zone. A meta-analysis study ([PERSON] et al. 2015) showed SOC storage for herbaceous perennials (miscanthus and switchgrass) of between 1.14 to 1.88 mg C ha/year and for woody perennials (willow and poplar) a range from 0.63 to 0.72 mg C ha/year. However, these authors emphasized that long-term field trial data (>25 years) are missing and are needed to confirm the long-term sustainable soil carbon enrichment (because the stability of recently built up SOC stores is uncertain).  
Willows as well as herbaceous crops such as giant reed or miscanthus are appropriate crops for being grown under climate conditions in Ukraine, but also in Germany and Italy, whereas in addition willow can be also grown under temporarily or permanently water-saturated soil conditions [16; 17; 19].  
The effect of perennials on building up SOC is particularly large in marginal land with low SOC levels [56]. Conversely, if biomass crops are established on land that already has high SOC levels, such as long-abandoned land with dense shrub and/or forest vegetation coverage or wetlands, this may lead to a serious decline in carbon (both above and below ground); that being said, in the context of low ILUC-risk production, there are already regulatory safeguards in place to prevent such land conversions.  
It is worthwhile noting that the production of lignocellulosic crops in contaminated soils may cause the accumulation of heavy metals and other contaminants in the crops, hence compromising the quality of biomass [17].
## 4 Simulation and Integration Results
### The Spatiotemporal Analysis of Methane Concentration Based on LrGIS
Here, one of the methane sensors located 10 m away from the working face in the outtake side of the lineway is selected to perform the data validation with the monitored data, as presented in Figure 10. The numerical simulation shows that, after the algorithm converges in 10 min, and the airflow distributes fully and stably within the lineway. The simulated methane concentration maintains at a stable level and agrees well with the monitored data after 10 min. Thus, some detailed information can be provided for decision makers, such as how long the methane distribution will be stable with the effect of the ventilation system, the peak value of methane concentration, and its specific position, which can guide miners about when to avoid the methane accident if the value is beyond the maximum allowable value. Besides, the methane distribution can also guide the production intensity, and reasonable arrangement of underground coal mine equipment.
# A Survey of Methods and Technologies for Congestion Estimation Based on Multisource Data Fusion
## 5 Representation of Methods Used in Data Fusion
### DNN Methods
Then, the results are combined into one framework. In many papers, authors use Artificial Intelligence, advanced filters (Kalman filter, Treiber-Helbing filter) or matrix factorization, to fuse the data or decisions based on collected data. In Table 5, DF methods are presented, and it is obvious that two approaches dominate, namely statistical and deep learning methods. We can also see that statistical methods are applied in the time horizon from 2002 to 2020, and deep learning methods are currently being applied, starting from 2019, as expected because of the popularity deep learning recently.
# A Multi-Scale Deep Back-Projection Backbone for Face Super-Resolution with Diffusion Models
## 3 Image Super-Resolution via Repeated Refinement  
### Gaussian Diffusion Process  
We were given a dataset of input-output image pairs, denoted \(D=\{LR_{i},HR_{i}\}_{i=1}^{N}\), which represents samples drawn from an unknown distribution \(p(HR|LR)\). The conditional distribution \(p(HR|LR)\) is a one-to-many mapping in which many target HR images may be consistent with a single source LR image. In diffusion models, there are two crucial processes: the diffusion process and the inference process, both of which are parameterized Markov chains. We refer to them as process \(q\) and process \(p\), respectively.  
SR3 [23] learns the parameter estimation of \(p(HR|LR)\) by a stochastic iterative refinement process based on DDPM [18], which maps the source image LR to the target image HR. The forward Markov process \(q\) can be defined as follows:  
\[q(HR_{1:T}|HR_{0})=\prod_{i=1}^{T}q(HR_{t}|HR_{t-1}), \tag{1}\]  
\[q(HR_{t}|HR_{t-1})=\mathcal{N}(HR_{t}|\sqrt{\alpha_{t}}HR_{t-1},(1-\alpha_{t} )\textbf{I}), \tag{2}\]  
where \(0<\alpha_{t}<1\) (\(t=1,2,\ldots,T\)) is a scalar parameter, which determines the variance of the noise added at each iteration. We used \(t\) to represent a certain moment in the diffusion process \(q\), and \(T\) to represent the end moment. \(T\) also corresponds to the inference step. Note that \(HR_{t-1}\) is attenuated by \(\sqrt{\alpha_{t}}\) to ensure that the variance of the random variables remains bounded as \(t\rightarrow\infty\). The original HR image \(HR_{0}\) gradually loses its distinguishable features as the step \(t\) becomes larger. Eventually, after \(T\) iterations, \(HR_{T}\) is equivalent to an isotropic Gaussian noise.  
In addition, an important conclusion of the diffusion process is that the \(HR_{t}\) image at any moment can be derived from the source image \(HR_{0}\), as shown in Equation (3):  
\[q(HR_{t}|HR_{0})=\mathcal{N}(HR_{t}|\sqrt{\gamma_{t}}HR_{0},(1-\gamma_{t}) \textbf{I}), \tag{3}\]  
where \(\gamma_{t}=\prod_{i=1}^{t}\alpha_{i}\) (\(t=1,2,\ldots,T\)). Since the \(\alpha_{i}\) (\(i=1,\ 2,\ \ldots,\ t\)) in the cumulative multiplication is a decimal, we can easily infer that \(\gamma_{t}\) decreases as \(t\) increases. To make the diffusion and inverse diffusion process more continuous, SR3 [23] samples the hyperparameter \(\gamma_{t}\) from a uniform distribution \(p(\gamma)=\sum_{i=1}^{T}\frac{1}{T}U(\gamma_{t-1},\gamma_{t})\). In the training phase, we sampled random pairs of time steps \(t\) to obtain a pre-defined sequence of hyperparameters \(\gamma_{t}\). This is where iterative optimization takes place. Using uniformly distributed hyperparameters \(\gamma_{t}\) makes the diffusion and inverse pre-post relationship more noticeable than using discrete time sampling.
# Parameter Estimation for Some Probability Distributions Used in Hydrology
## 2 Probability Distributions
### Pearson III Distribution (PE3)  
The Pearson III distribution belongs to the Gamma distribution family, being a generalized form of the two-parameter Gamma distribution [11], with shifted \(x\), and a particular case of the four-parameter gamma distribution [5; 12; 13].  
\[f(x)=\frac{(x-\gamma)^{\alpha-1}}{\beta^{\alpha}\cdot\Gamma(\alpha)}\cdot\exp \left(-\frac{x-\gamma}{\beta}\right)=\frac{1}{\beta}\cdot dgamma\Big{(}\frac{x- \gamma}{\beta},\alpha\Big{)} \tag{1}\]  
\[F(x)=1-\int\limits_{\gamma}^{x}f(x)dx=1-\frac{1}{\beta\cdot\Gamma(\alpha)}\cdot \int\limits_{\gamma}^{x}\left(\frac{x-\gamma}{\beta}\right)^{\alpha-1}\cdot \exp\left(-\frac{x-\gamma}{\beta}\right)dx=\frac{\Gamma\left(\alpha,\frac{x- \gamma}{\beta}\right)}{\Gamma(\alpha)} \tag{2}\]  
\[F(x)=1-pgamma\left(\frac{x-\gamma}{\beta},\alpha\right)=1-pchisq\left(2\cdot \frac{x-\gamma}{\beta},2\cdot\alpha\right) \tag{3}\]  
\[x(p)=F^{-1}(x)=\gamma+\beta\cdot\Gamma^{-1}(1-p;\alpha)=\gamma+\beta\cdot qgamma \big{(}1-p,\alpha\big{)} \tag{4}\]  
where \(\alpha,\beta,\gamma\) are the shape, the scale and the position parameters and \(x\) can take any values of range \(\gamma<x<\infty\) if \(\beta>0\) or \(-\infty<x<\gamma\) if \(\beta<0\) and \(\alpha>0\); \(\mu,\sigma\) represent the mean (expected value) and standard deviation. If \(\beta<0\) (negative skewness) then the first argument of the inverse of the distribution function Gamma, \(\Gamma^{-1}(1-p;\alpha)\) becomes \(\Gamma^{-1}(p;\alpha)\).  
The expressions of the inverse function \(x(p)=F^{-1}(x)\), using the mean and standard deviation, are valid only for the method of ordinary moments.
# A Survey on Node Clustering in Cognitive Radio Wireless Sensor Networks
## 4 Objectives and Advantages of Node Clustering in CR-WSNs  
Clustering in CR-WSNs distributes network-wide spectrum sensing and allocation issues to small groups, which solves their issues in the local area. Cluster members contribute to spectrum sensing that helps collect spectrum information from that particular area [1; 19]. More information about spectrum availability reduces interference with PUs of the channels.  
Clustering in CR-WSNs pursues the following objectives: (a) better local spectrum information; (b) efficient resource allocation; (c) mitigation of interference with incumbent license holders, the PUs, and cognitive users (or secondary users); (d) protect the PUs' right to access the spectrum; (e) reduce the cost of equipping all sensors with CR; (f) provide network scalability; (g) implement energy conservation; (h) reduce communications overhead; and (i) obtain better data aggregation.  
The following are the advantages of clustering in large-scale and dense CR-WSNs.