esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
### Related Works
In this method, since the truck and the drone work independently of each other, the routes of the truck and the drone were considered independent from each other, and route calculations were made separately to solve this problem. As with FSTSP, PDSTSP was an NP-hard problem. Heuristic methods have been proposed to solve this problem [31]. The aim of the study by [PERSON] et al. called The Traveling Salesman Problem with Drone Station (TSP-DS), was to solve the shortcomings of a PDSTSP. Drones have a range where they can operate in a healthy way. In the study, it was stated that the advantage of PDSTSP disappears when the number of delivery points within the distance that drones can serve from the central warehouse is very low. TSP-DS proposed using a \"drone station\" that can manage multiple drones to solve this problem. It was planned to deliver the packages by truck to the drone station, which was located far from the central warehouse. Afterward, it was thought that the truck would continue to deliver while the drones, which accepted the drone station as the center, delivered. To reduce the complexity of the situation, the problem was divided into two parts: the TSP and the parallel identical machine scheduling problem (PMS) [32]. In [33], the concept of the Traveling Salesman Problem with Drone (TSP-D) was emphasized. The most important difference between this scenario and FSTSP is that the location where the drone will meet with the truck after completing the delivery is the point where it starts moving from the truck. FSTSP does not allow this situation. The aim of the study was to minimize the service time, as in previous studies, and a dynamic programming-based solution method was presented. In the study by [PERSON] et al., the aim was to reduce the overall travel cost of drones. At the same time, in this study, the heterogeneity of vehicle means was explained. A heterogeneous drone is a team of drones with different sensing capabilities and different movement constraints that work together. The main process in the study was to transform a Heterogeneous, Multiple Depot, Multiple Traveling Salesman Problem (HMDMTSP) into a single, asymmetric TSP [34]. [PERSON] et al. have defined the Multiple Traveling Salesman Problem (MTSP) in their study. According to the MTSP, a team of drones should visit target points in the most efficient way possible. In this approach, trucks were not used in the realization of deliveries, and primarily, the destination points were clustered using the K-means clustering method. Afterwards, these clusters were analyzed with a heuristic structure of their choice [35].
## 5 System Experiments  
### Control System Construction  
Figure 9 presents the prototype of the UBSHR system. It consists of the \"PC\" and the \"UMAC\". The PC is fitted with an Intel Core i7 series and win7-32-bit processor to complete the functions of the human-computer interaction, code initialization and compilation, and condition monitoring. The UMAC is used to accomplish data calculation, digital to analog conversion, and motion control. The servo driving system consists of MR-J4-70A and MR-J4-100A servo drivers, and HG-SR102 BJ and HG-KR73 BJ AC servo motors. System experiments are implemented on the UBSHR's prototype system to verify the effectiveness of the proposed SC-AFNN method.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & **CE-SOSMC** & **SC-AFNN** \\ \hline Steady-state reaching time & \(z\) (s) & 2.08 & 0.26 \\ \(\beta\) (s) & 1.36 & 0.72 \\ Maximum tracking error & \(z\) (\(\times 10^{-4}\) m) & 72.05 & 14.45 \\ \(\beta\) (\(\times 10^{-3}\) rad) & 14.01 & 7.88 \\ RMSE & - & 0.54 & 0.30 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The numerical values of the steady-state performance and root mean square error (RMSE) (desired trajectory 2).  
Figure 8: The tracking error curves of the UBSHR’s end-effector (desired trajectory 2). (**a**) Tracking error curves in \(z\)-direction (6 s); (**b**) Tracking error curves of \(\beta\) (6 s).
## 2 Materials and Methods
### Fine-Tuning for Semantic Address Matching  
Fine-tuning is an implementation of deep transfer learning, which refers to adding task-relevant structures and parameters to an already-trained model, and then retraining on a task-relevant corpus [56]. We therefore used a newly constructed labelled address matching corpus for the semantic address matching, adding a new neural network structure for a fine-tuned learning model and training framework based on the classification task. The network structure is first superimposed with a layer of fully connected feedforward neural networks for nonlinear transformation, with an activation function of _tanh_, which is mathematically formulated as follows:  
\[\mathrm{t}(x)=\frac{e^{x}-e^{-x}}{e^{x}+e^{-x}} \tag{4}\]  
After obtaining the probability distribution features using the fully connected neural network, we then connected the fully connected neural network without the activation function for linear transformation. Since semantic address matching is a binary classification task of whether to match, the output of this layer is two-dimensional. Finally, we passed the output probability distribution score of this layer into the SoftMax normalization function to predict the probability of matching or not matching the address pair, respectively. We designed the deep semantic address matching model (abbreviated as DSAMM) with the following objective function. Here, given that the size of the number of address string pairs per batch iteration is _batch_size_, the predicted probability output is _prob(batch_size,2)_, and the true label sequence is _label(batch_size)_, the true label probability for each address pair is as follows:  
\[true\_prob(batch\_size)=gather(prob,Label) \tag{5}\]  
The final objective function is obtained by taking logarithmic values of the probabilities and then summing them (i.e., log transformation) and averaging them. The objective function is specified below:  
\[\frac{\sum_{i=1}^{batch\_size}\log(true\_prob[i])}{batch\_size}\to 0 \tag{6}\]  
The accuracy metrics used in this study include precision, recall, and F1 score [57]. Precision calculates the proportion of true positive samples out of those predicted to be positive; recall reflects the rate at which positive examples in this are predicted to be accurate and, in semantic address matching, refers to the percentage of correctly matched pairs out of all address pairs that should be correctly matched; and the F1 score is the harmonic mean of precision and recall.
# Energy and Spectrally Efficient Modulation Scheme for IoT Applications
## 1 Introduction
Furthermore, the OSP-MSK achieves a higher spectral efficiency than the recent MIMO-CEM system by increasing the number of bits per symbol without additional bandwidth. * OSP-MSK Performance Analysis: Firstly, a generic closed form expression for assessing the average bit error rate (ABER) performance of the proposed OSP-MSK modulation over an additive white Gaussian noise (AWGN) is presented thoroughly. Afterwards, this expression is used to derive closed-form expressions for the ABER performance of the OSP-MSK over the generalized Rayleigh and Nakagami-m fading channels. * OSP-MSK Bandwidth Utilization: The bandwidth utilization of the OSP-MSK is evaluated by deriving a closed form expression of its power spectral density (PSD). This PSD is used to assess the ability of the OSP-MSK to boost the spectral efficiency compared to the MIMO-CEM system. The rest of this paper is structured as follows. Section 2 addresses the system model of the proposed OSP-MSK modulation. The performance analysis of the OSP-MSK is driven in Section 3. The simulation results are introduced in Section 4. Finally, the paper is concluded in Section 5. Notation: Throughout this paper, vectors are represented using bold lowercase letters and matrices are represented using bold capital letters. Complex Gaussian distributed random variables (RVs) are expressed by \(\mathcal{CN}\left(\mu,\,\sigma_{o}{}^{2}\right)\), where \(\mu\) and \(\sigma_{o}{}^{2}\) denote the mean and the variance, respectively. We use \(p_{\Psi}(\Psi)\) to refer to the probability density function (PDF) of a random variable \(\Psi\). Moreover, the notations \(log\left(.\right)\), \(\left|.\right|\), and \(\Re(. )\) are used to indicate the natural logarithm, the magnitude, and the real part of a complex quantity, respectively. The symbol \(\left( \begin{array}{c}n\\ k\end{array} \right)\) indicates the binomial coefficient of choosing \(n\) outcomes from \(k\) possibilities. The functions \(Q(. )\), \(erfc\) (. ), and \(\Gamma(. )\) are used to indicate the \(Q\)-function, the complementary error function, and the gamma function, respectively. Finally, we use \(2F_{1}(a,b;c;z)\) to indicate the Gaussian hypergeometric function, where \(a\), \(b\), and \(c\) are Gaussian hypergeometric function arbitrary parameters, and \(z\) is the function argument.
# System of Systems Lifecycle Management--A New Concept Based on Process Engineering Methodologies
## 6 The Arrowhead Framework
### Service Levels of Arrowhead
#### 6.1.1 Mandatory Core Systems  
The central, mandatory systems of an Arrowhead cloud [97] provide the necessary functionalities of SOA. This group includes the Orchestration System (mainly for service discovery and late binding), Service Registry (service providers can announce their active services), and Authorization System (to provide authorization, authentication, and accounting functions).
# Effect of Prestress Levels and Jacking Methods on Friction Losses in Curved Prestressed Tendons
## 5 Conclusions  
The initial prestress force has a dominant effect on the member force of a PSC girder, and the friction loss between the prestressing tendon and sheath tube strongly affects this prestress force. In this study, friction loss experiments were conducted using full-scale 30-m long PSC girders. Some of the findings derived from the experimental results are as follows:  
1. The prestress force measured at midspan corresponded to 86.4% and 81.1% of the prestress force at the jacking end with both ends jacked and one end jacked, respectively.
2. The friction losses in the prestressing tendon continuously decreased as the prestress level increased. The measured friction loss was much greater than the theoretical friction loss at low prestress levels, but it approached the theoretical friction loss at high prestress levels.
3. Jacking at both ends is recommended for the prestressing method because it generates a much lower friction loss compared with the loss obtained with jacking at only one end.
4. In the design codes, the friction loss is evaluated solely by the curvature friction coefficient and wobble friction coefficient, with no regard to the prestressing method and prestress level. However, the experimental results in this study showed that the prestressing method and prestress level strongly affect the friction losses. Therefore, further studies are required to confirm this observation.  
**Acknowledgments: This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) and funded by the Ministry of Education (2016R1A6A3A11931804).**  
**Conflicts of Interest: The authors declare no conflict of interest.**
# CNN-Based Damage Identification of Submerged Structure-Foundation System Using Vibration Data
###### Abstract  
This study presents a convolutional neural network (CNN) deep learning approach for identifying damage in submerged structure-foundation systems using vibration data. Firstly, foundation damage in a lab-scale caisson-foundation system is simulated to measure time-history responses. Singular value decomposition (SVD) responses are derived from the time-history responses. Secondly, the 1-D CNN deep learning model is trained using both the time-history responses and SVD responses. Finally, the trained CNN models are implemented to evaluate the foundation damage under conditions of noise contamination and partially untrained data. The experimental results demonstrate the effectiveness of CNN models for damage identification and highlight the comparative strengths of time-history and SVD data. The CNN model trained using SVD data outperforms the other model when under noise contamination conditions, while the CNN model trained using time-history data maintains better accuracy in partially untrained data conditions. Integrating both types of data enhances the accuracy of damage classification.  
submerged structure-foundation system; vibration monitoring; time-history response; singular value decomposition response; 1-D CNN; deep learning; damage identification +
Footnote †: journal: Applied sciences  
There has been a lack of research comparing CNN models trained using different vibration features, and investigating how integrating results could improve prediction performance. This study uses both time-history responses and SVD responses for the CNN model. The advantages and disadvantages of each type of training data are identified by evaluating the CNN model under noise contamination and partially untrained data conditions. The main contributions of this study are as follows:  
* A CNN deep learning approach is developed to identify damage in submerged structure-foundation systems using both time-history and SVD data.
* The performance of the CNN model is evaluated with partially untrained cases, where certain damage levels were excluded from the training and validation datasets.
* A comparative study between CNN trained using time-history and SVD data is conducted. The results from the two models are integrated to strengthen the damage classification performance.  
To achieve these objectives, a CNN deep learning approach for identifying damage in submerged structure-foundation systems using both time-history and SVD data is developed. Firstly, foundation damage in a lab-scale caisson-foundation system is simulated to measure time-history responses. SVD responses are derived from the time-history responses. Secondly, the 1-D CNN deep learning model is trained using both the time-history responses and SVD responses. Finally, the trained CNN models are implemented to evaluate the foundation damage under conditions of noise contamination and partially untrained data.
## 5 Simulation Results
### Multi-Domain Coverage Planning in Static Environments
#### 5.1.3 Velocity and Acceleration  
The HUAV is commanded to follow the waypoints provided by its path planning system. The dynamic states of the vehicle achieving this trajectory are shown in Figure 17. In this case, the HAUV performed differently in multiple environments. The maximum velocity in the air was 9.5 m/s, and it was 2.74 m/s underwater; and the maximum acceleration in the air was 3 m\({}^{2}\)/s, and it was 0.78 m\({}^{2}\)/s underwater. All the outputs of the motion parameters conform to the HAUV's motion capability. The trajectory passed through all the target spots given by the GLNS solver safely. Figure 17 shows the corresponding resultant velocity and acceleration of the HAUV as it tracked through the trajectory. It can be clearly seen that the HAUV was in a different mode when operating in dynamic environments: moving in the air or moving underwater. Therefore, the values of velocity and acceleration would be helpful for HAUV control.
## 2 Materials and Methods
### Data Preparation  
We selected about 3% of the dataset for manual annotation. This part of the data was first segmented using the SAM (ViT-H) to obtain model-generated vegetation regions. Manual corrections were then applied, and correction points were annotated. The correction points are divided into two types. The first type is erroneous segmentation correction points, meaning areas incorrectly classified as vegetation. Equation (1) shows this process. The second category is the correct nonsegmented points. They refer to vegetation regions that were not correctly classified into the set. They are named \(T\). Equation (2) shows this process.  
\[P = \bigcup\limits_{i=1}^{N}\left(V_{model}^{(i)}\ -\ V_{true}^{(i)}\right) \tag{1}\]  
\[T = \bigcup\limits_{i=1}^{N}\left(V_{true}^{(i)}\ -\ V_{model}^{(i)}\right) \tag{2}\]  
\(V_{model}^{(i)}\) represents the vegetation region divided by the model in the \(i\) picture, \(V_{true}^{(i)}\) is the real vegetation region in the \(i\) picture, and the error segmentation correction points of all  
\begin{table}
\begin{tabular}{c c c} \hline
**Weather Type** & **Quantity** & **Proportion** \\ \hline Normal (sunny, cloudy) & 687 & 50\% \\ Rainy (ordinary) & 68 & 5\% \\ Rainy (rainstorm) & 273 & 20\% \\ Light haze & 172 & 12.5\% \\ Dense haze & 172 & 12.5\% \\ \hline \end{tabular}
\end{table}
Table 1: The proportion distribution of data among different climate types.  
\begin{table}
\begin{tabular}{c c c} \hline
\begin{tabular}{c} Baidu Map, \\ 2018–2020, \\ 1372 images \\ \end{tabular} \\ \hline \end{tabular}
\end{table}
Table 2: Streetscape data information.  
samples form a set \(P\). \(N\) is the total number of images processed, and its value is 50. During the manual annotation process, two uniformly trained annotators independently annotated using a cross-validation method. The annotation consistency was evaluated through the kappa coefficient (kappa > 0.85). Disagreements were reviewed by an expert group to form the final annotation results. All annotated data were verified through multiscale IoU and boundary matching degree detection to ensure that the spatial error of the vegetation area was controlled within 5 px.  
The street view data was then cropped and regionally matched to the remotely sensed data. The original streetscape data labeled \(S_{z\times w}^{(j)}\) represents the data of size \(z\times w\) numbered \(j\). The _select_ function filters the street view data from the study area \(bt\), finally obtaining the image group \((k_{1},\ k_{2},\ \dots,k_{n})\) of the matched data, labeled \(S_{size\times size}^{(k_{1},\ k_{2},\ \dots,k_{n})}\). The specific method is shown in Equation (3).  
\[S_{size\times size}^{(k_{1},\ k_{2},\ \dots,k_{n})}\ =\ select\left(S_{z\times w}^{(j)},\ bt\right) \tag{3}\]
# A Study on \(H_{\infty}\)-Fuzzy Controller for a Non-Linear Wind Turbine with Uncertainty
###### Abstract  
In this study, an \(H_{\infty}\)-fuzzy controller is proposed for application in wind turbines with uncertainties and nonlinearities. The performance of the proposed controller was validated via dynamic simulations using a commercial aero-elastic code and wind tunnel experiments employing a scaled wind turbine. The simulation and the experimental results were then compared with those of the conventional PI and LQR control algorithms presented in our previous study. In the simulation, the perturbation and the sensor noise were applied to reflect uncertainty and nonlinearity effects. In addition, in the wind tunnel experiment, a control system using a commercial Bachmann PLC was established with an accelerometer to estimate the fatigue load exerted by the rotor thrust. It was confirmed through experiments that the robustness and adaptation of the control system improved in the situation of pitch system failure. As a result of the experiment, the proposed \(H_{\infty}\) controller was able to reduce the rotor speed fluctuation by 39.9%, the power fluctuation by 32.0%, and the fatigue load by 2.4% compared with the LQR fuzzy controller, which had better performance than the conventional PI controller. In addition, it was confirmed through experiments that the robustness and adaptation of the control system were well maintained. This was even true in the situation of one-blade pitch system failure.  
\(H_{\infty}\) control; fuzzy logic; proportional-integral (PI) control; linear-quadratic regulator (LQR); wind tunnel experiment
###### Abstract  
With the development of the Internet of Things (IoT), the amount of data is growing and becoming more diverse. There are several problems when transferring data to the cloud, such as limitations on network bandwidth and latency. That has generated considerable interest in the study of edge computing, which processes and analyzes data near the network terminals where data is causing. The edge computing can extract insight data from a large number of data and provide fast essential services through simple analysis. The edge computing has a real-time advantage, but also has disadvantages, such as limited edge node capacity. The edge node for edge computing causes overload and delays in completing the task. In this paper, we proposes an efficient offloading model through collaboration between edge nodes for the prevention of overload and response to potential danger quickly in emergencies. In the proposed offloading model, the functions of edge computing are divided into data-centric and task-centric offloading. The offloading model can reduce the edge node overload based on a centralized, inefficient distribution and trade-off occurring in the edge node. That is the leading cause of edge node overload. So, this paper shows a collaborative offloading model in edge computing that guarantees real-time and prevention overload prevention based on data-centric offloading and task-centric offloading. Also, we present an intelligent offloading model based on several scenarios of forest fire ignition.  
edge computing offloading computation; distributed collaboration; dynamic offloading; IoT; forest fires +
Footnote †: journal: Applied Sciences
# MobiDabet: Mobile Telemonitoring System for Patients with Type 2 Diabetes Mellitus (T2 DM)
## 3 Results: Telemonitoring System
### Mobile-Web-Application Focused on the Patient
#### 3.2.2 Health Education Module  
In this module, patients can view different videos related to their illness and treatment. These videos are produced and provided by IMSS medical specialists (see Figure 38). The purpose of these videos is to educate and increase awareness among patients about their illness and the benefits of adopting healthy eating habits to avoid complications.