esa-sceva/satcom-chunk-collection huggingface dataset:
# Collaborative Optimization of Aerodynamics and Wind Turbine Blades
## 4 MDO of the Wind Turbine Blade
### Surrogate Model
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Variable** & **Initial Values** & **Range** & **Optimized Results** \\ \hline \(a_{1}\) & \(-1.7135\) & [\(-1.74\), \(-1.7\)] & \(-1.7242\) \\ \(a_{2}\) & \(3.9152\) & [\(3.9\), \(3.94\)] & \(3.9108\) \\ \(a_{3}\) & \(-3.3233\) & [\(-3.34\), \(-3.3\)] & \(-3.3081\) \\ \(a_{4}\) & \(1.2836\) & [\(1.26\), \(1.3\)] & \(1.2907\) \\ \(b_{1}\) & \(-81.1153\) & [\(-82\), \(-80\)] & \(-80.4060\) \\ \(b_{2}\) & \(177.4159\) & [\(176\), \(178\)] & \(177.7467\) \\ \(b_{3}\) & \(-137.1777\) & [\(-138\), \(-136\)] & \(-136.2567\) \\ \(b_{4}\) & \(44.5987\) & [\(43\), \(45\)] & \(44.4364\) \\ \(d_{1}\) (mm) & \(20\) & [\(15\), \(25\)] & \(17.7579\) \\ \(d_{2}\) (mm) & \(20\) & [\(15\), \(25\)] & \(16.0984\) \\ \(d_{3}\) (mm) & \(18\) & [\(15\), \(25\)] & \(15.3651\) \\ \(d_{4}\) (mm) & \(16\) & [\(15\), \(25\)] & \(15.1506\) \\ \(D\) (N\(\cdot\)m) & \(295.22\) & N\(/\)A & \(325.04\) (\(+10.1\%\)) \\ \(M\) (t) & \(0.40475\) & N\(/\)A & \(0.3845\) (\(-5\%\)) \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Optimization results of the wind turbine blade. Figure 23: CO framework to the design of a wind turbine blade.
# Designing a Collaborative Virtual Conference Application: Challenges, Requirements and Guidelines
## 4 Results
### Usage Frequency of Features on the Virtual Conferencing Application  
The frequency of feature usage describes how frequently participants utilized the features available on the virtual conference application during the study. The features included were items such as the web camera, the microphone, the chat function, and so on. Based on the results of the questionnaire, the most frequently used tools in the study when performing collaborative activities were primarily the share screen feature and the microphone feature (18%). The less frequently used tools were video recording, the reaction to indicate emotions, the raising hand feature (i.e., to signal permission to speak up), the breakout room feature, the file sharing feature, the whiteboard feature, the chat feature, and the web camera feature (i.e., enabling the camera to be switched on). This suggests that the participants only used the features marginally. It is possible that the participants were not familiar with the virtual conference application and had not fully explored all of its features. However, it strongly indicates that, in general, only a few features were necessary to carry out collaborative tasks during a virtual conference. Figure 3 depicts a diverging stacked bar chart of feature usage frequency for the current virtual conference application for collaborative purposes.
# Behavior Analysis of Active and Proactive Headrest during Low-Velocity Rear-End Collisions
## 1 Introduction  
The social and economic costs of road accidents due to car collisions are continuously increasing, which requires urgent attention. The accidents caused by car collisions can be divided into three categories: front collision, side collision, and rear-end collision, among which the accident incidence rate of rear-end collisions is the highest [1; 2; 3]. Because neck injuries are the most frequent injuries caused by rear-end collision, the headrest needs to be redesigned to minimize such injuries. Whiplash injuries occur at relatively low velocity changes (typically \(<\)25 km/h) [4; 5] and in impacts from all directions, although rear impacts are most frequently featured in accident statistics [6]. To this end, several studies focused on investigating the effect of design variables such as stiffness of a car seatback cushion, backscatter, and head contact time on the injury, and static and dynamic tests applied for establishing the headrest evaluation criteria are becoming more strict [7; 8]. In addition, the factors causing neck injuries during rear-end collision were estimated by developing dummies and human models [9; 10]. Determining neck injury mechanisms and developing methods to measure neck-injury related parameters are of importance for current crash-safety research [11]. However, the optimization of trajectory and connection structure of the headrest to reduce neck injuries was rarely discussed in the literature. Consequently, it is difficult to include these parameters in the seat design.  
Several studies focused on the structural analysis of seats to prevent neck injury by suppressing the relative movement between head and torso [12; 13]. Furthermore, several test methods based on injury indices were reported to determine the degree of neck injury of the passenger during rear-endcollision [3, 14]. However, the practical implementation of neck injury indices obtained from human bodies may not be appropriate because the measurement accuracy of the existing human body model is limited.  
Although similar rules apply to all the tests, when a test is conducted using a human body model, which is normally used in a car collision test, the measurement of test data often fails due to the disconnection of the sensor cable during the test. Furthermore, when the initial verification of the human body model is not completed, in addition to sensor measurement, behavior analysis provides an indicator of injury indices [15, 16].  
In this study, the neck injury indices corresponding to the active and proactive headrests during rear-end collision tests were analyzed. The predictability of injury indices was investigated through the behavior analysis of the head and seat. The characteristics of head-neck behavior and the structure of the headrests were analyzed. Furthermore, the neck injury indices of the two headrests were compared. The predictability of major neck injury indices was examined, which may provide a useful reference for developing an active deployment system to complement the headrest deployment characteristics.
## 3 Algorithm Test  
### Test Data and Preprocessing  
The study region is located in the territory of Hengshui, Hebei Province in China (37.52\({}^{\circ}\)-37.73\({}^{\circ}\) N, 115.44\({}^{\circ}\)-115.71\({}^{\circ}\) E) (Figure 2), and covers approximately 25 km \(\times\) 25 km. This region is dominated by fragmented cultivated land as shown in Figure 2a that is mixed with small areas of urbanized land (Figure 2b) and water bodies (Figure 2c). Broken parcels of cultivated land in this area are a typical feature because small land parcels are the basic management unit for farmers in China's agricultural policy. These characteristics make this area a good place to test the applicability of USTARFM to identify fragmented agricultural units in the landscape.  
Figure 1: Flowchart of the USTARFM algorithm.  
The study dataset includes two Landsat 8 OLI images and two date 500 m daily MODIS surface reflectance products (MOD09 GA) acquired on 19 August and 4 September 2014 (Table 1), which were downloaded from the USGS website (URL = [[http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/)]([http://earthexplorer.usgs.gov/](http://earthexplorer.usgs.gov/))).  
Images from these dates are of good quality and represent clear sky conditions. The Landsat 8 data products have been processed in geometric correction based on the terrain data. The main pre-processing included two steps: radiance calibration and atmospheric correction, which were conducted using FLAASH tools [36], then capable of comparing with the corresponding band of MODIS [26]. MOD09 GA is the standard surface reflectance and provides MODIS band 1-7 daily surface reflectance at 500 m resolution accompanied with high-precision geolocation (approximately 50 m at NADIR) [1]. These MODIS images were re-projected from the native SIN projection to a UTM-WGS84 geospatial system and sampled by means of the MRT (MODIS Re-projection Tool). Similar to previous research, no further geometric correction was needed because the MODIS data were assumed to be well co-registered and matched the geographical position with the Landsat 8 data [1]. Additionally, compared to the models of bilinear filtering and cubic convolution, nearest neighbor resampling is an effective method to inherit the maximum original remote sensing spectral value. The MODIS at 500 m resolution were resampled into 480 m in order to match 16 times of 30 m of Landsat images, which facilitated the subsequent decomposition of MOD09 GA pixels [1,31]. The green, red, and near-infrared bands, like [1,30], were chosen to test USTARFM performance.  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**Data** & **Acquisition Date** & **(Path/Row)** & **Data Usage** \\ \hline Landsat 8 (OLI) & 8/19/2014 & 123/034 & Classification and similar pixels selection (\(t_{k}\)) \\  & 9/4/2014 & Accuracy assessment (\(t_{0}\)) \\ \hline MOD09 GA & 8/19/2014 & h27/v05 & Unmixing data acquisition \\  & 9/4/2014 & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: The main characteristics of Landsat 8 and MODIS data.  
Figure 2: The location of study area.
# Opportunistic Multi-Technology Cooperative Scheme and UAV Relaying for Network Disaster Recovery
## 8 Conclusions  
This work investigates drone-assisted communications for disaster recovery scenarios. It proposes a dynamic scheme of communication that opportunistically leverages multiple network technologies integrated in to mobile devices, while leveraging the heterogeneity of such devices in terms of available energy levels. Extensive simulations have been conducted and results have shown the benefits of the proposed scheme from both, the drone and the survivors perspective. On the one hand, the proposed scheme allows to maintain a longer and maximum network coverage considering a cooperative scheme which enables, with the support of high-energy nodes, low-energy nodes to preserve their battery for longer time. On the other hand, our proposed solution reduces the energy consumption of the drone by minimizing the number of nodes it visits (i.e. anchor points) and therefore, it reduces the drone path length. However, the advantages of such a solution can be further exploited by introducing a further control parameter - the drone height. Indeed, extending the evaluation presented here to take account of such a parameter is a promising future work.  
[PERSON] and [PERSON] were responsible for the main idea. They conceived and designed the algorithm, and wrote the manuscript; [PERSON] carried out the simulations and run the experiments. All authors have read and agreed to the published version of the manuscript.  
This work was partly supported by CPER DATA and the Hauts de France PIPA project.  
The authors declare no conflict of interest.
## 2 State of the Art
### Structural Design Optimization  
Design optimization deals with creating better structures iteratively by using different algorithms in the process. It can be performed for reliability constraints or deterministic values by following existing methods proposed by researchers. However, a seamless workflow is needed for Architecture, Engineering, and Construction (AEC) specialists to optimize their design, since the available methods heavily depend on commercial software and technical knowledge. Most of the literature relies either on standalone analysis tools, commercial software, or algorithms coded specifically as part of the research [11; 12; 13; 14; 15]. For example, in [13], the authors optimized prestressed truss structures by considering shape and size variables by following AISC-ASD specifications and employing the parameter-free Java algorithm via custom computer software coded for the study.  
These state-of-the-art studies, along with others, highlight the current bottleneck in the literature on structural optimization within a BIM environment, aiming to make it more accessible and easier to use for designers who are not experienced in coding but in design. Nevertheless, some studies employ BIM for optimization purposes and leverage the advantages of BIM for enhanced collaboration, operation, and maintenance. The referenced review article [16] explores the literature on structural optimization through standardized interfaces and visualization platforms while discussing the implementation and impact of BIM in structural engineering practices.  
In the same direction, the referenced article [8] provides a streamlined framework using VP and parametric modelling techniques to optimize space trusses through Autodesk tools such as Revit, Dynamo, and RSA. This workflow has been enhanced by employing API technology to speed up the FEM process and reduce the overhead between Dynamoand RSA in [17]. Another example [18] explores the possibility of creating a custom plugin to use Revit for the structural optimization of steel structures by following existing codes by employing RSA through API along with LCA through Tally to obtain environmentally conscious designs in a BIM environment. Moreover, the referenced study [19] used a combination of platforms with Grasshopper and Rhino and explored shell structure optimization to maximize structural efficiency and minimize the maximum displacement under vertical loads via user-defined and AI-generated design spaces.  
In line with the presented literature, this research proposes an optimization workflow in a BIM environment that enables the user to optimize the design under uncertain constraints and find the optimum design by considering two conflicting objectives: mass and reliability. Because of the complex nature of this problem, employing AI-based MAs is common practice. Various algorithms have been developed and improved to address the increasing complexity of such optimization problems. A brief literature review was performed to select an MA for the examples section.
# Optimal Sliding Mode Control for an Active Suspension System Based on a Genetic Algorithm
###### Abstract  
In order to improve the dynamic quality of traditional sliding mode control for an active suspension system, an optimal sliding mode control (OSMC) based on a genetic algorithm (GA) is proposed. First, the overall structure and control principle of the active suspension system are introduced. Second, the mathematical model of the quarter car active suspension system is established. Third, a sliding mode control (SMC) controller is designed to manipulate the active force to control the active suspension system. Fourth, GA is applied to optimize the weight coefficients of an SMC switching function and the parameters of the control law. Finally, the simulation model is built based on MATLAB/Simulink (version 2014a), and the simulations are performed and analyzed with the proposed control strategy to identify its performance. The simulation results show that the OSMC controller tuned using a GA has better control performance than the traditional SMC controller.  
active suspension system; optimal sliding mode control; genetic algorithm; switching function; weight coefficients +
Footnote †: journal:  
_Article_  
Optimal Sliding Mode Control for an Active Suspension System Based on a Genetic Algorithm
## 3 Method
### Proposed Federated Learning Algorithm
\[\mathcal{W}_{t+1}=\frac{1}{|C_{s}|}{\sum}i\in C_{s}W_{t+1}^{i} \tag{7}\]  
The pseudocode of the proposed algorithm is as follows, as illustrated in Algorithm 1. During the selection process, \(S_{i}\) represents the performance score of each client's local model, while \(S_{T}\) is the threshold value used to select clients based on this score. A client is only selected if its score exceeds this threshold, and it becomes part of the set \(C_{s}\). Only the local updates from clients in this set are used to improve the global model. The proposed algorithm evaluates clients' local models based on metrics, selecting only those that meet a user-defined criterion for inclusion in the aggregation process to update the global model. Updates from clients that could degrade the performance of the global model due to lower quality are excluded. This approach ensures that only updates from clients contributing positively to the model's performance and efficiency are considered. ```
1:Input: Set of all clients \(C\), datasets \(D_{i}\) of each client \(i\)
2:Output: Aggregate global model parameters from selected clients \(W\)
3: Initialize global model parameters \(W\)
4: for each round \(t\) do
5: if t == 0 then
6: Select all subset of clients \(C_{s}\)
7: else
8: Select clients \(C_{s}\) from previously selected clients
9: end if
10: for each client \(i\in C_{s}\) in parallel do
11: if client \(i\) is selected then
12: Local training on client \(i\)
13: for each local epoch \(e\) to \(E\) do
14: for each batch \(b\) in local dataset \(D_{i}\) do
15: Update local model \(W_{i}\)
```  
**Algorithm 1** Local Model Evaluation based Client Selection
## 5 Conclusions and Future Work  
This paper presented the design of a new stress wave-based PPM communication system using piezoceramic transducers and its verification test. To realize stress wave-based communication, the system integrates a PPM modulation component and an amplifier into a stress wave generator that can produce high-voltage, modulated, information-containing waveforms. To reduce the need for rigid coupling, the new PZT transducer is fabricated into a cylindrical shape that can transmit stress waves through simple contact on a pipe surface. The design of the PZT transducer enables the transmission of the stress wave from the transducer to a pipeline upon touching. Through an easy-to-use user interface, end users can control the data to be transmitted. Finally, the experimental results verified the effectiveness of this new stress wave communication system with the piezoceramic transducer. Currently, the data transmission rate of the prototype system can reach 67 bits per second (bps). In the future, the data transmission rate can be further improved by adjusting the input voltage and output frequency, among other parameters. Furthermore, the proposed system can be easily adapted and applied in future research on stress wave-based PPM communications along pipelines. After fully investigating the channel characteristics, quantitative estimation of the entire system performance will be performed.  
**Author Contributions:** [PERSON] and [PERSON] conceived the idea of the paper. [PERSON] designed the PPM generator. [PERSON] and [PERSON] designed the PPM communication experiment. [PERSON] conducted the experiments under [PERSON]'s guidance. [PERSON] and [PERSON] wrote the paper. [PERSON] and [PERSON] proofread and revised the paper.  
**Funding:** This research was funded by the National Natural Science Foundation of China and Subsea Systems Institute, grant number [51604038 and 51541408], and grant number [#6 582-17-74270].  
**Acknowledgments:** The first author would like to thank the partial support by the National Natural Science Foundation of China (No. 51604038 and No. 51541408). The rest of the authors acknowledge the partial support with federal funding from the Department of the Treasury through the State of Texas under the Resources and Ecosystems Sustainability, Tourist Opportunities, and Revived Economies of the Gulf Coast States Act of 2012 (RESTORE Act) via Subsea Systems Institute (GAD # 6 582-17-74270). The content, statements, findings, opinions, conclusions, and recommendations are those of the author(s) and do not necessarily reflect the views of the State of Texas or the Treasury.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# A Practical Approach to Deploying a Drone-Based Message Ferry in a Disaster Situation +
## 4 Evaluation
### Results and Discussion
For example, to transfer a data size of 300 MB at a distance of 10 km, the proposed message ferry using Wi-Fi (IEEE 802.11 ac protocol) achieves an effective transmission speed of 2.3 Mbps compared to 25 kbps for Wi-SUN and 119 kbps for LoRa.
## 6 Comparison Algorithms
### Cerml  
Given \(n\) videos, CERML fuses both Euclidean data (i.e., feature means) \(X=\{x_{1},\cdots,x_{n}\}\subseteq R^{D}\) and the Riemannian representations (i.e., SPD matrices) \(\mathbb{Y}=\{Y_{1},\cdots,Y_{n}\}\subseteq Sym_{++}^{D}\) from videos. Data are transformed from the original Euclidean space and Riemannian space into RKHS via two transformation matrices \(W_{x}\in R^{n\times d},W_{y}\in R^{n\times d}\). Transformed data in RKHS can be defined as \(X^{R}=\left\{x_{1}^{R},\cdots,x_{n}^{R}\middle|x_{i}^{R}\!=\!W_{x}^{T}x_{iCol} ^{X}\right\}\subseteq R^{d}\) and \(Y^{R}=\left\{y_{1}^{R},\cdots,y_{n}^{R}\middle|y_{i}^{R}\!=\!W_{y}^{T}\!K_{iCol }^{y}\right\}\subseteq R^{d}\), where \(K_{iCol}^{x}\in R^{n\times n}\) and \(K_{iCol}^{y}\in R^{n\times n}\) are the \(i\)-th column of RBF kernel of \(X=\{x_{1},\cdots,x_{n}\}\subseteq R^{D}\) and \(\mathbb{Y}=\{Y_{1},\cdots,Y_{n}\}\subseteq Sym_{++}^{D}\), respectively.  
The first constraint is to minimize the distances between data with the same label, and maximize distances between data with different labels:  
\[D_{1}\left(W_{x},W_{y}\right)=\sum_{j=1}^{n}\sum_{i=1}^{n}a_{ij}\left\|x_{i}^{ R}-y_{j}^{R}\right\|^{2},\ a_{ij}=\begin{cases}1&l_{i}^{x}=l_{j}^{y}\\ -1&others\end{cases}. \tag{36}\]  
The second constraint aims to keep Euclidean and Riemannian geometric relations in RKHS:  
\[D_{21}(W_{x})=\sum_{i=1}^{n}\sum_{j=1}^{n}b_{ij}\|x_{i}^{R}-x_{j}^{R}\|^{2},\ b_{ij}=\begin{cases}w_{ij}^{x}&l_{i}^{x}=l_{j}^{x} \&\kappa\Lambda_{1}\left(x_{i},x_{j}\right)\\ -w_{ij}^{x}&l_{i}^{x}\
eq l_{j}^{x}\&\kappa\Lambda_{2}\left(x_{i},x_{j}\right),\\ 0&others\end{cases}, \tag{37}\]  
\[D_{22}\left(W_{y}\right)=\sum_{i=1}^{n}\sum_{j=1}^{n}c_{ij}\|y_{i}^{R}-y_{j}^{ R}\|^{2},\ c_{ij}=\begin{cases}w_{ij}^{y}&l_{i}^{y}=l_{j}^{y}\&\kappa\Lambda_{1} \left(y_{i},y_{j}\right)\\ -w_{ij}^{y}&l_{i}^{y}\
eq l_{j}^{y}\&\kappa\Lambda_{2}\left(y_{i},y_{j}\right),\\ 0&others\end{cases}, \tag{38}\]  
where \(\Lambda_{1},\Lambda_{2}\) are neighborhood number. Then, the objective function with balancing parameters \(\lambda_{1}>0,\lambda_{2}>0\) can be written as  
\[\begin{split}\underset{W_{x},W_{y}}{\text{min}}&\lambda_{1}D_ {1}\big{(}W_{x},W_{y}\big{)}+\lambda_{2}\big{(}D_{21}(W_{x})+D_{22}\big{(}W_{ y}\big{)}\big{)}D_{21}(W_{x})\big{(}W_{y}\big{)}\\ +&\lambda_{3}\|W_{x}K^{x}\|^{2}+\lambda_{3}\|W_{y}K^{y}\|^{2}\\ &\text{Subject to }W_{x}^{T}K^{x}W_{x}=I_{d};\ W_{y}^{T}K^{y}W_{y}=I_{d} \end{split} \tag{39}\]
# Two-Path Spatial-Temporal Feature Fusion and View Embedding for Gait Recognition
## 3 Proposed Method
### View Embedding Module
#### 3.3.1 View Prediction  
As shown in Figure 6, \(F_{space}\) and \(F_{SL-term}\) in the two-path parallel structure are concatenated along the channel dimension as the multi-scale spatial-temporal features \(F_{ST}\), which can be formulated as:  
\[F_{ST}=F_{spec}\Theta F_{SL-term} \tag{12}\]  
where \(\Theta\) denotes a merge operation on the channel dimension.  
The view classification feature can be realized by:  
\[\widetilde{F}_{view}=FC\Big{(}P_{Global\_{Arg}}(F_{ST})\Big{)} \tag{13}\]  
where \(FC\) means a fully connect layer and \(P_{Global\_{Arg}}\) indicates the global average pooling.  
Subsequently, the view probability of the input silhouette, denoted as \(P_{view}\) and the maximum probability of the predicted view, represented as \(\widetilde{P}_{view}\) are determined by calculating the following formulas, respectively, as:  
\[P_{view}=W_{view}\widetilde{F}_{view}+B_{view} \tag{14}\]  
\[\widetilde{P}_{view}=Argmax(P_{view}) \tag{15}\]  
where \(P_{view}\in R^{V}\), \(V\) means the number of discrete views, \(W_{view}\) are the view weight matrix of \(FC\) layer, \(B_{view}\) denotes the bias terms of \(FC\) layer and \(\widetilde{P}_{view}\in\{0,1,2\cdots V\}\).  
Regarding the predicted discrete view \(\widetilde{P}_{view}\), a group of view projection matrix will be trained as \(Z_{\widetilde{P}_{view}}|=\{W_{i}|i=1,2,\cdots,n\}\), where \(W_{i}\in R^{D\times D}\) is the projection matrix, and it will be used in Section 3.3.2.