esa-sceva/satcom-chunk-collection huggingface dataset:
# Multiscale Feature Fusion with Self-Attention for Efficient 6D Pose Estimation
## 2 Related Works
### Lightweight Design  
As deep learning is being widely applied across various fields, the demand for high-performance models is increasing, with lightweight design gaining attention. The main aim is to design more efficient computational networks, reducing network parameters without sacrificing performance and making it easier to deploy networks on mobile devices. SqueezeNet [43] was one of the first networks to feature a squeeze layer, using \(1\times 1\) convolutional kernels to compress features from higher layers and reduce the dimensionality of the features. MobileNet [44] introduced a new type of convolution called depthwise separable convolution, in which convolution is first performed for each channel individually, and then the resulting features are combined, reducing the number of parameters. ShuffleNet [45] employs a similar approach by splitting standard convolution into two steps--input feature map grouping and convolutions within each group--thereby reducing the convolutions' computational cost.  
The lightweight module proposed in this paper uses depthwise separable convolution and inverted residual blocks to improve the efficiency of computations during the feature extraction process, significantly reducing both the parameters and computational complexity. Compared to the traditional ResNet18 architecture, the proposed lightweight design not only maintains high accuracy while reducing the model size but is also better suited to real-time deployment on mobile devices. These improvements have yet to be fully explored and applied in existing 6D pose estimation methods.
## 5 Results and Discussion
### Mold Model Test Results
#### 5.2.4 Effects of Reinforcements (Comparing Cases 1, 5, 6, 7, and 8)  
At this point, the effects of reinforcements on NMC clay shall be analyzed. In the present study, unreinforced NMC clay had a high water content (30%) and low bearing capacity. To be able to use soft clay as an efficient embankment material, reinforcements must be provided. As depicted in Figure 14, the bearing capacity of NMC clay increases when reinforcements are added. The results showed that, when composite geotextiles (three layers) are added to layers of NMC clay (Case 7), the bearing capacity of the NMC clay increased. Yet, this improvement is small, so another type of reinforcement should be considered. Here, when crushed gravel was layered with the NMC clay (Case 5), the bearing capacity significantly increased. Therefore, when both composite geotextiles (three layers) and crushed gravel were layered with NMC clay (Case 6), the bearing capacity greatly increased compared to unreinforced NMC clay (Case 1). Moreover, when one more layer of composite geotextile was added to the top (Case 8), the bearing capacity increased tremendously and was far higher than the unreinforced NMC clay (Case 1). Basically, the results implied that clayey soil with a high water content can obtain a high bearing capacity when combined with crushed gravels and composite geotextiles.  
Figure 13: The comparison of the bearing capacity between loose sand (Case 3) and dense sand (Case 4).  
In addition, Figure 15 shows the BCR relationships of reinforced NMC clay over unreinforced NMC clay. Here, Case 5 showed a higher BCR than Case 7, which means that composite geotextiles are not strong enough as lone reinforcements; hence, crushed gravel is better. Yet, when the two reinforcements were combined (Case 6), the bearing capacity increased and then showed a higher BCR than Case 5. Moreover, adding one more layer of composite geotextile (Case 8) exhibited about two times higher bearing capacity than Case 6 at 0.25\(B\) and the highest BCR among the other reinforced NMC clay samples.  
Figure 14: The comparison of the bearing capacity of unreinforced NMC clay versus reinforced NMC clay.  
Figure 15: The load-settlement curve of unreinforced NMC clay versus reinforced NMC clay.  
It can be inferred that the parametric strength of crushed gravel and composite geotextiles may have contributed to the increase in the soft clay's bearing capacity. The lone, soft clay is weak and has poor engineering properties. Therefore, it may not be suitable as an embankment material. However, the soft clay layered with crushed gravel and reinforced with composite geotextiles exhibited a high bearing capacity, which may imply that it can be utilized as an efficient embankment material.
## 3 Architecture and Problem Formulation  
### Network Model  
The network model for the hybrid cellular MTC and HTC access framework is shown in Figure 1. Each MTC node considers the following steps for the realization of an energy efficient access solution for MTC nodes, which are to be accommodated in the existing cellular network. First, all MTCDs estimate their respective channel response (channel state information - CSI) to enable accurate modeling and information retrieval of the current experienced network environment. Service and node constraints are also considered for MTC nodes to be effectively integrated along with traditional HTC devices to avoid any service disruptions. Second, after determination of the device and context information, access provision to MTC nodes is performed. This includes user scheduling considerations for mMTC and uRLLC nodes according to their specific service QoS constraints. mMTC devices are arranged in clusters to simplify resource access as well as to address any congestion conditions, which have a high probability of occurring because of the massive connectivity requirement of these devices. Similarly, uRLLC devices are scheduled according to their requirements because of the event-driven nature of their traffic. Third, an SDG is formulated for the proposed energy-aware MTC design with a focus on the transmit power control of MTCDs. A mean field (MF) approximation is proposed as a possible solution to the formulated SDG which depends entirely on the accuracy of mean field modeling of the state dynamics of MTC nodes within a combined MTC/HTC cellular infrastructure. Lastly, a strategy based on the MF approximated solution is achieved by using a finite difference method for solving the MF equations to obtain converged energy-aware power and scheduling control. The equilibrium condition for the proposed game theory design is discussed and analyzed and includes the uniqueness, feasibility, and suitability with respect to the described network architecture.
## 2 Materials and Methods
### Extraction of Land Parcels  
The aim of this study is to delineate a procedure to exploit the high temporal and geometric resolution of PlanetScope data to extract agricultural parcels. A processing chain comprising three steps was developed, with the aim of leveraging the temporal variable. These steps include:  
* Mosaicking and color correction of PlanetScope imagery to generate a homogeneous time series.
* Calculation of the Normalized difference Vegetation Index (NDVI) for the whole time series.
* Image segmentation for extraction of agricultural parcels.
## 1 Introduction
Thus, computational efforts devoted to identifying the effects of the mainshock-aftershock sequence unanimously conclude that aftershocks would undoubtedly aggravate the damage scenario. [PERSON] and [PERSON] [13] presented the framework and application of seismicity clustering and damage accumulation for the mainshock-aftershock sequence. They concluded that spatiotemporal clustering plays an instrumental role in seismic risk estimation. Many other fragility functions reported in the existing literature are also seismic sequence fragility functions because the damage  
Figure 1: Damage to a residential building in Kathmandu by the 2015 Gorkha earthquake sequence: (**a**) after the 25 April mainshock; (**b**) after the 12 May aftershock (image courtesy of [PERSON]). grade assigned to each structure would be the final damage grade once major events of a sequence occur. For example, fragility functions created by [PERSON] et al. [3], [PERSON] et al. [5], [PERSON] and [PERSON] [14], [PERSON] et al. [15], [PERSON] and [PERSON] [16], [PERSON] [17], and [PERSON] et al. [18], among others, are the fragility functions derived from the damage data collected after earthquake sequences. Additionally, mainshock-aftershock vulnerability and resilience of bridges are studied by several researchers (see, e.g., [19; 20]). Although notable attention is paid in terms of seismic vulnerability of buildings under the mainshock-aftershock sequence, foreshock-mainshock-aftershock sequences have seldom been considered so far. Since earthquake sequences similar to the 2016 Central Italy event are likely to occur in the future too, there exists a gap in the existing literature in terms of quantification of vulnerability under the foreshock-mainshock-aftershock sequence, as strong foreshocks may cause significant damage to a structure. To address the gap, we present seismic fragility analysis of a representative RC building from Nepal. To replicate the realistic scenario, infill walls with solid bricks are modeled.
# Biomedical Flat and Nested Named Entity Recognition: Methods, Challenges, and Advances
## 3 Deep Learning Methods for Flat NER
### Sequence Labeling  
The sequence labeling approach is the most commonly used method in flat NER (Figure 2a). In this approach, a sentence is split into individual tokens, which are taken as input and processed through an encoding layer. The encoding layer enables the model to interpret each token within the context of the entire sentence. Following this, a token classification layer is applied to assign a label to each token based on the contextual information learned from the previous layer.  
[PERSON] et al. [23] utilize a transformer model, trained on the biomedical text and named BioBERT, in the encoding layer. They also employ a feed-forward neural network (FFNN) and _softmax_ function as the token classification layer. BioBERT is initialized with the weights of BERT [20] and retrained on the biomedical domain corpora, including PubMed abstracts and PMC full-text articles. It outperforms BERT on most BioNER datasets, proving the effect of the domain-specific representation model.  
A model developed by [PERSON] et al. [24] employs a fused representation by concatenating four representations: word-level representation [78], character-level representation [31], BioELMo [79], and BioBERT [23]. They apply an attention-based bi-directional LSTM (Bi-LSTM) as the context layer and a conditional random field (CRF) as the token classification layer. The fused representation outperforms the one using BioBERT alone. However, the complex representation requires substantial computational resources and memory, which may hinder its efficiency in real-time processing and resource-constrained environments.
## 1 Introduction
### Greenhouse Gas (GHG) Emissions and Decarbonization  
Electricity consumption associated with water and wastewater operations are a major contributor to GHG emissions [35; 36; 37; 38; 39]. Internationally recognized GHGs, regulated under the United Nations (UN) Kyoto Protocol [40], linked to the UN Framework Convention on Climate Change (UNFCCC) [41], are composed of long-lived and short-lived GHGs. Long-lived GHGs are mostly CO\({}_{2}\) and nitrous oxide (N\({}_{2}\)O), which can remain in the atmosphere for hundreds of years [42]. Short-lived GHGs are mostly methane (CH\({}_{4}\)), hydrofluorocarbons (HFCs), perfluorocarbons (PFCs), sulfur hexafluoride (SF\({}_{6}\)), and nitrogen trifluoride (NF\({}_{3}\)), and they remain in the atmosphere for much shorter periods, from days to decades [42; 43]. Most of the emissions embedded in water are usually from CO\({}_{2}\), N\({}_{2}\)O, and CH\({}_{4}\)[35]. The level of global warming is determined by the cumulative emissions of CO\({}_{2}\) over time and the annual emissions of short-lived GHGs. To stabilize the climate to below 3 degrees Celsius (\({}^{\circ}\)C), as stipulated by the UNFCCC [41], carbon neutrality or decarbonization will be needed by 2100 [43]. The energy intensity (responsible for long-lived gases emissions) of the Middle East and Africa region including the UAE increased by 41% from 1970 to 2010 [44]. The highest key category of GHG emissions in the Emirate of AD comes from the energy industries accounting for 46.8% [45]. Both the key driver and key category are the consumption of fossil fuels [44; 45]. The atmospheric concentration of CO\({}_{2}\) keeps increasing as long as emissions are positive, and it decreases if emissions are brought to zero, depending on the cumulative emissions over time. Thus, to stabilize long-lived gas concentrations, both CO\({}_{2}\) and N\({}_{2}\)O are required to be brought to zero and short-lived GHGs (CH4, HFCs, PFCs, SF\({}_{6}\), NF\({}_{3}\)) are to be kept constant [43].  
In 2019, three water challenges prevail in AD. First, the energy intensity of desalination; second, access to TSE; and third, high irrigation demand.  
Figure 4: Energy need in each step of the water process [31].
# Speed Optimization for Incremental Updating of Grid-Based Distance Maps
## 4 The Methodology
### Algorithm Pseudocode
#### 4.2.3 Construct DM-Based Subgoal Graphs  
By making use of the collision test equation shown in Equation (1), we modify the subgoal placement condition of the original construction algorithm; therefore. a collision-free subgoal graph for a certain safety radius \(R\) can be efficiently computed from the underlying DM.  
Given an agent with safety radius \(R\geq 0\), Table 3 shows how to construct a subgoal graph, \(Gs:=(Vs\),\(Es)\), from the underlying DM. The entire construction process consists of two sequential phases, i.e., firstly placing collision-free subgoals on the corners of the expanded obstacle boundaries (line 45 to line 49), and secondly, adding edges to connect those subgoals which are mutually direct-h-reachable (line 50 to 53).  
A time-consuming and important part in the algorithm shown in Table 3 is how to identify all direct-h-reachable subgoals from a given subgoal \(s\) (line 51), and this can be done by determining the direct-h-reachable area around \(s\). As shown in Table 4 the algorithm proposed by [PERSON] et al. [30] works in two steps. The first step (line 56 to 58) identifies the closest subgoals in each of the eight cardinal and diagonal directions, and the second step (line 59 to 70) incrementally finds out the other subgoals that can be reached via moves in two directions (i.e., either a diagonal direction or one of its two corresponded cardinal directions). In order to meet the strong requirements of Definition 5, we replace the Clearance(\(R\), \(V\), \(s\), \(d\)) function in the original algorithm with SafeClearance(\(R\), \(V\), \(s\), \(d\)), thus the resulting direct-h-reachable paths between two subgoals can be collision-free when taking safety radius \(R\) into consideration (e.g., the tests in line 73 ensure that every step of the incremental exploration is collision-free). For more detail about the function GetDirectHReachable, please refer to [PERSON] work [30].  
\begin{table}
\begin{tabular}{|c|} \hline \multicolumn{2}{|c|}{**ConstrucDMBasedSubGraph(safetyRadius R)**} \\
44. \(Vs\leftarrow\varnothing\), \(Es\leftarrow\varnothing\) \\
45. **for each**_unblocked cells_\(s\)**do** \\
46. **for each** pair of perpendicular cardinal directions \(c_{1}\) and \(c_{2}\)**do** \\
47. **if**\(C\)(\(R\),\(s\)+\(c_{1}\)+\(c_{2}\))=1 **then** \\
48. **if**\(C\)(\(R\),\(s\)+\(c_{1}\))=0\(\wedge C\)(\(R\),\(s\)+\(c_{2}\))=0 **then** \\
49. \(Vs\gets VS\bigcup\) \(\{s\}\) \\
50. **for each**\(s\in Vs\)**do** \\
51. \(S\)\(\leftarrow\) GetDirectHReachable(\(Vs\), \(R\),\(s\)) \\
52. **for each**\(s^{\prime}\)\(\in S\)**do** \\
53. \(Es\gets Es\bigcup\) \(\{\)(\(s\),\(s^{\prime}\))\(\}\) \\
54. \(Gs\leftarrow(Vs\),\(Es\)) \\ \hline \end{tabular}
\end{table}
Table 3: The pseudocode for constructing DM-based subgoal graphs.
# Adaptive Channel-Enhanced Graph Convolution for Skeleton-Based Human Action Recognition
## 3 Method
### Metrics  
To validate the effectiveness of the proposed method, this study evaluates the performance of the ACE-GCN model using the Top-1 classification accuracy (%) metric. Top-1 classification accuracy measures the proportion of instances where the predicted class matches the true class, directly reflecting the model's accuracy in the human skeleton action recognition task. The formula for calculating the Top-1 classification accuracy is shown in Equation (15):  
\[Top-1_{acc}=\frac{1}{N}\sum_{j=1}^{N}1(\hat{y}_{j}=y_{j})\times 100\% \tag{15}\]  
\(N\) is the total number of samples in the dataset. \(\hat{y}_{j}\) represents the predicted class for the \(j\)-th action sample, and \(y_{j}\) is the true class of the \(j\)-th action sample. Furthermore, \(1(\hat{y}_{j}=y_{j})\) is an indicator function that takes the value of 1 when the predicted class \(\hat{y}_{j}\) is the same as the true class \(y_{j}\) and is 0 otherwise.
## 4 Main Contribution of the Control Design
### The Control Challenge
 \tag{21}\]  
The application of the result derived in (19) to the expression of (18) leads to the following description of the derivative of \(V\big{(}\Delta,\bar{K},t\big{)}\):  
\[ \begin{split}\frac{d}{dt}V\big{(}\Delta_{t},\bar{K}_{t},t\big{)}& \leq\Omega(\Delta_{t})\big{(}\Delta_{t}^{\top}(Ric_{A}(P)-Q_{0}) \Delta_{t}+\beta\big{)}+\\ tr\bigg{\{}\bar{K}_{t}^{\top}\bigg{(}\bigg{(}\frac{d}{dt}\gamma _{t}+\alpha\gamma_{t}\bigg{)}\bar{K}_{t}+2\gamma_{t}\frac{d}{dt}\bar{K}_{t}+2 \Omega_{t}P\Delta_{t}\Delta_{t}^{\top}\bigg{)}\bigg{\}}-\alpha\gamma_{t}tr \big{\{}\bar{K}_{t}^{\top}\bar{K}_{t}\big{\}}\end{split}  \tag{22}\]  
where \(\Omega(\Delta)=2\frac{\left[\|\Delta\|_{P}^{2}-\epsilon\right]_{+}}{\Delta^{+}- \left[\|\Delta\|_{P}^{2}-\epsilon\right]_{+}^{2}}\). Taking into account the assumption in the theorem statement about \(Ric_{A}(P)<0\), as well as the adjustment law for \(\bar{K}_{t}\) leads to  
\[\frac{d}{dt}V\big{(}\Delta_{t},\bar{K}_{t},t\big{)}\leq-\Omega(\Delta_{t}) \big{(}\Delta_{t}^{\top}Q_{0}\Delta_{t}-\beta\big{)}-\alpha\gamma_{t}tr\big{\{} \bar{K}_{t}^{\top}\bar{K}_{t}\big{\}}. \tag{23}\]  
Taking into account the case when \(\Delta_{t}^{\top}Q_{0}\Delta_{t}-\beta\geq\alpha\big{(}\|\Delta_{t}\|_{P}^{2}- \epsilon\big{)}\), yielding to  
\[\frac{d}{dt}V\big{(}\Delta_{t},\bar{K}_{t},t\big{)}\leq-\alpha\Omega(\Delta_{t} )\big{(}\|\Delta_{t}\|_{P}^{2}-\epsilon\big{)}-\alpha\gamma_{t}tr\big{\{}\bar{K} _{t}^{\top}\bar{K}_{t}\big{\}}, \tag{24}\]in view of the description of the function \(\left[\left\|\Delta\right\|_{P}^{2}-\epsilon\right]_{+}^{2}\) the expression in (24) can be presented as  
\[\frac{d}{dt}V\big{(}\Delta_{t},\tilde{K}_{t},t\big{)}\leq-\alpha\Omega(\Delta_ {t})\Big{[}\|\Delta\|_{P}^{2}-\epsilon\Big{]}_{+}^{2}-\alpha\gamma_{t}tr\{\tilde {K}_{t}^{\top}\tilde{K}_{t}\}. \tag{25}\]  
Consider the following lemma. **Lemma 1**. : _For any constant \(\rho\) such that \(|\rho|<1\), the following inequality is satisfied: \(\rho\leq-\log(1-\rho)\leq\frac{\tilde{\rho}}{1-\rho}\)._  
Using the inequality in the previous lemma for the logarithmic function, the differential inequality for the first Lyapunov candidate function can be transformed to  
\[\frac{d}{dt}V\big{(}\Delta_{t},\tilde{K}_{t},t\big{)}\leq-\alpha\ln\left(\frac {\Delta^{+}}{\Delta^{+}-\left[\left\|\Delta\right\|_{P}^{2}-\epsilon\right]_{ +}^{2}}\right)-\alpha\gamma_{t}tr\{\tilde{K}_{t}^{\top}\tilde{K}_{t}\}. \tag{26}\]  
This expression is equivalent to \(\frac{d}{dt}V\big{(}\Delta_{t},\tilde{K}_{t},t\big{)}\leq-\alpha V\big{(} \Delta_{t},\tilde{K}_{t},t\big{)}\), which implies that if \(\Delta\in\Xi(\Delta)\), then \(V\big{(}\Delta_{t},\tilde{K}_{t},t\big{)}\leq V\big{(}\Delta_{0},\tilde{K}_{0 },0\big{)}e^{-\alpha\cdot t}\). This expression implies the origin is an asymptotic stable equilibrium point of \(\Delta\).
## 3 Results  
### Wind Condition during the Experiment  
During the study period, the 70 m tower within the wind farm registered an average monthly wind speed of 7.2 m/s at 70 m above the ground level (AGL) with a peak wind speed of 24.2 m/s. The majority of wind speeds ranged from 2 to 10 m/s (Figure 7a). The dominant wind directions were northwest (NW) and northeast (NE), with directions spanning from 0\({}^{\circ}\) to 90\({}^{\circ}\) and 270\({}^{\circ}\) to 360\({}^{\circ}\) comprising over 60% of the total observations (Figure 7b).
## 3 Methodology
### Experimental Design  
In order to analyze the major elements, we designed a simulation experiment to generate the criteria of rumor spreading by applying Expected Integrated Mean Squared Error (EIMSE), which was proposed by [PERSON] et al. [41]. According to [PERSON], [PERSON], and [PERSON] [41], EIMSE optimal design could be more accurate in regard to low prediction errors than alternatives, such as D-optimal design. Thus, EIMSE can minimize the expected meta-model prediction errors while improving the accuracy of prediction. By combining the variance and bias errors in a single criterion, the EIMSE criterion yields a simple interpretation of the expected squared errors, which can be defined in the equation below:  
\[\text{EIMSE}(\bar{z})=E_{\eta,\mathbf{x},\varepsilon}(\left[\hat{Y}\{\varepsilon, \mathbf{x},\eta(\mathbf{x}),\ \xi\}-\ \eta(\mathbf{x})\right]^{2}) \tag{3}\]  
where \"\(\hat{Y}\{\mathbf{x},\eta(\mathbf{x}),\ \xi\}\)\"are regression model predictions and \"\(\eta(\mathbf{x})\)\" is the true function value at the prediction point \"\(\mathbf{x}\)\". The experimental design is represented by \"\(\xi\)\", and \"\(\varepsilon\)\" is a vector of the experimental random errors. The symbol \"\(E\)\" represents the statistical estimations of the variables about which the experimenter is uncertain, which include the future random errors \"\(\varepsilon\)\", the points at which predictions will be calculated after the experiment \"\(x\)\", and via the true model \"\(\eta(\mathbf{x})\)\". Moreover, the EIMSE criterion can be expressed as  
\[\text{EIMSE}(\xi)=\sigma^{2}\text{Tr}[\mu_{11}(X_{1},X_{1})^{-1}]+\text{Tr}[K_ {2}\Delta] \tag{4}\]  
where  
\[\Delta=A^{\prime}\mu_{11}A-\mu_{12^{\prime}}A-A^{\prime}\mu_{12}+\mu_{12}\]  
\[A=(X_{1}^{\prime}X_{1})^{-1}X_{1}^{\prime}X_{2}\]  
\[\mu_{\text{ij}}=\int_{R}f_{i}(x)f_{j}^{\prime}(x)p(x)\text{d}x\ \ \ \ \text{for}\ i,j=1,2\ \text{and}\ i\leq j\]  
\[K_{2}=E[\beta_{2}\beta_{2}^{\prime}]\]  
The inputs and outputs of the factorial experimental design are shown in Table 2. All parameters have been adjusted to scale [1; 2; 3; 4]. The \"% of infected\" response indicates the percentage of individuals who heard the rumor and still believed it through 75 replications. The analysis of the experimental design was performed using Sagata Regression software.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Run** & \(\mathbf{a}\) (\(\mathbf{\times}\mathbf{1}\)) & \(\mathbf{E}\) (\(\mathbf{\times}\mathbf{2}\)) & \(\mathbf{L}\) (\(\mathbf{\times}\mathbf{3}\)) & \(\sigma\) & **Highly Strength (\(\mathbf{\times}\mathbf{5}\))** & **\%Infected** \\ \hline  
101\) & \(-0.065\) & \(-0.112\) & \(-0.974\) & \(31.89\) \\ \hline \hline \end{tabular}
\end{table}
Table 2: Inputs and outputs of the Expected Integrated Mean Squared Error (EIMSE) experimental design.