esa-sceva/satcom-chunk-collection huggingface dataset:
_applied_  
_sciences_  
_Article_  
Prediction and Optimization of Matte Grade in ISA Furnace Based on GA-BP Neural Network  
**[PERSON] 1\({}^{\copyright}\)**, **[PERSON] 1\({}^{,*}\)**, **[PERSON] 2\({}^{,*}\)**, **[PERSON] 1**, **[PERSON] 1** and **[PERSON] 1**  
1 Faculty of Metallurgical and Energy Engineering, Kunming University of Science and Technology,  
Kumming 650093, China  
2 Yunnan Copper Industry Co., Ltd., Kunming 650102, China  
*Correspondence: [EMAIL_ADDRESS] (D.Z.); [EMAIL_ADDRESS] (D.L.)  
**Abstract: The control of matte grade determines the production cost of the copper smelting process. In this paper, an optimal matte-grade control model is established to derive the optimal matte grade with the objective of minimizing the cost in the whole process of copper smelting. This paper also uses the prediction capability of the BP (Backpropagation) neural network to establish a BP neural network prediction model for the matte grade, considering various factors affecting matte grade (including the input copper concentrate amount and its composition content, air drumming amount, oxygen drumming amount, melting agent amount, and other process parameters). In addition, the paper also uses the optimal matte grade to optimize the dosing, air supply/oxygen supply, and oxygen supply for the ISA and other furnaces. When using BP networks only, it is a nonconvex problem with gradient descent, which tends to fall into local minima and has some bias in the prediction results. This problem can be solved by optimizing its weights and thresholds through GA (Genetic Algorithm) to find the optimal solution. The analysis results show that the average absolute error of the simulation of the BP neural network prediction model for ice copper grade after GA optimization is 0.51%, which is better than the average absolute error of 1.17% of the simulation of the single BP neural network model.**  
**Keywords: pyrometallurgical process; ISA furnace; matte grade; lowest cost; BP neural network prediction model; GA optimization; process optimization**
###### Abstract  
In this study, we propose to define a connectivity factor as the inverse of the diffusional tortuosity to measure quantitatively the connectivity of whatever type of structure. The concept of connectivity used here is related to the diffusional accessibility of the structure voids. This definition of connectivity factor arises from the consideration that, if we ideally imagine to decrease progressively the porosity of a regular structure, the porosity itself reaches a limit value below which the inner pores are not interconnected anymore. This leads to an evident situation of _zero connectivity_ and _infinite tortuosity_, where there is no continuous diffusion path able to connect the structure voids. According to the proposed definition, the connectivity factor is comprised within [0, 1], with zero corresponding to a completely disconnected structure and unity to a completely connected one. To show the efficacy of the presented approach, a case study on the regular structure of mono-sized (mono-disperse) spherical particles (Simple Cubic (SC), Face-Centred Cubic (FCC), Body-Centred Cubic (BCC) and Tetragonal structures) is provided. In particular, the tortuosity of such structures is evaluated by Computational Fluid Dynamics simulations, calculating the connectivity factor consequently. The morphological modification with porosity is induced by changing the surface-surface interparticle distance, allowing us to take both positive (detached particles) and negative values (overlapping particles). For each structure, a comparison between the calculated trends and some correlations of literature is made, and a novel _\"hidden\"_ morphological parameter has been identified, that is, the here-called _Limit Porosity Value_, below which the connectivity is zero. The presented approach represents a systematic methodology to quantify the connectivity of any structure and to compare the morphology of membranes, catalysts, and porous media in general.  
connectivity; tortuosity; topology; morphology; particles; structures +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Measurements
#### 2.3.9 Oxygen Permeability  
The oxygen permeability was measured as follows: an edible film is sealed between two specially designed metallic cups, each of which has a diameter of 6 cm and a depth of 3 cm. Both cups have two channels. Oxygen enters the lower cup from the down left entrance and exits from the down-right channel of the same cup. A stream of nitrogen enters the upper cup from the up-right entrance and comes out from the up-left exit of the same cup. The nitrogen acts as a carrier that transfers the oxygen permeating through the film (from its one side to the other) into a wet analysis system. The design of this system is mainly based on iodimetry according to the ASTM D3985-05 [41] standard. It consists of a conical flask containing an aqueous manganese II sulphate and alkaline iodide solution [42]. The gas mixture (N\({}_{2}\) and permeated O\({}_{2}\)) passes through the wet system for a specific time period and, in the presence of oxygen, manganese II hydroxide is, rapidly and quantitatively, converted to manganese III hydroxide by the following reaction:  
4 Mn(OH)\({}_{2}\) + O\({}_{2}\) + 2 H\({}_{2}\)O \(\rightarrow\) 4 Mn(OH)\({}_{3}\)\(\downarrow\)  
The brown precipitate formed, is rapidly dissolved causing the oxidation of the iodide ions present to iodine:  
Mn(OH)\({}_{3}\) + I\({}^{-}\) + 3 H\({}^{+}\) \(\rightarrow\) Mn\({}^{2+}\) + 1/2 I\({}_{2}\) + 3 H\({}_{2}\)O  
The liberated iodine is then titrated with a standard thiosulphate solution:  
2 S\({}_{2}\)O\({}_{3}\)\({}^{2-}\) + I\({}_{2}\) \(\rightarrow\) S\({}_{4}\)O\({}_{6}\)\({}^{2-}\) + 2 I\({}^{-}\)  
Finally, the Oxygen Permeability (\(OP\)) of the film is calculated by the equation:  
\[OP=\frac{m\cdot d}{A\cdot t\cdot\Delta P} \tag{8}\]  
where \(m\) is the mass of O\({}_{2}\) permeated through the film with a thickness of \(d\) and an area of \(A\), over the measured time interval \(t\), and \(\Delta P\) is the difference in the O\({}_{2}\) pressure between the two sides of the film.
###### Abstract  
In this work, three new convolutional neural network models--spatio-temporal convolutional neural network versions 1 and 2 (ST_CNN_v1 and ST_CNN_v2), and the spatio-temporal dilated convolutional neural network (ST_Dilated_CNN)--are proposed for solar forecasting and processing global horizontal irradiance (GHI) data enriched with meteorological and astronomical variables. A comparative analysis of the proposed models with two traditional benchmark models shows that the proposed ST_Dilated_CNN model outperforms the rest in capturing long-range dependencies, achieving a mean absolute error of 31.12 W/m\({}^{2}\), a mean squared error of 54.07 W/m\({}^{2}\), and a forecast skill of 37.21%. The statistical analysis carried out on the test set suggested highly significant differences in performance (\(p\)-values lower than 0.001 for all metrics in all the considered scenarios), with the model with the lowest variability in performance being ST_CNN_v2. The statistical tests applied confirmed the robustness and reliability of the proposed models under different conditions. In addition, this work highlights the significant influence of astronomical variables on prediction performance. The study also highlights the intricate relationship between the proposed models and meteorological and astronomical input characteristics, providing important insights into the field of solar prediction and reaffirming the need for further research into variability factors that affect the performance of models.  
solar forecast; dilated CNN; spatio-temporal; deep learning +
Footnote †: journal: _Applied_
# AI in Context: Harnessing Domain Knowledge for Smarter Machine Learning
## 5 Case Studies: Success Stories and Lessons Learned
### Detailed Case Studies from Various Industries
Healthcare  
In healthcare, integrating domain knowledge into AI systems has been pivotal in revolutionizing clinical operations and patient care. The Mayo Clinic's partnership with Google Cloud exemplifies the profound impact of integrating AI and ML from the ground up. Their collaboration focused on creating a solid data and infrastructure foundation to make healthcare data more meaningful, accessible, and interoperable [128]. This partnership facilitated patient care and empowered research and the discovery of new cures. The initiative was built upon a \"three-layer cake\" strategy, encompassing a core build, data foundations, a software delivery pipeline, data migration, security operations, IT operations [129], and an AI factory, all contributing to supporting AI and ML with an advanced model development environment [130]. This comprehensive approach allowed for the integration of clinical, operational, and financial data, facilitating a holistic view of the patient journey and enabling the effective use of AI and ML in advancing healthcare [131]. The research \"Human-in-the-Loop Machine Learning: A Survey\" by [PERSON] (2015) [119] explores integrating human expert knowledge into ML models, particularly in high-stakes domains like medical decision-making. This approach emphasizes the importance of human expertise in enhancing ML systems' performance and reliability, especially when decisions significantly impact patient outcomes. Frameworks that facilitate collaboration between human experts and ML algorithms allow for a more interactive and iterative learning process. By incorporating feedback from domain experts, models can be refined to better reflect real-world complexities that purely data-driven approaches might overlook, improving accuracy and interpretability. Similarly, the study by [PERSON] (2024) [120] develops a framework to assess the performance equity of health AI technologies. The framework quantitatively evaluates how AI models perform across different demographic groups, addressing potential disparities in healthcare outcomes. By incorporating domain knowledge, the framework ensures AI technologies do not inadvertently exacerbate existing inequities in healthcare, contributing to more equitable healthcare delivery. The collaboration between TidalHealth Peninsula Regional and IBM implemented an AI-based clinical decision support system incorporating medical expertise and clinical protocols. By embedding detailed knowledge about disease presentations, diagnostic procedures, and treatment guidelines, the AI solution streamlined information searching, significantly reducing the time required for clinical searches. This integration allows medical professionals to access precise and relevant information quickly, which generic AI systems without such knowledge might not efficiently provide, enabling clinicians to dedicate more time to patient care [134; 135; 136; 137; 138].
## 4 Design of the Algorithm
### Improved Ant Colony Algorithm
The parameter values (e.g., \(\alpha\), \(\beta\), \(\rho\), and \(Q\)) were determined using systematic single-factor experiments, where each parameter was varied individually while keeping others constant. This method enabled the identification of values that balanced convergence speed and solution stability. Step 2: Select a node as the starting point of the traverse randomly for each ant. Step 3: Update the nodes that the ant has traversed in the taboo list. Step 4: Calculate the probability of the next possible node based on Equation (7) and select the next node based on the roulette strategy. Repeat this process until all ants have traversed all nodes. Step 5: Calculate the optimal route and update the pheromone intensity on the road. Step 6: Repeat steps 3 to 5 until the maximum number of iterations is reached or the termination requirement is met. Step 7: Output the optimal result.
## 4 The Accurate and Stable MDTF Fault Identification Framework and Its Experimental Evaluation
### The gearbox Testbed and Data Acquisition
After performing down-sampling, the vibration sub-bands are obtained, which have the time length of one second, the sampling frequency of 21,845 Hz (65,536/3), and frequency range from 0-10,922 Hz. \begin{table}
\begin{tabular}{c c} \hline \hline
**Devices** & **Specification** \\ \hline Vibration sensor & Sensitivity (V/g): 10.2 mV/(m/s\({}^{2}\)) \\ \cline{2-3} (Accelerometer 622B01) & Operational frequency range: 0.42 to 10 kHz \\ \cline{2-3}  & Resonant frequency: 30 kHz \\ \cline{2-3}  & Measurement range: \(\pm\)490 m/s\({}^{2}\) \\ \hline
4-Channel DAQ & 18-bit 40 MHz AD conversion, a sampling frequency of 65.536 kHz is used for each of two channels simultaneously \\ \hline Displacement & Distance from the head of a transducer to a hole: 1.0 mm \\ \cline{2-3} transducer & Diameter of a hole: 12.80 mm \\ \cline{2-3}  & Sensitivity: 0 to \(-\)3 dB \\ \cline{2-3}  & Frequency response: 0-10 kHz \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specifications of the sensors and data acquisition system. Figure 8: The observed defect types of the multi-degree tooth faults on the gear wheel and examples of vibration signals at 600 RPM: (**a**) PC, (**b**) DT1, (**c**) DT2, (**d**) DT3, (**e**) DT4, (**f**) DT5, and (**g**) DT6, respectively. The vibration sub-bands are provided as an input to LADT for reducing the noise and enhancing the useful fault-related information, which represents multi-degree tooth fault behaviors. LADT applies ANR-GRS to each PFS. Through localized adaptive optimization, the new denoising methodology tries to find the localized optimal parameter set of the noise-simulated reference signal, which is appropriate to each specific PFS. The outputs of LADT are the optimized vibration sub-bands, which maintain the original defect-related frequency tones (meshing frequency harmonics and sideband frequencies) and reduced background noise. Those defect representative ingredients are key factors for identifying the defect types of an MDTF gearbox under the condition of the variable speed. Those fault types proceed as analogous behaviors reflecting the vibration characteristic, the differences between them might be degrees of amplitudes of informative tones, their proportions, or occurrence events in the tiny range of separation. Thus, the image-based enriching feature pool configuration methods are needed to sort them out for condensation.
## Appendix E Hybrid-DSPEB Model  
Previous work in deep learning for computer vision has proposed multi-input convolutional neural networks to improve performance on tasks where different views of the same input are useful for approximating a single output. This can be done by grouping multiple neural networks, or duplicating a single network architecture, through an MLP-like architecture near the output of the merged network [53, 54, 55]. In this experiment, we follow a similar methodology to create a hybrid model (H-DSPEB). We merge the output layers and the last fully connected layers of each of the two pretrained C-DSPEB and W-DSPEB models, forming the final output head of H-DSPEB. The architecture of H-DSPEB can be seen in Figure A4 (right).  
The MLP head which we append to the end of the two pre-trained models is composed of two new fully connected layers which connect to the last hidden layer of each sub-model, in addition to the output layer of each of the sub-models. The single output of the MLP head corresponds to the final output of H-DSPEB. We tested various architectures for the MLP head but noted little difference in performance. During training, we freeze all previously trained weights in C-DSPEB and W-DSPEB, as indicated by the dotted lines in Figure A4.  
The aim of this architecture is to evaluate whether color and celerity information can be automatically combined for enhanced estimation, due to the different conditions in which these two model types function. By including higher-level features from the final layer, our goal is that the hybrid model learns to estimate depth using both color and celerity information. Because the two approaches are complementary for clear and turbid waters, contrary to previous deep learning bathymetry inversion applications, the combination of the two unlocks the potential of inversion of the bathymetry from a satellite at any coast worldwide. While the principal contribution of this work is the W-DSPEB method, we find the H-DSPEB idea to be motivating and deserving of further study.
## 3 Results
### Volatile Components of NGBTs  
#### 3.2.1 Identification and Analysis of Volatile Substances  
Volatile compounds in the five NGBTs were profiled using HS-SPME-GC-MS. A total of 97 volatile compounds were identified across chemical classes: acids (3), alcohols (23), aldehydes (9), benzenoids (6), esters (21), heterocyclics (2), ketones (7), and alkenes (26). Total volatile content varied significantly among teas, with JSH exhibiting the highest abundance, followed by DXY, DXE, QTZ, and YHJ. Alcohols and esters constituted the dominant classes across all samples, though their distribution differed markedly: JSH contained the highest alcohol content while YHJ showed the lowest. Notably, JSH displayed the most abundant alkenes profile (Figure 4A), which included multiple aroma-active monoterpenes and sesquiterpenenes characteristic of black tea fragrance.  
Figure 3: Multivariate statistical analysis of non\(-\)volatile components in the 5 NGBTs. (**A**) PCA score plot, the dashed circle represents the 95% confidence range; (**B**) OPLS\(-\)DA score plot; (**C**) cross\(-\)validation results: the intercept of the Q2 regression line of the cross\(-\)validation model, with 200 tests of alignment was less than 0, indicating that the OPLS-DA discriminant model was not overfitted and the model was relatively reliable. (**D**) VIP score plot: pink bars represent non-volatile compounds with VIP > 1; green represents VIP < 1.  
Unique compositional signatures were observed: JSH, DXY, DXE, QTZ, and YHJ contained 13, 13, 6, 10, and 4 unique volatiles, respectively (Figure 4B), against a background of 13 shared aroma compounds. Heatmap analysis of these shared compounds (Figure 4C) revealed distinct enrichment patterns: JSH showed significantly higher relative abundance of (_E_)-\(\beta\)-ionone, benzaldehyde, geraniol, \(\beta\)-myrrene, and phenethyl alcohol; DXY exhibited elevated _T_-muurolol, \(\alpha\)-cadinol, methyl salicate, and (_E_)-pyranolol linalool oxide; DXE had limited enrichment; QTZ displayed uniformly low abundance; and YHJ was characterized by pronounced linalool and methyl hexadecanoate content. This multivariate variability suggests that volatile profiles contribute substantially to the sensory differentiation of NGBTs.
## 3 Preliminaries
### Data Outsourcing  
In IoT scenarios, most IoT devices lack the computational resources required for complex encryption and decryption operations. To address this limitation, [PERSON] et al. [18] proposed offloading these operations to high-performance devices, referred to as compute nodes (CNs) in this paper, which assist the controller (CR). During the encryption phase, the CR performs an initial encryption and sends the data to the CN for secondary encryption. In the decryption phase, the CR generates proxy keys and transmits them to the CN for initial decryption to produce the intermediate decryption message (\(IDM\)). Finally, the \(IDM\) is then returned to the CR for a second decryption to fully recover the plaintext. This process ensures that the CR's attribute key remains secure and confidential, as it is never exposed to other devices.
## 3 Materials and Methods
### Simulation Measurements
Considering how the signals were obtained, each simulation/case was reused (data augmentation) to generate additional cases by rotating the original image and reordering the transmitted and received signals. Hence, from each simulation, an additional 180 cases were obtained through matrix rotation and signal reordering. In this way, the number of cases significantly increased from 146 cases to 26,280 cases with the minimum computational cost.
# InSAR-CTPIM-Based 3D Deformation Prediction in Coal Mining Areas of the Baisha Reservoir, China
## 3 Simulation Experiment  
A simulation experiment was carried out in this paper to verify the feasibility and reliability of the method. The satellite parameters used in the simulation experiment are set based on the Sentinel-1A satellite SAR image parameters in the real experiment (see Section 4), with the geological parameter \(G_{1}\) = [2 m, 612 m, 588 m, 7\({}^{\circ}\), 82\({}^{\circ}\), 200 m, 500 m, 0.52 m/d], the PIM parameter-simulated value \(G_{2}\) = [0.711, 1.251, 0.326, 83.29, 7.71 m, 11.22 m, 0.167] and the time function model parameter \(c\) of 8.261; the 3D deformation field is generated as the real deformation according to Equations (4) and (5), respectively, as shown in Figure 4, from which 300-pixel points of 3D deformation values are extracted to be analyzed in quantitative comparison with the 3D deformation-calculated values generated by InSAR-3D-CTPIM.  
Figure 5 shows the comparison between the simulated and predicted vertical deformation values of pixels for four time periods; as can be seen from the figure, the simulated actual values and the predicted values of deformation are in good agreement, and statistically, the pixels with the error values distributed between [-2, 2] mm account for 100%, 99%, 94.67%, and 91% of the total number of pixels, respectively, where the maximum errors are 0.8 mm, 1.2 mm, 3.2 mm, 4.8 mm, respectively. Meanwhile, the root mean square errors (RMSE) of the model parameters were calculated to quantitatively verify the simulated and actual values of the model parameters. As shown in Table 2, the relative errors of the eight model parameters are less than 7%; the RMSE between the simulated actual values of vertical deformation and the predicted values of deformation generated by the InSAR-3D-CTPIM model is 2.2 mm, which confirms the reliability of the model and parameter estimation methods in this paper.  
Figure 4: Partially simulated three-dimensional time-ordered deformation field.
## 2 Automatic Geometric Corrections  
The proposed automatic geometric correction workflow is a result of an upgrade of the STORM geometric correction module that is described in [25]. STORM is a fully automatic image processing chain that performs all of the processing steps from sensor-corrected or ortho-ready optical satellite images to web-delivered map-ready products. Modules of a pre-processing stage and the final product stage compose the chain. One of the most important pre-processing modules is the geometric corrections module. This module automatically generates orthoimages from various high resolution (HR) and very-high resolution (VHR) images. The processing workflow varies slightly, depending on the input image resolution--see Section 2.1--but it is always composed of three sub-modules: automatic extraction of GCPs that is based on matching the roads detected from satellite image onto either reference rasterized roads (two-step algorithm) or reference rasterized roads and orthophotos (three-step algorithm), which is followed by automatic sensor modelling, during which either the proprietary physical sensor model or RFM are utilised, and finally orthorectification.  
While the algorithms and results for HR images were partly presented in [26] and improved and adapted algorithms that also support VHR images up to 2 m were tested in [25], this paper focuses on further improvements and upgrades to enable the processing of sub-meter panchromatic images of complex urban land cover (e.g., when a large portion of densely built city areas are present on the images). The most important improvements are made to the GCP extraction sub-module, which is the most critical automatic step.