esa-sceva/satcom-chunk-collection huggingface dataset:
# Automatic Breast Tumor Screening of Mammographic Images with Optimal Convolutional Neural Network
## 3 Experimental Results
### Multilayer CNN-Based Classifier Testing and Validation
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Fold** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score** \\ \hline
1 & 96.70 & 96.20 & 97.00 & 0.9640 \\
2 & 99.00 & 96.20 & 99.00 & 0.9760 \\
3 & 90.80 & 93.40 & 93.20 & 0.9210 \\
4 & 95.60 & 93.40 & 95.40 & 0.9230 \\
5 & 99.50 & 97.60 & 98.80 & 0.9860 \\
6 & 95.30 & 95.70 & 96.20 & 0.9550 \\
7 & 66.00 & 95.60 & 77.20 & 0.7800 \\
8 & 96.70 & 98.60 & 98.00 & 0.9770 \\
9 & 97.40 & 91.50 & 95.40 & 0.9440 \\
10 & 99.00 & 94.80 & 97.40 & 0.9690 \\ \hline Average & 93.60 & 95.30 & 94.60 & 0.9395 \\ \hline \hline \end{tabular}
\end{table} 
Table 14: Comparisons of average accuracy (%) for the CNN-based classifier with different convolutional windows in first to third convolutional layers. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Fold** & **Precision (\%)** & **Recall (\%)** & **Accuracy (\%)** & **F1 Score** \\ \hline
1 & 96.70 & 96.20 & 97.00 & 0.9640 \\
2 & 97.60 & 95.30 & 96.60 & 0.9570 \\
3 & 95.10 & 93.40 & 95.40 & 0.9420 \\
4 & 97.10 & 94.00 & 96.20 & 0.9540 \\
5 & 97.20 & 97.10 & 97.60 & 0.9680 \\
6 & 93.00 & 94.00 & 94.40 & 0.9340 \\
7 & 95.60 & 92.40 & 95.00 & 0.9400 \\
8 & 98.00 & 97.20 & 98.10 & 0.9760 \\
9 & 96.50 & 95.70 & 96.00 & 0.9610 \\
10 & 95.20 & 95.50 & 95.00 & 0.9540 \\ \hline Average & 96.30 & 95.04 & 95.93 & 0.9553 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Experimental results of K-fold cross-validation (\(K_{f}=10\)) for Model #1-3 with 16 Kernel convolutional windows and 16 maximum pooling windows in each convolutional-pooling layer. benign tumor (B), the center coordinates of the tumor are (1900, 317), the background tissue is D, and the circumscribed masses are labeled (CIRC) [33; 34]. In this study, using the automatic screening system developed [55], as per the pre-selected priority order of screening (1)-(6)), the sequence of ROI blocks is shown in Figure 8, and the automatic screening results show four TP (1),2),3), and (6)) and two TNs (4)and(5)). In this case, the large tumor spans four ROI blocks1-(3)and(6). Therefore, the screening results show TP for identifying a possible breast tumor, the reliability of the classifier output judged to be abnormal is larger than 0.50, and the abnormality is flagged by a red message. The screening system can be switched to manual mode. Similar to the automatic screening results, the four ROI blocks1-(3)and(6)can be manually circled, screenshotted, and stored in the queue in the order of manual screenshots. The classifier automatically performs image recognition in sequence, and the corresponding recognition results and messages are returned so that the clinician can confirm the possible tumor conditions.
## 2 Experimental Methods
### Experimental Procedures  
The carbon dioxide in the air and the bicarbonate in the seawater can react with the alkaline compounds in the pore solution, leading to the carbonation of the concrete [37; 38]. The real pH of the pore solution in a long-duration marine structure is always lower than 12. Therefore, the carbonated pore solution was used as the test solution. The test solution was first prepared as a pure saturated Ca(OH)\({}_{2}\) solution. Then, the pH of the solution was adjusted to 11 by adding NaHCO\({}_{3}\). In addition, NaCl was added into the solution to give a Cl\({}^{-}\) concentration of 0.2 mol/L. The multi-electrode sensor was firstly pre-passivated in the pure saturated Ca(OH)\({}_{2}\) solution for over 12 h. Following, they were transferred to the test solution. After the OCP of the electrodes became stable in the test solution, switches were kept closed. The coupled potential and the galvanic current were kept for continuous monitoring. The measurement frequency of the coupled potential and the galvanic current was 0.5 Hz. The OCP of the electrodes was measured every 24 h. The test period of each OCP measurement was only 1 min and it would not influence the corrosion processes on the electrodes. The test cell was placed in an air-conditioned room with the temperature fluctuation at 26 \(\pm\) 1 \({}^{\circ}\)C. The test solution was directly exposed to the air. After the sensor had been immersed in the test solution for nearly 60 h, the inhibitor NaNO\({}_{2}\) was introduced  
Figure 2: The measurement principle diagram of the coupled multi-electrode array sensor system.  
into the solution. According to the reference [24; 39], NaNO\({}_{2}\) would have a good inhibition effect in the pore solution when the concentration ratio of the NO\({}_{2}\)\({}^{-}\) and Cl\({}^{-}\) is above 0.3. Therefore, the ratio of the NO\({}_{2}\)\({}^{-}\) and Cl\({}^{-}\) is set as 0.5. Moreover, 0.1 mol/L NaNO\({}_{2}\) was introduced into the solution to make the ratio of the NO\({}_{2}\)\({}^{-}\) and Cl\({}^{-}\) was 0.5. Its inhibition effect on the corrosion of weldments in pore solution was studied by the sensor system. The pH of the test solution had a little decrease from 11 to 10.6 after the inhibitor was added for 40 h. After the inhibitor was added for 50 h, the multi-electrode array was taken out and the surface morphologies of the electrodes were observed by KEYENCE VH-8000 optical microscope.  
The corrosion coupon was also pre-passivated in the pure saturated Ca(OH)\({}_{2}\) solution for over 12 h. Then it was immersed into the test solution for 144 h. After the coupon was carried out from the test solution, the surface morphology of the coupon was shot by a digital camera and it was also observed by KEYENCE VH-8000 optical microscope.
## 3 Testing Methodology
### Dynamic Proof Tests
During both ILTs and BLTs, the same instrumentation and layout that were adopted for AVTs and displayed in Figure 5a were used. An additional AVT was also performed on the truck that was used for ILTs and BLTs, in order to determine the resonant frequencies of the truck in the three main orthogonal directions with the aim to check, and likely to exclude, any possible bridge-truck interaction due to the resonant frequency of the bridge and the truck very close to each other, which can induce unexpected dynamic amplifications [25,26]. The truck dynamics was measured through 3 PCB low-noise accelerometers that were positioned above the central axes of the chassis, at a height of about 1.50 m above the ground, with measurement directions in the longitudinal, transverse, and vertical directions of the truck (orthogonal to each other). At the end of the static and dynamic proof tests, namely the unloaded condition (U2), AVTs were also carried out on the foundation of the 4 pylons in order to investigate the compliance of the soil-foundation system (i.e., its translational and rotational stiffness), and to evaluate its relevance in view of the subsequent interpretation of the experimental results through the numerical model of the bridge. As shown in Figure 6, two sensor configurations were adopted for this purpose, one for the foundation of the south pylons (SP#) and the other one for the foundation of the north pylons (NP#). For each pylon basement, 3 sensors were positioned to measure the vertical accelerations, in order to identify the foundation rocking, while 1 sensor was positioned to measure the accelerations along the longitudinal direction of the bridge. Finally, only for the downstream pylons (SP1 and NP1), another sensor was positioned to measure the accelerations in the transverse direction. For both measurement configurations, 3 reference  
Figure 5: Dynamic proof test instrumentation and sensor arrays: (**a**) accelerometers layout for AVTs, ILTs, and BLTs, (**b**) pictures that are relevant to the adopted instrumentation, and (**c**) truck passing over a bump during ILTs. sensors (highlighted in Figure 6) were placed on the deck in the same positions that were adopted for the vibration measurements during the proof test. The use of reference sensors was needed to combine the measurements on pylons and on the deck, by suitably scaling the amplitudes of the frequency contents according to well-known methods that are available in the literature [27].
## 2 Modeling of the Problem  
In this section, the general approach to model this complex problem will be explained, where a good number of simplifications are assumed to transform the real aeronautical problem into a simple experimental rig and its corresponding numerical version, both containing the most relevant physics from the original case.  
When all the complexity of the industrial case is attempted to be described, a problem where an immiscible multiphase fluid confined inside a deformable moving tank emerges. The tank moves with a trajectory given by the deformations of the aircraft wing, which gives rise to a great number of vibration modes and therefore to complex movement. Both fluids, liquid and gas, are viscous with surface tension between them and initial formation of menisci caused by the walls. The movements of both fluids inside the tank are characterized by high Reynolds numbers, and therefore turbulence and non-linear effects are relevant. The numerical simulations must model those turbulent scales that normally the computational mesh does not resolve. Gravity and inertial forces due to acceleration are also present and especially relevant in this problem. It should also be remarked that a fluid-structure interaction case is faced, where in addition to the possible deformations of the container, there is a two-way interaction between the fluid and the tank through the pressure fields and viscous stresses produced, which makes the dynamics of the tank and those of the fluid clearly coupled.  
Given such a number of complexities, if a simple model that contains the physical phenomena that are taking place has to be manufactured, some simplifications need to be assumed:  
* The tank is manufactured in such a way that very small deformations appear, and then it can be considered as a rigid solid.
* Even though the tank moves with complex 6 degrees of freedom (DoF) kinematics inside the wing, the movement in the vertical direction is much larger than in any other, and as a consequence, the tank movement will be simplified to a vertical 1-DoF movement with no rotations.
* Despite that the fluid has a 3D movement inside the tank providing a 3D pressure field, 3D computations are very costly in terms of computational time, and the 2D results are accurate enough to obtain good performance (see [26]); thus, the computational fluid dynamics will be performed using a 2D domain.  
Assuming all these simplifications, the fuel tank inside the aircraft wing will be simplified to an experimental rig that consists of a vertically guided tank that is partially filled with liquid and air. An initially deformed spring system generates a decaying oscillating movement in the tank and the confined fluid. A more detailed description of the system is performed in the next Section 3.1.
# Research on Dynamic Evolution Model and Method of Communication Network Based on Real War Game
## 1 Introduction
In the literature ([PERSON], 1996), the maximum connectivity is used as an indicator to study the similar characteristics of the power information-physical dependency network and verify that the low degree nodes increase the strategy of connecting edges. The Information Age Combat Model (IACM) ([PERSON], 1996) proposed by [PERSON] adopts a network structure and uses nodes to represent the energy flow or information flow between entities. The interactions between the four typical types of nodes constitute a complete Observe-Orient-Decide-Act (OODA) cycle. The network loop is the source of combat effectiveness. The Perron-Frobenius of the IACM network adjacency matrix is proposed as an indicator to measure the robustness, indicating that there is a close relationship between this indicator and the effectiveness of the system's combat effectiveness; [PERSON] et al. (2018) further the rationality of the PFE index, which was verified by simulation. However, there are obvious shortcomings: the network ring can only reflect the influence of the network structure of the combat system on the robustness of the OODA cycle ([PERSON], 1996), and the influence of the component system capabilities is ignored; at the same time, the PFE can only represent the number of network rings in the combat system network. It cannot reflect the number of effective networks that exert actual combat effectiveness. [PERSON] (2018, 2019) proposed the communication system in the network-centric warfare (NCW) has been analyzed from the perspective of the SoS, which consists of a combat system and a network system so that the two reflect each other's effects. Then, he put forward simulation of a SoS model, which consists of a combat model and a network model, which has been used to analyze the performance of network-centric warfare in detail. The main contributions of this paper are as follows:  
1. Overcoming the disadvantage of the generation algorithm in the traditional combat SoS, and using the joint distribution strategy of power law distribution and Poisson distribution to model the communication network;
2. Using simulation methods to study the changes in the combat network under continuous attacks on communication nodes;
3. Using the method of control experiment to verify the rationality and effectiveness of the communication network modeling, which provides a reference value for the construction of the communication network topology in war.
# Climate, CO2, and Anthropogenic Drivers of Accelerated Vegetation Greening in the Haihe River Basin
## 3 Results
### Dominating Factor of NDVI Variance  
The element with the largest absolute contribution rate was identified as the dominant factor that contributed to the changes in NDVI. As shown in Figure 7a,b, CO\({}_{2}\) and AAF controlled the increase in NDVI across most areas of the study region. In mountainous areas, the growth in NDVI was dependent on CO\({}_{2}\) and AAF. At the same time, CO\({}_{2}\) was also the principal driving force for enhanced NDVI in plain areas. The dominant driving elements of decreased NDVI were Tem and Rad, especially in the mountainous areas. Rapid urban expansion reduced the NDVI in the plain, and the detrimental influence of urban expansion on NDVI change was revealed via NTL. Subsequently, we integrated the driving contributors into CLI, HUM, and CO\({}_{2}\), as shown in Figure 7c. Overall, CO\({}_{2}\) was the dominant contributor to the greening trend in more than 54% of counties, and HUM was the driving contributor in over 33% of the 334 counties (Figure 7c,d). The positive effects of HUM in the northern and western mountainous areas were attributed to vegetation restoration programs, which conserved and expanded forests to mitigate land degradation. However, the greening of the plain areas was primarily driven by elevated CO\({}_{2}\) concentration.  
Figure 7: Dominant variable to (**a**) normalized difference vegetation index (NDVI) increase and (**b**) NDVI decrease. (**c**) Spatial distribution of driving contributors (CO\({}_{2}\): rising CO\({}_{2}\), CLI: climate change, and HUM: human activities) to NDVI trends. (**d**) Fractional area (%) of counties ascribed to different contributors over mountainous and plain areas. The label at the top of the bar is the fraction of counties dominantly driven by each contributor. A postfix ‘+’ indicates a positive effect on NDVI trends, whereas ‘\(-\)’ indicates a negative effect.
## 1 Introduction
adopted the fields of experts to learn image priors with contrastive divergence [22]. [PERSON] et al. proposed an image prior based on a Markov random field [23]. [PERSON] et al. used a multi-layer perceptron to learn an image deconvolution scheme on a dataset of natural images with large quantities [24]. [PERSON] et al. trained a set of convolutional neural network priors, which act as a constraint of model-based optimization methods to cope with the image restoration problems [25]. In this paper, we propose a deblurring framework consisting of two stages. First, an accurate blur kernel is estimated from an input blurry image via a nonzero constrained function as the image prior. Compared to the conventional sparsity-based techniques, the optimization procedure of our method has a lower computational complexity due to the analytical solution of the mathematical model. Subsequently, we propose a deconvolution algorithm on sparse representation for latent image restoration. Experimental results and comparisons indicate that our method outperforms others. This study makes a novel contribution to the literature as summarized below:  
* An image prior based on nonzero measurement on four orientations of the image gradient domain is proposed. The image histogram charts show that the frequency of nonzero values in the gradient domain of a blurry image is far more than that in a clear one, and the nonzero measurement is suitable for a constraint for image deblurring. The solution for the cost function with the proposed image prior is also analyzed and discussed. * The blur kernel is obtained under a ridge regularization on the PSF because the measurements on an image are enough to estimate the blur kernel in the maximum a posteriori (MAP) framework. During the optimization, we propose a solution based on a conjugate gradient method combined with [PERSON]'s method; this solution could prevent us from calculating the inversion of a Hessian matrix and solve the cost function efficiently. * Considering the statistical features of natural images, we presented a non-blind image deconvolution algorithm by applying the concept of hyper-Laplacian distribution-based prior. Its target image is constrained by an \(\ell_{p}\) quasi-norm in the cost function. We analyze and discuss the solutions for different \(p\) values. * We tested our method on both simulated motion blurs and atmospheric turbulence blurs in real-life applications. In addition, we comparatively analyzed our method, in terms of the cost durations, estimated accuracy of the blur kernels, and quality assessment of the restored images, and adopted several approaches related to blur removal. The remainder of this paper is organized as follows: Section 2 introduces the imaging system and the principle of image blurs. Subsequently, we present the details of our proposed deblurring method, including the blur kernel estimation technique and the image deconvolution algorithm. The results of the experiments and comparative analysis performed are described in Section 3. Section 4 summarizes the study and presents concluding remarks.
# Experimental Evaluation of an IoT-Based Platform for Maritime Transport Services
## 3 Reference Scenario
* The Telco Operator Network is a Telco terrestrial network, mainly including front- and back-hailing segments and the Telco backbone network to convey data from different access networks (e.g., 5G cellular network) as well as external networks (e.g., satellite). The Telco operator cloud deploys the OneM2M system (6.a) along with specific application components (6.b) developed exploiting, in the context of this paper, the Maritime Platform for delivering sophisticated logistics services. * The 5G terrestrial cellular network (with base station and core network elements) is placed at the port terminal (8) and, as an additional option, can also collect IoT data from the vessel. The vessel can be in open sea, in near sea or docked at the port terminal. Correspondingly, we distinguish between two different operating domains for the Maritime Platform, whether the NB-IoT devices are attached to the terrestrial cellular network or to the on-board one to convey data to the OneM2M platform. More specifically:  
* Terrestrial domain--when the ship is in near sea or docked in a port terminal, the data produced by the NB-IoT devices will reach the Telco operator network (and then the OneM2M IoT system) by means of the terrestrial 5G cellular network. We consider near sea the part of the sea where a maritime telecommunications operator is not authorized to turn on its equipment. According to the Italian regulation, this area includes the sea within 2 nautical miles from the coast;
* Maritime domain--when the ship is in open sea, the data produced by the NB-IoT devices will reach the Telco operator network (and then the OneM2M IoT system) by means of a 5G cellular network deployed on-board the vessel and the satellite network. Figure 1: Reference scenario. We consider open sea the part of the sea where a maritime telecommunications operator is authorized to turn on their equipment. According to the Italian regulation, this area begins 2 nautical miles from the coast.
# Towards the Feasibility of Long Range Wireless Power Transfer over an Ocean Surface
###### Abstract  
In this work, we have realized [PERSON]'s style of wireless power transfer over a seawater surface. Method: The problem can be thought of as a surface electromagnetic wave propagating along an interface between a low-loss non-polar medium and seawater. A 10 m long water-filled plastic duct was connected to two separated water tanks, with one tank identified as the transmitting end and the other the receiving end. At the transmitting end, the water tank was excited with a 100 kV plasma from a spark gap transmitter at 44 MHz. At the receiving end, surface power was harvested in an open-circuit manner with the help of a suspended monopole antenna. Results: Without any antenna, no power was received at the receiving end. However, when two monopole antennas were individually connected to the transmitter and the receiver, a power was noticeably detected and successfully delivered to multiple loads even if the water's conductivity was low. As the salinity level increased from 0 to 5 g/L, the transmission efficiency was increased from 10% to 99%. Consistent with [PERSON]'s law of transmission, the transmission distance leading to the first maximum efficiency was found to be approximately proportional to square of the antenna heights. Conclusion: A vertically mounted monopole antenna enables power to be wirelessly transmitted along the interface between a low-loss dielectric medium and seawater.  
Zenneck waves; surface plasmon polariton; surface plasmons; dispersion; line of sight; LOS; spark gap +
Footnote †: journal: Applied  
1 Faculty of Engineering, Vietnamese-German University, Binh Duong New City 75000, Binh Duong, Vietnam 1  
[PERSON]  
2 Faculty of Engineering, Eastern International University, Binh Duong New City 75000, Binh Duong, Vietnam 2
## 2 Results and Discussion  
To study the performance of the proposed antenna, the radiating structure is designed, simulated, and analyzed using 3D electromagnetic simulator CST Microwave Studio (MWS) Suite 2021. A waveguide port is used as a source for the excitation of the designed antenna. The reflection coefficient (S11) is evaluated with open-add-space boundary conditions using a transient solver in CST Microwave Studio.