esa-sceva/satcom-chunk-collection huggingface dataset:
# CKGAT: Collaborative Knowledge-Aware Graph Attention Network for Top-N Recommendation
## 2 Related Work
### Propagation-Based Recommendation Methods
#### 2.3.1 User Representation-Refinement Approaches  
The user representation-refinement approaches use the embeddings of the items interacted with by the user and the embeddings of the entities (i.e., items) in multi-hop ripple sets to learn user representations. For example, [PERSON] et al. [23] proposed the RippleNet framework, which uses preference propagation to learn ripple set embeddings to generate user embeddings. [PERSON] et al. [4] proposed the collaborative knowledge-aware attentive network (CKAN) method, which uses a heterogeneous propagation strategy to obtain multi-hop ripple sets, and then inputs the ripple sets into the attentive network to learn user representations and item representations. [PERSON] et al. [24] proposed the relation-enhanced knowledge graph reasoning for recommendation (RE-KGR) method, which calculates all user-item interaction paths to refine user embeddings, thereby generating recommendations. However, this type of methods has two limitations: one is that it ignores the topological proximity structures of the entities in multi-hop ripple sets, leading to inaccurate ripple set embeddings; the other is that it simply uses vector addition or concatenation operations to aggregate all the ripple set embeddings, thus reducing the accuracy of user/item embeddings.
## 4 Discussion
The experiment of adding VEGF-blocking-peptide (VBP) to prevent the formation of bound VEGF further confirmed the respective effects of soluble and bound VEGFs. Adding VBP resulted in a higher concentration of soluble VEGF in the culture medium than not adding VBP. Therefore, we could confirm that soluble VEGF mainly stimulated the endothelial cells to change from a round to elongated form, which might benefit the center-hollowed tube structure development [43]. On the other hand, the bound VEGF mainly stimulated the endothelial cells to migrate and ultimately formed a high integrity network. In summary, the soluble and bound VEGF had different vital functions in inducing endothelial cells to differentiate into a capillary-like network. The soluble VEGF dissolved in the culture medium activated the cell morphogenesis quickly. The cells became elongated, which was conducive to the formation of network cords. Simultaneously, the VEGF binding to the biogel provided a slow and continuous chemical stimulation, guiding the cells to move and aggregate, forming a capillary-like structure with complete integrity. According to previous literature, VEGF has multiple functions to regulate the growth, division, migration, and shape changes of endothelial cells [44]. Our results confirmed that the cell survival ratio is significantly lower in the cases with no VEGF added. We also showed that endothelial cells could secrete VEGF themselves if no exogenous VEGF was provided. These two phenomena might have some correlation in our cultural condition. Furthermore, our results explained why some theoretical studies could obtain cell networks under specific culture conditions conforming to the autocrine experimental setups [37; 45; 46]. However, to develop a stable capillary-like network in vivo, the VEGF source needed should be paracrine, secreted by other kinds of cells [34; 47].
## 2 Forward Modeling Method
### Solving the Discretization of the Target Region
The matrix forms of Equations (11) and (12) are  
\[\mathbf{Cell}_{F}{}^{\mathrm{T}}\mathrm{diag}\Big{[}\mathbf{A}_{F2C}^{\mathrm{T}} \mathbf{v}\Big{]}\mathbf{C}\mathbf{E}=-i\omega\mathbf{C}\mathbf{ell}_{F}{}^{\mathrm{T}}\mathrm{ diag}\Big{[}\mathbf{A}_{F2C}^{\mathrm{T}}\mathbf{v}\Big{]}\mathbf{B}, \tag{15}\]  
\[\mathbf{Cell}_{E}{}^{\mathrm{T}}\mathbf{C}\mathrm{diag}\left[\mathbf{A}_{F2C}^{\mathrm{T}} \Big{(}\mu_{0}^{-1}\Theta\mathbf{v}\Big{)}\right]\mathbf{B}=\mathbf{Cell}_{E}{}^{\mathrm{ T}}\mathrm{diag}\Big{[}\mathbf{A}_{E2C}^{\mathrm{T}}(\mathbf{\sigma}\mathbf{\sigma}\mathbf{v}) \Big{]}\mathbf{E}+\mathbf{Cell}_{E}{}^{\mathrm{T}}\mathrm{diag}\Big{[}\mathbf{A}_{E2C}^{ \mathrm{T}}\mathbf{v}\Big{]}\mathbf{J}_{s}, \tag{16}\]  
where \(\mathbf{\sigma}\) is the vector of conductivity values at all grid centers \(\mathbf{\sigma}=[\sigma_{1},\sigma_{2},\ldots,\sigma_{n}]^{\mathrm{T}}\), \(\mathbf{v}=[v_{1},v_{2},\ldots,v_{n}]^{\mathrm{T}}\) represents the volumes of all grid cells, \(\mathbf{E}=\left[\mathbf{E}_{x}^{\mathrm{T}},\mathbf{E}_{y}^{\mathrm{T}},\mathbf{E}_{z}^{ \mathrm{T}}\right]\) denotes the electric field components sampled at the centers of all grid cell edges with corresponding edge basis functions \(\mathbf{Cell}_{E}=\left[\mathbf{Cell}_{E_{x}}^{\mathrm{T}},\mathbf{Cell}_{E_{y}}^{\mathrm{ T}},\mathbf{Cell}_{E_{z}}^{\mathrm{T}}\right]\), \(\mathbf{B}=\left[\mathbf{B}_{x}^{\mathrm{T}},\mathbf{B}_{y}^{\mathrm{T}},\mathbf{B}_{z}^{ \mathrm{T}}\right]\) represents the magnetic field components sampled at the centers of all grid cell faces with corresponding face basis functions \(\mathbf{Cell}_{F}=\left[\mathbf{Cell}_{F_{x}}^{\mathrm{T}},\mathbf{Cell}_{F_{y}}^{\mathrm{ T}},\mathbf{Cell}_{F_{z}}^{\mathrm{T}}\right]\), \(\Theta\) is the Hadamard matrix product operator denoting element-wise multiplication of two matrices at the same positions, and \(\mathrm{diag}[\ \cdot\ ]\) is the diagonal matrix operator. The entire modeling domain has a constant magnetic permeability \(\mu_{0}\). From the above Equations (15) and (16), the discrete form of Equation (10) is derived as  
\[\Big{(}\mathbf{C}^{\mathrm{T}}\mathbf{M}_{F2\mu}\mathbf{C}+i\omega\mathbf{M}_{E2\sigma}\Big{)} \mathbf{E}=\mathbf{S}, \tag{17}\]  
where \(\mathbf{M}_{F2\mu}=\mathrm{diag}\Big{[}\mathbf{A}_{F2C}^{\mathrm{T}}\Big{(}\mu_{0}^{ -1}\Theta\mathbf{v}\Big{)}\Big{]}\), \(\mathbf{M}_{E2\sigma}=\mathrm{diag}\big{[}\mathbf{A}_{E2C}^{\mathrm{T}}(\sigma\Theta \mathbf{v})\big{]}\), \(\mathbf{S}=-\mathrm{i}\omega\mathrm{diag}\big{[}\mathbf{A}_{E2C}^{\mathrm{T}}\mathbf{v} \big{]}\mathbf{J}_{s}\).
# A Novel Fast Parallel Batch Scheduling Algorithm for Solving the Independent Job Problem
## 7 Conclusions and Future Works  
The paper analyzed the parallel batch scheduling problem of minimizing the makespan, where arbitrary sizes of scheduling jobs are allowed and machines have different capacities. Each machine can only deal with jobs whose sizes do not exceed that machine's capacity. We developed an efficient 4.5-approximation algorithm for this problem. The experimental results show that the algorithms can obtain a reasonable solution in a finite time. A 2-approximation algorithm is achieved under the particular circumstances of equivalent processing times. Computational experiments show that the fast algorithm can help to improve the efficiency of resource consumption and give researchers more choices to balance the quality of the solution and the running time in the parallel batch scheduling problem.  
Several important related directions for this problem are worth researching in the future. First of all, how do we improve the fast algorithm to get closer to the optimal solution in shortest time? In addition, jobs with release times are more common BPM problems in the manufacturing industry. How to develop a fast scheduling algorithm for this problem is an import direction. Finally, BPM problems with different service levels can be considered as well.  
Methodology--Y.S. and B.Z., Data analysis--B.Z. and D.W., Writing--Original Draft Y.S., D.W. and K.L., Writing--Edit and Review, B.Z., Y.S., D.W., K.L. and J.X., Visualization--J.X., Funding acquisition--B.Z. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the CERNET Innovation Project (NGII20190605), High Education Science and Technology Planning Program of Shandong Provincial Education Department under Grant (J18 KA340, J18 KA385), National Natural Science Foundation of China (61976125, 61772319, 61976124, 61771087, 51605068), A Project of Shandong Province Higher Educational Science and Technology Program (No.J16 LN51), the Graduate science and technology innovation fund of Shandong Technology and Business University (2018 yc038), Yantai Key Research and Development Program (2019 XDHZ081).  
We thank the anonymous referees for their constructive comments, which helped to improve this paper.  
The authors declare no conflict of interest.
# Shaft Wall Damage to High-Depth Inclined Ore Passes under Impact Wear Behavior
## 2 Shaft Wall Damage Model for High-Depth Inclined Ore Passes  
### Basic Model  
High-depth inclined ore passes have been widely employed in deep mines, both domestically and internationally [16; 24]. Based on the design blueprint of a main ore pass in a deep mine in Yunnan, China, a schematic diagram illustrating the basic structure of this high-depth inclined ore pass is presented (Figure 1). In the diagram, \(\theta\) is the ore-pass dip angle; \(\alpha\) is the inclined angle of the chute; \(D\) is the shaft diameter; \(H\) and \(H^{\prime}\), respectively, indicate the depth of the upper- and lower-middle production levels of the ore pass; and \(h_{1}\), \(h_{2}\), and \(h_{3}\), respectively, indicate the heights of the ore discharge port, the inclined chute, and the bottom ore bin.  
The ore extracted from the upper- and lower-middle production levels is discharged, respectively, from the ore discharge ports of the corresponding loading pockets. Due to the unique location of the loading pocket in the lower-middle production level, the discharged ore collides with the shaft wall after leaving the inclined chute. The impact wear of the ore may result in problems such as a deep depression and large wear range in the shaft wall, and in severe cases, it may induce large-scale collapse and damage to the shaft wall. The damage areas are mainly concentrated in the vicinity of the junction between the inclined chute and the shaft.  
When a large ore block collides with the shaft wall, the ore presses into the wall with the normal impact force \(F_{n}\) and slides relative to the shaft wall surface with the tangential impact force \(F_{t}\), resulting in damage to the shaft wall. \(\delta\), \(l\), and \(a\) are, respectively, the ore indentation depth, wear length, and contact radius between the ore and the shaft wall. As shown in Figure 2, the ore indentation depth \(\delta\) and the wear length \(l\) are coupled together to form the shaft wall damage volume \(Q\).
## 2 Results and Discussion
### Taste-Active Compounds' and Quality Markers' Accurate Quantitative Profiling by LC-UV/DAD
Valley is characterized by a fingerprint that drives its independent clustering; it is described by a peculiar pattern of _flavour-3-ols_ (Figure 3b) such as _epigallocatechingallate_ (EGCG), _epigallocatechin_ (EGC), _catechin_ (C), and _epicatechingallate_ (ECG). Experimental results on Darjeeling black tea are consistent with those obtained by [PERSON] and co-workers in 2004 [23]. Ceylon, Assam, and Kenya teas' sub-classification is mostly driven by theaflavins and _flavonol-3-o-glycosides_. On the other hand, tea samples from the Azores, Yunnan, and Darjeeling Castleton inversely correlate with these variables (weaker profiles). The quantitative distribution of monitored markers will have an impact on teas taste and trigeminal perception. Flavan-3-ols, originally present in tea leaves, can undergo substantial changes during post-harvest treatments (oxidative phenomena during fermentation), leading to the formation of high-molecular-weight dimeric (theaflavins) and oligomeric (thearubigins) derivates. For this reason, in fermented black tea, quantitative differences in flavan-3-ols and theaflavins can be ascribed to both the geographical origin and the technological processing. Conversely, the levels of flavonoids and _ caffeine_ remain unchanged during tea manufacturing, and thus, their variations are mostly influenced by the origin [3; 42]. The high amount of flavan-3-ols but the low content of theaflavins in the Darjeeling Testa Valley infusion suggests lower oxidation during post-harvest treatment, which preserves the original amount of these analytes. On the other hand, teas from Ceylon, Assam, and Kenya, due to the higher levels of dimeric analytes, are likely more fermented. In addition, theaflavins are responsible for the characteristic dark orange-red color of black tea infusions, a piece of evidence that was confirmed by a visual inspection of the infusions. Indeed, teas from Darjeeling had a light yellow color due to the low concentration of these markers, in contrast with Ceylon tea. Tea from the Azores was confirmed to be characterized by a poor non-volatile fraction for all compounds, suggesting a low astringency capacity. _Caffeine_ is present in similar concentrations in all samples (230-260 mg/L), with the only exception of Azores tea (143 mg/L); this outcome is probably related to the standardization of the infusion process (incomplete extraction), which does not allow its exhaustive extraction from the matrix.
# Throwaway Shadows Using Parallel Encoders Generative Adversarial Network
## 4 Experimental Setup  
In this section, we present the experimental setting of our shadow removal network. First, we created a synthetic shadow database and then trained our shadow removal network on it. For fair comparison, we retrained state-of-the-art works such as Edge-Connect [10], Partial Convolution [14], Gated Convolution [15] and Ghost-free Shadow removal [6] on a new synthetic database. At evaluation time, we also show results on real world shadow images, collected from the internet.  
**Database:** We trained our shadow removal network in a supervised manner. We started with 20,000 randomly selected images from the CelebA Face dataset [44] and created a synthetic shadow database. CelebA face images contain various celebrity images with wild backgrounds and were taken in diverse conditions. We used OpenFace dlib [45] to align the faces using facial landmark positions. This alignment helps the model to generate the face semantics (e.g., eyes) at the right place on the face. Finally, we generate synthetic images by placing the shadow of various objects using Adobe Photoshop. We consider shadows of various objects of different sizes and scales and placed them at various positions in the face image. Corresponding shadow mask images were also created to train the shadow detection stage of our network. Compared to the shadow dataset created by [46; 47], we focus on creating shadow images that contain shadows of various objects instead of producing a relit image with hard cast shadows.  
**Training setting:** The convolutional auto-encoder network is fed with an input shadow image and generates a binary map of the object's shadow in the input image. The generator of our shadow removal network then takes the pair of input shadow image and its corresponding mask generated by the convolutional auto-encoder and produces an output image without shadow. While the job of discriminator is to differentiate between the generated and ground truth images without shadows. We trained our network with a joint objective function as in Equation (1). The generator network produces an output face image without the shadow. The data split is 70% for training and 30% for testing. Thereis no subject overlap between the training and testing sets; both have distinct images.  
We used the Adam optimizer [48] with learning rate \(2\times 10^{-4}\) and momentum 0.5 to train the shadow removal network. Random crop and random flip techniques were used for data augmentation. We fed 10 images together in a batch. At the start, the discriminator trained quickly and the generator became weak. We first trained the generator network for one hundred epochs and then both the generator network and the discriminate network were trained for five hundred epochs to avoid this problem. We implemented our network on python using the TensorFlow platform [49]. Our training took around three days on an NVIDIA GeForce 1080 Ti graphic card. The code with the pre-trained model will be published on GitHub after acceptance of this manuscript.
## 4 Experimental Design and Results
### Comparison Algorithms
#### 4.4.2 Compression Algorithms  
* Better Portable Graphics (BPG) [55]: BPG is an image compression format that is a more efficient alternative to JPEG. It is based on the HEVC (High Efficiency Video Coding, H.265) standard, which provides superior compression while maintaining high image quality. Due to its advanced compression capabilities, high visual fidelity, and efficient storage, BPG is a significant development in image compression, making it a strong candidate for replacing traditional JPEG in modern applications.
* Lossless JPEG Standard (JPEG-LS) [56]: JPEG-LS (JPEG Lossless Standard) is a lossless and near-lossless image compression algorithm developed by the Joint Photographic Experts Group (JPEG) as part of the ISO/IEC 14495-1 standard. It is designed to provide efficient and computationally lightweight compression while ensuring perfect image reconstruction. Due to its simplicity, speed, and efficiency, JPEG-LS is an excellent choice for applications requiring high-quality lossless image compression while maintaining a low computational cost.
* Advanced Image Compression (JPEG2000) [57]: JPEG2000 is an advanced image compression algorithm developed by the Joint Photographic Experts Group (JPEG) as part of the ISO/IEC 15444 standard. Unlike traditional JPEG, it utilizes wavelet-based compression instead of the Discrete Cosine Transform (DCT), offering superior image quality at higher compression rates. Due to its high compression efficiency, superior image quality, and flexibility, JPEG2000 is an essential algorithm for applications requiring high-fidelity image preservation and advanced compression capabilities.  
These algorithms were selected based on their widespread adoption, efficiency, and compression capabilities. The DEA was evaluated against other encoding algorithms, while the S+DEA was compared with well-established lossless compression algorithms.
# Modal Verification and Strength Analysis of Bladed Rotors of Turbine in Rated Working Conditions
###### Abstract  
The Top-pressure Recovery Turbine (TRT) uses the blast furnace gas generated in the iron and steel manufacturing process to push the turbine which drives the generator to generate electricity, and the generated electric energy is supplied to in-plant equipment. In this paper, we investigate the aerodynamic force, centrifugal force, and maximum stress on the structure of the TRT rotor in rated working conditions and the positions of occurrence using the Finite Element Method (FEM), as well as discuss the dynamic characteristics of bladed disks during TRT operation through Campbell and SAFE diagrams. To confirm the effectiveness of the finite element models, the mode shapes and natural frequencies in the FEA-based modal analysis of the TRT rotor will be captured and compared with those of the practical structures through the Experimental Modal Analysis (EMA). To verify the agreement between the mode shapes of the finite element analysis and those of the actual structure, the Modal Assurance Criterion (MAC) is introduced here to confirm the reliability of the finite element model. The stress distribution on the structure in the rotation is obtained by centrifugal force analysis. The TRT rotor is driven as the blast furnace top pressure pushes the moving blade; when the rotor rotates, the moving blade bears centrifugal and periodic aerodynamic forces. The stress distribution is investigated on the structure when these forces act simultaneously using aerodynamic analysis. To discuss whether the bladed disks will resonate with the external force under the operating conditions, Campbell and SAFE diagrams are used for evaluation, and the modal parameters obtained from the EMA are used to estimate the strength and durability of the blades. According to the analysis results when the TRT rotor is in working conditions, the fatigue failure may occur at the maximum stress existing on the dovetail slot.  
Top-pressure Recovery Turbine (TRT); bladed disks structures; aerodynamic force; centrifugal force; experimental modal analysis (EMA); finite element analysis (FEA); Campbell diagram; SAFE diagrams +
Footnote †: journal: Applied Sciences
# No-Reference Blurred Image Quality Assessment by Structural Similarity Index
## 3 The Proposed IQA Metric
### Parameter Setting
#### 4.3.1 Filter Type and Parameter for Re-Blur  
As mentioned in Section 3.2, we apply a Gaussian low-pass filter to get the re-blurred image of the input image. It should be noted that we compared three types of filters for re-blur, i.e., Gaussian blur, motion blur and defocus blur. The results on LIVE II dataset are illustrated in Table 3. We can see that Gaussian blur leads to the highest SROCC. It is easy to understand such results, noting that the filter for re-blur is of the same type as the distortion of the image in LIVE II dataset. Thus we chose Gaussian filter for re-blur in the experiments. Furthermore, the size and deviation of Gaussian filter will impact running time. According to the experimental results shown in Table 4, we take the Gaussian filter of \(11\times 11\) with deviation 1.5.
## 3 Experimental Results and Discussions
### Results Discussions
#### 3.2.2 Conference Paper Dataset Results  
The confusion matrix of our model based on the Conference Paper dataset is presented in Figure 4, together with the mutuality matrix of the 10 feature segments.  
The results presented in Figure 4a show that the model classifies the WWW label better than the other labels. Using the mutuality matrix defined for the 10 feature segments as presented in Figure 4b, we also obtain information about the interrelationships between the feature segments, where segment \(X_{4}\) has the highest combined mutuality.  
Looking further into the mutuality of the features in each segment, we also found that the segment \(X_{4}\) with the highest combined mutuality contained features which were semantically related to the WWW label and whose combined mutuality was amongst the highest in all segments, such as the words \"dynamic\", \"efficient\" and \"fast\", as shown in Figure 4c. Nevertheless, the low normalized true positive (TP) value for the WWW label implies some features in the label were not semantically classified by the model under the WWW label. The incorrect semantic classification of features with respect to the labels using mutual value allocation may account for the low TP of the other labels.
## 3 Sliding Mode Path following and Control Allocation
### Control Allocation
#### 3.2.2 Over-Actuated Mode  
We propose the steps to compute \(\alpha_{i}\) and \(w_{i}\):  
Step 1: Initially, set the tilt angles \(\alpha_{i}=0\) ( \(i=1,2,3,4\)).  
Step 2: Compute \(f\) and \(\tau\) from (32) and (29).  
Step 3: Compute the rotor velocities from (35).  
Step 4: Compute the tilt angles from (23)-(25) as follows:  
\[\Bigl{(}k_{f}s_{2}\Bigr{)}w_{2}^{2}+\Bigl{(}-k_{f}s_{4}\Bigr{)}w_{4}^{2}=\tau _{\phi} \tag{38}\]  
\[\Bigl{(}Lk_{f}c_{2}-k_{m}s_{2}\Bigr{)}w_{2}^{2}+\Bigl{(}-Lk_{f}c_{4}+k_{m}s_{4 }\Bigr{)}w_{4}^{2}=\tau_{\phi} \tag{39}\]  
\[\Bigl{(}-k_{f}s_{1}\Bigr{)}w_{1}^{2}+\Bigl{(}k_{f}s_{3}\Bigr{)}w_{3}^{2}=\tau _{\theta} \tag{40}\]  
\[\Bigl{(}-Lk_{f}c_{1}-k_{m}s_{1}\Bigr{)}w_{1}^{2}+\Bigl{(}Lk_{f}c_{3}+k_{m}s_{ 3}\Bigr{)}w_{3}^{2}=\tau_{\theta} \tag{41}\]  
where \(s_{i}=\sin\alpha_{i}\) and \(c_{i}=\cos\alpha_{i}(i=1,\ 2,\ 3,\ 4)\). Using the triangular identities \(s_{i}^{2}+c_{i}^{2}=1\) (\(i=1,\ 2,\ 3,\ 4\)), one can use the numerical method to solve for \(\alpha_{i}\) in the system of nonlinear equations.  
Step 5: Go to Step 2 to continue the iteration.