esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Relevant Theoretical Models
### Transformer Feature Fusion
#### 2.3.3 Domain Adaptive Classifier  
This paper introduces an adaptive fusion module designed to integrate self-attention and cross-attention features, aiming to remove redundant information across different feature sets. The adaptive fusion process consists of three main stages: global average pooling (GAP), feature compression, and feature expansion. Initially, the input feature undergoes GAP to produce the feature vector \(Z\). Subsequently, \(Z\) is linearly transformed and compressed to one quarter of its original dimension, yielding the compressed vector \(Z_{cmps}\):  
\[Z_{cmps}=ReLU\big{(}FC\big{(}Z,W_{cmps}\big{)}\big{)},Z_{cmps}\in R^{\frac{ \pi}{4}} \tag{24}\]  
where \(ReLU\) stands for the nonlinear activation function and \(FC\) for the fully connected layer. Since \(W_{cmps}\) is a learnable parameter, dynamically modifying the parameter during the backpropagation process can reduce the redundant information among the feature sets. Finally, the features are mapped to the 0-1 interval through the Sigmoid activation function, and the fusion weights of the learned feature matrix and the original feature matrix are multiplied:  
\[Z_{exc}=\varphi\big{(}FC\big{(}Z_{cmps},W_{exc}\big{)}\big{)},Z_{exc}\in R^{n} \tag{25}\]  
Figure 2: Cross-attention mechanism.  
where \(W_{exc}\) is the expansion coefficient and \(\varphi(x)=\frac{1}{1+e^{-x}}\) represents the Sigmoid activation function.  
Figure 3 depicts the flowchart of the proposed method. Firstly, a bearing fault dynamics model is established, and it is solved using the Runge-Kutta theory. The obtained simulation/experiment data is subjected to Fast Fourier Transform to obtain frequency-domain signals with fault characteristics. The time-frequency signals are processed through the CNN module to obtain global features. The cross-attention mechanism captures the global features extracted by the CNN module. Based on the dynamic weight allocation, larger weights are assigned to the signals with more obvious fault features. Finally, the precise classification results are output.
# BrushGaussian: Brushstroke-Based Stylization for 3D Gaussian Splatting
## 3 Methods
### Three-Dimensional Gaussian Splitting  
In 3 DGS, each Gaussian primitive is explicitly defined by its 3D covariance matrix \(\Sigma\in\mathbb{R}^{3\times 3}\), which determines its shape and orientation, and its center \(\mu\in\mathbb{R}^{3\times 1}\), which defines its position in 3D space. To render a 2D Gaussian ellipse from a 3D Gaussian primitive, the center of the projected 2D Gaussian ellipse is given as follows:  
\[\mu^{\prime}=P\mu, \tag{1}\]  
where \(P\) is the model-view-projection (MVP) matrix, which projects 3D points onto a 2D image plane.  
To transform a 3D Gaussian distribution from world coordinates to camera coordinates, we follow the approach introduced by [PERSON] et al. [77], which applies a view transformation matrix \(V\in\mathbb{R}^{3\times 3}\), followed by projection onto a 2D image plane via a local affine transformation \(J\in\mathbb{R}^{3\times 3}\). The transformed 3D covariance matrix \(\Sigma^{\prime}\) is computed as follows:  
\[\Sigma^{\prime}=JV\Sigma V^{T}J^{T}. \tag{2}\]  
Following [PERSON] et al. [77], we obtain an approximate 2D covariance matrix by removing the third row and third column of \(\Sigma^{\prime}\). This \(2\times 2\) symmetric matrix consists of three elements, \(\sigma_{x}\), \(\sigma_{xy}\), and \(\sigma_{y}\), which correspond to the variances and covariance of the projected Gaussian distribution in the image plane. These elements define the shape and orientation of the projected 2D Gaussian ellipse.  
For color rendering, 3 DGS adopts a volume rendering approach similar to NeRF-style volumetric rendering. The final pixel color is computed by accumulating colors from \(\mathcal{N}\) Gaussian primitives, blending them from front to back according to the conventional volume rendering equation:  
\[C=\sum_{i\in\mathcal{N}}c_{i}\alpha_{i}\prod_{j=1}^{i-1}(1-\alpha_{i}), \tag{3}\]  
where \(c_{i}\) is the color of each Gaussian, and \(\alpha_{i}\) is the opacity computed using the 2D covariance matrix \(\Sigma^{\prime}\) and a learned per-point opacity parameter \(o_{i}\).  
Specifically, \(\alpha_{i}\) is given by the following:  
\[\alpha_{i}=o_{i}e^{-\frac{1}{2}\pi_{i}^{T}\Sigma^{\prime-1}x_{i}}, \tag{4}\]  
where \(x_{i}\) represents the offset vector between the current pixel position and the center of the 2D Gaussian ellipse \(\mu_{i}\), measuring the pixel's spatial distance from the Gaussian center in the image space.
# Multivariable Adaptive Super-Twisting Guidance Law Based on Barrier Function
## 3 Barrier Function-Based Multivariable Super-Twisting Guidance Law Design
### Design of Adaptive Multivariable Super-Twisting Controller Based on Barrier Function  
In consideration of a first-order system with the following form:  
\[\dot{s}(t)=u(t)+\delta(t) \tag{8}\]  
where \(s(t)\) is the output variable; \(u(t)\) is the super-twisting controller; and \(\delta(t)\) is Lipschitz disturbance such that:  
\[\left|\dot{\delta}(t)\right|\leq M \tag{9}\]  
where \(M\) is the upper bound of \(\left|\dot{\delta}(t)\right|\), which is always unknown in practice.  
The main goal of the super-twisting controller design is to counteract the Lipschitz disturbance \(\delta(t)\) and drive both \(s(t)\) and \(\dot{s}(t)\) to zero. The standard super-twisting controller for system (8) can be described as [23]:  
\[\left\{\begin{array}{ll}u(t)=-a_{1}|s|^{1/2}sign(s)+y(t)\\ \dot{y}(t)=-a_{2}sign(s)\end{array}\right. \tag{10}\]  
where \(a_{1}\) and \(a_{2}\) are designed gains, which satisfy \(a_{1}=1.5\sqrt{M}\) and \(a_{2}=1.1M\), respectively.  
By introducing a twisting state variable \(y(t)\), which converges to a neighborhood of zero in finite time, the controller (10) can meet the main goal. However, there are two distinct flaws in the standard super-twisting controller:  
1. The design of the controller gains \(a_{1}\) and \(a_{2}\) requires the information of \(M\);
2. The value and convergence speed of \(y(t)\) relies on the designed gains \(a_{1}\) and \(a_{2}\).  
In order to address these problems, an adaptive super twisting controller based on barrier function is proposed in [35], which is given by:  
\[\left\{\begin{array}{ll}u(t)=-L(t,s)|s|^{1/2}sign(s)+y(t)\\ \dot{y}(t)=-L^{2}(t,s)sign(s)\end{array}\right. \tag{11}\]  
where the adaptive gain \(L(t,s)\) is formulated as:  
\[L(t,s)=\left\{\begin{array}{ll}\overline{L}(t),&0\leq t<\overline{t}(s_{0}) \\ L_{b}(s),&t\geq\overline{t}(s_{0})\end{array}\right. \tag{12}\]  
where \(s_{0}=s(0);\overline{t}(s_{0})\) is the first time for which \(s(t)<\varepsilon/2\); \(L_{b}(s)\) has the same form as (7); and \(\overline{L}(t)\) is given by:  
\[\overline{L}(t)=l_{0}+l_{1}t \tag{13}\]  
where \(l_{0}\) and \(l_{1}\) are designed positive constants.  
Reference [35] has proved that there exists a finite time \(\tilde{t}(s_{0})\), and after that, the barrier function gain \(L_{b}(s)\) can drive \(s(t)\) to a small neighborhood of zero. However, the super-twisting controller (10) can not be utilized directly for the multivariable system, such as:  
\[\dot{\mathbf{s}}(t)=\mathbf{u}(t)+\mathbf{\delta}(t) \tag{14}\]  
Therefore, a multivariable adaptive super-twisting controller with the barrier function gain (12) is proposed in this paper, which can be expressed as:  
\[\left\{\begin{array}{l}\mathbf{u}(t)=-\mathbf{L}(t,\mathbf{s})\circ\frac{\mathbf{s}}{\|\mathbf{s }\|^{1/2}}+\mathbf{y}(t)\\ \dot{\mathbf{y}}(t)=-\mathbf{L}^{2}(t,\mathbf{s})\circ\frac{\mathbf{s}}{\|\mathbf{s}\|}\end{array}\right. \tag{15}\]
# Energy Demand Forecasting Scenarios for Buildings Using Six AI Models
## 2 Methodology
### Model Development
#### 2.3.1 Mathematical Model (ANN)  
Energy demand forecasting uses artificial neural networks that seek out complex relationships between input characteristics and energy use. Primarily, the design consists of interlaced layers, beginning with the input layer that simply receives raw data such as past energy consumption, temperature, and economic indicators. The mathematical model for this process, described in Equations (1)-(16), shows how data enter the hidden layers by means of neurons interconnected by weighted connections and activation functions, which can catch even the finest patterns. Finally, an estimation is made for future energy demand. Table 2 also shows how the ANN is trained on historical data and appropriately weighted to minimize prediction error and generalize well to new data, thereby causing significant insights to energy suppliers and policy makers [45]:
# Energy-Aware Online Non-Clairvoyant Scheduling Using Speed Scaling with Arbitrary Power Function
## 2 Related Work
HDF is optimal [10] in online clairvoyant settings for the objective of fractional weighted/importance-based flow time plus energy. In clairvoyant job scheduling, the size of a job is known at arrival time but the same is not true in case of non-clairvoyant scheduling, therefore HDF cannot be applied in non-clairvoyant setting. In this paper, a variant strategy of HDF is considered but in online non-clairvoyant setting for the objective of minimizing the scaled importance based flow time plus energy. Authors proposed a new strategy Highest Scaled Importance First (HSIF) in which rather than the complete importance of a job the scaled importance of a job is considered. The scaled importance of a job increases if the job is new and it does not get the chance to execute; consequently, the starvation condition is avoided. If a job executes then the scaled importance will decrease. This balances the speed and energy consumption. The speed of a processor is a function of the total scaled importance of all active jobs. 2-competitive HSIF is analysed using the amortized potential function against an offline adversary and arbitrary power function. The results of HSIF and other related online non-clairvoyant job scheduling algorithm are provided in Table 1.
## 3 Mathematical Modeling and Design Methodology
### System Design Methodology
#### 3.4.1 System Reliability  
Reliability evaluation is considered as a good measure for the planning and operation stages of the power system, by precisely computing many reliability indices [42]. These performance indices evaluate the process of meeting the load demand for an HRES. Reliability is a major concern to meet the load demand over a given time period. This is due to the irregular nature of the power supply of RES, which significantly influences the HRES power generation and supply. This will also impact the load demand in standalone and on-grid HRES applications [5].  
In many studies, the reliability of the system is taken as either a constraint or a goal to be achieved in the optimization process. In this paper, the value of the index of reliability (IR), shown in Equation (13) [5; 29], will be optimized using the PSO algorithm and WOA to find the optimal size of the HRES configuration. Other reliability indices, such as loss of power supply probability (LPSP), loss of load probability (LOLP), and energy expected not served (EENS), will be computed corresponding to the optimal value of IR.  
Here, LPSP is the probability, over a given time period, in which the power demand is not fully satisfied by the generated power. It is widely used in the literature to assess the system reliability due to the RES' intermittent nature. It is mathematically represented as shown in Equation (14) for a one-hour time step [29; 30]. Furthermore, the power system reliability can be evaluated based on the LOLP index. The LOLP index is defined as the total failure hours of the energy deficit divided by the number of sample hours for the entire period, which is 8760 h for the year of 2018 in this paper [43]. This index is widely used in order to evaluate whether the system is able to serve the load demand, by indicating the percentage of time that the system is insufficient at supplying the load. This index can be computed as shown in Equation (15) [6; 25]. Moreover, EENS is a probabilistic index for the reliability analysis of a power system. It represents the expected energy not supplied to the load due to the energy deficit as in Equation (16) [5; 44]:  
\[IR=1-\frac{\sum_{t=1}^{8760}[P_{L}(t)-P_{G}(t)]}{\sum_{t=1}^{8760}[P_{L}(t)]} \tag{13}\]  
\[LPSP=\frac{\sum_{t=1}^{8760}[P_{L}(t)-P_{G}(t)]}{\sum_{t=1}^{8760}[P_{L}(t)]} \tag{14}\]  
\[LOLP=\frac{\sum_{t=1}^{8760}\text{ hours}\left[\left(P_{PV_{L}}(t)+P_{T}(t) \right)<P_{L}(t)\right]}{8760} \tag{15}\]  
\[EENS=\sum_{t=1}^{8760}[P_{L}(t)-P_{G}(t)] \tag{16}\]
## 4 Correlation Analysis of Shield Tunneling Parameters
### Correlation Analysis and Tunneling Strategy Optimization
(6) Cutter Speed and Total Thrust  
In the volcanic ash strata, there is an extremely weak correlation between the cutter torque and the total thrust. The cutter speed primarily influences the cutting efficiency of the soil. However, the total thrust is also impacted by the soil viscosity, density, hardness, and other factors. Increasing the cutter speed enlarges the contact area between the cutter and the soil, thereby enhancing the cutting force. Yet, during actual tunneling, variations in the contact area may be influenced by factors like the shield machine's orientation and the soil heterogeneity. Hence, the relationship between the cutter speed and the total thrust may not be strictly linear. (7) Excavation Speed and Cutter Speed  
In the volcanic ash strata, there is an extremely weak negative correlation between the excavation speed and the cutter speed. Within a certain range, the excavation speed is positively correlated with the cutter speed, as a higher cutter speed enhances the tool's cutting ability, leading to an increased excavation speed. However, in the volcanic ash strata, factors like the tool wear and formation resistance weaken this positive correlation.
# Capacitor-Less Low-Power Neuron Circuit with Multi-Gate Feedback Field Effect Transistor
## 2 Fbfet
### Multi-Gate FBFET for Multiple Inputs  
Furthermore, we designed a multi-gate FBFET to implement neuronal network characteristics because neuron networks need to receive multiple signals from previous neurons. The proposed structure consists of four gates and one control gate. It is shown in Figure 7.  
This structure can implement spatial integration by receiving multiple inputs through multi-gates. It also can store electric charges in the floating body, enabling temporal integration. Its simulation results are shown in Figure 8. We also used TCAD simulation for this. Figure 8a shows the turn-on time difference according to the number of gates applying voltage. Figure 8b shows the turn-on time of the FBFET according to the number of biased voltage gates. As the number of gates increases, the turn-on time exponentially decreases. Because as the number of applied gates increases linearly, the accumulated electrons in the potential well under the control gate also increase linearly. Additionally, as the number of electrons increases, the valence band height of the control gate decreases linearly. When the valence band height is linearly lowered, the accumulated holes increase exponentially. The associated energy band equation is as follows [20].  
Due to this feedback, as the gates applied increase, the turn-on time decreases exponentially. Therefore, when multiple inputs are applied, it indicates that the charges are stored in the floating body, and the temporal integration is performed by the floating body.  
Figure 8: Multi-gate FBFET simulation result. (a) Compared on/off time between two and three gates that applied voltage (b) Graph of turn-on time according to the number of gates applied with a voltage. The time decreases exponentially as the number of gates increases (Control gate V = 0.9 V).  
Figure 7: Proposed multi-gate FBFET structure.
# Parametric Analyses of the Response of Masonry Walls with Reinforced Plaster
## 3 Numerical Simulations
### Unreinforced Masonry--Effect of the Joint Mortar Strength  
The performance of a masonry wall is strongly dependent upon the properties of its constituents. The mortar joints are usually the weakest elements of an assemblage, and their non-linear response is one of the most relevant features of masonry response. Moreover, it is well known that the difference between the elastic properties of the unit and mortar is the main cause of the failure of these structures. For these reasons, different grades of mortar were considered, with different ratios of cement, lime, and sand, to investigate the effect of the mortar's strength on the overall behavior of the masonry walls. The mechanical properties of the mortars (Table 4) were selected as in [41], where an experimental characterization of mortar cubes is presented.  
Figure 11 shows the results in terms of load vs. displacement curves, while Figure 12 shows the damage pattern at failure for the different configurations. For lower values of mortar strength, the peak load was lower and the damage that developed in the mortar joints, was spread over a wider area. For the strong mortars, a main crack developed along the compressed diagonal, involving both the joints and the bricks, with additional smaller cracks around the main crack. Moreover, more ductile behavior was detected at the peak load.  
\begin{table}
\begin{tabular}{c c c c} \hline
**Mortar (Cement:Line:Sand)** & **Failure Strain** & \(\sigma_{\text{ccu}}\) & E \\  & & **(N/mm\({}^{2}\))** & **(N/mm\({}^{2}\))** \\ \hline Weak (1:0:6) & 0.0102 & 3.1 & 545 \\ Modified weak (1:0.7:5.5) & 0.0092 & 5.5 & 1100 \\ Intermediate (1:0.5:4.5) & 0.0083 & 15.2 & 3300 \\ Strong (1:0.3) & 0.0099 & 20.6 & 3750 \\ \hline \end{tabular}
\end{table}
Table 4: Mechanical properties of mortars.  
\begin{table}
\begin{tabular}{c c c} \hline
**Test** & **Investigate Parameter** & **Reinforced/Unreinforced** \\ \hline
1 & Mortar strength & unreinforced \\
2 & Presence of weak bricks & unreinforced \\
3 & Anchors orientation & reinforced \\
4 & Plaster reinforcement & reinforced \\
6 & Anchor density & reinforced \\
6 & Plaster strength and fracture energy & reinforced \\
7 & Plaster thickness & reinforced \\ \hline \end{tabular}
\end{table}
Table 3: Summary of the performed simulations.  
From the results, it was confirmed that, when the mechanical properties of the mortar are close to those of the brick, the peak stress is reduced, the overall response of the wall is more homogeneous, the masonry components are fully exploited, both in terms of resistance and stiffness, and more ductile behavior is achieved.
## 4 ADMM-Based Distributed Solution Strategy
### Problem Decomposition and Joint Optimization Algorithm
* Constraints: Each user must be served by either SLEO or BS (but not both). The LEO satellite has limited power and needs to avoid interfering with GEO satellite transmissions. When the user's resolution (rUi) remains fixed, the network side must ensure sufficient resource allocation for that demand. If resources become constrained, the network (SDN controller) may signal one or both users to downgrade their resolutions to ensure stable service. Although modest in scale, this example highlights how our scheme coordinates resource allocation among multi-layer satellite links and ground stations. By solving the user-side and network-side subproblems with ADMM, we iteratively assign service nodes, video resolution levels, and transmission parameters in a way that balances performance, interference mitigation, and resource limitations. Scaling up to more satellites, users, and base stations follows the same core logic while adding corresponding subproblems and constraints.
# Drill Hole Orientation: Its Role and Importance on the Compression Response of Pure Magnesium
## 3 Results and Discussions
### Experimental Results
#### 3.2.3. Summary of Experimental Results  
Experimentally, the following observations were made:  
1. Compression tests showed the ductility and ultimate tensile strength reduced as the drill hole orientation changed from vertical to angular to horizontal, respectively.
2. The load to failure for each of these individual components indicates that the horizontal drill hole orientation fails first and the vertical drill hole orientation lasts the longest.  
Figure 10. Compressive fracture surface for (**a**) monolithic Mg, (**b**) vertical drill hole orientation, (**c**) angular drill hole orientation and (**d**) horizontal drill hole orientation with insets showing the respective fractured specimen.  
Another observation was that all three drill hole orientations increased the yield strength of the component if the 0.2% offset method was considered. This can be attributed to the lower stiffness of each of these components in general when compared to the monolithic condition, thereby giving rise to local flexure and local yield and thereby, longer duration to global yield.
4. Microscopic observations indicated that the presence of the drill hole did not change the microscopic deformation and failure mechanisms with the shear mode still being prevalent in all cases.
5. Macroscopic observation of the fractured sample indicated a difference in orientation of the final crack. While the monolithic and vertical drill hole orientations experienced near 45\({}^{\circ}\) failure crack propagation, the angular drill hole experienced a failure angle of more than 45\({}^{\circ}\) with the horizontal and horizontal drill orientations experienced a failure angle of less than 45\({}^{\circ}\) with the horizontal. These deviations are shown in the insets of Figure 10.
## 4 Proposed Task Execution Policy
### Task Selection Policy  
A fog node in \(ETE_{FN}\) after regular intervals calculates the total number of received tasks and makes the decision whether to process these tasks on its own or forward them to the cloud server based on the following conditions:  
1. If the vehicle's remaining time of attachment with the RSU is less than its executed task downloaded time, then the task is forwarded to the cloud for task execution, and the cloud is supposed to download the task to a fog node placed at the vehicle's next attached RSU.  
Figure 1: System model.  
If all the valid requested tasks are less than its task execution capacity, then it processes all tasks itself, and no task will be forwarded to the cloud server.  
A complete task processing criteria is shown in Algorithm 1.  
```
1\(y_{i}\leftarrow\)Current task
2\(t_{i}\leftarrow\)Remaining time of attachment with RSU
3\(T_{c}\leftarrow\)Executed task downloading time
4\(i\leftarrow\)current vehicle ID
5\(C\leftarrow\)Task execution capacity of fog node
6\(Y\leftarrow\)Max. no. of tasks requested by a vehicle
7\(V\leftarrow\)Max. no. of tasks requesting vehicles
8\(y_{i}\leftarrow\)content size requested by \(i^{\text{th}}\) content
9 Task execution policy
10 for\(x=1\)to\(v\)do
11 for\(y_{x}=0\)to\(Y\)do
12 If\(t_{i}\leq T_{c}\)\(\leftarrow\)Forward the task to cloud server
13 Else
14\(\leftarrow\)Include the task in task processing list \(x=x+1\)
15 EndIf
16
17 end for
18\(\leftarrow\)Compute all tasks (\(K\)) in task processing list
19 If\(K\leq C\)\(\leftarrow\)Fog node executes all tasks
20 Else
21\(\leftarrow\)Fog node apply 0/1 knapsack to scrutinize tasks EndIf
```  
**Algorithm 1**Task processing Criteria  
If requested tasks are more than the task execution capacity, then it optimally scrutinizes tasks to be performed at the fog node by applying 0/1 knapsack, and the rest of the tasks are forwarded to the cloud server. The value of 0/1 knapsack in the proposed scheme is derived from a utility function that is computed by the fog node for each offloaded task.