esa-sceva/satcom-chunk-collection huggingface dataset:
# Human-Centered Dynamic Service Scheduling Approach in Multi-Agent Environments
## 3 Human-Centered Dynamic Service Scheduling Approach
### Multi-Agent Scheduler
#### 3.2.2 Operation Manager
If a user wants jobs J1 and J4 to be executed together, such as by asking for a meal and coffee to be delivered together, the service can be performed separately by two different agents simultaneously or by a single agent. If two agents are running in parallel, the operation manager uses the freezing function and freezes the last operation of J1 and J4 until both are in the candidate queue, and the two agents are available for simultaneous delivery. The combining function is used in a single-agent execution, and a new operation is created that combines the last operations of J1 and J4 to be performed by the agent as one. When all the previous operations are completed, the new operation O14 cb is fetched and replaces the last two operations in the candidate queue. The combining function can only be applied when the two operations have the same operation type, object, start position, and end position. In addition, only agents that can perform both operations can execute a combined operation. The duration of the combined operation was arbitrarily set to be longer than that of both operations. The scheduler also utilizes human monitoring and agent monitoring, which allows multiple agents to provide services at user-specified preferred times. For instance, the operation manager freezes the operation in the candidate queue until the human monitoring condition is met. The operation status in the candidate queue can be \"ready\", \"frozen\", and \"execute\". There is no \"finished\" status for operations because when an operation is complete, the agent status changes to \"finished\", and the operation is dequeued from the candidate queue by the operation manager. As described above, the operation manager is implemented according to the pseudocode reported in Algorithm 2.
# Study on Temperature Correction of Asphalt Pavement Deflection Based on the Deflection Change Rate
## 3 The Representative Temperature of the Asphalt Layer
### Analysis and Results  
The average modulus of the asphalt layer obtained from the backcalculation was averaged according to the temperature, and the modulus-temperature relationship was modeled by statistical regression. It was found that the logarithm of the modulus is linearly related to temperature, which is shown in Equation (1):  
\[\begin{array}{c}\log(E_{T})=a+m\times T\\ E_{T}=10^{a+mT}\end{array} \tag{1}\]  
where \(T\) is the test temperature in \({}^{\circ}\)C, \(E_{T}\) is the backcalculation modulus at the test temperature, in MPa, and \(a\) and \(m\) are different regression coefficients.  
Then, the temperature correction coefficient was exponentially related to temperature. Therefore, an exponential relationship model was used to regress the modulus-temperature data, as shown in Equation (2):  
\[\lambda_{E}=\frac{E_{T_{0}}}{E_{T}}=10^{-m(T-T_{0})} \tag{2}\]  
where \(T_{0}\) is the standard temperature in \({}^{\circ}\)C, \(E_{T_{0}}\) is the modulus corrected to the standard temperature, in MPa, and \(\lambda_{E}\) is the correction factor.  
When the temperature at one-third depth was used for modeling, the fitted curves and model parameters obtained were shown in Figure 2 and Equation (3):  
\[\left\{\begin{array}{c}\log(E_{T})=4.4066-0.013T\\ \lambda_{E}=10^{0.013\bullet(T-T_{0})}\\ R^{2}=0.741\end{array}\right. \tag{3}\]  
When the temperature at the middle depth was used for modeling, the fitted curves and model parameters obtained were shown in Figure 3 and Equation (4):  
\[\left\{\begin{array}{c}\log(E_{T})=4.4475-0.015425T\\ \lambda_{E}=10^{0.015425\bullet(T-T_{0})}\\ R^{2}=0.855\end{array}\right. \tag{4}\]  
Figure 2: Modulus–temperature model (at 1/3 depth).  
From the above results, it can be seen that both models can achieve a better fitting effect, but in comparison, the fitting curve of \(R^{2}\) is larger and the fitting effect is better when the temperature at the middle depth is chosen as the representative temperature. Therefore, this paper uses the temperature at the middle depth as the representative temperature of the asphalt layer.
## 3 Results
### Synthesis of the Results  
Separate meta-analyses were performed by pooling quantitative data from two RCTs [20; 21] that compared orthodontic pain between the bite wafer (soft and/or hard) and Ibuprofen groups and three RCTs [2; 40; 41] that compared the bite wafer and Acetaminophen (Paracetamol) groups (for adolescent and/or adult participants). Moreover,  
Figure 3: Risk of bias [2; 4; 9; 20; 21; 40; 41].  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** & **[PERSON]** \\  & **et al.**[20] & **et al.**[9] & **et al.**[21] & **et al.**[2] & **et al.**[4] & **et al.**[4] & **et al.**[41] & **et al.**[40]** \\ \hline Random sequence generation & Low & Low & Low & High & Low & Low & Low \\ \hline Allocation concealment & Low & High & High & High & Low & Low & High \\ \hline Blinding of participants and researchers & High & High & High & High & High & High & High \\ \hline Blinding of outcome assessment & High & Low & High & High & High & High & High \\ \hline Incomplete outcome data & Low & Low & Low & Low & Low & Low & High \\ \hline Selective outcome reporting & Low & Low & Low & Low & Low & Low & Low \\ \hline Other bias & High & Low & High & Low & Low & Low & Low \\ \hline Overall & High & Moderate & High & High & Moderate & Moderate & High \\ \hline \hline \end{tabular}
\end{table}
Table 5: Risk of bias of the included randomized controlled clinical trials.  
subgroup analyses were performed based on the timepoints of pain interval evaluation. The RCTs included in the meta-analyses assessed self-reported orthodontic pain for the following timepoints: 2 h; 6 h; bedtime; 24 h; 1 day; 2 days; 3 days; 4 days; 5 days; 6 days; and 7 days. None of the timepoints individually indicated a significant difference in self-reported pain scores between bite wafer and control groups (Ibuprofen and Acetaminophen), except on day 3, indicating significantly lower pain scores in the bite wafer versus Acetaminophen (Paracetamol) groups (Figures 4 and 5).
# A Generalized Framework for the Estimation of Edge Infection Probabilities
## 4 Evaluation
### Method Details and Experimental Setup  
The inverse infection tasks in this paper were constructed according to the following procedure.  
1. For a given graph, reference edge weights and the initially infected vertices are selected. The reference edge weights are not used in the inverse infection model, so they are omitted afterward.
2. An infection model is selected and simulated using a modified version of CompleteSim, published in [28]. The original method computes two observations for the IC model, but it is easy to adapt it to other models. The set of infection models considered includes: 1. The Independent Cascade Model (SIR with \(\tau_{i}=1\)). 2. The SIR model with a \(\tau_{i}\) infectious period. 3. The SEIR model with \(\tau_{e}\) latency and a \(\tau_{i}\) infectious period. 4. The SI model.
3. During the simulation process, the set of reference observations for all nodes is computed. The observations are taken at the following time steps: 1. Two observations are available at \(t_{0}=i_{0}\) and \(t_{1}=t_{max}=i_{max}\). 2. Observations for all iterations of the discrete infection process are available \(T:\{t_{0}=i_{0},\ldots,t_{max}=i_{max}\}\).
4. Observations may be in the following form: 1. \(o_{v_{t}}=p_{v_{t}},t\in T\) for all \(v\in V(G)\), that is, _real infection values_ for all vertices of the graph denoting the probability of infection up to time step \(t\). \(T\) may contain an arbitrary number of observations with respect to the restrictions in Section 3.1. 2. \(o_{v_{t}}=b_{v_{t}},t\in T\) for all \(v\in V(G)\), that is, _binary flags_ for all nodes of the graph signaling that the node was in an infectious state at time step \(t\). \(T\) may contain an arbitrary number of observations with respect to the restrictions in Section 3.1.
5. An error function is attached to the task. 1. The RMSE function averaged over all real-valued observations. 2. The ROC AUC value for the last observation with binary observations.
6. The Particle Swarm Optimization method computes the estimated edge infection probabilities and observations. The method is used with the number of agents ranging from 100 to 250 depending on the size of the task. The method is stopped if it does not find a better solution in 50 iterations. We experimented with other settings and found that the accuracy of the method cannot be significantly improved by increasing the PSO parameters.  
The final error value measures the goodness of the estimation. This procedure is repeated for a range of graph classes, sizes and infection scenarios.
# Infrared Stripe Correction Algorithm Based on Wavelet Analysis and Gradient Equalization
## 3 Stripe Correction New Algorithm Based on Wavelet Analysis and Gradient Equalization
### Guide Filtering Removes Vertical Component Noise  
Guided filter uses guidance image \(I\) to destriping the input image \(P\). Local linear model of guided filter assumes that filtered output \(q\) is a linear transform of the guidance \(I\). Therefore, guidance image \(I\) directly affects the output [18; 28]. The formula defined between the two is as follows:  
\[q_{i}=a_{k}I_{i}+b_{k},\ \forall_{i}\in w_{k} \tag{13}\]  
where \(a_{k}\) and \(b_{k}\) are continuous linear constant coefficients in window \(W_{k}\). \(I\) is the guidance image, \(W_{k}\) is a window centered at pixel \(k\), The constraint equations for \(a_{k}\) and \(b_{k}\) in window \(W_{k}\) are:  
\[E(a_{k},b_{k})=\sum_{i\in w_{k}}\bigl{(}\left(a_{k}I_{i}+b_{k}-p_{i}\right)^{2 }+\varepsilon a_{k}{}^{2}\bigr{)} \tag{14}\]  
where \(\varepsilon\) is a regularization parameter, the purpose is to make \(a_{k}\) converge.  
By minimizing the constraint equation to minimize the deviation between the input image and the output image, the coefficients \(a_{k}\) and \(b_{k}\) are:  
\[a_{k}=\frac{\frac{1}{\left|\frac{1}{\omega}\right|}\sum\limits_{i\in w_{k}}p_{ i}I_{i}-\overline{p_{k}}u_{k}}{\sigma_{k}^{2}+\varepsilon}, \tag{15}\]  
\[b_{k}=\overline{p_{k}}-a_{k}u_{k}; \tag{16}\]  
where \(u_{k}\) and \(\sigma_{k}^{2}\) are the mean and variance of the guidance image \(I\) in the window \(W_{k}\), \(\overline{p_{k}}\) is the mean of the input image \(p\) in window \(W_{k}\). \(|w|\) is the number of pixels in window \(W_{k}\).  
According to the formula, if the regularization parameter \(\varepsilon\) is given, the size of filtering window \(h\) will affect the output of guided filtering. In this paper, \(H\) represents the height of the image. Figure 6 shows the results of using different window sizes. If we set the window sizes \(h\) to a low value, many obvious stripes noise still remain visible in the smoothed output, as shown in Figure 6b. Conversely, setting the value of h to a large value, there will be obvious fuzzy phenomenon in Figure 6d,e, as highlighted in yellow. In our experiment, \(h\) = 0.3\(H\) was used, and the regularization parameter \(\varepsilon\) was 0.2\({}^{2}\).  
Figure 6: Stripe removal results with different window heights. From left to right (**a**) Raw infrared image (**b**) \(h\) = 0.1\(H\), (**c**) \(h\) = 0.3\(H\), (**d**) \(h\) = 0.5\(H\), (**e**) \(h\) = 0.7\(H\). \(H\) denotes the height of the image. Regularization parameter \(\varepsilon\) is set to 0.2\({}^{2}\). Please zoom in to see details in the pictures.
# A Survey of Algorithms and Systems for Evacuating People in Confined Spaces
## 3 Evacuation Wayfinding Algorithms
### Off-Line Evacuation Wayfinding Algorithms
#### 3.1.3 Fluid-Dynamics Model-Based Algorithms  
Fluid-dynamics models imitate evacuee flows as fluids to study the density and speed adaptation during an evacuation process. Compared with microscopic models, the macroscopic fluid-dynamics models are better at simulating and analysing the behaviours of large crowds. For instance, the research in [68] derived several equations that governed the motion of a pedestrian flow from the \"continuity equation\" of fluid mechanics in physics; the proposed equations led to two possible regimes of pedestrian flow: the fast-moving, low-density \"supercritical\" flow in which disturbances spread within the flow and the slow-moving high density \"subcritical\" flow in which disturbances are swept along by the flow; several partial differential equations that govern the crowd behaviours of a flow that contains different types of pedestrian were also studied; a pedestrian type was determined by the destination, walking speed and perception; the analysis and experimental results showed that pedestrians tended to reach each immediate destination in the minimum time rather than arriving at all destinations in the overall minimum time. The study in [69] presented a continuum model to investigate the relation between evacuee density and walking speed during a process of evacuees leaving a corridor through a door; the proposed model was based on the [PERSON] [70] and [PERSON] [71] model, which is used to simulate vehicle flows; specifically, this model describes the decrease in the outflow through a door caused by the panic \"over-compression\" effect of evacuees in front of the door; the analytical results indicated that the rise of panic can dramatically decrease the outflow of evacuees when the door is narrow.
# Detecting SARS-CoV-2 in CT Scans Using Vision Transformer and Graph Neural Network
## 3 Materials and Methods
### GNN-Based Spatial Representation Module
#### 3.6.2 GCN Architecture  
With the graph defined, we apply a Graph Convolutional Network (GCN) to refine the node features. The GCN propagates information across these connections through message passing, where each node updates its feature representation by aggregating information from its neighbors. The GCN in our implemented model consists of two graph convolutional layers, which sequentially update the node features. The first GCNlayer transforms the initial node features, while the second layer further refines these representations. Mathematically, the update of the feature for a node \(i\) is defined as  
\[\mathbf{h}_{i}^{\text{(new)}}=\sigma\left(\sum_{j\in\mathcal{N}(i)\cup\{i\}} \frac{1}{c_{ij}}\mathbf{W}\mathbf{h}_{j}^{\text{(old)}}\right) \tag{1}\]  
where:  
* \(\mathcal{N}(i)\) represents the set of neighboring nodes connected to node \(i\).
* \(c_{ij}\) is a normalization factor based on the degrees of the nodes to ensure numerical stability.
* \(\mathbf{W}\) is a learnable weight matrix that applies a linear transformation to the features.
* \(\sigma\) is a non-linear activation function (ReLU).  
Each GCN layer in our model follows three key steps.  
1. Linear transformation, where we project the feature vectors of the nodes into a new feature space using \(\mathbf{W}\).
2. Neighborhood aggregation, where we sum the transformed features of neighboring nodes using the adjacency matrix.
3. Nonlinearity, where we use a Rectified Linear Unit (ReLU) activation function to add complexity to the feature representations. To avoid overfitting, we add dropout regularization after each GCN layer.
## 4 Case Study: The Kimberley Climate Adaptation Project (KCAP)
### Modeling and Visualization Workflow  
The novelty of the following approach, compared to conventional landscape design workshops [50], is that visualizations of drivers and scenario narratives are aggregated in a virtual globe as a data-driven, interactive, multi-dimensional and comprehensive 3D landscape model that is used as a shared communication platform. The virtual globe model can be used interactively, or still images and animations can be taken for use in other media such as presentations and posters.  
Development scenarios for potential future residential buildout in and around the town of Kimberley were modeled using CommunityViz Scenario 360. The first build-out was based on existing land use zoning, Official Community Plan land use designations, approved land development proposals and pending development applications, including several large areas outside the town core. Assumptions were made about the rate development would occur within these areas over time, with the different stages of development depicted visually as high level oblique views. A second build-out scenario assumed compact infill development. These two scenarios were presented as both static views from key viewpoints, and as animations, depicting the rate and extent of growth over time.  
Vulnerabilities associated with urban growth, given current trends and practices, were modeled based on local development scenarios and downscaled climate change projections. Overall, four local climate change related impacts or sectoral vulnerability models were integrated in GIS, each of which was visualized in Google Earth: downscaled hydrological modeling for snowpack changes with climate change (from PCIC); current potential flood areas within the City (based on an older municipal flood study); regional mountain pine beetle susceptibility (originally modeled by CALP, with modeling from the Ministry of Forests, and forecast under climate change by CALP); and, the potential spread of a forest fire with an ignition point in the Nature Park west of Kimberley (modeling from the City's fire consultant). The models are further explained in [PERSON] _et al._[51].  
The visualization workflow (Figure 3) was kept as simple and accessible as possible to make it replicable for other small communities. Google Earth was chosen as the main presentation medium because it is widely accessible, making it potentially replicable in the future for other smaller communities. In addition, the virtual globes Biosphere3D (B3D) and ESRI ArcGIS Explorer were usedas additional tools because B3D is open-source and specializes in the representation of vegetation, and ArcGIS Explorer is well integrated with ArcGIS. Feedback from two sessions with stakeholders, the city planner/councilors, and the KCAP facilitator led to a third round of data gathering, modeling, analysis, and synthesis, and improved 3D visualizations for the final Public Open House in June 2009.
# Integrated Model Selection and Scalability in Functional Data Analysis Through Bayesian Learning
## 3 Methods
### Scalable Update Strategy  
The scalability of our algorithm so far is primarily challenged by the need to optimize the variational lower bound, \(\mathcal{L}\), over \(K\) basis functions. As indicated by Equation (22), the time complexity is \(\mathcal{O}\big{(}K^{6}\big{)}\) (or, alternatively, \(\mathcal{O}\big{(}K^{2}P\max_{i}(N_{i})\big{)}\), typically dominated by the former), which becomes prohibitive when \(K\) is large. In practice, however, only a small subset of these basis functions is necessary for an accurate representation--those with non-negligible weights under our sparse prior.  
To address this, we focus the updates on the subspace of active basis functions, denoted as \(K^{(a)}\), which comprises only those functions with non-negligible weights. The remaining basis functions, whose influence is minimal, are held fixed during optimization. Furthermore, the number of active principal components is noted as \(J^{(a)}\) and set equal to \(K^{(a)}\), ensuring that the model spans the full range of possible ranks from \(1\) to \(K^{(a)}\). Consequently, we optimize \(\mathcal{Q}^{(a)}\) using updates derived with regard to the objective \(K^{(a)}\)-dimensional lower bound \(\mathcal{L}^{(a)}\) as an efficient surrogate of the full updates of \(\mathcal{Q}\) with regard to the full lower bound \(\mathcal{L}\), using only \(K^{(a)}\) active basis functions. Meanwhile, the active dimensionality of the model is adjusted dynamically during optimization by activating or deactivating basis functions based on their precision parameters. For clarity, variables associated with the active subspace are annotated with the superscript \((a)\) (e.g., \(a_{a_{j}}^{(a)}=a_{0}+\frac{K^{(a)}}{2}\) versus \(a_{a_{j}}=a_{0}+\frac{K}{2}\)).
## 3 Testing Results  
### Typical Stress-Strain Responses  
As depicted in Figure 4a-d, the samples were subjected to uniaxial increasing-amplitude fatigue loading paths. Influenced by rock bridge length, the marble samples exhibit varying fatigue loading stages and cycles before failure. The fatigue lifetime increases with longer rock bridge lengths, measuring 258, 316, 353, and 394 cycles, respectively, for samples with rock bridge lengths of 10 mm, 20 mm, 30 mm, and 40 mm. Irreversible plastic deformation causes the loading curve to deviate from the unloading curve, resulting in the formation of a hysteresis loop whose shape changes over time. After measuring axial strain (\(\epsilon_{\mathrm{a}}\)) and lateral strain (\(\epsilon_{\mathrm{r}}\)), volumetric strain (\(\epsilon_{\mathrm{v}}\)) can be calculated using the formula \(\epsilon_{\mathrm{v}}=\epsilon_{\mathrm{a}}-2\epsilon_{\mathrm{r}}\).  
Volumetric strain in rock provides a more comprehensive reflection of its deformation characteristics by integrating both axial and lateral deformations. The shape of the hysteresis loop on the volumetric stress-strain curve varies across different stages of fatigue loading. Changes in the sparse and dense characteristics of volumetric strain exhibit distinct trends. The shape of the hysteresis loop is intricately linked to the behaviors of crack initiation, propagation, and coalescence. Before reaching the last fatigue loading stage, the hysteresis loop consistently shifts from sparse to dense at the onset of each loading stage. This shift indicates greater plastic deformation occurring with each increase in stress amplitude. However, during the final loading stage, the hysteresis loop gradually becomes sparser until the granite samples eventually fail. This progression suggests that significantly more energy is expended to form the crack coalescence pattern, resulting in greater energy dissipation during the last cyclic stage compared to the preceding loading stages.
# A Cluster-Based Hierarchical-Approach for the Path Planning of Swarm
## 4 Model of the Proposed System
### Synchronization and Connectivity  
In order to further examine the synchronization of the Vicsek MAS as well as the connectivity of the related neighbor graphs, we have to first define what synchronization is.  
**Definition 2**.: _The aforementioned Vicsek MAS model achieves synchronization if the headings of every agent meet the following condition,_  
\[\underset{t\rightarrow\infty}{\lim}\theta_{i}(t)=\theta,\ i=1,\ \ldots\,\ N \tag{16}\]  
_whereas, \(\theta\) may vary according to the initial states\(\{\theta_{i}(0),\ x_{i}(0),\ y_{i}(0),\ i=1,\ldots,N\}\)and the model constraints \(v\), and \(r\)._  
The subsequent two propositions will set up the synchronization for the Vicsek MAS and its linear form under the parameters of the initial states and the system constraints \(r,\ v\), and \(N\).  
**Proposition 1**.: _For the Vicsek MAS model in Equations (8) and (9), let \(\ \{\theta_{i}(0)\in(-\frac{\pi}{2},\frac{\pi}{2}),\ i=1,\ \ldots\,\ N\}\), and the initial neighbor \(\ \mathbb{G}_{0}=\{\mathcal{V},\ \mathcal{E}_{0}\}\) is connected. So, the model will achieve synchronization if it meets the following condition,_  
\[v\leq\frac{d}{\triangle_{0}}\left(\frac{\cos\overline{\theta}}{N}\right)^{N} \tag{17}\]  
_where N is the number of agents, and_  
\[\left\{\begin{array}{c}\overline{\theta}=\underset{i}{\max}|\theta_{i}(0)| \\ d=r-\underset{i,j\in\mathcal{E}_{0}}{\max}d_{ij}(0)\\ \triangle_{0}=\underset{i,j}{\max}\big{\{}\tan\theta_{i}(0)-\tan\theta_{j}(0 )\big{\}}\end{array}\right. \tag{18}\]  
**Proposition 2**.: _For the linearized Vicsek model (8) and (13), let \(\theta_{i}(0)\)\(\in\) [0, 2\(\pi\)), and the initial neighbor graph is connected. So, the model will achieve synchronization if it meets the following condition,_  
\[v\leq\frac{d\left(\frac{1}{N}\right)^{N}}{2\pi} \tag{19}\]  
_where d is defined as in Equation (18)._