esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Video prediction, which is the task of predicting future video frames based on past observations, remains a challenging problem because of the complexity and high dimensionality of spatiotemporal dynamics. To address the problems associated with spatiotemporal prediction, which is an important decision-making tool in various fields, several deep learning models have been proposed. Convolutional long short-term memory (ConvLSTM) can capture space and time simultaneously and has shown excellent performance in various applications, such as image and video prediction, object detection, and semantic segmentation. However, ConvLSTM has limitations in capturing long-term temporal dependencies. To solve this problem, this study proposes an encoder-decoder structure using self-attention ConvLSTM (SA-ConvLSTM), which retains the advantages of ConvLSTM and effectively captures the long-range dependencies through the self-attention mechanism. The effectiveness of the encoder-decoder structure using SA-ConvLSTM was validated through experiments on the MovingMNIST, KTH dataset.  
SA-ConvLSTM; encoder-decoder; video prediction; spatiotemporal; self-attention memory module +
Footnote †: journal: applied sciences
## 4 Results and Discussion
### Variation of the \"Weak\" FIV Signals
#### 4.2.1 Wear State Change Analysis  
Figure 7 depicts the change in the friction coefficient during the wear test. The average value of the friction coefficient was calculated every half minute and taken as the representative of the corresponding time to obtain the fitting curve. The fit curve showed that the friction coefficient was approximately 0.272 initially, decreased to 0.115 rapidly in five minutes, and then fluctuated smoothly during the remainder of the test.  
Figure 5: Frequency components with amplitude greater than threshold and their distribution.  
According to references [29, 30, 31], the friction coefficient commonly varied with the wear process and was related to the wear stages and the initial wear process with an upward or downward trend of the friction coefficient could be regarded as the running-in wear stage. While the friction coefficient became stable, the friction pair entered a stable wear stage. When the friction coefficient increased suddenly and sharply, severe wear was indicated. Therefore, the wear process in this study can be divided into two distinct stages: the running-in stage and the stable wear stage, as shown in Figure 7.
## 2 Background
### Knowledge Distillation  
Knowledge Distillation (KD), as proposed by [PERSON] et.al. [20], is a type of knowledge transfer that was developed around convolutional neural networks, the idea behind KD is to train a large, complex model using high computational resources and use the learned parameters from the model to teach a small model with fewer computational resources. The process usually utilized two network structures, which are named teacher and student. The teacher is a model with a strong capability, while the student is a simple shallow neural network model which mimics the behavior of the teacher model by transferring significant knowledge from the teacher model [21].  
The process of knowledge distillation is well-represented in Figure 3, where two models are defined with different architectures. On the top is teacher model with multiple convolutional layers and hidden layers, whereas the picture of student model with only a convolutional layer and one hidden layer is on the bottom. This indicates that the two models differ physically, and therefore, they have different computational requirements. The student models mimic the behavior of the teacher model by training on the soft level generated by the teacher model, otherwise called the distilled knowledge from the teacher. Unlike transfer learning, this technique does not require the Internet, and it does not require that the student model architecture be the same as the teacher.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**S/N** & **Ambient Source** & **Technology** & **Strength** & **Weakness** \\ \hline
1 & Solar & photovoltaic & Easier and cheap & Not suitable in all environments \\
2 & Thermal & Thermoelectric converters & Can be used both in indoor and outdoor situations & Lower power density than the rest of the methods \\
3 & Vibration & Electromagnetic & and others & Has high power density & Difficult to fabricate, and the performance \\
4 & Wind & Electromechanical converter & Has high power density and is easy to fabricate & The reliability of the power heavily depends on the wind speed \\
5 & Radiofrequency & Electromagnetic converter & The sources are largely available nowadays & Low power density and expensive to fabricate \\
6 & Acoustic noise & Piezolectric converter & Can be installed both indoors and outdoors & Expensive and difficult to fabricate \\ \hline \hline \end{tabular}
\end{table}
Table 1: Energy Harvesting Techniques.
## 4 Software System
### Coordinate Transformation from Scanning Frame to External Work Coordinate System
#### 4.3.2 Transformation from Tool Coordinate System to External Work Coordinate System  
During operation, the robotic arm holds the shoe last and moves it toward a fixed spray gun. Guided by the spraying path generated by the scanning system, the arm maneuvers the shoe last such that the adhesive application area closely aligns with the spray baffle, allowing the fixed spray head to accurately apply treatment or adhesive onto the upper surface. To achieve this, the spraying path--initially defined relative to the tool coordinate system--must be converted into the external work coordinate system, whose origin is located at the center of the spray baffle. This final coordinate transformation ensures that the robotic arm can move the shoe last precisely along the pre-defined path relative to the fixed spraying apparatus.  
To accurately execute the adhesive spraying path relative to the fixed spray baffle, the spraying trajectory--originally defined in the tool coordinate system (\(T\))--must be transformed into the external work coordinate system (\(W\)). This transformation requires a sequential application of two homogeneous transformation matrices:  
\(M_{T}^{B}=\left[\begin{array}{cc}R_{T}&P_{T}\\ 0&1\end{array}\right]\) is the transformation from the tool frame (end-effector) to the robot base coordinate system.  
\(M_{B}^{W}=\left[\begin{array}{cc}R_{W}&P_{W}\\ 0&1\end{array}\right]\) is the transformation from the robot base to the external work coordinate system, whose origin is set at the center of the spray baffle.  
The overall transformation from the tool frame to the work frame is given by  
\[P_{W}=M_{B}^{W}\cdot M_{T}^{B}\cdot P_{T} \tag{8}\]  
where \(P_{T}=[\mathcal{X}_{T},\mathcal{Y}_{T},\mathcal{Z}_{T},1]^{T}\) represents a point on the spraying path in the tool coordinate system.  
The matrix \(M_{T}^{B}\) is dynamically provided by the robot controller through its forward kinematics module based on the current joint states. Meanwhile, \(M_{B}^{W}\) is established via a one-time calibration process by positioning the tool center point (TCP) at known locations on the spray baffle and recording their corresponding base frame coordinates.  
As illustrated in Figure 21, this transformation chain enables the robot to convert spraying positions defined relative to the lasted upper (held by the TCP) into precise poses relative to the external environment. It ensures that the nozzle aligns accurately with the bonding line on the upper surface during adhesive application.
# Degree-Constrained Steiner Problem in Graphs with Capacity Constraints
## 6 Approximations
### Case of \(V_{1}=\emptyset\)  
**Theorem 3**.: _Let us suppose that there is no node with degree bound \(1\) (\(V_{1}=\emptyset\)). The cost of a DCMStH can be approximated within a ratio \(\rho\frac{D}{D-1}\), where \(\rho\) is the ratio of the approximation of the Steiner tree and \(D=\min_{\rho\in V}D(v)\)._  
Proof.: The cost of the minimum Steiner tree \(T_{S}\) without the degree constraints is a lower bound for all connected spanning structures of the group \(M\). The Steiner problem is NP-hard. We let \(T_{A}\) be an approximation of the minimum Steiner tree with ratio \(\rho\). The tree \(T_{A}\) can be decomposed into a set \(S=\{S_{i},i=1,\ldots,k\}\) of edge-disjoint stars. In each star \(S_{i}\), a spanning hierarchy \(H_{S_{i}}\) respecting the degree constraint of the (eventually multiplied) central node \(c_{i}\) can be built. Since \(V_{1}=\emptyset\), possible duplicates concern the best cost edges in the construction of \(H_{S_{i}}\). In the worst case, the leaves are in \(V_{2}\) and only one return is possible from each leaf. The cost of the hierarchy \(H_{S_{i}}\) spanning the star \(S_{i}\) is bounded by \(\frac{D(c_{i})}{D(c_{i})-1}c(S_{i})\) where \(c(S_{i})\) is the cost of the star (cf. [28]). The hierarchies \(H_{S_{i}},i=1,\ldots,k\) spanning the \(k\) different stars can be connected, and a hierarchy \(H\) spanning the node set \(V\) and respecting the degree constraints is obtained. With \(D=\min_{i=1,\ldots,k}D(c_{i})\), since \(\frac{D(c_{i})}{D(c_{i})-1}\leq\frac{D}{D-1},i=1,\ldots,k\), the cost of the spanning hierarchy is bounded:\[c(H)=\sum_{i=1}^{k}c(H_{S_{i}})\leq\sum_{i=1}^{k}\frac{D(c_{i})}{D(c_ {i})-1}c(S_{i})\leq\\ \leq\frac{D}{D-1}c(T_{A})\leq\rho\frac{D}{D-1}c(T_{S})\leq\rho \frac{D}{D-1}c(DCMStH) \tag{14}\]  
\(\square\)  
The decomposition of the Steiner tree into a set of connected stars is illustrated in Figure 3. In the worst case, the degree bound of all nodes is two, \(D\) is equal to two, and the ratio of costs is also two. If there are nodes with large degree bounds, the heuristic computes hierarchies close to the optimum.
# MASA: Multi-Application Scheduling Algorithm for Heterogeneous Resource Platform
## 5 Experiments
### Ablation Experiment
#### 5.3.1 Reward Dynamic Alignment Analysis  
In Figure 10, we plot the convergence of the training results of the MASA algorithm with and without the RDA optimization method during training at different application arrival rates. The introduction of the RDA method makes it clear that the network converges to a stable state earlier and the final training results in better performance. The RDA optimization method maintains the average application execution time in a more stable state, even with changes in the arrival rate of applications. RDA solves the reward inaccuracy problem by buffering the rewards under clock signals, thus improving the performance of network training. RDA is a lightweight optimization method that can be directly applied to similar application scenarios, as it only memorizes the decision start and end times of the task in comparison to the traditional RL method.
## 2 The Concept of an EMC Topological Model  
It should be emphasized that the proposed model is intended for numerical calculations used in simulations of various interference scenarios reflecting situations occurring during ship's operation. This involves calculating the effective RMS (Root Mean Square) value corresponding to the value measured with a broadband electric field meter at the indicated place on the ship. The value of the electromagnetic interference (e-m) is determined by the electrical component of this interference, which is expressed by Equations (1)-(5).  
Estimating this value is very useful both in the initial design analyses and also in the specific operating conditions of the ship. The sources of this interference are usually radiocommunication and radiolocation transmitters, as well as intentional e-m signals coming from different sources. This makes it possible to predict threats to the ship's operation devices resulting from such disturbances and, on this basis, to protect these devices using appropriate shielding and/or appropriate locations in the ship's structure. The most susceptible systems to such disturbances are automatic ship command systems, especially certain elements of this system, e.g., the torpedo tube control subsystem. For obvious reasons, the details of this are not the subject of the reviewed article. For this reason, the topological model presented in the article as described by matrix equations was presented at a high level of generality when expressing the idea of these calculations. In these simulations, only the attenuation of the ship's walls and attenuation in the spaces between these walls were taken into account, which are described in detail below (p. 2.2).  
In addition, the applied description of the propagation of interference from their sources to the indicated place on the ship was not rigorous; it was not based on a vector description and was not of a waveform nature. This approach would be virtually impossible in such dynamic conditions on the ship.
###### Abstract
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Author** & **Developed Inversion Process** & \begin{tabular}{c} **Generation of Dispersion** \\ **Curves** \\ \end{tabular} & **Drawbacks** \\ \hline [PERSON] [9] & Based on forward modeling & \begin{tabular}{c} A modified version of the \\ Haskell–Thomson matrix \\ solution [10; 11] \\ \end{tabular} & \begin{tabular}{c} Quite tedious and \\ time-consuming \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [13] & \begin{tabular}{c} [PERSON]’s conjugate directions \\ method for optimization \\ \end{tabular} & \begin{tabular}{c} The discrete layer stiffness \\ matrix method initially \\ developed by [PERSON] [20] and \\ [PERSON] and [PERSON] [21] \\ \end{tabular} & \begin{tabular}{c} The nontranscendental \\ quadratic eigenvalue problem \\ \hline [PERSON] and [PERSON] [16] & \begin{tabular}{c} [PERSON] and [PERSON]’s simplex \\ method [22] \\ \end{tabular} & \begin{tabular}{c} Automated using \\ optimization techniques with \\ a least-squares criterion \\ \end{tabular} & \begin{tabular}{c} The number of iterations \\ needs to be increased \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [15] & \begin{tabular}{c} Linearized least-squares \\ approximation \\ \end{tabular} & - & - \\ \hline [PERSON] and [PERSON] [18] and & \begin{tabular}{c} Back-calculation neural \\ networks \\ \end{tabular} & - & \begin{tabular}{c} The network required a \\ greater number of more \\ complex mappings for \\ training \\ \end{tabular} \\ \hline [PERSON] and [PERSON] [17] & \
\begin{tabular}{c} The SASW-INVERT program \\ \end{tabular} &
\begin{tabular}{c} The maximum vertical \\ flexibility coefficient of the \\ layered soil system \\ \end{tabular} & - \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Studies related to SASW inversion analysis. methods and Downhole Tests (DHT) and determined the unknown nonlinear relationships between SASW results and those obtained by means of the method of DHT, the latter of which have been treated as real values. The results show that the backpropagation neural network could predict the shear wave velocity between wells accurately. Therefore, due to the performance of ANN in SASW inversion by previous researchers, an ANN model will be adopted in this study and compared to other ML algorithms.
# Numerical Insight into the Kelvin-Helmholtz Instability Appearance in Cavitating Flow
## 3 Numerical Procedure
### Cavitation Model  
We know multiple models of cavitation in which we can model liquid and vapor evaporation and condensation. Bubble dynamics models, introduced by [PERSON] et al. [22], where he used a linear portion of the Rayleigh-Plesset equation to explain the evolution of bubble radius as a function of surrounding pressure have been the most used cavitation models in CFD in recent years. The proportion of the vapor phase and hence the density of the mixture is calculated by bubble radius and bubble number density. Other cavitation models were derived relying on the pressure and bubble radius dependencies [23], based on the Rayleigh-Plesset equation. However, with all cavitation models, it is conditioned to specify parameters, such as the bubble number density or the initial size of the bubble,which are very hard to define. The Schnerr-Sauer [24] model is the cavitation model based on bubble dynamics that is most widely used in commercial CFD software packages, where the mass transfer source terms \(R_{e}\) and \(R_{c}\) (see Equation (7) are defined as:  
when local pressure is equal to or smaller than vapor pressure--\(p\leq p_{v}\):  
\[R_{e}=F_{evap}\frac{\rho_{v}\rho_{l}}{\rho}\alpha(1-\alpha)\frac{3}{R_{b}} \sqrt{\frac{2}{3}\frac{(p_{v}-p)}{\rho_{l}}} \tag{8}\]  
when local pressure is larger than vapor pressure--\(p>p_{v}\):  
\[R_{c}=F_{cond}\frac{\rho_{v}\rho_{l}}{\rho}\alpha(1-\alpha)\frac{3}{R_{b}} \sqrt{\frac{2}{3}\frac{(p-p_{v})}{\rho_{l}}} \tag{9}\]  
where the default values of empirical calibration coefficients of evaporation \(F_{evap}\) and condensation \(F_{cond}\) are 1 and 0.2, respectively. To link the fraction of the vapor volume to the number of bubbles per liquid volume \(n_{b}\), the Schnerr-Sauer cavitation model uses:  
\[\alpha=\frac{n_{b}\frac{4}{3}\pi R_{b}^{3}}{1+n_{b}\frac{4}{3}\pi R_{b}^{3}} \tag{10}\]  
The parameters that must be determined in this model are bubble radius \(R_{b}\) and bubble number density \(n\). The compressibility of water and water vapor was not considered in the modeling.
# Measuring Gas Flaring in Russia with Multispectral VIIRS Nightfire
## 1 Introduction
Other global-scale remote sensing products capable of detecting and estimating the radiative output of the gas flares include short-wavelength infrared (SWIR) adaptation of fire radiative power (_FRP_) estimates based on the Active Fire Products wildfire detection method [12] and Nightfire adaptation for the Sentinel-3A SLSTR sensor [13], although no continuous annual national scale and point-specific gas flaring volume estimates have been presented in both at the time of writing. Reviews of remote sensing in gas flare detection and monitoring have been published recently [14; 15]. In recent studies, the Nightfire algorithm has been further extrapolated to the regional data and hotspots other than gas flares. VNF was demonstrated to be significantly more sensitive in the case of gas flare detection compared with MODIS and VIIRS based Active Fire Products [16]. For China as a case study region, Nightfire data have been successfully applied to categorize the industrial facilities based on their temperature and persistence characteristics [17]. A correlation of VIIRS Nightfire data with reported flaring volume data in Nigeria was developed by the author of [18]. VIIRS Nightfire data were also applied to evaluate gas flaring in Texas and North Dakota [19; 20; 21; 22]. VIIRS Nightfire data were used to assess the impact of gas flaring reduction possibilities on meeting Paris Climate Agreement emission reduction goals [23], and potential global gas flaring black carbon emission levels were calculated using SLSTR Nightfire adaptation [13]. [PERSON] et al. examined VIIRS Nightfire detection limits with the introduction of the VIIRS M11 SWIR band for night-time observations [24]. [PERSON] et al. conducted the ground truth validation of the VNF on the controlled gas flares [25]. A comparison of VIIRS Nightfire estimates with regionally reported offshore flaring volumes was conducted by the author of [26].
# Reliability Analysis of Response-Controlled Buildings Using Fragility Curves
## 4 Combination of Different Types of Dampers  
### Combination of Hysteresis and Oil Dampers  
To further improve the seismic performance of the damper-equipped models and to use the results of the fragility curves and the response energy of the hysteresis and oil dampers, two different combinations of hysteresis and oil dampers were considered. The first combination was dubbed the Hysteresis-Oil-Hysteresis Model (HOHM), because the hysteresis dampers in the center portion of the hysteresis model were replaced with the same number of oil dampers, while the hysteresis dampers in the two outer sections of the model remained the same as shown in Figure 9a. In contrast, the latter combination had the reverse arrangement, namely, the Oil-Hysteresis-Oil Model (OHOM), as shown in Figure 9b.
## 1 Introduction
To identify and rate BC, the majority of research employ traditional ML and transfer learning methods. However, the biggest problem with classical ML (such as support vector machine (SVM)) is the lengthy training period for big datasets [14]. The most problematic shortcomings in transfer learning systems, however, are negative transfer and generalization. The concept that pre-trained classification techniques are usually trained on the ImageNet database, which contains images irrelevant to medical imaging, is one of its disadvantages. As a result, developing efficient CADS to rapidly and accurately distinguish BC from ultrasound remains challenging. The DeepbreastcancerNet DL model is recommended to address these limitations. It makes use of filter-based feature extraction, which can aid in achieving excellent classification performance. Convolutional layer, clipped ReLu, and Leaky ReLu activation functions were used to develop the proposed model, which extracts the most essential and detailed features from the chest BC images. The proposed framework has 24 layers, including six convolutional layers, nine inception modules, and one fully connected layer. Through the use of a max-pooling operation, the design may reduce a number of weight characteristics. The suggested model, which is a novel method for BC detection and classification, includes batch and cross channel normalizing procedures as well as both clipped Relu and leaky ReLu activation functions. Furthermore, we adopted the strategy of TL for BC detection using nine pre-train DL model i.e., ResNet101 [15], ResNet-50 [16], ResNet-18 [17; 18], GoogLeNet [19], ShuffleNet [19], AlexNet [20], SqueezeNet [21].XceptionNet [22] The frozen weights of pretrain models are transformed to the target cancer dataset and the final softmax layer is replaced by the new one, as we have three classes. Therefore, the new softmax is adjusted three. The main contribution of this research paper is as follows. 1. We proposed a DeepBreastCancerNet deep learning model for breast cancer detection and classification. 2. We use data augmentation to increase model performance and avoid the problem of overfitting. The rest of the paper is organized as follows. Section 2 provides a literature review, Section 3 materials and method, Section 4 is about experiments and results, and Section 5 has a conclusion and future work.