esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Methodology
### Bearing Fault Signatures  
The rolling element bearing (REB) is an important component in rotating machines and generally carries heavy loads with expectations to operate at high reliability and efficiency. Usually, the inner ring and outer ring of the bearing are mounted on a rotating shaft and stationary housing, respectively. The balls, tapered rollers, cylindrical rollers, and barrel rollers are the rolling elements, enclosed in a cage with equal spacing. To allow the rolling element to contact the ring at a single point, the radii are slightly smaller than the track of rotation and help to distribute the load to a very small surface. General representation of rolling element bearing, and two different types of fault analyzed in this study are represented in Figure 5.  
Therefore, the cage isolates the rolling elements to avoid bad lubrication surroundings through the operation. A large variety of REBs exists, including the deep groove ball bearings, and are mostly used in home appliances, industrial equipment, and automotive applications. Various types of damage such as pitting, spalling, waviness, and misaligned races generally occur due to abrasive wearing, improper installation, material fatigue, manufacturing error, and so on.  
Generally, each bearing element obtains a representative frequency. When damage occurs on a bearing element, the interaction of defects produces pulses of very small duration, which result in a rise in vibration energy at that particular frequency. The damage frequency can be determined with the help of the geometry of the bearing and element rotational speed from Equations (1)-(4).  
\[\text{Outer race defect}:\ f_{outer}=\frac{N_{ball}}{2}\times f_{m}\  
and load torque also occur as bearing damage produces radial displacement of the rotor relative to the stator and results in amplitude, phase, and frequency modulation of the motor-current signals. The bearing fault motor current equation is described in [54] as:  
\[i(t)=\sum\limits_{k=1}^{\infty}i_{k}\cdot\cos(\omega_{c_{k}}t+\varphi) \tag{5}\]  
where \(\varphi\) and \(\omega_{c_{k}}\) represent the phase angle and angular velocity, respectively, and  
\[\omega_{c_{k}}=\frac{2\pi f_{bearing}}{p}\]  
\[\text{where, }f_{bearing}=\left|f_{s}\pm mf_{\varphi}\right| \tag{6}\]  
Here, \(f_{bearing}\), \(p\), and \(f_{s}\) denote harmonic frequency of the fault current, the pole pair number of the corresponding machine, and fundamental frequency, respectively. The fundamental frequency is referred to as the electrical supply frequency. This is the frequency of three phase power supply connected to the stator of the induction motor. Hence, m = 1, 2, 3,... are the harmonic indexes; and \(f_{\varphi}\) can be \(f_{inner}\), inner race frequency, or \(f_{outer}\), outer race damage frequency. However, the noise frequency and harmonics produced by the bearing fault become very close or tend to overlap each other, complicating bearing fault detection [55]. In our study, we analyzed three types of bearing conditions: healthy, inner race fault, and outer race fault.
# An Algorithm for Scene Text Detection Using Multibox and Semantic Segmentation
## 3 Proposed Algorithm
### Semantic Bounding Box Module  
The SBM contains two modules: CRF processing and bounding box search. The CRF processing solves the problem of boundary blur and stickiness in text semantic segmentation. The bounding box search obtains the optimal text semantic segmentation bounding box according to the CRF processing result. The SBM process is shown in Figure 6.  
Figure 5: Flow chart of the BEM.  
The semantic segmentation result has a defect, which is that it is easy to cause adhesion when words are close. The CRF algorithm is used to correct the pixel-level prediction of the semantic segmentation result. Text edges are sharper and less sticky after CRF processing. CRF has been employed to smooth noisy segmentation maps [28; 29]. These methods use short-range CRF to couple neighboring nodes, favoring same-label assignments to spatially proximal pixels. In this work, the goal should be to recover the detailed local structure rather than smooth it further. Therefore, the fully connected CRF model [30] is integrated into our network. The CRF model employs the energy function:  
\[\mathrm{E}(x)=\sum\
olimits_{i}\theta_{i}(x_{i})+\sum\
olimits_{ij}\theta_{ij }\big{(}x_{i},x_{j}\big{)}, \tag{1}\]  
where the \(x\) is the label assignment for pixels and the unary potential is defined as follows:  
\[\theta_{i}(x_{i})=-\log P(x_{i}), \tag{2}\]  
where \(P(x_{i})\) is the label assignment probability of pixel \(i\) computed by semantic processing.  
The pairwise potential has a form that allows for efficient inference while using a fully-connected graph, i.e., connecting all pairs of image pixels, \(i,j\). In particular, as in [30], the following expression is used:  
\[\theta_{ij}(x_{i},x_{j})=\mu\big{(}x_{i},x_{j}\big{)}\Bigg{[}\omega_{1}exp \Bigg{(}-\frac{\parallel p_{i}-p_{j}\parallel^{2}}{2\sigma_{\alpha}^{2}}- \frac{\parallel I_{i}-I_{j}\parallel^{2}}{2\sigma_{\beta}^{2}}\Bigg{)}+\omega _{2}exp\Bigg{(}-\frac{\parallel p_{i}-p_{j}\parallel^{2}}{2\sigma_{\gamma}^{ 2}}\Bigg{)}\Bigg{]}, \tag{3}\]  
where \(\mu\big{(}x_{i},x_{j}\big{)}=1\) if \(x_{i}\
eq x_{j}\), and zero otherwise. The remaining expression uses two Gaussian kernels in different feature spaces; the first, bilateral kernel depends on both pixel positions \(p\) and RGB color \(I\), while the second kernel depends on pixel positions. The hyperparameters \(\sigma_{\alpha}\), \(\sigma_{\beta}\) and \(\sigma_{\gamma}\) control the scale of Gaussian kernels. The first kernel has similar tags for pixels with similar colors and positions, whereas the second kernel considers spatial proximity while smoothing the boundaries. Figure 7 shows the effectiveness of the CRF processing.  
Figure 6: Semantic bounding box module flow chart. The yellow circle areas in the figure are clearer in the text boundary portion after the CRF processing.
## 6 Experiment
### Experimental Setup
#### 6.1.5 Experimental Environment and Parameters  
Here is a detailed description of the experimental setup used to conduct data poisoning attack experiments, which includes both hardware and software environments:* Hardware environment GPU: NVIDIA GeForce RTX 4090 (NVIDIA, Santa Clara, CA, USA), utilized for its robust processing capabilities in handling complex computations required for deep learning. CPU: Intel(R) Xeon(R) Silver 4210R CPU @ 2.40 GHz (Intel, Santa Clara, CA, USA), capable of efficiently managing multiple tasks. Memory: 128 GB of RAM, to ensure smooth operation of large datasets and extensive computational processes without bottlenecks.
* Software environment Operating System: Ubuntu 20.04 LTS, chosen for its stability and compatibility with various development tools and libraries. Development Environment: Set up in a virtual environment on Anaconda, which helps manage package dependencies easily. Deep Learning Framework: PyTorch (version 1.12.0), chosen for its efficiency and flexibility in building neural network models. Recommendation System Library: RecBole (version 1.2.0), which provides comprehensive support for implementing and evaluating recommendation algorithms.
* Details of the data poisoning attack experiments Algorithm: DQN with a Dueling Network architecture to enhance decision-making capabilities. Learning Rate: 0.001, to control the speed of model weight updates. Reward Decay Factor (\(\gamma\)): 0.95, which determines the current value of future rewards and affects the discounting in the learning process. Exploration Rate: Begins at 1.0 and linearly reduces to 0.01, to balance exploration of new actions against exploiting known actions. Experience Buffer Size: 10,000, used to store past interactions for replay, crucial for experience-based learning. Batch Size: 128, determining the number of samples per training iteration. Target Network Update Frequency: Updated every 100 episodes to regulate the update rate of the target Q-network and stabilize learning.
# Efficient Route Management Method for Mobile Nodes in 6 TiSCH Network
## 4 Performance Analysis
### Scheduling Performance Analysis
#### 4.2.3 Stability Analysis of Mobile Nodes  
Figure 11 shows the ratio of the total time the moving node is connected to the network and communicable, with Orchestra + EMA-RPL sharing cells for RPL control messages globally; however, cell allocation for data packet communication is automatically determined through MAC hash, allowing participation in the network. However, existing MSF scheduling has a low participation rate because the speed of movement increases, as RPL control messages must compete with existing shutdown nodes through shared cells.  
Figure 12 shows the number of packets generated by a moving node that failed to reach the root node as the moving speed increased. Although MSF and Orchestra + EMA-RPL increase rapidly depending on the speed of movement, the implementation algorithm can confirm that the number of packets that failed to transmit is exceedingly small.  
Figure 11: Rate of participating in the network of mobile nodes with node speed.  
Figure 12: Number of packets that failed to transmit from the mobile node according to the node speed.  
We compare Orchestra + EMA-RPL with the distributed scheduling MSF proposed by the existing 6 TiSCH working group. Mobile-root node fixed-path construction and fast parent selection of moving nodes are two techniques that show that nodes with a fast moving speed can quickly participate in the network, thus, minimizing missing packets to the root node and delivering less latency.
## 4 Experiments and Evaluation
### Adaptive Scheduling: Performance Analysis
#### 4.5.2 The Effect of Different Operational Limits on the Performance
In our case, the selection of interval 0.5 would be the best option (also computationally lightweight); however, a better computational resource would allow for smaller interval sizes and so less fluctuations.
## 2 Numerical Modelling and Validation
### CAE Modelling of Hybrid DED Product  
After the heat source model creation phase, in order to initialize a semi cross hybrid DED part, design volume has been created for topology optimization as shown in Figure 8. The topology optimization model has been evaluated under three point bending conditions.  
The base material is defined as a non-design area, and inside of the cross is described as a design area, namely changeable zone in terms of creating local stiffeners on the parts.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Heat Source** & **Laser Scan Speed** & **D** & **H** & **D + H** & **Dilution Rate** \\
**Power (W)** & **(mm/sn)** & **(mm)** & **(mm)** & **(mm)** & **(mm)** \\ \hline
1500 & 12 & 3.368 & 5 & 8.368 & 0.402486 \\
1500 & 8 & 3.723 & 5 & 8.723 & 0.426803 \\
1300 & 12 & 3.178 & 5 & 8.178 & 0.388604 \\
1300 & 8 & 3.589 & 5 & 8.589 & 0.41786 \\
1100 & 12 & 3.006 & 5 & 8.006 & 0.375468 \\
1100 & 8 & 3.496 & 5 & 8.496 & 0.411488 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Dilution results.  
Figure 8: Boundary conditions and optimization model for cross model.  
Figure 7: Comparison of effects of heat source on dilution rate with different scanning velocities.  
Topology results have been obtained as shown in Figure 9. After this step, the structure should be derived and redesigned considering topology optimization results. It has been redesigned defined as Table 2.  
Structural strength analysis has been carried out taking the thermomechanical results obtained from the DED simulation into account. Therefore, this means all thermal effects from the DED process can be included mapping on the parts during structural simulation.
## 2 Methodology  
The methodology of this study is outlined below:  
1. The literature related to the mechanism of differential settlement and treatment techniques was analyzed.
2. For this research, field surveys were conducted to study the settlement in the already-completed pavement. Subsequent site visits were conducted to investigate soil, topography, traffic patterns, and pavement distresses.
3. A geotechnical investigation of the soil was conducted. Samples were collected from test pits and boreholes; three main test pits for the foundation (natural ground), the old road, and the new road (widened portion) with loose clayey soils were identified. Moreover, the direct shear test was performed to calculate the shear strength properties of three identified soil samples.
4. Using the finite element software Abaqus CAE 2021, the interaction between the old and new road portions was established.
5. To explore the settlement features under various circumstances, several models for 2D simulation were established for analysis. For a detailed study, a weak section of the highway was selected for numerical model validation.
6. Reinforcement techniques in the embankment widening to control differential settlement in the pavement structure were used to evaluate the effect of the techniques, such as geogrid reinforcement and installation of CFG piles, separately and in combination. In addition, lightweight foamed concrete (LWFC) was used as a soil replacement in the widened portion of the road.
## 2 Experimental Investigations and Data Acquisition  
### Construction of Hybrid Towers  
Hybrid towers consist of a combination of concrete and steel structures. Such a tower is shown in Figure 2. The concrete substructure consists of individual ring segments stacked on top of each other without additional connecting elements. This enables the construction of large diameters and at the same time simplifies the delivery and assembly of individual concrete segments.  
The steel tower is placed on top of the concrete structure. The force-fit connection is affected by the transition piece (see Figure 3). The tendons are anchored in this area. From here, the tendons run inside the concrete tower to the foundation (see Figure 4a). Inside the tower, they lie freely and, at particular sections, are in contact with the tower's inner wall, as the rings' diameters change non-linearly over the tower's height. The pre-stressing anchors are located at the bottom of the tower. Traditionally, the foundation has a tensioning chamber to provide the necessary space for pressing and tensioning work. This conventional type of foundation is, as a consequence, particularly massive. A much flatter foundation is achieved with the anchors freely suspended above the foundation. Thus,  
Figure 2: Illustration of a hybrid tower with its structural components.  
they are located in the entrance area at ground level. This novel design contributes to a significantly more efficient use of materials.  
Both types of construction are relevant to the tests carried out. The initial plan was to analyze only the novel anchorage. Accordingly, the test specimens for laboratory tests were constructed with the novel anchorage (see Section 2.2). However, ultimately, the  
Figure 4: Sensor installation at the wind turbine in _Karholz_: (**a**) Internal view of the concrete substructure with internally routed tendons. (**b**) Photo of the locations for sensor 3 at the transition piece and sensor 7 at the foundation with a conventional anchorage.  
Figure 3: Illustration of the transition piece at the top of the concrete substructure and the foundation with conventional and novel anchorage.  
operational monitoring could only be carried out on a system with conventional anchoring (see Section 2.3). The structural differences have an effect on the signal transmission from the tendon end to the foundation and must, as a consequence, be taken into account when analyzing the results. The anchorage at the transition piece is the same in both cases.
# DRL-Based Dynamic SFC Orchestration Algorithm for LEO Satellite Networks
###### Abstract  
Low-Earth-Orbit (LEO) satellite networks face unique challenges in service function chain (SFC) orchestration due to their dynamic topology and resource constraints, making traditional terrestrial network solutions inadequate. This study addresses the challenge of maximizing service provider benefits through efficient SFC deployment and readjustment in LEO satellite networks. We propose PPOSFC, a novel dynamic SFC orchestration algorithm based on Proximal Policy Optimization (PPO), which incorporates future topology information into the decision-making process. The algorithm models the orchestration problem as a Markov decision process and employs a dual-objective optimization approach considering both deployment success rate and readjustment costs. Simulation results demonstrate that PPOSFC achieves a 13.07% increase in cumulative profit and improves deployment success rates by 6.78% compared to existing algorithms. The algorithm exhibits superior performance in both high and low user service request intensity, effectively balancing service quality and operational efficiency. Furthermore, our analysis reveals that incorporating predicted topology information significantly enhances orchestration performance.  
a Article
# Computational Modeling of U-Shaped Seismic Dampers for Structural Damage Mitigation
## 1 Introduction
The complex geometries and localized deformation mechanisms inherent to these devices necessitate careful consideration of mesh density and element type to ensure solution accuracy and minimize computational cost [3; 32; 33]. Insufficient mesh refinement can lead to inaccurate stress and strain predictions, particularly in regions of high stress concentration, ultimately compromising the reliability of design optimizations based on these models. Therefore, reliable numerical models are needed to evaluate local stress, strain, and energy dissipation, providing a basis for design optimization. More detailed models that can thoroughly characterize local strain and stress are essential to develop high-performance U-shaped dampers for seismic applications and optimize their geometry to improve energy dissipation and performance. This work presents the development of a computational model that reliably predicts the performance behavior of U-shaped metallic dampers through the integration of an elastoplastic model with rigorous mesh convergence analysis, enhancing the accuracy of predictions related to local deformation and energy dissipation capacity often overlooked in prior research. By addressing the local behavior, this approach lays the groundwork for more sophisticated models that consider damage prediction and fatigue behavior, ultimately leading to safer designs for structural resilience during seismic events. Nonlinear finite element analysis is employed, with material characterization derived from the reported stress-strain curve of low-carbon steel. The sensitivity of the numerical results to mesh refinement is assessed across various loading protocols, ensuring the robustness of the applied method.
## 4 New Routing Protocol for MWSNs
### Our Efficient Routing Strategy in MWSNs
#### 4.1.3 Routing Path Recovery
The relay node in the original path that received the RNF packet, e.g., \(RN_{j}^{k+2}\), forward it to the next relay node (\(RN_{j}^{k+3}\)) and reset its isRelay = 0 to remove routing information. _Step 2_ Build new routing path without failed node  
After received the RNF packet, \(Agent_{i}\) removes the failure node \(RN_{j}^{k}\) from the set of all candidates \(Set(V)\) and executes our new protocol to re-build new optimal routing path with \(Set(V)\)-\(\{RN_{j}^{k}\}\). The subsequent steps are the same as routing recover for mobile moves away the current agent, which are described in Section 4.1.2.
# A Design of a Time Synchronization Protocol Based on Dynamic Route and Forwarding Certification
## 3 Design of a Time Synchronization Protocol Based on Dynamic Routing and Forwarding Certification
### Authorization of Node Forwarding Certification  
In the process of dynamic routing for time synchronization, nodes to be synchronized always select the best performance nodes as their synchronization references. Therefore, nodes with a poor performance have no chance of being selected as the references, even though the neighbors receive information from these poor nodes. In such a condition, network traffic will be heavily burdened due to large amounts of redundant information and energy is wasted. In order to improve the performance, the authorization of node forwarding certification is put forward.  
A threshold marked \(R_{cor}\) is set as the evaluation index, which represents the percentage of qualified nodes in the neighbor domain that are eligible to be put forward. \(R_{cor}\) can be set according to the status of energy consumption. Each node compares its \(Std\) with the others' and ranks itself in the neighbor domain. When its rank is larger than \(R_{cor}\) in the neighbor domain, its forwarding certification will be terminated. Due to this strategy, useless information transmission will be effectively controlled and the energy consumption will be reduced.  
Flag_QA is defined as the mark of forwarding certification. In the first round of time synchronization, all nodes are qualified to forward information, that is, the Flag_QA of each node is set to 1. After several rounds, the performance parameters of each node in previous rounds have been cached. Then, the process of forwarding certification is initiated. Each node broadcasts its \(Std\) and receives others to rank itself in the neighbor domain. If its rank is less than \(R_{cor}\), Flag_QA is set to 1, which means that this node needs to forward its time synchronization information after completing time synchronization; otherwise, Flag_QA is set to 0, which means that this node just ends this round after synchronization, without forwarding time synchronization information to others.
# Predicting Student Achievement Based on Temporal Learning Behavior in MOOCs
## 4 Prediction Method
### Multi-Layer LSTM Neural Networks  
We used a multi-layer LSTM neural network to predict students' achievement and reflect students' learning process. The LSTM neural network can remember important information and forget invalid information. This solves the problem of gradient disappearance and gradient explosion in long sequences in RNNs. A LSTM unit consists of an input gate, an output gate, and a forget gate. The input gate inserts the sequential data, and the forget gate selectively forgets the information transmitted by the previous node. The output gate determines the information that can be passed to the next node. The structure of the LSTM cell used is shown in Figure 2.  
The calculation process of the output results is shown in Equations (1)-(7). The new memory, \(C_{t}\), is generated from the output, \(h_{t-1}\) and \(x_{t}\), of the previous unit. Input gate \(i_{t}\) controls the retention of new memory, forgetting gate \(f_{t}\) controls the retention of C\({}_{t-1}\) before forgetting gate \(f_{t}\), and the output of final memory is controlled by output gate \(o_{t}\).  
\[f_{t}=\sigma\big{(}W_{f}[h_{t-1},x_{t}]+b_{f}\big{)} \tag{1}\]  
\[i_{t}=\sigma(W_{i}[h_{t-1},x_{t}]+b_{i}) \tag{2}\]  
\[\overline{C}_{t}=tanh(W_{c}[h_{t-1},x_{t}]+b_{c}) \tag{3}\]  
\[C_{t}=f_{t}\circ C_{t-1}+i_{t}\circ\overline{C_{t}} \tag{4}\]\[o_{t}=\sigma(W_{o}[h_{t-1},x_{t}]+b_{o}) \tag{5}\]  
\[C_{t}=tanh(C_{t}) \tag{6}\]  
\[h_{t}=o_{t}\circ C_{t} \tag{7}\]  
A multi-layer LSTM network generally consists of three layers: (1) An input layer, (2) a hidden layer, and (3) an output layer. Our LSTM adopts a five-layer architecture, including three-layer hidden layers. Part (b) of Figure 1 displays the network.