esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion and Conclusions  
This study introduces a new technique to boost the quality of industrial CT image reconstruction by combining lightweight attention-enhanced super-resolution networks with Radon-domain artifact suppression. Results from experiments show that the suggested method significantly improves PSNR and SNR metrics while preserving important structural details and successfully reducing metal artifacts, particularly in high-density areas.  
Radon-domain processing, in contrast to traditional image-domain correction methods, makes use of the fundamental physics of artifact creation to produce more focused and efficient suppression. The structure of the super-resolution module is simple, consisting of a single residual block enhanced by a channel attention mechanism. Because of its low computing complexity and improved feature representation, this architecture makes the approach ideal for use in industrial settings with limited resources.  
Nevertheless, there are certain drawbacks to the existing approach. Slice-wise 2D processing may introduce inter-slice inaccuracy in volumetric reconstructions, and it may have trouble generalizing when faced with extremely varied or irregular artifact patterns. Future research will concentrate on expanding the architecture to three-dimensional networks for better volumetric consistency and implementing adaptive artifact modeling techniques in the Radon domain in order to overcome these problems. Furthermore, using spectral CT data will facilitate material fusion and decomposition, improving the model's performance on intricate, multi-material components.  
In summary, the suggested method shows scalability and practical efficacy in eliminating metal artifacts from industrial CT images while maintaining structural integrity. The technique opens the door for wider uses in nondestructive testing and digital inspection of intricately engineered parts by offering a viable approach for high-quality and deployable CT reconstruction.  
Conceptualization, H.L.; methodology, B.W.; software, Z.Z.; writing--original draft preparation, B.W.; writing--review and editing, Z.Z.; visualization, R.W.; funding acquisition, Z.Z. All authors have read and agreed to the published version of the manuscript.  
The authors gratefully thank the Science & Technology Program of Hebei [No. 246Z1002G] for its support.  
Not applicable.  
The data presented in this study are available on request from the corresponding author. And the data are not publicly available due to privacy.  
We gratefully thank each of the authors of the references and acknowledge the use Quillbot software v4.4.15 to check for grammar errors.  
The authors declare no conflicts of interest.
## 3 Research Method
### Sensor Arrangement  
The variables in the system can be divided into the known variables and the unknown variables. The known variables used as input for data reconciliation include two types: characteristic data and measurement data.  
Characteristic data are generally based on characteristic parameters of the equipment or design values of system operation because of their relative stability in most situation. The characteristic data used in this paper are shown in Table 1.  
For the measurement data, it is necessary to arrange sensors in each part of the whole system. There are three types of sensors: pressure, temperature and mass flow. The arrangement of sensors in this paper is shown in Table 3. It can be seen that three types of sensors are mainly arranged around the S-CO\({}_{2}\) bottom cycle, while some sensors in the gas turbine cycle mainly supplement some necessary data and provide little redundancy for data reconciliation. There are more temperature sensors than sensors for pressure and mass flow, and several temperature sensors are arranged in one stream of waste heat recovery system in some cases.  
The measurement uncertainty represents the reasonable distribution of the measured value under a specific probability. It can quantify the possible deviation of the measured value. It should be noted that the uncertainty here is not equivalent to the uncertainty level of the measurement instrument, but is only used in data reconciliation calculations. It is similar to a weight coefficient, reflecting the uncertainty of the data before and after the reconciliation.  
For each pressure, temperature and mass flow sensor, there is a certain uncertainty. Even small fluctuations in environmental conditions will affect the measurement performance and lead to deviations. In general, the measurement uncertainty can be empirically assumed as either the instrument accuracy or the maximum permissible deviation in practice [28]. In this paper, the specific assumptions of uncertainty are as follows: the absolute uncertainty is taken as 2 \({}^{\circ}\)C for temperature sensors and parameters, the relative uncertainty is taken as 2% for pressure sensors and parameters, the relative uncertainty is taken as 5% for the mass flow sensors and parameters and the relative uncertainty is taken as 10% for the characteristic parameters such as ratio and efficiency.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Stream Number** & **Pressure Number** & **Temperature Number** & **Mass Flow Number** \\ \hline Stream 1 & & TS01 & \\ Stream 2 & & TS02 & MFS01 \\ Stream 3 & PS01 & TS03 & \\ Stream 4 & PS02 & TS04 & \\ Stream 5 & PS03 & TS05 & MFS02 \\ Stream 6 & & TS06 & \\ Stream 7 & & TS07 & \\  & & TS08 & \\ Stream 9 & & TS09 & \\  & & TS10 & \\ Stream 10 & & MFS03 \\ Stream 11 & & TS11 & \\ Stream 13 & & TS12 & MFS04 \\  & & TS13 & \\ Stream 14 & PS04 & TS14 & \\  & & TS15 & \\ Stream 15 & PS05 & & \\ Stream 19 & & TS16 & \\  & & TS17 & \\  & & TS18 & \\ Stream 20 & & MFS05 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Arrangement of sensors in different stream of waste heat recovery system.
# Biological Methods in Biodiesel Production and Their Environmental Impact
## 5 Conclusions  
The transport sector is one of the largest greenhouse gas emitters in the world. For this reason, there has been a need to find environmentally friendly solutions, including in technologies of the production of alternative fuels', especially from renewable plant resources. One such fuel is biodiesel, which is produced by the method of transesterification of fats with different alcohols and in the presence of different catalysts. The traditional methods of biofuel production with the use of acid and base catalysts were under strong investigation and considered as close to their limits, and are currently under marginal development. Moreover, transesterification with these catalysts has some disadvantages, which can be overcome by replacing them with biological ones. The use of an enzymatic catalyst enables to carry out the process at lower temperatures and with the use of raw materials of lower quality (containing free fatty acids). Other significant advantages are: increased reaction efficiency, reduced product and glycerol separation costs, and the possibility to work under different environments (aqueous and nonaqueous). The lower temperature of the enzymatic process and lower cost of separation of the catalyst and the final products translate into lower GHG emissions compared to a chemically catalyzed process. This is associated with the need to provide less heat to the process.  
Unfortunately, despite numerous advantages, the wide application of the enzymatic biodiesel production method is still limited due to disadvantages such as sensitivity of enzymes to inhibitors (methanol, glycerin) or high costs of enzyme production. Therefore, the use of the biological method of biodiesel production on a larger scale requires further work to minimize the disadvantages associated with the process. For this reason, in order to improve the qualitative and quantitative performance and economic efficiency of the process, it is necessary to continue research, e.g., in the area of new enzymes and their sources, selection of solvents, or optimization of process conditions.  
Biocatalytic transesterification is a promising technology for biodiesel production. After reducing the process barriers, this technology will be able to compete with the chemical catalysis methods and will be universally applicable.  
Conceptualization, K.B., A.M., I.S.-B. and M.O.; methodology, A.M., I.S.-B. and M.O.; writing--original draft preparation, A.M., I.S.-B. and M.O.; writing--review and editing, K.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods
### Materials
#### 2.1.5 Monomers  
_Methacrylic acid (MAA)_ produced by \"Fluka Chemicals\" (Taufkirchen, Germany) was used without pre-treatment with a purity \(
# Satellite Remote Sensing of Surface Urban Heat Islands: Progress, Challenges, and Perspectives
## 6 Research Challenges
### Impacts of Clouds and Other Factors on LST Data
In addition, even though the cloudy pixels have been removed, cloud effects on the adjacent non-cloud pixels still exist and will impact LST interpolation and downscaling methods [207]. New developments for improved approaches or the trade-off between efficiency and accuracy of the existing methods is still needed.
# Indoor Localization System Using Fingerprinting and Novelty Detection for Evaluation of Confidence
## 3 LocFiND--Localization Using Fingerprinting and Novelty Detection
### Hybrid Approach: Combining Prediction Probability with Novelty Detection  
The previous Sections 3.1 and 3.2 are more reliable and accurate but, as we show in Section 4, it discards and ignores a high number of position estimations, leaving the system with little position information. Thus, in this section, we propose a new technique called Hybrid Approach. We call it hybrid because it combines the confidence assessment of the two previous configurations, each one being applied to a threshold used in different steps and techniques. The first threshold is a probability estimate, while the second is a novelty factor. Thus, this hybrid approach combines the first and second approaches.  
To understand the hybrid approach, we need to first understand the behavior of the probability threshold \(\alpha\). The lower the probability threshold, the higher the classification errors, resulting in a higher average localization error. However, in this case, we also have a higher number of position estimations, which is good for the application. If we increase this threshold, we improve accuracy but also reduce the number of accepted classifications, leaving the system without enough position estimations.  
In the hybrid approach, we initially use the same probability threshold \(\alpha\) as in the previous sections. However, instead of simply rejecting the classifications that do not pass this filter, we use novelty detection to give a second chance for some of these classifications that would be discarded otherwise. For this, we use a second, lower probability threshold \(\beta\) and use novelty detection to confirm the classifications above this second threshold.  
The result of this decision process, which is depicted in Figure 5, is that classifications with high prediction probabilities (threshold \(\alpha\)) are not checked by the novelty detection. Classifications with lower prediction probabilities, but still higher than the threshold \(\beta\), will be confirmed by the novelty detection. Finally, classifications with prediction probabilities lower than \(\beta\) or that did not pass the novelty check, are discarded.  
Figure 4: Evaluation of confidence using the novelty detection technique: accuracy is improved by further discarding unreliable classifications.  
Figure 5: Evaluation of confidence using the hybrid approach: a compromise between accuracy and number of position estimations.
# Electromechanical Behavior of Afyonkarahisar Clay Under Varying Stress and Moisture Conditions
## 3 Test Results and Discussions
### Sensitivity Assessment
\[c_{u}=-1102.2\cdot(w/w_{L})+712.4, \tag{12}\]  
Figure 10 illustrates the correlation between electrical conductivity and the measured uniaxial compressive strength (\(q_{u}\)) at different levels of relative water content (\(w/w_{L}\)). Figure 8: Shear strength (\(c_{u}\)) vs. electrical conductivity (_EC_). Figure 9: Undrained shear strength (\(c_{u}\)) vs. relative water content (\(w/w_{L}\)). The results demonstrate that electrical conductivity, which increases with higher moisture content, is inversely related to the uniaxial compressive strength of the clay. As relative water content rises, the electrical conductivity of the soil increases due to enhanced ionic mobility and pore water connectivity, while the compressive strength decreases due to the softening and loss of interparticle cohesion. The correlation is nonlinear and reflects the complex interaction between the soil's hydraulic and mechanical properties (see Equation (13)). This relationship suggests that electrical conductivity measurements could potentially serve as a proxy for estimating strength parameters in situ, especially in conditions where direct mechanical testing is not feasible. However, the variability introduced by different moisture levels must be carefully considered in predictive models. \[\text{EC}=-0.3543\cdot(\text{w}/\text{w}_{\text{L}})^{2}+0.4617\cdot(\text{w}/ \text{w}_{\text{L}})-0.055, \tag{13}\]  
The results also show that the relationship between electrical conductivity (\(EC\)) and relative water content (\(\text{w}/\text{w}_{\text{L}}\)) is not linear but follows a decreasing trend. This indicates that as relative moisture increases, the electrical conductivity initially rises but eventually stabilizes or declines at a slower rate, reflecting saturation effects and changes in ion mobility at high water contents. For practical applications, a more suitable correlation is provided in Figure 11, which presents an exponential equation (see Equation (14)) fitted to the experimental data. This empirical model offers improved predictive capability for estimating conductivity based on moisture content, particularly in field conditions where accurate and rapid assessments are required. \[w=6.7\cdot e^{15.342\cdot EC}, \tag{14}\]  
The results confirm that the use of relative humidity \(\text{w}/\text{w}_{\text{L}}\) is crucial for a realistic assessment of clay strength under different hydrological conditions. The empirical nonlinear relationship allows the engineer to easily assess the safety state even under variable conditions. The use of index consistency, together with laboratory data, allows for more reliable prediction of soil behavior. Figure 10: Electrical conductivity (\(EC\)) vs. relative water content at max. Uniaxial compression stress.
## 5 Conclusions and Future Works  
In this paper, we propose a new scheme for autonomous monitoring of non-cooperative object behavior in civil aviation airport scenarios, which provides new ideas for the application of abnormal behavior detection algorithms in specific industrial areas. An NC-ABLD framework for the localization and detection of abnormal behaviors of typical non-cooperative targets in airport areas has been developed through the design of three modular networks ranging from coarse to fine-grained. In addition, an IIAR-30 airport video dataset is proposed on which the overall performance of NC-ABLD is validated, and a dataset contribution is provided for subsequent studies targeting specific industrial areas. The NC-ABLD framework achieves 67 FPS on the GPU-3090 ti and 15 FPS on the edge-side device Jetson-Orin -AGX inference despite having a high computational complexity of 98.7 GFLOPs. As Table 5 shows, the average error of the temporal node localization of abnormal behavior in the field is 0.4 s, which is almost equivalent to the ability of human eyes to detect abnormal behavior.  
Although the framework performs well in handling detection in light-occlusion situations, there are still some limitations. For example, when the human body is highly occluded or the shape of the occluder is similar to that of the target object, the model may fail to recognize the target correctly or produce incorrect detection results. In addition, our model detects poorly in low-light environments. In future work, we will also explore the use of infrared cameras to improve the performance of the system in low-visibility conditions, thereby increasing its robustness and reliability in a variety of operational situations.  
[PERSON] searched and processed experimental data and wrote the code. [PERSON] wrote the paper and analyzed the data. All authors have read and agreed to the published version of the manuscript.  
Figure 15: Visualization of experimental results for single versus multi-person abnormal behavioral scenarios.  
**Funding:** This work was supported by the National Natural Science Foundation of China (No. U2033201), the Fundamental Research Funds for the Central Universities (No. NJ2023029), Open Project Funds for the Key Laboratory of Space Photoelectric Detection and Perception (Nanjing University of Aeronautics and Astronautics), Ministry of Industry and Information Technology (No. NJ2023029-2), and Aeronautical Science Foundation of China (No. 20220058052001).  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** This dataset can be obtained by contacting the corresponding author by email.  
**Conflicts of Interest:** The authors declare no conflicts of interest.
# Air Entrainment and Air Demand in the Spillway Tunnel at the Jinping-I Dam
## 4 Discussion
### Estimation Method of Air Demand
#### 4.2.1 The First Type of Formula
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Project**} & \multirow{2}{*}{\(q_{\text{w}}\) **(m\({}^{3}\)/s-m)**} & \multicolumn{4}{c}{**Aerator**} & \multirow{2}{*}{**Air Duct**} & \multirow{2}{*}{\(K\)} & \multirow{2}{*}{\(\overline{K}\)} \\ \cline{3-4} \cline{6-7}  & & & **Location** & & & & \\ \hline Shitouhe & 3.49–31.67 & water-conveyance tunnel outlet & 0.00 & 0.35 & sym & 0.025–0.037 & 0.034 \\ Baishan & 25.50–70.17 & surface spillway aerator & 0.00 & 1.66 & sym & - & 0.024 \\ Jinping-I & 112.4–246.1 & spillway tunnel No. 2 aerator & 0.40 & 1.50 & sym & 0.037–0.045 & 0.041 \\ Fengman & 6.53–26.67 & spillway dam aerator & 0.20 & 0.00 & asym & 0.010–0.011 & 0.010 \\ Guri & 6.89–150.0 & spillway dam chute 1 lower aerator & 0.25 & 2.84 & sym & - & 0.026 \\
15.29–120.0 & spillway dam chute 2 lower aerator & 1.50 & 2.20 & sym & - & 0.073 \\ \hline Fengjiashan & 27.08–76.1 & spillway tunnel upper aerator & 0.60 & 0.00 & sym & 0.019–0.021 & 0.020 \\  & & spillway tunnel lower aerator & 0.30 & 0.00 & sym & 0.025–0.029 & 0.028 \\ \hline \multirow{4}{*}{Foz do Areia} & \multirow{4}{*}{7.58–46.74} & spillway No. 1 aerator & 0.20 & 0.00 & sym & 0.026–0.034 & 0.030 \\  & & spillway No. 2 aerator & 0.15 & 0.00 & sym & 0.031–0.036 & 0.033 \\  & & spillway No. 3 aerator & 0.10 & 0.00 & symmetric & 0.026–0.035 & 0.030 \\ \cline{2-7}  & & spillway No. 1 aerator & 0.20 & 0.00 & asym & 0.019–0.021 & 0.020 \\  & & spillway No. 2 aerator & 0.15 & 0.00 & asym & 0.022–0.030 & 0.026 \\ \hline \multirow{4}{*}{Wujiangdu} & 51.85–163.1 & left ski-jump spillway upper aerator & 0.61 & 0.00 & asym & 0.012–0.015 & 0.013 \\  & & left ski-jump spillway lower aerator & 0.85 & 0.00 & sym & 0.015–0.021 & 0.017 \\ \cline{1-1}  & & right ski-jump spillway lower aerator & 0.85 & 0.00 & sym & 0.013–0.016 & 0.015 \\ \cline{1-1}  & & 52.15–164.6 & No.2 spillway opening aerator & 0.85 & 0.00 & sym & 0.014–0.020 & 0.015 \\ \cline{1-1}  & & 38.89–197.8 & left spillway tunnel retardator & 0.38 & 0.00 & sym & 0.012–0.025 & 0.020 \\ \cline{1-1}  & & 50.33–226.7 & right spillway tunnel upper aerator & 0.29 & 0.00 & sym & 0.033–0.036 & 0.034 \\ \hline \hline \end{tabular} Notes: \(\overline{K}\) is the average value of coefficient \(K\) ; \(\overline{\chi}\) “sym” is the abbreviation of symmetric and “asym” is the abbreviation of asymmetric.  
\end{table} 
Table 5: The coefficient \(K\) derived from prototype observation of some projects.
# Radiation-Assisted Synthesis of Polymer-Based Nanomaterials
## 2 Carbon-Based Nanomaterials  
The basic constituent of these nanomaterials is carbon in the form of carbon nanotubes (CNT), graphene and fullerenes. One of the major problems associated with this all-carbon nanomaterial family is their poor dispersion in aqueous and even organic media due to their hydrophobic and fused aromatic structures. The formation of agglomerates, clusters of these carbon-based nanomaterials limits their use for some applications such as in biomedical devices, drug delivery platforms, etc. CNTs, graphene and fullerenes are generally used as nanofiller for the preparation of polymer nanocomposites to achieve superior properties. However, their poor compatibility with commodity and engineering polymer matrices prevents these materials from achieving their full potential. To overcome these problems, surface modification of carbon nanomaterials by covalent and noncovalent functionalization has been elaborated [4]. Covalent functionalization of these fully aromatic compounds is best achieved via reactions to be initiated by irradiation with gamma rays or accelerated electrons.  
Pre-irradiation of CNTs with gamma rays can be applied to disintegrate the agglomerates and disperse them into a polymer matrix and to maintain stable dispersions instead of using a dispersant or reducing agent [5]. Chemical functionalization and subsequent dispersion were better achieved for the irradiated CNTs as compared to pristine CNTs. Enhanced interaction between CNTs and polymer matrix help in uniform dispersion improving the electrical and mechanical properties of corresponding composites. Fullerene was shown to be functionalized with -OH groups by gamma irradiation in methanol solution for biosensor applications [6]. Water-soluble poly(acrylic acid) grafted multiwalled carbon nanotubes (MWCNTs) were prepared by two-step gamma irradiation [7]. Solution processable reduced graphene was prepared first by radiation-induced reduction of graphene oxide later grafted with poly(vinyl pyrrolone) by pre-irradiation technique [8]. Green synthesis of water-dispersed graphene nanosheets was achieved by gamma irradiation and using natural capping agents [9]. The surface of CNTs was functionalized by radiation-induced grafting of acrylic acid, methacrylic acid, glycolidyl methacrylate, maleic anhydride and 4-vinylphenylboronic acid [10]. The CNT/polymer nanocomposites were shown to be successfully prepared by radiation curing using epoxy resins [11]. These works and many other clearly show that ionizing radiation is a very useful tool to functionalize CNTs, graphene and fullerene to make them compatible with a variety of polymer matrices for the preparation of nanocomposites.