esa-sceva/satcom-chunk-collection huggingface dataset:
# Transmissive Polarizer Metasurfaces: From Microwave to Optical Regimes
## 6 Review of Recent Progress of MSs Based Polarization Conversion
### Reconfigurable Transmissive MS Based Converters
#### 6.3.1 Controllability through Circuit Elements  
The tunability of MSs was experimentally demonstrated with meta-atoms controlled by external knobs involving varactor or pin diodes in these meta-atoms. Each unit can be controlled by applying an appropriate voltage to the diode.  
There have been interesting controlling circuit elements for the MS-based polarization converters. For example, a switchable LP-to-CP converter based on MS was proposed by [PERSON] et al., where controllability can be achieved by changing the states of double PIN diodes [104]. Incident LP waves can be converted into CP waves whose handedness is determined by the state of PIN diodes waves. Diagonal asymmetry in the unit cell of the polarizer is loaded with PIN diodes (SMP 1345-079 LF). Figure 6(a) shows the unit cell for the proposed polarization converter. Top and bottom rings are fabricated on each layer so that biasing by DC power can be done easily. The orientation of the slots of top and bottom rings is varied (vertical and horizontal, respectively). The biasing of PIN diodes is done by pairs of DC lines shown on the left of Figure 6(a). Incident LP waves are converted to CP waves when the PIN diodes of the top ring and bottom rings are in different states. A similar concept was used by [PERSON] et al., who converted the incident LP wave into the transmitted CP wave by using a double-layered active MS. The structure works by tuning the working states of the PIN diodes in its unit cells [105]. Figure 6(b) shows the unit cell of the polarizer. Figure 6(c)-e show three states of the polarizer. In state 1, as shown in Figure 6(c), diodes 1 and 3 are switched on, while diodes 2 and 4 are off. In state 2, diodes 1 and 3 are shut off while diodes 2 and 4 are switched on, which is shown in Figure 6(d). Lastly, in state 3, as shown in Figure 6(e), all the four diodes are switched on, leading to cross-polarization conversion.  
A similar design consisting of active MSs based on voltage-controlled varactor diodes was proposed by [PERSON] et al. [106], where the transmission response from the MS was tailored by the varactor diode; it behaved as a multi-functional polarization conversion. When an LP wave is made incident on the MS, it becomes converted to a CP one. Dual-band operation is realized in a single voltage configuration, resulting in transmitted orthogonal CP waves within two bands of operation. Moreover, in a single voltage configuration, two circular polarized waves with different handedness can be obtained at different frequencies by varying the applied voltage along the MS. When PIN diodes are on, MS operates in reflection mode and reflects the incident wave with the same polarization. Thus, differentcircuit elements can be used to achieve controllability in polarization conversion and multi-functional operations.
## 1 Introduction
In a normalized confusion matrix, which is a square matrix, the main diagonal element represents the correct prediction results, and the remaining lower or upper diagonal elements represent the incorrect prediction result for each class. As can be seen from all three of these graphs, the false pseudo-labels in most of the foreground classes are mostly confused with the background class (class 0), not with the other foreground classes. We believe that this result is due to a lack of information and predictive certainty in semi-supervised semantic segmentation, considering that all areas except foregrounds of interest should be assigned as background. That is, unlike each foreground class that may have common image information, the background class may have a complex and diverse image information structure, which can further cause confusion. In many real-world applications, this problem can be considered even more important in that it aims to classify all remaining areas as background areas except for some foregrounds of interest, and it is necessary to perform good predictions on unseen data. To alleviate this problem, we propose a new approach called Multi-View Integrated Ensemble (MVIE), which can better distinguish between the background and foreground in semi-supervised semantic segmentation. MVIE is based on a novel ensemble approach based on output perturbation, and is described in detail in Section 3. We evaluated the proposed MVIE under various training settings in PASCAL VOC 2012 [20], where many different kinds of objects are assigned background areas. Our experimental results on unlabeled data show that our proposed approach has better background or foreground discrimination capabilities than recent SOTA semi-supervised semantic segmentation models. Furthermore, we used our MVIE model to first determine pseudo-labels as background or foreground, and then experimented with semi-supervised semantic segmentation SOTA models using pseudo-labels under that discrimination. In this way, all experimental results combining our MVIE model and recent SOTA models show better performance than those found using single SOTA models. Specifically, our contributions include the following:  
* We find a common problem with pseudo-labels in semi-supervised semantic segmentation SOTA models. The reason for false foreground pseudo-labels is not primarily due to confusion among other foreground classes, but rather, it is mainly a problem of confusion with the background class. * To alleviate the above problem, we propose a novel ensemble approach based on output perturbation. Our method outperforms existing SOTA models in background and foreground classification performance on unlabeled data. * When training an existing SOTA model with the help of our model, although the computational cost of training increases, the inference process for practical use incurs the same cost as each existing model. Therefore, from a practical usage perspective, further improved performance can be achieved without increasing computational costs.
## Materials and Methods  
This section describes the algorithm for matching the position of the firefighter to the building layout. In order to be consistent with the terminology presented in Section 1, we use the term _individual_ instead of firefighter. However, in this case, the individual has a specific movement pattern consistent with firefighters' movement during search and rescue operations. We also introduce the term _software agent_ (or _agent_ for short) which is the reflection of the individual in the computer program. Our definition of the agent is consistent with that presented in [34].  
The goal of the algorithm is to minimize the location error by the map-aiding approach. The algorithm updates the velocity vector of an individual by finding the closest collision free velocity to the current velocity. The updates in velocity are recorded and then used to correct the incoming positions.  
Our main assumption in the selection of a map-matching algorithm is based on the collision avoidance velocity, derived from a firefighting tactic. This tactic suggests that in dense smoke with low visibility, firefighters should move along walls, maintaining hand contact with them [19]. The left-hand rule in this situation is also recommended (or at least the same hand all the time).  
The observation of the movements of firefighters during our blind tests confirms that individuals tend to apply this left-hand rule. They move along the walls while maintaining contact with them. Therefore, we decided that our algorithm will be based on the proximity of walls. We assumed that the walls will be a source of correction of the firefighter's position.  
The tests of our generic INS (without additional error correction) revealed that the error increases with the number of rotations performed by individuals. The bigger the number of rotations, the bigger the error. Even when individuals try to move in a straight line, small sway of their feet causes deviation from the course. The reflection in the rotation is very important because small error in the angle of rotation causes large error in the position. This was especially reflected in the long walks, causing significant error in the location. Figure 1 depicts the situation where small error in angle reflection causes large error in location.  
The main idea is to estimate the inter-step path reconstructed by INS and correct it by local collision detection and a collision avoidance velocity algorithm. Collision detection needs to be accomplished with regards to the current position and formal definition of a map. Next, the smallest possible correction of agent velocity is performed to avoid the obstacle. The velocity vector is defined as subtraction of the last position obtained from INS and the current agent position.
## 3 Results  
### Results Considering No PV Surplus Energy Exportation  
Figure 13 presents the summarized results of the three proposed actions of this study and the current state (base case) in terms of the (total) net present cost and amount of electricity consumption.  
While Action 1 and Action 2 do not require essential equipment investment, consisting mainly of reviewing the contracts and the connection configuration between the loads and the grid (electrical company issues), Action 3 requires special attention to avoid oversizing the PV.  
Additionally, in Figure 13, the adverse effect on the LCC due to the oversizing of the PV panels in Action 3 can be verified if we move from the right to the left side on the plotted points of scenarios A, B, and C.  
The right side of the highlighted optimal points A, B, and C represents the region where the increase in PV panels corresponds to a LCC reduction. The left side represents the region of oversizing,  
Figure 13: Summary of proposed actions and base case (i.e., the current situation of the Logistics Center).  
where an increase in the PV panels increases the total costs, meaning that it is unfeasible from an LCC perspective, unless an appropriate use for the surplus energy is addressed, as commented above (see Section 1).  
Action 1 represents 5.43% of cost reductions after revising the contracted powers for each separate unit of the study. In the same direction, Action 2, maintaining the same energy consumption from the grid as Action 1 and the base case, achieved a reduction of up to 13.47% in costs (compared to the base case). Recall that Action 2 would imply the combination of the demands of all units into one combined demand. A more detailed annual cost (i.e., annual cash flows) breakdown of Actions 1 and 2 can be found in Figure 14, and the changes to be implemented are shown in Figure 15.  
Action 3, and more specifically, its optimal scenario A (see Figure 13), can present as much as a 26.13% cost reduction when compared to the base case. This solution would also contribute to 26.8% in the reduction of grid dependency. The other scenarios and their optimal and suboptimal solutions can also potentially provide cost reductions over the lifecycle of the study.  
Figure 14: Breakdown of Actions 1 and 2 when compared to the base case.  
Figure 15: Optimized power per unit for Action 1 when compared to the base case (BC, i.e., current situation), overall optimized power for Action 2, and their relative shares depending on the three periods of Endesa’s 3.0A tariff.
# Analysis of a Nature-Inspired Shape for a Vertical Axis Wind Turbine
###### Abstract  
Wind energy is gaining special interest worldwide due to the necessity of reducing pollutant emissions and employ renewable resources. Traditionally, horizontal axis wind turbines have been employed but certain situations require vertical axis wind turbines. With a view to improve the efficiency of a vertical axis wind turbine Savonius type, the present work proposes a bioinspired design blade profile relying on the Fibonacci spiral. This shape is repeatedly presented in nature and thus it leads to a bio-inspired blade profile. A numerical model was carried out and it was found that the Fibonacci shape improves the performance of the original Savonius shape, based on semicircular blade profiles. Particularly, the Fibonacci blade profile increases around 14% the power in comparison with the Savonius blade profile. Besides this comparison between Savonius and Fibonacci, a research study was carried out to improve the efficiency of the Fibonacci turbine. To this end, the effect of several parameters was analyzed: number of blades, aspect ratio, overlap, separation gap, and twist angle. Improvements on the average power greater than 30% were obtained.  
wind turbine; VAWT; CFD; Savonius; Fibonacci Article Analysis of a Nature-Inspired Shape for a Vertical Wind Turbine  
1  
[PERSON], [PERSON], [PERSON].  
Regarding VAWTs, two types can be found in the literature. The Darrieus turbine, mainly based on lift, and the Savonius turbine, mainly based on drag. The main propulsor effect in the Savonius turbines is the drag force acting on them, while the Darrieus rotating principle is mainly based on the lift force actuating on the blades. The Savonius wind turbine was initially proposed by [PERSON] [7] in the 1930s. This turbine is integrated by two semi-circular sections vertically positioned along the rotation axis. The main advantages are its simplicity, high start-up, low noise, and independence of the wind direction [8]. In comparison with a Darrieus turbine, the Savonius is more recommended for urban environments due to its lower starting torque, i.e., it needs lower wind velocities for starting. In offshore applications, the Savonius turbine is appropriated as a hydrokinetic turbine to generate energy from tidal currents [9; 10; 11; 12]. Despite these advantages, the main disadvantage of the Savonius turbine is its low efficiency [13; 14]. For this reason, these types of turbines have been scarcely employed until recent decades. Many modifications have been proposed along the years in order to increment the efficiency of the Savonius turbine. The most significant parameters analyzed in the scientific literature (number of blades, aspect ratio, overlap, separation gap, and twist angle) are analyzed below, and a schematic representation of them is shown in Figure 1.
# Prediction of Microwave Characteristic Parameters Based on MMIC Gold Wire Bonding
## 3 Model and Analysis
### Building an Artificial Neural Network
We choose the mean square error (MSE) as the loss function to characterize the spatial distance between the model output results and the actual labels, and the optimization goal is to train the neural network by reducing this spatial distance as much as possible. For network training, the dataset is randomly divided in the ratio of 1:9, where 1 becomes the test set and 9 becomes the training set. The data in the training set will be involved in the training of the neural network so that the neural network back propagates to learn the data features, and the test set data will not be involved in the neural network training. Before the neural network model starts training, an initial assignment of the parameters within the network model weights, which is relatively small, is required so that the starting values of the repeatedly trained networks do not differ much and play a positive role in evaluating the different parameters for the training of the network model. During the training process of the neural network model, the model is not unique considering that the neural network has the property of infinite approximation. When the network training reaches a certain level, the test set data is input to the network for model evaluation, and when the test set data works well, the model can be considered to reach the convergence condition, terminate the training and save the network training model. The schematic diagram of the whole neural network training process is shown in Figure 7.  
At the early stage of model training, the convergence of the model is judged by the change of the loss value, and when the loss curve appears to be divergent or repeatedly turbulent, the model is judged to be non-convergent and adjustments need to be made to the model. In this experiment, three sets of data are trained under the same model, and the loss function curve is shown in Figure 8 for 1000 iterations in order to reduce the error. In this experiment, when the model was first trained, the loss function value tended to decline in a straight line, then the speed slowed down, and eventually the speed leveled off and converged near a tiny value. From the training and prediction loss function values, the error was small, and we set the variance threshold to 1.5. When the variance value was less than 1.5, the network was judged to be accurate in prediction, and vice versa. We selected 1300 sets of data in the test set, and 1274 sets of data met the prediction readiness, with an  
Figure 7: Model flow diagram. accuracy rate of 98%, at which time we considered that the network training reached the convergence state, terminated the network training and saved the network training model.
# TRC-Based High-Precision Spot Position Detection in Inter-Satellite Laser Communication
## 5 Experiment
Figures 12 and 13 show the output results of the direct FFT method, the Kalman filter method and the TRC method when the SNR is \(-17.86\) dB and the spot radius is \(0.71\) mm. It can be seen that the detection curve of the TRC method is significantly smoother than that of the direct FFT method, and the Kalman filter method cannot detect the spot position correctly at all. This means that the TRC method has a strong ability to suppress random noise. Figure 11: When \(\omega\) = 0.53 mm, detection results of TRC method under different SNR conditions. Figure 12: When \(\omega\) = 0.71 mm and SNR = \(-17.86\) dB, detection results of TRC method and direct FFT method. (a) The Calculated coordinate curve; (b) Absolute error. Figure 13: When \(\omega\) = 0.71 mm and SNR= \(-17.86\) dB, detection results of TRC method and Kalman filter method. (a) The Calculated coordinate curve; (b) Absolute error. In addition, we use maximum error and root-mean-square error to compare the performance of the two methods. Maximum error is defined as:  
\[\delta_{\text{max}}\ =\ \max_{i}(|\delta_{xi}|) \tag{26}\]  
and root-mean-square error is defined as:  
Figure 8:0  
The comparison between the two methods is shown in Figure 8:1 When the SNR is relatively high, the two methods have little difference in the ability to suppress noise, and the direct FFT method is slightly worse. However, when the QD output SNR is only \(-17.86\) dB, the errors of the direct FFT method increase sharply. In contrast the root-mean-square error of the TRC method is still less than \(0.0135\) mrad, and the maximum error is also limited within \(0.0238\) mrad.
###### Abstract  
This review paper aims to understand the current processing plant maintenance systems and further identify on-stream phased array corrosion mapping (PACM) to reduce turnaround maintenance (TAM) activity during plant operations. Reducing the TAM duration and extending the TAM interval are common goals of most researchers. Thus, a detailed review was performed to understand the maintenance systems and the problems faced. Furthermore, a review of the current PACM application and the possibility of applying it during on-stream inspection was also performed. PACM has better detectability for localized corrosion, and the results can be obtained for a range of thicknesses, which is the main advantage of this method. However, applying PACM during on-stream inspections at elevated temperatures presents challenges owing to the limitations of the ultrasonic properties and increased probe contact. Future research is needed to evaluate the effectiveness of PACM on piping systems that can be utilized for inspection during plant operation at elevated temperatures. This will enable the detection of general and localized corrosion in common materials, thereby reducing the TAM duration and extending TAM intervals. Detecting and monitoring corrosion growth without shutdown is critical for ensuring the safety and reliability of the processing plants. This literature review provides a more precise direction for future research to address these challenges and to advance the field of on-stream corrosion monitoring.  
non-destructive testing (NDT); phased array ultrasonic testing (PAUT); risk-based inspection (RBI); turnaround maintenance (TAM) +
Footnote †: journal: Applied Sciences
# Dynamic Compensation of a Fluxgate Magnetometer Based on a Hybrid Optimizing Algorithm
## 3 Simulation and Signal Test
### Simulation Results and Discussion  
In Section 2, the dynamic characteristics of the fluxgate magnetometer were tested, and the system was determined to be a first-order model. The first-order transfer function is obtained by identifying the test data through the Simulink system, and the difference equation of the magnetometer system is determined according to Equation (2):  
\[1.5505 yk-0.9835y(k-1)=0.2835x(k)-0.2835x(k-1)\]  
The dynamic response of the magnetometer is set as the input of the dynamic compensator, and the measured signal is set as the target output of the dynamic compensator. The parameters of the dynamic compensator are solved by the optimization algorithm so as to realize the solution of the dynamic compensator. Since the step signal contains relatively rich frequency components, the step response signal of the magnetometer is selected as the input of the dynamic compensator, and then, the parameters of the dynamic compensator are solved. In this way, the problem that the dynamic compensator obtained by selecting the dynamic response signal of a single frequency as the input is only compensated for the signal of a single frequency is avoided.  
In Simulink, the input signal of the magnetic sensor is set as a step signal with an amplitude of 1. The input signal generates a step at 25 ms, and the simulation time is 150 ms. The step response curve of the magnetometer model is shown in Figure 10. The response time is 10.70 ms. It means that the output of magnetometer model reaches 98% of the steady-state value after 10.70 ms after the step of the input signal, which has a certain lag.