esa-sceva/satcom-chunk-collection huggingface dataset:
# A Study on the Roof-Cutting and Pressure Releasing Technology of Roof Blasting
## 3 Simulation of Hard Roof Blasting
### Analysis of Simulation Results
#### 3.3.3 Reflection and Transmission of Stress Waves  
Reflection and transmission occur when stress waves encounter faults and joints during propagation. The energy consumption increases with the number of reflections. The more times the stress wave is reflected, the less efficient it is at breaking the rock. Six monitoring points were selected on both sides of the joint surface to analyze the pressure variation on both sides of the joint surface, as shown in Figure 5.  
Figure 5 shows that the impact pressure curve is in a state of first increasing and then decreasing. The shock wave reflects multiple times at the joint, showing a sawtooth shape at the monitoring points m\({}_{1}\) and k\({}_{1}\) located directly below the charge. Extracting the incident angle of each monitoring point found that the pressure value of the unit first decreases and then oscillates when the incident angle ranges between 0 and 40\({}^{\circ}\). However, unlike the normal incidence, the pressure value during the oscillation change does not continuously decrease. Reflection and transmission coexist, and the transmitted stress wave continues to propagate through the rock interior, which causes rock mass damage to a certain extent. Therefore, oblique incidence should be selected as far as possible at the joint surface to reduce reflection in engineering applications.
# A Review of Image Inpainting Methods Based on Deep Learning
## 4 Deep-Learning-Based Image Inpainting Algorithm
### Attention Mechanism
#### 4.3.2 Transformer  
High-resolution image inpainting typically employs a multistage approach; the process begins with low-resolution inpainting, followed by upsampling, and attention mechanisms are specifically applied during the high-resolution inpainting stage. [PERSON] et al. [83] proposed an iterative inpainting method with a confidence map as a feedback mechanism and used the attention mechanism in the next stage to borrow high-resolution feature blocks from the input image to achieve high-resolution inpainting results. [PERSON] et al. [84] introduced a context-residual aggregation mechanism for super-high-resolution image inpainting. The context-residual aggregation mechanism designed based on the context attention layer idea uses high-level feature maps to compute attention scores, then transfers attention in multiple low-level feature maps, achieving the matching of context information between multiple abstraction layers. [PERSON] et al. [85] devoted the first stage to restoring missing semantic information. In the second stage, a spatial channel attention (SCA) module was introduced to obtain fine-grained texture. [PERSON] et al. [86] proposed a new three-stageinpainting framework for local and global refinement. Similarly, attention mechanisms are used for global refinement in the last stage. [PERSON] et al. [87] proposed two coarse-stage attention mechanisms. A progressive context module is used to find image-block-level feature similarity in the original image reconstruction, and a spatial channel context module is used to find essential spatial and channel features in chroma image reconstruction. Attention-based image inpainting methods obtain information from the background area far from the defect and propagate it to the defect area. However, during the propagation process, because the information from the newly restored defect area is misleading, it produces blurred results. The above methods that optimize and improve the attention mechanism are quantitatively evaluated on commonly used datasets, as shown in Table 3.  
CNNs, due to their inherent inductive biases, often struggle to capture global features. These biases stem from their local receptive fields, which prioritize nearby information over distant or holistic patterns. [PERSON] et al. [88] introduced ADCT domain learning and contextual modeling, achieving the computation of the correlation between the missing regions and all surrounding pixels. This provides higher-quality image imputation. Additionally, two ADCT context loss functions were introduced to enhance training stability. An integrated optimization was also introduced, which not only unifies the learning mechanisms of pixels and the ADCT domain but also their contextual modeling and attention.
# Application of Waste Tire Carbon for Iron-Containing Dust Reduction in Industrial Processes
## 3 Results
### Optimal Reaction Conditions for Iron Reduction
This suggests that a low-O\({}_{2}\) environment (<8%) in an operational blast furnace will not significantly affect iron reduction. For large-scale reaction systems, as the O\({}_{2}\) content increases, the reducing atmosphere is enhanced. This leads to a decrease in the amount of carbon used in direct reduction reactions, while the carbon combustion rate increases. These two effects counterbalance each other, resulting in a maximum carbon utilization efficiency at an O\({}_{2}\) supply rate of 19 Nm\({}^{3}\)/kg [49]. Additionally, Figure 6e shows that during the redox reaction between SMTD and WTC, the generated gases are primarily CO, CO\({}_{2}\), and minor H\({}_{2}\). When the O\({}_{2}\) content is between 2 and 6%, as the O\({}_{2}\) content increases, more carbon participates in the reduction reaction, leading to more CO\({}_{2}\) production and an overall increase in the total gas volume. The highest amount of H\({}_{2}\) is observed at 4% O\({}_{2}\). When the O\({}_{2}\) content further increases to 8%, the reducing atmosphere is slightly disrupted, reducing the production of CO and CO\({}_{2}\), and thus the total gas volume decreases. However, this phenomenon is not significantly reflected in the XRD patterns. The O\({}_{2}\) content (0-8%) does not significantly impact the reduction effectiveness of the iron-bearing dust. From Figure 6d, it can be seen that under suitable conditions, when the reaction time is 15 min, significant MFe formation is already observed in the reduced residues. When the reaction time is 45 min, the iron phase in SMTD can be almost completely reduced to MFe. A similar study shows that extending the reaction time by 30 min can increase the iron reduction rate by more than 30% [50]. Considering the reduction effectiveness, reduction efficiency, and energy consumption, 45 min is identified as the optimal reaction time. In summary, the optimal reaction conditions for reducing SMTD with WTC are a temperature of 1000 \({}^{\circ}\)C, a mixing ratio of SMTD to WTC of 2:1, 0% O\({}_{2}\) content, and a reduction time of 45 min. The reduction process is illustrated in Figure 7.
## 2 The Proposed Method
### Loss Function  
The proposed method combines the technical features of teacher-student networks, generators, and an enhanced anomaly discriminator to optimize network training through teacher reconstruction loss, student reconstruction loss, knowledge distillation loss, generative adversarial loss, and enhanced anomaly discriminator loss. These loss functions work synergistically to improve the learning capability and detection accuracy of the model. In the input image (\(I\)), the loss function is defined as in Equations (11)-(17):  
For the loss of the teacher network generator \(L_{T}\):  
\[L_{T}=(I-\mathrm{G}_{\mathrm{T}}(\mathrm{E}(I)))^{2} \tag{11}\]  
For the loss of the student network generator \(L_{S}\):  
\[L_{S}=(I-\mathrm{G}_{\mathrm{s}}(\mathrm{E}(I)))^{2} \tag{12}\]  
Knowledge Distillation Loss, \(L_{DIST}\):  
\[L_{DIST}=\sum_{i=1}^{\mathrm{m}}\left(F_{T}^{i}-F_{S}^{i}\right)^{2} \tag{13}\]  
where \(F_{T}^{i}\) and \(F_{S}^{i}\) are the feature representations of the teacher and student generators at the layer \(i\), respectively, and m is the total number of feature layers.  
Generation of antagonistic losses \(L_{GEN}\):  
\[L_{GEN}=\log(1-\mathrm{D}(\mathrm{G}_{\mathrm{s}}(\mathrm{E}(I)))) \tag{14}\]  
Discriminator loss \(L_{DIS}\):  
\[L_{DIS}=\log(\mathrm{D}(I))+\log(1-\mathrm{D}(\mathrm{G}_{\mathrm{s}}(\mathrm{ E}(I)))) \tag{15}\]  
In summary, the goal of the generator in this model is to minimize the generator's loss function \(L_{G}\), making the generated data as realistic as possible.  
\[\underset{G}{\text{min}}L_{G}=\min\Bigl{(}\lambda_{t}L_{T}+\lambda_{s}L_{S}+ \lambda_{\text{dist}}L_{DIST}+\lambda_{\text{gen}}L_{GEN}\Bigr{)} \tag{16}\]  
The goal of the enhanced anomaly discriminator is to maximize its loss function \(L_{D}\) in order to better distinguish between real and generated data.  
\[\underset{D}{\text{max}}L_{D}=\max\Bigl{(}\lambda_{\text{dis}}L_{DIS}\Bigr{)} \tag{17}\]  
where \(\lambda_{t}\), \(\lambda_{s}\), \(\lambda_{\text{dist}}\), \(\lambda_{\text{gen}}\), and \(\lambda_{\text{dis}}\) are the hyperparameters corresponding to their respective losses.
## 3 Numerical Simulation
### The Vibration Reduction Characteristics of the System  
Based on the analyses done in Sections 3.1 and 3.2, the influence of damper mass, damper vibration stiffness, and external excitation amplitude on the vibration reduction characteristics of the system are studied in this section. Relevant parameters were set as in Table 1. The other parameters are given in the following simulation.  
\(E_{ds}\) is the vibrational power reduction rate of the steady-state response of the blade. As the steady-state response is periodic, \(T\) is the minimum period of the steady-state response, which is equal to the period of the external excitation with the value of \(2\pi/\omega_{0}\). Therefore, \(E_{ds}\) can be expressed as Equation (8). \(E_{dt}\) is the average power-reduction rate of the transient response of the blade, and can be expressed as Equation (9). The relative displacement of the blade without an under-platform damper is \(y^{\prime}{}_{1}\), and the moment when the system without an under-platform damper reaches steady-state is \(t^{\prime}{}_{0}\). Similarly, the response of the blade before \(t^{\prime}{}_{0}\) can be defined as \(y^{\prime}{}_{1t}\), and after \(t_{0}\) can be defined as \(y^{\prime}{}_{1s}\). The response is divided into \(y^{\prime}{}_{1t}\) and \(y^{\prime}{}_{1s}\).  
\[E_{ds}=\frac{\int_{T}y^{\prime 2}_{1}dt/T-\int_{T}y^{\prime 2}_{1}dt/T}{\int_{T}y^{ \prime 2}_{1}dt/T}=\frac{\int_{T}y^{\prime 2}_{1}dt-\int_{T}y^{2}_{1}dt}{\int_{T}y^{ \prime 2}_{1}dt}=\frac{\int_{T}(y^{\prime 2}_{1}-y^{2}_{1})dt}{\int_{T}y^{ \prime 2}_{1}dt} \tag{8}\]  
\[E_{dt}=\frac{\int_{0}^{t_{0}^{\prime}}y^{\prime 2}_{1}dt/t_{0}^{\prime}-\int_{0} ^{t_{0}}y^{\prime 2}_{1}dt/t_{0}}{\int_{0}^{t_{0}^{\prime}}y^{\prime 2}_{1}dt/t_{0} }=\frac{\int_{0}^{t_{0}^{\prime}}y^{\prime 2}_{1}dt-\int_{0}^{t_{0}}t_{0}y^{ \prime 2}_{1}dt/t_{0}}{\int_{0}^{t_{0}^{\prime}}y^{\prime 2}_{1}dt}=\frac{\int_{0}^{t_{0}^{ \prime}}(y^{\prime 2}_{1}-t_{0}^{\prime}y^{2}_{1}/t_{0})dt}{\int_{0}^{t_{0}^{\prime}}y^{ \prime 2}_{1}dt} \tag{9}\]  
The maximum values of \(\left|y_{1t}\right|\) and \(\left|y_{1s}\right|\) are \(y_{1 tm}\) and \(y_{1 sm}\) respectively, Similarly, the maximum values of \(\left|y^{\prime}{}_{1t}\right|\) and \(\left|y^{\prime}{}_{1s}\right|\) are \(y^{\prime}{}_{1 tm}\) and \(y^{\prime}{}_{1 sm}\) respectively. \(A_{ds}\) is the reduction rate of \(y_{1 sm}\) and \(A_{dt}\) is the reduction rate of \(y_{1 tm}\). \(A_{dt}\) and \(A_{ds}\) are expressed in Equation (10).  
\[\left\{\begin{array}{l}A_{ds}=\frac{y^{\prime}{}_{1 sm}-y_{1 sm}}{y^{\prime}{} _{1 sm}}\\ A_{dt}=\frac{y^{\prime}{}_{1 sm}-y_{1 sm}}{y^{\prime}{}_{1 sm}}\end{array}\right. \tag{10}\]
## 4 System Analysis and Experiment
### Experiment and Analysis
If the system was supposed to finish the process, we recorded the results and calculated the average values of the coordinates. The single receiving experiment situation is shown in Figure 10b below, and the dual-receiving experiment situation is shown in Figure 10c. Among the 12 coordinates to be measured, only (1, 1.5), (2.5, 2), and (2.5, 1.5) had larger positioning errors, which were 0.1579 m, 0.1882 m, and 0.1758 m, respectively. The errors of other coordinates were all less than 0.15 m. There were few differences among these coordinates. At the same time, some accidental factors may also need to be considered, such as indoor temperature, the unstable effect of piezoelectric tweeters, etc. It was quite certain that selecting a higher-quality omnidirectional facility as an essential part of the acoustic anchor would greatly reduce the contingency and uncertainty of our experiments. Once more experiments were repeated, the overall results might be different, and the error distribution would gradually tend to normal distribution in total. After the system completed CTGSA-AFSA optimization and applied the NLOS acoustic signal coping mechanism, it could be seen that the novel method proposed in this paper achieved excellent results with the help of a four-pattern data set and dual-receiving structures. The data set that contained the four patterns of NLOS situations was proved to have a positive impact on the NLOS identification ability in complex and flexible situations compared with the single receiving method. These precise positioning results will help indoor sensor networks or cognitive IoT networks adjust the user's location and play a better role. In detail, they are shown in Table 4 as follows: \begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Average Error of X Axes Coordinate (m)** & **Average Error of Y Axes Coordinate (m)** & **Coordinate Error (m)** \\ \hline (1, 1) & 0.0351 & 0.0622 & 0.0714 \\ (1, 1.5) & 0.0356 & 0.1538 & 0.1579 \\ (1, 2) & 0.0240 & 0.0474 & 0.0531 \\ (1, 2.5) & 0.0920 & 0.0981 & 0.1345 \\ (1, 5, 2.5) & 0.0497 & 0.0623 & 0.0797 \\ (2, 2.5) & 0.0682 & 0.0654 & 0.0945 \\ (2, 5, 2.5) & 0.0578 & 0.1009 & 0.1163 \\ (2, 5, 2) & 0.1006 & 0.1591 & 0.1882 \\ (2, 5, 1.5) & 0.0925 & 0.1495 & 0.1758 \\ (2, 5, 1) & 0.0973 & 0.0994 & 0.1391 \\ (2, 1) & 0.0667 & 0.0525 & 0.0849 \\ (1, 5, 1) & 0.0954 & 0.0578 & 0.1115 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Positioning error of each coordinate.
## 3 Results and Discussion
### Hg Levels in Arctic Animals and Humans (Historical and Modern)  
In historical samples of hair of human mummies of the Aleutian Islands (Alaska) dating 1450 AD, mean total mercury concentration (5.8 \(\pm\) 0.9 \(\upmu\)g/g) is comparable to the levels observed in hair of modern residents of the northern polar territories (Alaska, Canada, Faroe Islands) [59]. That confirms the main contribution of the traditional nutrition based on fish and meat of marine mammals to the accumulation of mercury for residents of these territories. The Egyptian, Chilean, and Peruvian mummies had mercury exposures below the US EPA reference level of 1 \(\upmu\)g/g and were considerably lower than that of northern pre-industrial populations [60]. Hg concentrations in hair samples of historical (10.42 \(\pm\) 1.31 \(\upmu\)g/g) and modern (10.42 \(\pm\) 2.45 \(\upmu\)g/g) arctic foxes were similar and strongly correlated with ecotype and available food source [22]. Unlike humans and foxes, Hg concentrations in the hair of Greenland polar bears showed a significant increase from 0.52 to 4.9 \(\upmu\)g/g (from 1300 to 2000 years) [61]. Comparison between Hg levels in the hair of the ancient dogs of the Seward Peninsula (0.657 \(\pm\) 0.273 ng/g [62]) and the modern Alaska fish-fed dogs (0.54 \(\pm\) 0.11 \(\upmu\)g/g, [51]) did not show significant difference. Thus, when environmental exposure (atmosphere and water) to mercury is low, the increased levels of mercury in the bodies of ancient humans and animals are primarily associated with their diet. Mammals of the mammoth fauna have low Hg levels, since they are herbivores that get mercury from plants and accumulate it in their bodies (and hair). Biomagnification along the food chain (as seen in aquatic ecosystems and fish-eating animals and humans) is not observed.
## 8 Results and Discussion  
### Case Study  
This case study focuses on a residential home in Liaoning Province, China, which is equipped with a hybrid energy system integrating PV, WTs, and backup energy sources including hydrogen storage, batteries, and EVs. The primary objective is to analyze the performance and energy management under different DR scenarios, leveraging local weather data for accurate assessments. The proposed HEMS has been applied in a real case study in China, providing valuable insights into its practical implementation.  
Liaoning Province is located in the northeastern part of China, known for its diverse climate and industrial base. It experiences significant seasonal variations with cold winters and warm summers. The province has abundant solar and wind resources, making it suitable for renewable energy generation. The highest recorded irradiation was 895.16 Wh/m\({}^{2}\), and the peak wind speed reached 8.89 m/s. The temperature varied from 8.73 \({}^{\circ}\)C to 25.8 \({}^{\circ}\)C, and the load demand ranged from 642.67 W to 1658 W. These values significantly influence the energy generation capacity of the PV and WTs installed at the residence. The daily profiles of load demand, solar irradiation, temperature, and wind speed are depicted in Figures 6-9, respectively. The load demand data for a specific day in May 2023 shows significant variation throughout the day. Early morning demand started at 916 W and decreased slightly around 642.67 W by mid-morning. As the day progressed, the load demand gradually increased, peaking at 1658 W in the late afternoon. This variation in load demand throughout the day corresponds to typical household activities and the increased use of cooling systems during the warmer parts of the day.  
Figure 6: Daily load demand pattern of the chosen area.  
Four scenarios were studied to understand the impact of DR programs on energy management and cost efficiency. The first scenario, without any DR programs, serves as the baseline. This scenario reflects the energy usage patterns and costs without any adjustments or incentives to shift energy consumption. In the second scenario, a DR program with a 20% reduction in peak energy usage was implemented. This scenario incentivized the household to move energy consumption to non-peak periods, resulting in a significant reduction in energy costs and optimized use of backup systems. The third scenario involved a more aggressive DR program with a 30% reduction in peak energy usage. This further reduction in peak demand demonstrated additional cost savings and enhanced the efficiency of the energy management system. The final scenario examined the effects of a 40% DR program, the most aggressive of the four scenarios. This scenario showcased the maximum potential for cost savings and optimal utilization of RESs and backup systems.  
Figure 8: Daily temperature profile of the selected region.  
Figure 7: Daily solar irradiation profile of the selected region.
# UPGCN: User Perception-Guided Graph Convolutional Network for Multimodal Recommendation
## 4 Experiments
### Baselines  
To demonstrate the effectiveness of the proposed model, we compare it with several representative baseline recommendation models in two categories. The first category consists of general collaborative filtering (CF) models, which rely solely on historical interaction relationships between users and items to generate recommendations. The second category encompasses several representative multimodal models that leverage both historical interaction information and the multimodal features of items to provide recommendations for users.  
1. **General CF Models:**
2. **BPR** [1] is a classic collaborative filtering method that optimizes the latent representations of users and items within a matrix factorization (MF) framework through a BPR loss;
3. LightGCN [4] is a simplified graph convolution network that omits feature transformation and nonlinear activation layers, focusing on linear propagation and neighbor aggregation.
4. **Multimodal Models:**
5. VBPR [5] employs the CNN to obtain visual features from product images, subsequently integrating these visual features and ID embeddings of each item as its representation.
6. MMGCN [13] constructs the user-item interaction graphs for different modalities based on the features of different modalities and user-item interaction information. It subsequently employs graph convolutional on this graph to obtain user and item representations in different modalities. The user and item representations utilized for the final recommendation are derived from concatenating the user and item representations learned from the above steps across different modalities.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Dataset 1** & **Users** & **Items** & **Interactions** & **Sparsity** \\ \hline Baby & 19,445 & 7050 & 160,792 & 99.88\% \\ Sports & 35,598 & 18,357 & 296,337 & 99.95\% \\ Clothing & 39,387 & 23,033 & 278,677 & 99.97\% \\ \hline \hline \end{tabular} 1  
\end{table}
Table 1: Statistics of three datasets used in experiments.  
* GRCN [14] has designed a graph refining layer that further refines the interaction graph between user and items by detecting false-positive edges and noisy edges, thereby preventing the propagation and aggregation of noise in the GNN.
* DualGNN [23] initially extracts user representations from the user-item interaction graph across various modalities, subsequently integrates these representations, and constructs an additional user co-occurrence graph.
* LATTICE [24] constructs distinct modality-specific item-item graphs and obtains a latent item-item graph by integrating item-item graphs from all modalities.
* SLMRec [39] is a self-supervised learning framework for multimedia recommendation, which designs three different granularity data augmentation methods to build auxiliary tasks for contrastive learning.
* FREEDOM [25], based on the LATTICE framework, freezes the graph prior to training and employs a degree-sensitive edge pruning method to reduce noise in the interaction graph between users and items.
###### Abstract  
The scattering of surface waves by a three-dimensional shallow cavity of arbitrary shape at the surface of a homogenous, isotropic, linearly elastic half-space is theoretically investigated. A novel analytical approach based on a reciprocity consideration is introduced in this article to determine the particle displacements of the scattered wave field generated by the interaction between the surface waves and the cavity. In the usual manner, the scattered field was shown to be equivalent to the radiation from the distribution of tractions, calculated from the incident wave, on the surface of the cavity. The radiation of surface waves subjected to the computed tractions applied at a single location was found using reciprocity theorems. The field scattered by the cavity was subsequently obtained from the superposition of displacements due to all the forces applied on the cavity surface. Solutions for the scattering of surface waves by a spherical, a circular cylindrical (coin-shaped) and a square cylindrical cavity are presented in detail. We here derive the closed-form expressions of the displacement amplitudes, which represent the far-field scattered waves produced by each of the cavities. An experimental setup using the ultrasonic pulse-echo technique was then carried out to record the scattered echoes of surface waves from these cavities in order to provide practical validation of the analytical findings. The vertical displacements measured at a significant distance of about twenty-five wavelengths from the cavities of the same width and different depth were compared with the corresponding theoretical predictions. The comparisons show excellent agreement for the case of a spherical cavity and good agreement in the cases of a circular and a cylindrical cavity in terms of trends and magnitudes. It is followed by a discussion on the results of the comparison and the limitations of the proposed approach regarding the degree of smoothness and the size of cavity.  
surface wave; half-space; three-dimensional cavity; reciprocity +
Footnote †: journal: Applied Physics Letters