esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Dark Current Measurement Based on Focal-Plane Temperature and Dark Current Correction Experiment
### Long-Wave QWIP Dark Current Noise Correction Experiment
The noise values before and after correction are calculated as:  
\[g_{\text{noise}}(CH,i)=\sqrt{\frac{1}{f-1}\sum_{F=1}^{f}\left[g_{\text{pixel}} (CH,i,F)-\overline{g}_{\text{pixel}}(CH,i)\right]^{2}}, \tag{17}\]  
\[\overline{g}_{\text{pixel}}(CH,i)=\frac{1}{f}\sum_{F=1}^{f}g_{\text{det}}(CH,i,F), \tag{18}\]  
where \(g_{\text{noise}}(CH,i)\) is the noise of a pixel, \(\overline{g}_{\text{pixel}}(CH,i)\) is the mean response of pixels in f frames, f is the total number of frames (f in this study is 1000), F is the frame serial number, and \(g_{\text{det}}(CH,i,F)\) is the response value of the pixels. The correction effect is shown in Table 5, and the noise values of the target pixels before and after correction using this method are calculated with Equations (17) and (18). The average reduction in temporal noise after correction was calculated to be 57.69%. The remaining noise that was not reduced consists of photonic noise of the background, several types of inherent noise in detectors (including Johnson noise, the generation of composite noise, 1/f noise, and scattered particle noise), the noise of the signal sampling carried out by the information acquisition circuit, and the temporal noise caused by the spectrometer's temperature change [31]. This method links the two ends of the response model. It is innovative to take a single datum and correct another mixed datum consisting of multiple components. Finally, the prospect of reducing the QWIP dark current noise research is described: (1) the variation in  
Figure 8: Response values before and after dark current correction. \begin{table}
\begin{tabular}{c c c c} \hline
**Pixel** & **Temporal Noise** & **Temporal Noise after** & **Noise Reduction** \\
**Coordinates/(CH, i)** & **before Correction** & **Correction** & **Ratio** \\ \hline \((1,10)\) & 23.74 & 10.02 & 57.79\% \\ \((9,160)\) & 22.15 & 9.41 & 57.51\% \\ \((2,310)\) & 22.23 & 9.32 & 58.07\% \\ \((17,10)\) & 22.13 & 9.43 & 57.38\% \\ \hline \end{tabular}
\end{table} 
Table 5: Dark current correction effect. the temperature measurement pin voltage is very small, so a better-performing ADC can be used to sample. (2) The focal-plane temperature data combined with the response of dark pixels for correction may be more suitable.
## 6 Description of Tests and Their Procedure
### Simple Regression Using RBF Networks  
The study was performed using network models created with the use of the Netlab library and was carried out on a set of arithmetic means of the values of the training patterns and the target represented in binary form. The data consisted of a vector of input (**X**) and a vector of output (**T**). RBF networks with three different activation functions were subjected to the learning process. The representation of the patterns has been changed for this series of tests. The input dataset is a vector with a range of integers from 0 to 1187 (number of musical notation symbol patterns). The target data were a vector containing:  
(a) in the first case, the average values of the distorted images of symbols in musical notation,  
(b) in the second case, the average values of the reference images of the musical notation symbols correspond to the number of the specific pattern.  
Series of studies were conducted to obtain the results for the 40 epochs, and the number of a single hidden layer = 100.  
The following activation functions were used to activate neurons: Gauss, spline and logarithmic. The arithmetic means of distorted values and standard symbols of musical notation were used as training patterns. As a result of the research, it was found that increasing the number of learning epochs did not significantly improve the network operation. On the other hand, increasing the number of neurons, the neural network parameters improved, despite the small number of learning epochs. Increasing both the number of epochs and the number of neurons led to over-parameterization, causing the value obtained from the function to diverge in relation to the value of the training patterns, which can be seen by observing Figures 13-16 (mainly Figures 15 and 16). The best generalization behavior was obtained using the Gaussian function as the neuron activation  
Figure 11: Plot of the density function [39].  
Figure 12: Density function plot for selected clusters [39].
# Effects of Water-Reducing Agents on the Mechanical Properties of Foamed Phosphogypsum
## 4 Conclusions  
(1) When the water-reducing agent content was increased, the strength of the foamed phosphogypsum first increased and then decreased and the dry density of the foamed phosphogypsum first decreased and then gradually increased. There was an optimal dosage range for water-reducing agent in phosphogypsum. The optimal content of FDN and PCE in this experiment was found to be 0.4%. Compared to PCE, FDN is a better match for the foamed phosphogypsum slurry;  
(2) The effect of the water-reducing agent content on the apparent viscosity and shear stress of the phosphogypsum slurries was in the order of 0% > 0.4% > 0.3% > 0.5% > 0.2% > 0.1%. The phosphogypsum slurry doped with 0.4% FDN had higher apparent viscosity and shear stress than the slurry mixed with 0.4% PCE;  
(3) FDN and PCE both enlarged the pore size distribution, increased the size and proportion of large pores, and decreased the total pore content of foamed phosphogypsum; however, the influences of PCE were found to be more significant. The foamed phosphogypsum slurry mixed with 0.4% FDN had the highest total pore content. Among the samples, the total pore content of foamed phosphogypsum A reached 91%, and the total pore content of foamed phosphogypsum B reached 77%. The lowest proportion of large pores was 17% for foamed phosphogypsum A and 7% for foamed phosphogypsum B;  
(4) The water-reducing agents mainly reduced the water consumption of the phosphogypsum slurries; improved the viscosity and shear stress of the slurries; influenced the stability of foam in the gypsum slurries; changed the pore size and distribution in the phosphogypsum slurries; and affected the strength and dry density of the foamed phosphogypsum samples. The viscosity of the gypsum slurry doped with 0.4% FDN better matched that of the foam, having the highest macro-strength and the lowest dry density.  
Conceptualization, J.Y.; methodology, J.Y. and H.W.; validation, J.Y. and H.W.; writing--original draft preparation, J.Y.; writing--editing, J.Y. and H.W.; review, H.Y. and J.G.; formal analysis, F.W. and H.Y.; data curation, F.W.; Chart processing, J.G.; funding acquisition, H.W. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the 14 th Five-Year \"National Key Research and Development Plan Project\" (Grant No. 2022 YFC3902704).  
Data Availability Statement: Data are contained within the article.  
We would like to express our heartfelt gratitude to China Building Materials Academy Co., Ltd. for providing the experimental instruments and workstations, as well as to [PERSON] and [PERSON] for their contributions to the experimental research.  
[PERSON], [PERSON] and [PERSON] were employed by the company China Building Materials Academy Co., Ltd. [PERSON] and [PERSON] that the research was conducted in the absence of any commercial or financial relationships.
## 3 Experimental Section
### Real-Time PCR Experiment  
Quantitative real-time PCR experiments were performed in an Applied Biosystems 7500 apparatus using the sequence-specific primer set (900 nM each), specific probes (200 nM each) (Table 1) labelled at the 5'-end with 6-carboxy-fluorescein (FAM) and at the 3'-end with tetramethylrhodamine (TAMRA), 0.5 \(\upmu\)L of the first strand cDNA and 12.5 \(\upmu\)L of 2X TaqMan Universal PCR Master Mix (Applied Biosystems, Foster City, CA, USA) in a total volume of 25 \(\upmu\)L. Negative controls were performed by omitting the template in the PCR reaction mix. Experiments were conducted in triplicate and using cDNAs obtained from at least two independent experiments. Real-time PCR cycles were as follows: 50 \({}^{\circ}\)C for 2 min (1 hold); 95 \({}^{\circ}\)C for 10 min (1 hold); 95 \({}^{\circ}\)C for 15 s, 60 \({}^{\circ}\)C for 1 min (40 cycles). Quantification of transcripts was carried out using the comparative quantitation module, as described for the ABI 7500 Sequence Detection System, (User Bulletin 2; Applied Biosystems)based on the \(2^{-\Delta\Delta\mathrm{C}}\) method [26]. The relative expression was normalized against \(\beta\)-actin and calculated using the untreated samples as a calibrator, whose expression was arbitrarily set to one.
## 2 Role of Ports in the Decarbonisation of Maritime Transport
### Key Regulations Driving Change  
The maritime industry is undergoing a major transformation, propelled by regulatory frameworks designed to reduce greenhouse gas emissions and promote sustainable  
Figure 1: Schematic diagram of marine port power supply system microgrid concept taken from [12].  
fuels. These policies set ambitious targets to enhance energy efficiency, implement stricter emissions standards, and develop infrastructure for alternative fuels. By expediting the adoption of cleaner technologies and ensuring compliance with evolving environmental requirements, these regulations are reshaping the future of maritime transport.
* International Maritime Organization (IMO): The IMO's Initial Strategy for Reducing GHG Emissions from Ships aims to cut emissions by at least 50% by 2050 compared to 2008 levels. The introduction of the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) regulations in 2023 enforces compliance with stricter efficiency standards [1].
* European Green Deal: The European Union (EU) has adopted the Fit for 55 legislative packages, including the Fuel EU Maritime Initiative. This initiative mandates a gradual shift towards alternative fuels and sets specific reduction targets for GHG emissions from shipping by 2030 and 2050 [5].
* Alternative Fuels Infrastructure Regulation (AFIR): The EU has proposed new infrastructure requirements for alternative fuel supply, ensuring ports are equipped with refuelling facilities for sustainable fuels like hydrogen, methanol, ammonia, and bio-LNG [13], among others.  
Although regulatory frameworks such as the IMO's Initial GHG Strategy, the EU's Fit for 55 legislative packages, and the Alternative Fuels Infrastructure Regulation are driving the maritime sector's shift toward decarbonisation, this research focuses primarily on the technical dimension of that transition--specifically, the role of met-ocean modelling tools in supporting feasibility studies for offshore renewable energy systems in port environments. Therefore, while these policies provide essential context, the discussion remains at a general level to maintain the main scope on methodological approaches and environmental data analysis rather than detailed regulatory evaluation.  
Following its April 2025 meeting, the International Maritime Organization (IMO) adopted a revised GHG reduction strategy that sets a firm target of achieving net-zero greenhouse gas emissions from international shipping by 2050. The updated strategy includes strengthened intermediate checkpoints--at least a 30% reduction by 2030 and 80% by 2040 compared to 2008 levels--and emphasizes the development and uptake of zero- and near-zero emission fuels, as well as the implementation of lifecycle-based fuel standards and a global pricing mechanism for maritime emissions [14]. Meanwhile, the EU's Emissions Trading System (ETS) has been extended to maritime transport, creating financial incentives for emissions reductions [15].
# Differences Research on Human Overall Comfort Under Low Pressure
## 2 Method  
### Human Overall Comfort Evaluation Method  
Under standard atmospheric and low-pressure conditions, human comfort is affected by the thermal, luminous, and acoustic environment [38]. In the evaluation process, a relatively concise evaluation approach involves the following: (1) making the evaluation indicators dimensionless through a specific method and (2) weighting them into a comprehensive evaluation value using a synthesis model. This constitutes the overall evaluation method based on the effective function.  
Compared with other overall evaluation methods, the effective-function method has two main characteristics. First, it presents the conclusions intuitively, which facilitates the evaluation and analysis of human overall comfort. Second, its evaluation process is direct for each component without cross information interference and offers multiple selectable methods for each component. Since thermal comfort, luminous comfort, and acoustic comfort reflect different physiological mechanisms in response to the surrounding environment, it was therefore reasonable to establish an overall comfort evaluation model using the effective-function method. Meanwhile, the practical application examples of the effective-function method at home and abroad are also worthy of reference [39; 40; 41].  
The effective-function expression of the overall evaluation method is given by Equation (1):  
\[C=\xi(y_{\mathrm{i}},\omega_{\mathrm{i}}/\mathrm{i}=1,2,\cdots,\mathrm{n}) \tag{1}\]  
where \(C\) is the human overall comfort evaluation value; \(\xi\) is the synthesis model of \(y_{\mathrm{i}}\) and \(\omega_{\mathrm{i}}\); \(y_{\mathrm{i}},=(\mathrm{x_{i}})\) is the single factor dimensionless value; \(x_{\mathrm{i}}\) is the single factor variable; and \(\omega_{\mathrm{i}}\) is the single factor weight value. The specific description of the symbols can be found in Appendix A.  
\(\xi\) represents the synthesis model of evaluation indicators, meaning that it uses a mathematical function to combine multiple evaluation indicators into a single overall evaluation indicator. Moreover, the physical meaning of the individual indicators must be consistent with that of the overall evaluation value. \(\omega_{\mathrm{i}}\) denotes the weight coefficient. Since each indicator's influence on human overall comfort varies in importance, the quantitative value of each indicator's significance must be determined through an appropriate weighting method. Thermal comfort, luminous comfort, and acoustic comfort exhibit different physiological responses to the indoor environment, requiring different methods to obtain their dimensionless values \(y_{\text{i}}\).  
Therefore, when applying the effective-function method to evaluate human overall comfort, three components need to be determined: (1) the single-factor function \(f_{\text{i}}\), (2) the weight value \(\omega_{\text{i}}\), and (3) the synthesis model \(\xi\).
## 1 Introduction
Against this background, we propose HawkEye, an artificial intelligence (AI)-based software platform designed to optimize the metallographic analysis of nodular cast iron. Our system integrates advanced CV techniques and DL models to automate graphite morphology classification, microstructural phase segmentation, carbide detection, and metallurgical quality assessment. HawkEye software v2024.12 includes a dynamic image type classification module that adapts the workflow depending on the image. This way of working improves processing accuracy and resource efficiency. All components are unified via a user-centric interface, which is suitable for laboratory use and also compatible with automated inspection pipelines. The main contributions of this work are as follows:  
* A complete and adaptive analysis pipeline for both etched and non-etched images, enabling robust image type classification and workflow adaptation. * Integration of DL models for accurate segmentation and classification of graphite nodules and microstructural phases such as ferrite, perlite, and carbides. * A modular and user-friendly software interface supporting both manual operation and integration with automated metallographic analysis systems. * A quality evaluation framework that translates image features into metallurgical classifications aligned with international standards. The remainder of this paper is organized as follows. Section 2 describes the work and the steps undertaken to achieve the proposed solution. Additionally, this section discusses data generation, the methodology, and the development of the software system. Section 3 presents the results achieved with the proposed approach. Here, we introduce the achieved results for several configurations, as well as the final solution created. Finally, Section 4 concludes this paper and the research, providing a discussion of the solution and suggesting future work and potential improvements to our proposal.
## 4 Analysis
### Complexity Analysis  
We evaluate the computational complexity of the proposed RIS at each iteration. Let \(N\) represent the dimension of localization. Calculating \(\mathbf{a}_{i}^{(k)}\) requires \(2N^{2}+2N+2\) multiplications and \(N^{2}+3N-2\) additions, while \(b_{i}^{(k)}\) involves \(3N+1\) multiplications and \(4N+3\) additions. The computation of \(\widetilde{\mathbf{X}}_{\mathbf{\mu}}^{(k)}\) demands \(M(N^{2}+N)\) multiplications and \(MN^{2}+N^{2}\) additions. The inversion of \(\widetilde{\mathbf{X}}_{\mathbf{\mu}}^{(k)}\) has a complexity of \(O(N^{3})\). Equation (65) necessitates \(MN+2N^{2}+M\) multiplications and \(N^{2}+M-N\) additions. Consequently, the overall computational cost per iteration of our method is \(O(MN^{2})\). This complexity is comparable to that of the Gauss-Newton iteration, which is also \(O(MN^{2})\).
# Blockchain-Based Lightweight Trust Management in Mobile Ad-Hoc Networks
## 3 Proposed Solution
### Delegation Process  
Based to the dynamic and limited features of MANETs, even though a node is chosen as the validator, there could be the time when it cannot do the block generation process. Therefore, we consider a delegation process for the validator node that is influenced by the delegated proof-of-stake (DPoS) algorithm, where the validator from the PoS mechanism can vote on whether or not other nodes should be trusted as delegates. Similarly, in the proposed scheme, the original validator can select a delegated node to perform the role of the original validator on its behalf. If the validator node is subjected to the following conditions, the delegation process will occur. Otherwise, it will be put in blacklist and cannot get the chance to join this network anymore.  
* **Moving away from the network**: If the validator node is moving away from the network, it must elect another delegate node to act as a new validator node. Based on Equation 6, the validator node can determine when it must elect a delegate node. \[n+d<t\_range,\] (6) where \(n\) is the number of neighbors, \(d\) is the distance between each neighbor, and \(t\_range\) is the transmission range.  
* **The validator node is a very rich node**, meaning it has been the MPR node for a long time and accumulated a very high TV (its trust value is already one, which is the maximum value). Therefore, it should give opportunities to other trusted nodes by delegating the block generation process.
* **The validator node is in power-saving mode**. In this case, the validator node does not have sufficient energy to perform additional tasks.  
This is when the voting node for the original validator will receive a reward. When choosing a delegate, the validator node will select the node with the highest TV in its neighborhood. However, to give more chances to its original voter, the validator node will temporarily increase that voter's TV based on Equations 1 and 3. The TV will be increased to the same value as that of the MPR node. Based on this temporarily increased TV, if the original voter becomes the delegate, the temporary TV will be given as a reward for performing blockchain processes. Otherwise, it can only get a portion of the reward for voting for the original validator node.  
The demonstration of how to select the validator and how it could delegate to another node is shown in Figure 7. Among the validator claim messages broadcasted by every node in the network, according to node E and F, D has the highest trust value. Even though D is chosen as the validator node, since it is a rich node with trust value \"1\" it has to perform the delegation process consequently. Among its neighbors, B, E, F and G, it compares their trust values. However, for node E and F, the ones which claimed it as the validator, it compares their value by adding the temporary value, 0.5 to them. Node F with the highest trust value among D's one-hop neighbors becomes the delegate to create the block.
# Routing Protocols for UAV-Aided Wireless Sensor Networks
## 2 Communication Technologies for UAWSNs  
A UAWSN consists of several components such as the UAV, sensors, and ground station. In a UAWSN, different kinds of communications are utilized, such as communication between sensor and cluster head, inter-UAV communication, and communication between the UAV and ground station. In UAWSNs, it is very important to provide survivability and reliability through redundant communications. Significant packet drop may happen because of link failure between the UAV and sensor nodes. In UAV networks, IEEE 802.11 medium access control (MAC) protocols are widely used for the communications between UAV and the ground station because they are suitable for long-range communication and can handle higher bandwidth with high data rate. In inter-UAV communications, the link quality fluctuates because of the high mobility of UAVs, dynamic topology change, and communication distances between UAV nodes.  
For the above-mentioned three kinds of communication links, different protocols are used. In Table 1, various communication technologies used in UAWSNs for different applications are summarized and compared based on physical characteristics and communication parameters at the physical and data link layers. In UAWSNs, the two categories of communication technologies are employed, which are wireless personal area networks (WPANs) [21; 22; 23] and wireless local area networks (WLANs) [24; 25; 26; 27; 28; 29; 30].  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
## 3 Results
### Effects of CWP on Concrete Microstructure  
Table 6 summarises the research findings on the effects of CWP on the concrete microstructure using various testing methods. Using methods such as Scanning Electron Microscopy (SEM), Energy Dispersive X-ray Spectroscopy (EDS), X-ray Diffraction (XRD), Thermogravimetric Analysis (TGA), and the Frattini test, researchers have explored how CWP enhances the microstructure of concrete. Key observations include reduced CH content due to pozzolanic activity, increased C-S-H gel formation, and improved densification of the interfacial transition zone (ITZ). The findings demonstrate the potential of CWP to refine the pore structure and contribute to stronger and more durable concrete.