esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Our Approach
#### 2.2.2 The Pose Estimation Algorithm
For each super-point p in P\({}_{j}\), we compute the super-point p\({}^{\prime}\) in the model, which contains the point M\({}_{j}\)p. Using their features, then the error between P and P\({}_{j}\) is defined as  
\[E=\frac{1}{n}\sum_{i=1}^{n}\left\|\overrightarrow{f}_{i}-\overrightarrow{f}_ {i}^{\prime}\right\|_{2} \tag{17}\]  
where \(n\) is the number of super-points in P\({}_{j}\); \(\overrightarrow{f}_{a}\) is the feature vector of the \(i\)-th super-point p\({}_{i}\) in P\({}_{j}\); \(\overrightarrow{f}_{i}^{\prime}\) is the feature vector of the corresponding super-point p\({}_{i}^{\prime}\) in the model. The proposed model-based pose estimation algorithm is summarized as shown in Algorithm 3. ```
Input: A single RGB image \(I\) Output: The pose parameters of \(I\) and the registration error \(E\) Method:
1. Input \(I\) to the \(D\)\(V\)\(V\)\(V\)\(V\) to obtain the viewpoint class \(v\) and retrieve the template \(\Phi_{v}\) from the prestored database. 2. Set the pose parameters of \(I\) to be depth image and external camera parameters of \(\Phi_{v}\). 3. Compute the 3D point clouds \(\mathrm{p}_{I}=(\mathrm{p}_{a})_{a=1}^{r}\) and \(\mathrm{p^{\prime}}_{\Phi_{v}}=(\mathrm{p}_{a}^{\prime})_{a=1}^{r}\) for \(I\) and \(\Phi_{v}\), respectively using (2) and (4). 4. Perform the \(MPAA\) to compute the super-points and super-pixels of \(I\) and \(\Phi_{v}\). 5. Perform the \(RANSAC\) algorithm to generate the pixel correspondences \(\Omega_{I}^{\Phi_{v}}=\big{(}\chi_{a}^{\Phi_{v}},\chi_{a}^{\Phi_{v}}\big{)}_{a=1}^ {r}\) between \(I\) and the RGB image in \(\Phi_{v}\) in terms of their visual features of key points (super-pixels). 6. Perform the ICP algorithm to register P\({}_{I}\) and \(\mathrm{p^{\prime}}_{\Phi_{v}}\), and compute the transformation matrix \(\mathrm{M}_{I}^{\sigma}=[\mathrm{R}_{j,3\times 3}^{\sigma}]\big{|}\sigma_{j,3}^{ \sigma}\big{|}\). 7. Fine tune the external camera parameters of \(I\) to be M\({}_{4\times 4}\) using (16). 8. Compute the final point cloud P\({}_{I}\) of \(I\) and perform the \(RANSAC\) algorithm is used to establish the 3D super-point correspondence set between P\({}_{I}\) and the model point cloud P\({}_{m}\) in terms of the feature vectors of super-points, which are defined in (6). 9. Perform the ICP algorithm to register P\({}_{I}\) and P\({}_{m}\), and compute the transformation matrix \(\mathrm{M}_{I}^{m}=[\mathrm{R}_{j,3\times 3}^{\sigma}]\big{|}\sigma_{j,3}^{ m}\big{|}\). 10. Compute the inspection error \(E\) using (17). 11. if\(E\) is not changed again, return the pose parameters of \(I\) and \(E\); else goto 4. ```  
**Algorithm 3** Model-based Pose Estimation
# Performance Comparison of Deep Learning Models for Damage Identification of Aging Bridges
## 3 Proposed Framework  
This paper proposes a deep learning framework for detecting damaged objects in bridges [12]. Initially, object-detection performance for each damage type is enhanced by using super-resolution (SR) techniques to improve and normalize resolution, thereby augmenting the dataset's diversity and consistency. By using the SR technique, it is possible to build a better learning set because it maintains the quality of the images used for learning. Thereafter, we construct an optimized detection model tailored for each damage type using the bridge-damage-identification deep learning combination-module, which is based on separate training for each type. These models are then integrated into a single model and presented as an optimized solution for detecting damaged objects in bridges. The framework is specifically designed for six types of bridge damage: efflorescence, concrete scaling, concrete spalling, cracks, corrosion, and water leaks. The architecture of this framework is shown in Figure 1. The deep learning models used in combination are Mask R-CNN, BlendMask, and SWIN.
# Study on Fluctuating Wind Characteristics and Non-Stationarity at U-Shaped Canyon Bridge Site
## 4 DWT Reconstruction and Analysis
### DWT Reconstruction Results  
In the analysis of 1669 ten-minute wind speed samples with U-component speeds exceeding 4 m/s at bridge deck height, all fluctuating wind speed samples reconstructed using db10 wavelet decomposition at levels greater than or equal to 9 passed the ADF stationarity test at a significance level of 0.05. Figure 9 illustrates the reconstruction results of selected samples at various decomposition levels.  
Figure 9: Comparison of wavelet reconstruction results across different decomposition levels.  
To further examine the impact of different decomposition levels on pulsating wind speed characteristics, the wavelet components at levels 1 through 11 were reconstructed, as shown in Figure 10. As the decomposition level decreases, the overall fluctuation trend becomes increasingly apparent, and the instantaneous wind speed variability becomes clearer. This indicates that when using the wavelet method, selecting an appropriate number of wavelet levels is crucial.  
If the decomposition level is too low, excessively detailed time-varying mean wind components may be extracted, leading to the loss of fluctuating wind characteristics.
## 1 Introduction
The probability map of the lesion distribution and the lesion's location was obtained by the researchers using the LRP module. CNN networks were preferred in the classification process. In the dataset consisting of CT images of 160 patients, the researchers obtained 82.45% accuracy values [11]. Image visualization is a branch of computer science that involves analyzing, processing, and displaying digital images. Image visualization technology can process pictures, video recordings, maps, and other similar digital data [12]. This process makes the data visually understandable and provides a better understanding of these images. It is used in image visualization, medicine, communications, games, cinema, and many other industries. Especially when combined with big data management and analysis, image visualization can facilitate data analysis and provide a faster understanding of images. In this study, Grad-CAM and LIME techniques were used to interpret hydatid cyst CT images more easily by experts and to prevent mistakes. In addition, these images visualized using Grad-CAM and LIME techniques were used in the developed model. The same image's various attributes were acquired in this manner. This improved the generated model's accuracy. Artificial intelligence is used in many industries today. These areas are health, finance, education, production, energy, transportation, and defense. Health is one of the most widely used areas of artificial intelligence. In this study, artificial intelligence techniques were used to classify hydatid cyst images. It is possible to summarize the innovations and contributions of the study as follows:  
* In this study, artificial intelligence techniques were used to classify hydatid cyst images. The developed model obtained more successful results than similar studies and pretrained models in the literature. The accuracy of the model developed in the classification of hydatid cyst images was 94 percent. This value indicates that the proposed model can be applied to the categorization of CT images of hydatid cysts. * Image visualization process was carried out so that hydatid cyst images can be interpreted more easily by experts and to prevent mistakes. * Grad-CAM and LIME techniques are used for image visualization, in which CNN architectures are used as the base. In the second part of the article, data visualization, dataset, deep learning architectures, the Kruskal algorithm, classifiers, and the model developed for the classification of hydatid cyst images are examined. In the third chapter, the results obtained from the original dataset and the results of the proposed model are presented. In the last sections, the discussion and conclusion are provided.
## 4 Measurements  
The S\({}_{21}\) was measured using a Rohde & Schwarz ZVA 25 for a subject standing and walking. The measurements are made in a room 8.1 \(\times\) 7.9 m\({}^{2}\) with furniture and reinforced concrete walls and floors. The person is walking along one side of the room as shown in Figure 6a. The first antenna is located on the right-side shoe toe-cap and the second antenna is located above the right wrist, as shown in Figure 7. Three different frequency bands are analyzed as shown in Figure 6b:  
* 50 MHz centered at 2.45 GHz
* 500 MHz centered at 3.95 GHz
* 500 MHz centered at 7.25 GHz  
The three frequencies represent the centre frequencies for the narrowband and UWB physical layer for wireless BAN networks in IEEE 802.15.6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Frequency** & \multicolumn{2}{c}{**Maximum realized**} & \multicolumn{2}{c}{**3-dB beamwidth**} & \multicolumn{2}{c}{**3-dB beamwidth**} & \multicolumn{2}{c}{**Radiation**} \\
**[GHz]** & \multicolumn{2}{c}{**gain magnitude [dBi]**} & \multicolumn{2}{c}{**for \(\varphi=0^{\circ}\) [deg]**} & \multicolumn{2}{c}{**for \(\varphi=90^{\circ}\) [deg]**} & \multicolumn{2}{c}{**efficiency**} \\ \cline{2-9}  & **Wrist** & **Footwear** & **Wrist** & **Footwear** & **Wrist** & **Footwear** & **Wrist** & **Footwear** \\ \hline
**2.45** & \(-2.09\) & \(-0.16\) & 131.3 & 131.9 & 86.6 & 84.3 & 21\% & 30\% \\
**3.95** & 5.37 & 4.07 & 86.6 & 83.4 & 61.4 & 80.5 & 54\% & 55\% \\
**7.25** & 4.41 & 4.46 & 57.1 & 42.7 & 44.3 & 46.1 & 65\% & 64\% \\ \hline \end{tabular}
\end{table}
Table 2: Directivity parameters for the on-body antennas.  
Figure 6: (**a**) Layout of the room; (**b**) investigated frequencies with bandwidth.  
To avoid obstruction of the cables by the subject walking, the VNA was placed at the side of the room and the cables located behind the subject. In this way the cables trail the walking subject, without impacting on the gait. Photos of the measurement setup are shown in Figure 8.  
In total there are five measurements with 330 frequency sweep samples for a subject walking and standing. The first 10 s are related to a stationary subject at one side of the room. Then the subject walks to the other side of the room. Finally the subject stands still at the other side of the room. There is a measurement every \(\sim\)100 ms. The total time for each measurement is \(\sim\)33 s. The measurements were made on a subject of mass 70 kg and 1.75 m tall. Path loss results are displayed in terms of Cumulative Distribution Function (CDF) and compared with the Rician distribution using the Maximum Likelihood Estimation (MLE) criteria.  
Figure 8: Photos of the on-body antennas.  
Figure 7: Walking subject with antennas on wrist and toe-cap footwear.
# Optimization of Effective Throughput in NOMA-Based Cognitive UAV Short-Packet Communication
## 1 Introduction
### Motivation and Contributions  
In mission-critical communication networks, information is always transmitted in short packets so we consider a UAV short-packet communication network. In addition, we find that the cognitive radio and non-orthogonal multiple-access techniques have been applied in previous studies to solve the spectrum scarcity problem and improve the spectrum efficiency in short-packet communication. In [31], the NOMA technique was applied in short-packet communication and the effective throughput of users with better channel gain was taken as the optimization objective. In [32], the authors improved the energy efficiency of the UAV short-packet communication system by optimizing the parameters such as the sensing time, transmit power, etc. Together with the above analysis, we consider a NOMA-based cognitive UAV short-packet communication system. The UAV in this system flies around the ground base station (GBS) in a circular path and detects its status by spectrum sensing. When the ground base station is idle, the UAV provides short-packet communication to secondary users using the NOMA technique.  
The contributions of this paper are summarized in the following three points:  
1. In the NOMA-based cognitive UAV short packet communication system, we present the problem of maximizing the effective throughput of far secondary users under the constraints of the transmission power and effective decoding error probability.  
2. To solve this problem, the sensing time, information bits, and transmit power are optimized in the system.
3. The proposed optimization algorithm has low complexity and leads to similar results as the exhaustive method. The NOMA scheme can achieve higher throughput compared to some benchmark schemes.
## 2 Materials and Methods
### Stochastic Adjustment Model  
Proper numerical processing of geodetic survey data involves the adjustment of observations (according to the method of least squares) and assessment of the accuracy of the results. To this end, it is necessary to transform the mean errors of the original observations (\(\overline{d}\), \(\alpha\), \(\beta\), \(l\), \(s\)--see Figures 2 and 3) into mean errors of the pseudo-observations. To do this, one can employ the propagation of mean error [7; 25]. The mean error of the pseudo-observations \(d_{jk}\) (5), which are distances between the instrument station \(j\) and signal \(k\) (target), is expressed as:  
\[m_{jk}^{(d)}=\sqrt{\left(\frac{\partial d_{jk}}{\partial\overline{d}_{jk}} \right)^{2}\cdot\left(m_{jk}^{(\overline{d})}\right)^{2}+\left(\frac{\partial d _{jk}}{\partial\alpha_{jk}}\right)^{2}\cdot\left(m_{jk}^{(\alpha)}\right)^{2 }+\left(\frac{\partial d_{jk}}{\partial i_{j}}\right)^{2}\cdot\left(m_{j}^{( i)}\right)^{2}+\left(\frac{\partial d_{jk}}{\partial s_{k}}\right)^{2}\cdot \left(m_{k}^{(s)}\right)^{2}} \tag{9}\]  
Partial derivatives (\(\partial\)) of each variable (survey result) are determined as follows:  
\[\frac{\partial d_{jk}}{\partial\overline{d}_{jk}}=\frac{\overline{d}_{jk}+ \left(i_{j}-s_{k}\right)\cdot\cot\alpha_{jk}+\overline{d}_{jk}\cdot\cot^{2} \alpha_{jk}}{d_{jk}}\]  
\[\frac{\partial d_{jk}}{\partial\alpha_{jk}}=\frac{-\overline{d}_{jk}^{2} \cdot\cot\alpha_{jk}-\overline{d}_{jk}\cdot\left(i_{j}-s_{k}\right)}{d_{jk} \cdot\sin^{2}\alpha_{jk}}\]  
\[\frac{\partial d_{jk}}{\partial i_{j}}=\frac{\overline{d}_{jk}\cdot\cot\alpha _{jk}+i_{j}-s_{k}}{d_{jk}}\]  
Figure 4: Determination of the height difference between two ground points \(L\) and \(R\). The mean error of the pseudo-observations \(d_{LR}\) (4), which are distances between the left (\(L\)) and right (\(R\)) target (Figure 3), is determined as shown below (also using the propagation of mean error):  
\[\begin{array}{c}m_{LR}^{(d)}=\sqrt{ \begin{array}{c}\left(\frac{\partial d_{ LR}}{\partial d_{SL}}\right)^{2}\cdot\left(m_{SL}^{(\overline{d})}\right)^{2}+\left( \frac{\partial d_{LR}}{\partial d_{SR}}\right)^{2}\cdot\left(m_{SR}^{( \overline{d})}\right)^{2}+\left(\frac{\partial d_{LR}}{\partial a_{SL}}\right) ^{2}\cdot\left(m_{SL}^{(\kappa)}\right)^{2}+\left(\frac{\partial d_{LR}}{ \partial a_{SR}}\right)^{2}\cdot\left(m_{SR}^{(\kappa)}\right)^{2}+\\ \qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad+\left(\frac {\partial d_{LR}}{\partial p_{ISR}}\right)^{2}\cdot\left(m_{LSR}^{(\beta)} \right)^{2}+\left(\frac{\partial d_{LR}}{\partial s_{L}}\right)^{2}\cdot\left( m_{L}^{(\kappa)}\right)^{2}+\left(\frac{\partial d_{LR}}{\partial s_{R}}\right)^{2} \cdot\left(m_{R}^{(\kappa)}\right)^{2}\end{array} } \tag{10}\]  
Partial derivatives of each variable (survey results) are expressed with the following equations:  
\[
# Improved Road Segment-Based Geographical Routing Protocol for Vehicular Ad-hoc Networks
## 3 Improved Road Segment-Based Geographical Routing Protocol
### Head Node Selection  
The head node (HN) is an appropriate forwarding vehicle node for information dissemination in a traffic environment. The HN selection process starts for every segment after the completion of segment formation operation on the nearby road map. Each vehicular node shares the information of its own position, direction, and link quality to its neighbor nodes within the segment. The protocol checks the position and direction of each neighboring vehicle for selecting the HN. The direction is calculated entirely in respect to the current location and available road trajectories in different possible directions. Then protocol check the position of node at time t and t+1, as expressed \(P(t)\) and \(P(t+1)\) in Equations (1)-(3) for the calculation of road direction-oriented distance. The segment-centric distance of node decreases towards the destination, as shows in Equation (4).  
\[P(t)\ =\ (x_{1},\ y_{1}) \tag{1}\]  
\[P(t+1)\ =\ (x_{2},\ y_{2}) \tag{2}\]  
\[Distance(c)\ =\ P(t+1)-P(t) \tag{3}\]  
After this process, the position is calculated with the help of the Pythagoras theorem, as mentioned in Equation (4).  
\[Distance\ (c)\ =\ \sqrt{\left(x_{2}-x_{1}\right)^{2}+\left(y_{2}-y_{1}\right) ^{2}} \tag{4}\]  
When there is a tie between two or more nodes in terms of distance and direction and the nodes are present in the central position, the protocol checks the link quality-based score of the nodes for further prioritizing the nodes as mentioned below.  
Figure 1: Trajectory and segment formation scenario for the improved road segment-based geographical routing (ISR) protocol.
## 6 Case Simulation
### Cascade Small Hydropower-Pumped Storage Transformation and Capacity Calculation Results  
According to the topology diagram of the cascade small hydropower plants, the No. 6 hydropower reservoir and No. 10 hydropower reservoir form a hydraulic cascade relationship between the upper and lower reservoirs; it can be seen from the reservoir capacity parameters that the regulating reservoir capacity of No. 6 can meet the regulation requirements of joint operation and independent operation of runoff daily regulation and energy storage peak regulation, which meets the requirements of the upper reservoir as pumped storage. The No. 10 reservoir capacity is the largest in the basin, which meets the requirements of the lower reservoir water storage, and both No. 6 and No. 10 have regulated storage capacity, so the \"integrated combination\" method is used to carry out pumped storage transformation of cascade small hydropower. In order to improve the utilization rate of the original generator set and not increase the reconstruction cost, a water pump is added to the No. 6 hydropower station as the pumped storage unit to complete the transformation.  
The cascade small hydropower-pumped storage transformation aims to expand the pumped storage function while ensuring the basic function of water supply and power generation. Therefore, this paper matches the total power generation regulation capacity and total water pumping regulation capacity of the entire cascade of small hydropower plants as the primary basis for determining the capacity of the pumped storage unit. The calculation results of the maximum regulation capacity for each hydropower station are shown in Table 3.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Number**} & \(\mathbf{V_{begin}}\) & \(\mathbf{V_{end}}\) & \(\mathbf{V_{c\,min}}\) & \(\mathbf{V_{c\,max}}\) & \(\mathbf{q_{min}}\) & \(\mathbf{q_{max}}\) \\  & **/(10\({}^{4}\) m\({}^{3}\))** & **/(10\({}^{4}\) m\({}^{3}\))** & **/(10\({}^{4}\) m\({}^{3}\))** & **/(10\({}^{4}\) m\({}^{3}\))** & **/(m\({}^{3}\)/s)** & **/(m\({}^{3}\)/s)** \\ \hline
1 & 40 & 40 & 0 & 16 & 0 & 16 \\
2 & 70 & 70 & 0 & 36 & 0 & 20 \\
3 & 150 & 150 & 0 & 116 & 0 & 30 \\
4 & 1450 & 1450 & 0 & 119 & 0 & 50 \\
5 & 500 & 500 & 0 & 74 & 0 & 87.8 \\
6 & 1500 & 1500 & 0 & 844 & 0 & 56.2 \\
7 & 32 & 32 & 0 & 3 & 0 & 67.6 \\
8 & 425 & 425 & 0 & 20 & 0 & 84.8 \\
9 & 265 & 265 & 0 & 111 & 0 & 77.6 \\
10 & 6400 & 6400 & 0 & 114 & 0 & 119.7 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Table of reservoir capacity parameters for each hydropower station.  
Figure 3: Cascade small hydro topological structure.  
According to the calculation results, the total regulation capacity of the cascade small hydropower plants is 15.7 MW. In order to supplement the pumping and regulating capacity of cascade small hydropower plants, the capacity of the installed pumped storage unit must be no less than the total regulation capacity. Therefore, the total capacity of the pumped storage unit installed at Hydropower Station No. 6 is 16 MW.
# Fourier Ptychographic Reconstruction Method of Self-Training Physical Model
## 2 Principle of FPM
### Evaluation Indicators
#### 2.4.1 Peak Signal-to-Noise Ratio  
Peak Signal-to-Noise Rate (PSNR) is the most normally and extensively used objective evaluation index. It is an image quality evaluation grounded on the error between the corresponding pixels, that is, grounded on the sensitive error. PSNR is an engineering term for the ratio of the maximum possible power of a signal to the destructive noise ratio that affects its representation accuracy. Because many signals have a very wide dynamic range, PSNR is often expressed in logarithmic decibels (dB). In the field of image evaluation, its mathematical definition is:  
\[\begin{array}{c}MSE=mean\Big{(}(I_{1}-I_{2})^{2}\Big{)},\\ PSNR=-10*\log(MSE)\end{array} \tag{12}\]  
Among them, MSE is mean squared error (MSE), which is also a common loss function which represents the original image, represents the reconstructed image, and is the same size. The value of PSNR is generally 0-100 dB. The larger the value of PSNR, the smaller the distortion and the better the image quality.
## 1 Introduction
For example, the equalizer with the Volterra structure can effectively eliminate the nonlinear damage of the signal. In [4], a 50 km transmission of a 50 Gb/s PAM4 signal is demonstrated using a direct modulated laser with a bandwidth of 10 GHz, in which a 275-tap Volterra equalizer is used. The transmission achieved \(-6\) dBm at a bit error rate of \(4.7\times 10^{-3}\). There is also an 80 km transmission with 50 Gbps PAM4 using C-Band DML (Direct Modulation Laser), in which a Volterra-DFE post-equalizer is used. The BER finally reaches SD-FEC (Soft Decision-Forward Error Correction) limit (\(2.2\times 10^{-2}\)) [12]. In addition, equalization algorithms using maximum likelihood sequence estimation (MLSE) have also been studied recently. A transmission of 56 Gb/s PAM-4 signals over 26.4 km SSMF (Standard Single-Mode Fiber, YOFC G652) has been demonstrated using 23 GHz MZM (Mach-Zehnder Modulator) and PIN-TIA detectors (Discovery Semiconductors, DSC10 ER) [13]. There are also researchers achieving 112 Gbps C-band transmission by using transmitter-side Tomlinson-Harashima precoding (THP) and MLSE [14]. Although there is relatively good compensation for signal transmission damage, the structure of the Volterra equalizer and maximum likelihood sequence estimation algorithm is complex and requires large memory and power consumption, which will increase the operation cost of the system, so there is still a long distance to go before commercialization. Thus, many scholars have worked to simplify such equalizers. For example, a sparse Volterra filter has been proposed to realize nonlinear pre-distortion in DML-based OFDM transmission, in which the proposed pre-distorter is specially designed to prevent significant changes in the powers of transmitted subcarriers [15]. There are also researchers proposing and experimentally demonstrating a trellis-compression MLSE in a 210 Gb/s PAM-8 signal transmission over a 2 km SSFM transmission. They found trellis-compression MLSE (TC-MLSE) can reduce the complexity by 98% with only a 0.2 dB penalty compared with conventional MLSE [16]. In this paper, the Volterra structure is simplified by the derivation of signal transmission and the absolute value operation is proposed to replace the square operation, by which a simplified nonlinear-DFE equalizer is realized. The complexity of this equalizer is compared with other related equalizers. Finally, in our experiments based on a 25 GHz bandwidth EML, 112 Gbit/s and 128 Gbit/s PAM-4 signals can transmit over 10 km. By comparing the equalization effects of different equalizers, the feasibility of the proposed algorithm is proved.