esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion  
We presented a series-parallel GAN architecture for translating fundus structure image to fluorescence angiography. The proposed algorithm is aimed at translating fundus structure image to three-phase FFA images. Compared with the other algorithms [19; 22; 23], it achieves better performance due to its high utilization rate of the mutual information. This is achieved in two ways. First, it uses a series-parallel architecture-based generator that contains one encoder and three decoders. The encoder is used to extract the features from the inputted fundus structure image including mutual information of three-phase FFA images with the supervision of all the three-phase labeled FFA images. Three different  
Figure 7: Two representative examples of our image translation are presented. The first example is from a wide field of view and the second example is from a narrow field of view. The images with red square boxes show that our algorithm can successfully translate ONH leakages.  
weighted decoders are used to generate the three-phase FFA images, respectively. Second, it uses a single discriminator that takes the fundus structure image and three-phase FFA images as the input and then evaluates the FFA images based not only on single FFA image information but also on mutual information among the fundus structure image and three-phase FFA images.  
Here, we make a comparison between our algorithm and other algorithms [19; 22; 23] in more detail. Compared with the three independent parallel GAN architecture [22; 23], our algorithm has three advantages. First, our algorithm has lower computation costs for both the training and testing processes, due to the sharing of the encoder and the usage of a single discriminator. Second, our algorithm requires a smaller training set, because each translated FFA image is supervised by three-phase real FFA images at the same time. Third, instead of training each phase of the FFA image independently, our model is trained by the loss function that takes account of the fundus structure image and three-phase FFA images, which makes our training process more robust. Compared with Sequence GAN [19], our algorithm also has lower computation cost due to the sharing of the encoder and the usage of a single discriminator. In addition, our algorithm series-parallel GAN architecture improves the image translation quality of the first-phase FFA image by increasing its utilization rate of mutual information.  
Because the translation from fundus structure image to multiple-phase FFA images is not yet available in commercial devices, there is still room for improvement. Here, we provide two options for future research. One option is finding a new GAN architecture to further improve the utilization rate of mutual information between the different phases of FFA images. The other option is translating fundus structure image to more than three phases of FFA images, which needs to capture more phases of FFA images and create the algorithm tailored for the datasets.
# A Software-Defined Radio for Future Wireless Communication Systems at 60 GHz
## 2 5G Testbed Overview
### FMC126/FMC230 Data Interface
Additionally, both IQ up-/down- conversion stages can be performed by the multiplication of the desired signal by a complex exponential operating at a certain center frequency (\(f_{c}\)), as it is illustrated in Figure 9. At the transmitter, the baseband signal spectrum is frequency shifted to be centered at (\(f_{c}\)), resulting in a non-symmetric spectra [5]. Consequently, applying \(e^{-\beta\pi f_{c}}\) at the receiver, will move the transmitted signal spectra to be centered at DC, thus converting the received signal back to baseband. Such complex exponential function was implemented using a Coordinate Rotational Digital Computer (CORDIC) DSP algorithm, which is responsible for implementing an IQ-mixer with angular frequency proportional to the desired \(f_{c}\) value. However, since both DAC/ADC converters employ an eight parallel processing path technique, similar to the FIR filter introduced above, a distributed CORDIC with eight paths operating at 156 MHz is also required to perform either an up-conversion or down-conversion. To this end, the distributed CORDIC must generate samples of the same complex exponential but at shifted sample instants in each processing path. It is worth noting that each CORDIC path in the TX FPGA firmware should be connected to the parallel structure of OSERDES, in order to perform the data serialization required in the data interface of the FMC230. In the RX firmware, to shift the signal back to baseband, a similar distributed CORDIC approach was employed but with  
Figure 10: Graphical representation of uniform filter bank (image adapted from [28]). negative phase increment. Then, it was connected to a parallel structure of ISERDES, which enables data parallelism and consequently reduction of the data clock. Finally, to integrate the developed SysGen OFDM with the transceiver firmware, it was necessary to package and export such modulation into two distinct TX/RX IP customized blocks. That can be seen in the high-level block diagram illustrated in Figure 11.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion
### Path Planning
#### 3.2.2 Optimal Sequence  
After the 72 points of interest were identified, the optimal visiting sequence was determined to minimize the total trip time. Here, the number of possible solutions is  
\[{}^{n}C_{r}=\frac{n!}{r!(n-r)!}=2556,\]  
where \(n\) is the number of points of interest and \(r\) represents the two actions of entering and exiting a point. The solution of the traveling salesman problem is used to determine an optimal visiting sequence known as the Hamiltonian cycle, as depicted in Figure 9.  
Table 1 shows the distance between nodes of the traveling salesman problem solution. It was noted that the distance between some nodes is very high, suggesting that the terrain  
Figure 8: Boundary region for mutual visibility.  
Figure 9: Before and after solving the traveling salesman problem.  
is irregular or obstructions are close. This further suggests that the optimal path between the nodes is not a simple straight line, an issue that was overcome using the \(A^{*}\) algorithm.
# Melody Extraction Using Chroma-Level Note Tracking and Pitch Mapping
###### Abstract  
A new architecture for melody extraction from polyphonic music is explored in this paper. Specifically, chromagrams are first constructed through the harmonic pitch class profile (HPCP) to measure the salience of melody, and chroma-level notes are tracked by dynamic programming. Then, note detection is performed according to chroma-level note differences between adjacent frames. Next, note pitches are coarsely mapped by maximizing the salience of each note, followed by a fine tuning to fit the dynamic variation within each note. Finally, voicing detection is carried out to determine the presence of melody according to the salience of fine-tuned notes. Note level pitch mapping and fine tuning avoids pitch shifting between different octaves or notes within one note duration. Several experiments have been conducted to evaluate the performance of the proposed method. The experimental results show that the proposed method can track the dynamic pitch changing within each note, and performs well at different signal-to-accompaniment ratios. However, its performance for deep vibratos and pitch glides still needs to be improved.  
melody extraction; chromagram; harmonic pitch class profile; dynamic programming; Euclidean algorithm +
Footnote †: journal: Applied Sciences
# Safety Analysis of Secondary Lining of Yulinzi Tunnel Based on Field Monitoring
## 5 Numerical Simulation Analysis of Long-Term Stress on Secondary Lining
### Analysis of Numerical Simulation Results  
#### 5.2.1 Deformation Analysis of Secondary Lining  
In order to further analyze the displacement and deformation of the secondary lining structure, the vertical displacement curves of the vault and invert positions, as well as the horizontal displacement curve of the right side of the arch waist, are plotted separately, as shown in Figures 20 and 21.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Mechanical Parameters** & **Formation Type** & **Parameter Value** \\ \hline Density (kg/m\({}^{3}\)) & Loess Strata & 2135 \\ Elastic Modulus (MPa) & Loess Strata & 3.7.65 \\ Poisson’s ratio & Loess Strata & 0.33 \\ Cohesion (kPa) & Loess Strata & 27.04 \\ Angle of internal friction & Loess Strata & 6.34 \\ G\({}_{\text{M}}\)(kPa) & Loess Strata & 27.44 \\ H\({}_{\text{M}}\) (kPa-h) & Loess Strata & 343.75 \\ G\({}_{\text{K}}\) (kPa) & Loess Strata & 10.95 \\ H\({}_{\text{K}}\) (kPa-h) & Loess Strata & 20.69 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Mechanical parameters of loess.  
Figure 19: Cvisc model components.  
Figure 18: Numerical simulation model.  
According to the analysis of Figure 20, the values of the vault and invert positions at time zero were \(11.9\) mm and \(-12.1\) mm, respectively. This indicates that before the creep deformation of the surrounding rock, the secondary lining structure of the tunnel would experience arch crown settlement and invert uplift under the instantaneous deformation pressure of the surrounding rock. The relative displacement of the arch crown and invert positions remained basically unchanged in the following 100 years, indicating an overall settlement of the structure. The overall settlement of the secondary lining could reach \(51\) mm within 100 years.  
According to the analysis of Figure 21, the lateral displacement of the right arch foot at time zero was \(6.2\) mm. The tunnel structure exhibited outward extrusion deformation before the creep deformation of the surrounding rock. With the passage of time, the lateral extrusion deformation of the structure gradually increased. After 20 years of creep, the lateral deformation gradually stabilized, and the lateral displacement reached \(7.2\) mm. Compared to the vertical settlement displacement, the lateral displacement of the secondary lining structure was relatively small. Therefore, it is necessary to control the overall settlement displacement of the secondary lining structure in the later operation of the loess tunnel.
# Comparison of Machine Learning Algorithms Used for Skin Cancer Diagnosis
## 4 Method
### Classsification of Skin Lesions
#### 4.3.5 Random Forest  
The random forest algorithm is based on the use of multiple decision trees, which translates into greater accuracy of the model than in the case of using single decision trees. Each individual tree in a random forest returns a prediction of belonging to the selected class, and the class with the most votes becomes the final prediction of the model.
# VOCs Are Relevant Biomarkers of Elicitor-Induced Defences in Grapevine
## 2 Results and Discussion
### Analysis of Stilbenes in Bastid(r)- and Chitosan-Treated Grapevine Plants  
Stilbene phytoalexins were analysed as a reference, as markers of defence elicitation and resistance to diseases [47]. The test was performed with Bastid(r), used as a reference elicitor in this study, and also with chitosan because it showed the highest level of protection against downy mildew (Figure 4). Four stilbenes, i.e., piceid (_trans_- and _cis_- isomers) and  
Figure 4: Induced resistance assays. Six elicitors were tested for their efficiency in protecting grapevine against downy mildew (_P. viticola_). Bastid® (0.8 mL/L, 4 assays), Romeo® (2 g/L, 2 assays), Redeli® (1 g/L, 2 assays), OG (2.5 g/L, 2 assays), and chitosan (1 g/L) and Bion® (0.5 g/L, 1 assay). Protection levels (%) were calculated based on comparisons of the sporulating leaf area with the H\({}_{2}\)O control considered as 0% protection.  
resveratrol (_trans_- and _cis_- isomers) were analysed. Both elicitors induced the accumulation of these compounds (Figure 5). In the water control, the concentrations of resveratrol (0.39 to 1.60 \(\upmu\)g/g DW) and piceid (3.40 to 14.38 \(\upmu\)g/g DW) were low throughout the 8 days of the experiment compared to those measured after the two elicitor treatments (up to 7.39 and 75.29 \(\upmu\)g/g DW for the resveratrol and piceid isomers, respectively). After chitosan treatment, the piceid and resveratrol isomers were only significantly accumulated on D8-3- and 13-fold more than in the H\({}_{2}\)O control, respectively. Bastid(r) promoted the accumulation of both stilbenes from D2 till D8. It induced higher levels of stilbenes than chitosan did, particularly on D6. Both elicitors triggered the accumulation of phytoalexins. Bastid(r) induced earlier and higher phytoalexin accumulation than chitosan did, and also higher VOC emission. This observation suggests that the protection of grapevine against downny mildew provided by Bastid(r) and chitosan involves different modes of action. For Bastid(r), the induction of phytoalexin synthesis probably plays a key role. Chitosan has long been studied as an elicitor of plant defence [48]. It is very effective to reduce grapevine downny mildew severity in controlled conditions probably thanks to both its direct antimicrobial and indirect elicitor effects. The present study shows that it also induces the emission of terpen. It may be tempting to conclude that chitosan is a less effective VOC inducer than Bastid(r) (Table S1). However, it should be noted that the tent experiment was only performed twice.
# Shape Memory Effect in Micro-Sized Shape Memory Polymer Composite Chains
## 3 Morphology of Shape Memory Polymer/Nickel (SMP/Ni) Chain Arrays in Horizontal Plane  
The distribution of nickel particles in SMP/DMF solution is essential for the formation of chain-like structures. After well stirring of the nickel-particle/SMP/DMF mixture, the nickel particles distribute uniformly and randomly at the initial time. As time flows, the nickel particles slowly deposit on the bottom of the petri dish. Hence, the uniform and random distribution of nickel particles in SMP/DMF solution is difficult to be observed by an optical microscope or SEM. Alternatively, to observe and study the formation characteristics of nickel chains, the magnetic field was applied in the direction parallel to the bottom of petri dish. The nickel chains formed, and finally solidified in horizontal plane with the volatilization of DMF solvent. Figure 4 indicates the distribution morphology of nickel particles under the magnetic field in horizontal plane. The straight nickel-chain structures along with the direction of magnetic field are obvious especial for the low volume fraction of nickel fillers, namely 0.25%, 0.5%, 1%, 2%, 3%, and 5%. The width of nickel chains gradually increases with the increasing of nickel contents. For the 0.25% sample, most of the cross sections of nickel chains only contain one nickel particle (3-7 um width), and the length of nickel chains is general over 1000 um, with an aspect ratio around 200. For the samples with higher nickel contents, they contain more nickel particles in one cross section, and the length limits of nickel chains approach 1500-3000 um. In summary, for the varied contents of nickel fillers (0.25%, 0.5%, 1%, 2%, 3%, and 5%), the general widths of nickel chains are 3-7 um, 10-50 um, 20-80 um, 20-100 um, 100-300 um, and 200-400 um, respectively, and the general lengths are 1000-3000 um.  
Figure 3: Typical chain structures of nickel particles in Shape Memory Polymer (SMP) matrix (3 vol% Ni). (**a**) Morphology of SMP/Ni vertical chain pillar; (**b**) Zoomed-in view of (**a**); (**c**) Typical morphology of nickel particles chains.
## 2 Related Work  
### Autonomous 3D Reconstruction in Robotic System  
Autonomous 3D reconstruction of objects using robots is a long-standing problem. Previous works have employed various robotic platforms for this task, including Unmanned Aerial Vehicles (UAVs) [19; 20], wheeled mobile robots [17; 23], and fixed-base robotic arms [9; 21; 22; 24].  
To model the entire geometry of an object, a robot must have access to the entire view space that allows for the observation of all sides of the object. In this regard, UAVs and wheeled mobile robots are frequently used robotic platforms as they can cover a \(360^{\circ}\) view of the object without space restriction. Specifically, [PERSON] et al. [17] generate finite-view candidates that cover the whole-object geometry, allowing the mobile robot to access view candidates. [PERSON] et al. [19] use UAVs to freely access the view space within a continuous SE(3) manifold.  
In the fixed-based robotic arm setup, the workspace of the robot is limited, resulting in the inability to access the view space of static objects, such as the back or bottom side of the objects. To address this limitation, [PERSON] et al. [9] utilize a multi-robot system to expand the view space of static objects. [PERSON] et al. [24] employ an additional rotary table to enable a single robot system to access the \(360^{\circ}\) view space of the object.  
Most 3D reconstruction approaches in robotic applications often rely on RGB-D data captured by sensors such as Microsoft Kinect, leveraging both color and depth information for high-accuracy reconstructions [25; 26; 27; 28]. For instance, KinectFusion [26] uses RGB-D data to perform real-time 3D reconstruction by aligning and merging depth frames. Similarly, [PERSON] et al. [25] formulate volumetric information gain using RGB-D sensors, enabling active reconstruction with mobile robots. However, these methods depend on specialized depth sensors, which can be costly, less scalable, and sensitive to factors like lighting, calibration, and object reflectivity.  
In our work, we propose a method that overcomes these limitations by utilizing only RGB cameras combined with neural implicit representation. By directly manipulating objects within a single robotic arm setup, our approach enables complete 3D reconstruction, including occluded regions, without the need for depth sensors, making it more cost-effective and robust.  
Figure 1: Overview. We propose an active learning framework that automates the full 3D reconstruction of novel objects using a single robot manipulator system. As shown in the red box, a fixed-base single manipulator system can only partially reconstruct the front part of an object due to the constraints of the robot’s view space. By utilizing our proposed See-Then-Grasp method, the robot can directly interact with the object, granting access to the entire view space of the object. As illustrated in the blue box, this allows for full 3D reconstruction.
## 1 Introduction
Both of these two experimental validations are tested with smaller (1 Ah) and larger (2 Ah) initial values in EKF than the true value of 1.5 Ah (Ah: ampere-hour). * Thanks to the superposition principle, two sub-models are extracted. For the nonlinear one, an observability analysis is conducted. The local observability for battery ECM parameters and battery usable capacity are analyzed separately. * The necessary observability conditions for the extended ECM are derived and clearly listed. It shows that the necessary conditions for local observability depend on the battery current value, the initial value of the battery capacity, and the square of the derivative of the OCV with respect to the SOC. * A new cascaded framework for the LIB state and parameter estimation is proposed based on the obtained theoretical analysis results. The battery ECM parameter estimation and battery capacity estimation are divided into two parts. A simultaneous estimation of OCV will connect these two parts. * The derived local observability conditions and the new proposed framework extend the traditional battery monitoring study. The remainder of this paper is organized as follows. Firstly, details of the extended battery ECM will be introduced in Section 2. Secondly, a theoretic and systematic observability analysis for the nonlinear extended battery ECM will be developed in Section 3, where the necessary local observability conditions of the battery state and parameter estimation will be clearly listed. Thirdly, a new cascaded framework for battery monitoring, with a main improvement for the battery usable capacity estimation, will be proposed in Section 4. Furthermore, Section 5 is dedicated to the numerical evaluation of the estimation framework. Last, but not least, conclusions will be drawn in Section 6.
## 4 Methodology
### Generative Adversarial Network (GAN) Module
Therefore, the overall loss functions of the GAN model are defined as Equation (3):  
\[L\_\{GAN\}=E\_\{x\sim p\_\{data\}(x))\{log(D(x))]+E\_\{z\sim p\_z(z)\}[log(1-D(G(z)))] \tag{3}\]  
The loss function consists of two components: the first term encourages the discriminator to classify real samples as true, i.e., to output values as close to 1 as possible; the second term encourages the discriminator to classify synthetic (generated) samples as false, i.e., to output values as close to 0 as possible. To improve training stability, we adopt the binary cross-entropy (BCE) loss function in our experiments. The formulation is given as Equation (4):  
\[BCE(p,\;y)=-[y\cdot log(p)+(1-y)\cdot log(1-p)] \tag{4}\]  
Here, \(y\in\{0,1\}\), where 0 denotes a generated sample and 1 denotes a real sample. During training, the Adam optimizer is used to update the parameters of the generator and discriminator independently. Adam combines the advantages of AdaGrad and RMSProp, providing adaptive learning rates and momentum, which improves both the speed and stability of deep neural network training [26]. Moreover, Adam has been widely adopted in the training of GANs and has demonstrated robust convergence and stability in practice [27]. In each training epoch, the generator's parameters are first fixed while the discriminator is trained using both real and generated samples. Then, the discriminator's parameters are fixed, and the generator is updated to enhance its sample generation capability. This alternating optimization continues until the adversarial process converges, allowing the generator to produce high-quality synthetic fault samples in a stable manner. After training is complete, the generator is used to generate synthetic fault samples in quantities comparable to the normal samples. These generated samples are then combined with the original real fault samples and normal samples to construct a new, class-balanced dataset. This augmented dataset serves as the training foundation for the subsequent CNN-LSTM classification model. To evaluate the effectiveness of this data augmentation strategy, the experimental section compares classification performance under both non-augmented and GAN-augmentedconditions using the same classification model framework. This allows for a comprehensive assessment of the GAN practical impact on mitigating the class imbalance problem.