esa-sceva/satcom-chunk-collection huggingface dataset:
# Space-Time Prediction of PM\({}_{2.5}\) Concentrations in Santiago de Chile Using LSTM Networks
## 4 Pre-Processing of Data and Selection of Neural Model  
### The Dataset  
The database used in this work was obtained from monitoring sites of the Ministry of the Environment of Chile (National Air Quality Information System (SINCA), [[http://sinca.mma.gob.cl](http://sinca.mma.gob.cl)]([http://sinca.mma.gob.cl](http://sinca.mma.gob.cl)), accessed on 1 January 2020).  
For this study, the following observations were selected: temperature (temp), relative humidity (hrel), wind direction (dirv), wind speed (velv), particulate matter with diameter less than 10 microns (PM\({}_{10}\)), particulate matter with diameter less than 2.5 microns (PM\({}_{2.5}\)) and nitrogen dioxide (NO\({}_{2}\)).  
As the objective of our study focuses on the Metropolitan region, the information recorded from 2010 to 2018 by the meteorological stations located in the capital was used, which are: Cerrillos, Cerro Navia, El Bosque, Independencia, La Florida, Las Condes, Pudahuel, Puente Alto, Quilicura, Parque O'Higgins and Talagante. Figure 3 shows their geographical locations.  
Figure 3: Google map of the the Metropolitan region (Chile) with the locations of the \(11\) meteorological stations.  
Figure 2: LSTM recurrent network scheme: the current state of the network depends on the previous state and the input, which are weighted by gate functions.  
Table 1 shows the base structure of the data obtained by SINCA from the 11 meteorological stations, in which the name of the file indicates the polluting or meteorological variable that is recorded (a). Within these files, 3 columns are stored, which identify the date (b), the time (c) and the observed value (d).
## 2 Experimental Program
### Electrochemical Characterizations  
In order to determine the corrosion state of the rebar, non-destructive electrochemical measurements such as half-cell potential (\(E_{corr}\)), linear polarization resistance (LPR) and electrochemical impedance spectroscopy (EIS) were carried out using a potentiostat (PARSTAT2263, Ametek(r), Elancourt, France). Subsequently, the corrosion current density was calculated. Electrochemical characterizations were performed after the accelerated corrosion test, following depolarisation of the rebar.  
Half-cell potential measurements (\(E_{corr}\)) are correlated to a probability of corrosion [8; 47]. The ASTM C876-15 standard [8; 48] provides a correlation between potential values and probability of corrosion. Potential values with electronegativity exceeding \(-350\) mV/SCE indicate a high probability of corrosion (\(>\)90%).  
The corrosion current density \(J_{corr}\) (\(\mu\)A/cm\({}^{2}\)) was calculated based on the Stern and [PERSON] equation [49]  
\[J_{corr}\ =\frac{B}{R_{p}S} \tag{1}\]  
where \(B\) is a constant (26 mV), \(R_{p}\) (Ohm) is the linear polarization resistance (determined through the measurement of LPR and EIS) and \(S\) (cm\({}^{2}\)) is the polarized steel surface corresponding to the electrochemical cell design. In order to calculate this surface area, it was assumed that the corrosion will take place under the platinum mesh, which has a length of 275 mm. The steel bar was thus subjected to corrosion over a length of 275 mm, i.e., a surface area of 172.78 cm\({}^{2}\). According to the RILEM recommendations [50], values below 0.1 \(\mu\)A/cm\({}^{2}\) indicate a negligible level of corrosion. Those between 0.1 and 0.5 \(\mu\)A/cm\({}^{2}\) are indicative of a weak corrosion level. From 0.5 to 1 \(\mu\)A/cm\({}^{2}\), the corrosion level is moderate, and values higher than 1 \(\mu\)A/cm\({}^{2}\) indicate a high level of corrosion.
## 3 Case Studies
### DCMG MATLAB/Simulink Model Challenges and Limitations  
One of the major challenges of the MATLAB/Simulink model was selecting the sampling time. A sampling time of 1 us set to accurately capture PQ issues such as voltage ripple and transients. This fine resolution is necessary to represent the fast dynamics of DCMG components, including converters and controllers. However, using a small sampling time significantly increases the computational load, making real-time simulation impractical. This scaling ensures that detailed PQ phenomena can be analyzed without exceeding the computational limits but compromises, for example, the analysis of the real-time state of charge of the BESS. Additionally, the simulation model employs idealized models of some components, such as inverters, converters, and storage systems, which may not capture all real-world inefficiencies like thermal effects or aging of components.  
Optimizing the system design of a DCMG system is essential for improving PQ. Key strategies include strategically placing components like BESS and RES to minimize voltage fluctuations and reduce ripple currents. Using low-impedance conductors and optimizing bus lengths help reduce losses and maintain voltage stability. Voltage regulation techniques, such as droop control, and the integration of filters or capacitors, further enhance PQ by mitigating high-frequency noise and transients. Additionally, fault management strategies and modular, scalable designs ensure consistent power quality even under varying loads or during faults. The lack of norms and standards for DCMG applications also represents an obstacle.
# Quantification of SPECT Concentric Ring Artifacts by Radiomics and Radial Features
## 2 Materials and Methods
### Visual Phantom Readings  
Automated algorithms written in IDL 8.4 were run on all transaxial phantom data that generated a series of output jpg image files [7]. Because some accrediting agencies request displays of all transaxial slices [3], multiple jpg files were generated for each phantom for all transaxial slices spanning the entire height of the phantom, and a separate composite jpg file was generated, showing a composite of threeimages (Figure 1): a summed rod section, a single section through spheres with the highest contrast, and a section of uniform activity concentration. The jpg files were reviewed and scored by two observers for the presence and severity of concentric ring artifacts.  
To gauge inter-rater agreement about the presence and severity of concentric artifacts, two experienced medical physicists ([PERSON] and [PERSON]), each with over twenty years'  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Manufacturer** & **Model** & **Studies** \\ \hline General Electric & Millenium—dual detector & 17 \\ Philips & Skylight—dual detector & 10 \\ Philips & Adac Argus—single detector & 4 \\ Philips & Adac Argus—single detector & 3 \\ Siemens & Symbia Intevo—dual detector & 2 \\ Siemens & Symbia Intevo—dual detector & 2 \\ Siemens & Symbia Intevo—dual detector & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 1: SPECT systems and related number of studies selected for concentric ring artifact evaluation.  
Figure 1: Example of composite image. In this case, both readers agreed there were no perceptible artifacts in any phantom section.  
One physicist ([PERSON]) re-scored the images blinded to his initial scores and to those of the other reader, to enable the assessment of intra-rater reproducibility. Readers had no knowledge of any texture analysis metrics values; these values were not computed until after all readings were completed by both readers.  
To compute cut-off values for the presence of ring artifacts, scale ratings were dichotomized so that any reading >2 was assigned 1 and any reading <2 to 0. Ambiguous readingsqual to 2 were excluded from this analysis.
# New Frontiers in Sustainable Geotechnics
## 2 Laboratory Testing for Geotechnical Design
### Geotechnical Evaluation of Diesel-Contaminated Clayey Soil [10]  
Soil contamination by different hydrocarbons has rapidly expanded worldwide, surpassing the self-purification capacity of soils and increasing the number of contaminated sites. Although considerable effort has been devoted to studying the effects of diesel contamination on the geotechnical properties of soil, there is still limited available information about it. Presented results showed that the soil could only retain 12.6% of the added diesel and the excess was expulsed. At such a diesel concentration, the saturation rate of the soil was lower than 80%. Diesel contamination increased the plasticity and the internal friction angle of the soil, while its cohesion was considerably decreased. It should be noted that the matric suction of contaminated soil was lower than the one obtained for natural soil. However, its osmotic suction was considerably higher. This indicates that osmotic suction must be considered to evaluate the shear strength of contaminated soils [10].
# An Efficient Stochastic Constrained Path Planner for Redundant Manipulators
## 1 Introduction  
Mapping the path of an end-effector onto a configuration trajectory for the robot to accomplish a desired collision-free task is a well-known problem in robotics [1]. The consideration of redundancy, where the actuated degrees of freedom of the manipulator exceed the end-effector variables defining its functionality in the task space, adds an interesting dimension to the planning problem. It effectively facilitates a scheme where additional objectives can also be incorporated along the way. Beyond obstacle avoidance, constraints such as minimal energy, jerk-free paths, anthropomorphism and so forth can thus be considered. A particularly attractive scenario in motion planning is the avoidance of undesirable singularities in joint space [2], which limits the ability to move in certain task space directions. Increasing the manipulability of the robotic system at each time step is regarded as an effective means of moving away from the neigborhood of such configurations [3], thus reducing the hazardous condition whereby small task space movements may translate to large joint velocities. Avoiding near-singular regions is also a particularly concerning situation when the manipulator might be operating in close proximity to human operators. Moreover, operating away from singularity regions also relaxes the effect of undesirable dynamics that otherwise impose additional perturbances to the robot controllers, hence permitting superior end-effector precision whilst executing the desired task.  
This brief proposes a stochastic method that exploits the particular kinematics of closed-chain mechanisms with redundant actuation and a well-known manipulability measure [4] to track the desired end-effector task-space motion in an efficient manner. The approach departs from global solutions with high computational costs, or optimal formulations that can only be solved numerically, without any guarantee of success except for simple obstacle-free problems. The approach has been tested through simulation ona number of redundant multibody topologies, and via experimental deployment on the Rethink Robotics 7R Sawyer arm.  
The rest of the paper is organised as follows: Section 2 provides broad coverage of techniques in relation with this motion planning for redundant manipulators in the presence of obstacles. Next, Section 3 describes the kinematics of redundant manipulators and the exploitation of the null space. The stochastic algorithm to generate collision-free trajectories is described in Section 4. Section 5 presents a set of experiments carried out both in simulation and with a real platform. Finally, the main conclusions are described in Section 6.
# Analysis of MTF in TDI-CCD Subpixel Dynamic Super-Resolution Imaging by Beam Splitter
## 2 Sampling MTF of Subpixel Dynamic Super-Resolution Imaging  
### Partition Method of CCD  
The most important common feature of the crystal's inner structure is the periodicity of the molecules (atoms and ions) in the space [22]. The Wigner-Seitz cell is the smallest periodic unit of lattice, which is selected according to the following method: in the space lattice, take any node as the origin point; select all nodes near the origin point (if necessary, then consider the second nearest neighbor) and then draw the perpendicular bisectors of these lines [23] to form a rectangle spatial unit surrounded by these perpendicular bisectors (Figure 3) [24; 25; 26].  
Considering the similarity characteristics between the inherent periodicity of CCD pixel spatial distribution and the Wigner-Seitz lattice (Figure 3), the lattice division theory can be used to describe the spatial sampling function of CCD with the traditional square shape (Figure 3a) or the other irregular shapes (Figure 3b). Solid line boxes are regarded as Wigner-Seitz lattice.  
Setting the center distance of the pixel in every CCD as \(p\), the pixel size is \(c\), due to now most of the CCD duty cycles tend to 100% because of the use of microlens, \(c/p=1\). Therefore, we defined the Wigner-Seitz function \(w(x)\), normalized with respect to the area of the Wigner-Seitz (WS) lattice as the following:  
\[w(x)=\left\{\begin{array}{ll}\frac{1}{p}\times\frac{c}{p}=\frac{1}{p},&\text {for $x$ inside the WS cell}\\ 0,&\text{for $x$ outside the WS cell}\end{array}\right. \tag{1}\]
# Attention-Guided Wireless Channel Modeling and Generating
## 3 Modeling and Generating  
This research focuses on a MIMO communication system. There are \(N_{t}\) transmitting antennas at the base station (BS) end and \(N_{r}\) receiving antennas at the user equipment (UE) end [32]. Thus, a time-domain real channel with an orthogonal frequency division multiplexing (OFDM) symbol can represent \(\widetilde{\mathbf{H}}\in\mathbb{C}^{N_{t}\times N_{t}\times N_{d}}\), where \(N_{d}\) represents the number of delay paths. Further, the real channel is reformulated \(\mathbf{H}\in\mathbb{R}^{N_{u}\times N_{t}\times 2}\) with a normalized amplitude \([-1,1]\), where \(N_{a}=N_{t}N_{r}\) represents the logarithm of the antenna, and the last dimension with a value of 2 represents the real and imaginary parts of the original complex channel, respectively.  
The channel modeling involves identifying a generated channel distribution \(p_{g}\) that is tailored to match the distribution \(\widetilde{\widetilde{p}}_{r}\) of the real channel. This can be described as follows:  
\[\begin{split}\underset{p_{g}}{min}\,L\left(\widetilde{p}_{r},p_{ g}\right),\end{split} \tag{1}\]  
where \(\widetilde{p}_{r}\) is the true distribution of the real channel \(\mathbf{H}\), but theoretically an infinite number of real channel data are needed to obtain \(\widetilde{\widetilde{p}}_{r}\). Therefore, a small dataset of real channel \(\mathbb{H}=\{\mathbf{H}|\mathbf{H}\sim p_{r}\}\) characterized by the distribution \(p_{r}\) is used for channel modeling. This paper introduces a channel modeling and generating solution SE-GAN based on WGAN-GP, which can model the channel distribution \(p_{r}\) more accurately by utilizing less channel data. \(\hat{\mathbf{H}}\) represents the generated channel and generates the channel dataset \(\widehat{\mathbb{H}}=\{\mathbf{H}|\mathbf{H}\sim p_{g}\}\), and \(\left|\hat{\mathbb{H}}\right|\gg\left|\mathbb{H}\right|\). Therefore, Equation (1) can be approximated as follows:  
\[\begin{split}\underset{p_{g}}{min}\,L\left(p_{r},p_{g}\right). \end{split} \tag{2}\]  
In this study, we do not aim to explicitly find a minimum or maximum value in the traditional sense. Instead, the optimization process in the GAN is generally about approximating the optimal solution rather than directly solving for the optimal value. Therefore, related formulas do not explicitly represent the minimum or maximum, but rather depict the adversarial training process between the generator and discriminator. The objective functions in these formulas are not defined to directly minimize or maximize a particular value, but are optimized to approach an optimal generation outcome.
## 4 Validation of Inelastic Seismic Analysis
### Results of Inelastic Seismic Analyses
#### 4.2.2 Validation of Acceleration Time History Responses  
The investigation of the acceleration time history responses at the pipe is important in pointing out that the seismic strain responses in the nozzle depend on the piping's seismic behavior. Figures 21 and 22 present the acceleration time histories at the locations of Accelerometer-2 and Accelerometer-3 (see Figure 5). As shown in the figures, the overall response waveforms are very similar to the test results of Figures 11 and 12. The largest acceleration response occurs in the NS (Z) direction. This will dominantly excite Mode 1 and Mode 3, invoking torsional responses at the nozzle.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{3}{*}{**Strain Gauge ID**} & \multicolumn{4}{c}{**Tests (\%)**} & \multicolumn{4}{c}{**Inelastic Seismic Analysis (\%)**} \\ \cline{2-9}  & \multicolumn{2}{c}{**PGA = 0.3 g**} & \multicolumn{2}{c}{**PGA = 0.6 g**} & \multicolumn{2}{c}{**PGA = 0.3 g**} & \multicolumn{2}{c}{**PGA = 0.6 g**} \\ \cline{2-9}  & **Max(\(\epsilon_{\text{max}}\))** & **Min(\(\epsilon_{\text{min}}\))** & **Max(\(\epsilon_{\text{max}}\))** & **Min(\(\epsilon_{\text{min}}\))** & **Max(\(\epsilon_{\text{max}}\))** & **Min(\(\epsilon_{\text{min}}\))** & **Max(\(\epsilon_{\text{max}}\))** & **Min(\(\epsilon_{\text{min}}\))** \\ \hline SG-1 & \(0.139\) & \(-0.144\) & \(0.185\) & \(-0.202\) & \(0.101\) & \(-0.139\) & \(0.184\) & \(-0.247\) \\ SG-2 & \(0.125\) & \(-0.119\) & \(0.212\) & \(-0.211\) & \(0.123\) & \(-0.120\) & \(0.199\) & \(-0.197\) \\ SG-3 & \(0.139\) & \(-0.158\) & \(0.249\) & \(-0.262\) & \(0.119\) & \(-0.099\) & \(0.255\) & \(-0.189\) \\ SG-4 & \(0.148\) & \(-0.161\) & \(0.217\) & \(-0.239\) & \(0.127\) & \(-0.128\) & \(0.273\) & \(-0.194\) \\ SG-5 & \(0.152\) & \(-0.168\) & \(0.228\) & \(-0.230\) & \(0.165\) & \(-0.167\) & \(0.290\) & \(-0.216\) \\ \hline \hline \end{tabular}
\end{table}
Table 10: Comparison results of principal strain responses.  
Figure 20: Inelastic seismic analysis results of principal strain time history responses for PGA = 0.6 g: (a) SG-1; (b) SG-2; (c) SG-3; (d) SG-4; (e) SG-5.  
Figure 23 presents the comparison results of the response spectrum, calculated from the acceleration time history responses at the pipe end (Accelerometer-3). As shown in the figure, the results obtained from the inelastic seismic analysis reveal good agreement with those of the tests.
## 3 Experiment Results and Discussion
### Dataset
#### 3.1.1 Bounding Box Label  
The left-top and right-bottom coordinates of a vehicle provided as a front view were converted to the top view coordinate system. The coordinates were then rotated using the yaw value. Then, the center point as well as the width and height of the bounding box (\(x\), \(y\)) were calculated.
## 3 Experimental Results and Analysis
### Analysis of Experimental Results
#### 3.3.2 Comparative Analysis Experiment
In order to visually demonstrate the detection performance of the proposed algorithm, the detection results of the proposed algorithm in the test set were compared and analyzed with those of Faster R-CNN, YOLOv5, SSD-VGG, and Efficientdet-D0 algorithms. The detection results were presented in Figure 11. As shown in Figure 11, the first column represents images with GLS disease, the second column represents images with Aphids pest, and the third column represents images with NLB disease. It can be observed from Figure 11 that while Faster R-CNN detected a larger number of targets, it also had a significant number of false detections. On the other hand, YOLOv5, SSD-VGG, and Efficientdet-D0 displayed missing detections in certain instances. Additionally, the Efficientdet-D0 algorithm misclassified GLS disease as NLB disease in the first column. In the third column, both Faster R-CNN and YOLOv5 were able to detect the fuzzy small target, while the other three algorithms failed to detect it. Overall, in all three columns of images except for the fuzzy small target in the third column, all other targets of pests and diseases were successfully detected. Based on this analysis, it can be concluded that compared with the comparison algorithm, the proposed algorithm is more accurate in the detection of pests and diseases. Figure 10: mAP curves for six different object detection models.
# Generating Music with Data: Application of Deep Learning Models for Symbolic Music Composition
## 2 Materials and Methods
### Models
#### 2.2.1 Framework for Music Generation with Performance RNN  
Table 1 demonstrates that this model was expected to have high musicality due to its careful pre-training by Google researchers. However, its potential is limited compared with more advanced neural architectures, such as the ones utilized in transformers-based models, despite the potential improvement in quality that can be achieved through transfer learning.  
The first step of this framework was to obtain the weights of the pre-trained model. As detailed bySimon and [PERSON] [24], the weights of the models, as well as their source code, are made available by Google (the financier of the original research) through its Magenta repository on GitHub. The use of this model is linked to dependencies of other Python libraries, and thus, the creation of a virtual environment with all the necessary dependencies was found to be interesting for reproducibility purposes. The conda technology was used to create a virtual environment with all the necessary dependencies. Custom scripts for using and manipulating the model for MIDI generation were also developed in this work.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model Name** & **Model Type** & **Pre-Trained** &
\begin{tabular}{c} **Musicality** \\ **Expectation** \\ \end{tabular} \\ \hline Custom GRU & RNN—GRU & No & Low \\ Performance RNN & RNN—LSTM & Yes & High \\ Custom GPT2 & Transformer & No & Average \\ Music Transformers & Transformer & Yes & Very high \\ MuseNet & Transformer & Yes & Very high \\ \hline \hline \end{tabular}
\end{table}
Table 1: List of models used in this research.  
Finally, as a result of the sum of the aforementioned elements, we obtained the structure for generating MIDI files with the pre-trained Performance RNN model. The structure can be synthesized in the following steps:  
* Obtaining the model weights and source code.
* Creation of a virtual environment for dependency resolution and reproducibility.
* Use of custom scripts to manipulate the model for MIDI generation.
## 3 Quad-Port MIMO Antenna
### Antenna Measurements  
The testing setup employed to evaluate the impedance and radiation characteristics is shown in Figure 18a-c. The impedance parameters (S\({}_{\text{XX}}\) and S\({}_{\text{XY}}\), where X and Y range from 1 to 4) are recorded using Vector Network Analyzer, as depicted in Figure 18. For S\({}_{\text{XX}}\) (reflection) measurements, port X of the fabricated antenna is connected with the first port VNA while the others are connected with 50-ohm terminations. For S\({}_{\text{XY}}\) (transmission) measurements, the antenna's X and Y ports are connected with ports 1 and 2 of the VNA, respectively.  
Figure 16: Parametric analysis (L3 = 6.5 mm to 8.5 mm).  
Figure 17: Fabricated prototype (**a**) top view and (**b**) ground view.