esa-sceva/satcom-chunk-collection huggingface dataset:
# Focused and TSOM Images Two-Input Deep-Learning Method for Through-Focus Scanning Measuring
## 2 Materials and Methods  
The process of the two-input deep-learning TSOM method is shown in Figure 1. Firstly, the TSOM imaging system is built to capture experimental images. Secondly, by scanning along the optical axis, a series of images of the measuring target and the background of the imaging region are obtained, respectively. After the background images are subtracted from the target images, a series of images of the target removing the background noise are obtained, consisting of a focused image and lots of defocused images. These images are stacked to form an image data cube (TSOM cube) according to their spatial positions. Then, the image definition of all images in the TSOM cube is evaluated to obtain the best focused image. At the same time, a sectional view of the TSOM cube is extracted and processed by smoothing, interpolation and pseudo-color methods to generate the TSOM image. Next, the obtained focused images and TSOM images are input into the two-input CNN network for training and testing. Finally, the network outputs the measuring parameters of the corresponding target.  
Sections 2.1-2.4 introduce the TSOM imaging system, the acquisition of the dataset, the network structure and model training in detail, respectively.
# Real-Time Visual Feedback in Singing Pedagogy: Current Trends and Future Directions
## 3 The Oscillatory Subsystem
### Vocal Folds Contact Variations
As shown in both maps, this singer has two main vibratory patterns, depending on \(f_{\mathrm{o}}\) and SPL. Above approximately 300 Hz, phonation is mainly achieved by reducing vocal fold addiction, as shown by a clear color change from yellow to green in the \(Q_{\Delta}\) map (left panel) and from green to light blue in the \(I_{\mathrm{c}}\) map (right panel). Figure 11: Examples of voice maps displayed by FonaDyn software for (1) firm and (2) breathy phonation, produced by a male singer while observing the EGG waveshape (red boxes) in real-time and trying to match the EGG waveshape model of the intended phonation type (yellow boxes). The result is also presented in real-time as an individual cell in the voice map (red arrows). Note: SPL, sound pressure level; \(f_{\mathrm{o}}\), fundamental frequency. Figure 10: Real-time voice maps of a female singer’s voice displayed by FonaDyn software. (**Left panel**): normalized peak derivative (\(Q_{\Delta}\)), with red and green indicating high and low values, respectively. (**Right panel**): index of contact (\(I_{\mathrm{c}}\)), with red and blue indicating high and low values, respectively. As expected, EGG voice maps differ between individuals. This is particularly relevant when training a singer; teaching tools should be tailored to the student and not the other way around [48]. For example, when comparing the voice maps in Figures 10 and 12, pertaining to a female trained jazz singer and to a female amateur singer, respectively, they differ substantially. As compared with the amateur singer, the jazz singer phonated with stronger vocal fold contact over a wider range of frequencies and intensities. The singer reduced vocal fold contact only at higher pitches (approximately above 500 Hz). This can be seen as the green area in the \(Q_{\Delta}\) map, which corresponds to a slower vocal fold contact speed, and the light blue area in the \(I_{\rm c}\) map, corresponding to a weaker collision force. Voice maps can also be used to investigate whether implemented teaching approaches resulted in the intended pedagogical goals. For example, immediate effects of flow ball exercises were analyzed as differences between pre- and post-exercise voice maps. The results showed that the use of flow ball phonation assisted with the development of less pressed phonation and gentler vocal fold collisions [49].
# Calibration of Camera and Flash LiDAR System with a Triangular Pyramid Target
## 4 Experiments
### Simulation  
#### 4.1.1 Experiment Settings  
We first evaluate our extrinsic calibration method by simulation data and ground truth. We set up a virtual camera and flash LiDAR system. The intrinsic parameters of the virtual camera and flash LiDAR are shown in Table 1. The rotation and translation from LiDAR to camera are set as \([30^{\circ},-40^{\circ},70^{\circ}]\) and \([0.4\text{ m},-0.2\text{ m},0.6\text{ m}]\) separately. The base and height of the triangular pyramid are set as 1 m and 0.4 m, respectively. The size of each cell of chessboard is 0.05 m \(\times\) 0.05 m. Each plane of triangular pyramid has 6000 LiDAR points and 100 image corner points. In the following experiments, we employ \(e(\mathbf{R})\) and \(e(T)\) to evaluate the errors of rotation and translation:  
\[\left\{\begin{array}{l}e(\mathbf{R})=2\text{arccos}(0.5\sqrt{1+tr(\mathbf{ RR}_{real}^{T})})\\ e(T)=\|T_{real}-T\|\end{array}\right., \tag{23}\]  
Figure 3: Flowchart of intrinsic and extrinsic calibration of the proposed method.  
where **R** and \(T\) are extrinsic parameters figured out through our method, **R**\({}_{real}\) and \(T_{real}\) are ground truth of rotation matrix and translation vector. \(e(\textbf{R})\) represents a single angle for **R** to rotate to coincide with **R**\({}_{real}\).
# Energy Ratio Variation-Based Structural Damage Detection Using Convolutional Neural Network
## 2 Basic Theories and Methods  
### Wavelet Packet Decomposition  
As opposed to the physical quantities directly related to the characteristics of the structure, such as stiffness, natural frequency, and damping, energy plays a significant role in signal processing, which is only the energy of signal analysis angle and does not have the significance of energy in the real physical world. After structural damage, the acceleration signal will be partially disturbed, resulting in the energy change of each frequency band after the wavelet packet decomposition. The energy change in the frequency band corresponds to the damage done to the structure, offering a better reflection of the damage information. With the index of energy, a set of new feature vectors can be obtained by rearranging the frequency band information, so as to describe the damage scenarios of the structure more clearly. The process of WPD of the frequency band energy and the three-layer WPD tree in Figure 1 is as follows [29].  
After the signal is decomposed by the j-layer wavelet packet, the initial signal \(f(t)\) is expressed as:  
\[f(t)=\sum_{i}^{2j}f_{j}^{i}(t) \tag{1}\]  
Its linear combination form can be expressed as:  
\[f_{j}^{i}(t)=\sum_{\infty}^{+\infty}c_{j,k}^{i}(t)u_{j,k}^{i}(t) \tag{2}\]  
The wavelet packet coefficient can be expressed as:  
\[c_{j,k}^{i}=\int_{R}f(t)u_{j,k}^{i}(t)dt \tag{3}\]  
where \(u_{j,k}^{i}(t)\) and \(c_{j,k}^{i}(t)\) denote the wavelet packet function and the wavelet packet coefficient, respectively. When \(m\
eq n\), the orthogonality condition of the wavelet packet function can be expressed as:  
\[u_{j,k}^{m}(t)u_{j,k}^{n}(t)=0 \tag{4}\]  
The formula for total energy is:  
\[E_{j}=\int_{R}f^{2}(t)dt=\sum_{m}^{2j}\sum_{n}^{2j}\int_{R}f_{j}^{m}(t)f_{j}^{ n}(t)dt \tag{5}\]  
According to the orthogonality condition of the wavelet function, we can obtain:  
\[E_{j}=\sum_{i=1}^{2j}E_{j}^{i} \tag{6}\]  
The energy distributed over the i-band of the j-layer can be expressed as:  
\[E_{j}^{i}=\int_{R}f_{j}^{2}(t)dt \tag{7}\]
## 5 Measurement Setup  
A transmission experiment was performed to showcase the effectiveness of the adaptive FFEs. In this work 850 nm-940 nm oxide-confined GaAs VCSELs with an anti-waveguiding AlAs-rich core with apertures ranging from 3 um to 7 um[2] are used. This type of VCSEL enables highest reported bandwidths and thus enables very high data transmission under direct modulation. Figure 9 depicts the experimental measurement setup [22]. A 22 GHz bandwidth VCSEL wire-bonded to an RF connector and coupled into a lensed MMF fiber was used. It was connected to a Bias-T. The Bias-T combines an AC path, which is connected to an Arbitrary Waveform Generator (AWG) and a DC path, which is fed by a current source driving a bias current of 5 mA. The AWG is used to create a 21 along, 800 mV pp Pseudo-Random Binary Sequence (PRBS) which is pre-emphasized with a UI-spaced linear or adaptive FFEs with six Transmitter (TX) taps (two pre, one main, three post). The number of taps was chosen based on the length of the relaxation oscillations and the trade-off between better equalization (high amount of taps) and low computation complexity (low amount of taps). Since the filter effectively dampens the relaxation oscillations, increasing the amount of taps only gives diminishing returns after a certain number. The integrated 32 GHz receiver of the sampling oscilloscope is used to receive the optical signal of the VCSEL. For transmission experiments, OM-5 fiber was connected in-line. Synchronization of the system is attained by generating a clock signal with the second channel of the AWG, driving the phase reference module and trigger input of the sampling oscilloscope.  
Figure 8: (**a**) Adaptive FFE training sequence, (**b**) overlaid pulse responses for all 12 combinations after training.  
BER performance was evaluated by exporting the Probability Density Function (PDF) eye diagram from the jitter and noise analysis software of the oscilloscope. Vertical bathtubs were calculated with the common method for Additive White Gaussian Noise (AWGN) channels of fitting normal distributions to the four PAM-4 levels and adding Q-functions [23]. The bathtub curves for every one of the three eyes were then combined into one vertical bathtub curve. This method of calculating BER is undertaken because commercially available stand-alone linear receivers have limited bandwidth and are difficult to de-embed, thus limiting the performance in combination with a bit-error tester.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 4 Numerical Simulations
### Simulation Cases
#### 4.2.1 Case 1  
In this case, the MTTP for FFSR1 is studied. Figure 13 represents the variation trends of fitness values using IGA, showing that the minimum maneuver time of FFSR1 is 5.196 s. Figure 14 represents the movements of FFSR1, where the red points in each subfigure denote the corresponding values at the waypoints. The following explanation is useful to understand the subfigures: Figure 14a,b represents the time histories of the position and attitude of the end effector, respectively; Figure 14c,d represents the time histories of the seven joint angles; Figure 14e,f represents the time histories of the seven joint angular velocities. Figure 14a,b shows that the optimal waypoint sequence is \(1\to 4\to 7\to 9\to 2\to 3\to 10\to 8\to 5\to 6\), and all waypoints are accurately visited. Figure 14c,d shows that the joints move smoothly, while Figure 14e,f shows that the joint angular velocities at each waypoint are zero. Moreover, Figure 14c-f shows that the joint angles and the joint angular velocities are within the predefined region. In Figure 14, each joint angle \(q_{i}(i=1,...,7)\) denotes one component of the joint configuration \(\mathbf{q}=\left[q_{1},...,q_{7}\right]^{T}\), and each joint angular velocity \(dq_{i}\) denotes the 1 st derivative of \(q_{i}\).  
Figure 12: Optimal fitness values of IGA and AL1 in two cases. (**a**) MTTP for FFSR1 and (**b**) MTTP for FFSR2.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & **Position (m)** & **Attitude (deg)** \\ \hline
1 & \(\left[1.61,\,0.15,\,0.15\right]\) & \(\left[148,\,-27,\,75\right]\) \\
2 & \(\left[1.00,\,-0.50,\,-0.45\right]\) & \(\left[150,\,-30,\,80\right]\) \\
3 & \(\left[1.50,\,0.00,\,0.00\right]\) & \(\left[100,\,0,\,100\right]\) \\
4 & \(\left[1.00,\,-1.00,\,0.50\right]\) & \(\left[80,\,10,\,100\right]\) \\
5 & \(\left[0.00,\,1.00,\,1.00\right]\) & \(\left[60,\,30,\,120\right]\) \\
6 & \(\left[-0.50,\,0.50,\,1.00\right]\) & \(\left[80,\,80,\,80\right]\) \\
7 & \(\left[0.60,\,-0.50,\,1.00\right]\) & \(\left[0,\,80,\,80\right]\) \\
8 & \(\left[1.00,\,0.50,\,1.00\right]\) & \(\left[0,\,-10,\,10\right]\) \\
9 & \(\left[1.00,\,-0.50,\,1.20\right]\) & \(\left[0,\,0,\,0\right]\) \\
10 & \(\left[0.90,\,-0.50,\,1.40\right]\) & \(\left[20,\,0,\,-20\right]\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Position and attitude of ten waypoints.
# Multi-Feature Fusion with Convolutional Neural Network for Ship Classification in Optical Images
## 4 Experimental Results and Analysis
### Simulation Environment  
To validate the performance of the proposed method, several experiments were performed on the two ship datasets described in the previous section to facilitate training and evaluation of the proposed improved CNN. The experimental environment comprised of an Inter(R) Core(TM) i9-7980 [EMAIL_ADDRESS] GHz processor along with an NVIDIA TITAN Xp Pascal graphics card. All experiments were performed using Python 3.5, MATLAB, and the TensorFlow framework.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**No.** & **Class** & **Train** & **Test** \\ \hline
1 & Medium-other & 99 & 86 \\
2 & Merchant & 103 & 71 \\
3 & Medium-passenger & 78 & 62 \\
4 & Sailing & 214 & 198 \\
5 & Small & 342 & 313 \\
6 & Tug & 37 & 20 \\  & Total & 873 & 750 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Number of training and test samples using the VAIS dataset.  
Figure 14: Five visible samples from each of the main classes of the VAIS dataset.
## 2 Materials and Methods  
### FTIR Passive Remote Sensing Model  
Passive remote sensing of gas clouds is based on the analysis of the infrared absorption and radiation by molecules in the atmosphere. The propagation of radiation through the atmosphere is described by the theory of radiative transfer [23]. The atmosphere in front of the FTIR is divided into multiple homogeneous layers, as shown in Figure 1. For each homogeneous layer, it receives radiation from the previous layer and transfer radiation to the next layer. Taking into account the transmittance and scattering of the target material, interferers and atmosphere in the homogeneous layer, the output spectral radiometry can be expressed as:  
\[L_{i}=(1-\tau_{a}\tau_{b}\tau_{c})B_{i}+\tau_{a}\tau_{b}\tau_{c}L_{i-1}+\rho_{i} \tag{1}\]  
where \(B_{i}\) is the radiance of blackbody at temperature of layer \(i\), \(L_{i-1}\) is the radiance of layer \(i-1\), \(\tau_{a}\tau_{b}\tau_{c}\) are the transmittance of the gas, the interference and the atmosphere, respectively, and \(\rho_{i}\) is the scattering contribution.  
For horizontal, low elevation, or ground to ground measurements, a simplified model with three layers can be used, as shown in Figure 2. Radiation from the background, for example, the building, the sky or the ground (Layer 3) propagates through the gas cloud (Layer 2) and the atmosphere between the cloud and the Fourier transform infrared spectrometer (Layer 1) [1].  
In this three-layers model, Layer 1 and Layer 2 are considered homogenous. The radiation measured from all layers by the spectrometer is:  
\[L_{1}=(1-\tau_{1})B_{1}+\tau_{1}[(1-\tau_{2})B_{2}+\tau_{2}L_{3}] \tag{2}\]  
where \(\tau_{i}\) is the transmittance of layer \(i\) (\(i=1\), 2, 3), \(B_{i}\) is the radiance of blackbody at temperature \(T_{i}\), \(L_{3}\) is the radiance transmitted by the background layer to gas cloud layer. The scattering contribution is negligible [1]. When the second layer is in thermal balance with the surrounding environment, that is, \(B=B_{1}=B_{2}\), Equation (2) can be simplified as:  
\[L_{1}=(1-\tau_{1}\tau_{2})B+\tau_{1}\tau_{2}L_{3} \tag{3}\]  
Figure 1: Multilayer atmosphere radiative transfer model.  
Figure 2: Three-layer simplified atmosphere radiative transfer model.  
It is generally assumed that the transmittance of Layer 1 \(\tau_{1}\approx 1\), then Equation (3) can be simplified with a two-layers model:  
\[L_{1}=(1-\tau_{2})B+\tau_{2}L_{3} \tag{4}\]
# UWB Localization with Battery-Powered Wireless Backbone for Drone-Based Inventory Management
## 3 UWB Localization System Design
### Position Calculation  
The most likely spatial position of the mobile tag is calculated by combining multiple distance estimations. Ranging messages are received both by the mobile tag, as well as on a back-end server. As such, the position can be calculated centralized (e.g., on a remote server) or locally on the tag using a localization algorithm (e.g., particle filter [28] or Kalman filter [29]).  
For the position calculation, we implemented a particle filter which is described in Section 4 of [30]. The particle filter uses a set of \(N\) particles that represents different possible states. The states of each particle are updated with each incoming range, resulting in a position update for every reporting message. The particle server requires incoming ranges from at least three different anchors for a good 2D position estimate, and at least four different anchors on different heights for a 3D estimate.
## 2 Hyperspectral Imaging Configurations
### Image Acquisition Modes  
Generally, there are four approaches to the acquisition of hyperspectral image data: point scanning, line scanning, area scanning and single shot, as shown in Figure 1. Point scanning captures spectral data for each pixel each time, and scans the full spatial scene by moving either the detector or the object continuously in the two spatial dimensions to obtain 3-D image cubes. This approach is usually used in microscopic imaging, where double scanning is required; however, it is very time consuming, and also requires repositioning the hardware to ensure the repeatability of results. Hence point scanning is rarely used for food quality or safety evaluation. Line scanning is a push-broom method, in which a line of spatial information with a full spectrum for each pixel is captured, and a complete volume of spatial-spectral data is obtained by moving the sample line by line. This approach is well suited for conveyor belt systems in the production line for online inspection of food items. Unlike point scanning and line scanning, which are spatial scanning methods, area scanning is a spectral scanning method, in which the sample is kept stationary and the scan with full spatial information is performed sequentially over a full spectral range. This approach is suitable for applications where sample movement is not required. Last, the single shot approach uses a large area detector to acquire a 3-D image cube with one exposure, without scanning in either spectral or spatial domain. This approach is very attractive due to its fastest speed, and it is emerging recently as a viable method for rapid acquisition of hyperspectral images. However, it has limited spatial resolutions and narrow spectral ranges.
# The Campus as a Smart City: University of Malaga Environmental, Learning, and Research Approaches
## 2 UMA Smart-Campus
### Methodology  
The actions of this initiative follow six main application categories, or pillars (see Figure 4). Emissions, Energy and Water, Nature and Environment, Health and Well-Being, Mobility, ICTs, and the implication of the University community by Research, Teaching, and Innovation. All of these pillars have a close relationship among each other and must be interpreted as part of a global objective.  
While the first four pillars are common categories in the Smart-City concept, the latest two (ICTs and Research, Teaching, and Innovation) have special relevance for SmartUMA and therefore represent the main focus of this work. Hence, the goal of the ICTs is to apply technology to address all the other pillars with different lines of action:  
* Deployment of measuring devices (sensors, cameras, etc.) throughout the campus.
* Development of telecommunication networks and protocols to communicate the different data sources with open platforms in the cloud.
* Processing and analysis by means of artificial intelligence and big-data techniques (the information provided by the data sources).
* Deployment of actuators, which take actions driven by the AI algorithms in response to the data sources.  
Moreover, the university implication is the last pillar that further distinguishes the Smart-Campus from a general Smart-City. In this field, the objectives are defined in three areas:  
* Research: To engage researchers in UMA Smart-Campus design and development in multiple fields, including agriculture, ecology, energy management, urban farming, telecommunications, and interdisciplinary approaches in combination with ICTs, such as the work in [17].
* Teaching: To translate research and teaching into practice on UMA's campuses by facilitating participation in pilot projects and testbeds for practical lessons. To foster a new generation of environmental leaders providing mentoring, networking, and professional development opportunities. To support the creation of new sustainability-related curricula, programs, and cross-disciplinary opportunities.  
Figure 4: UMA Smart-Campus (SmartUMA) application categories (pillars).  
Figure 3: Objectives.  
* Innovation: To set up an Open Innovation framework, engaging students, teachers, researchers, and companies. To create new business opportunities concerning Smart-Cities.  
Following these lines, UMA annually develops a Plan of Objectives and Environmental Goals that develops annual matches for these strategic lines, setting specific goals and actions to materialize on the campus throughout the year.  
Moreover, in order for the UMA Smart-Campus to be feasible, it is necessary to simultaneously work on a series of transverse pillars that are essential for the proper implementation of its objectives. These pillars structure a set of different challenges, actions/methods, and indicators, which form the foundation for the methodologies followed by the UMA Smart-Campus, as presented in Table 1.
# Evaluation of Biosynthetic Pathway and Engineered Biosynthesis of Alkaloids
## 5 Ergotamine
The resultant d-lysergyl-monopeptide intermediate is extended further with L-phenylalanine and L-proline by LPS1 to yield the d-lysergyl-tripeptide, which remains tethered to the phosphopantetheinyl arm of the third thiolation domain of LPS1. The terminal cyclization or lactamization domain is thought to catalyze the release of the d-lysergyl-tripeptide from LPS1 as _N_-(D-lysergyl-L-Ala)-L-Phe-L-Pro-lactam (L,L-ergotamam). Finally, L,L-ergotamam is converted into ergotamine (**21**) by the hydroxylation activity of the non-heme dioxygenase EasH (Fe\({}^{2+}\)/2-ketoglutate-dependent dioxygenase) [64]. Very recently, heterologous partial reconstitution of the ergot alkaloid biosynthetic pathway was accomplished in _Aspergillus nidulans_ by introducing a segment of the _A. fumigatus_ genome covering a portion of the ergot alkaloid biosynthetic gene cluster into the _A. nidulans_ chromosome [65]. The integrated _A. fumigatus_ genomic DNA segment contained four genes, dimethylallyltryptophan synthase-coding _dmaW_, dimethylallyltryptophan _N_-methyltransferase-coding _easF_, oxidoreductase-like protein-coding _easE_ and catalase-like protein-coding _easC_. The transformed _A. nidulans_ was able to produce **16** and **17**, early intermediates in the d-lysergic acid biosynthetic pathway (Figure 10). Using this heterologous production system, gene deletion experiments were performed to determine for the first time that the four genes were sufficient for the biosynthesis of **17**[65]. This successful production of ergot alkaloid biosynthetic intermediates in _A. nidulans_ would facilitate the effort toward achieving engineered biosynthesis of this class of natural products. Such a heterologous biosynthetic  
Figure 11: Nonribosomal assembly of the peptide portion of ergotamine **21**, initiated with d-lysergic acid **20** as a primer unit [64]. LPS, d-lysergyl peptide synthetase; C, condensation domain; A, adenylation domain; T, thiolation domain; Cyc, cyclololization domain. platform would promote the study on the complex mechanism of ergot alkaloid biosynthesis and advance the drug discovery efforts that evolve around ergot-type natural products.