esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Proposed Cross-Working Condition RUL Prediction Method
### JTMN-Based RUL Prediction  
Traditional intelligent prediction models require that the target and training data obey independent and identical distribution. But cross-working condition situations frequently occur in real industrial environments, so the distribution discrepancies of data for training and testing often hinder promotion. While DA techniques are widely used in RUL forecasting to address this issue, RUL prediction performance needs to continue to improve to meet production needs. The performance of discrepancy metric criteria depends on the RUL prediction results using DA technology. Therefore, to build a more satisfactory intelligent prediction model aimed at cross-working condition situations, a joint domain adaptation loss is proposed to embed into a mapping regression network to build a joint transfer metric network (JTMN). The joint loss is designed to acted on the final layer of the mapping regression network combining RSD and VDR, which can weaken data distribution discrepancies and ultimately obtain cross-domain invariant features. Based on the proposed JTMN-based RUL prediction model, the RUL of rolling bearings under cross-working conditions can be predicted more precisely.
## 4 Discussion
Furthermore, the integration of more sophisticated image processing techniques, including optical flow or frequency-domain analysis, could improve detection sensitivity and robustness in noisier conditions. These enhancements would extend the utility of our system not only for cardiac monitoring but also for analyzing other physiological parameters such as respiration or vascular dynamics. Although validation of the heart rate range is not sufficient to quantitatively assess the performance or reliability of the estimation method, it is useful for initial screening and for roughly checking whether the estimates are deviating significantly. Motion extraction using grayscale conversion and frame difference is a standard and general-purpose method in video processing and is easy to implement in that it does not require special preprocessing or highly accurate region extraction. Compared to other advanced heartbeat detection methods, it may be slightly inferior in accuracy, but it has the advantage of being computationally low and quick and easy to apply. Compared to complex methods aimed at high-precision measurement, this method is suitable for preliminary experiments and rapid screening applications, making it an appropriate choice depending on the usage environment and purpose. This paper does not solve the problem of limited scalability in high-throughput experiments, but it proposes a simple heartbeat detection method that may replace existing expensive solutions in the future.
# Research on Fault Diagnosis of Rotating Parts Based on Transformer Deep Learning Model
###### Abstract  
The rotating parts of large and complex equipment are key components that ensure the normal operation of the equipment. Accurate fault diagnosis is crucial for the safe operation of these systems. To simultaneously extract both local and global valuable fault feature information from key components of complex equipment, this study proposes a fault diagnosis network model, named MultiDilatedFormer, which is based on the fusion of transformer and multi-head dilated convolution. The newly designed multi-head dilated convolution module is sequentially integrated into the transformer-encoder architecture, constructing a feature extraction module where the complementary advantages of both components enhance overall performance. Firstly, the sample is expanded into a two-dimensional feature map and then input into the newly designed feature extraction module. Finally, the diagnostic output is performed by the designed patch feature fusion module and classifier module. Additionally, interpretability research is conducted on the proposed model, aiming to understand the decision-making mechanism of the model through visual analysis of the entire decision process. The experimental results on three different datasets indicate that the proposed model achieved high accuracy in fault diagnosis with relatively short data windows. The highest accuracy reached 97.95%, which was up to 10.97% higher than other models. Furthermore, the feasibility of the model is also verified in the actual dataset of the rotating parts of the injection molding machine. The excellent performance of the model on different datasets demonstrates its effectiveness in extracting comprehensive fault feature information and also proves its great potential in practical industrial applications.  
fault diagnosis; deep learning; multi-head attention; model interpretability +
Footnote †: journal: applied sciences
## 1 Introduction
In this paper, an adaptive method of parameter tuning for VSG-based VSCs providing both inertia response (IR) and PFR for counteracting the load disturbances is presented based on quantitative performance analysis. The contributions of this work could be summarized as follows:  
(1) The analysis of frequency response dynamics, based on the simplification of the transfer function down to the second-order form, is conducted. Performance indices are proposed, and the frequency responses influenced by different parameters are analyzed. (2) It is noted that the performance indices are influenced by composite parameters, but the hierarchical stages of IR and PFR operate in chronological order. Therefore, a parameter tuning algorithm is designed in which the parameters are also determined following a chronological sequence. (3) The adaptive parameter tuning approach is verified when load disturbances in different magnitudes happen. The proposed parameter approach could be used to enhance the flexibility and response time as well as maintain the required frequency stability. The rest of the paper is organized as follows: Section 2 presents the VSG algorithm to perform frequency response. Section 3 analyzes the parameter influence. Through analyzing the composite influence on system performances by different parameters, Section 4 proposes a parameter tuning algorithm by consideration of the time sequence of performance indices in the multiple frequency control stages. Section 5 verifies the proposed algorithm by simulation.
# A Solution Procedure to Improve 3D Solid Finite Element Analysis with an Enrichment Scheme
## 2 Finite Element Formulation Enriched by Cover Functions  
In this section, we revisit the formulation of enriched 3D solid finite elements that has a well-established mathematical background and is free from the linear dependence problem [5; 24]. Among 3D solid finite elements, we consider the 5-node pyramidal, 6-node prismatic, and 8-node hexahedral elements shown in Figure 1, which utilize a set of piecewise linear shape functions as the partition of the unity function for each element, and 4-node tetrahedral element based on the shape function of the standard finite element method. For the piecewise linear shape function of the 5-node pyramidal, 6-node prismatic, and 8-node hexahedral element, each element is divided into several tetrahedral sub-domains using the set of points including \(n\) nodes and \(n_{quad}\) centers of the quadrilateral faces as shown in Figure 2.  
Each set of points is assigned as the vertices of a tetrahedral sub-domain. A total of 24, 14, and 8 sub-domains are defined for the 5-node pyramidal, 6-node prismatic, and 8-node hexahedral elements, respectively [24]. Information on the tetrahedral sub-domain for each element is omitted for simplicity in this paper and can be found in Ref. [24]. Note that the 4-node tetrahedral element does not have a tetrahedral sub-domain. For each tetrahedral sub-domain, the linear shape functions corresponding to node \(i\) are given by  
\[h_{i}^{n}=(a_{i}+b_{i}r+c_{i}s+d_{i}t)/n, \tag{1}\]  
in which coefficients \(a_{i}\), \(b_{i}\), and \(c_{i}\) for the piecewise linear shape functions are not included in this paper for the sake of simplicity, see Ref. [24] for detailed information, and \(n\) is the number of nodes in the element. Figure 1: Coordinate systems for 3D solid finite elements: (**a**) global Cartesian coordinate system \((x,y,z)\), local Cartesian coordinate system \((\overline{x},\overline{y},\overline{z})\), and nodal local coordinate systems \((\xi_{i},\eta_{i},\zeta_{i})\) for each node, and (**b**) the natural coordinate systems for 8-node hexahedral, 6-node prismatic, 5-node pyramidal, and 4-node tetrahedral solid elements. Figure 2: Set of points for the tetrahedral sub-domains for (**a**) the 8-node hexahedral, (**b**) 6-node prismatic, and (**c**) 5-node pyramidal element. The interpolation of geometry for the enriched 3D solid finite elements is given by  
\[\mathbf{x}(r,s,t)=\sum_{i=1}^{n}h_{i}^{n}(r,s,t)\mathbf{x}_{i}\text{ with }\mathbf{x}_{i}=[x_{i} \quad y_{i}\quad z_{i}]^{\text{T}}, \tag{2}\]  
where the vector \(\mathbf{x}_{i}\) denotes the position of node \(i\) in the global Cartesian coordinate system in Figure 1a, \(n\) is the number of nodes, and \(h_{i}(r,s,t)\) is the shape function corresponding to node \(i\) defined in the natural coordinate system in Figure 1b.
# An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks
## 3 Results
### Protocol Composition
#### 3.1.5 Overhead Analysis  
As the proposed protocol depends on the Immediate Dependency Relation protocol [14], the size of the control information of message \(m\) depends on the number of concurrent messages that form an IDR with \(m\).  
In the internal group, all of the messages are sequentially numbered. As a message \(m\) cannot form an IDR with more than \(n\)-1 messages (\(n\) is the number of processes in a system), its dependency vector cannot contain more than the \(n\)-1 set bits. However, the set bits can be separated by cleared bits. Let \(m_{1}\) be the message with the lowest sequence number to form an IDR with \(m\). Then a bit vector can have no more bits than the number of messages concurrent to \(m_{1}\) that exist in a system. As message delay is finite, then each process can generate a finite number of messages concurrent to \(m_{1}\). Thus, a total number of messages concurrent to \(m_{1}\) is also finite and it is proportional to a number of processes in a system producing an overhead of O(\(n\)) bits.  
In the external group, message dependencies are represented as a combination of dependencies on external messages and dependencies on internal messages. The number of elements that represent dependencies on external messages are limited by the number of processes in an external group (peers and super peers), thus limiting a number of pairs that represent message dependency to O(\(l\)) (\(l\) is the number of peers and super peers in the external group). As vector \(l\) in the external message, its size is limited by the number of messages that can be generated in the external and internal groups producing an overhead of vector \(l\) to O(\(n\)) (the same as overhead in the internal group).  
We notice that in our protocol, as for the minimal causal algorithm in [14], the likelihood that the worst case will occur approaches zero as the number of participants in the group grows. This is because the likelihood that \(k\) concurrent messages occur decreases inversely proportional to the size of the communication group. This behavior has been shown in [14].
# A Multi-Agent Optimization Approach for Multimodal Collaboration in Marine Terminals
## 2 Problem Description and Modeling Approach
### Problem Description
#### 2.1.1 Truck Scheduling at Marine Terminals  
Uncoordinated arrivals of trucks in marine terminals is a major problem caused by the unpredictability and rising volume of truck arrivals. A widely adopted solution to this problem in the literature is the implementation of a Truck Appointment System (TAS) [6]. Appointment systems are designed to reduce the impact of a concurrent influx of arrivals by limiting the number of trucks admitted during each time period. A time period is the temporal interval used to measure access rights in an appointment system (e.g., 1 h). TASs are often considered mandatory [7] for all trucks entering the port. This study also assumes a mandatory appointment system, where all logistics companies must request specific slots for their trucks. A slot grants a truck the right to access the terminal within a designated time period. The proposed system, however, does not operate on a simple First-Come, First-Serve (FCFS) basis. Decentralization has been proposed as a way to enable collaborative appointment scheduling between trucking companies and terminals [8]. Inspired by this concept, our work adopts a similar philosophy. In line with [9], the proposed system arbitrates between conflicting truck requests based on company preferences, with the overall objective of minimizing the maximum deviation cost from the original requests for all logistics companies. This objective introduces fairness in truck scheduling operations among different logistics companies. Fairness is introduced through an arbitration process that aims to reduce the incurred deviations per company.  
Trucks are anticipated to enter the marine terminal to fulfill specific tasks. These tasks can be categorized into pickup tasks, involving the retrieval of cargo from the terminal, and delivery tasks, which entail transporting cargo to be exported through the terminal. The complexities of truck drayage within a marine terminal vary with the type of task [10]. In this study, all scheduled tasks are assumed to be related to a vessel's arrival at the terminal. This operational framework aligns with the principles outlined in [11], wherein the concept of vessel-dependent time-windows is introduced. A Vessel-Dependent Time-Window (VDTW) is defined as a set of time periods during which specific trucks, based on their tasks, are granted terminal access. Separate time-windows are designated for pickups and deliveries: delivery jobs are expected to be scheduled near the vessel's arrival, while pickup jobs occur after the vessel has unloaded its cargo. This study omits any interactions between trucks performing different jobs, similar to [12], with aspects such as minimizing empty runs [9; 13] not being considered.
## 5 Results and Discussions
### Feasibility Analysis
This comparison elucidates the speed error performance of both the STSM-MARS and conventional PI-MARS control systems during motor startup, load surge, and speed surge events, as depicted in Figure 6.  
As depicted in Figure 9, upon motor initialization, the rotor position error of the STSM-MARS control system proposed in this paper is 0.012 rad, whereas the rotor position error of the conventional PI-MARS control system is 0.036 rad. At 0.3 s, when the load abruptly increases to 20 N, and there is a sudden increase in speed, the rotor position error of the STSM-MARS control system is \(-\)13 r/min, while the rotor position error of the conventional PI-MARS control system is \(-\)17 r/min. Similarly, at 0.5 s, the rotor position error of the STSM-MARS control system is 0.009 rad, while the rotor position error of the conventional PI-MARS control system is 0.019 rad. This comparison elucidates the rotor position error performance of both the STSM-MARS and conventional PI-MARS control systems during motor startup, load surge, and speed surge events. As illustrated in Figure 10, under external disturbances and model uncertainties, the proposed STSM-MARS control system outperforms the conventional PI-MARS control system by achieving significantly smaller rotor speed error and faster convergence to steady-state conditions. Compared to the conventional PI-MARS algorithm, the STSM-MRAS algorithm demonstrates a capacity for diminishing rotor speed fluctuation and position estimation errors while achieving quicker convergence toward zero. This observation suggests that the STSM-MRAS algorithm exhibits superior steady-state performance and robustness compared to the traditional PI-MARS algorithm.
# CFormerFaceNet: Efficient Lightweight Network Merging a CNN and Transformer for Face Recognition
## 2 Related Works
MobiFace [19] uses a residual bottleneck layer with an extended layer to construct the model, thereby optimizing the information contained within the output vector, delivering outstanding results, and achieving a notable accuracy of 99.73% on the LFW dataset. ELANet [17] was proposed based on MobileFaceNet and it was able to acquire diverse multi-scale, multi-level characteristics and also discern local features. However, these methods are all based on CNNs. UnifiedFace [39] proposed an efficient and lightweight CNN based on GhostNet [14] for embedded or mobile devices known as GhostFaceNet, which significantly reduced the computational requirements of the model. This paper presents a novel lightweight face recognition model that combines a CNN and Transformer by merging the two architectures organically. The contributions of this work are as follows:  
1. A novel and efficient lightweight model known as CFormerFaceNet, which combines a CNN and Transformer for face recognition, is proposed in this study. This model significantly reduces the number of parameters and computational complexity while maintaining high performance. 2. A Group Depth-Wise Transpose Attention (GDTA) block is designed to effectively capture both local and global representations to mitigate the issue of limited receptive fields in CNNs, without increasing the parameters and Multiply-Add (MAdd) operations. 3. Cross-covariance attention is used to incorporate the attention operation across the feature channel dimension instead of the spatial dimension, which reduces the quadratic complexity of the original self-attention operation in terms of the number of tokens to a linear complexity. As a result, global information is effectively encoded implicitly.
# Geometrical Degrees of Freedom for Cellular Structures Generation: A New Classification Paradigm
## 2 Design and Geometrical Consideration
### Overview of Representative Volume Elements
#### 2.2.3 Triply Periodic Minimal Surfaces  
Minimal surface areas have been researched for more than 200 years. A minimal surface area is a surface with the smallest area bounded by a contour [86]. Triply Periodic  
Figure 1: Strut-based unit cells. From left to right: grid, \(X\) (or BCC), diamond, vintiles, octet. Made using IntraLattice [82].  
Figure 2: Extruded 2D unit cells with visualizations capturing a three-unit cell, a top view, and an isometric view of each configuration: (**a**) circular (c = 9.8 mm, t = 0.6 mm); (**b**) hexagonal (c = 3.125 mm, t = 0.6 mm); and (**c**) triangular (c = 9.14 mm, t = 0.6 mm) [85].  
Minimal Surfaces (TPMS) are minimal surfaces in all three x, y and z directions.They are defined by an implicit function such as \(f(x,y,z)=t\) describing the locus of points at which the function takes the value \(t\). To generate a solid structure or to describe a volume, the equation is replaced by an inequality, for instance \(f(x,y,z)<t\). The function is used to define one unit cell, and the unit cell is replicated throughout the structure. The constant \(t\) can be adapted regionally to allow different volume fractions ([PERSON] and [PERSON], 2011) (see Figure 3).  
More specifically, a change in \(t\) will result in a different volume fraction. Since TPMSs are defined by functions, the transition between two unit cells is smooth, which means the structure has fewer stress concentrations. [PERSON] et al. (2016) have described an image-based algorithm and an implicit function to properly define a unit cell. Their unit cell is easy to discretize using finite element and it also minimizes the use of overhangs. Figure 4 shows a list of typical TPMS cells. Sometimes, there are simiarities between strut-based unit cells and TPMS. For example, [PERSON] et al. (2016) have highlighted that controlling the volume fraction of the gyroid structure, one can get something close to the BCC lattice structure.
# Framework for Video Steganography Using Integer Wavelet Transform and JPEG Compression
## 5 Analysis and Discussion of Experimental Results
### Complexity Analysis  
The complexity of the research work is calculated depending on the concealing and retrieval time of secret information. For complexity, we have also calculated the concealing capacity of the work. Concealing time is the quantity of time essential to hide secret data. Retrieval time is the amount of time required to retrieve the hidden secret data from the carrier file. Concealing capacity is how much data can be concealed in the cover file without the chances of exposure of secret data.  
The amount of time varies with the format and type of multimedia file we are concealing. Time also varies with the format cover file. For hiding a 64 \(\times\) 64 image, whose size is 20 KB, the proposed technique takes a time of 21 s. For retrieval of secret information, it  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **PSNR** & **MSE** & **SSIM** & **CC** \\ \cline{2-5} Gaussian Noise & 53.3 & 0.301 & 0.81 & 0.82 \\ Attack & 55.8 & 0.280 & 0.89 & 0.90 \\  & 57.5 & 0.221 & 0.93 & 0.94 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Performance Metrics for Stego File.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Video Cover Frame** & **Secret Image** & **Stego Frame** & **Retrieved Secret** & **Retrieved Cover** \\ \hline  & & & & \\  & & &  
has taken a time of 5 s. Concealing capacity is calculated to be 34 BPS. In the case of mp4 videos, concealing and retrieval time is found to be low as compared to any other format of video.
# Effect of Surface Micro-Hardness Change in Multistep
###### Abstract
Due to the change of tool milling path, the degree of softening in near surface layer was the most obvious factor that affected friction and wear performance. The degree of softening determined the material spalling method and the severity of the plastic deformation, as well as the wear mechanism of the material. [PERSON] et al. [9] studied the wear behavior and mechanism of hardened die steel AISI D2 under different sliding speed and hardness level by dry sliding conditions. It was found that the wear loss was very sensitive to sliding speed and hardness. At the same time, due to the influence of hardness difference, different wear mechanisms were proposed. The main wear mechanism was adhesive wear under low hardness conditions and delamination wear under high hardness (referring to relative hardness). [PERSON] et al. [10] and [PERSON] et al. [11] found through experiments that the subsurface microstructure was refined and evenly twisted along the cutting direction, which could improve the surface friction and wear properties of the material. The parts were usually obtained by a variety of machining methods and subjected to friction and wear during using. Especially for the advanced equipment, the performance and accuracy would be affected. Therefore, it was important to improve the friction and wear resistance. However, the effect of the machined surface obtained by multistep machining on the friction and wear properties of titanium alloys needed to be further revealed. Therefore, this paper aimed to study the effect of surface micro-hardness change (indicated by standard deviation) in multistep machining on the friction and wear characteristics along the feed direction. This will be of great significance to provide the basis for accurately controlling the high efficiency machined surface quality and adaptively improving the surface wear resistance of titanium alloy components for high wear resistance applications.