esa-sceva/satcom-chunk-collection huggingface dataset:
# UAV Support for Mission Critical Services
## VMCS Concept  
This section will present the VMCS concept. The concept is based on:  
* The analysis of the services offered by TETRA and LTE-MC discussed in Section 2, as well as shortcomings of the technologies. In both cases, the set of services is predefined. Such an approach is relatively simple but has two drawbacks. Foremost, it does notallow using commercial terminals of wireless networks not supporting LTE-MC, for example, LTE/WiFi smartphones. Using such terminals is of premium importance in the case of disasters as it allows for establishing communication with any user of the commercial mobile network via MCS without using dedicated MCS terminals. The second problem is the necessity of supporting by MCS the existing IoT or SCADA solutions that lost connectivity to their service platforms. Both mentioned solutions provide no such support nor support for programmability.
* The analysis of the concepts concerning the use of UAVs as flying communication nodes (or networks) with computing capabilities presented in Section 3. The UAV transportation and endurance capabilities enable to use them as can be used for quick provisioning of connectivity on a relatively large area.
* The analysed in Section 4 concepts that propose the use of UAV computing capabilities, typically marked as edge computing or MEC.
* Analysis of the two scenarios presented in Section 4 shows that, in some cases, MCS can be a part of a larger networking/computing solution or can be deployed on an island basis. The second case raises issues of management and orchestration of ad hoc deployed MCS.  
To solve some of the above-mentioned issues, the VMCS approach introduces the following novelties:  
* Uses a combined Fog/Edge/Cloud approach, i.e., exploits computing capabilities of all system nodes, including terminals. Due to Fog, the use of all system resources is maximised, and terminals can be dynamically adapted to service needs (there is no need to make dedicated terminals, commercial smartphones can be a part of the system).
* Uses distributed, combined Fog/Edge/Cloud orchestration with redundancy that is needed in an unreliable, distributed computing environment.
* Exploits the mobility of UAVs as a part of the orchestration.
## 4 Experiments and Results
### Performance Evaluation  
To evaluate the performance of the proposed method against the state-of-the-art algorithms, the same experimental protocol used in the corresponding works is adopted to conduct an impartial comparison. More specifically, the image classification rate at the image level is estimated as:  
\[\textit{Image Classification Rate}=\frac{N_{correct}}{N_{all}} \tag{13}\]  
where \(N_{correct}\) denotes the number of images that are correctly classified at each magnification factor and \(N_{all}\) denotes the total number of cancer images in the test set.  
In addition, the model's performance when the training data set is unbalanced was evaluated using precision, recall, and F-Score. The metrics' formulations are explained as follows:  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Magnification** & **Benign** & **Malignant** & **Total** \\ \hline
40\(\times\) & 625 & 1370 & 1995 \\
100\(\times\) & 644 & 1437 & 2081 \\
200\(\times\) & 623 & 1390 & 2013 \\
400\(\times\) & 588 & 1232 & 1820 \\ \hline Total & 2480 & 5429 & 7909 \\ \hline Patients & 24 & 58 & 82 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Image distributions by their magnification factors and classes.  
Figure 8: Slides of malignant breast tumors at different magnification factors: (**a**) 40\(\times\), (**b**) 100\(\times\), (**c**) 200\(\times\), and (**d**) 400\(\times\). The green rectangle is an interesting area that is detailed in slide obtained at the next magnification factor.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Methods** & \begin{tabular}{c} **Size of the** \\ **Input Layer** \\ \end{tabular} & \
\begin{tabular}{c} **No. and Sizes of** \\ **Fully Connected Layers** \\ \end{tabular} &
\begin{tabular}{c} **Size of the** \\ **Output Layer** \\ \end{tabular} \\ \hline Proposed method 1 & 12,288 & 1:64 & 2 \\ Proposed method 2 & 12,288 & 2:64, 64 & 2 \\ Proposed method 3 & 12,288 & 3:64, 64, 32 & 2 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Three configurations of the FCNN classifiers that are used in the proposed methods.  
\[Precision=\frac{true\ positive}{true\ positive+false\ positive} \tag{14}\]  
\[Recall=\frac{true\ positive}{true\ positive+false\ negative} \tag{15}\]  
\[F-Score=\frac{2*Precision*Recall}{Precision+Recall} \tag{16}\]  
where the number of malignant samples that were successfully identified as malignant is represented by \(true\ positive\), and the number of benign samples that were correctly identified as benign is represented by \(true\ negative\). Additionally, the \(false\ positive\) and \(false\ negative\) values show the number of benign samples that were mistakenly categorized as malignant and malignant samples that were mistakenly classed as benign, respectively.
# A Durability Prediction Method for Historical Square Rebar Reinforced Concrete Buildings
## 5 Applications of the Proposed Method
### The Reliability and Applicability of the Proposed Method
#### 5.4.3 The Realistic Evaluations of the Two Cases  
The authors conducted durability studies and structural analyses of these two buildings. In the light of the data obtained from the studies, the special conservation designs were presented. The main research results and engineering measures are as follows.  
The rest durability prediction of the Songfengge Building was more than 10 years. All of the concrete beam and column components of the structure were in a good condition, without an obvious durability problem. Except for some paint falling and local tile damage, there was no visible cracking on RC members to the building. Therefore, the authors presented a daily maintenance design for this building.  
The service time of the Main Hall of the Tomb of Yu had exceeded the durability prediction life of 2 years. Some concrete slabs showed signs of water seepage and rebar exposure. Long corrosion induced cracks and some concrete beam rebar were exposed in some beams and columns, including major important components and a critical component. The durability status of the building was in a poor condition. The authors presented the urgent conservation design for this building, and the slabs, beams, and columns were repaired or strengthened according to their different necessities.  
According to the realistic engineering experience, it is thought that the durability prediction method proposed in this study can meet the accuracy and safety requirements of durability evaluation for engineering practice and application.
## 3 Circuit Analysis and Operation Modes
### Operation Mode III (\(t_{2}-t_{3}\))  
The equivalent circuit of Mode III is shown in Figure 10c. This mode starts when the current \(i_{LPFC}\) decreases to zero. In this mode, \(S_{1}\) keeps conducting. The resonant current continually supplies energy via the center-tapped transformer to the output.
# Multi-Objective BiLevel Optimization by Bayesian Optimization
## 2 Preliminaries
EHVI is an updated version of expected improvement (EI) to HVI and determined by \(J(\mathbf{x})=\mathbb{E}_{p(f(x)|D_{u})}[HVI(f(\mathbf{x}))]\). Previous work has considered unconstrained sequential optimization with ParEGO [15]. ParEGO is often optimized with gradient-free methods. qParEGO supports parallel and constrained optimization [17], and exact gradients are computed via auto-differentiation for acquisition optimization. This approach enables the use of sequential greedy optimization of \(q\) candidates with proper integration over the posterior at the pending points. Multi-objective bilevel optimization problems have two levels of multi-objective optimization, such that a feasible solution to the upper-level problem must be a member of the Pareto-optimal set of lower-level optimization problems. Each level has its own variables, objectives and constraints. For the given upper-level vector \(\mathbf{x}_{u}\) the evaluation of the upper-level function is valid _only if_ the \(\mathbf{x}_{l}\) is an optimum of the lower-level problem. A general multi-objective bilevel optimization problem can be described as follows:  
\[\underset{\mathbf{x}_{u}\in\mathcal{X}_{u}\mathbf{x}_{l}\in \mathcal{X}_{l}}{\text{minimize}}\left\{F_{1}(\mathbf{x}_{u},\mathbf{x}_{l}),...,F_{M_{u}}(\mathbf{x}_{u},\mathbf{x}_{l})\right\}\] (1) subject to \[\mathbf{x}_{l}\in\underset{\mathbf{x}_{l}}{\text{argmin}}\left\{f_{1}( \mathbf{x}_{u},\mathbf{x}_{l}),...,f_{M_{u}}(\mathbf{x}_{u},\mathbf{x}_{l}); \,g_{j}(\mathbf{x}_{u},\mathbf{x}_{l})\leq 0,\,\,j=1,2,\ldots,J\right\}\] \[G_{k}(\mathbf{x}_{u},\mathbf{x}_{l})\leq 0,\,\,k=1,2,\ldots,K.\]  
In Equation (1) the upper-level objective functions are \(F_{i}(\mathbf{x}_{u},\mathbf{x}_{l})\), \(i=1,2,\ldots,M_{u}\) and the lower-level objective functions are \(f_{i}(\mathbf{x}_{u},\mathbf{x}_{l})\), \(i=1,2,\ldots,M_{l}\), where \(\mathbf{x}_{u}\in\mathbb{X}_{u}\) and \(\mathbf{x}_{l}\in\mathbb{X}_{l}\). \(G_{k}(\mathbf{x}_{u},\mathbf{x}_{l})\) and \(g_{j}(\mathbf{x}_{u},\mathbf{x}_{l})\) represent upper- and lower-level constraints, respectively. \(j\) and \(K\) values represent the number of constraints at the upper- and lower-level. The lower-level optimization problem is optimized with respect to \(\mathbf{x}_{l}\) considering \(\mathbf{x}_{u}\) as a fixed parameter.
# A Serious Gaming Approach for Crowdsensing in Urban Water Infrastructure with Blockchain Support
## 2 Related Work
### Serious Gaming for Water Resource Management
Another example of a SG is presented in [77] and consists in the development of ways to adapt to climate change on the rivers in the Netherlands. The aim of the game is to manage a virtual river, consisting of several players who are part of two different teams. The parties involved are either Dutch water managers or academics. Consensus also plays a key role in decision-making involving the stakeholders and several areas of activity. [PERSON] and [PERSON] [78] present a serious multiplayer game approach targeting farmers and water issues that may arise, which allows cooperation between players to observe differences between the collaborative and the individual behavior. [PERSON] et al. [79] present the game WATERSTORY, which is based on an interactive learning environment (ILE) offering the parties involved the opportunity to raise awareness and exchange knowledge in the process of managing water demand on the island of Maui. The authors consider that this approach can achieve a consensus on how to manage water resources, with a minimum of conflicts and a maximum of objectives achieved, taking into account the rules imposed by the authorities. The SimDelta Program is an extension of the Dutch Delta Program [80], being an interactive online simulation model based on policy development for freshwater supply and to avoid flooding in the Rhine-Meuse delta. However, [PERSON] and [PERSON] [81] conclude that SimDelta is not really a serious game, although it fulfills some of the criteria, because users cannot interact with each other, but only with the experts who present the solution. Another interesting approach to serious games is presented by [PERSON] et al. [82], where the authors used the game to evaluate the decisions made in case of a drought. Participants were chosen from different fields, such as students, graduates and stakeholders in the water domain. The presented research emphasizes the fact that serious games have been and are involved in the water sector, being an important tool in the understanding by third parties of socio-technical-economic problems in terms of water resource management. Lately, the integration of games with other complex systems has become mandatory for good awareness, cooperation and optimal results, while gamification in MCS for urban water reporting, such as defined in this paper, is not commonly found in related studies.
# Iterative MLFMA-MADBT Technique for Analysis of Antenna Mounted on Large Platforms
## 3 Formulation
### MADBT Method
Therefore, the MADBT method is insensitive to the mesh size. Thus, the platform can be modeled with extremely coarse mesh if no loss of geometry information of the platform. It can be further concluded that the iterative MLFMA-MADBT method is mesh-independent while keeping high accuracy.
## 6 Results and Discussion
### Training Data Size vs. Recall  
As shown in Figure 5 and Table 2, the influence of training data size on recall performance is significantly higher in the RPL IDS. The FT-CID achieves higher recall performance for all three training data sizes in the federated setup (40%, 60%, and 80%) and for 20 communication rounds in the AMI heterogeneous IIoT network. MLP is ranked second, and LR recall is third--7.45 percentage points lower than the MLP and 27.03 percentage points lower than the FT-CID. The proposed FT-CID model improves intrusion detection performance, particularly in small training samples. Figure 5 shows that the recall obtained by the proposed FT-CID model is better than that obtained by the MLP and LR models for IIoT dataset for all the training data sizes. According to modelling of the federated environment for heterogeneous IIoT applications, the performance of the FT-CID is better than that of the ML algorithms on the MLP and LR on the IIoT dataset. Moreover, the FT-CID yields 23.7 and 27.9 percentage-points higher recall than the baseline MLP and LR models, respectively, with 60%  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Training Data Size (\%)**} & \multicolumn{3}{c}{**Precision (\%)**} \\ \cline{2-4}  & **Proposed FT-CID** & **CNN** & **MLP** \\ \hline
**40** & 82.97 & 68.64 & 51.25 \\ \hline
**60** & 84.32 & 75.63 & 60.83 \\ \hline
**80** & 85.18 & 78.92 & 71.09 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparative experimental precision results.  
Figure 4: Precision performance for FT-CID, CNN, and MLP.  
of training data due to the advantage of gaining knowledge from other IIoT datasets, as well as the source dataset, of the attack patterns during global model training and updating.
## 2 Wave Energy and Converting Devices  
The wind's action produces the waves and, therefore, is an indirect form of solar energy, but can also be created through landslides, earthquakes, the gravitational attraction of the sun and the moon, or any disturbance on the water surface ([PERSON], 2018). A wave can be characterized by different parameters, such as the period (\(T\)), which represents the time required for two successive crests (or troughs) to pass a certain point; the height (\(H\)), which is the vertical distance between the crest and the wave trough; and the depth (\(h\)) in which they propagate. These characteristics can be seen in Figure 2([PERSON], 2018).  
Figure 2: Wave characterization (adapted from [PERSON] and Darlymple ([PERSON], 2018)).  
Other parameters that characterize the wave can also be theoretically determined from those already mentioned. The first is the wavelength (\(\lambda\)), being determined as the distance between two successive crests (or troughs). Another is the free surface elevation (\(\eta\)), which is the free surface position in relation to its average level and is defined by Equation (1) [32], and the last parameter is the wave amplitude (\(A\)), representing the maximum elevation compared to the average water level.  
\[\eta(x,t)=A\cos(kx-\omega t)+\frac{A^{2}k\cosh(kh)}{4\text{sinh}^{3}(kh)}[2+ \cosh(2 kh)]\cos 2(kx-\omega t) \tag{1}\]  
where \(h\) is the depth (m), \(\omega\) is the frequency (rad/s), \(t\) is the time (s), \(k\) is the wave number (m\({}^{-1}\)), \(x\) is the coordinate representing the main direction (m), and \(A\) is the wave amplitude (m).  
The wave power is proportional to the square of its amplitude and period, calculated per wavefront meter [5]. The south and southeast regions are subject to more energetic waves associated with the cold fronts at certain times of the year, totaling 22 GW of wave power [33]. The southern region of the State of Rio Grande do Sul stands out as having the most significant energy potential of the entire Brazilian coast in wave energy, with an available power that can reach 40 kW/m [18].
## 4 Mathematical Modeling and Analysis
### Simulation Results  
To evaluate the performance of the proposed scheme, we consider three scenarios:  
1. Scenario 1: Different channel models
2. Scenario 2: Different transmission powers
3. Scenario 3: Different traffic loads  
Figures 6 and 7 depict these scenarios. In Figure 6, there are two significant interferers to ABS mode coverage like DUE-Tx and CUE. In this case, the interference from CUE can be suppressed through the FFR-ABS scheme, but interference from DUE-Tx cannot be avoided. We simulate this model with various transmission powers and channel models. Figure 7 describes a model in which all of the DUEs and CUEs are randomly distributed. In this model, we compare throughput for each node with different traffic loads.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Assumption/Value** \\ \hline Cellular layout & Hexagonal grid, 7 cells sites \\ Path Loss Model (D2D-NLOS 100\%) & \(24.82+35.31\times log_{10}d\) \\ Path Loss Model (D2D-NLOS 80\%) & \(28+40\times log_{10}d\) \\ Path Loss Model (CUE-NLOS 100\%) & \(31.25+33.76\times log_{10}d\) \\ Path Loss Model (CUE-NLOS 80\%) & \(30.35+36.7\times log_{10}d\) \\ CUE transmit power & 24 dBm \\ DUE transmit power & 20 dBm \\ Noise power density & \(-174\) dBm \\ Inter-site distance (radius of the cell) & 500 m \\ Carrier frequency & 2 GHz \\ Bandwidth & 10 MHz \\ Number CUEs & 10 \\ Number of DUE & 20 \\ ABS pattern period & 10 ms \\ Distance between D2D & 20–60 m \\ Traffic patterns & Full-buffered \\ Monte Carlo number & 10,000 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Computational parameters.  
Figure 6: Scenarios 1 and 2: Isolated D2D user equipment (DUE)-pairs and cellular user equipment (CUE) model.
# NMR Analysis of Pulegone in Food Products
## 4 Conclusions  
Essential oils from trade could be measured directly after dissolving in MeOD/CDCl\({}_{3}\) at a ratio of 1:1 (_v/v_) without any complex sample preparation. In the case of foodstuffs, pulegone had to be extracted beforehand. Hydrodistillation according to ISO 6571 [28] is more effective than ultrasonic-assisted extraction.  
The linearity of the concentration determination of pulegone was ensured for all matrices. The accuracy and selectivity of the NMR method was confirmed for the determination of pulegone in Chinese mint, corn mint, and muna oil and for the extracts (distillates) of spearmint and pennyroyal herbs. If carvone is present (e.g., in spearmint samples), its signals overlay of the pulegone signals and impedes quantification. The pulegone contents measured in this study were mostly consistent with the literature values. The manufacturer's consumption recommendations do not exceed the TDI. Therefore, the consumption of peppermint oil and corn mint oil is assumed to be not toxicologically relevant. The use of pennryroyal and spearmint as seasonings also poses no health risks to humans. Based on the available data, it can be assumed that NMR is a suitable method for determining pulegone in food.  
Conceptualization, D.W.L.; methodology, D.W.L. and T.K.; validation, Y.Y.; formal analysis, Y.Y.; investigation, Y.Y.; resources, T.K.; data curation, Y.Y.; writing--original draft preparation, Y.Y.; writing--review and editing, D.W.L., and T.K.; visualization, Y.Y.; supervision, D.W.L. and T.K.; project administration, D.W.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors sincerely thank the departments of nuclear magnetic resonance and plant-based foods at CVUA Karlsruhe for their invaluable assistance and support.  
The authors declare no conflicts of interest.
## 4 Results and Discussion  
### Symmetrical Deflection of Both Cascades  
The impact of the thrust control configuration on the aircraft's thrust, lift, and pitch moment was measured under different angles of symmetrical deflection of the thrust control plates. The experimental results for the lift coefficient, as depicted in Figure 14, indicate that, during the symmetrical deflection of the thrust control plates, the additional lift coefficient remained above zero and exhibited an increasing trend as a function of the deflection angle. The increase in lift was due to the fact that the symmetrical deflection of the upper and lower cascades effectively formed a contracting flow channel along the direction of the airflow. This contraction caused the airflow to accelerate within the channel, thereby increasing the inlet airflow velocity to the fan. As a result, the additional lift generated by the BLI fans, due to the suction of the boundary-layer air, was enhanced. However, past a \(50^{\circ}\) deflection angle, there was a noticeable reduction in additional lift due to the airflow separation or stall-like conditions induced by the excessive deflection.  
Figure 15 outlines the behavior of the pitch moment with varying deflection angles. Initially, within a deflection range from 0 to \(20^{\circ}\), the pitch moment remained minimal, suggesting a balanced aerodynamic force distribution along the aircraft's center of gravity. As the deflection angle increased beyond this range, the pitch moment increased significantly, reaching a peak when the thrust control plates were fully deflected. This can be explained by the forward shift in the center of pressure due to the deflected plates, creating a larger arm to the center of gravity and, hence, a higher pitch moment.  
As shown in Figure 16, the thrust coefficient decreased progressively with an increase in the deflection angle, approaching a minimum when the plates were fully closed. This trend is consistent with the aerodynamic principle that, as the deflection of the control plates increases, the effective exit area for the jet decreases, which, in turn, reduces the jet velocity and the resulting thrust due to the conservation of the mass flow rate through the propulsors.
# Mechanical Assembly Monitoring Method Based on Semi-Supervised Semantic Segmentation
## 2 Related Work
### Fully Supervised Semantic Segmentation  
Fully supervised learning uses labeled datasets to train models. The fully supervised semantic segmentation method uses accurately labeled datasets for network training. The labeled dataset can enhance detailed information and local features, which helps to enhance the precision of the semantic segmentation network. [PERSON] et al. [12] are the first to present a fully convolutional network (FCN) by using convolution instead of full connection to realize image segmentation. FCN directly upsamples the image by a factor of 8, leading to rough segmentation results. For the problem of FCN, [PERSON] et al. [13] proposed a U-Net network, which is a U-shaped structure based on an encoder-decoder. The U-Net network uses multilayer skip connections and upsampling to improve the segmentation effect. To solve the problems of FCN, the DeepLab series takes another approach. In DeepLabv2, to fuse multiscale features, [PERSON] et al. [14] first proposed an atrous spatial pyramid pooling (ASPP) technique. Then, DeepLabv3 [15] and DeepLabv3 plus [16] improved the method of ASPP and achieved higher segmentation accuracy. Supervised semantic segmentation demands a mass of labeled data to achieve high semantic segmentation accuracy, but data labeling is very difficult. Therefore, this method is not suitable for industrial applications.