esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Case Study
### Direct Causes  
A number of sources have indicated that three components are required for a fire to occur--oxygen, fuel, and an ignition or heat source, which is known as the \"fire triangle\" [16; 17]. These elements were used to identify the direct causes of the incident in this study, as illustrated in Figure 2.  
The same author [16] further expressed that if we \"remove any one of these and the fire will be extinguished\". Failure to extinguish the fire when the fire was initiated was also identified as a direct cause to the incident since the machine was equipped with an automatic fire suppression system.  
Figure 2: Fire triangle for the PGM mine LHD HP incident.  
These causes are summarized below:  
1. **Oxygen source:** The machine was positioned in a fresh air zone with a force fan, which continued to supply the oxidizing agent to sustain the fire. The force fan power was not immediately switched off. This was, however, classified under the contributing causes in the FTA based on its reliance on management systems.
2. **Fuel Source:** The inital source was hydraulic oil-soaked cables and hoses. The hydraulic oil was a result of leaks and spills that had not been adequately cleaned. When the fire propagated, it then resulted in ignition of diesel.
3. **Heat or ignition source:** The fire was initiated by a short circuit on the electric cables for the additonal systems that had been installed on the machine. It was then reignited by burning rubber of the hydraulic hoses. The cables for the additional systems were not installed in consultation with the original equipment manufacturer to ensure adequate risk assessment. In addition to this, these cables were installed in close promixity to hot spots on the machine.
4. **Failure to extinguish fire at initiation:** The automatic fire suppression system was unable to detect the fire and automatically discharge. Even after maual activation, the system was unable to extinguish the fire at the source. This was because the fire suppression system nozzles were not directed to all the possible sources of fire.
## 2 Related Works
### Evaluation of Speech Changes
#### 2.2.2 Articulation  
The phenomenon of articulatory undershooting has been described for several aspects of parkinsonian speech leading to imprecise stop consonant articulation [58, 59, 60]. Articulation deficits in PD patients are primarily due to the reduced amplitude and velocity in the lips, tongue, and jaw movements. In the field of research on changes in articulation in people with PD, the most common analysis is performed using a pronounced vowel with prolonged phonation or repeated syllables. A small proportion of studies use spontaneous speech. Most studies focus on the analysis of speech signal formats [39] due to the fact that, based on the position of the tongue, a reduction in the articulation extension of this articulator can subsequently affect the frequency range of the formants [61,62]. The transitions between phonemes and between voiced and unvoiced segments have also been employed in the automatic detection of PD using spectrograms and convolutional neural networks (CNNs) [63], PLP and Gaussian Mixture Model-Universal Background Model (GMM-URM) [64], i-vectors and Probabilistic Linear Discriminant Analysis (PLDA) [64], or MFCC and Band Bark Energies (BBEs) with SVM classifiers [65], yielding, in most of the cases, accuracies over 85% [39]. The analysis performed in [39] indicates that individual segments associated with specific articulation movements, such as those of plosives, provide more relevant information for the detection of PD in comparison to detection schemes that do not select or set the attention to any specific segment. Similar studies employing voiced and unvoiced segments separately to detect PD obtain better classification results using the unvoiced segments [66].
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 2 Space Debris Detection
### Spatial-Temporal Feature Fusion  
The STFM is used for outputting the resulting saliency map by fusing the spatial features and temporal features obtained by SFEM and TFM, respectively. The STFM consists of four stacked refinement blocks, each of which takes as input the output feature map \(F^{i}_{td}\) of the previous refinement block in the top-down stream and the output feature map \(F^{i}_{bu}\) from SFEM in the bottom-up stream. This design can mitigate the effects of the loss of spatial details caused by a series of convolutional layers and downsampling operations from SFM. The main workflow of STFM includes three steps: (1) concatenating \(F^{i}_{td}\) and \(F^{i}_{bu}\), (2) feeding them to a convolutional layer with a \(3\times 3\) kernel size and 16 channels, (3) up-sampling the output to ensure that \(F^{i}_{td}\) and \(F^{i}_{bu}\) have the same spatial resolution by a bilinear interpolation operation and that \(F^{i+1}_{td}\) can be obtained. Note that \(F^{1}_{td}\) is the same as the output feature \(C_{T}\) from TFM. Lastly, a probability map with the same resolution as the input image by a decoder composed of two convolutional layers can be generated.
## 6 Prediction of Field Strength by Construction Log Data and Future Perspectives
### Analysis of Construction Log Data  
The construction log data shown in Figure 14 were analyzed to evaluate the mixing condition every meter along the depth, and the analyzed result is shown in Figures 15-17. Figure 15 shows the distribution of the cement factor along the depth, which is defined as the weight of cement (kg) over the soil volume (m\({}^{3}\)). The cement factor is almost constant at approximately 150 kg/m\({}^{3}\) as the design criteria in the clay-1, clay-2, and alluvial clay layers. In the alluvial sand layer, the cement factor is scattered, ranging from 118 to 289 kg/m\({}^{3}\), as the cement slurry and water were injected for the bottom treatment and flushing the delivery tube.  
Figure 14: Example of DM construction log data.  
Figure 15: Distribution of binder factor along depth.  
The _W/C_ ratio was calculated by Equation (4) using the estimate water content of soil at the depth and plotted along the depth in Figure 16. In the clay-1 layer, the _W/C_ ratio is almost constant at approximately 4.8, except at approximately \(-7\) m, where 0.48 m\({}^{3}\) of water was injected to flush the delivery tube after the withdrawal injection. In the clay-2 and alluvial clay layers, the _W/C_ ratio is smaller than that of the clay-1 layer, even though the amount of cement is almost the same, which is due to the low natural water content of the layers rather than that of the clay-1 layer. The _W/C_ ratio in the alluvial sand layer is scattered from 4.3 to 9.3, which is due to the scatter in the amount of injected cement slurry and water.  
The estimated unconfined compressive strength of laboratory soil, \(q_{ul}\), is shown along the depth in Figure 17. The predicted strength at a depth of approximately \(-7\) m is approximately 2.1 MPa, which is approximately 20% smaller than that of the others and is due to the water injection there. In the alluvial clay layer, the predicted strength is almost constant at approximately 3.9 MPa. In the alluvial sand layer, the predicted strength decreases with depth. The estimated strength at a depth of approximately \(-26.0\) m is quite small at approximately 1.9 MPa.  
The core sample was taken from the column, and unconfined compression tests were carried out to measure the field strength, \(q_{ul}\). These measured strengths are plotted in Figure 17 by solid circles. The measured strengths in clay-1 and clay-2 are smaller than the estimated strength. The ratio of the measured and estimated strength, \(q_{ul}/q_{ul}\), is roughly obtained as approximately 0.63. It should be noted that the measured strength is scattered rather than the estimated strength, which might be due to the local soil condition, mixing condition (entrained rotation phenomenon), and the coring and unconfined compression testing machine and techniques.  
Figure 16: Distribution of W/C ratio along depth.  
Figure 17: Distribution of predicted laboratory strength and measured field strength along depth.
## 5 Summary and Conclusions  
We applied the GNSS-R remote sensing technique based on a dataset of spaceborne observations of reflected GPS signals over the land to detect and map the recent flood in the southeastern part of Iran. The flood occurred in the Sistan and Baluchestan province after the heavy rain in mid-January 2020. The dataset used was acquired from the data products of the NASA CYGNSS mission. The main parameter of interest used in the analysis was the delay doppler map SNR, which was retrieved from the level-1 data product. First, a data preparation procedure was applied to remove outliers and discard low-quality data. In the next step, inverse bistatic radar formula was used to calculate the corrected SNR, which was closely related to surface reflectivity and hydrological conditions. The corrected SNR values were calibrated and interpolated to a regular grid over the study area. After calibration and gridding, the corrected SNR was verified with the MODIS optical image. A threshold of about 11 dB or more could be distinguished between the inundated and noninundated areas in the regions of interest. Finally, the flood-affected areas were mapped on Google Maps. The area of the flooded regions was estimated to be about 19,644 km\({}^{2}\) or 10.8% of the province. Many cities, roads, and other infrastructures were affected by the flood in these regions. The results indicate the regions close to depression, lakes, and coastal areas are at a high risk of flooding in this province. This study confirms that CYGNSS data is of value for hydrological investigations, particularly flood detection in the Sistan and Baluchestan province. Despite a relatively short revisit time of CYGNSS observations, the spatial resolution of the data products needs to be improved for mapping purposes. This issue could be addressed in future missions by, e.g., increasing the number of onboard processing channels, as well as by processing the reflected signals from other GNSS constellations such as GLONASS, Galileo, and BeiDou.  
Conceptualization, H.N., M.R.; Data curation, M.R., M.H.; Formal analysis, M.R.; Funding acquisition, M.R. and H.N.; Investigation, M.R. and M.H.; Methodology, M.R.; Software, M.R. and M.H.; Supervision, H.N.; Validation, M.R.; Visualization, M.R.; Writing--original draft, M.R.; Writing--review and editing, H.N. and M.H. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Iran Ministry of Science, Research and Technology and the APC was funded by Norwegian University of Science and Technology.  
The authors would like to acknowledge the valuable comments of the editors and two anonymous reviewers, which significantly improved the presentation and quality of this paper. Also, the authors would like to thank the NASA CYGNSS and MODIS science--operation teams for providing the data products which made this study possible.  
The authors declare no conflict of interest.
## 3 Materials and Methods
### Volatile Compound Analysis
#### 3.3.1 Solid-Phase Microextraction  
Solid-phase microextraction (SPME) was used to analyze and quantify the compounds isoamyl alcohol, isobutanol, ethyl lactate, and ethyl hexadecanoate. Sample preparation, sampling, and instrument analysis followed a previously outlined procedure with modifications [74]. For each sample, 10 mL aliquot (model wine solutions or wine samples) was pipetted into 20 mL round-bottomed glass vials (Gerstel, Linthicum, MD, USA) containing 3 g of NaCl (ACS reagent, Sigma Aldrich, Sigma Aldrich Inc., St. Louis, MO, USA) and 6.18 \(\upmu\)L of 2-undecanone (Sigma Aldrich, Sigma Aldrich Inc., St. Louis, MO, USA) was used as the internal standard. Vials were topped with metal screw caps with 1.3 mm septa, blue silicone, and PTFE (Gerstel, Linthicum, MD, USA). Samples were analyzed in triplicate and the run order was randomized.  
For instrument analysis the SPME fiber DVB/C-WR/PDMS, 50/30 \(\upmu\)m, 10 mm (Restek, Restek Corporation, Bellefonte, PA, USA) was used. Samples were heated to 40 \({}^{\circ}\)C and agitated for 10 min at 250 rpm before sampling by the SPME fiber. The fiber was exposed to the sample for 30 min at 40 \({}^{\circ}\)C with an agitation speed of 250 rpm. The SPME fiber was desorbed in split mode with a 20:1 split ratio, the fiber was kept in the inlet for 10 min and the inlet temperature was held at 240 \({}^{\circ}\)C. The samples were analyzed using an 8890 gas chromatography system (Agilent, Agilent Technologies Inc., Santa Clara, CA, USA) as well as an MPS autosampler (Gerstel, Linthicum, MD, USA). A DB-Wax capillary column, 30 m, 0.250 mm, 0.25 \(\upmu\)m (Agilent, Agilent Technologies Inc., Santa Clara, CA, USA) was used. Helium was used as the carrier gas at a constant flow or 1 mL/min. For analysis the oven parameters were as follows: 40 \({}^{\circ}\)C for 5 min, followed by an increase of 3 \({}^{\circ}\)C/min to 180 \({}^{\circ}\)C, then 30 \({}^{\circ}\)C/min to 240 \({}^{\circ}\)C where the temperature was held. The MSD interface was also held at 240 \({}^{\circ}\)C. Compounds were analyzed using deconvolution ranked by height and 50 retention time (RT) window size. Compounds were identified by the NIST 20 MS Search Version 2.4 using their respective qualifying ions shown in Supplemental Table S8.
# Infrared and Visible Image Fusion with a Generative Adversarial Network and a Residual Network
## 2 Related Work
### The Framework of GAN and ResNet
#### 2.2.1 Introduction of Generative Adversarial Network  
In 2014, [PERSON] [56] and his colleagues first published the article about GAN which made a shock in academia on DL research. This work can be seen as an innovative combination of graph and game theory. The GAN model, in essence, consists of two adversarial models: a generative model G that captures the data distribution and generates the new samples, and a discriminative model D that estimates the probability whether a sample came from the training data or generated by G. The optimization process of GAN corresponds to a minimax two-player game. The goal is to achieve the Nash equilibrium when G almost reconstructs the data distribution and D cannot distinguish the two kinds of data. The core principle and adversarial process of GAN shows in Figure 1 and can be expressed by a mathematical equation as follows:  
\[\min_{G}\max_{D}V_{\text{GAN}}(G,D)=E_{x\sim p_{data}(x)}[\log D(x)]+E_{z\sim p _{z}(z)}[\log(1-D(G(z)))]. \tag{1}\]In the function \(\mathrm{V_{GAN}}(G,D)\), the first item represents the entropy of data from real distribution judged by D. D tries to maximize the result. The second item is the entropy of the data generated by G from random inputs discriminated by D. D tries to make this item bigger and equivalently minimize the \(D(G(z))\) to guarantee its ability to distinguish falsehood from truth. In short, D wants to maximize the function \(\mathrm{V_{GAN}}(G,D)\). On the other hand, the task of G is completely the opposite which tries to maximize the \(D(G(z))\) and minimize the function \(\mathrm{V_{GAN}}(G,D)\). This kind of training of GAN just meets the minimax two-player game of game theory.  
GAN has many advantages compared with traditional ones. It can be trained only with backpropagation and there is no need for any Markov chains or unrolled approximate inference networks during either training or generation of samples [56]. What is more, the parameter updating of G is not directly from data samples, but also related to the backpropagation of D. However, the disadvantages are also obviously summarized as poor interpretability and stability. There is no explicit representation for the distribution of G, and that D must be synchronized well with G during training [56]. Nowadays, there are some improved models have been proposed to overcome these disadvantages like DCGAN [57], CGAN [58], InfoGAN [59], WGAN [60], LSGAN [61], EBGAN [62], and so on.
# Numerical and Experimental Investigations of Particle Dampers Attached to a Pipeline System
## 3 Design and Simulation of Particle Damper
### Analysis of the Influence of Particle Damper Materials and Structural Parameters
#### 3.4.3 Particle Density  
Particle density was an important parameter in the simulation. The influence of different particle densities (2000, 5000, 7850, 11,000, and 14,000 kg/m\({}^{3}\)) on the total energy dissipation of the particle system was investigated at three filling rates (20%, 60%, and 80%).  
As shown in Figure 10, the total energy dissipation of the particle damper system increased with particle density at all three filling rates. When the particle size is the same, the mass of the particles with a lower density is much smaller. Under vibration conditions, the motion of the particle system was relatively low, and the contact with the damper system as a whole was relatively small. Therefore, the overall energy consumption of the particle system was low. Meanwhile, denser particles have a relatively large mass, and the particle system makes more contacts. The collisions and friction between particles and between particles and the container walls were more prominent. Therefore, a larger energy dissipation was achieved.  
Figure 8: Total energy dissipated by particle system with the same particle size.  
Figure 9: Total number of particle collisions for different particle sizes.
## 3 Results
### Model Performance Evaluation
#### 3.2.1 Evaluation Metrics  
The assessment of algorithm performance in this study employed various evaluation metrics, as outlined by [26; 27; 28; 29]. These metrics include accuracy, precision, recall, F1-score, and the area under the ROC curve. Accuracy is the ratio of correctly classified instances to the total instances in the validation dataset. Precision represents the proportion of correctly predicted positive observations relative to the total predicted positive observations, indicating how accurately positive predictions are made. Recall is the ratio of correctly predicted positive observations to all actual positive observations, measuring the model's ability to capture all positive instances. F1-score, the harmonic mean of precision and recall, offers a balanced evaluation of a model's overall performance. The area under the ROC curve (AUC) serves as a metric quantifying the model's capability to distinguish between positive and negative classes, with higher AUC values indicative of superior performance.
# Design and Testing of a LUT Airfoil for Straight-Baded Vertical Axis Wind Turbines
## 4 Results and Discussion
### Static LUT Airfoil Performance at Low Reynolds Number
#### 4.1.1 Pressure Coefficient Distribution on the Surface for the LUT Airfoil
Comparing the case of \(R_{e}\) = 3 \(\times\)\(10^{5}\) to the case of \(R_{e}\) = 5 \(\times\)\(10^{5}\), it is observed that the free transition position is more toward the leading edge with the increase in \(R_{e}\). An angle of attack of \(\alpha\) = 8\({}^{\circ}\), for example, results in a transition position located at about 29% of the chord when \(R_{e}\) is 5 \(\times\)\(10^{5}\). In particular, in the case of \(R_{e}\) = 5 \(\times\)\(10^{5}\), the pressure on the lower surface significantly drops at an angle of attack of \(\alpha\) = 21\({}^{\circ}\). This shows that obvious separation on the lower surface exists in the LUT airfoil at an angle of attack of \(\alpha\) = 21\({}^{\circ}\). The above discussions indicate that the distribution of the pressure coefficient of the LUT is better before a stall angle of \(\alpha\) = 14\({}^{\circ}\) for both the case of \(R_{e}\) = 3 \(\times\)\(10^{5}\) and \(R_{e}\) = 5 \(\times\)\(10^{5}\); moreover, the free transition position at about 30% chord almost keeps constant before the stall, it shows that the pressure distribution on the upper surface is steady between \(R_{e}\) =3 \(\times\)\(10^{5}\) and \(R_{e}\) =5 \(\times\)\(10^{5}\) for the LUT airfoil. However, in the case of \(R_{e}\) = 5 \(\times\)\(10^{5}\), the pressure distribution on the lower surface is poor where the separation of the flows is obvious after a stall angle of \(\alpha\) = 14\({}^{\circ}\).
# Designing an Adaptive Underwater Visible Light Communication System +
## 9 Discussion
#### Analysis  
After several experimental trials, it was deduced that adaptive communication is sensitive to external environmental factors like background light. When external light falls on either side of the respective aligned position, the power received changes. This indicates that the power measurement results shown in Figures 17, 20, and 21 are actually the power of the received signal plus the background light. This explains why the power values are not identical at angles symmetrically offset by the same amount from 90 degrees. Performing the experiment in a dark room would give more exact results on received power. However, the signal can still be received with minor loss. Overall, this paper's findings suggest that transmitting an optical signal via PPM using Li-Fi technology and an SDR gives reasonable preliminary results that need further refinement, particularly regarding synchronization accuracy. Better synchronization algorithms could mitigate the challenges faced during data transmission and reception.
## 3 LOADng Protocol Overview
### LOADng Data Message Forwarding  
In the data message sending process, the node should use the path created in the route discovery process to deliver the data message to its correct destination. Thus, the node consults its Routing Set looking for an entry that matches the message destination. In sequence, the node should forward the message to the next hop of the found route entry. According to the latest LOADng specification, a node should always refresh the valid time of a route entry that it uses. The intermediate node that receives a data message should forward to the next hop of the path based on the information in its Routing Set. This process occurs until the message reaches its final destination. If an intermediate node does not find a route entry that matches the message destination, it should perform a new route discovery process to recover the broken path. If the path recovery does not succeed, the node should generate an RERR message to inform the data message originator of the impossibility of delivering the message successfully.