esa-sceva/satcom-chunk-collection huggingface dataset:
# Shadow Estimation for Ultrasound Images Using Auto-Encoding Structures and Synthetic Shadows
## Appendix A Algorithm for Generating Synthetic Shadows  
Algorithm A1 describes the process for generating synthetic shadows that correspond to convex probes. The parameters in the algorithm were set \(p=[-128,64]\), \((d_{\min},d_{\max})=(250,290)\)[deg], \((\theta_{\min},\theta_{\max})=(0,10)\)[deg], \((R_{\min},R_{\max})=(256,256)\), \(r_{\min}=128\), \(\delta_{\theta}=1\), \(k=10,\sigma=1.55\), and \(v_{\max}=1\) in all the experiments. The parameter \(v_{\min}\) is decided by the grid search as described in Section 4.1.  
```
0: Parameters for annular sectors (center coordinate \(p\in\mathbb{R}^{2}\), range of direction \(d_{\min},d_{\max}\), range of angle \(\theta_{\min},\theta_{\max}\), range of outer radius \(R_{\min}\), \(R_{\max}\), and minimum inner radius \(r_{\min}\)), blurring parameters \(\delta_{\theta},k,\sigma\), and range of shadow intensity \(v_{\min},v_{\max}\).
0: Image of a synthetic shadow \(s\in[0,1]^{H\times W}\).
1:\(d\gets U(d_{\min},d_{\max})\).
2:\(\theta\gets U(\theta_{\min},\theta_{\max})\).
3:\(R\gets U(R_{\min},R_{\max})\).
4:\(r\gets U(r_{\min},R)\).
5:\(v\gets U(v_{\min},v_{\max})\).
6:\(s\gets 0_{H,W}\) (a zero matrix shaped \(H\times W\)).
7:for\(i=-(k-1)/2,\ldots,(k-1)/2\)do
8: Let \(s_{k}\in[0,1]^{H\times W}\) be a image that filled with \(1\) inside an annular sector which center is \(p\), outer radius is \(r\), angle is \(d+(i\delta_{\theta})\), and direction is \(\theta\), and \(0\) otherwise.
9:\(s\gets s+s_{k}\).
10:endfor
11:\(s\gets v(s/\max(s))\).
12:\(s\gets 1-s\).
13: Apply Gaussian blur with variance \(\sigma^{2}\) to \(s\).
```  
**Algorithm A1** Generation of annular sector shaped synthetic shadows. A function \(U(\cdot,\cdot)\) draws a sample from a uniform distribution.
# Mechanical Properties Analysis of WAAM Produced Wall Made from 6063 Alloy Using AC MIG Process
## 3 Results and Discussion  
### Macrostructure Characterization  
The results of the basic macrostructural characterization of the as-built samples (11, 12, 13 and 14) are presented in Table 3, while images of the as-produced samples are shown in Figure 3. Analyzing the values obtained from measurements of the height and width of the samples produced with an AC, a linear correlation with the AC balance settings is evident. When the AC balance is set to 50 (the longest negative period), the highest and narrowest  
Figure 1: Dimensions and locations of samples for tensile testing.  
Figure 2: Tensile testing setup with addition of Aramis system.  
walls are produced. Conversely, when the AC balance is set to \(-50\) (the longest positive period), walls with the smallest height and the greatest width are produced.  
These results are directly correlated with the heat distribution during the welding process. With the balance set to \(50\) and the longest negative period, heat is more directed toward the filler material, increasing the melting rate of the wire and resulting in a higher deposition rate. Consequently, in the same period of time, higher and narrower walls can be produced compared to those fabricated using an AC with a balance of \(0\) or \(-50\).  
Additionally, when comparing the sample produced using a DC to those produced with an AC, it can be observed that the width is greater, while the height falls between the values observed for the AC with balance set to \(-50\) and \(0\). The width is largest when using a DC due to the heat distribution being concentrated on the wall, which increases the fluidity of the weld pool. As a result of the enhanced fluidity of the weld pool and the stability of the electric arc, the profile of the sample produced with a DC is significantly more uniform. When analyzing the homogeneity of the AC-produced samples, it is evident that the sample produced with the balance set to \(-50\) has the fewest pores, and these pores are the smallest in diameter. This is also a direct consequence of the heat distribution; in this case, the heat is more concentrated on the wall, leading to a slower cooling rate. This allows gas bubbles to migrate through the molten pool more effectively.  
These results are consistent with those of [PERSON] et al. (2015), who showed that an increased electrode-negative pulse ratio leads to higher and narrower deposits with lower porosity. The improved geometry was primarily attributed to a more stable metal transfer, which enables uniform and efficient material deposition.
# Coordinated Mitigation Control for Wideband Harmonic of the Photovoltaic Grid-Connected Inverter
## 5 Simulation Case Study
### Verification of Dead Zone Compensation Method  
The harmonic content of PV output current under the two different conditions mentioned in the previous section is compared in this section. The experiment uses a three-phase ideal power supply, and the experiments verify the validity and correctness of the low-order harmonic mitigation algorithm.  
After the above compensation of the low-order harmonics caused by nonideal switching, Figure 21a shows via the experimental waveform that the inverter's output current is about 45 A. Figure 21b shows the FFT result. Compared with the data before compensation, the fifth harmonic current is reduced from 2.436% to 0.481%, and the seventh harmonic current decreases from 2.19% to 0.844%. In addition to the significant reduction of harmonic content, the sinusoidal degree of the current waveform is greatly increased after the compensation to the dead zone. There is a significant reduction in the distortion when the current is over zero, and the voltage avoids being compensated incorrectly with the precise extraction of the current sequence.  
Figure 22 shows the modulated voltage waveforms before and after the compensation. The indirect control of the inverter achieves the effect of output current by establishing a equation between the modulated and the grid side voltages. Experimental results show  
Figure 21: Experiment of the dead zone compensation.  
that the compensated voltage accurately catches the modulation voltage polarity, so the distortion at the over-zero point is significantly reduced after compensation.  
The paper also demonstrates the proposed compensation method's effectiveness under the condition that the current is about 20 A. Figure 23 shows the FFT results, and the total current distortion rate is reduced from 5.98% to 3.46%. Among these, the fifth harmonic decreased significantly from 4.243% to 0.71%, and the proportion of the decline is more than 83%. The seventh harmonic current is also reduced from 3.052% to 1.867%. Except for the relatively high content components, the harmonics of other frequencies are each reduced to a certain extent, since the author mainly focuses on the 6 \(k\pm 1\) order harmonics under the premise that the harmonics of other frequencies are not significantly higher. The comparative experiments show that the dead zone compensation method suits multiple conditions.
# Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation
## 3 Performance Analysis  
In this section, we assume that the chaotic sequences' length \(\beta\) is large enough so that the Gaussian approximation (GA) method can be applied [25] to derive the theoretical BER and secrecy capacity expressions over the AWGN channel and the Rayleigh fading channel.
## 3 Experimental Results and Analysis
### Analysis of Object Detection Results
#### 3.5.2 Generalization Performance Comparison  
To verify the detection performance and generalization capability of YOLOv8-URE on external datasets, the model was trained on the publicly available WiderPerson dataset and compared with other mainstream algorithms. The experimental results show that the YOLOv8-URE method achieves a recall of 80.4% on the test set, which is higher than other mainstream algorithms. Moreover, YOLOv8-URE is the smallest, at only 4.46 MB, making it highly suitable for lightweight deployment. The experimental results are shown in Table 8.
## 3 Experiments and Results  
### Dataset and Implementation Detail  
Three-dimensional data of the Terracotta Warrions are acquired by using Creaform VIU 718 hand-held 3D scanners. Furthermore, the Terracotta Warrions are unearthed from the K9901 pit of Emperor [PERSON]'s Mausoleum Site Museum. The scan resolution is 0.05 mm, which is conducive to scan speed, but the scanning accuracy is rough. The software Artect is used to automatically or manually align the scanned terracotta warrions to synthesize the entire model. Because the original point cloud has a large number of points, it occupies a very large memory and affects the efficiency of the algorithm. Therefore, the downsampling procedure is adopted to obtain a relatively sparse point cloud (as shown in Figure 5).  
There are 8000 point cloud patches (7356 for training and 644 for testing) extracted from 40 whole CHarts to train the network. The original point cloud \(P\) is generated by sampling 2048 points from each patch uniformly. The default point number \(N\) of each patch is 2048, and the upsampling ratio is 2. Therefore, the input is randomly selected \(r=\left\{\frac{1}{2}N,\frac{1}{4}N,\frac{1}{8}N\right\}\) points from \(P\) (see Figure 6), and the target point cloud is \(\left\{N,\frac{1}{2}N,\frac{1}{4}N\right\}\). For example, the input point cloud data with 256 points require upsampling to the 512 target points cloud data. The network is implemented using PyTorch and Python 3.7, and the Adam algorithm [41] is adopted as the optimization function with two parameters of 0.5 and 0.9. Where 0.5 is the exponential decay rate of the first-order moment estimation, 0.9 is the exponential decay rate of the second-order moment estimation. In the training phase, the generator and discriminator are trained alternately, and the initial learning rates are both set to 0.0001. We gradually decrease the learning rate by a decay rate of 0.5 per 50 k iterations until \(10^{-6}\). We employ batch normalization (BN) and RELU activation units at the generator. We trained the network for 120 epochs with a batch size of 28. We set \(\alpha=0.1\), \(\beta=100\) and \(\gamma=0.1\) for all our experiments, with the hardware of AMD Ryzen7 2700 (2.39 GHz), 64 GB memory, and NVIDIA RTX TITAN.
# Strength Analysis of Cylindrical Shells with Tangential Nozzles under Internal Pressure
###### Abstract  
Pressure vessels having the structure of a cylindrical shell with a tangential nozzle are often used in engineering for some process requirements. But there are no accurate methods in engineering codes for the strength design of this special structure. In this paper, the limit-load analysis was performed to evaluate the weakening effects of the tangential nozzles on the strength of the cylindrical shells under internal pressure. A so-called strength-weakening coefficient was defined to reflect the weakening degree of the load-bearing capacity of the cylindrical shells by the tangential nozzles or specifically by the three dimensionless structural parameters, namely diameter ratio (\(d_{o}/D_{i}\)), diameter-thickness ratio (\(D_{i}/T\)) and thickness ratio (\(t/T\)). Results show that when increasing \(d_{o}/D_{i}\) and \(D_{i}/T\) or decreasing \(t/T\), the strength-weakening coefficient increases, which means that the strength-weakening effect of the tangential nozzle on the cylindrical shell increases. With sufficient simulation results, regression equations for the strength-weakening coefficient were obtained which provides a reference for the strength design of cylindrical shells with tangential nozzles under internal pressure.  
cylindrical shell; tangential nozzle; limit-load analysis; strength design Article
## 2 Materials and Methods  
### Study Area  
Xinjiang, located in the heart of the Eurasian continent (\(73^{\circ}40^{\prime}\) E-\(96^{\circ}18^{\prime}\) E, \(34^{\circ}25^{\prime}\) N-\(48^{\circ}10^{\prime}\) N), encompasses a total area of 1.6649 million square kilometers. It forms a closed inland basin system that exemplifies a typical arid ecological unit globally (Figure 1a). The unique topographical pattern, characterized by the 'Three Mountains Surrounding Two Basins'--the Altai Mountains, Tianshan Mountains, and Kunlun Mountains encircling the Junggar Basin and the Tarim Basin--creates a significant topographic barrier that obstructs the transport of maritime moisture [18; 19; 20]. This results in an extremely arid climate with average annual precipitation (PRE) below 200 mm across most areas. This PRE exhibits a notable gradient, ranging from 150-250 mm in northern Xinjiang to 25-100 mm in the south [21; 22].  
The pronounced spatial differentiation in water and heat drives the vertical zonation of the mountain-oasis-desert system, where natural grasslands, covering 31.5% of the total area (Figure 1b), function as ecological corridors, playing an irreplaceable role in maintaining the regional carbon-water cycle balance [23]. Notably, influenced by the coupling effects of westerly wind circulation and local topography, PRE is distributed unevenly throughout the year (with over 60% occurring in summer), which, combined with intense land surface evapotranspiration processes (with average annual potential evapotranspiration reaching 1800-2200 mm), leads to an extreme sensitivity of grassland productivity to climatic fluctuations. In the context of global warming, the rate of TEM increase in this region significantly exceeds the global average [22], further exacerbating the competition for water resources in vegetation. Consequently, VPD, a key driver of plant water stress and evapotranspiration, exhibits a distinct spatial pattern characterized by lower values in mountainous areas and higher values in plains/basins, with a weaker increasing trend observed in the mountains compared to the plains/basins. This pattern is attributed to the combined effects of topography, climate, and vegetation type. Furthermore, influenced by the zonal differentiation of regional hydrothermal conditions and vegetation types, GPP of vegetation across Xinjiang displays significant spatial heterogeneity, generally manifesting as higher values in Northern Xinjiang and lower values in Southern Xinjiang. These contrasting spatial patterns of VPD and GPP highlight the complex interplay between atmospheric demand, water availability, and ecosystem productivity in this arid region, making the grassland ecosystems a critical indicator of climate-ecological coupling responses in arid areas.  
Figure 1: Overview of the Xinjiang Area. (**a**) Geographic Location and Elevation. (**b**) Grassland distribution and average grassland LAI. (**c**) Trend of average temperature change. (**d**) Trend of average precipitation variation.
# Propagation Characteristics of High-Power Vortex Laguerre-Gaussian Laser Beams in Plasma
## 3 Numerical Methodology  
The Laguerre-Gaussian laser beams are numerically generated by utilizing the FDTD method based on the total field/scattering field (TF/SF) source condition [12; 16]. It is possible to generate any types of laser beams with predefined pattern and parameters using this technique. Within the framework of FDTD method, assuming that the TF/SF interface is perpendicular to the \(z\) axis and locates at \(z=(k_{\text{s}}-1/4)\Delta z\), the \(x\) component of scattering magnetic field \(H_{\text{s},x}^{(n+1/2)}\) on the plane \(z=(k_{\text{s}}-1/2)\Delta z\) at time instant \(t=(n+1/2)\Delta t\) can be updated by:  
\[H_{\text{s},x}^{(n+1/2)}(i,j+\frac{1}{2},k_{\text{s}}-\frac{1}{2})=H_{\text{s },x}^{(n+1/2)}(i,j+\frac{1}{2},k_{\text{s}}-\frac{1}{2})_{\text{FDTD}}-\frac{ \Delta t}{\mu\Delta z}E_{\text{LG},y}^{(n)}(i,j+\frac{1}{2},k_{\text{s}}) \tag{10}\]  
where the first item on the right side of (10) is the FDTD updating equation for \(H_{\text{s},x}^{(n+1/2)}\) and the last item is the \(y\) component of the electric field of the to-be-generated laser beam,  
\[E_{\text{LG},y}^{(n)}(i,j+\frac{1}{2},k_{\text{s}})=E_{\text{LG},y}[i\Delta x,(j+\frac{1}{2})\Delta y,k_{\text{s}}\Delta z,n\Delta t] \tag{11}\]  
Similarly, the \(y\) component of scattering magnetic field:  
\[H_{\text{s},y}^{(n+1/2)}(i+\frac{1}{2},j,k_{\text{s}}-\frac{1}{2})=H_{\text{s },y}^{(n+1/2)}(i+\frac{1}{2},j,k_{\text{s}}-\frac{1}{2})_{\text{FDTD}}+\frac{ \Delta t}{\mu\Delta z}E_{\text{LG},x}^{(n)}(i+\frac{1}{2},j,k_{\text{s}}) \tag{12}\]  
where the last item is the \(x\) component of the electric field of the to-be-generated laser beam:  
\[E_{\text{LG},x}^{(n)}(i+\frac{1}{2},j,k_{\text{s}})=E_{\text{LG},x}[(i+\frac{ 1}{2})\Delta x,j\Delta y,k_{\text{s}}\Delta z,n\Delta t] \tag{13}\]  
Typically, the TF/SF interface is set at or near the plane of beam waist for convenience purposes, where the electric field has the simplest expression and there is no \(z\) component of electric field. The bilinear transform (BT) approach [13] can be used to implement the complex dispersive model given by Equation (8) via the numerical implementation of the constitutive equation, \(\mathbf{D}=\varepsilon(\omega)\mathbf{E}\), within the frame work of FDTD method. The BT approach is not only accurate, but also stable with a stability limit that is equal to the Courant stability limit, \(\Delta t_{\text{C}}=\Delta s/c\sqrt{m}\), where \(\Delta s=\min\{\Delta x,\ \Delta y,\ \Delta z\}\) is the shortest side of Yee cell, \(c\) is the speed of light, and \(m\) is the dimensionality of the simulated problem.
## 5 Comparison of Theoretical Calculation and Simulation Analysis  
The computational results of the theoretical model are analyzed against the simulation results as shown in Figures 27-31.  
From Figures 27-31, it can be seen that the theoretical model analysis results match the simulation analysis results in the non-resonance region to a high degree, with a deviation of less than 5%, while the results in the resonance interval have a larger deviation, which is analyzed for the following reasons:  
At the resonance point, the vibration amplitude and velocity of the vibration system are relatively larger. This will increase the nonlinearity of eddy current damping, resulting in deviation.  
In the theoretical model calculation, the influence of the length of the conductor plate is not specifically considered, but is compensated with a correction factor, which can result in deviation.  
The magnetic field of the electromagnet array has complex coupling relationships. Using linear accumulation and empirical coefficients to consider coupling relationships can result in deviation.  
The theoretical model analysis is the calculated steady state result, while the simulation analysis result is the statistical result of reaching the steady state through transient analysis, so there are data statistical errors.
## 2 Materials and Methods
### Design of the Experimental Activities  
#### 2.2.1 Lab-Scale Phase  
The effectiveness of two coagulants, FeCl\({}_{3}\) (40% solution, density: 1.45 g\(\cdot\)L\({}^{-1}\); Fe\({}_{\rm tot}\): 13%) and polyaluminium chloride (PAC--density: 1.35 g\(\cdot\)L\({}^{-1}\); active Al: 9%) have been evaluated. In the lab-scale phase, five diverse doses were studied to determine the optimal dosage. COD has been selected as a target parameter to evaluate the effectiveness of the process.  
In order to define the proper flocculant, an anion-active reagent (ZETAG 4139--AAF) and a cation-active reagent (ZETAG 9246 FS--CAF), both at a 0.1% concentration in solution, were selected in this study. In this case, turbidity has been selected as the target parameter.  
A multi-chamber mixer with five reactor vessels has been used. A total of 1 L of BWW was fed into each reactor vessel, keeping it at a temperature of 25 \({}^{\circ}\)C. A paddle mixer (170 RPM) was used to stir all the vessels to achieve homogeneity, and H\({}_{2}\)SO\({}_{4}\) solution (20%) was used for neutralization. Within 2 min, a steady pH value was achieved.  
Five different amounts of coagulant agent were tested in the study. The concentrations of FeCl\({}_{3}\) were 290, 435, 580, 725, and 1088 mg\(\cdot\)L\({}^{-1}\), and the concentrations of PAC were 270, 405, 540, 675, and 1013 mg\(\cdot\)L\({}^{-1}\).  
After the coagulation (3-5 min), the stirrer speed was decreased to 30 RPM and samples were treated with two different (40 mg\(\cdot\)L\({}^{-1}\) and 90 mg\(\cdot\)L\({}^{-1}\)) flocculating solutions (20-25 min). The sludge formed and precipitated rapidly. The settling time was 30 min. Tests have been carried out to test different doses and types of flocculating solutions for every possible combination of samples.
# An Optimal Online Resource Allocation Algorithm for Energy Harvesting Body Area Networks
## 5 Performance Analysis
### Bounded Data Queues  
In this subsection, we give a theorem about bounded data queue of sensor \(n\) carried by patient. In order to ensure the stability of the BAN system, the data queue must be bounded. We use the constant \(I^{max}\) to represent the upper bound of data queue. In Theorem 3, we give a theorem on upper bound of data queue.  
**Theorem 3**: _The upper bound of data queue consists of weight \(V\), slope of user utility function \(\
u\) and the maximum of collecting rate \(c^{max}\). It is given by \(I^{max}=V\
u+c^{max}\). The length of data queue does not always exceed the upper bound:_  
\[0\leq I_{n}(t)\leq I^{max},\forall n\in N. \tag{17}\]  
_This inequality is satisfied at time \(t=0\)._  
Proof.: See proof of theorem 3. From Theorem 3, the upper bound of data queue increases linearly with the weight \(V\). To enable the BAN system to operate under the proposed resource allocation algorithm, the sensor \(n\) must be equipped with a data buffer of size \(I^{max}\).
## 4 Future Research Directions
### Open Research Direction for DERs
#### Optimal Placement of DERs  
The optimal placement of DERs is very important for a given DSM. The placement of DERs plays an important role in loss minimization which can, in turn, improve the DSM schemes' efficiency.