esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Geographic Polarization Suppression Model
#### 2.2.3 Geographic Polarization Suppression Model  
By integrating real-time positioning solutions with orientation information solution methods, a geographic polarization suppression model is developed, as shown in Equation (19).  
\[\begin{array}{l}G\big{(}\alpha,\varphi,T_{time},w_{x,y,z}\big{)}=G\big{[}f_ {1}(\alpha,\varphi,T_{time}),f_{2}\big{(}w_{x},w_{y},w_{z}\big{)}\big{]}\end{array} \tag{19}\]  
In the model, \(f_{1}(\alpha,\varphi,T_{time})\) represents the real-time positioning solution function, while \(f_{2}\big{(}w_{x},w_{y},w_{z}\big{)}\) denotes the orientation information solution function. The model initially acquires GPS data of the measurement point, using the latitude, longitude, and time information as input parameters for the function \(f_{1}(\alpha,\varphi,T_{time})\) to calculate the solar or lunar elevation angle and the angle of incidence of light rays. Subsequently, the angular velocity information of the monitoring platform, captured by MEMS sensors, is used as input parameters for the function \(f_{2}\big{(}w_{x},w_{y},w_{z}\big{)}\). The output of \(f_{2}\big{(}w_{x},w_{y},w_{z}\big{)}\) is then utilized to mitigate the interference of sea surface wave fluctuations on the monitoring platform.  
Ultimately, a geographic polarization suppression model is successfully constructed, capable of achieving all-weather, adaptive, and stable polarization suppression of sea surface glare. Figure 4 illustrates the glare suppression method process based on the geographic polarization suppression model.
# A Semantic Segmentation Method with Emphasis on the Edges for Automatic Vessel Wall Analysis
## 2 Materials and Methods  
### Network Architecture  
We proposed the Edge Vessel Segmentation Network (EVSegNet), a highlighted edge convolution network for arterial vessel wall and plaque segmentation. The architecture of the EVSegNet is illustrated in Figure 1. It consists of a regular stream and an edge stream. The regular stream has an encoder-decoder architecture to process texture information. In the encoder, four downsampling convolutional blocks are followed by a bottleneck block. Overall, the downsampling block reduced the spatial dimension down to 1/16 of the input size to extract high-level semantic information. In the decoder, we introduced Dense Upsampling Convolution (DUC) to replace traditional bilinear interpolation upsampling to predict segmentations from features extracted by the encoder. Motivated by [PERSON] et al. [14],the bottleneck block is a hybrid dilated convolution (HDC) module, which is composed of three stacked HDC blocks to expand receptive fields but to not increase the weights number in the filter. The HDC block consists of three convolution layers with kernel size 3 and dilation rates of \(r=1\), \(2\), \(3\), respectively, three normalization layers, and three ReLU activation functions. The edge stream is composed of a sequence of residual blocks followed by an edge gated layer, except for the bottleneck, which was fed into the edge stream as the first residual block input of the edge stream. Each resolution upsampling feature map from the decoder and its corresponding downsampling feature map from the encoder of the regular stream were fused and combined with previous residual block as the edge gated layer inputs. Specifically, the edge information was generated by utilizing the Canny filter. Finally, the output of the edge stream was concatenated with the output of the regular stream. Two streams shared a dual loss function besides their dedicated loss layers.
## Materials and Methods
### Training and Testing Procedure  
The training and testing were conducted with Caffe and Keras models. Caffe was chosen because of its direct support in NVIDIA DIGITS. Keras was chosen because it allowed easy optimization to improve system accuracy. For the Caffe GoogleNet model in NVIDIA DIGITS, the LMDB format was used. In the Keras model, hierarchical data format version 5 (HDF5) was used. HDF5 provides a simple format to read/write over a large dataset. It is easy to add data to a dataset without creating copies. In addition, it supports different languages such as R, C, and Fortran, making it easier to use across various platforms. Once the dataset was processed and converted into the respective file format, it was processed separately with Caffe and Keras models. Once both the models were trained using training images, they were tested with a testing dataset of 53,594 images. The K-fold cross-validation method was used in this study. In k-fold cross-validation, the complete dataset is randomly divided into k folds or groups. One of the groups is used for testing and the remaining groups are used for validation. This process is repeated for all the groups until the complete dataset is trained [38]. In each iteration of k-fold cross validation, the data were spilt into a training and testing dataset, with 30% for testing and 70% for training. We tested for k = 5, 10, and 12. We found that k = 5 was optimal.
# A Multi-Technology Communication Platform for Urban Mobile Sensing
## 3 Architecture Overview
### Network Elements
#### 3.1.2 Mobile Nodes  
Mobile nodes are sets of different mobile entities that can comprise bicycles, cars, or drones (aerial or aquatic), increasing the network heterogeneity. Our platform, as already mentioned, focuses on mobile nodes equipped with both WiFi and LoRa communication capabilities. They are not only a fundamental element in the data collection task, since a mobile node interacts with DCUs in order to gather their stored information, but they are also responsible for the data forwarding among mobile elements over an opportunistic delay-tolerant network, where intermediate nodes act as relays in a store, carry, and forward fashion.  
The multi-technology capabilities allow the mobile nodes to send information to a LoRa gateway, complementing the WiFi opportunistic network with long-range technology, and consequently enlarging the range of applications that the platform can achieve. For instance, applications such as the rescue of DTN expired data packets in order to concede them a new opportunity to be delivered using LoRa communication, or even the periodic dispatch of reports about the mobile node's geographical location are possibilities enabled by it. Figure 3 shows the mobile node software architecture.  
Figure 2: Data-collecting unit (DCU) software architecture. IPC: Inter-Process Communication; UART: Universal Asynchronous Receiver-Transmitter; I2C: Inter-Integrated Circuit.
# Nonlinear Trajectory Tracking Controller for Underwater Vehicles with Shifted Center of Mass Model
## 5 Conclusions  
The paper proposes a control scheme that is suitable for marine vehicles whose model includes dynamic couplings. The method implied transforming a model with a symmetric inertia matrix using a velocity transformation into a model where this matrix was diagonal. A control strategy consisting of two controllers, namely kinematic and dynamic, was then developed. The presented approach is particularly useful when the position of the center of mass differs from the geometric center and competing algorithms do not deliver satisfactory results. Simulation verification was carried out on a five DOF model of an ROPOS vehicle used in marine research with conditions that correspond to real operating conditions. Finally, an analysis of the results was given.  
The QV control scheme made it possible to achieve acceptable performance with both weak and strong couplings for two different trajectories and under the assumption of engine limitation (according to vehicle specifications obtained from the literature). From the tests performed, it can be concluded that including the couplings arising from the shift in the center of mass in the inertia matrix is sensible because it leads to better results than when they are omitted. If the perturbation functions have small values (e.g., in the order of several N or Nm), they can represent small uncertainties in the model and environment perturbations, but inaccuracies due to a lack of symmetry should be included in the inertia matrix. Using a model with a symmetric inertia matrix resulted in better controller performance than if the model with a diagonal inertia matrix was applied.  
The proposed control method is particularly useful at the pre-experimental stage (design, decision to possibly perform an experiment on a real object), as it allows the effect of dynamic couplings on the performance of the controller to be estimated.  
In the future, more detailed research should point out the implications of vehicle model reduction for controller performance and include tests to determine whether the algorithm under study (known or designed) is effective when dynamic couplings are omitted from the dynamics model.  
The work was supported by Poznan University of Technology grant No. 0211/SBAD/0123.  
**Institutional Review Board Statement:** Not applicable.  
**Informed Consent Statement:** Not applicable.  
**Data Availability Statement:** The original contributions presented in the study are included in the article.  
**Conflicts of Interest:** The author declares no conflicts of interest.
## 1 Introduction
The simultaneous retrieval of AOD and SSA with high temporal resolution is expected to improve the accuracy of aerosol products, the understanding of the radiometric effects of aerosols and the estimation of trends in aerosol distributions over Asia. Thus, by using these products, further investigations of the role of aerosols in Asia on climate and air quality change can be performed. A general description of the validation and ancillary dataset is given in Section 2. In Section 3, the general design of the algorithm is presented and details of each process are described in subsections. Results obtained from the developed algorithm are compared with AERONET, CALIOP and OMI products and are presented in Section 4. In Section 5, a reference test is performed to estimate retrieval uncertainty. Further development of the algorithm based on the uncertainty analysis is discussed in Section 6. The final section provides a summary of the results.
# Design of a Compact Microstrip Decoupled Array
## 2 Materials and Methods  
### Antenna Configuration  
Two aspects are considered in the miniaturization design of a two-element microstrip patch array. The patch can be miniaturized by means of the slotting method, and the antenna size can be significantly reduced by decreasing the element spacing. However, reducing the element spacing will lead to an impedance mismatch between ports, which can be solved by changing the structure of the feed network and correctly using matching branches. The overall size change is shown in Figure 1.  
Figure 1: Comparison of antenna size between a conventional microstrip two-element array antenna and the proposed microstrip two-element array antenna.  
The final antenna configuration comprises two slotted patches and a decoupled feed network, both of which are miniaturized, as shown in Figure 2. Its substrate is pressed using two layers of F4B (\(\varepsilon_{r}\) = 2.65, tan\(\delta\) = 0.002) substrate, with a grounding layer between the two layers. The antenna unit is located on the top layer of the substrate, and U- and rectangular slots are added to it. The decoupling feed network is printed on the bottom layer of the substrate and is miniaturized by adding 12 branches. A matching branch is added to the external port to realize impedance matching with the external ports. A ground plane exists between the two substrates, and the feed network is fed to the two-element antenna array through the bottom feed. Two holes with a radius slightly larger than the coaxial line are introduced on the ground plane. The SMA connector is used as the feed launcher. All parameters of the antenna system are listed in Table 1.
## 7 Observations and Recommendations
### Recommendations and Research Challenges
#### 7.3.5 Benchmarking  
As previously stated, anomalies are rare obscure events in practice which makes detecting them very challenging. It has previously been stated that in the simulated scenario they are artificially created and often clearly defined. This will vary between different simulations and also will not necessarily translate well into practice. It thus becomes important for researchers to develop benchmarking tests that would enable other researchers to evaluate their schemes against an industry standard instead of developing their own tests, which could be misleading. These tests should take the practical implications of the application environment into consideration. This would give researchers a solid foundation on which to design their proposed even though the end results may be deployed in vastly different applications.
# Design and Implementation of an Autonomous Charging Station for Agricultural Electrical Vehicles
## 2 Materials and Methods
### Decision-Tree Based Architecture  
The software architecture is based on a decision-tree algorithm [32]. Each sequence is a set of conditions that need to be successfully fulfilled in order to move to the following sequence. The intuitive sequence of the commands that the ACS performs follows what a human would perform when performing the same task: going to where the charging cable sits, grab the cable, move towards the vehicle, and then plug the cable in. When the vehicle is finished charging, he/she would then unplug the cable, and sit it back in its original place for later use.  
To automate the whole process using the ACS, we set two types of commands, those performed by the mobile platform (moving the whole ACS), and those performed by the robot arm (moving only the griper). When an AEV enters the garage, it sends a _charge request_ to the ACS, which sits in an idle state. It is worth noting that the idle state of the ACS means that the gripper (and consequently the camera mounted on it), are facing the wall and that the wall-plug identifier is visible, as seen in Figure 1). When receiving the _charge request_, the ACS aligns itself to the wall-plug (only the mobile platform moving at this stage). Once the ACS is aligned to the wall-plug (at a predefined distance from the wall-plug), the _unplug cable from wall-plug_ sequence begins, taking the wall-plug marker as a reference for the gripper to grasp the power-outlet. Once the power-outlet is unplugged from the wall-plug, the base of the robot arm turns 180 degrees to face the AEV. A new aligning sequence starts to align the ACS to the AEV. Once the ACS is at a predefined distance from the AEV, the plugging sequence begins. Once plugged in, the ACS informs the AEV that it successfully plugged the cable in, and the charging process begins. Once the AEV has recharged its batteries, or reached an acceptable level that allows it to continue its field tasks, it sends an _unplug request_ to the ACS, the latter starts unplugging the power-outlet from the AEV. Once the power-outlet is unplugged from the AEV, the robot arm turns again 180 degrees to face the wall, and the ACS aligns itself to the wall-plug. When the ACS is aligned again to the wall-plug, the ACS plugs the power-outlet into the wall-plug, and returns to the idle state, waiting for new commands from other ASVs. The whole decision-tree is illustrated in Figure 5.  
Figure 5: The decision-tree scheme of the ACS.
# Power Control Techniques for Interference Management-
## 2 Systematic Review Method
### Selection and Data Extraction
#### 2.3.2 Inclusion and Exclusion Criteria  
The procedure of a systematic literature review must be detailed in sufficient detail for literature reviews to be dependable and independently repeatable [27]. Other researchers can use this information to follow the same procedures and obtain the same conclusions. The report should describe the reasoning behind each inclusion and exclusion criteria [32; 33], focusing on the inclusion and exclusion criteria. The following inclusion and exclusion criteria were defined:  
1. The criteria for inclusion were the following: * If the content of the article seemed to discuss modelling in 5G wireless networks, we obtained its full reference (including author(s), year, title, etc.), interference management approach(es) or technique(s), and the country. * Articles were limited to those that included specific strategies for resource management in 5G wireless networks. * Journal articles that mentioned power control in 5G wireless networks. * This paper suggests collecting data from the following databases: Scopus, Web of Science, and IEEE Xplore Digital Library. * Journal articles written in English. (ii) The criteria for exclusion are: * Journal articles that described no resource management modelling or techniques in 5G wireless networks were excluded. * Any study that is not about 5G wireless networks. * The article does not describe any resource management models in 5G wireless networks. * Studies before 2018. * Published data not available.  
Figure 2: PRISMA flow diagram procedure. Adapted from [31].  
After applying the inclusion and exclusion criteria, 2029 journal articles remained for the next step of the procedure.
## 5 Mitigation of Pharmaceutical Pollutants by Nanomaterials
### Interaction of Pharmaceuticals and Chemical Nature of Adsorbents  
With the increased usage of medicines in people and animals, there is a growing demand for pharmacological expertise. The fundamental physicochemical properties of pharmaceutical compounds, particularly their sorption characteristics, have a considerable influence on their environmental behavior. Adsorption through physical means: the sorption of hydrophobic, non-ionic substances is principally caused by hydrophobic (including weak van der Waals) and electron donor-acceptor interactions [108]. Pharmaceutical compounds contain moieties with a wide variety of chemical characteristics (polar, apolar, and charged sections), making it difficult to evaluate their sorption behavior using a single measure, such as logP or solubility [133].  
Surface complexation: its inner- and outer-spheres generate multiatom structures (e.g., complexes) with unique metal-functional group interactions, which is important for the adsorption of pharmaceuticals on carbon adsorbents. Surface complexation and electrostatic interaction impact the adsorbent's adsorption capacity, and the inner charges of the adsorbent may influence more to the adsorption capacity than the adsorbent's surface charges [134].  
Ion exchange and electrostatic interaction: ion exchange between pharmaceuticals and protons on oxygen-containing functional groups, such as carboxyl and hydroxyl groups, is one of the key mechanisms for pharmaceuticals adsorption by carbon adsorbents. For ionizable pollutants, pH determines the amounts of coexisting charged species (i.e., anion, cation, zwitterion, and neutral) and their sorptive interactions with complementary charged sorbents [135]. If the adsorbent surface's isoelectric point is reduced from 6.0 to 4.8, electrostatic contact between the adsorbent and ions rises throughout a larger pH range.
## 3 Results and Discussion
### Multi-Objective Optimization Results and Analysis  
#### 3.4.1 Sensitivity Analysis Results and Discussion  
The sensitivity of seven optimization parameters to the turntable's natural frequency, the maximum deformation, and the total mass were analyzed. The results are as follows:  
It can be seen from Figures 24-26 that parameters \(\mathrm{n_{2}}\), \(\mathrm{n_{4}}\), \(\mathrm{n_{5}}\), and \(\mathrm{n_{7}}\) are sensitive to the first-order natural frequency and are negatively correlated with the first-order natural frequency. Parameters \(\mathrm{n_{2}}\), \(\mathrm{n_{4}}\), \(\mathrm{n_{5}}\), and \(\mathrm{n_{7}}\) are sensitive to the maximum deformation and positively correlated with the maximum. Parameters \(\mathrm{n_{1}}\), \(\mathrm{n_{3}}\), and \(\mathrm{n_{7}}\) are sensitive to total mass, where \(\mathrm{n_{1}}\) and \(\mathrm{n_{7}}\) are negatively correlated with total mass and \(\mathrm{n_{3}}\) is positively correlated with total mass. Since parameter \(\mathrm{n_{6}}\) has a small contribution to the optimization, the other parameters are sensitive to the dynamic and static characteristics of the direct-drive turntable. Therefore, only parameters \(\mathrm{n_{1}}\)-\(\mathrm{n_{5}}\) and \(\mathrm{n_{7}}\) were retained for size optimization.  
It can be seen from Figures 27-29 that the optimization parameters are related to the first-order natural frequency, maximum deformation, and mass, but the correlation is irregularly distributed. Increasing or decreasing a certain parameter cannot improve the dynamic and static characteristics or the mass of the direct-drive turntable. Since it is difficult for all the optimization parameters to achieve the optimal solution simultaneously, it is necessary to carry out further multi-objective optimization to obtain the optimal value.  
The main index to evaluate the response surface is the decision coefficient R\({}^{2}\), with the best value being R\({}^{2}\) = 1. The response surface determination coefficients of the first-order natural frequency, minimum deformation, and total mass of the direct-drive turntable are 0.99873, 0.9887, and 1. The optimum root means the square error is 0, and the root means the square errors of response surface for the first natural frequency, minimum deformation, and total mass are 0.39775, 2.3254 \(\times\) 10\({}^{-6}\), and 1.8594 \(\times\) 10\({}^{-13}\), respectively. The response surface model has a good fitting degree in terms of the above parameters.  
The closer the normalized curve of the test points and the verification points are to the X- and Y-angle bisector, the better the prediction result is. The prediction error is greater if it is far away from the X- and Y-angle bisector. The normalized curve of this test is shown in Figure 30.  
By observing Figure 30, we can see that a few points are not on the X- and Y-angle bisector. The other points are almost on the X- and Y-angle bisector, and the prediction accuracy is higher.