esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Simulation Results and Discussion
Case 3: To test the position tracking performance of the LPV scheme subject to dynamic payload and wind disturbance at the same time that required large tilting angle, variation in the payload was introduced at \(t=7.5\) s, and wind disturbance was applied at \(t=10\) s.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **Performance Parameters** & **LPV** & \(\mathcal{H}_{\infty}\) & **\% Improvement** \\ \hline \multirow{4}{*}{
\begin{tabular}{c} **P**\(\mathrm{\text{acc}}\) \\ **P**\(  
A horizontal wind model in SIMULINK was used for wind generation. A constant north-east wind speed of 5.3 m/s was assumed. The water level variation over time is shown in Figure 20. Figure 21 presents the reference signals and the position of the quadrotor. The corresponding attitude angles are shown in Figure 22. Figures 23 and 24 depict the control signals and motor commands. As can be seen, the quadrotor tracks the reference signal more effectively in the presence of time-varying payload and wind disturbance as compared to the LTI controller, which introduces significant oscillations and also results in actuator saturation as the demand for tilt angles gets higher. Figure 16: The positions \([x,y,z]\) with 20% variation in the inertia. Figure 17: The attitude angles \([\phi,\theta,\psi]\) with 20% variation in the inertia. Figure 18: The control inputs \([U_{1},U_{2},U_{3},U_{4}]\) with 20% variation in the inertia. Figure 11:0 The positions \([x,y,z]\) with payload variation and wind disturbance. Figure 11:1 Variation in the water level with time. Case 3: Gaussian noise \(N(\mu,\sigma^{2})\) was introduced into the quadrotor outputs in this case [37]. Noise \(N(0,(1\,\mathrm{cm})^{2})\) was added to the x, y, and z positions, and Noise \(N(0,(0.51^{\circ})^{2})\) was introduced to the yaw, pitch, and roll angles. The water level variation over time is shown in Figure 11:2 Figure 25 shows the reference signals, the position of the quadrotor,  
Figure 11:3 The control inputs \([U_{1},U_{2},U_{3},U_{4}]\) with payload variation and wind disturbance. Figure 11:4 The attitude angles \([\phi,\theta,\psi]\) with payload variation and wind disturbance. Figure 11:5 The control inputs \([U_{1},U_{2},U_{3},U_{4}]\) with payload variation and noise. Figure 11:6 The attitude angles \([\phi,\theta,\psi]\) with payload variation and noise.
# Designing UAV Swarm Experiments: A Simulator Selection and Experiment Design Process
## 3 Generalized Swarm Process Consideration during Development
### Network Topologies and Communication
Future research should focus on developing efficient network topologies and communication protocols tailored to the specific needs of UAV swarm systems, considering factors such as scalability, robustness, energy efficiency [61], and adaptability to dynamic environments. By improving network topologies and communication, UAV swarms can achieve enhanced coordination, cooperation, and performance, enabling them to tackle a wide range of complex tasks and operate effectively in diverse scenarios.
## 3 Controller Design
### Proposed Controller Design
#### 3.2.1 Adaptive Radial Basis NN (ARBNN)
Furthermore, both \(O_{2}\) and \(\delta\) are positive, while \(|s|\geq 0\), such that \(-O_{2}^{T}\delta\otimes|s|\leq 0\), thereby indicating that the entire system is Lyapunov stable. The NFTSMC-based controller is able to provide the basic control required for nonlinear systems; however, it cannot ensure stability and robustness in the face of multiple complex disturbances and uncertainties. On the one hand, switching control input \(U_{r}\) requires a known maximum upper bound on overall interferences; however, this is seldom satisfied in practical scenarios. Note also that this control strategy depends on the design of the sliding surface parameters \(c_{1}\) and \(c_{2}\), the debugging of which would be a tedious process. We sought to overcome the shortcomings of this controller by employing an additional controller to deal with other (multiple) forms of disturbance and uncertainty while reducing dependence on the NFTSM control strategy. As mentioned previously, the dual-axis manipulator addressed in this paper includes payload \(U_{p}\), external disturbances \(d(t)\), system uncertainties \(\Delta M(q)\hat{q}\), \(\Delta C\big{(}q,\hat{q}\big{)}\hat{q}\), \(\Delta G(q)\), friction force \(f\big{(}\hat{q}\big{)}\), and backlash \(V(U)\). We assigned the payload to an estimator and the other factors to the controller. In dealing with the payload, ARBNN was used for estimation and compensation tasks. Manipulators commonly encounter problems related to the payload when grasping heavy objects or using end-effectors. We previously defined \(\mathcal{N}=M_{0}^{-1}\big{(}-U_{p}\big{)}\) as the portion of the payload to be estimated by the ARBNN, which can be expressed as follows:  
\[\mathcal{N}=\mathcal{N}^{*}+\Delta\mathcal{N}={W^{*}}^{T}\mu+\Delta\mathcal{N} \tag{34}\]  
where \(\mathcal{N}^{*}\) is the optimal estimate achievable by the neural network, \(\Delta\mathcal{N}\) is the neural network reconstruction error, and \(W^{*}\) is the optimal weight. We can express the control input \(U_{NN}\) estimate \(\hat{\mathcal{N}}\) of the neural network as follows:  
\[U_{NN}=\hat{\mathcal{N}}=\hat{W}^{T}\mu=[u_{NN1}\;\;u_{NN2}]^{T} \tag{35}\]  
where \(\hat{W}\) is the estimated weight, such that estimation error \(\widetilde{\mathcal{N}}\) can be derived in a straightforward manner, as follows:  
\[\widetilde{\mathcal{N}}=\mathcal{N}-\hat{\mathcal{N}}=\mathcal{N}^{*}+\Delta \mathcal{N}-\hat{\mathcal{N}}=-\widetilde{W}^{T}\mu+\Delta\mathcal{N} \tag{36}\]  
\[\widetilde{W}=\hat{W}-W^{*} \tag{37}\]
# Machine-Learning-Based Model for Hurricane Storm Surge Forecasting in the Lower Laguna Madre
## 3 Modeling Scenarios and Data Processing
### Forecasted Gridded Weather Data
#### 3.2.5 Normal Conditions: June 2020  
This month was especially dry with a precipitation total for the contiguous U.S. of about 0.21 inches below average. Two tropical storms were recorded in the Atlantic, Amanda and Cristobal. Amanda made landfall in Guatem and its remnants developed to form Cristobal, which eventually made landfall in Louisiana, just east of Grand Isle.  
It is important to note that while data are abundant, a bias was identified, and its impact on the performance is later discussed at the end of the paper. The root of the bias comes from the data available in the area where the study was conducted. Data from NAM provided a total of 13 hurricane seasons for training. Each season translates to six months of data, meaning a total of more than 6 years of continuous data was available. During this period of time, the Lower Laguna Madre saw the impact of around 20 abnormal sea-level conditions due to weather events. On average, the duration of such abnormal events was one week. This translates to 6% of the data being representative of storm surge conditions. This is problematic, since it evidences a bias in the data towards normal conditions; however, this was expected since disastrous impacts of hurricanes are scarce on a local level in the South Texas region.
## 5 Discussion
### Implication for a Shift in Tectonic Setting  
Early Cretaceous tectonic evolution history in the Jiaodong Peninsula remains unclear, although it is well documented that its Mesozoic geodynamics are closely associated with the Palaeo-Pacific plate subducting beneath the East China continental margin [11; 102; 103], and an Early Cretaceous tectonic transition has been identified [8; 13]. Large-scale extension-related magmatism [47; 52; 104] and gold mineralization [6; 105], as well as the normal movement along the TLF [9] all suggest an extensional tectonic setting during c. 130-110 Ma in the Jiaodong Peninsula. However, [8] proposed that a tectonic transition from extension to compression occurred in the Jiaodong Peninsula during c. 125-122 Ma, resulting from the shift in subduction direction of the Palaeo-Pacific Plate.  
It is widely accepted that the Jiaodong Peninsula has undergone lithospheric thinning during an extensional tectonic setting since the Mesozoic [106; 107; 108], which is evidenced by Early Cretaceous high Ba-Sr granitoids in the Jiaobei terrane [33]. However, there is an abrupt change in composition between these two subgroups of Early Cretaceous high Ba-Sr granitoids (Figures 5-9), which indicates a change from juvenile mafic lower crust and continental lower crust sources in the middle Early Cretaceous (132-122 Ma) [33] to continental lower crust and enriched subcontinental lithospheric mantle-derived sources in the late Early Cretaceous (120-110 Ma). Furthermore, the two types of coeval lampprobyres (i.e., low-Ti and high-Ti lampprobyres) emplaced at c. 121 Ma are suggested to originate from an enriched subcontinental lithospheric mantle and an asthenospheric mantle, respectively [47]. It was suggested that the melting of inferred lower crust and juvenile mafic lower crust was caused by asthenosphere upwelling and lithospheric thinning (Figure 13 in [48]), which is in accordance with the Palaeo-Pacific plate WNW-ward subduction, leading to E-W extension in the Jiaodong Peninsula during the middle Early Cretaceous (132-122 Ma) [13; 109]. The lithospheric mantle-derived melt for the late Early Cretaceous granitoids (120-110 Ma), combined with the asthenosphere mantle melting for the high-Ti lampprobyres, indicates the Jiaodong Peninsula probably underwent a catastrophic lithospheric thinning event at c. 120 Ma, which is consistent with an abrupt change in the direction of the Palaeo-Pacific plate subducting from WNW to NW at c. 121 Ma [13]. Therefore, this suggests a transition in tectonic setting from E-W extension to NW-SE extension (Figure 12), as also supported by the least principal stress axis shifting from E-W through WNW-ESE to NW-SE c. 119 Ma based on the Palaeostress analysis by the quartz veins and lamprophyre dikes at the Sizhuang gold deposit in the Jiaobei terrane [6].
## 1 Introduction
### Problem Definition  
This work presents the final phase of morphing wing development, building upon our aerodynamic analysis of the SMTE flap [36], and structural evaluation of the proposed elephant trunk mechanism [37]. Previous studies assessed the aerodynamic performance of the SMTE flap, followed by a structural analysis of the elephant trunk mechanism and flexible skin. With these results in hand, the present study aims to develop a prototype of the UAS-S45 wing, equipped with both the SMTE flap and the ETM to experimentally validate the previous results. To achieve this, a section of the UAS-S45 wing was prototyped. This section integrated the elephant trunk mechanism, flexible skin and actuation control system within the wing box. The final model consisted of main components, including wing box, four elephant trunks, four actuation motors and the flexible skin. Figures 1 and 2 illustrate the UAS-S45 wing equipped with an SMTE flap and the ETM actuation mechanism inside the SMTE flap, respectively.  
Figure 1 shows the SMTE flap after morphing. Since there are no discontinuities between the flap and the wing body, the flap was seamlessly integrated with the wing body. Figure 2 illustrates the internal wing structure in detail. As shown in Figure 2, four ETMs were incorporated inside the SMTE flap, oriented as specified above. Previous studies have conducted both aerodynamic and structural analyses of the SMTE flap and the ETM actuation mechanism (see [36; 37] for more detail).  
This study underscores the critical role of precise manufacturing processes and advanced structural innovations in the development of functional and scalable morphing wing systems. By incorporating detailed manufacturing processes into the development of morphing wing systems, this study highlights the critical role that precision in fabrication plays. The integration of manufacturing considerations not only enhances the structural integrity but also ensures that the morphing wing system is ready for real-world application.  
Figure 1: Schematic of the UAS-S45 wing equipped with a morphed SMTE flap.  
Figure 2: Illustration of the (**a**) elephant trunk mechanism (ETM), (**b**) assembly of four ETMs inside the SMTE flap.  
In the present study, a section of the UAS-S45 wing was prototyped for its testing in a wind tunnel. The wing section was designed to conform to the dimensions of the wind tunnel. This prototype included the SMTE flap, the ETMs inside the flap, a flexible skin and an actuation system embedded within the wing box. Figure 3 displays the fully assembled wing section, showcasing all its components.  
The assembled prototype underwent several tests in the LARCASE Price-Paidoussis subsonic wind tunnel. During the tests, the real-time actuation of the wing was evaluated at various angles of attack and flap deflections. These tests allowed a performance assessment of the actuation system under operational conditions and facilitated the validation of the experimental data with the numerical aerodynamic results obtained in previous studies.
## 2 Materials and Methods
### Spatial Attention Modules  
Local features generated by the fully convolutional network (FCN) may cause object classification errors [31]. [PERSON] et al. [32] believe that the spatial attention module can capture remote context information. The spatial attention module (SA module) enhances its representation ability by encoding extensive contextual information as local features. The structure of the spatial attention module is shown in Figure 4.  
After inputting the global feature map \(\mathbf{U}\in\mathbb{R}^{C\times H\times W}\) into three \(1\times 1\) convolutional layers (conv1, conv2 and conv3), conv1 and conv2 are transformed into feature spaces \(\mathbf{B},\mathbf{C}\in\mathbb{R}^{C1\times(H\times W)}\). The spatial attention map \(\mathbf{S}\in\mathbb{R}^{(H\times W)\times(H\times W)}\) can be calculated using a softmax layer shown in Equation (4).  
\[\mathbf{s}_{ji}=\frac{\exp(\beta_{ij})}{\sum_{i=1}^{N}\exp(\beta_{ij})},\text { where }\partial_{ij}=\text{B}_{i}\cdot\text{C}_{j} \tag{4}\]  
The convolutional layers conv3 is transformed into a feature map \(\mathbf{D}\in\mathbb{R}^{C\times H\times W}\). The value of \(s_{ji}\) indicates the degree of influence of the \(i\)-th position on the \(j\)-th position. The new feature map \(\mathbf{o}=\{\mathbf{o}_{1},\mathbf{o}_{2},\dots,\mathbf{o}_{N}\}\) is obtained by performing matrix multiplication with the spatial attention map \(\mathbf{S}\) and the feature map \(\mathbf{D}\) shown in Equation (5).  
\[o_{j}=\sum_{i=1}^{N}s_{ji}\text{D}_{i},\text{ where }N=H\times W \tag{5}\]  
Figure 4: Spatial attention module.  
The element-wise summation between feature attention map o and feature map U is the final output of spatial attention module \(y\), as shown in Equation (6),  
\[y_{j}=\beta o_{j}+\text{U}_{j} \tag{6}\]  
where \(\beta\) is the weight obtained through learning.
# Smart City Actions Integrated into Urban Planning:
## 4 Results and Discussion
### Thematic Areas for Smart City Management
For cluster 3 (blue), the words learning, big data, and artificial intelligence appear, linking networked applications and services, showing the health and assistance area as a catalyst for artificial intelligence solutions. Cluster 4 (purple) approaches the interconnection between intelligent systems, communication, and transportation, with mobility as a central topic. Finally, cluster 5 (yellow) establishes connections between security, network architectures, and information management in the security and protection area. To expand the knowledge, a word co-occurrence map from the articles' abstracts was constructed, systematized, and standardized using words with a minimum frequency of 30 times, structured into clusters (Figure 5). The co-occurrence map includes 263 words in three clusters. Cluster 1 (red) connects the central terms study, research, cities, strategies, impacts, innovation, and governance through actions and solutions in thematic areas of governance and engagement, sustainable economy and consumption, education and training, technology and innovation, and living environment and infrastructure. In cluster 2 (green), the main relations are related to the internet, network, communication, algorithm, devices, and security, covering thematic areas of energy, safety and security, and mobility. Cluster 3 (blue) includes the terms precision, machine, dataset, and detection, highlighting actions in the thematic area of technology and innovation. The co-occurrence analysis provided an opportunity to understand the groupings and relationships between the main recurring words in applied research with an emphasis on smart cities, contributing to expanding knowledge about the perspective of smart cities' thematic areas. Figure 5: Word co-occurrence map. With the literature review and bibliometric analysis of the words' co-occurrence, the reference matrix was structured, including the identification of 11 essential thematic areas for the development of strategic actions for the innovative management and planning of urban spaces, focused on smart cities' performance (Table 1). In smart cities, thematic areas influence each other, improving urban planning actions and quality of life. Solutions aimed at improving urban spaces are developed through the interaction and communication between thematic areas. For example, the thematic area of technology and innovation impacts several areas, since implementing the IoT in energy systems enables remote management of urban lighting, influencing the areas of energy and economy, as well as sustainable consumption. Also, real-time water quality monitoring, with information made available through mobile applications, includes interactions between the technology and innovation area and the living environment and infrastructure area.
# Survey of Dense Video Captioning: Techniques, Resources, and Future Perspectives
## 3 Techniques for Dense Video Captioning
### Encoder-Decoder Based Approaches
#### 3.1.2 Multimodal Fusion Encoder-Decoder Models  
Multimodal fusion approaches aim to integrate information from video, audio, and text to generate more accurate captions. [PERSON] et al. [20] introduced an attention-based method for multimodal fusion in video description, as shown in Figure 3. This method utilizes temporal attention mechanisms to selectively focus on encoded features from specific time frames. By combining visual, motion, and audio features, it captures a more comprehensive representation of the video content, enhancing the quality of the generated captions.  
Building upon the idea that videos contain both visual and auditory information, [PERSON] et al. [21] proposed a model that leverages both visual and audio features. Their  
Figure 3: Redrawn architecture of multimodal fusion encoder-decoder models (adapted from [20]) for video caption generation.  
Figure 2: Redrawn schematic of the pioneering video caption encoder-decoder network, adapted from [19].  
model employs separate encoders for each modality and generates captions based on a fused multimodal representation. Similarly, [PERSON] et al. [22] introduced a model that incorporates early linguistic information fusion through a visual-semantic embedding framework. However, these methods often require costly additional annotations for non-video modalities and often rely on simple fusion techniques, such as concatenation or element-wise summation. These techniques may not fully exploit the complementary nature of multimodal information, highlighting the need for more sophisticated fusion strategies.
## 2 Generalized Dynamic Model
### Establishment of Global Dynamics Model
#### 2.1.4 Planetary Carrier Element
The spring supporting elements \(k_{jx}\), \(k_{jy}\) (\(j\) = s, r, c) represent the supporting stiffness of the parts (sun gear, inner gear ring, and planetary carrier, respectively) in planetary gear set along the local coordinate system \(x_{i}y_{i}\). The spring supporting element \(k_{cp}\) represents the supporting stiffness of the three planetary gear sets of respective planetary frames to N planet gears. Spring element \(k_{\text{spi}}\), \(k_{\text{rpi}}\) (\(l\) = 1, 2,..., N, N is the number of planet gears), respectively, represent the meshing relationship between the three gear sets of respective sun gears and the i-th planetary gear, and the meshing relationship between the three gear sets of respective inner ring gears of the sun gears and the planetary gear. \(c_{\text{spi}}\), \(c_{\text{rpi}}\) are the corresponding meshing damping values of \(k_{\text{spi}}\), \(k_{\text{rpi}}\) in their respective meshing relations. The dynamic model of the whole three planetary gear set transmissions is discretized into sub-elements, including a shaft element, a bearing element, an external meshing element, an internal meshing element, a planetary carrier element, a bearing element, and so on, by using the idea of finite element. The motion differential equations of each sub-element are listed by lumped mass method and converted into the mass matrix, damping matrix, and stiffness matrix, respectively. The motion equation of the planetary gear set transmissions as a whole is shown in Equation (15). \[M\ddot{x}(t)+C[\dot{x}(t)-\dot{e}(t)]+K(t)[x(t)-e(t)]=P_{0} \tag{15}\]  
where \(M\) is the overall mass matrix of the system; \(C\) is the damping matrix of the system; \(K(t)\) is the stiffness matrix of the system; \(x(t)\) is the displacement column vectors of all mass nodes in the system, \(P_{0}\) is the external load column vector of the system. \begin{table}
\begin{tabular}{c c} \hline \hline
**Stiffness** & \\
**Symbol** & **The Meaning of Stiffness Symbol** \\ \hline \(k_{c1}\) & Connection stiffness between input shaft and S1 when clutch CL1 engages \\ \(k_{c2}\) & Connection stiffness between input shaft and C1 when clutch CL2 engages \\ \(k_{12}\) & Connection stiffness between sun gear S1 and sun gear S1 \\ \(k_{j2x}\) & Connection stiffness between ring gear R1 and planetary carrier C2 \\ \(k_{j2y}\) & Connection stiffness between planetary carrier C2 and connecting gear \\ \(k_{23}\) & Connection stiffness between connecting gear and sun gear S3 \\ \(k_{c3}\) & Connection stiffness between connecting disc and ring gear R3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The meanings of each connection stiffness in Figure 6.  
Figure 7: Dynamics model of planetary gear sets. (**a**) Dynamics model of the planetary gear set 1 or 2; (**b**) dynamics model of the planetary gear set 3.
# A Dynamic Surface Gateway Placement Scheme for Mobile Underwater Networks +
## 3 Background
### Design Challenges
#### 3.1.3 Energy Constraints  
Underwater sensor nodes are typically powered by a battery, which can be difficult or impossible to recharge. Thus, in most cases, the life time of a sensor node is restricted to a limited power supply. This power resource constraint imposes strict energy conservation requirements on the deployment strategy. Algorithms requiring frequent redeployment will not be suitable for application to UWSNs.