esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Conclusions  
The application of UAV stereo imagery as a practical sampling tool of forests is limited by the ground surface elevation under the forest canopy. Most studies on the extraction of forest spatial structures using UAV stereo imagery used the DTM from lidar data. Therefore, they are mostly limited within the area where the lidar DTM is available. In this study, we proposed a new way to extract the deciduous forest canopy height without the help of the lidar DTM. The central idea is that the synthesis of UAV stereo imagery acquired under leaf-on and leaf-off condition. The point cloud derived from the leaf-off UAV stereo imagery was firstly processed by the proposed algorithm of iterative median filtering to remove points from forests. The digital terrain model (DTM) was produced by the rasterization of filtered point cloud. The digital surface model (DSM) of the forest canopy top was generated by rasterizing the point cloud derived from the leaf-on UAV stereo imagery. The mean forest canopy height model (MCHM) was derived from the DSM subtracted by the DTM. The results showed that the terrain information in the DSM could be removed in the MCHM.  
The extracted MCHM was further used for the mapping of forest AGB. Results showed that forest AGB maps from UAV stereo imagery were highly correlated with those from the lidar data with R\({}^{2}\) higher than 0.94 and RMSE lower than 10.0 Mg/ha. The underestimation of AGB by UAV data was observed. The reason for this underestimation should be explored over more test sites in the future.  
Conceptualization, W.N.; methodology, W.N. and J.D.; software, J.D.; validation, W.N., Y.P. and X.T.; formal analysis, W.N.; investigation, W.N.; resources, Z.Z.; data curation, W.N.; writing--original draft preparation, W.N.; writing--review and editing, G.S.; visualization, W.N. and J.D.; project administration, E.C.; funding acquisition, Z.L. This work was funded in part by the National Key R&D Program of China under Grant 2017 YFA0603002, National Basic Research Program of China (Grant No. 2013 CB733404, 2013 CB733401), and in part by the National Natural Science Foundation of China (Grant Nos. 41471311, 41371357, 41301395). Special thanks to the forest field inventory team members for the valuable field inventory data including [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON]. Conflicts of Interest: The authors declare no conflict of interest.
## 2 Theory
### Theory of the PSO  
PSO is a population-based algorithm that begins with random solutions that are named particles [70]. [PERSON] and [PERSON] first introduced the concept, which was based on the group behavior of birds and used it for the optimization of continuous non-linear functions [71]. PSO shares many similarities with other evolutionary, population-based algorithms. In an optimization problem, each particle can be considered as a solution. The first stage of the optimization process is the random distribution of particles during the search. The personal best (\(p_{best}\)) and global best (\(g_{best}\)) values are the optimal solution that has a particle and the optimal solution calculated by the swarm, along with its particle index, respectively. Therefore, the particle velocity (in the next step) can be calculated using \(p_{best}\) (cognitive component), \(g_{best}\) (social component), and the current particle velocity. The cognitive and social components are both randomly selected [72]. The \(p\)th particle is introduced by:  
\[X_{pd}^{iter+1}=X_{pd}^{iter}+v_{pd}^{iter+1}, \tag{10}\]  
where \(X_{pi}\) stands for the \(i\)th data point in the \(D\)-dimensional space. \(G=\{g_{1},g_{2},\ldots,g_{D}\}\) and \(p_{p}=\{p_{p1},\)\(p_{p2},\ldots,p_{pD}\}_{\text{ss}}\) represent the best position among all particles and the acceptable performance for the \(p\)th particle, respectively. The particle velocity is represented by \(V_{p}=\{V_{p1},\)\(V_{p2},\)\(\ldots,V_{pD}\}\).  
Figure 1: The adaptive neuro-fuzzy inference system (ANFIS) structure.  
The particle position changes due to its speed, and it is updated in each iteration. The particle moves based on its speed, which is calculated as follows:  
\[v_{pd}^{iter+1}=\omega v_{pd}^{iter}+C_{1}rand(0,1)\Big{(}p_{pd}-x_{pd}\Big{)}+C_{ 2}rand(0,1)\Big{(}g_{d}-X_{pd}\Big{)}. \tag{11}\]  
The new position is computed as follows:  
\[X_{pd}^{iter+1}=X_{pd}^{iter}+v_{pd}^{iter+1}. \tag{12}\]  
The factor \(\omega\) represents the inertia weight. The positive constants \(C_{1}\) and \(C_{2}\) are denoted as learning factors and help particles to move toward a more appropriate space to reach a better solution. PSO updates the inertia weight for each iteration using [73]:  
\[\omega_{iter}=\omega_{max}-\frac{\omega_{max}-\omega_{min}}{iter_{max}}\times iter, \tag{13}\]  
where \(\omega_{iter}\) denotes the inertia weight and \(iter_{max}\) represents the maximum number of iterations. According to experimental reports, the values of \(\omega_{max}=0.4\) and \(\omega_{max}=0.9\) are apt [72].
# Effect of Post-Weld Heat Treatment on the Solid-State Diffusion Bonding of 6061 Aluminum Alloy
## 3 Results and Discussion
### Effect of PWHT on Diffusion Bonded AA6061 joints
This indicated that, during the SLS test, the T4-tempered joints experienced fast fracture and weak metallurgical bonds, indicating low bonding strength. On the other hand, the fractography of the T6-tempered joints in Figure 10b,d showed elongated and finer dimples (1-2 \(\upmu\)m) than those of AA6061 joints not tempered with T6-PWHT (Figure 7d). As compared with the non-PWHT samples, the fine dimples were related mainly to the smaller grain size and finer precipitations in the bonded region [37] as the artificial aging temperature (160 \({}^{\circ}\)C) of T6-PWHT was relatively lower than that of the diffusion bonding temperature of 500-530 \({}^{\circ}\)C. This type of fracture is indicative of plastic deformation and good bonding in the localized area, which is consistent with the increased bonding strength after T6-PWHT. These findings support the above claim that T6-PWHT successfully enhanced the joint strength of diffusion bonded AA6061 samples. Figure 8: Microstructure of AA6061/AA6061 bonding interface after diffusion bonding at **(a)** 500 \({}^{\circ}\)C with T4-PWHT (**b)** 500 \({}^{\circ}\)C with T6-PWHT, **(c)** 530 \({}^{\circ}\)C with T4-PWHT, and **(d)** 530 \({}^{\circ}\)C with T6-PWHT. Figure 10: Fractography of AA6061 joints diffusion bonded at temperatures of **(a)** 500 \({}^{\circ}\)C with T4-PWHT, **(b)** 500 \({}^{\circ}\)C with T6-PWHT, **(c)** 530 \({}^{\circ}\)C with T4-PWHT, and **(d)** 530 \({}^{\circ}\)C with T6-PWHT. Figure 9: Bonding strengths of AA6061 joints diffusion bonded at 500–530 \({}^{\circ}\)C for 30–240 min after T4- and T6-PWHT. In summary, our study shows that diffusion bonding of AA6061 for large area joint applications is feasible, and the joint strength can be further enhanced with T6-PWHT. Such a method should be important in bonding AA6061 structural components for two reasons: it is cost effective due to the simple manufacturing process, and the joint strength is sufficient for industrial needs.
## 1 Introduction
Another problem that researchers have to face is how to label the great amount of acquired material generated by a sensor recording continuously. Manual identification of sound sources in large audio files takes up a significant amount of research time. Some projects have used crowdsourcing [40], where volunteers tagged the audio files using internet platforms like Zooniverse. Since this method of labelling often produces low-quality results, other approaches, such as Active Learning [41] or Semi Supervised Learning [42] are very interesting. These methods are grounded on the idea that by means of actively choosing the most accurately predicted data, algorithms can increase their performance while utilizing less training data. Pseudo-labelling [43], also called self-training [44] is one of the techniques that has garnered the greatest interest in recent years. This method, involves training a classifier on labelled data, predicting the labels for the unlabelled data and retraining the model adding confident predicted data to the training data. In this paper, besides presenting the new NoisenseDB, we propose several state-of-the art urban sound events classification systems. We also analyse the feasibility of a semi-supervised training approach to cope with the labelling of the large amounts of data produced by such continuous monitoring systems. This paper is organized as follows: Section 2 describes the data-acquisition device and recording locations; Section 3 explains the creation of the NoisenseDB database, its taxonomy and structure; Section 4 introduces the tested sound event classification systems; Section 5 shows the experimental results using these systems with NoisenseDB. Finally, some conclusions are presented in Section 6.
## 5 Experiments and Results
### Ablation Study
#### 5.1.1 Which Fusion Technique Is Suitable?  
We conducted this study to analyze and evaluate the model's performance when using different common fusion techniques. The goal was to determine which fusion method was more suitable for our model. We examined three fusion methods: concatenation, element-wise addition, and averaging. Table 2 shows the results of each technique.  
The table compares three fusion techniques: concatenation, element-wise, and average-wise, using metrics like F1-score, mAP, and EM. Average-wise outperformed others in all metrics, indicating its effectiveness in integrating features from two views. Consequently, we selected average-wise as our preferred fusion method.
### Topology Optimization Considering Multiple-Loading Conditions
To overcome the aforementioned difficulties, this paper proposes a new method called the bisection constraint method (BCM). This method attempts to address the min-max compliance formulation of TO problems considering multiple-loading conditions. The BCM method offers a unique, user-preference-independent solution for TO problems amidst multiple-loading conditions. The paper is organized as follows: In Section 2, the basics of TO using the SIMP approach are explained, regarding single and multiple-loading conditions. Section 3 provides a detailed description of the proposed method. Representative examples are presented in Section 4 to demonstrate the effectiveness of the proposed method in solving the min-max compliance formulation of TO considering multiple load cases. Section 5 presents a comparative study discussing not only compliance minimization but also the stress levels arising in structures obtained via different methods. Finally, conclusions are provided in Section 6.
## 5 Internal Control Techniques
### Wind Turbine Control  
In variable-speed wind turbines, the use of blade pitch angle (BPA) control is becoming common to control the output power and protect generators from sudden wind gusts. During normal operation, when wind speed exceeds the threshold value, blade pitch angle controllers can modify the speed of rotor windings to maintain tip speed ratio. In this case, output power can be kept at the rated value, and the electrical and mechanical stress of wind turbines can be limited [61; 62].  
During the transient period, grid voltage dips can lead to imbalance between the high input power from wind and the low electrical output power to the grid, which may result in overspeed. Blade pitch controllers can protect rotor windings from overspeed and possible runaway conditions. Furthermore, an advanced control technique was proposed to absorb the wind energy which cannot be injected to the power grid during faults. This control scheme converts additional power to kinetic energy of wind  
Figure 19: Control effect comparison of stator-side external retrofit techniques in (**a**) terminal voltage, (**b**) DC-link voltage.  
turbines by reducing rotor torque, which may also lead to the rotor speed increasing during grid faults. The blade pitch angle control is triggered once the rotor speed exceeds the threshold value. Therefore, the hybrid control scheme can improve wind energy efficiency on the premise of safe operation.  
To improve the control effect during severe disturbances and avoid the torque ripple caused by conventional proportion integration differentiation (PID) controllers, adaptive fuzzy-PID controllers can be utilized in variable-speed wind turbines [63]. Fuzzy-PID controllers endow blade pitch angle controllers with the capability to maintain control even under unbalanced faults, as well as handle situations with unstable wind power.
## 2 Materials and Methods
To maximize the separation between both lasers' respective subimages, the lenslet array was rotated by 45 degrees, which in turn changed the size of the subaperture regions from 18 \(\times\) 18 pixels to 12.7 \(\times\) 12.7 pixels as shown in Figure 3. For simplicity, the telescope magnification was slightly adjusted such that the subimages lie within 13 \(\times\) 13 pixel boxes. The 11 cm laser separation ensured the subimages formed by the lenslet array were centered within these respective regions. During periods of strong turbulence, the subimages can spill outside of their allocated regions affecting data processing and profile computation. Each lenslet has a 146 \(\upmu\)m diameter and a 5.2 mm focal length. The lenslet  
Figure 1: Overhead view of the sensing path. The four sonic anemometers are placed evenly 100 m apart along the path. The trailer on the western end of the path contains the boundary layer scintillometer receiver and the helium-neon lasers. The eastern trailer houses the HTS and the scintillometer source. array has a 150 \(\upmu\)m pitch [12]. The spaces between are masked with chrome to block light not passing through the lenslets [15]. The Phantom camera captures the subimage motion in 600 \(\times\) 800 pixel frames, which are cropped to 572 \(\times\) 572 pixels for processing. A mask is then applied to the data frames to cover subapertures that are not fully illuminated due to proximity to the telescope aperture and the central obscuration. Flawed or blurred subapertures are also covered by the mask. This has a negligible effect on the solution due to the large sample size of additional subapertures available for the statistical calculations. Once the mask is applied, an auto-focusing algorithm is used to calculate the centroid of the mean frame of each subimage and then correct the tracking windows to center on the same respective points. This collection of data is used to calculate the differential tilt variances for all crossing and non-crossing sensing paths. A comparison of an initial HTS data frame against a cropped, background subtracted, masked, and auto-focused frame is shown in Figure 4.  
Figure 3: Dashed 18 \(\times\) 18 pixel grid compared to the solid 12.7 \(\times\) 12.7 pixel grid following a 45 degree mask rotation. The alternating laser subimages are centered within 13 \(\times\) 13 pixel regions after the telescope magnification is adjusted. Figure 2: Experimental setup of the (a) Hartmann Turbulence Sensor, (b) dual laser beacons, and (c) sonic anemometers.
## 4 Proposed Solution and Algorithm
### EDs Preference List  
Let \(\textit{deg}^{d}(i)\) be the degree of vertex \(i\) in \(\mathcal{G}_{d}\), hence the number of incident edges on \(i\) in \(\mathcal{G}_{d}\). The preference list is built considering, for each \(i\) in \(\mathcal{D}\), the maximum delay \(\delta_{i,j}\) associated to its incident edges, hence the maximum delay suffered from the ED \(j\in\mathcal{V}_{i}\). Each \(i\in D\) considers the delay associated to the direct link communication mode toward the SN \(\mathcal{S}\), and the delivery delay resulted by its selection as relay node. Furthermore, both the potential delays are weighed by the degree of the considered node and by its coefficient of reputation. Hence, each ED \(i\in\mathcal{D}\) evaluates both  
\[\delta^{\prime}_{i}=\frac{\delta_{S,i}}{\textit{deg}^{d}(i)\rho_{i}+1}; \tag{8}\]and  
\[\delta^{\prime\prime}_{i}=\frac{\delta_{\mathcal{S},i}+\delta_{ij}}{deg^{d}(i) \rho_{i}+1}; \tag{9}\]
# Optically Transparent Nano-Patterned Antennas: A Review and Future Directions
## 2 Transparent Antennas: A Review
### Au and Ag Grids  
In Reference [36], a gold grid layer (AuGL) CPW-fed planar monopole lozenge antenna operating at 60 GHz is proposed. The structure of AuGL is deposited on a 0.2-mm thick fused silica 7980 Corning substrate with a relative permittivity of 3.8 and a loss tangent of 0.0001. The AuGL has better optical transmittance and sheet resistance than others. The sheet resistance decreases as the metal area increases. The theoretical sheet resistance and the optical transmittance of the AuGL are 0.04 \(\Omega\)/sq and 83%, respectively (thickness of layer: 1 mm, pitch of the grid: 200 \(\upmu\)m, and gold strip grid-width: 10 \(\upmu\)m). Figure 10a shows the structure of the fabricated reference antenna and mesh transparent antenna. Notably, there is no difference in radiation efficiency between the reference antenna and the transparent antenna despite the very short wavelength of 54 GHz in Figure 10b.  
Figure 10: Photographs of the coplanar-fed monopole lozenge antennas printed on fused silica 7980 Corning substrates under measurement. (**a**) Light reflecting reference antenna and meshed transparent antenna; (**b**) Measured radiation patterns (co-polarization and cross-polarization) of the two monopole antennas at 54 GHz. Solid line: reference antenna; dotted line: optically transparent antenna [36]. (With permission from IEEE).  
In Reference [37], a multilayer H-shaped slot antenna composed of three silver grid layers (AgGLs) printed on each glass substrate is proposed, as depicted in Figure 11a. It operates in the FDD-UMTS band (1920-2170 MHz), shown in Figure 11b. It is built on three 300 mm \(\times\) 300 mm \(\times\) 1.1 mm 1737 Corning glass substrates (with a relative permittivity of 5.7 and a loss tangent of 0.006 at 2 GHz). AgGL features the lowest sheet resistance of 0.054 \(\Omega\)/sq compared to 8 \(\Omega\)/sq of ITO and 4.5 \(\Omega\)/sq of AgHT, so AgGL is used. The theoretical optical transmittance in this antenna structure is between 51.6% and 70.7% over the whole visible light spectrum. It also has a radiation efficiency of 60% (around 5 dBi of antenna gain), compared with 90% of the ideal opaque antenna.  
Metal Grid  
In Reference [38], a broadside radiating patch antenna consisting of a diamond grid representing the fundamental TM\({}_{010}\) mode at 28 GHz is designed. The patch is detailed for a 2000-A thick Ag alloy with various dielectrics. The patch width and length are 2.5 and 1.8 mm, respectively. This value is obtained by a numerical analysis of multiple substrates consisting of a transparent film insulator, OCA, and display panel. The diamond-grid patch antenna achieves an impedance bandwidth of about 800 MHz, which can cover the Federal Communications Commission (FCC) assigned to the 28 GHz band spectrum for 5G. The measured radiation gain of the diamond-grid patch antenna is confirmed to be 3.2 dBi.
# VLSI-Friendly Filtering Algorithms for Deep Neural Networks
## 4 Minimal Filtering Algorithms
### Algorithm 2, \(M=5\)  
Let \(\textbf{X}_{9\times 1}=[x_{0},x_{1},x_{2},x_{3},x_{4},x_{5},x_{6},x_{7},x_{8}]^{ \text{T}}\) be a vector that represents the input data set, \(\textbf{W}_{5\times 1}=\left[w_{0}^{(5)},w_{1}^{(5)},w_{2}^{(5)},w_{3}^{(5)},w_{4 }^{(5)}\right]^{\text{T}}\) be a vector that contains the coefficients of the impulse response of a 5-tap FIR filter, and \(\textbf{Y}_{5\times 1}=\left[y_{0}^{(5)},y_{1}^{(5)},y_{2}^{(5)},y_{3}^{(5)},y_{4 }^{(5)}\right]^{\text{T}}\) be a vector describing the results of using a 5-tap FIR filter:  
\[\textbf{Y}_{5\times 1}=\left[\begin{array}{ccccc}x_{0}&x_{1}&x_{2}&x_{3}&x_{4} \\ x_{1}&x_{2}&x_{3}&x_{4}&x_{5}\\ x_{2}&x_{3}&x_{4}&x_{5}&x_{6}\\ x_{3}&x_{4}&x_{5}&x_{6}&x_{7}\\ x_{4}&x_{5}&x_{6}&x_{7}&x_{8}\end{array}\right]\left[\begin{array}{c}w_{0}^{ (5)}\\ w_{1}^{(5)}\\ w_{2}^{(5)}\\ w_{3}^{(5)}\\ w_{4}^{(5)}\end{array}\right] \tag{10}\]  
As can be seen, calculating the product (10) requires 25 multiplications and 20 additions.  
Figure 2: Data flow graph of the algorithm for implementing the basic filtering operation for the case \(M=3\).  
We can devise a streamlined algorithm to compute \(\textbf{Y}_{5\times 1}\) by employing the following matrix-vector calculation procedure:  
\[\textbf{Y}_{5\times 1}=\textbf{T}_{5\times 14}^{(5)}\textbf{D}_{14}^{(5)} \textbf{T}_{14\times 9}^{(5)}\textbf{X}_{9\times 1} \tag{11}\]  
where  
\[\textbf{T}_{5\times 14}^{(5)}=\left[\begin{array}{ccccccccc}1&1&1&1&&&&\\ 1&1&1&1&&1&&\\ &1&1&&1&1&&1&\\ &&1&&1&1&1&1\\ &&&1&&1&1&1\end{array}\right],\]  
\[\textbf{T}_{14\times 9}^{(5)}=\left[\begin{array}{ccccccccc}1&-1&-1&1&-1&&&&\\ 1&&-1&&&&\\ &&1&-1&&&&\\ &&1&&&&&\\ -1&1&-1&-1&&\\ &&1&&&&&\\ &&-1&1&&\\ &&-1&1&-1&-1&\\ &&1&&&&&\\ &&1&-1&-1&-1&1\end{array}\right],\]  
and  
\[\textbf{D}_{14}^{(5)}=\textit{diag}\Big{(}s_{0}^{(5)},s_{1}^{(5)},\ldots,s_{13} ^{(5)}\Big{)},\]  
\[s_{0}^{(5)}=w_{0}^{(5)},\quad s_{1}^{(5)}=w_{0}^{(5)}+w_{1}^{(5)},\quad s_{2}^{ (5)}=w_{0}^{(5)}+w_{2}^{(5)},\quad s_{3}^{(5)}=w_{0}^{(5)}+w_{1}^{(5)}+w_{2}^{(5 )}+w_{3}^{(5)},\]  
\[s_{4}^{(5)}=w_{1}^{(5)},\quad s_{5}^{(5)}=w_{0}^{(5)}+w_{4}^{(5)},\quad s_{6}^{ (5)}=w_{1}^{(5)}+w_{3}^{(5)},\quad s_{7}^{(5)}=w_{2}^{(5)}+w_{3}^{(5)},\]  
\[s_{8}^{(5)}=w_{2}^{(5)},\quad s_{9}^{(5)}=w_{1}^{(5)}+w_{4}^{(5)},\quad s_{10} ^{(5)}=w_{3}^{(5)},\quad s_{11}^{(5)}=w_{2}^{(5)}+w_{4}^{(5)},\]  
\[s_{12}^{(5)}=w_{3}^{(5)}+w_{4}^{(5)},\quad s_{13}^{(5)}=w_{4}^{(5)}.\]  
Figure 3 illustrates a data flow graph of the proposed algorithm for implementing the basic filtering operation for a 5-tap FIR filter.