esa-sceva/satcom-chunk-collection huggingface dataset:
# Impact of Disruption on Ship Emissions in Port: Case of Pandemic in Long Beach
## 4 Experiment and Analysis
### Description of the Study Area and Data Source  
On the west coast of the United States, Long Beach, a city in Los Angeles County, California, is home to the Long Beach Port. The relevant latitude and longitude ranges are 33\({}^{\circ}\)43\({}^{\prime}\)49\({}^{\prime\prime}\) N to 33\({}^{\circ}\)46\({}^{\prime}\)57\({}^{\prime\prime}\) N, and 118\({}^{\circ}\)10\({}^{\prime}\)48\({}^{\prime\prime}\) W to 118\({}^{\circ}\)15\({}^{\prime}\)05\({}^{\prime\prime}\) W. According to the official POLB map, Figure 2 illustrates piers and amenities. The Port of Long Beach is a leader in innovative cargo transportation, security, environmental stewardship, and sustainability, and is one of the United States' most vital ports for trans-Pacific trade. In addition, the POLB is a large container port in the US, processing $170 billion in annual trade and supporting more than 175 ship lines with connections to more than 220 seaports globally [49]. According to the POLB authorities' data report, the POLB's annual container throughput has risen during the last three years, from 7,632,032 TEUs in 2019 to 9,384,368 TEUs in 2021 [50], a year-on-year growth of 22.96%.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \multirow{2}{*}{**Engine Type**} & **CO\({}_{2}\)** & **NOx** & **SOx** & **PM** & **CO** \\  & **(g/kWh)** & **(g/kWh)** & **(g/kWh)** & **(g/kWh)** & **(g/kWh)** \\ \hline ME & 658 & 13.54 & 0.41 & 0.19 & 0.54 \\ AE & 696 & 10.53 & 0.43 & 0.19 & 0.54 \\ BO & 962 & 2.0 & 0.57 & 0.10 & 0.20 \\ \hline \end{tabular}
\end{table}
Table 4: Emission factors of ME, AE, and BO, by using MGO 0.1% sulphur.  
Since 2014, the Port of Long Beach has introduced a shore power policy. As seen in Table 5, the proportion of shore power is growing over time [51]. In conclusion, a final multiplier of 30% should be applied to the figure for 2019 emissions. A figure of 20% should be multiplied by the result for 2020 results to calculate ship emissions more precisely.  
The National Oceanic and Atmospheric Administration's Office of Coastal Management and Bureau of Ocean Energy Management provide the AIS data for this paper, with the distinction between vessel types based on the NAIS definition. The system enables information storage, processing, etc., through the land-, marine-, and space-based AIS radio frequency infrastructure to receive and transmit data from AIS-equipped vessels operating on the U.S. coast, interior waterways, and ports.  
Furthermore, this study also refers to the AIS User Manual in determining the information corresponding to each AIS code. For ships identified by the IMO code in the AIS information, the detailed information of these ships, such as design speed, length, width, and engine power, can be downloaded from Lloyd's database, the ABS ship database, and the Marine Traffic system. For ships not available from the above queries, the methods of estimation in Section 3 using known information about the ships are adopted.
# Estimating Algorithmic Information Using Quantum Computing for Genomics Applications
###### Abstract  
Inferring algorithmic structure in data is essential for discovering causal generative models. In this research, we present a quantum computing framework using the circuit model, for estimating algorithmic information metrics. The canonical computation model of the Turing machine is restricted in time and space resources, to make the target metrics computable under realistic assumptions. The universal prior distribution for the automata is obtained as a quantum superposition, which is further conditioned to estimate the metrics. Specific cases are explored where the quantum implementation offers polynomial advantage, in contrast to the exhaustive enumeration needed in the corresponding classical case. The unstructured output data and the computational irreducibility of Turing machines make this algorithm impossible to approximate using heuristics. Thus, exploring the space of program-output relations is one of the most promising problems for demonstrating quantum supremacy using Grover search that cannot be dequantized. Experimental use cases for quantum acceleration are developed for self-replicating programs and algorithmic complexity of short strings. With quantum computing hardware rapidly attaining technological maturity, we discuss how this framework will have significant advantage for various genomics applications in meta-biology, phylogenetic tree analysis, protein-protein interaction mapping and synthetic biology. This is the first time experimental algorithmic information theory is implemented using quantum computation. Our implementation on the Qiskit quantum programming platform is copy-left and is publicly available on GitHub.  
algorithmic information theory; universal distribution; Kolmogorov complexity; quantum algorithms; quantum circuit model; quantum Turing machine; genomics; viral genomics; meta-biology  
Academic Editor [PERSON]  
3 February 2021  
12 March 2021  
17 March 2021  
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**Copyright:** 2021 by the authors. License MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([[https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/)]([https://creativecommons.org/licenses/by/4.0/](https://creativecommons.org/licenses/by/4.0/))).
# Experimental and Numerical Evaluation of a Wildland-Urban Interface Fire Scenario
## 1 Introduction
[36]. [PERSON] et al. [41] used FDS to reproduce the model outlined in the AS 3959 standard [27] and to perform additional parametric studies by varying wind velocity, fuel load, and moisture content of the vegetation. It was found that by modifying the previously mentioned parameters, much larger incident radiant heat fluxes to the structure could be found. [PERSON] et al. [36] tested different modelling strategies, namely the Particle Model, the Boundary Fuel Model, and the Level Set Method, for different length scales, showing the suitability of FDS for accurately modelling fire spread at different scales. Some relevant studies have already been conducted concerning firebrands' generation and transport by fire buoyancy [37; 40], assessing the influence of firebrand characteristics and fire intensity in the observed distribution. Generally, it was demonstrated that the developed models were qualitatively similar to the experimental data for short-range spotting for Eucalyptus vegetation [40; 42]. In this investigation, a field fire test was performed, assessing the impacts of a wildfire on a building component (sandwich panels). The building component was instrumented with disk type-K thermocouples to monitor the temperature evolution during the fire test. Additionally, an IR camera, static cameras, and drones were used to monitor both fire spread and temperature evolution. Based on the detailed characterization of the field test, numerical models using FDS were developed, modelling explicitly the terrain, the vegetation, and the building component (construction product). The goal was to reproduce, in detail and accurately, the fire scenario in the real test in order to quantify, using the CFD model, the basic heat transfer mechanisms occurring from the wildfire to the building component (heat release rate, radiative heat fluxes, gas, and surface temperatures, etc.). In particular, for this analysis, topography, meteorology, materials, and fuels were modelled explicitly following the information collected during the experimental test. This investigation contributes to the definition of a performance-based design approach for buildings in the WUI by characterizing and quantifying the thermal action. Note that significant limitations still exist, and that further research is needed to ensure the full integration and coupling between CFD and the Finite Element Model (FEM). This integration is necessary to evaluate the mechanical behaviour of structures exposed to wildfires, considering explicitly the degradation of material properties as temperature increases.
# Solar-Supplied Satellite Internet Access Point for the Internet of Things in Remote Areas
###### Abstract  
As satellite communications provide ubiquitous coverage, they play a key role in providing Internet connectivity in remote or marginalized areas, so as to enable the vision of a truly global connectivity of the Internet of Things (IoT). However, these areas often lack reliable electricity supply. Thus, this paper proposes a satellite internet access point powered by solar energy, so that a stable Internet connection can be provided. The access point provides Wi-Fi coverage so that sensors, IoT, and devices can connect to the access point using the Wi-Fi, a common wireless technology. Our design took some cost-saving measures to make it affordable and selected the components that require minimal maintenance operations. The satellite access point costs about USD $500, and can provide four days of Internet connectivity without solar energy.  
satellite internet; IoT; solar supply; solar panel; Wi-Fi; geosynchronous equatorial orbit (GEO) satellite networks; low earth orbit (LEO) satellite networks; remote areas +
Footnote †: journal: Sensors
# Comparative Analysis of Machine Learning Methods for Predicting Energy Recovery from Waste
## 5 Conclusions  
This goal of this paper was to evaluate the most effective machine learning technique for predicting energy recovery from waste. It uniquely compares three methods: ElasticNet, Decision Trees, and Neural Networks, considering quality indicators such as R, MSE, RMSE, RIE, and MAE. ElasticNet had an R-value of 0.90716, but it also showed higher error metrics, such as an MSE of 2372.8, compared to Decision Trees and Neural Networks. Decision Trees had a lower MSE of 253.57 and an R-value of 0.9905, indicating better performance than ElasticNet. Neural Networks, with an R-value of 0.9911 and the lowest MSE of 242.09 along with the lowest MAE of 11.1631, slightly outperformed Decision Trees, suggesting it was the most accurate model among the three. The use of 'early stopping' to avoid overfitting and the Gaussian-like distribution of errors in the neural network error histogram suggest robust training methodologies were employed.  
The comparison between ElasticNet, Decision Trees, and Neural Networks demonstrates that while all three methodologies offer valuable insights into energy recovery processes, Neural Networks exhibit a superior ability to handle the complex, non-linear relationships inherent in waste-to-energy data. This finding underscores the importance of adopting advanced computational techniques to enhance the predictive modeling of energy recovery systems.  
This research opens the way for further studies to explore other machine learning algorithms, incorporate more diverse datasets, and extend the analysis to other regions and waste types. Investigating the economic and environmental impacts of implementing waste-to-energy solutions at scale can also provide valuable insights for sustainable development.  
Conceptualization, J.K., M.K. and W.C.; methodology, M.K. and J.P.; software, M.K. and J.P.; validation, M.K. and M.C.; formal analysis, W.C.; investigation, J.K.; resources, J.K. and W.C.; data curation, M.C.; writing--original draft preparation, J.K., M.K., J.P., M.C. and W.C.; writing--review and editing, J.K, M.K., J.P., M.C. and W.C.; visualization, M.K. and J.P.; supervision, J.K.; project administration, J.K. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in the study are included in the article, further inquiries can be directed to the corresponding author.  
The authors declare no conflicts of interest.
## 1 Introduction
[41] made a detailed statistics of failure events during the construction period, and basically established the relationship between the surrounding rock failure and the rock strength-stress ratio under the high in situ stress. It was found that the lower the strength-stress ratio is, the more events are. [PERSON] et al. [42] established the layout design method of large underground caverns based on rock strength-stress ratio, and put forward the specific quantitative formulas about spacing, rock pillar thickness and axis direction of a cavern group. To sum up, the existing methods are basically derived from the on-site engineering experience and not summarized well in parameter selection of the excavation procedure. It is also common and rigescent for the relevant provisions to select the excavation procedure parameters without considering high in situ stresses in detail. The current numerical simulation methods [43; 44; 45; 46; 47], such as Finite Element Method (FEM), Fast Lagrangian Analysis of Continua (FLAC), Discrete Element Method (DEM) and Discontinuous Deformation Analysis (DDA), are quite different in generalizing geological structure and predicting the excavation damage zone, especially for high in situ stress. It can be seen that the existing excavation procedure design method is more and more unsuitable for stability control of underground cavern excavation of large hydropower stations with increasingly complex construction conditions, especially in the case of high in situ stress and low rock strength-stress ratio. To this end, a short introduction on the basis of the stress environment and excavation procedure technology development in China was given firstly. In addition, fifty-one underground powerhouses in China were summarized and listed to compare the excavation procedures and to analyze their correlations with in situ stress levels. Afterwards, the strength-stress ratio was introduced to evaluate the cavern excavation stability. The recommended partition values for excavation procedure parameters were summarized and the blasting excavation design based on the stress transient unloading control was supplemented. It is beneficial to quantify the correlation between excavation procedure parameters and in situ stress, and to recommend a better excavation procedure optimization.
## 3 Analysis and Design of Key Components
### Driving Module for Simulating Coring Tests
#### 3.2.2 Composition of the Dynamic System  
This system includes the driving pump station and an ultrahigh-pressure rotary dynamic sealing system [6], and the main function is to provide a power source for simulated coring tests and activation of the pressure seal inside the cabin. The ultrahigh-pressure rotary sealing structure consists of three groups of sealing rings, as shown in Figure 8. Each set of sealing rings comprise an \"O\"-type rubber ring, a \"Z\"-type sealing ring with grooves on the inner surface, and a supporting sleeve. Among them, the \"Z\" ring is made of polytetrafluoroethylene (PTFE). PTFE has the advantages of a low friction coefficient and high wear resistance, and it can adapt to ultrahigh-pressure rotary dynamic sealing conditions. On this basis, a pair of combined grooves is provided on the main sealing surface and matched with the rotating axis of the \"Z\" ring. Combined grooves can cover impurities and prevent the sealing surface from being damaged. Additionally, lubricating oil can be stored inside the combined grooves and produce a lubricating oil film on the main sealing surface during operation and effectively prevent wear and destruction of the main sealing surface.  
Figure 7: Structure of the lifting motion mechanism.
## 4 Comprehensive Link Budget Analysis of the BDS-3 ISL
### Analysis of Inter-Satellite Link Loss  
During the propagation of ISL signals, the signal power does not remain constant but decreases due to various losses, including free-space transmission loss, feeder loss, polarization loss, and pointing loss. Among these, free-space transmission loss is the predominant factor.  
Free-space transmission loss refers to the signal attenuation caused by the propagation of radio signals in free space. The corresponding formula for the BDS-3 ISL signal can be expressed as follows:  
\[L_{BDS\_ISL}=(\frac{4\pi d_{BDS\_ISL}}{\lambda_{BDS\_ISL}})^{2}=(\frac{4\pi d _{BDS\_ISL}f_{BDS\_ISL}}{c})^{2} \tag{9}\]  
where \(L_{BDS\_ISL}\) is the free-space transmission loss of the BDS-3 ISL signal, \(d_{BDS\_ISL}\) is the geometric propagation distance between satellites in m, \(f_{BDS\_ISL}\) is the ISL signal carrier frequency in Hz, \(\lambda_{BDS\_ISL}\) is the ISL signal carrier wavelength in m, and \(c\) is the speed of light, with a value of \(3\times 10^{8}\) m/s. Then the received signal power of satellites can be expressed as follows:  
\[P_{r\_BDS}=\frac{P_{t\_BDS}G_{t\_BDS}G_{r\_BDS}}{L_{BDS\_ISL}} \tag{10}\]  
where \(P_{r\_BDS}\) is the received signal power of satellites in W, \(P_{t\_BDS}\) is the transmission signal power of satellites in W, \(G_{t\_BDS}\) and \(G_{r\_BDS}\) are the transmitting antenna gain and the receiving antenna gain in dBi, respectively.  
Typically, we use the Equivalent Isotropic Radiated Power (EIRP) as an important metric to evaluate the transmission capability of a satellite. It can be expressed in dB as follows:  
\[[\text{EIRP}]=[P_{t\_BDS}]+[G_{t\_BDS}]-[L_{S\_BDS}] \tag{11}\]  
where \(L_{S\_BDS}\) is the transmission circuit feed loss in dB. To better characterize the signal reception and amplification capability of the satellite receiving system, we typically use the receiver antenna quality factor for evaluation, which can be expressed in dB as follows:  
\[[G/T]=[G_{r\_BDS}]-[T_{s}] \tag{12}\]  
where \(G/T\) is the receiver antenna quality factor in dB/K, and \(T_{s}\) is the total noise temperature of the receiving system in K.  
During the transmission of ISLs, in addition to free-space transmission loss, there are a series of other losses, including receiver feeder loss, antenna polarization loss, and antenna pointing loss. Taking into account various losses and to better reflect the performance of the satellite transmission and reception systems, Equation (10) can be rewritten in dB form as follows:  
\[[P_{r\_BDS}]=[\text{EIRP}]+[G/T]+[T_{s}]-[L_{BDS\_ISL}]-[L_{N\_BDS}]-[L_{P\_BDS }]-[L_{D\_BDS}]-[L_{M}] \tag{13}\]  
where \(L_{N\_BDS}\) is the receiver feeder loss in dB, \(L_{P\_BDS}\) is the antenna polarization loss in dB, \(L_{D\_BDS}\) is the antenna pointing loss in dB, and \(L_{M}\) is the link margin of the system in dB.
## 4 Experimental Results  
In the EECP application, the proposed metaheuristic based on the LSTM network is simulated using a Python software environment on a computer with 64 GB random access memory, a TITAN graphics processing unit with Intel core i7 processor and Ubuntu operating system. The effectiveness of the proposed metaheuristic based on the LSTM network in EECP is validated by comparing its performance with benchmark models, such as a Bi-LSTM with CNN [16], an ensemble-based deep learning model [17], a CNN with GRU model [32], a Bi-LSTM with dilated CNN [33], and multilayer bi-directional GRU with CNN [34] on the AEP and IHEPC datasets. In this research, the experiment is conducted using four performance measures, MAPE, MAE, RMSE, and MSE, for time series data prediction. The MAPE is used to estimate the prediction accuracy of the proposed metaheuristic based on the LSTM network. The MAPE performance measure represents accuracy in percentage, as stated in Equation (11):  
\[\text{MAPE}=\frac{1}{n}\sum_{1}^{n}\lvert\frac{y-\hat{y}}{y}\rvert \tag{11}\]  
The MAE is used to estimate the average magnitude of the error between actual and predicted values by ignoring their direction. The MSE is used to determine the mean disparity between actual and predicted values. The mathematical expressions of MAE and MSE are stated in the Equations (12) and (13). Correspondingly, the RMSE is used to find the dissimilarity between the actual and predicted values, and then the mean of the square errors is computed. Lastly, the square root of the mean values is calculated, where the mathematical expression of RMSE is stated in Equation (14):  
\[\text{MAE}=\frac{1}{n}\sum_{1}^{n}\lvert y-\hat{y}\rvert \tag{12}\]  
\[\text{MSE}=\frac{1}{n}\sum_{1}^{n}(y-\hat{y})^{2} \tag{13}\]  
\[\text{RMSE}=\sqrt{\frac{1}{n}\frac{\sum_{1}^{n}(y-\hat{y})^{2}}} \tag{14}\]  
where \(n\) represents the number of instances, \(y\) the actual value and \(\hat{y}\) the prediction value.
## 3 Materials and Methods
### Annotation, Augmentation, and Training Parameters  
YOLOv8 requires annotated images to perform the classification task. Therefore, before the training phase, we manually annotated each image into two categories (myopic and normal) according to the primary objective of the study. The annotations were completed using free online software [34], and the two annotated datasets were uploaded. We performed several image preprocessing steps, including auto-orientation and resizing the images to a resolution of 640 \(\times\) 640 pixels, which is the recommended size for YOLOv8. To mitigate the risk of overfitting, we applied various data augmentation techniques, such as horizontal and vertical flips, 90-degree rotations in both clockwise and counterclockwise directions, and flipping the images upside down. These transformations expanded our dataset to 699 images, which were then split into a training set (569 images), an internal validation set (80 images), and a test set (50 images). The batch size, which determines the number of samples processed before updating the model parameters, was initially set to 4. Although the standard batch size is typically 32, we chose a smaller batch size to conserve computational resources during each epoch. Each model was trained for 100 epochs, and we implemented an early stopping criterion: if no improvement was observed over ten consecutive epochs, training was halted, as the model was likely to have converged. After identifying the best-performing model variant during the testing phase, we increased the batch size to 8 and adjusted the input image size to 1024 \(\times\) 1024 pixels. We then repeated the inference on the test dataset. The experiment was conducted on a system with an Intel Core i7 processor, 64 GB of RAM, and an 8 GB NVIDIA 3070 Ti graphics card.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number of Participants** & **Myopic** & **Non-Myopic** \\ \hline
169 & 124 (74 F) & 45 (27 F) \\ \hline
**Age (SD)** & 26.74 (13.78) & 40.33 (17.74) \\ \hline
**Number of eligible pictures in each dataset** & 238 & 86 \\ \hline
**Mean SER (SD)** & \(-\)3.29 (3.13) D & +0.92 (1.32) D \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary description of patients’ characteristics and dataset details.