esa-sceva/satcom-chunk-collection huggingface dataset:
# Monocular Vision Guidance for Unmanned Surface Vehicle Recovery
## 1 Introduction
The recognition of ArCuo markers is used for robot localization. [PERSON] [26] utilized the internal circular refueling port on rotary-wing aircrafts as a marker for recognition. This marker is used in autonomous docking and refueling tasks for unmanned drones. Autonomous recovery technology for USVs based on vision is currently a research focus in the field of marine technology. To solve the problem of large GPS positioning errors within a short range which prevent accurate guidance of unmanned surface vehicles (USVs) returning to their mother ships, this paper proposes a close-range recovery guidance method based on monocular vision for USVs. Firstly, a two-level circular artificial marker is designed for target localization at different distances. Secondly, the marker is initially extracted using color segmentation, and then, ellipse fitting based on least squares estimation is employed to obtain the center points of the eight ellipses corresponding to the marker. Subsequently, a correction method is proposed to address the issue of noncoincident center points of the two circles in the marker on the imaging plane. Finally, based on the corrected pixel coordinates of the four circular markers, the PNP method is utilized for localization, guiding the USV back to the mother ship. The main contributions of this paper are as follows:  
* Designed an artificial marker consisting of a circular ring for close-range visual localization. * Utilized the least squares method to obtain the pixel coordinates of the centers of the two circles within the ring and addressed the issue of noncoinciding centers caused by perspective transformation using the cross-ratio invariance. * Analyzed the influence of different distances on positioning angles and the influence of different angles on positioning distances. Through experimental comparison, it has been demonstrated that the method proposed in this paper is practical for close-range positioning and can be applied to recovery guidance for unmanned boats. The structure of this article consists of four parts. The first part is the introduction, which provides a detailed description of the research background and significance, and discusses the structure and contribution of the present study. The second part is the content of the research methods, which provides a detailed description of the content and innovation of the proposed methods. The third part is the experimental results and analysis, which validates and analyzes the proposed methods through experiments. The fourth part is the conclusion, which summarizes this study and outlines the limitations of the proposed methods and potential future directions.
# GAN-Based Inversion of Crosshole GPR Data to Characterize Subsurface Structures
## 2 Methodology
### High-Resolution GAN  
The low-resolution GAN establishes an end-to-end non-linear relationship between crosshole GPR data and the corresponding low-resolution defect reconstruction image that has only 64 \(\times\) 64 pixels. In order to image the inverted targets more precisely, we designed an additional high-resolution GAN to refine the defect reconstruction image derived from the low-resolution GAN and improve the inversion resolution to 256 \(\times\) 256. The task of the high-resolution GAN is no longer to establish the mapping relationship between the crosshole GPR image and the defect reconstruction image. Instead, it is used to denoise and enhance the resolution of the low-resolution defect reconstruction image. In the high-resolution GAN, a U-Net structure named high-resolution feature extraction network is used to extract high-resolution features from the defect reconstruction images produced by the low-resolution GAN. The image is first enlarged to 256 \(\times\) 256 \(\times\) 1 by four deconvolutional layers and then fed into ResNet50 V2 for high-order feature extraction. Then, 12 cascaded deconvolutional layers are implemented to enlarge the size of the extracted feature map to 256 \(\times\) 256 \(\times\) 1. Horizontal connections are also set between the same-size feature maps of ResNet50 V2 and the deconvolutional network to comprehensively utilize the feature vectors in different stages and suppress gradient vanishing. After high-resolution feature extraction, we use a high-resolution inversion network with the same structure as the low-resolution inversion network to invert the high-resolution features to high-resolution defect reconstruction images. We also use a neural network with a fully connected layer of 256 neurons and five cascaded convolutional layers as the high-resolution discriminator to form the GAN architecture. The high-resolution discriminator is employed to judge the authenticity of the high-resolution defect images. Table 1 summarizes the main layers in the proposed framework and the shape of each layer. For each training batch, the shape of the feature map flowing in the network is three-dimensional, and the three dimensions are width, height, and channel (W, H, C), respectively. In general, the larger the number of channels in the feature map, the more abstract the information contained in the feature map [25].
# Compact Ultra-Wideband Series-Feed Microstrip Antenna Arrays for IoT Communications
## 1 Introduction
According to the reported works, it can be seen that log-periodic structures are occasionally used, using different radiating elements. However, they are rarely applied at high frequencies and appropriately designed to allow for high bandwidths, ensuring high gains and compact dimensions, which enable solutions suitable for the present and future of communications, namely in IoT networks. In this paper three different log-periodic microstrip antenna arrays are designed to operate at a central frequency of 28 GHz but with wideband characteristics and with high gain. Besides the combination of these two properties, these solutions also have reduced dimensions, allowing them to be considered when integrating sensors in IoT networks. This paper is divided into four sections. In Section 1, the work done in this paper is introduced. Section 2 explains the approaches and methods used in the design of the antenna arrays according to the proposed objectives. Section 3 presents the characterization of the designed structures, in terms of simulation and measured results. Lastly Sections 4 and 5 present the discussion of the obtained results and reports the main conclusions, respectively.
# Time-Series Classification Based on Fusion Features of Sequence and Visualization
## 3 Model Design  
The proposed framework consists of two parts, LSTM-A and CNN-A (as shown in Figure 1). LSTM-A is pre-trained with the loss function \(AUX_{1}\) on raw TS data and CNN-A is pre-trained with the loss function \(AUX_{2}\) on Area Graphs converted from the raw TS data. After LSTM-A and CNN-A are well-trained, raw test TS data are input to the LSTM-A to extract sequence features and corresponding Area Graphs to CNN-A for visualization features. Fusion features are obtained through a fully-connected layer concatenating the sequence features and visualization features. The final classification operation is realized by a softmax layer. An Attention mechanism is used to make the model focus on key sub-sequences and sub-regions which contain more discriminative information for TSC.
# Study on Surface Roughness of Sidewall When Micro-Milling LF21 Waveguide Slits
## 1 Introduction
Research has shown that the ANN method is more suitable for the prediction of surface roughness in the micro-milling process. The GMDH on a polynomial basis is a feed-forward neural network commonly used for data mining, knowledge discovery, the prediction of complex system modeling, optimization and pattern recognition. It has higher stability compared with other ANNs, as it is capable of adaptive synthesis by automatically filtering the model's structure and its variables, for example, avoiding the over-fitting and under-fitting of the model structure, and reducing the influence of subjective factors on the network. Moreover, it can express the functional relationship of input and output as polynomial-based equations. These explicit model equations reveal the correlation and dependence amongst the variables [17]. Therefore, it is appropriate to apply the GMDH to surface-roughness prediction. In summary, spindle speed and feed per tooth have a significant effect on the surface roughness in micro-milling. As far as we know, previous work has focused only on the surface roughness of sidewalls when micro-milling slots, i.e., only the Ra obtained after single machining is studied when, actually, the study of surface roughness is of critical significance for revealing machining mechanisms. We conducted experiments on the micro-milling of LF21-slotted waveguide antennae and studied the influence principle of cutting parameters (i.e., the spindle speed and the feed per tooth) on surface roughness. Furthermore, we built a surface-roughness prediction model based on the GMDH. In the construction of this model, machining output variables and input variables such as spindle speed and feed per tooth were considered with regard to surface roughness. The quadratic K-g polynomial was also selected, along with the Regularization criterion, i.e., organizing the reference function and the screening during the training process for the GMDH. The results signified that the GMDH model helped to produce surface-roughness predictions with a high level of accuracy.
# Fault Analysis of a Small PV/Wind Farm Hybrid System Connected to the Grid
## 2 Proposed Hybrid System Architecture  
A schematic with only one line of a small hybrid PV/wind farm system is shown in Figure 1. Two PV arrays that can produce a combined maximum of 145.2 kW at 1000 W/m\({}^{2}\) of solar irradiation make up the PV farm. Each section of the PV array is made up of 66 strings parallel, each of which is made up of five modules joined in series. A DC/DC converter is attached for every PV array. The results of the boost converters are linked to a 500 V common DC bus. Each MPPT controls its own boost. To achieve the highest power output from the PV array, the MPPTs employ the Perturb and Observe (P and O) technique in order to change the voltage between the terminals. A three-level VSC translates 500 V DC to 260 V AC while maintaining a power factor of one. The inverter is coupled to the grid by a three-level coupling transformer with a 400 kVA 260 V/25 kV rating. Power from the wind farm, which comprises four 1.5 MW wind turbines, is exported to a 120 kV grid via a 5 km 25 kV feeder and is coupled to a 25 kV system of distribution. A simulation of a wind farm with a capacity of 6 MW is created by utilizing two groups of wind turbines that each have a 1.5 MW output. Wind turbines employ SCIG. A wind turbine with variable pitch powers the rotor while a direct connection to the 60 Hz grid exists between the stator winding. When wind speeds surpass the standard velocity (9 m/s), the pitch angle is adjusted to maintain the generator's output power at its standard level. The constant no-load requirement is met by the fixed bank of capacitors that are attached to the low voltage bus of each wind turbine (for every set of 1.5 MW turbines, 400 KVAR is required). The grid model is made up of conventional 120 kV equivalent transmission lines and 25 kV distribution feeders.
# Performance Evaluation of IEEE 802.11 ah Networks With High-Throughput Bidirectional Traffic
## 4 Performance Evaluation and Discussion
### Simulation Environment and Setup
#### 4.1.3 Immediate Reply  
We also improved bidirectional scheduling in IEEE 802.11 ah by enabling the scheduling of immediate replies at the AP. If a station wakes up to transmit data in its corresponding RAW slot, it remains awake after the completion of the transmission until the end of the RAW slot. This enables the AP to check if the resulting reply can still fit in the same RAW slot. If so, the AP can immediately transmit the frame, and thus significantly reduce the round trip time. In case the reply does not fit in the current RAW slot, the AP is forced to schedule its transmission after the next DTIM beacon, prolonging the RTT for the length of the DTIM beacon interval. To be able to send immediate replies, the AP must keep track of whether stations are active by checking the last sent DTIM bitmap or whether data was received from the station during the current RAW slot.  
In reality, it is unlikely for an AP to host the TCP server itself, introducing extra delay because of the RTT between the AP and the TCP server. For sufficiently large delays, the ability to reply within the same slot becomes infeasible. However, if the AP spoofs ACKs like in satellite networks [35] this problem could be somewhat mitigated. On the other hand, this could introduce other problems such as losses in the rare cases in which the spoofed ACK does not correspond to reality, i.e., in case that ACK was never received, or in case the re-transmission time is exceeded. In these cases, the spoofed ACK does not correspond with the actual fact that the packet never arrived.
## 5 Conclusions  
The evolution of iceberg A68A, a giant tabular iceberg calved from the Larsen C Ice Shelf in July 2017, was analyzed in terms of area, drift speed, rotation, and freeboard using Sentinel-1 SAR images, CryoSat-2 profiles, and environmental factors predicted by the MERRA-2 reanalysis data. The iceberg showed little change in area over time, except in mid-August 2017 and mid-May 2018 when it decreased sharply due to ice calving, and in September 2018 when it increased possibly due to its longitudinal stretching by melting of surrounding sea ice. Air temperature trends had little effect on iceberg area. At the end of November 2018, the iceberg area (5660.1 km\({}^{2}\)) was just 2% less than at the time of formation (5786.3 km\({}^{2}\)). The drift speed and rotation of the iceberg prior to mid-July 2018 were very small due of the drift being interrupted by the Larsen C Ice Shelf, a high concentration of surrounding sea ice, and a shallow seabed. The iceberg escaped from the bay-shaped region in front of the Larsen C Ice Shelf in mid-July 2018, but only rotated without significant movement due to the shallow seabed near the Bawden Ice Rise. The iceberg freeboard measured using CryoSat-2 observations has dramatically decreased since February 2018 (\(-\)0.80 \(\pm\) 0.29 m/year) owing to basal melting by warm seawater in summer and an increasing relative velocity in winter 2018. The longitudinal stretching could also contribute to the decrease in freeboard. Considering the increase in snow depth, and assuming that the maximum radar return measured by CryoSat-2 is from the snow surface on the iceberg (0.82 \(\pm\) 0.06 m/year), the rate of thickness change was estimated to be \(-\)12.89 \(\pm\) 3.34 m/year between February and November 2018. The volume loss of the iceberg by the ice thinning and calving was approximately 1.12 \(\times\) 10\({}^{5}\) tons.  
These observations reflect the initial evolution of a large tabular iceberg that is unable to drift freely due to surrounding terrain and sea ice. The study demonstrates how Sentinel-1 SAR and CryoSat-2 SIRAL observations can be very useful for investigating the evolution of icebergs. In the near future, iceberg A68A is expected to show much larger-scale changes than have been observed so far. Future study will analyze the influence of such rapid changes on the Antarctic Peninsula marine environment and the flow characteristics of the Larsen C Ice Shelf.  
Conceptualization, H.H.; Methodology, H.H.; Formal analysis, H.H., S.L., J.-I.K., S.H.K. and H.-c.K.; Writing--original draft preparation, H.H., S.L., J.-I.K. and S.H.K.; Writing--review and editing, H.H., S.L., J.-I.K. and S.H.K. This research was funded by the Korea Polar Research Institute (KOPRI), grant number PE18250 and PE19120. The authors declare no conflict of interest.
## 3 Model and Research
### Adaptive Control Navigation System
Similar idea is presented for the \(y\) axis, limiting the target velocities during the high speed flight. The L1 adaptive controller is presented to control the drone velocity. The idea is to consider all nonlinear forces (gravity and aerodynamics) as disturbing factors. Then, the control action remains linear, and the task is reduced to the implementation of a nonlinear system with a stability margin sufficient to operate over the entire range of disturbances. In this case, the difference between position holding and high-speed flight disappears in the system, and the system becomes completely controllable. Figure 8 represents its test and tuning results. Figure 8a depicts the time transient for the \(z\) axis with various coefficients m and c.  
At the Figure 8\(V_{x}\) is the drone velocity and \(A_{x}\) is the drone acceleration. The simulation results for different model dynamics coefficient m and filter coefficient c are presented in time (Figure 7a) and phase trajectory (Figure 7b) domains. As can be observed, the coefficient m is in charge of the system's dynamics, while the coefficient c acts as a damping coefficient and influences its stability. The phase trajectory Figure 8b for the provided system is the \"stable focus\" type, allowing us to discuss the similarities of transients to linear systems. As a result of the comparison, the system with coefficients m = 10 and c = 7.5 produces the best outcomes. The final tuning is performed using parametric optimization. The optimization criterion is (7):  
\[\mathbf{Q}=\mathbf{K_{V}}\mathbf{\int_{0}^{T}\;(V_{ct}-V_{c})dt}+\mathbf{K_{V }}\mathbf{\int_{0}^{T}(\psi_{t}-\psi)dt} \tag{7}\]  
Figure 7: Control system elements: (**a**) L1 controller; (**b**) dynamic limits block. where Q is the quality criteria, T is the simulation time, \(\rm V_{ct}\) is velocity target and \(\rm\psi_{t}\) is heading target. The resulting variables are: \(\rm m_{t}=5.55\), \(\rm c_{t}=18.15\), \(\rm m_{p}=2.37\), \(\rm c_{t}=47.84\), \(\rm m_{r}=1.75\), \(\rm c_{r}=24.45\), \(\rm m_{y}=2.1515\), \(\rm c_{y}=188.28\). The simulation with final settings will be shown below. As a result, an adaptive system for control of the aircraft is obtained and optimized. The resultant mechanism is universal and can be used for both fast flight and position holding.
## 3 Network Anomaly Detection
### The Structure of Network Anomaly Detection
#### 3.2.2 Enhancement via Adversarial-Based High-Dimensional Feature Transformation  
Our methodology leverages the adversarial concept in conjunction with an autoencoder to bolster the detection sensitivity towards subtle anomalies. This section further elucidates how this integration operates and the key components involved in this process.  
Specifically, \(AE_{2}\) is composed of the encoder \(E\) and the decoder \(D_{2}\). This module functions as the discriminator, initiating an adversarial competition with \(AE_{1}\). Analogous to \(AE_{1}\), \(AE_{2}\) also aims to reconstruct the input data, taking the generated features \(\mathcal{W}\) delivered by \(AE_{1}\) as input and providing high-dimensional features \(\mathcal{W}^{\prime}\) as output. However, a contrast to \(AE_{1}\) is that \(AE_{2}\) seeks to maximize the reconstruction error \(\left\|\mathcal{W}^{\prime}-\hat{\mathcal{W}}\right\|_{2}\).  
In this configuration, the generator \(AE_{1}\) strives to generate progressively realistic data, while the discriminator \(AE_{2}\) improves its sensitivity and accuracy in distinguishing the original features from the generated features. Through this adversarial learning process, both the generator and discriminator mutually and iteratively refine their performances, fostering their capabilities via a competitive dynamic. This procedure ultimately augments the model's ability to discern subtle anomalies.  
The integration of these components forms a composite model, exhibiting enhanced robustness. The model not only improves the anomaly detection accuracy of autoencoders, but also addresses potential issues such as training non-convergence and pattern collapse associated with the GANs.  
In conclusion, the fusion of these processes and components forms an innovative approach, enhancing the model's overall robustness. The effectiveness of the autoencoder in anomaly detection is amplified, and challenges typically associated with GANs are effectively managed. This results in a more stable training process and improved performance in anomaly detection.
## 2 Proposed System
### Safety Control  
By taking into account both the safety and comfort of a person in the presence of the robot during task execution [27; 28], the system conducts speed control based on their relative distance. It regulates robot speed according to the distance between a person and a robot, as shown in Figure 4[23]. The robot halts completely when it enters the region in red, which is set to be 0.13 m and 1.3 m for temperature measurement and medicine delivery, respectively, and decreases its speed when it is within the region in yellow, set to be 0.4 m and 1.7 m as the warning regions; otherwise, a normal speed is maintained, although these distances are subject to robot speed and other considerations. To achieve a smooth transition from the initial normal speed \(V_{ini}\) to an adjusted deceleration speed \(V_{adj}\), a sigmoid function \(U\), shown in Figure 5, is utilized, as described in (5):  
\[V_{adj}=U(D)\times V_{ini} \tag{5}\]  
where \(D\) is the distance between the person and the robot. \(D_{dec}\) is for deceleration, and \(D_{stop}\) is for stopping.  
Figure 3: The medication-taking action is further divided into five sub-actions.  
In addition to speed control, to further raise the comfort and expectation of the subjects during temperature measurement, the system will also constrain the approaching direction of the robot arm toward his/her forehead. As shown in Figure 6, the robot arm is constrained to move toward the subject's forehead in a direction within a 60\({}^{\circ}\) range. To achieve it, the system will search for joint solutions corresponding to the points on the approaching path that satisfy this directional requirement. Based on the discussions above, the algorithm developed for temperature measurement is organized as follows:  
Algorithm for temperature measurement, restricting the end-effector orientation of the robotic arm and adjusting speed based on distance for patient safety and comfort:  
1. Confirm that the patient is seated and stationary.
2. Utilize the depth camera on the robotic arm to calculate the coordinates of the patient's forehead.
3. Compute the path from the robotic arm to the forehead and restrict the orientation of the end-effector.
4. Dynamically adjust the arm's speed based on the distance from the patient.  
Figure 4: Speed regulation according to the distance between human and robot.  
Figure 5: Speed smoothing between normal navigation, deceleration, and full stop.  
Figure 6: The robotic arm is regulated to approach the human’s forehead within a 60\({}^{\circ}\) range of orientation.