esa-sceva/satcom-chunk-collection huggingface dataset:
# Modeling of Voltage Fluctuations Generated by Arc Furnaces
## 5 Summary
The presented model, the statistic evaluation of voltage fluctuations generated by arc furnaces, and the presented results of measurements of energy quality parameters may constitute a theoretical and practical contribution to the development of new models of arc devices; - The proposed method makes it possible to determine the increase in voltage fluctuations when connecting successive arc devices for parallel operation; - Based on the proposed model tests, a preliminary assessment of changes in voltages and currents consumed by arc furnaces is necessary in the selection and design of compensation devices; - In the case of arc furnaces, only changes in one phase can be accepted for the analysis of voltage fluctuations and fluctuation of light; - On the basis of the analysis of measurement data, it can be concluded that there is a very high correlation between the flicker indicators and voltage fluctuations in individual phases; - For the determined significant correlation between changes in the flicker Pst indicators and changes in the amplitude of voltage fluctuations (where the correlation coefficient is \(\rm{r_{PStAU}}=0.906\)), both fast-changing voltage fluctuations and flicker indicators can be used to assess the impact of arc devices on the power system; - In the case of arc furnaces, where the frequency of voltage changes does not change significantly, the increase in the amplitude of voltage fluctuations is proportional to the increase in flicker indicators; - The result of adopting the disturbance analysis in one phase is the development of a single-phase diagram of the arc device replacement; - The recorded measurement data can be the basis for further research on the impact of arc furnaces on the power system. This research received no external funding. The following nomenclatures are used in this manuscript:  
\(\rm{I_{M}}\) arc furnace melting current  
\(\rm{I_{N}}\) nominal current  
\(\rm{P_{It}}\) short-term flicker severity  
\(\rm{P_{st}}\) short-term flicker severity  
\(\rm{R_{L}}\) power line resistance  
\(\rm{R_{T}}\) resistance high-current path with the furnace transformer  
\(\rm{S_{C}}\) short-circuit power  
\(\rm{SD(I)}\) standard deviation of current  
\(\rm{n_{i}}^{*}\) cumulative amount  
\(\rm{f_{i}}^{*}\) cumulative periodicity  
\(\rm{SD(U)}\) standard deviation of current  
\(\rm{S_{intr}}\) power of the furnace transformer  
\(\rm{THD}\) total harmonic distortion  
\(\rm{U_{Arc}}\) arc voltage  
\(\rm{U_{H1}}\) RMS first voltage harmonic  
\(\rm{U_{L}}\) supply voltage  
\(\rm{U_{mean}}\) RMS voltage mean  
\(\rm{U_{N}}\) rated voltage  
\(\rm{U_{S}}\) steelworks supply voltage  
\(\rm{X_{L}}\) power line reactance  
\(\rm{X_{T}}\) reactance high-current path with the furnace transformer  
\(\rm{\Delta I_{AB}}\) arc furnace current fluctuations  
\(\rm{\Delta U}\) voltage fluctuations
## 3 Materials and Methods
### Calculation of the Entropy-CRITIC Combined Weights
#### 3.3.2 CRITIC Weighting  
(1) Based on Equation (2), the standard deviation of the \(j\)-th indicator, \(S_{j}\), is calculated.  
\[S_{j}=\sqrt{\frac{\sum_{l=1}^{m}\left(Y_{ij}-\overline{Y}_{j}\right)^{2}}{n-1}} \tag{9}\]  
(2) Based on Equation (2), the correlation coefficient between the \(b\)-th and \(j\)-th indicators, \(r_{bj}\), is calculated.  
\[r_{bj}=\frac{\sum_{b,j=1}^{n}\left(Y_{ib}-\overline{Y}_{b}\right)(Y_{ij}- \overline{Y}_{j})}{\sqrt{\sum_{b=1}^{n}\left(Y_{ib}-\overline{Y}_{b}\right)^{2 }\sum_{j=1}^{n}\left(Y_{ij}-\overline{Y}_{j}\right)^{2}}} \tag{10}\]  
(3) Calculate the conflicting characteristic between the \(b\)-th and \(j\)-th indicators, \(A_{j}\).  
\[A_{j}=\sum_{b=1}^{n}\left(1-r_{bj}\right) \tag{11}\]  
(4) Calculate the quantity of information contained in \(j\)-th indicator, \(C_{j}\).  
\[C_{j}=S_{j}\ A_{j} \tag{12}\]  
(5) Calculate the weight coefficient of the \(j\)-th indicator, \(u_{j}\).  
\[u_{j}=\frac{C_{j}}{\sum_{j=1}^{n}C_{j}} \tag{13}\]
## 6 Biological Properties
### Phytooxic Effects/Pityochemical Activity  
Recent studies have demonstrated the potential of plant phytotoxins, such as ailanthone from _A. altissima_, as bioherbicides.  
_A. altissima_ has produced phytotoxic compounds and the main toxin identified is the ailanthone quassinoid, first isolated by [PERSON] and [PERSON] [30]. Both tested the herbicidal effect of the methanol extracts from the stem and bark of _A. altissima_ on 17 weed and crop species and found that their application provided a strong herbicidal effect on all tested species. Numerous studies confirmed that quassinoids have a wide range of biological activities, including antileukemic and anticancer, antiamoebal, antimalarial, insecticidal and antifeedant, antiviral, antifungal, antitubercular, and herbicidal activities [59]. It is considered that the bioactivity of quassinoids is based on the plasma membrane NADH oxidase inhibition [60]. The phytotoxic effect seemed to correlate better with the extracts of higher phenolic contents; such results have previously been reported in _Psychotria leiocarpa_[61]. Phytochemical studies have shown that the main allelochemicals are ailanthone, ailantinone, chaparrin, and ailantinol with the first component being the most potent allelochemical. Moreover, both the methanolic extract and the hydrodistilled residue presented a significant phytotoxic activity [40]. [PERSON] et al. [51] extracts showed an excellent inhibitory action on the germination of seeds and growth of lettuce seedlings.  
Regarding inhibiting progestin, [PERSON] et al. [62] analysed 13 Chinese plant species, including _A. altissima_, in which they used the extract of the stem part. With the treatment of the T47D human breast cancer cell line with 314.46 ng/mL of progesterone and with three different levels of concentration of 10 ug/mL, 20 ug/mL and 40 ug/mL of ethanolic extracts, it was possible to verify that these concentrations could significantly prevent the action of the hormone in a dose-dependent manner.  
Despite all the beneficial effects, some reports depict the contrary. _A. altissima_ has been reported to negatively affect human health, with effects such as allergies, dermatitis and myocarditis. In addition, this invasive tree can also produce toxic and noxious environments for other species in the neighbourhood from the soil since the main compound responsible for its allelopathy (ailanthone) is present in high concentrations in the root bark and when released into the soil [6,7,63].
## 3 Results and Discussion
### Operation Carried out on the \"Partly Cloudy Day\" (With and without Fully Charged Batteries)
Another concern for the system operations is the potential brine disposal, which is a crucial environmental issue and, in more concentrated seawater applications, may comprise up to 33% (worst case) of the total cost of the seawater desalination process [73]. In large-scale seawater desalination plants, brine is commonly discharged into the sea, with costs ranging from US$ 0.05/m\({}^{3}\) to US$ 0.3/m\({}^{3}\)[74; 75], causing significant environmental issues on marine ecosystems [73]. In small-scale brackish water desalination plants, brine can be discharged into the sewer system (if available), with disposal costs between US$ 0.3/m\({}^{3}\) and US$ 0.7/m\({}^{3}\)[74; 75]. For inland plants, deep-well injection and evaporation ponds are suitable brine disposal choices, with a wide range of costs (US$ 0.5-10/m\({}^{3}\)) [74; 75], respectively. Land applications are mainly used for low brackish water brine volumes, as well as the availability of suitable land and groundwater conditions, which cost in the range of US$ 0.7-2/m\({}^{3}\)[74; 75]. In this work, the relatively low recovery of low-pressure RO or NF membranes assures a low salinity concentrate stream, and the possibilities for using the disinfected waste stream for washing and livestock watering can result in zero concentrate generation, depending on the feedwater quality [58]. A general guide for groundwater salinity and stock tolerances in South Australia has been reported [76]. For example, it is noted that the requirements to maintain the conditions of sheep and beef cattle are up to 21,600 and 8300 uS/cm, while the maximum values for health growth are 10,000 uS/cm and 6700 uS/cm, respectively [76]. Hence, a concentrate stream can be used for these purposes when the value remains at a level below these limits. In summary, fully charged batteries enabled an increase of production by \(\sim\)16%, from 664 to 767 L/d, with the water quality also improving by just over 3%, from 304 to 294 uS/cm. The average parameters are summarized in Table 1 in Section 3.3.
## 4 Results
### Sensitivity Analysis
#### 4.5.5 Effects of Penalties for Non-Compliance  
The evolution of strategies under different intensities of default penalties is shown in Figure 13. In the no-regulation case, x and y first converge to 0 and then to 1 after 101 and 69 iterations, respectively; after 211 and 156 iterations, respectively, they stabilize at 1; while z and u keep converging to 1, with rates of 0.004 and 0.010, respectively. In the high-intensity regulation case, x, y, z, and u converge quickly to 1, with iterations of 46, 61, 55, and 49, respectively. However, in the initial stage of evolution, y and z first converge to 0 before shifting towards 1, with inflection points at (26, 0.35) and (22, 0.42), respectively. Compared with the no-regulation (Figure 13a) and base cases (Figure 5), high penalties can significantly shorten the fluctuation interval and markedly increase X's willingness to implement abatement strategies. High-intensity regulation (Figure 13b) evolves similarly to the no-perturbation case (Figure 7a), suggesting that further increasing regulation intensity will not achieve a significant effect and may even cause conflicts between X and downstream agents, hindering synergistic cooperation.  
Figure 12: Evolution pathways of the four parties under different synergistic capabilities. (**a**) Evolution pathway for X; (**b**) Evolution pathway for Y; (**c**) Evolution pathway for Z; (**d**) Evolution pathway for U.
## 7 Mechanisms of PGPB (Plant Growth Promoting Bacteria) towards Salinity Tolerance
### Direct Mechanisms
#### 7.1.2 Phosphate Solubilization  
Phosphorus (P) is another essential nutrient needed by living organisms for transduction, biosynthesis of macromolecules, energy transfer, respiration, and photosynthesis [180]. Most of the soil phosphorus is found in the form of insoluble phosphates and phytate, which cannot be used by plants [181]. Environmental factors are likely to affect the organization and efficiency of phosphate-solubilizing microorganisms, particularly under conditions [182], namely the efficiency of soil bio weathering of phosphates [183]. Recently it was highlighted that many microorganisms (_Pseudomonas, Ervinia, Achromobacter, Enterobacter, Flavobacterium, Servatia, Mycobacterium, Bacillus, Escherichia_, and _Agrobacteriumum_) improve their inorganic phosphate solubilization rates when NaCl is present in moderate concentrations [184]. However, this beneficial effect of moderate saline conditions is counterbalanced by the fact that salinity diminished the phosphorus adsorbed to soil particles and inhibits phosphate absorption by plant roots [185].  
Some PGPB are able to solubilize phosphate salts at high salinity levels (10% NaCl) [186]. It is possible that this efficiency of PGPB under high salinity is related to the fact that the production of organic acids responsible for phosphate solubilization (2-ketogluconic, gluconic, oxalic, acetic, citric, succinic, and malic acids), are also responsible for a local decrease of the alkaline pH usually associated with saline soils [187, 188]. For instance, the isolate _Pseudomonas stutzeri_ SGM-1 could grow at increasing salt concentrations up to 12% w/v NaCl with wide temperature and pH ranges. _Pseudomonas stutzeri_ SGM-1 exhibited diazotrophy by growing on nitrogen free media along with expression of other PGP traits such as phosphate solubilization. The amelioration of the salt stress to the _Cicer arietinum_ plant by isolate's bio-fertigation was observed up to 300 mM of NaCl [189]. A novel halo-tolerant strain _Kocuria rhizophila_ Y1 was isolated from maize rhizosphere soil. This strain tolerated up to 10% NaCl and showed growth promoting traits like phosphate solubilization [190].
## 4 Results
### Analysis of Temperature Distribution
Similarly, for laser powers of 18 W and 22 W, the maximum deviations are 67.3% and 37.2% at \(z\) = 7 mm and \(z\) = 8 mm, respectively, with corresponding minimum deviations of 9.4% at \(z\) = 5 mm and 3.5% at \(z\) = 4 mm. In contrast, for the \(\alpha\) = 0 kJ/kg model, the minimum deviation exceeds 40%, and the maximum deviation reaches up to 500%. Hence, the model with \(\alpha\) = 2260 kJ/kg proves to be more suitable for analyzing the temperature distribution during the interaction between the laser and the tissue. The temperature distribution at various time points, particularly when the duration reaches 10 s, is illustrated in Figure 8. Notably, as the laser continues to irradiate points (0,1) and (0,3) with power levels of 10 W and 14 W, the temperature exhibits a noticeable rise, as depicted in Figure 8. A detailed comparison and discussion of the observed differences are provided. The experimental results align closely with the model employing \(\alpha\) = 2260 kJ/kg, in contrast to the \(\alpha\) = 0 kJ/kg model. At \(t\) = 10 s, the maximum temperature is recorded at 506 \({}^{\circ}\)C and 256 \({}^{\circ}\)C for the \(\alpha\) = 0 kJ/kg and \(\alpha\) = 2260 kJ/kg models, respectively. It was observed that the temperature rises rapidly initially, followed by a gradual slowdown until reaching its peak upon the cessation of laser irradiation, particularly evident in the \(\alpha\) = 0 kJ/kg model. Furthermore, the temperature rise process exhibits greater fluctuation  
Figure 6.0 Temperature distribution along the axial direction, obtained from simulation data under two distinct conditions and experimental measurements. (**A**–**D**) \(P\) = 10 W, 14 W, 18 W, and 22 W.  
for the \(\alpha=2260\) kJ/kg model compared to the \(\alpha=0\) kJ/kg model. The maximum deviation is observed to be 37.8% at \(t=13\) s, with a minimum deviation of 3.4% at \(t=50\) s for the point (0,1). Similarly, for the point (0,3), the maximum deviation is 24.1% at \(t=14\) s, with a minimum deviation of 4.9% at \(t=10\) s. The discrepancy in curve shape can be attributed to the varying states of aggregation of water, upon which \(\alpha\) is based, influencing the energy absorption dynamics.
# Optimal Intrinsic Mode Function Based Detection of Motor Bearing Damages
## 5 Results and Discussion
### Discussion  
1. Simplify HHT spectrum: Comparing Figures 15a and 22a, the GEMD-HHT spectrum can be used easily to observe the damage of the bearing.
2. Classify damage types: The paper focuses on three types of gearing which are healthy, seal damage, and race surface damage. The main frequency of GEMD-HHT spectrums of the healthy, seal damage, and race surface damage types are 400, 350, and 200 Hz, respectively. The specific damage type can be detected easily by using the proposed GEMD model.
3. Realize damage range/size: The proposed GEMD model can realize the size of the race surface damage. We found that the sub-frequency bands of the 0.5 mm and 1.0 mm damage types are at 25-75 Hz and 20-100 Hz, respectively, no matter whether the damages are located on the inner, outer, or both, of the race surface. Thus, by using the proposed GEMD model, the severity of the damaged bearing can be realized by observing the sub-frequency signals.
# A Traceable Spectral Radiation Model of Radiation Thermometry
## 2 The Direct (Physical) Model of Radiation Thermometry  
### The Full System of Measurement  
In radiation thermometry, thermal radiation from objects is transferred to the radiation thermometer according to the physical laws of radiation. In measurement systems, influences of the environment are usually inevitable; therefore, these influences are taken into account and compensated for in the measuring instrument. These influences include reflection, transmission through the air (and possibly optics and filter) and absorption to the sensor surface (Figure 1).  
Radiation thermometer sensors measure the object's radiant heat flux to its surface. Conventional thermometers use thermal sensors that absorb radiation on an exposed, absorbing surface, and convert it to thermal radiation. This is the case with the most commonly used types of thermopile: MEMS and pyroelectric sensors.  
The temperature difference between the exposed and the unexposed side of the absorbing surface is measured and calculated to the incident heat flux, using characteristic functions of the sensor that are beyond the scope of this research and are considered properly calibrated and traceable. The self-emission of the sensor is considered mathematically correctly compensated within the scope of this research, as it is purely dependent on the temperature of the sensor.  
Figure 1: Physical system of measurement in radiation thermometry with key influence parameters. Physical quantities and their notation correspond to Equation (3).  
The output data of the sensor and the complementary input-output characteristic of the sensor constitute the incident radiance, which is assumed as compensated for natural heat dissipation, both through the case and through the optics. These influences are considered mathematically easy to compensate for, and are beyond the scope of this research.  
The incident radiance data of the sensor are later converted by another mathematical model (within the instrument) into the temperature of the radiation source, taking into account the inevitable influences of the environment and reflective properties of the object.  
The operation and behaviour of the latter model, which is the inverse of the physical model, is the subject of this article. It represents the opposite of the physical model of radiation transfer, described in Figure 1.
## 4 Multimodal Fusion for Infrastructure Inspection
### Applications in Practice
#### 3.4.3 Multilayer Analysis  
Multilayer environments are ubiquitous in real-world GPR applications, including geotechnical assessments, archeological prospection, and subsurface utility mapping. These  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Fusion Type** & **Sensors Used** & **Observed Benefits** \\ \hline gprMax + CNN & Physics + DL & Simulated GPR & Improved realism \\ GPR + RGB Fusion & Sensor-level & GPR + Camera & Better localization \\ PINN \(-\) GPR & Equation-informed & GPR & Physics-consistent results \\ \hline \hline \end{tabular}
\end{table}
Table 4: Overview of hybrid and multimodal GPR models.  
scenarios typically involve multiple strata with varying electromagnetic properties, causing signal attenuation, overlapping reflections, and complex hyperbolic patterns. Accurately interpreting such data remains a significant challenge for AI systems due to issues of generalization, ambiguity, and the need for representative training data.  
Recent studies have begun to address these challenges using tailored AI models and hybrid approaches. (1) Deep Learning with Stratified Labeling: Research works proposed CNNs trained on synthetically generated multilayer datasets with known material transitions [35,64]. These models learn to distinguish layer interfaces and buried objects even when hyperbolic signatures overlap. (2) Physics-Informed AI: Integrating prior knowledge of electromagnetic wave propagation has shown promise in improving robustness across varying layer properties. For example, [65] introduced a hybrid CNN + physics framework where model outputs are regularized using physical constraints related to permittivity and layer thickness. (3) Domain Adaptation for Generalization: To reduce the domain gap between simulated and real multilayer GPR data, researchers employed adversarial domain adaptation and style transfer to enhance generalization from synthetic to field-collected data [66]. (4) Multi-Task Learning: Some models perform simultaneous detection and stratigraphic segmentation, thereby encouraging the network to learn contextual features across depth layers [67]. (5) Transformer-Based Architectures: Recently, researchers explored transformer-based models for sequence modeling of GPR B-scans, allowing the network to retain long-range dependencies across multiple reflections--an advantage in complex stratigraphy [68].  
These advancements highlight that AI-driven multilayer analysis is an active area of research, and continued progress will depend on high-quality annotated datasets, physically informed learning objectives, and methods for robust domain generalization.
## 3 EM Techniques for CH Diagnosis
### X-Ray Radiography (XRR) and Computed Tomography (CT) in CH Applications  
X-rays penetrate solid materials according to their density. Images are produced by means of photographic plates or detectors placed on the opposite side of the irradiating source. The analysis provides morphological and physical data of the internal structure [123] and may reveal [99]:  
* presence and distribution of different materials;
* structural elements, fillers, and voids;
* joints, repairs, and internal damages.  
Examinations may be performed in situ but require temporary shielding and restrictions on access during the examination. Soft organic materials, such as textiles, do not offer enough contrast to be effectively captured in X-ray radiography images. CT (Computed Tomography) is an imaging method that uses multiple radiography images taken at different angles. It is expensive and requires considerable expertise, so it is rarely used.  
Practical Application of XRR to CH  
XRR is commonly related to medicine, but its first application in the CH field dates back to the beginning of the last century for the examination of paper, papyrus, and book structures. Currently, XRR is used to investigate the inner techniques of artists; it allows for the analysis of canvases and the discovery of previous conservation interventions and underdrawings [82]. The use of X-radiography for studying the internal structure of paintings is also addressed by [98], while in [124], digital XRR has been used to analyze the union of the head and pin of six metallic nails from the Bronze Age to detect density inconsistencies that may reveal details about the manufacturing technique. Additionally, in [125], XRR is mentioned as the preferred method to examine manufacturing processes and object conditions without physical contact. Figure 7 shows the XRR of a detail of the painting in Figure 8.  
IAEA underlines the fact that radiography, including both X-ray and gamma radiography, is well accepted by the conservation community for non-destructive analysis, particularly of eagle paintings, status, archaeological objects, and musical instruments [93].  
Figure 8: _Madonna with Child and the Holies Crescentino and Domino_, [PERSON]. The results presented are from the area in the white box. Credit [PERSON] et al. [126]. Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. No changes were made to the figure but to capture.  
Figure 7: _Madonna with Child and the Holies Crescentino and Domino_, [PERSON]. XRR of the area around [PERSON]’s face. Credit [PERSON] et al. [126]. Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. No changes were made to the figure but to capture.