esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiobjective Optimization Design for a MR Damper Based on EBFNN and MOPSO
## 3 Electromagnetic Field Simulation of the Designed MR Damper
### Material Properties  
The magnetically conductive components of the MR damper, such as the magnetic core, magnetic rings, and magnetic sleeves, are made of No. 10 steel; while the non-magnetically conductive components, such as the nonmagnetic sleeve and nonmagnetic disks, are made of the stainless steel. Figure 3 shows the magnetization curve of No. 10 steel. In addition, MRF-J2 ST MR fluid, which was produced by Chongqing Institute of Materials in China [31; 32], was used in this work. Figure 4a shows the magnetization curve of MR fluid. The relationship between the magnetic flux density \(B\) and shear stress \(\tau\) is nonlinear, as shown in Figure 4b. The yield stress can be determined by the polynomial approximation:  
\[\tau=a_{3}\times B^{3}+a_{2}\times B^{2}+a_{1}\times B \tag{6}\]  
where \(a_{1}\), \(a_{2}\), and \(a_{3}\) are the polynomial coefficients of the yield stress at the damping gap varying with the magnetic flux densities. Here, \(a_{1}\) = 11.25 kPa/T, \(a_{2}\) = 58.92 kPa/T\({}^{2}\), \(a_{3}\) = \(-\)24.52 kPa/T\({}^{3}\).  
Figure 3: Magnetization curve of No. 10 steel.
## 3 Samples and Analytical Methods
### Analytical Methods
#### 3.2.2 Rock Eval Analysis  
The fundamental purpose of a Rock Eval analysis is to offer several pyrolysis parameters for assessing the abundance, type, and maturity of organic matter in source rocks. Multiple parameters are combined into the following three fundamental pyrolysis parameters: soluble hydrocarbon content S\({}_{1}\) (mg (hydrocarbon)/g (rock)), pyrolysis hydrocarbon content S\({}_{2}\) (mg (hydrocarbon)/g (rock)) and pyrolysis hydrocarbon peak temperature T\({}_{\rm max}\) (\({}^{\circ}\)C). The Rock Eval 7 rock pyrolysis instrument is used for rock pyrolysis analysis, and the analysis standard is GB/T 18602-2012.
# Radiometric Identification of Signals by Matched Whitening Transform
## 2 Framework for Radiometric Identification
### Classification by Matched Whitening  
The data are organized in an \(N\times M\) matrix \(\mathbf{X}=[\mathbf{x}_{1},\mathbf{x}_{2},\dots,\mathbf{x}_{M}],\mathbf{x}_{i }\in\mathbb{R}^{N\times 1}\) where \(M\) is the number of measurements and \(N\) is the number of variables, or dimensions. For the IQ data, \(N=2\) and \(M\) is the number of symbols in the record. Let \(\mathbf{W}_{i}\), \(i=1,2,\dots,m\) be the whitening transform matrices for \(m\) source signals \(\{c_{1},c_{2},\dots,c_{m}\}\). The class-dependent whitening matrices are computed offline from the training data. Since the IQ data are affected by phase and frequency offsets, the data need to be corrected before the whitening matrices are calculated. The test data are partitioned into blocks used to generate statistics. There is no \"correct\" block length. It depends on the rate of change of phase, frequency offset or Doppler shift. In the case of nonlinear phase offset, block lengths are chosen short enough to insure near stationary phase during phase estimation. More on how to choose the block length for reversing the frequency offset appears in Section 3.  
Let \(\mathbf{X}_{j}\in\mathbb{R}^{2\times M}\) be the \(j\)th block. The unknown measurement vector is repeatedly whitened by \(\mathbf{W}_{i},\forall i\).  
\[\mathbf{Y}_{i} =\mathbf{W}_{i}\mathbf{X}_{j},i=1,2,\dots,m \tag{2}\] \[cov(\mathbf{Y}_{i}) =\mathbf{I}\iff\mathbf{X}_{j}\in c_{i}\]  
The covariance matrix of the whitened data is an identity matrix if and only if \(\mathbf{W}_{i}\) matches the data that are projected on it. In other words, the whitening matrix can only whiten its own data. Conversely, if the unknown data are whitened, the data belongs to the same class that the whitening matrix came from.  
To illustrate this point, three multivariate normal populations are created and shown in Figure 1a. The 3 rd data set (in black) is used as the \"unknown\" source and is repeatedly projected on \(\mathbf{W}_{i},i=1,2,3\). After each projection, the scatter diagram is plotted and shown in Figure 1b-d. When the data from group 3 is whitened by \(\mathbf{W}_{1}\), Figure 1b, the major axis of the projected data appears at an angle to the principal axis of the projection matrix. This indicates that the data and the whitening matrix are mismatched. Repeated projections produce Figure 1b-d. It is only in Figure 1d that the whitening transformation produces a circular scatter diagram. The projection that produces the least correlated data identifies the brand. This property indicates that the source of the unknown data matches the whitening transform of group 3. The detector can be implemented as a bank of parallel matched filters shown in Figure 2.
## 2 Existing Brittleness Indices  
There are numerous ways to introduce brittleness of rock masses. Modern review papers [3; 4] highlight dozens of ways to calculate brittleness indices. [PERSON] et al., 2019 [4] consider three major groups of brittleness indices:  
1. Mineral-based brittleness indices (MBIs) calculated from the relative fractions of different minerals contained in the rock mass;
2. Log-based brittleness indices (LBIs) calculated from empirical correlations between particular well logs data and brittleness;
3. Elastic-based brittleness indices (EBIs) calculated from elastic moduli characterizing the mechanical behavior of the rocks.  
This classification is mainly based on the type of geophysical data used for brittleness evaluation. Not only properties of rocks in use are considered for brittleness index to be classified, but also their typical scale matters, as different types of data are related to either seismic, logging, and core scales. We will use a slightly modified version of this classification in the current paper. According to the classification described above, certain ways of calculating brittleness of dynamic elastic moduli belong to the third group ([PERSON] et al., 2019 [4] set them as a distinct sub-group of EBI). Nevertheless, widely applied methods of estimating brittleness from sonic logs (based on the definition suggested by [PERSON] et al., 2008 [16]) are purely elastic-based, yet use well logs as raw data. Therefore, we will change the third group from elastic-based brittleness indices (EBI) to test-based brittleness indices (TBI). According to this modified classification, TBIs are brittleness indices obtained from detailed analysis of deformation curves or measurements of elastic waves velocities in laboratory conditions, while LBIs use any form of well logs data interpretation, including dynamic elastic moduli profiles, as input for brittleness estimation. This modification is also needed to take scaling factor into consideration, as LBI and TBI correspond to log and core spatial scales respectively. Note that calculations of certain forms of LBIs imply usage of dynamic elastic moduli. These moduli can be obtained both from well logs and laboratory tests on core samples, so there is a direct opportunity to consider these LBIs both at well logs and core scales.  
Figure 1 represents the classification of brittleness indices used in the current study. Spatial scale of brittleness index derived from each approach is added into consideration.  
Brittleness indices have been extensively studied and discussed. Review papers by [PERSON] et al., 2019 [4] and [PERSON] et al., 2016 [3] provide a lot of information on ways to deal with brittleness. We will provide brief information of different ways to calculate brittleness indices with emphasizing the inner structure and scaling factors below.
# Utilizing Graphite Waste from the Acheson Furnace as Anode Material in Lithium-Ion Batteries
## 3 Results and Discussion  
### Characterization of Jet-Milled Graphite Waste  
The carbon purity of jet-milled graphite waste (JMGW) was thoroughly analyzed using both proximate analysis (PA) and X-ray fluorescence (XRF). The PA results indicate that JMGW consists of 98.5% fixed carbon, with a minimal presence of volatile matter (0.8%) and ash (0.7%) (Table S1). The high level of fixed carbon is a clear indicator of JMGW's suitability for applications requiring high-purity carbon. Furthermore, the XRF analysis confirmed that the carbon purity of JMGW is as high as 99.9% (Table S2). These complementary results collectively demonstrate that JMGW boasts excellent carbon purity, effectively removing the need for additional purification processes. Given this level of purity, JMGW could be directly utilized in various industrial applications, including electrode materials for energy storage systems and carbon-based composites. The direct application of JMGW could reduce overall production costs, energy consumption, and carbon footprint, as fewer resources would be required for further refinement or purification. Such features are especially appealing for industries aiming to implement more sustainable production practices.  
To further explore the crystalline properties of JMGW, X-ray diffraction (XRD) analysis was performed (Figure 3 and Table 1). The XRD results indicate that JMGW possesses high crystallinity, evidenced by a d-spacing value of 0.3360 nm for the (002) plane (d\({}_{002}\)) and a graphitization degree (GD) of 93.5%. In comparison, commercial anode materials (CAM) exhibit a d\({}_{002}\) value of 0.3359 nm and a GD of 94.2%. This close proximity in crystallinity between JMGW and CAM suggests that JMGW is highly compatible with commercial graphite anode materials, making it a strong candidate for integration into lithium-ion battery (LIB) applications (Figure S1 and Table S3). The ability of JMGW to meet the crystallinity standards required for LIBs underscores its potential for use in large-scale energy storage solutions.  
In addition to crystallinity, the particle size (PS), specific surface area (SSA), and tap density (TD) of JMGW were measured and presented in Table 2. The results show that JMGW has a PS of 9.3 \(\upmu\)m, a span of 1.16, an SSA of 14.8 m\({}^{2}\)/g, and a TD of 0.27 g/cc. These values highlight the material's potential but also suggest that improvements are necessary, particularly in SSA and TD, to meet the performance benchmarks set for LIB anode materials. To address these issues and improve JMGW's viability as a LIB anode material, a spheronization process was employed. Spheronization is widely recognized for its ability to modify particle morphology, thereby enhancing physical properties such as PS, SSA, and TD.
## 4 Evaluation of System Presentation and Efficacy
### System Evaluation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Agent** & **HMI Elements** & **DIR** & **Description** & **BI** & **Average BI** \\ \hline \multirow{6}{*}{PHI} & RDBOT & 1.303242 & A little bit needs to improve & \multirow{6}{*}{0.016884} \\  & CBOT & 0.926256 & A little bit needs to improve & \multirow{6}{*}{0.008430} \\  & RKBOT & 0.930887 & A little bit needs to improve & \multirow{6}{*}{0.003165} \\  & PDBOT & 0.926256 & A little bit needs to improve & \multirow{6}{*}{0.008430} \\ \hline \multirow{6}{*}{RI Agent} & GMAP & 1.266055 & A little bit needs to improve & \multirow{6}{*}{0.008430} \\  & GPS & 0.904325 & A little bit needs to improve & \multirow{6}{*}{0.008430} \\  & RDBOT & 0.904325 & A little bit needs to improve & \multirow{6}{*}{0.003165} \\  & NAVI & 1.000834 & Approximately optimal balance & \multirow{6}{*}{0.0085242} \\  & FB & 1.000834 & Approximately optimal balance & \multirow{6}{*}{0.005242} \\ \hline \multirow{2}{*}{BS Agent} & SBOT & 1.270882 & A little bit needs to improve & \multirow{6}{*}{0.005242} \\  & QBOT & 0.903256 & A little bit needs to improve & \multirow{6}{*}{0.005242} \\ \cline{1-1}  & WBOT & 0.903256 & A little bit needs to improve & \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Evaluation results of design preference to importance ratio (_DIR_) with average balancing index (_BI_) = 0.008430 of proposed agents.
## 4 Experiments
### Ablation Studies
#### 4.4.3 Visual Comparison  
In this section, based on the COCO dataset, the validation accuracy and loss changes were visually compared during the process of training models with different design elements, as shown in Figure 5. From the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics, it is clear that the models with different design elements exhibit three different levels of model performance. The Slicing and DSConv methods, each with only a single design element, are at the lowest level of model accuracy. The proposed SliceSamp and SliceUpsamp methods, with both slicing and DSConv elements, perform similarly to Strided Conv and show a trend of further improvement after the 50 th epoch. The SlicingConv3\(\times\)3 method, which combines Slicing and 3 \(\times\) 3 convolution, achieves the highest model performance yet, as mentioned before, it is more computationally expensive. In terms of the loss changes on the validation set, the loss values of all models also converged to three levels. All models showed a pattern of lower loss values with higher mAP values. However, it is noteworthy that the proposed method converges to lower loss values on Object loss after the 40 th epoch, which is similar to the Strided Conv method on Class and Box losses. It can be seen that our method has better generalization compared to the baseline Strided Conv.  
Meanwhile, based on the VOC dataset, the validation accuracy and loss changes were visually compared during model training with 300 epochs, as shown in Figure 6. As seen from the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics, the Slicing and DSConv methods still exhibit the lowest model accuracy. Although the SlicingConv3\(\times\)3 method achieves the highest model performance, the validation accuracy gradually decreases and exhibits overfitting after the 200 th epoch. However, the accuracy of the proposed SliceSamp and SliceUpsamp methods continues to steadily improve after the 200 th epoch. In particular, in terms of the [EMAIL_ADDRESS]:0.95 metric, our method is significantly higher than the Strided Conv method after the 50 th epoch. In terms of loss convergence on the validation set, on the Class, Object, and Box losses, both the Slicing and DSConv methods converge to higher values, and the  
Figure 5: Validation accuracy and loss on the COCO dataset during model training.  
SlicingConv3\(\times\)3 method converges to lower values. Although our methods (SliceSamp and SliceUpsamp) have slightly higher Class loss values than Strided Conv, they converge to lower loss values on the Object and Box losses. It is noteworthy that SliceUpsamp exhibits loss values close to those of the SlicingConv3\(\times\)3 method on the Object and Box losses after the 250 th epoch. Moreover, when SlicingConv3\(\times\)3 is overfitting on Object loss, SliceUpsamp still maintains a low loss value, showing good robustness.
# A Survey on Mission-Profile-Based Stress Testing of Electric Motor Drives
## 3 Mission-Profile-Based Stress Testing Methods
### Stress Testing Methods
#### 3.2.1 Testing Electric Motors
A drawback of the proposed method is that it utilizes only the temperature profile, while other factors are neglected. In [66], the electric machine is subjected to three different temperature profiles. These profiles, each with different average temperature values, were applied, and based on the experimental results, the time to failure was estimated. The paper [67] investigates the impact of PDs on stator insulation in motors fed by a PWM inverter. The occurrence of PDs is observed through two types of tests: off-line PD detection in the electric stator and on-line PD detection in the electric motor associated with a PWM power supply. A similar test procedure is presented in [68]. The proposed test method utilizes surge-test-based identification of stator insulation components with PD activity for induction machines. The method for mechanical stress testing of PMSM was developed in [69]. The mechanical stress on the rotor core is analyzed under different speed profiles. Experimental verification is also conducted by measuring the deformation of the rotor core in relation to the rotor's rotating speed. The impact of mechanical and electrical stress is further examined in [70]. The AST is conducted on the bearings of an electric motor to estimate their remaining useful life. According to the authors, the vibration profile over time is used to calculate the induced damage to the bearings. However, a limitation of this method is that it has not been tested on a real mission profile, which would allow for a more accurate estimation of the bearings' lifespan. The problem of voltage sags in electric machines is investigated in [71]. The induction machine is tested under sags type A, B, and C defined by IEEE 1159-1995 standard. An induction machine is tested under types A, B, and C of sags, as defined by the IEEE 1159-1995 standard. Experiments have shown significant insulation damage due to increased current in the windings. Mechanical damage and lifetime degradation are also observed. While the experiments highlight significant damage, further research is needed to assess the effectiveness of protective measures, like voltage sag compensators, in extending the lifespan of induction motors.
# Spherical-Model-Based SLAM on Full-View Images for Indoor Environments
## 4 Method Overview
### Keyframe and Map Point Insertion  
To maintain our system function, as the camera moves, new keyframes and map points are added to the system to include more information about the environment.  
After the tracking procedure, the current frame is reviewed to determine whether it is a qualified keyframe using the following conditions:  
1. The tracking did not fail at this frame;
2. The recovery procedure has not been activated in the past few frames;
3. The distance from the nearest keyframe is greater than a minimum distance;
4. The time since the last keyframe was added should exceed some frames.  
Map point insertion is implemented when a new keyframe is added. First, feature points in the new keyframe are considered as candidate map points. Then, we determine whether the candidate point is near successfully observed map points in the current view. This step helps us to discard candidate points that may already be on the map. Next, we select the closest keyframe by distance since map point insertion is not available from a single keyframe. To establish a correspondence between the two views, we use the spherical epipolar search to limit the search range to a small, but accurate range. Based on the tight search range, spherical matching for the candidate map point is more reliable. If a match has been found, the candidate map point is triangulated. However, until this step, we cannot guarantee the candidate map point is a new point which is not on the map. Therefore, we compare the 3D candidate map point with each existing map point by projecting them onto the first and current keyframe image. If both offsets on the two images are small, we conclude they are the same point, and then connect the current frame information to the existing map point. Finally, the remaining candidate map point is inserted into the map.
## 3 Results and Discussion
### Thermal Behaviour Analysis
It is important to note that DSC analysis is highly sensitive to measurement uncertainties, particularly due to factors such as baseline drift, instrument calibration, and small sample sizes. Minor variations in sample mass and potential contamination can influence heat flow measurements, which may introduce slight discrepancies in the reported values. While the DSC results in this study provide insights into the relative thermal behaviours of the composites, they may not fully capture the bulk properties of the composite materials.
# Chinese Lip-Reading Research Based on ShuffleNet and CBAM
## 4 Conclusions  
The paper proposes a lip-reading recognition model on our self-built dataset. The current state-of-the-art methodology consists of a ResNet network and a temporal convolutional network. We evaluate and analyze several advanced methodologies and find that our model achieves a reduction in computing power by 60% and comparable recognition accuracy compared with the ResNet network, which has the best accuracy. Firstly, we replace the ResNet network with the lightweight network ShuffleNet to fit into practical scenarios for mobile applications. Secondly, we improve the performance by inserting an attention model, which helps the network focus on the most important information in the feature maps. Thirdly, we train the model with variable-length frames and fixed-length frames, and the results show that the model trained with variable-length frames is more robust if random frames are removed at the cost of loss of recognition accuracy. Finally, we expand the dataset built by our laboratory and in the future we will collect more datasets for Chinese lip-reading research. Abbreviation shows the full name of used symbols in the paper.  
Data curation, Y.F.; Software, Y.L. and Y.F; Supervision, Y.L.; Visualization, R.N.; Writing--review & editing, Y.F. All authors have read and agreed to the published version of the manuscript.  
This research was supported by the National Natural Science Foundation of China (61971007 and 61571013).  
Not applicable.  
The authors declare no conflict of interest.  
The temporal Convolutional Networks  
Convolutional Block Attention Module  
Convolutional Neural Network  
Long Short-term Memory  
Connectionist Temporal Classification  
Lip Reading in the Wild  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Randomly Drop N Frames** & **N = 0** & **N = 1** & **N = 2** & **N = 3** & **N = 4** \\ \hline ShuffleNet (0.5\(\times\)) & Fixed-Length Training & 71.2\% & 62.1\% & 51.7\% & 43.8\% & 37.1\% \\ +CBAM & Variable-Length Training & 68\% & 66.3\% & 61.9\% & 57.1\% & 54.2\% \\ \hline \hline \end{tabular}
\end{table}
Table 5: Different performance of the model consisting of a ShuffleNet (0.5\(\times\)) with CBAM.
# Application of Image Fusion in Diagnosis and Treatment of Liver Cancer
## 5 Clinical Applications of Image Fusion in Liver Cancer Diagnosis and Treatment  
### Ultrasonography/MRI, Ultrasonography/CT and Ultrasonography/PET-CT  
Ultrasonography, a traditional, practical and convenient imaging technology, presents major advantages in the diagnosis of liver cancer. The recently developed ultrasound-based fusion imaging technology plays an important role in early diagnosis and ensuring minimally invasive procedures [170]. Several liver tumor types exist, including benign and primary liver malignant tumors and metastatic liver cancer. Benign liver lesions include hemangioma, focal nodular hyperplasia (FNH) and hepatic adenoma. Primary liver malignancies originate from hepatocytes, intrahepatic bile duct epithelial cells, endothelial cells and connective tissue. A number of tumor types, such as FNH and hepatic adenoma, have similar manifestations in enhanced CT or dynamically-enhanced MRI, and are therefore often difficult to distinguish. As the concept of fusion imaging technology was gradually implemented in clinical practice in the 1990s, ultrasonography was initially applied to the field of image fusion to identify the types of liver tumor [171].  
CEUS/CT and CEUS/MRI fusion images were successfully generated and employed to dynamically observe blood flow in lesions and the blood perfusion of tumors, facilitating the diagnosis of different types of intrahepatic lesions, and in particular, minor lesions. CEUS/CT and MRI fusion imaging clearly achieves higher diagnostic efficacy than CEUS, CT or MRI alone. At present, image fusion technology based on CEUS is mainly applied for (1) diagnosis and treatment of small liver cancer, (2) the evaluation of minimally invasive treatment methods, such as liver tumor TACE and RFA, and (3) the early diagnosis and treatment of new or recurrent liver cancers or liver metastases after surgery.