esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Key Technology of the A2S Integrated MIoT
### Resource Allocation
#### 3.1.2 NOMA Technique
This helps in predicting future demand and traffic distribution, allowing managers to better plan resource allocation strategies. Second, machine learning can adjust resource allocation plans in real time through continuous monitoring and feedback mechanisms. For example, when maritime traffic systems encounter unforeseen events or abnormal situations, machine learning can quickly respond by re-optimizing resource allocation, ensuring the efficient operation of the traffic system in complex environments. Furthermore, machine learning can optimize ship navigation paths and berthing locations, reducing congestion and waiting times, to improve overall traffic efficiency. By continuously updating models in real time, the system can adapt to changing conditions, making resource allocation more flexible and intelligent. In conclusion, the application of machine learning in the MIoT, through deep learning and intelligent optimization, enhances the efficiency and accuracy of resource allocation, making maritime traffic safer, more reliable, and sustainable.
# A Carrier-Based Gardner Timing Synchronization Algorithm for BPSK Signal in Maritime Communication
## 3 Materials and Methods
### Interpolator
#### 3.5.2 Interpolation Algorithm  
The interpolation method of CB-Gardner uses the four-point Lagrange cubic interpolation method with Farrow structure [33]. Once the positions of the error detection points (\(p_{y,n}\)) and each sampling point (\(P_{n,i}\)) are obtained, an interpolation algorithm is applied to resample them and obtain their true values without the influence of Doppler. For any resampling position \(P=m+u\), where \(m\) is the integer part of \(P\) and \(u\) is the fractional part of \(P\), let \(s\) be the received original passband signal, the interpolated value after resampling according to \(P\) is  
\[y_{\text{in}}=\mathbf{X}^{\text{T}}\mathbf{B}^{\text{T}}\mathbf{U} \tag{22}\]  
where \(\mathbf{X}\) consists of four sampling points in \(s\):  
\[\mathbf{X}=\left[\begin{array}{c}s(m+2)\\ s(m+1)\\ s(m)\\ s(m-1)\end{array}\right] \tag{23}\]  
\(\mathbf{B}\) is the Farrow cubic interpolation structure matrix:  
\[\mathbf{B}=\left[\begin{array}{cccc}0&0&1&0\\ -\frac{1}{6}&1&-\frac{1}{2}&-\frac{1}{3}\\ 0&\frac{1}{2}&-1&\frac{1}{2}\\ \frac{1}{6}&-\frac{1}{2}&\frac{1}{2}&-\frac{1}{6}\end{array}\right] \tag{24}\]  
and \(\mathbf{U}\) is the matrix determined by \(u\):  
\[\mathbf{U}=\left[\begin{array}{c}1\\ u\\ u^{2}\\ u^{3}\end{array}\right] \tag{25}\]  
The resampling value for the sampling point location \(P_{n,i}\) is \(y_{\text{in},n,i}\), and the combination of \(y_{\text{in},n,i}\) forms the final passband signal \(s_{CB}\) after Doppler elimination, i.e.,  
\[s_{CB}[(n-1)*sps+i]=y_{\text{in},n,i}\text{, }n=1,2,\dots;i=1,2,\dots,sps \tag{26}\]  
and \(s_{CB}(1)=s(1)\). The algorithm terminates when the number of symbols (\(n\)) in a packet reaches its maximum value (\(N\)). In the subsequent demodulation process, we only need to perform down-conversion, matched filtering, and equalization operations on \(s_{CB}\) to decode the transmitted symbols \(x\).  
Algorithm 1 summarizes the proposed CB-Gardner algorithm.  
```
1:\(s,sps,N\)
2:Initialization of \(\lambda_{0}=1,P_{1}=1,\eta_{1}=1,s_{CB}(1)=s(1)\);
3:for \(n=1:N\)
4:Step 1: Finding detection points
5: getting \(p_{y_{1},n},p_{y_{2},n},p_{y_{3},n}\)
6: interpolating to get \(y_{1},y_{2},y_{3}\)
7:Step 2: Timing Error detection
8:\(err_{n}=y_{2}*(y_{1}-y_{3})\) or \(err_{n}=y_{2}*(y_{3}-y_{1})\)
9:Step 3: Loop filter
10:\(E_{LF,n}=c_{1}err_{n}+c_{2}\sum\limits_{i=1}^{n}err_{n}\)
11:Step 4: Numerically controlled oscillator
12:\(W_{n}=1/sps-E_{LF,n}\)
13:\(P_{n+1}=\text{int}(P_{n})+1+\eta_{n}/W_{n}\)
14:\(\eta_{n+1}=\eta_{n}-[\text{int}(\eta_{n}/W_{n})+1]W_{n}+1\)
15:\(\lambda_{n}=sps/(P_{n+1}-P_{n})\)
16:Step 5: Calculating \(s_{CB}\)
17:for \(i=1:sps\)
18:\(P_{n,i}=P_{n}+i/\lambda_{n}\)
19:interpolating to get \(y_{in,n,i}\)
20:\(s_{CB}[(n-1)*sps+i]=y_{in,n,i}\)
21:end for
```  
**Algorithm 1**CB-Gardner algorithm.
## 2 Mathematical Formulation
### Model Interpretation  
The interpretation of the MINLP model defined from (1) to (11) is the following: Equation (1) defines the objective function of the optimization problem which adds the energy purchasing costs to the conventional generators (i.e., substation buses) as defined in Equation (2) with the annualized investments in PV sources including its maintenance and operating costs as defined by Equation (3). Equality constraints (4) and (5) present the active and reactive power equilibrium at each node of the system for each period of time. These equations are the most complex constraints in the studied problem since these are nonlinear, non-convex, and typically require numerical methods to be properly addressed. Box-type constraints (6) and (7) define the lower and upper bounds associated with the active and reactive power generation outputs in the conventional sources; the Box-type constraint (9) defines the PV lower and upper generation capabilities for the PV sources in case that the binary variable is activated; the Box-type constraint (10) presents the lower and upper voltage regulation limits allowed for all nodes of the network. This is a typical constraint imposed by regulatory entities and utility operation practices. The inequality constraint (10) limits the maximum number of PV sources that can be installed along with the distribution network; finally, constraint (11) shows the binary nature of the decision variable regarding whether or not to locate a PV source in a particular node of the network.  
It is worth mentioning that the main complication of the optimization model (1)-(11) is its MINLP nature, since binary and continuous variables are combined with nonlinear non-convex relations mainly defined by the power balance equations. To solve MINLP models, in the current literature, the use of master-slave optimization strategies is a popular method to decouple the binary problem from the continuous problem [20]. In the studied problem defined by (1)-(11), the Newton metaheuristic algorithm is used in the master stage and the successive approximation power flow method is used in the slave stage.
## 2 Methodology and Computational Model  
### Actuator Line Model  
An actuator line model (ALM) was employed in the present study to simulate the aerodynamics of a VAWT because of its high computational accuracy and relatively low cost. Based on computational fluid dynamics (CFD), ALM replaces the displacement boundary conditions of the blades with the body forces using blade element theory (BET). In BET, the aerodynamic forces of the blades are determined by AoAs, which are extracted from the flow field by velocity sampling. Several methods of velocity sampling have been proposed, especially for simulations of horizontal axis wind turbines, such as averaging the velocities adjacent to the blades [27; 28; 29; 30] or extracting the flow velocities at monitor points minus the induced velocities by bound circulation [31; 32]. In the present study, the planar velocities in the airfoil profiles at the aerodynamic centers were employed to calculate the AoAs. The previous literature by the authors described the details of the ALM employed, as well as several necessary modifications [33], so they are omitted here for brevity.  
The instantaneous aerodynamic force coefficient of a blade element (\(C_{Fi-be}\)) is  
\[C_{Fi-be}=\frac{F_{i-be}}{\frac{1}{2}\rho V_{\infty}^{2}cl_{be}} \tag{1}\]  
where \(F_{i-be}\) is the instantaneous aerodynamic force of a blade element (\(i\) refers to various directions: \(t\) for tangential, \(n\) for normal, \(x\) for downstream, \(y\) for lateral and \(z\) for upward), \(\rho\) is the air density, \(V_{\infty}\) is the freestream velocity, \(c\) is the blade chord length and \(l_{be}\) is the blade element length. The instantaneous aerodynamic force coefficient of a blade (\(C_{Fi}\)) is  
\[C_{Fi}=\frac{\sum F_{i-be}}{\frac{1}{2}\rho V_{\infty}^{2}CH} \tag{2}\]  
where \(H\) is the blade span length. The average aerodynamic force coefficient of a blade (\(C_{j}\)) is then  
\[C_{j}=\overline{C_{Fi}} \tag{3}\]  
where \(j\) refers to various directions (including thrust, lateral and vertical) and \(\overline{C_{Fi}}\) is the azimuth-averaged aerodynamic force coefficient of a blade.  
The power coefficient of a turbine (\(C_{P}\)) is  
\[C_{P}=\frac{P}{\frac{1}{2}\rho V_{\infty}^{3}A}=\frac{N\lambda c}{D}\overline{ C_{It}} \tag{4}\]  
where \(P\) is the power output, \(A\) is the swept area of the turbine with unfolded blades, \(\lambda\) is tip-speed ratio and \(D\) is the rotor diameter. Obviously, the rotating diameter of a blade element is changed with the folding movement of the blade, which results in the change of the swept area of the turbine simultaneously. However, these changes are ignored when calculating \(C_{P}\), in consideration of the direct comparison between results with different fold angles. Additionally, variations of the swept area are calculated and discussed solely below.
## 3 Methodology  
The objective of this work is divided into two folds: (i) effective fault types classification (internal (DC)/external (AC or DC)) and (ii) accurate prediction of fault event locations. Figure 2 shows the flowchart of the presented work that has been adopted for fault classification and location in the HVDC transmission system. The proposed classification scheme  
\begin{table}
\begin{tabular}{l l} \hline \hline
**System** & **Parameter** \\ \hline AC Systems & 230 kV, 2000 MVA, 50 Hz \\ \hline Transformers & Yg-D configuration, 230/100 kV, 250 MVA, \(X_{L}=0.15\)\(pu\) \\ \hline Phase Reactor & 0.15 pu \\ \hline \multirow{2}{*}{High-Pass AC Filter} & Tuned: 27 th Harmonic, 18 MVAr, Q = 15 \\ \cline{2-2}  & Tuned: 54 th Harmonic, 22 MVAr, Q = 15 \\ \hline
3 rd Harmonic DC Filter & \(R=0.14737\)\(\blacktriangle\), \(L=46.908\)\(mH\), \(C=12\)\(\mu F\) \\ \hline Smoothing Inductor & \(R=0.0251\)\(\blacktriangleright\), \(L=8\)\(mH\) \\ \hline Converter & \
\begin{tabular}{l} 100 MVA, rated DC voltage = 200 kV \((\pm 100\)\(kV)\), Rated DC \\ current = 500 A \\ \end{tabular} \\ \hline Transmission Lines &
\begin{tabular}{l} Symmetric monopolar lines, PI-configuration, Length = 100 km, \\ R = 0.0139 \(\Omega\)/Km, L = 0.159 mH/Km, C = 0.231 uF/Km \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: AC System and Converter parameters.  
Figure 1: Studied symmetrical monopolar VSC-MT-HVDC system.  
was designed based on the positive and negative pole voltage and current waveforms obtained at the relaying points (R-1 to R-8) present at each end of the transmission line. Afterward, different statistical features such as mean, standard deviation, entropy, and correlation coefficient of voltage and current were extracted in order to train and test the data-mining models. In the DM framework, five state-of-the-art ML models (ANN, SVM, KNN, RF, and ELM) and one DL model (DBN) were used for training and testing in order to recognize the fault types. In order to increase the efficiency of the DM model by reducing the computational burden, the sequential forward feature selection (SFS) was integrated with each ML-based DM model. Here, the accuracy of fault event classification was analyzed in a comparative manner. Again with an intention to predict the accurate fault location, six state-of-the-art ML-based regressors (ANN, SVR, KNN, ELM, LR, and RF) and one DL-based regressor (DBN) were used for training and testing. In this case, several error indices such as MSE, MAPE, RMSE, NRMSE, and R-value were calculated and compared for each model. For classification (fault type recognition) and regression (prediction of fault location) analysis, 5-fold cross-validation was used to validate the performance of each model.
## 1 General Background
However, similar to radio services, manpower is required to maintain satellite communication. However, an officer is also tasked with maintaining firmware for these technologies. A single boat can contain multiple devices, such as VHF radio, chart plotter, and depth finder. These may be produced by one or many manufacturers. Information sharing between these tools is guided by standards published by the National Marine Electronics Association. For instance, NMEA 2000 defines the hardware, as well as software, specifications. These include but are not limited to the hardware architecture, data communication between adjacent network nodes, packet routing and shared communication protocols specifications [5]. Figure 1 illustrates a limited number of protocols that may exist on a single marine vessel, as well as how these protocols may interact with other vessels, as well as with coastal services.
# Performance of Payement Temperature Prediction Models
## 3 Results
### Performance of Models at Different Geographical Locations  
The \(R^{2}\) value was the same for both calibrated and uncalibrated models, thus the goodness of fit remained unchanged. A summary of the results of statistical analysis is presented in Figure 3, with the detailed results in Table 3.  
The uncalibrated models performed better than the calibrated models, as shown by the higher \(RMSE\) and MSE values for the calibrated models. The Viljoen model had the lowest \(RMSE\) and \(MRE\) values at both sites and thus performed best. This was followed by the Diefenderfer model and then the SHRP Superpave model. Thus, calibration should be carried out for each location, using data obtained from the respective location to improve prediction accuracy.  
\begin{table}
\begin{tabular}{l l c c c c c c} \hline \hline \multirow{2}{*}{**Analysis**} & \multirow{2}{*}{**Location**} & \multicolumn{2}{c}{**Viljoen Model**} & \multicolumn{2}{c}{**SHRP Superpave Model**} & \multicolumn{2}{c}{**Diefenderfer Model**} \\ \cline{3-8}  & & **Before** & _After_ & **Edibration** & _After_ & **Before** & _After_ \\  & & **Calibration** & _Calibration_ & _Calibration_ & _Calibration_ & _Calibration_ & _Calibration_ \\ \hline \multirow{2}{*}{\(R^{2}\)} & Akumadan & 0.9772 & _0.9772_ & 0.9772 & _0.9772_ & 0.9567 & _0.9567_ \\  & Sogakope & 0.9971 & _0.9971_ & 0.9971 & _0.9971_ & 0.9976 & _0.9976_ \\ \hline \multirow{2}{*}{\(RMSE\)} & Akumadan & 3.38 & _12.60_ & 17.76 & _22.76_ & 8.95 & _20.22_ \\  & Sogakope & 2.18 & _11.26_ & 18.96 & _21.91_ & 11.20 & _16.69_ \\ \hline \multirow{2}{*}{\(MRE\) (\%)} & Akumadan & 15.14 & _37.59_ & 73.09 & _77.03_ & 45.12 & _60.27_ \\  & Sogakope & 14.38 & _42.21_ & 74.56 & _82.87_ & 47.60 & _58.39_ \\ \hline \multirow{2}{*}{\(VAF\) (\%)} & Akumadan & 80.30 & _83.47_ & 81.71 & _94.17_ & 66.04 & _45.68_ \\  & Sogakope & 34.63 & _36.78_ & 35.57 & _63.33_ & 26.91 & _17.32_ \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of statistical analysis of predicted temperature values at other locations.
## 3 Results  
### Study Selection  
The literature search and selection protocol are exhibited in the PRISMA flow chart (Figure 2). The electronic search retrieved 836 articles from six electronic databases. After removing duplicates, 354 studies remained, and following a manual search, two more articles were added (thus, 356 articles to be screened). The evaluation of the articles by title and abstract resulted in 329 eliminations. Finally, twenty-seven articles remained for eligibility assessment. After title, abstract, and full-text evaluations, 20 articles were excluded for different reasons explained in the list of excluded studies (Appendix A), leaving 7 for qualitative assessment. Seven RCTs [2; 4; 9; 20; 21; 40; 41] were included in the present qualitative analysis, and five RCTs were included in the quantitative analysis. Regarding missing data from the RCT's identified for quantitative analysis, a note was sent to the corresponding authors of the articles, but no responses were received.
## 6 Conclusions  
In this paper, a novel method of bridge damage detection using a statistical approach from acceleration-based BWIM data is proposed and its ability to quantify bridge damage is investigated. The bridge accelerations contain important bridge health condition information. As a result, the inferred BWIM weights are damage sensitive. Global uniform loss of bridge stiffness is used as a measure of the damage, and statistical properties of the inferred BWIM results, specifically standard deviation, are shown to be well correlated with bridge stiffness. In addition to numerical simulation, this new concept is tested in the field for global damage detection using one year of 5-axle truck data from the Sentvid bridge in Slovenia. In the field tests, the second derivative of the commonly used strain response is used as an estimate of acceleration, and temperature change is used as a proxy for damage. Both studies demonstrate that this novel approach is effective in detecting changes in bridge stiffness and is able to quantify the extent of that change. The main findings of this study are as follows:  
1. In numerical analysis, the relationship between the standard deviation of inferred GVWs and bridge damage percentage is found in simulations to linearly increase. This change in the standard deviation due to bridge damage is significant, sufficiently so to be able to detect reasonably small changes in bridge condition.
2. Analysis of the field data confirms a linear and increasing relationship between the standard deviation of inferred steer-axle weights and the mean internal bridge temperatures. As temperature affects stiffness, the ability to detect temperature change is strong evidence of an ability to detect a change in the damage state.  
Figure 18: Inferred steer-axle weight standard deviation against the bridge internal temperature.  
Bridge accelerations are damage sensitive, and the possibility of using a statistical approach for acceleration-based BWIM data for bridge damage detection has been shown to be promising using both simulations and field testing. This method can be practically applicable for long-term bridge monitoring and has a relatively low cost due to a smaller number of sensors being required than current practice methods. This method has also been shown to be applicable to SiWIM systems. An initial calibration of the bridge influence line using pre-weighed truck and a method of axle-detection is an essential element in any such installation.  
Conceptualization, E.O.; Formal analysis, M.A.K.; Funding acquisition, E.O.; Project administration, E.O., M.A.K. and D.P.M.; Resources, A.Z.; Software, M.A.K.; Supervision, E.O. and D.P.M.; Visualization, E.O., M.A.K. and D.P.M.; Writing--Original draft, M.A.K.; Writing--Review & editing, E.O., M.A.K. and D.P.M. All authors have read and agreed to the published version of the manuscript.  
This research has received funding from the Science Foundation Ireland (SFI) under the US-Ireland R & D partnership with the National Science Foundation (NSF) and Invest Northern Ireland (ID: 16/US/13277).  
The authors declare no conflict of interest.
# Unsupervised Color-Based Flood Segmentation in UAV Imagery
## 2 Materials  
We employed two publicly available datasets for this study to demonstrate the robustness and general applicability of our method. First, the dataset used to assess the efficacy of our approach and facilitate comparative analyses with alternative methodologies is called Flood Area, consisting of color images acquired by UAVs and helicopters [35]. It contains 290 RGB images depicting flood hit areas, as well as their corresponding mask images with the water region segmentations. The ground truth images were annotated by the dataset creators using Label Studio, an open-source data-labeling software. The images were downloaded selectively from the Internet; thus, the dataset exhibits a wide range of image variability, depicting urban, peri-urban, rural areas, greenery, rivers, buildings, roads, mountains, and the sky. Furthermore, there are image acquisitions relatively close to the ground as well as from a very high altitude and from diverse camera rotation angles around the X-axis (roll) and Y-axis (pitch). If pitch and roll are zeros, this means that the camera is looking down (top-down view). Hereafter, we use the term \"camera rotation angle\" to present the angle between the current view plane and the horizontal plane (top-down view). The images have different resolutions and dimensions with height and width ranging from 219 up to 3648 and 330 up to 5472, respectively. Representative images with their corresponding ground truths are shown in Figure 1.  
Figure 1: Sample images from the Flood Area dataset (**top**) and their corresponding ground truths (**bottom**).  
Second, to confirm the universal functionality of our approach, we employed the Flood Semantic Segmentation Dataset [36]. It consists of 600 and 63 color images for training and validation, respectively. Since our method is fully unsupervised, it does not require training, and, therefore, we used all 663 images for evaluation. Similarly to the initial dataset, this dataset comprises images obtained from UAVs, accompanied by their respective ground truth annotations, portraying diverse flooded scenes captured from various camera perspectives. The image sizes and resolutions also vary, but were all resized and, if necessary, zero-padded, to \(512\times 512\) by the creator, as shown in Figure 2.
# Robust Segmentation of Partial and Imperfect Dental Arches
###### Abstract  
Automatic and accurate dental arch segmentation is a fundamental task in computer-aided dentistry. Recent trends in digital dentistry are tackling the design of 3D crowns using artificial intelligence, which initially requires a proper semantic segmentation of teeth from intraoral scans (IOS). In practice, most IOS are partial with as few as three teeth on the scanned arch, and some of them might have preparations, missing, or incomplete teeth. Existing deep learning-based methods (e.g., MeshSegNet, DArch) were proposed for dental arch segmentation, but they are not as efficient for partial arches that include imperfections such as missing teeth and preparations. In this work, we present the ArchSeg framework that can leverage various deep learning models for semantic segmentation of perfect and imperfect dental arches. The Point Transformer V2 deep learning model is used as the backbone for the ArchSeg framework. We present experiments to demonstrate the efficiency of the proposed framework to segment arches with various types of imperfections. Using a raw dental arch scan with two labels indicating the range of present teeth in the arch (i.e., the first and the last teeth), our ArchSeg can segment a standalone dental arch or a pair of aligned master/antagonist arches with more available information (i.e., die mesh). Two generic models are trained for lower and upper arches; they achieve dice similarity coefficient scores of \(0.936\pm 0.008\) and \(0.948\pm 0.007\), respectively, on test sets composed of challenging imperfect arches. Our work also highlights the impact of appropriate data pre-processing and post-processing on the final segmentation performance. Our ablation study shows that the segmentation performance of the Point Transformer V2 model integrated in our framework is improved compared with the original standalone model.  
imperfect dental arch; 3D teeth segmentation; digital dentistry; mesh registration; deep learning +
Footnote †: journal: Applied sciences