esa-sceva/satcom-chunk-collection huggingface dataset:
# U-Net-Based Semi-Automatic Semantic Segmentation Using Adaptive Differential Evolution
## 2 Proposed Method
### U-Net  
The U-net [25] is a kind of convolution network using an autoencoder for semantic segmentation. Figure 3 shows the U-net architecture we applied. The first half of the U-shaped network is an encoder, and the second half is a decoder. The encoder consists of convolution layers and pooling layers, which repeatedly perform two 3 \(\times\) 3 convolutions, each followed by a rectified linear unit (ReLU) and a 2 \(\times\) 2 max pooling with stride 2 for downsampling. The number of feature maps increases as the layers get deeper, with the deepest layer having 1024 feature maps. The decoder consists of deconvolution layers and upconvolution layers with a 2 \(\times\) 2 upsampling and two 3 \(\times\) 3 convolutions, each followed by a ReLU. The feature maps of the encoder part at the same depth are concatenated with the feature maps obtained by upsampling, and a convolution is performed. The last layer performs a 1 \(\times\) 1 convolution and assigns a class to each pixel. It uses skip connections to combine the high-level semantic feature maps from the decoder and corresponding low-level detailed feature maps from the encoder. Due to the skip connection and the relatively small network described above, the U-net approach is often used in medical image segmentation.
# Learning-Free Text Line Segmentation for Historical Handwritten Documents
## 1 Introduction  
Digital handwritten documents are not easily explorable in their raw form but need to be transcribed further into machine readable text. Certainly, manual transcription of a large number of documents is not feasible in a reasonable time. Hence, there is a practical need for reliable handwritten document image processing algorithms. Text line segmentation is an essential operation and prerequisite for many document image analysis tasks. Advancement in text line segmentation performance will boost the performance of other tasks, such as word segmentation [1; 2] and word recognition [3; 4].  
Text line segmentation consists of text line detection and text line extraction. Text line detection locates each text line by its baseline or \(x\)-height representation. Text line extraction in turn leads to polygonal or pixel level representation of text lines. Extraction level representation is more precise and useful for higher level document image analysis tasks. With the advances in deep learning, numerous learning-based methods have been proposed for text line segmentation of handwritten documents. Learning-based methods [5; 6; 7; 8] can inherently handle the problems arising from complex layout of text lines and heterogeneity of documents. Henceforth, the recent competition datasets [9] (Figure 1) are more challenging than the prior ones [10; 11; 12; 13].  
In the last decade several learning-free methods for text line extraction of handwritten documents have been proposed [14; 15; 16; 17]. However, we observe only limited attempts where learning-free algorithms have been used for text line detection of challenging historical documents [9]. This is particularly interesting as learning-based algorithms require a vast amount of labeling effort.  
A learning-free algorithm for text line extraction is proposed in [17] where text lines are detected using multiscale second derivative of Gaussian filters and extracted using an energy minimization (EM) function. The present article extends this approach for detecting text lines of challenging historical documents. The new method proposes a robust character height estimation even with the presence of noise and chains of touching characters among multiple text lines. In addition, the extraction phase includes a mechanism to split the consequently touching text lines. Apart from these, we mathematically formulate automatic scale selection together with second derivative of anisotropic Gaussian and thoroughly present and discuss the evaluation of parameters.
# Dual-Band MIMO Antenna for n79 and sub-7 GHz Smartphone Applications
## 3 Results and Discussion
### MIMO Performance Parameters
#### 3.3.4 Channel Capacity Loss (CCL)  
Another important parameter of MIMO system performance is the channel capacity loss (CCL), which defines the loss of information during transmission through the correlated in-space antenna elements of the MIMO array.  
\[\text{CCL}=-\log_{2}\ det(\psi) \tag{4}\]  
The CCL simulated and measured data are presented in Figure 12 for all the simulated antenna pairs. The CCL values are computed for the same five antenna pairs as the ECC plot above. From the above analysis, it was found that the CCL values of all the antenna pairs are less than 0.4 bps/Hz, both in simulation and measurement. Consequently, the values of the upper bands are shifted to higher values. The simulated values spread over the lower bands are below 0.4 bps/Hz from [28], and the simulated values for the higher bands are below 0.5 bps/Hz.  
In addition, the results that have been obtained are compared with those of the simulated results, showing that the actual MIMO antenna is more efficient. This implies that the antenna design does not lose much channel capacity, hence enabling adequate transmission and reception of information in the MIMO system.  
The proposed MIMO antenna design is compared with recently published articles in Table 1. The proposed MIMO design has higher isolation and ECC than the published ones. This paper presents a novel open-ended scissor-shaped antenna with partial and connected ground for n79/n102/n104 bands. This design can benefit high-frequency applications for the future such as 5G and IoT applications, enhancing inherited higher impedance matching, radiation efficiency, and the variety of connections for modern wireless systems.  
Figure 12: Channel capacity loss of proposed ten-port MIMO antennas.
# Use of SAR and Optical Time Series for Tropical Forest Disturbance Mapping
## 2 Material and Methods
### Data Sets  
For Peru, the set of optical images consists of data from Landsat 8 (WRS: 008-064) and Sentinel-2 (granule: 18 MUU) from 03/2015-05/2017. We used optical data from 03/2015 to 03/2016 to generate the initial forest/non-forest mask. Forest disturbance mapping is based on all optical and SAR data from 03/2016 to the end of 12/2016. Optical data from 2017 were only used to confirm the mapped disturbances in the confirmation loop that is described in the respective chapter on data processing. Sentinel-1 data is from both ascending and descending relative orbits (orbit 43--ascending; orbit 49--descending). The time interval between two consecutive acquisitions with the same orbit orientation is 24 days. Images in ascending orbit are acquired three days after images in descending orbit. The polarization mode is single VV until the end of 2016. In short, for Peru, we generate a forest/non-forest mask for status March 2016 and a disturbance map for the period March-December 2016.  
Figure 1: Test site Peru.  
For Gabon, we used optical data from Landsat 8 (WRS: 185-061) and Sentinel-2 (granule: 32 MPD) from 2013 to 2017 and Sentinel-1 SAR data (VV and VH polarizations) from 2015 to 2017. We calculated the initial benchmark forest/non-forest mask based on Landsat 8 data from the year 2013. Initially, we calculated all changes from 2013-2017 from optical data only. For the aim of this study to combine optical and SAR data, we only evaluate the changes between 15 December 2015 and 8 April 2017 in order to have temporally consistent stacks of data from both optical and SAR sensors. In short, for Gabon, we have a FNF map for status 2013 and a forest disturbance map for December 2015-April 2017.
# Tolerance-Aided Interference Degradation for Optical OFDM in Power-Constrained Systems
## 4 Numerical Results and Comparison
This observation aligns with the fact that, for small-scale signals, the impact of peak power clipping can be marginalized, allowing us to position the decision threshold beyond the peak power value. This strategic placement effectively aids in noise reduction. Conversely, when the transmitted signals exhibit a relatively larger scale, the presence of peak power clipping distortion becomes non-negligible. Consequently, it necessitates a smaller decision threshold, falling below the peak power level, in order to effectively mitigate a portion of the added noise. This empirical result underscores the fact that, with a smaller peak power constraint imposed on the light sources, a lower value of the ratio \(\frac{P_{\max}-\Delta}{P_{\max}}\) is needed to achieve optimal BER. Figure 8: BER performance of the proposed algorithm with a varing decision tolerance \(\Delta\). 32-QAM is applied. Figure 7: BER performance of the proposed algorithm with a varing decision tolerance \(\Delta\). 32-QAM is applied, and an ideal channel is assumed.
# GNSS Spoofing Detection and Mitigation Based on Maximum Likelihood Estimation
## 3 Review on MLE-Based Positioning
### Maximum Likelihood Estimation  
The probability density function (pdf) of the measurement vector \(\mathbf{x}\) can be expressed as Equation (3) since the noise vector \(\mathbf{w}\) is zero-mean AWGN with variance \(\sigma^{2}\).  
\[p(\mathbf{x};\mathbf{\tau},\mathbf{f},\mathbf{a},\sigma^{2})=\frac{1}{(2\pi\sigma^{2})^{\frac {K}{2}}}exp\Big{[}-\frac{||\mathbf{x}-\mathbf{G}\mathbf{a}||^{2}}{2\sigma^{2}}\Big{]}. \tag{3}\]Denote \(\{\tau,f\}\) as \(r\). The maximum likelihood (ML) estimates of the unknown parameters are given by [25]:  
\[\hat{r}=arg\max_{\tau}\{\mathbf{x}^{H}\mathbf{G}(\mathbf{r})[\mathbf{G}(\mathbf{r})^{H}\mathbf{G}(\mathbf{r}) ]^{-1}\mathbf{G}(\mathbf{r})^{H}\mathbf{x}\} \tag{4}\]  
\[\hat{\mathbf{a}}=[\mathbf{G}(\hat{r})^{H}\mathbf{G}(\hat{\mathbf{r}})]^{-1}\mathbf{G}(\hat{\mathbf{r}}) ^{H}\mathbf{x}. \tag{5}\]  
\[\hat{\sigma}^{2}=\frac{1}{K}[\mathbf{x}-\mathbf{G}(\hat{\mathbf{r}})\hat{\mathbf{a}}]^{H}[\mathbf{x }-\mathbf{G}(\hat{\mathbf{r}})\hat{\mathbf{a}}]. \tag{6}\]  
\[J(\mathbf{r};\mathbf{x})=\mathbf{x}^{H}\mathbf{G}(\mathbf{r})[\mathbf{G}(\mathbf{r})^{H}\mathbf{G}(\mathbf{r})]^{- 1}\mathbf{G}(\mathbf{r})^{H}\mathbf{x} \tag{7}\]  
We call \(J\) the cost function of MLE in the rest of the paper. The ML estimates of the parameters can be obtained by maximizing the cost function.
# The Path Planning of Mobile Robots Based on an Improved Genetic Algorithm
## 2 Environment Mapping  
In the application of path planning, the grid method is a commonly used modeling approach. This method discretizes the continuous planning space into a series of equally sized grids (grid cells), simplifying complex environmental information into a simple one -dimensional array format. Each grid cell corresponds to a region in the actual environment, and the specific attributes of that region are represented by the state of the grid map. To ensure the effectiveness of path planning, the size of each grid cell is typically determined based on the robot's own dimensions, the size of obstacles, and the characteristics of the working environment, in order to be applicable to non-grid environments.  
In the grid map, black grid cells represent obstacles, while white grid cells indicate the feasible free-movement areas. Additionally, to prevent the robot from exceeding the boundaries of the map during the path planning process, it is typically assumed that the perimeter of the grid map is completely surrounded by obstacles, thus forming a safe boundary constraint, as shown in Figure 1.  
The coordinates (x, y) of each grid cell can be uniquely mapped to a grid index, and the relationship between the grid index and coordinates is given by the following formula:  
\[\left\{\begin{aligned} &\text{x}=\text{floor}\big{(}\frac{\text{num}}{ \text{row}}\big{)}+1\\ &\text{y}=\text{mod}(\text{num},\text{row})+1\end{aligned}\right. \tag{1}\]  
\[\text{G}(\text{x},\text{y})=0 \tag{2}\]  
where floor is the floor function, num is the grid index, row is the number of columns in the grid map, and G(x, y) is the matrix of feasible grid cells.
# Hybrid Precoder Using Stiefel Manifold Optimization for Mm-Wave Massive MIMO System
## 3 Problem Formulation  
To maximize the spectral efficiency, \(\mathds{R}\left(\mathbf{F}_{RF},\mathbf{F}_{BB}\right)\), and the maximum mutual information, \(\mathbf{F}_{RF}\) and \(\mathbf{F}_{BB}\) are designed using Gaussian signaling over the Mm-Wave channel in the following manner:  
\[\mathds{R}\left(\mathbf{F}_{RF},\ \mathbf{F}_{BB}\right)=log_{2}\left(\left|\mathds{I}+ \frac{\rho}{N_{S}\ \sigma_{n}^{2}}\mathbf{H}\mathbf{F}_{RF}\mathbf{F}_{BB}\mathbf{F}_{RF}^{H}\mathbf{H}^{H}\right|\right) \tag{3}\]  
To maximize the spectral efficiency (3), the precoder and the combiner are jointly optimized. This paper concentrates on the design of the precoder, so there is the need to decouple the precoder section and the combiner section. The performance of the optimal hybrid precoder is nearly close to that of the optimal precoder of matrix \(\mathbf{F}_{opt}(N_{\text{T}}\times N_{\text{s}})\), which is obtained from channel matrix \(\mathbf{H}\). \(\mathbf{F}_{opt}\) is the first Ns column of the eigenvectors of \(\mathbf{V}\) generated by \(\mathbf{H}=\mathbf{U}\sum\mathbf{H}^{H}\), i.e., the SVD of channel matrix \(\mathbf{H}\). So, the formulated problem for a narrowband system (4) and an OFDM system (5) based on \(\mathbf{F}_{opt}\) can be written as  
\[\begin{array}{c}\min_{\mathbf{F}_{RF}\mathbf{F}_{BB}}\left|\left|\mathbf{F}_{opt}-\mathbf{F }_{RF}\mathbf{F}_{BB}\right|\right|_{F}\\ s.t.\ \left|\left(\mathbf{F}_{RF}\right)_{ij}=1\right.\\ \text{and}\ \left|\left|\mathbf{F}_{RF}\mathbf{F}_{BB}\right|\right|_{F}^{2}=N_{s}\\ \left|\left|\mathbf{F}_{RF}\mathbf{F}_{BB}\right|\right|_{F}^{2}=N_{s}\\ =\left|\left|\mathbf{F}_{BB}^{H}\mathbf{F}_{RF}^{H}\mathbf{F}_{RF}\mathbf{F}_{BB}\right|\right| _{F}\\ =\sqrt{Tr}(\mathbf{F}_{BB}^{H}\mathbf{F}_{RF}^{H}\mathbf{F}_{RF}\ \mathbf{F}_{BB}\mathbf{F}_{BB}^{H}\mathbf{F}_{ RF}^{H}\mathbf{F}_{RF}\mathbf{F}_{BB})\end{array} \tag{4}\]  
Narrowband systems have poor performance due to the effect of multipath fading. Therefore, the multi-carrier concept is used in the above problem for the OFDM millimeter-wave system and is expressed as  
\[\begin{array}{c}=\min_{\mathbf{F}_{RF}\mathbf{F}_{BB}\left|\right.}\ \sum_{k=0}^{K-1}\left|\left|\mathbf{F}_{opt}\left[k \right]-\mathbf{F}_{RF}\mathbf{F}_{BB}\left[k\right]\right|\right|_{F}\\ s.t.\ \left|\left(\mathbf{F}_{RF}\right)_{ij}=1\right.\\ \text{and}\ \left|\left|\mathbf{F}_{RF}\mathbf{F}_{BB}\left[k\right]\right|\right|_{F}^{2}=N _{s}\end{array} \tag{5}\]  
where \(K\) signifies the number of subcarriers and \(k\in[0,\ \ K-1]\) is the index of subcarriers. The spectral efficiency can be increased by transmitting the number of data streams from different users. These data streams should verify that the condition of existence is less than the number of RF chains in the analog section.
# Can LiDARs Replace Meteorological Masts in Wind Energy?
## 2 Test Site and Measurement Setup
It has a vertical range of 220 m and is configured such that below 60 m, it measures the wind speeds at 40 m, 50 m and 57 m, while above 60 m, it measures at an interval of 20 m. The V2 LiDAR completes one DBS cycle every four seconds and it has a sampling rate of roughly 1 Hz. The LiDAR is placed about 12.6 m from the met-mast. Before the actual measurement, it was confirmed that all five radial beams could avoid the met-mast. Table 1 summarizes the installation and measurement details of these instruments. Figure 3: Picture of (**a**) the meteorological mast; (**b**) the V2 profiling LiDAR.
# Research on the Obstacle-Avoidance Steering Control Strategy of Tracked Inspection Robots
## 2 Tracked Robot Structure  
To adapt to complex working conditions, a tracked robot with four swing arms and six tracks was developed for this paper, which can be applied to daily inspection work in a coal mine [20]. The body structure of the tracked robot is shown in Figure 1. The related parameters of the tracked robot are listed in Table 1. The robot's four swing arms were free to rotate by 360\({}^{\circ}\). During the course of the tracked robot steering on a horizontal hard road, the contact area between the track of the swing-arm driving wheel and the road surface was small, which had little influence on the overall steering of the tracked robot. Therefore, the influence of the swing arm track on ground steering is ignored in the study of robot steering. When the tracked robot was driving on a soft and muddy road environment, the track of the swing-arm of the tracked robot had greater contact with the road surface, which had a greater influence on the driving trajectory of the robot. In this case, it was necessary to consider the influence of the swing-arm on the steering control of the tracked robot.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Symbol** & **Parameters** & **Value** \\ \hline G & Body weight & 120 kg \\ \hline L & Length & 830 mm \\ \hline B & Width & 790 mm \\ \hline h & Height & 360 mm \\ \hline D & Wheelbase & 650 mm \\ \hline R\({}_{\text{w}}\) & Radius of driving wheel & 102 mm \\ \hline i\({}_{\text{c}}\) & Rear reduction ratio & 1:2 \\ \hline i\({}_{\text{d}}\) & Motor reduction ratio & 80 \\ \hline \(\eta\) & Transmission efficiency & 0.94 \\ \hline w\({}_{\text{d}}\) & Motor angular speed & 140 rps \\ \hline T & Motor torque & 28.2 N\(\cdot\)m \\ \hline v & Inspection speed & 2 km/h \\ \hline \hline \end{tabular}
\end{table}
Table 1: Main parameters of tracked robot.  
Figure 1: Tracked robot with four swing arms and six tracks. 1. Body; 2. Left track; 3. Left driving wheel; 4. Right track; 5. Right driving wheel.  
The two motors of the tracked robot drove the driving wheels on both sides and then drove the driven wheels to rotate through the tracks. The tracked robot realized various motion controls by controlling the speed difference of the driving motors, such as linear motion and steering motion, with an arbitrary radius.
# Mass Estimation-Based Path Tracking Control for Autonomous Commercial Vehicles
## 3 Path-Tracking Controller
### Stability Constraints  
In order to improve the lateral stability of commercial vehicles, the maximum steering angle is constrained by load and speed. The vehicle roll dynamics model is adopted, as shown in Figure 5. When the vehicle is in a steady-state turn, the risk of vehicle rollover can be represented by the rollover index \(R_{LT}\).  
The roll index \(R_{LT}\) is expressed as follows [20]:  
\[R_{LT}=\frac{2m_{s}}{mgl_{w}}[H_{s}(a_{y}-h_{s}\dot{\beta})+gh_{s}\beta] \tag{33}\]  
In the formula, \(H_{s}\) is the distance from the sprung mass center to the ground, \(h_{s}\) is the roll arm length, \(L_{w}\) is the wheelbase, \(\beta\) is the roll angle, and \(m_{s}\) is the load.  
When the rollover index is \(R_{LT}=0\), the vehicle has no risk of rollover. When \(R_{LT}=\pm 1\), the vehicle is in a turn. The outer wheels bear all the vertical load, while the inner wheels interact with the ground. The wheels are at the critical stage of rollover. In order to ensure the safe driving of the vehicle, the rollover index is set to \(|R_{LT}|=0.7\); thus, the stability constraint of the vehicle is expressed as follows:  
\[\delta_{L}=\arctan\left(\frac{R_{LT}mg(l_{r}+l_{f})+2m_{s}h_{s}(l_{r}+l_{f})( \tilde{B}L_{w}-g\beta)}{2m_{s}V_{x}H_{s}L_{w}}\right) \tag{34}\]  
The roll angle is a small angle during steady-state cornering. Therefore, the maximum front wheel steering angle is mainly constrained by the vehicle's load and speed. When it exceeds the safety limit, the vehicle stability constraint is more stringent than the actuator physical constraint, causing the feasible region of the front wheel steering angle to shrink.
## 4 Structural Sensitivity Analysis
### Sensitivity Analysis of the Maximum Deflection to the Pipeline Friction Coefficient  
The pipe friction coefficient is determined by the material itself, so it will inevitably lead to the change in tension control stress, which directly affects the change in structural deflection [31,32]. Therefore, it is necessary to analyze the sensitivity of the pipeline friction coefficient, and subsequently control the value of the pipe friction coefficient.  
As shown in Figure 11, under the condition of the maximum cantilever, the maximum deflection at the cantilever end per span is insensitive to the variation of the pipeline friction coefficient. After changing the pipe friction coefficient, the maximum variation of  
Figure 10: Relationship between the change in internal prestress and the maximum variation value of the deflection per span.  
Figure 9: Variation of the maximum deflection at the cantilever end per span.  
the deflection of each span occurs at the mid-span position, as shown in Figure 12. Under the condition of the maximum cantilever, the maximum variation value of the deflection at the mid-span position of each span presents a linear relationship with the change in pipe friction coefficient. The variation ranges of the deflection corresponding to the first and second spans are the same and less than that of the other three spans. The variation values of the deflection of the third and the fifth span are the same. Among them, the deflection of the fourth span is most sensitive to the variation of the pipe friction coefficient. When the pipe friction coefficient changes by 5%, the deflection changes by 0.3%. Above all, the change in pipeline friction coefficient has almost no effect on the bridge alignment, which is a non-sensitive parameter.
# Design Approach and Mechanism Analysis for Cavitation-Tolerant Torque Converter Blades
###### Abstract
A large pressure gradient between the pressure side and the suction side will cause a jet to form on the suction side. This will worsen the load distribution on the blade surface, and the formation of high-speed jets will result in slot cavitation. Therefore, the conventional slotting method cannot fully suppress cavitation. Some researchers optimized the geometric profile or machined the groove structure near the leading edge of the suction surface  
Figure 1: Application of different slotting designs for cavitation suppression. (**a**) Cavitation suppression in hydrofoils; (**b**) cavitation-tolerant hydroturbine guide vane. to readjust the load distribution on the blade surface, but these methods cannot fully suppress cavitation, and they lead to increased local loss. The addition of an auxiliary flow control device can suppress a cavitation surge effectively, but it is difficult to apply to fluid machinery because of its high cost. Given these shortcomings in suppressing cavitation, two suppression slot methods are proposed in this study: (1) The fluid is sucked in from the pressure side and ejected out of the suction side; both sides of the pressure and suction sides are slotted (Plan 1). (2) The fluid is sucked in from the downstream of the suction side and ejected out of the upstream of the suction side; only one side of the suction side is slotted (Plans 2-4). The remaining content of this paper is organized as follows: Section 2 describes the computational fluid dynamics (CFD) calculation settings and transient cavitation simulation of the torque converter. It shows the distribution of cavitation in the flow field of the torque converter and the threat caused by cavitation. In Section 3, four slot designs are proposed for stator cavitation suppression, and the cavitation internal flow field and hydrodynamic performance of the slot solutions are compared. In Section 4, the authors quantitatively evaluate the cavitation risks of the four stator suppression slots, systematically discuss the stator cavitation under the various slot widths and slot positions, and reveal the cavitation suppression mechanism of stator cavitation. Then, the important conclusions of this paper are presented in Section 5. Exploring the influence of slot geometry on cavitation and proposing a practically valuable cavitation suppression method for HTC stators, this study offers reference value for turbomachine researchers.