esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
It not only presents a strong global search ability but, more importantly, the core control is less complicated, and the code is easy to implement. Based on these advantages, the MVO algorithm and its variants have been successfully applied to many engineering applications [31; 32; 33; 34]. Although MVO performance is superior, there are still some shortcomings. First, the control parameter that increases linearly with the number of iterations, wormhole existence probability (WEP), slows the evolution of the population in the early stage of the search, resulting in poor convergence of the algorithm. Second, the control parameter, travel distance rate (TDR), does not consider the current quality of each individual, which makes it difficult to balance the global search ability and local search ability of the algorithm. Finally, the variation strategy based on wormholes is difficult to further improve the quality of offspring individuals, resulting in a population convergence to global optimality. In this paper, an improved MVO algorithm named Dynamic Leader Multi-Verge Optimizer (DLMVO), was designed for the first time to effectively estimate the parameters of the PV model. In DLMVO, three improvements are proposed to overcome the disadvantage of the original MVO. First, the adaptive strategy of wormhole existence probability (_WEP_) based on a Gauss distribution is proposed to help losers find more promising regions further. Second, a new adaptive strategy for updating travel distance rate (TDR) is adopted to balance exploration and exploitation, preventing the search from falling into local optima. Third, we propose a dynamic leader search strategy to improve the probability of producing better solutions and speed up the convergence rate. The main contributions of this paper are summarized as follows:  
1. Dynamic Leader Multi-Verge Optimizer (DLMVO) was proposed by improving MVO. In DLMVO, three improved strategies were implemented to remedy the disadvantages of MVO. 2. The performance of the DLMVO algorithm was verified by applying it to parameter extraction of different PV models. 3. The compared and statistical experimental results presented the feasibility and effectiveness of the DLMVO in identifying parameters of different PV models. The remainder of this paper is organized as follows: Section 2 is the PV models and problem formulation. The fundamentals of the MVO algorithm will be introduced in Section 3. Section 4 presents the proposed DLMVO algorithm in detail. Section 5 shows the experiment results and analysis. Conclusions are presented in Section 6.
## 5 Average Secrecy Capacity Analysis  
The Average Secrecy Capacity (ASC) is a critical metric for assessing the active-eavesdropping's security performance and can be expressed as follows:  
\[\overline{C_{S}}=\int_{0}^{\infty}\frac{F_{S_{G}S_{E}}(\gamma)}{1+\gamma} \bigl{[}1-F_{S_{G}RD}(\gamma)\bigr{]}d\gamma \tag{23}\]  
Substituting (13) and (20) into (23) and after a few mathematical simplifications described above, we obtain\[\begin{split}&\overline{C_{S}}=\sum\limits_{j=1}^{n}H_{j}Y_{j}^{0.5} \exp\left(Y_{j}\right)\frac{1}{1+Y_{j}}\frac{M}{\Omega_{Sj}E}\sum\limits_{h=0}^ {M-1}C_{M-1}^{h}(-1)^{h}\\ &\times\left(\frac{\Omega_{Sj}E}{\hbar+1}-\exp\left(\frac{-Y_{j}} {\Omega_{S}E}\right)-\frac{1}{\frac{Y_{j}}{\Omega_{S}E}}\right)\exp\left(\frac {-Y_{j}}{\Omega_{S}E}\right)\frac{\xi_{\text{mod}}^{2}}{\text{r}(a)\text{r}(a )\text{r}(1)}H_{3,3}^{3,1}\left[\frac{a\rho\xi_{j}^{1}}{(b-1)\mu_{r}^{2}}\right] \quad(1-b,1),\left(\xi_{\text{mod}}^{2}+1,1\right),\left(1,\frac{1}{r}\right) \quad\right]\end{split} \tag{24}\]
## 3 Results and Discussion
### Effectiveness of Forecasting the Changes of Depth in Convergence Areas
We found that the depth distribution of the convergence area in the model and the actual condition were in good agreement with the forecasted curve with only a little difference. were different in different periods when the SD was fixed, the depth of the convergence area is related to that of the SOFAR axis, the difference between them, as well as the SD. The forecasting results using the melt function based on the melt parameter \(a\) (which is related to the width and location of the frontal zone) were in good agreement with the model and the actual results, which proves the effectiveness of this method. By calculating the forecasting results of other sound source depths together (Table 5), it was found that when the SD was shallow, the forecasting results were in good agreement with the actual results, but the degree of agreement was relatively poor at a deeper launch depth (SD = 500 m) because SD's closeness to the SOFAR axis makes most of the sound energy converge near the SOFAR axis and propagates forward. It is impossible to form an obvious convergence area, which can not be detected during some periods. After calculation, the RMSE between the forecasting results and model calculation results in the first detection convergence area was 74.8 m, and the second detection convergence area was 45.0 m; The RMSE between the forecasting results and the actual calculation results in the first detection convergence area was 82.3 m, and the second detection convergence area was 43.3 m. It shows that the RMSE of the second detection convergence area was smaller and the effectiveness of forecasting was better. Figure 15: The forecasting curve of the depth of the convergence area and the calculating results of the model and actual ocean front (SD = 100 m): (**a**) 1 February; (**b**) 1 July; (**c**) 1 August. The forecasting results using the melt function based on the melt parameter \(a\) (which is related to the width and location of the frontal zone) were in good agreement with the model and the actual results, which proves the effectiveness of this method. It proves that the melt function was applied to effectively achieve the depth forecast of the convergence area in the ocean front environment.
## 1 Introduction
Geometric transformation is usually applied for image data augmentation. However, there are limitations to augmenting the training data via geometric transformation, such as the linearization and standardization of data. The overall research objective is to recognize and classify ten components for picking, placing, and assembling work. However, this yields data deficiency when training the deep learning network to recognize and classify components because these components are uncommon, as previously mentioned. Therefore, we propose a method for effectively improving the object classification accuracy of the deep learning network in the case of a small dataset in various manufacturing domains. To accomplish the goal of object recognition, even if there are few images, we focus on a method generating the massive training data from image data augmentation methods. Our study makes three main contributions:  
* the extraction of the target object from the background while maintaining the original image ratio;
* color perturbation based on the predefined similarity between the original and generated images because of the decision of the reasonable perturbation range;
* data augmentation by combining color perturbation and geometric transformation. We eliminate the background, extract the target object, generate the image by tuning pixel values, and transform to implement this concept. Mainly, the background is eliminated by considering the characteristics of the CIE L*a*b* color space. Besides, an object in an image is extracted such that it is proportional to the ratio of the object in the original image. Next, we change the image by determining the reasonable range for color perturbation and tuning the pixel values based on color space, following which, we combine the tuned images with the geometric transformations. This is unlike the traditional method, which arbitrarily adds the pixel values to the RGB color space. Subsequently, we train the pre-trained network by using the generated images. This step is called transfer learning. To verify the proposed method, we compare and analyze the experiments with one another in terms of the color perturbation of three color spaces and two geometric transformations. We compare the classification result of the trained networks by using test images. The rest of this paper is organized as follows. In Section 2, we introduce works related to data augmentation, color spaces, similarity calculation, and transfer learning. In Section 3, we discuss the performance of the proposed method that applies deep learning to perform object recognition on a small dataset. In Section 4, we present the experimental results of the proposed method. Finally, in Section 5, we provide the conclusions.
## 1 Introduction
### Control Plane  
The control plane is liable for concentrating the control of the whole progression of data that circles by the data or the foundation layer. This comprises the approaches of sending or redirecting information, stream boards, and has the general point of view of the whole system facilitated in a  
Figure 1: Architecture, modes, controllers, and applications of software defined networking (SDN).  
SDN-based controller [4]. As shown in Figure 2, there is the API southbound interface (SBI), for example, the OpenFlow convention, which permits the controller like open sunlight, or the VMware NSX (e.g., network virtualization and security platform) to forward the arrangement of approaches and setups to every one of the gadgets that build up the information plane. The APIs are of vital significance for exacting partition of the elements of information and the control planes [1]. There are additionally northbound interface (NBI) APIs, for example, restful API [5] or the software defined mobile networks (SDMN) API [1], that characterize a crucial spot in the foundation to intervene among the global applications approaches and system strategies, permitting application planes to correspond with the control plane [1].
## 5 Discussion of the Results  
This article studies the \(2\)-coverage of a polygonal region \(R\). First it is shown how to completely cover \(R\) using a set of \(n\) antennas with minimum transmission range and then how to minimise the antennas' range so that a \(2\)-path between two points exists on \(R\). The first problem is solved using an algorithm that runs in \(\mathcal{O}(mn)\) time and space, \(m\) being the number of vertices of \(R\). However, this complexity is lowered to \(\mathcal{O}(m+n\log n)\) time and \(\mathcal{O}(m+n)\) space if the region is convex. With this in mind, the next problem was solved using a decomposition of \(R\) in convex pieces. The minimisation of the antennas' range to assure the existence of a \(2\)-path between two points on \(R\) proved to be harder to solve and so the first approach was directed at the decision problem. Therefore, if the antennas' power transmission range is \(r\in\mathbb{R}^{+}\) then decide if there is a \(2\)-path between two points on \(R\) takes \(\mathcal{O}(k\times nM)\) time (\(k\) is the number of rays added to decompose \(R\) in convex pieces and \(M\) the complexity of the convex piece with more vertices). Using the decision algorithm together with a binary search finally solves the main problem in \(\mathcal{O}(knM\log mn)\) time. The binary search is performed on a set of ranges which are needed to \(2\)-cover particular points found on \(R\) and \(\mathrm{VD}_{2}(S)\).  
Although it is assumed throughout the paper that the point defining the minimum range to \(2\)-cover a particular object is unique, this is not true in degenerate cases. The antennas' distribution might result in a scheme where there can be more than one of these points. However, bottleneck-points can be found using the same technique and they still fall into only two types. The classification of bottleneck-points does not change in the presence of degenerate cases.  
Although the antennas' range is optimised to assure the existence of a \(2\)-path on \(R\), it is never shown how to construct such path. Future work obviously passes through explaining how to construct a \(2\)-path between two points and even how to construct the shortest \(2\)-path between those points. It is not clear how to do this efficiently because the space where the \(2\)-path exists has a shape that is uncommon, some edges are arcs, others are line segments.
## 3 Results
### Differences between the GPS and Galileo Solutions
We received almost the same results for the IGS-type mean models (E3-G2), where the mean difference was 2.2 mm for PPP and 0.4 mm for DD (Figure 9). For individual stations, the differences varied from \(-\)11.2 mm to 16.6 mm. Importantly, the differences between approaches were similar for the compared pairs of solutions. We also observed a consistent effect when changing the source of G02 corrections from chamber calibrations to the IGS-type mean models between both approaches. As indicated earlier, using GPS-based correction caused a negative bias in the height (Figure 4). Here, we observed a positive inter-system GPS-Galileo bias in height (Figure 8). The overlapping of these two errors compensated for one another, as a result of which the E2 and E3 solutions were more consistent with the GPS solutions (Figures 8 and 9). This has already been confirmed by the IGS [39; 40] in a study of the inclusion of Galileo observations in analyses.
## 3 System Model
### Network Model
#### 3.1.1 Trusted/Untrusted Relays  
Relay nodes are categorized into two groups, namely, trusted and untrusted. Trusted relays implement stringent security measures, including robust cybersecurity defenses, such as advanced quantum-resistant firewalls and access control mechanisms (i.e., memory encryption), to prevent illegal access and unauthorized use of sensitive resources. They are typically deployed in highly secure environments to ensure network integrity.  
In contrast, untrusted relays have lower security guarantees, making them susceptible to interception by eavesdroppers. Their security measures are less robust, potentially rendering them vulnerable to unauthorized access and compromise. Recent studies have explored varying trust levels in QKD networks, introducing classifications such as full access trust (FAT), partial access trust (PAT), and no access trust (NAT), each defining the degree of trust required for relays within the key management system [35].  
For example, the trusted QKD relay network includes quantum nodes and quantum links, which continuously generate quantum keys for neighboring nodes. Multihop technology is employed for secure end-to-end key relays when remote communication is needed. The SDN controller assumes a central role in QKD decision-making, effectively separating network control from QKD equipment and facilitating flexible network management [23; 36]. The architecture is organized into four layers, as illustrated in Figure 2.  
This layer is composed of applications running on user terminals and ensures secure communication between applications using the keys provided by the key management layer. The control center formulates suitable strategies based on varying application key requests.
###### Abstract  
In the last few years, the application of adhesive joints has grown significantly. Adhesive joints are often affected by a specific type of defect known as weak adhesion, which can only be effectively detected through destructive tests. In this paper, we propose nondestructive testing techniques to detect weak adhesion. These are based on Lamb wave (LW) data and artificial intelligence algorithms. A dataset consisting of simulated LW time series extracted from single-lap joints (SLJs) subjected to multiple levels of weak adhesion was generated. The raw time series were pre-processed to avoid numerical saturation and to remove outliers. The processed data were then used as the input to different artificial intelligence algorithms, namely feedforward neural networks (FNNs), long short-term memory (LSTM) networks, gated recurrent unit (GRU) networks, and convolutional neural networks (CNNs), for their training and testing. The results showed that all algorithms were capable of detecting up to 20 different levels of weak adhesion in SLJs, with an overall accuracy between 97% and 99%. Regarding the training time, the FNN emerged as the most-appropriate. On the other hand, the GRU showed overall faster learning, being able to converge in less than 50 epochs. Therefore, the FNN and GRU presented the best accuracy and had relatively acceptable convergence times, making them the most-suitable choices. The proposed approach constitutes a new framework allowing the creation of standardized data and optimal algorithm selection for further work on nondestructive damage detection and localization in adhesive joints.  
artificial intelligence; adhesive joints; machine learning; Lamb wave +
Footnote †: journal: Medical Imaging  
1 Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal 22 INEGI--Institute of Science and Innovation in Mechanical and Industrial Engineering, 4200-465 Porto, Portugal 2
# Deep-Learning Based Algorithm for Detecting Targets in Infrared Images
## 4 CMF-3 DLSTM
### Context Information Extraction Network  
Figure 6 shows a 3D long- and short-term memory network, which can extract context information. The 3 DLSTM network firstly transforms the 3D feature image into the 2D feature image. Then, each row in the two-dimensional feature graph is regarded as a vector or a sequence, and each column in the two-dimensional feature graph is considered to be a time step. Finally, the context information of feature map extraction is transformed into the extraction of vector or sequence relations.  
We fix the feature map's length, width, and height separately and stretch the other two directions. In this way, the shape of the 3D feature map can be transformed into a 2D feature map. This two-bit information is then input into Bi-LSTM to extract contextual information along the fixed direction of the original feature map. Taking the length direction of the fixed feature map as an example, the 3D feature map becomes a 2D feature map (length, width \(\times\) height) after transformation, and the specific transformation process is shown in Figure 7.  
Finally, the features generated are connected. At this time, the feature graph input to the classification regression network contains the context information extracted from the  
Figure 6: 3 DLSTM network structure diagram.  
Figure 7: 3D feature-length expansion diagram.  
length, width, and height of the original feature graph, so the network is called 3 DLSTM. Compared with RNN, LSTM, and BI-LSTM, which extract 2D context information, this network is more conducive to improving the performance of the target detection model.
## 2 Trajectory Design and Optimization  
This section describes the mathematical model used to analyze the motion of an interplanetary spacecraft propelled by an SFGIS. Specifically, the spacecraft motion is studied in a three-dimensional heliocentric mission scenario, in which the deep space vehicle moves between two assigned Keplerian orbits. The characteristics of the parking and target orbits are described in Section 3 along with the optimal spacecraft transfer trajectory, while the next subsection describes the SFGIS thrust model used to schematze the propulsive acceleration vector. The SFGIS thrust vector used in this paper is a simplified version of the mathematical model recently proposed by [PERSON] et al. [1]. The expression of the propulsive acceleration vector is then used in Section 2.2 to describe the heliocentric dynamics of the SFGIS-propelled spacecraft using a set of non-singular orbital elements [7] proposed by [PERSON] et al. [8], i.e., the modified equinoctial orbital elements (MEOEs). In this regard, in order to simplify and speed up the discussion, details regarding the transition between classical and non-singular orbital elements are here omitted, while the interested reader can refer to the recent literature by the authors [9] for a more in-depth discussion of the use of MEOEs in describing the heliocentric dynamics of a spacecraft equipped with a propellantless propulsion system as the (photonic) solar sail or the Electric Solar Wind Sail [10; 11; 12].
# The Traffic Adaptive Data Dissemination (TrAD) Protocol for both Urban and Highway Scenarios
## 3 Traffic Adaptive Data Dissemination
### Broadcast Suppression Technique
#### 3.1.2 Traffic Adaptive Sorting Technique
For instance, we assume that the utility \(U_{TX}\) of vehicles is proportional to its ID number in Figure 4, e.g., \(U_{N2}>U_{N1}\), so after the first round of sorting, the priority list is {_N2_, _N4_, _N6_, _N8_, _N10_, _N12_}. In the second round, all neighbors are sorted and the final priority list is {_N2_, _N4_, _N6_, _N8_, _N10_, _N12_, _N1_, _N3_, _N5_, _N7_, _N9_, _N11_}. Figure 3: The projection of CBR on the weight \(\omega_{\text{CBR}}\) and channel status. The sender S inserts the priority list into the header of the data message before transmitting. The header format is < _Data ID_, _Originator ID_, _Sender ID_, _Originator Position_, _Sender Position_, _Priority List_ >. When the recipients receive a data message from \(S\), they extract the priority list and seek out their rank \(R\in[0,\ n-1]\), where n is the number of neighbors. TrAD gives one time interval \(ti\) between the former transmission and the latter one. \(ti\) includes the total time taken from the transmitter to the recipient, such as medium access delay, propagation delay and transmission delay. The delay (back-off time) of transmissions \(Delay_{TX}\) is obtained by using Equation (6):  
\[Delay_{TX}=ti\cdot R \tag{6}\]
## 2 Preliminaries and Problem Formulation
Lemma 4: _The error system in (3) is asymptotically stable with an \(H_{\infty}\) performance (5) if and only if there exist matrices \(P=P^{T}>\mathbf{0}\) and \(G\) such that_  
\[ \begin{bmatrix}P-G-G^{T}&\mathbf{0}&G^{T}A_{e}&G^{T}B_{e}\\ \star&-\mathbf{I}&C_{e}&D_{e}\\ \star&\star&-P&\mathbf{0}\\ \star&\star&\star&-\gamma^{2}\mathbf{I}\end{bmatrix} <\mathbf{0}. \tag{7}\]  
Proof: (Necessity) Letting \(G=G^{T}=P\), the condition (6) is expressed as (7) exactly. (Sufficiency) Assume that the condition (7) holds, and we can infer from (7) that \(G+G^{T}>P>\mathbf{0}\), which means that \(G\) is non-singular. Further, \((P-G)^{T}P^{-1}(P-G)\geq\mathbf{0}\) yields \(P-G-G^{T}\geq-G^{T}P^{-1}G\), and it is straightforward that (7) implies  
\[ \begin{bmatrix}-G^{T}P^{-1}G&\mathbf{0}&G^{T}A_{e}&G^{T}B_{e}\\ \star&-\mathbf{I}&C_{e}&D_{e}\\ \star&\star&-P&\mathbf{0}\\ \star&\star&\star&-\gamma^{2}\mathbf{I}\end{bmatrix} <\mathbf{0}. \tag{8}\]  
Pre- and post-multiplying (8) with \(\operatorname{diag}\{PG^{-T},\mathbf{I},\mathbf{I},\mathbf{I}\}\) and its transpose leads to (6). The proof is completed. Remark 1: _Lemma 3 establishes the necessary and sufficient condition ensuring that the error system (3) is stable under a given error bound. Note that (6) is hard to solve, for which there exist coupled terms between the Lyapunov variable \(P\) and \(A_{r},B_{r}\), and more significantly, both \(P\) and \(A_{r},B_{r}\) bear structural constraints. To address this issue, an equivalent condition (7) is developed. It is observed that (7) is easier to implement than (6) by eliminating the coupled terms between \(P\) and \(A_{r},B_{r}\), and thus facilitating parametrization on the positivity constraint._