esa-sceva/satcom-chunk-collection huggingface dataset:
# A Novel Resource Allocation Scheme in NOMA-Based Cellular Network with D2D Communications
## 4 Numerical Results and Discussions
### Sum Data Rate of CUs in the Same SC in the Network  
Figure 11 displays the sum data rate of the four algorithms versus the number of CUs on the same SC. In the four algorithms, the sum data rate of CUs increases with a changing speed that gradually slows down as the number of the CUs increases on the same SC. From Figure 11, the increment of sum data rate FSPA is smaller than the other three algorithms and the sum rate performance of FRPA becomes closer to TSA with the increasing of the number of CUs of the network.  
Summarizing the results obtained from Figures 9-11, we observe that the increasing speed of the data rate slows down with the increase of the number of the non-orthogonal CUs. This is because, with the increasing of the number of CUs, more multiple access interference is introduced into the network and the SINR of each CU decreases along with the data rate.  
Figure 10: Geometry average of data rates versus number of CUs on the same SC. We study how the increasing of users impacts on the geometry average of data rates of CUs on each SC where the number of the users changes from 1 to 7.
## 5 Conclusions  
This study presents results obtained through statistical models to investigate Mn and Co extraction in an acid medium at a room temperature of 25 \({}^{\circ}\)C. The effects of time and the ferromanganese crust/Fe(res) ratio were studied as predictive variables. The key findings are as follows:  
* Mn and Co extraction can be effectively modeled using multiple regression techniques, with strong goodness-of-fit indicators.
* Optimal Mn and Co extraction is achieved at longer durations (30 min) and lower ferromanganese crust/Fe(res) ratios (1/3) for both samples, S1 and S2.
* Gradient analysis shows that Mn and Co extraction for both samples is directly proportional to time and inversely proportional to the ferromanganese crust/Fe(res) ratio in most cases, except for Co extraction at higher time levels and lower ferromanganese crust/Fe(res) ratios.
* No precipitation of Co or Mn species was observed in the studied residues.  
While RSM and DOE offer substantial advantages in modeling leaching processes, alternative approaches such as Bayesian statistics and genetic programming could provide valuable additional insights and suggest interesting avenues for future research. Bayesian models, for instance, incorporate probabilistic elements to predict leaching outcomes based on prior and posterior knowledge, offering a novel perspective on process optimization [45]. Similarly, genetic programming can be utilized to develop predictive models for leaching processes, serving as a robust tool for understanding the relationships between process parameters and leaching efficiency [53]. These alternative methodologies could complement RSM and DOE, enabling a more comprehensive understanding of leaching processes.  
Writing-original draft and investigation K.P.; Writing-original draft and conceptualization N.T.; formal analysis and methodology, M.M., I.C. and M.S.; writing--review and editing and investigation, I.S. and F.M.G.M.; conceptualization, resources and funding acquisition, F.J.G. and E.M.; formal analysis and supervision, J.C.; investigation; supervision and project administration, P.C.H. All authors have read and agreed to the published version of the manuscript.  
This study was funded by the ATLANTIS project (PID2021-124553 OB-I00) of the Spanish State Investigation Agency (AEI), European contract EMODnet-Geology (EASME/EMFF/2016/1.31.2-Lot 1/SI2.750862), and the Horizon Europe projects GSEU (HORIZON-CL5-2021-D3-02-14, Project 101075609).  
Not applicable.  
Figure 14: Scanning electron microscopy elemental analysis for sample Subvent 1/DA06-4 (SEM-EDS).  
Data Availability Statement: The original contributions presented in this study are included in the article. Further inquiries can be directed to the corresponding authors.  
**Acknowledgments:** [PERSON] acknowledges the infrastructure and support of the PhD Program in Mineral Processes Engineering of the Universidad de Antofagasta. The authors are grateful for the support of ANID-Chile through the research projects FONDECYT Iniciacion 11230550 and ANID/FONDAP 1522A0006 Solar Energy Research Center SERC-Chile.
## 4 Overview: Signal Processing Methods  
Signal processing [18; 19; 20] is essential for analyzing and interpreting raw data by extracting meaningful information, reducing noise, and identifying patterns from input signals. This process enhances data quality, enables efficient storage and transmission, and facilitates subsequent analysis and interpretation in various applications.
## 4 Simulation and Experimental Results
### Throughput  
The number of the sent packet to the base station is known as throughput. The more sent packet, the better. This means that there was more information collected by the sensor nodes.  
Figure 7 shows the total number of sent packets to the base station in every round. From the figure, it is observed that the YSGAP sends more data packets to the base station than the other protocols. This was as expected since the proposed method prolongs the network lifetime, and consequently, the collected data is superior.
# Transformer-Based Hybrid Forecasting Model for Multivariate Renewable Energy
###### Abstract  
In recent years, the use of renewable energy has grown significantly in electricity generation. However, the output of such facilities can be uncertain, affecting their reliability. The forecast of renewable energy production is necessary to guarantee the system's stability. Several authors have already developed deep learning techniques and hybrid systems to make predictions as accurate as possible. However, the accurate forecasting of renewable energy still is a challenging task. This work proposes a new hybrid system for renewable energy forecasting that combines the traditional linear model (Seasonal Autoregressive Integrated Moving Average--SARIMA) with a state-of-the-art Machine Learning (ML) model, Transformer neural network, using exogenous data. The proposal, named H-Transformer, is compared with other hybrid systems and single ML models, such as Long Short Term Memory (LSTM), Gated Recurrent Unit (GRU), and Recurrent Neural Networks (RNN), using five data sets of wind speed and solar energy. The proposed H-Transformer attained the best result compared to all single models in all datasets and evaluation metrics. Finally, the hybrid H-Transformer obtained the best result in most cases when compared to other hybrid approaches, showing that the proposal can be a useful tool in renewable energy forecasting.  
time series; hybrid systems; transformers; Machine Learning; renewable energy +
Footnote †: journal: _Sciencedence_
## 2 Complete System Formation
### Luo Converter Mathematical Modeling  
Renewable technology comprises DC-DC topologies for yield of energy harvest with admissible proficiency. With respect to other DC-DC converters, modern Luo topology depicted in Figure 3 delivers reasonable cost, better power/density ratio and enhanced transformation efficiency. It comprises the least ripple content with geometric output voltage and surpasses the parasitic element action. The auxiliary benefit of this topology is switched components, which take ground as a reference. In addition to that, the input inductor smoothes the ripple present to input source. Employed capacitors get charged to stated value to accomplish high voltage leveled. Table 1 presents the designed parameters of Luo Converter used during practical implementation.  
Transfer gain voltage is evaluated as [24; 25]:  
\[\frac{V_{0}}{V_{S}}=\frac{2-d_{duty}}{1-d_{duty}} \tag{3}\]  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**S.N** & **Parameters** & **Values** \\ \hline
1. & Inductor (\(L\)) & 0.02 mH \\
2. & Capacitor (C and C\({}_{1}\)) & 20 μF, 15 μF \\
3. & Switching Frequency (\(f_{pulse}\)) & 10 KHz \\
4. & Duty Ratio (\(d_{duty}\)) & 0.58 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Luo converter parameter.  
Figure 3: Power Circuit Luo converter.  
Figure 2: Two diode PV cell model.  
Relation between inductor ripple current and duty cycle is expressed as:  
\[\Delta I_{L_{Ripple}}=\frac{V_{S}\times d_{duty}}{f_{Pulse}\times L} \tag{4}\]  
Capacitors (C=C\({}_{1}\)) values are determined mathematically as:  
\[C=C_{1}=\frac{\left(1-d_{duty}\right)\times V_{0}}{f_{Pulse}\times R_{Load} \times\Delta V_{0}} \tag{5}\]  
where,  
\(d_{duty}\) = Duty ratio  
\(f_{Pulse}\) = Frequency of Switched pulse  
\(V_{0}\) = Output Voltage of Luo Converter
# Energy Efficient Transceiver Design for NOMA VLC Downlinks with Finite-Alphabet Inputs
## 3 Transceiver Design for NOMA VLC  
In this section, to design an energy-efficient transceiver, we first develop an optimal power allocation strategy with unipolar PAMinputs by maximizing the minimum Euclidean distance (MED) of the received signals for signal transmission. Then, thanks to the nice structure of the received signal, we will propose a low complexity JDD algorithm for signal detection.
# Monitoring Changes in Water Use Efficiency to Understand Drought Induced Tree Mortality
## 2 Materials and Methods
### Statistical Analysis  
We used a regression tree approach to estimate maximum tree mortality rates (TPH) in California forests. Tree-based models are a supervised machine learning method commonly used in ecology for exploratory data analysis and prediction, due to their simplistic nonparametric design. This approach recursively partitions the data into increasingly homogenous groups based on values that minimize a loss function (i.e., Sum of Squared Errors (SSE) for regression). Tree-based models were developed in R [57] using the package rpart [58; 59]. The rpart package creates a regression tree based on binary splits that maximize homogeneity and minimizes impurity. The output is a single decision tree that can be further \"pruned\" or trimmed back using the cross-validation error statistic to reduce over-fitting. Regression trees are ideally suited for the analysis of complex ecological data [60]. We used this approach to estimate maximum tree mortality rates [58; 60; 61], and included \(\Delta\)WUE, WUEBASEINE, mean annual scPDSI in 2014 and 2015, and the frequency of severe drought (i.e., mean annual scPDSI \(<-3\)) from 2002-2015 in the model as explanatory parameters. We evaluated post drought maximum mortality rates using the FHP mortality data and used the FIA data to evaluate tree mortality patterns at the plot level. Data processing was carried out using the program R 3.3.2 [57] and the packages rgdal [62], and raster [63].
# Electrochemical Control of Biofilm Formation and Approaches to Biofilm Removal
## 2 Biofilms  
To understand the issue of biofilms in food technology, it is necessary to provide basic knowledge in the field of microbiology, especially an overview of pathogenic bacteria, technologically important biofilm substrates, and conditions under which biofilms form. Special attention is paid to the interactions of planktonic cells with conductive substrates, when the essence of the use of electrochemical approaches to biofilm formation control is already partially approximated.  
Bacteria can form biofilms as a part of their survival mechanisms, and biofilms are thus ubiquitous in nature [1]. Biofilm formation constitutes an alternative lifestyle in which microorganisms adopt a multicellular behavior that facilitates and/or prolongs survival in diverse environmental niches. Biofilms are formed on biotic and abiotic surfaces both in the environment and in healthcare settings. In hospital wards, the formation of biofilms on vents and medical equipment enables pathogens to persist as reservoirs that can readily spread to patients. Inside the host, biofilms allow pathogens to subvert innate immune defenses and are thus associated with long-term persistence [2]. Bacterial biofilms are clusters of bacteria that are attached to a surface and/or to each other and embedded in a self-produced matrix. Bacterial biofilms are complex surface-attached communities of bacteria held together by a self-produced polymer matrix mainly composed of polysaccharides, secreted proteins, and extracellular DNAs. Generally, bacterial biofilm formation is a complex process and can be described in four main phases which can be further subdivided according to their specification: bacterial attachment to a surface (i) including a reversible attachment phase, where bacteria non-specifically attach to surfaces, and irreversible attachment involving interaction between the bacterial cells and a surface using bacterial adhesins such as fimbiae and lipopolysaccharidesaccharide (LPS); (ii) production of extracellular polymeric substances (EPS) by the resident bacterial cells; (iii) biofilm maturation, in which bacterial cells synthesize and release signaling molecules to sense the presence of each other, conducing to the formation of a microcolony and the maturation of biofilms; and, finally, (iv) dispersal/detachment phase, where the bacterial cells depart the biofilms and revert to an independent planktonic lifestyle [3]. Within the biofilm, the bacteria adapt to environmental anoxia and nutrient limitation by exhibiting an altered metabolism, gene expression, and protein production, which may lead to a lower metabolic rate and a reduced rate of cell division [4; 5; 6; 7]. Biofilms have high cell densities ranging from 108 to 1011 cells per gram of the wet weight [8].
## 3 Simulation and Analysis
### Practical Application  
In actual application scenarios, the complex relationship between radar activation point, MGF data, and \(P_{ident}\) is further compounded by dynamic maritime environment, aircraft performance, and specific mission requirement. Owing to the non-linear interaction among these factors, analytically determining the single optimal radar activation point, either by maximizing \(P_{ident}\) at a given target location or by minimizing the combined extent of Level I and Level II regions, is computationally intractable and often operationally impractical.  
The proposed method circumvents this difficulty by providing planners with an explicit, probability-based comparison of candidate configurations. The Inputs consist of  
(1) The 3D MGF model;  
(2) The area around the MGF whose \(P_{ident}\) needs be calculated;  
(3) Radar resolution parameters;  
(4) Errors mentioned in Section 2.2;  
(5) A discrete set of candidate radar activation points that satisfy the constraints.  
The proposed method would then return the corresponding \(P_{ident}\) values, and the mission planners can select the most operationally suitable radar activation point from candidates without recourse to exhaustive numerical optimization.  
Example. Consider the narrow-shaped island scenario with two candidate radar activation points \(S_{2}\) and \(S_{6}\) due to the constraints. If surveillance intelligence indicates that a target is expected at location \(T_{a}\), and the computed probabilities are \(P_{ident}(S_{2})=0.9990\) and \(P_{ident}(S_{6})=0.5887\). The planner therefore selects \(S_{2}\), achieving a substantial increase in identification confidence.
## 4 Experiment Results
### Depth Map Acquisition Results  
We adopt the monoResMatch network to generate the monocular depth map. This network can obtain a high accuracy of up to 90% in the depth map on the KITTI data. The results of the depth map are shown in Figure 8. The top image is the colored depth map and the bottom image is their corresponding images.
# Exploring Data Augmentation Algorithm to Improve Genomic Prediction of Top-Ranking Cultivars
## 2 Materials and Methods
### Evaluation of Prediction Performance  
This research used a cross-validation methodology referred to as \"random-partition-line.\" In this approach, elucidated by [PERSON] et al. [24], each fold designated 20% of the lines as the test set, while the remaining 80% collectively formed the training set. The evaluation used 10 folds, with the average performance across these folds reported as the prediction outcome.  
To assess the genomic performance of the models, two metrics were used: the normalized root mean square error (NRMSE) and the Mean Arctangent Absolute Prediction Error (MAAPE; Sungil & Heeyoung [25]). The mean square error is computed as \(\text{MSE}=\frac{1}{T}(\sum_{i=1}^{T}{(y_{i}-f(x_{i}))})\), where \(y_{i}\) denotes the observed \(i\)th value, while \(\hat{f(x_{i})}\) represents the \(i\)th predicted value and the normalized root mean square error (\(NRMSE=\frac{RMSE}{\overline{y}}\)), where \(RMSE=\sqrt{\frac{1}{T}(\sum_{i=1}^{T}{(y_{i}-\hat{f(x_{i})})})}\) was used as a metric to evaluate the prediction accuracy.  
The Mean Arctangent Absolute Prediction Error (MAAPE) offers a metric to assess prediction accuracy by evaluating the arctangent of the absolute errors between the predicted and actual value, and it is computed as MAAPE \(=\)\(\frac{1}{n}\)\(\sum_{i=1}^{n}{\arctan\left|\frac{y_{i}\cdot\hat{f(x_{i})}}{y_{i}}\right|}\), where \(y_{i}\) and \(\hat{f(x_{i})}\) represent the observed and predicted values of the \(i\)th cultivar, respectively.
# An Efficient Satellite Resource Cooperative Scheduling Method on Spatial Information Networks
## 5 Numerical Experiment
### Parameter Setting  
We take a particular instantaneous network structure and assume that the number of task queues at that moment is 12. If a new task is added to the queue at the next moment, then the resource scheduling result needs to be recalculated. According to the numbering of the satellites in Table 3, we assume that satellite 1 and satellite 2 have already met the amount of mission resources requests. However, there are still tasks waiting to be executed with observing resource requirements of six photographs and storage resources of six GB. Next, we can schedule idle satellite resources to perform tasks currently waiting due to resource shortages. This experiment compares the resource scheduling algorithm \(RQRCS\) proposed in this paper with benchmark algorithms to verify its effectiveness. These algorithms include Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). Both PSO and GA are provided by the Python 3.6 supported toolkits  
Figure 8: Two-dimensional trajectory of satellite.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Id** & **Satellite** & **Mission Class** & **Mean** & **Eccentricity** & **Inclination** & **Argument of Perigee** & **RAAN** & **Mean Anomaly** \\ \hline
1 & COSMO-SkyMed 3 & Earth Observation & 0.0617566 deg/s & 0.0001561 & 97.8893 deg & 93.4363 deg & 348.021 deg & 266.704 deg \\
2 & ZiYuan 2C & Earth Observation & 0.0626305 deg/s & 0.004758 & 97.2866 deg & 62.7732 deg & 35.4333 deg & 297.833 deg \\
3 & IRIDUM-162 & Communications & 0.0601366 deg/s & 0.0023271 & 86.4485 deg & 96.0452 deg & 133.275 deg & 264.101 deg \\
4 & STARLINK-1490 & Communications & 0.0627664 deg/s & 0.0001502 & 53.0558 deg & 82.9425 deg & 45.8273 deg & 277.173 deg \\
5 & STARLINK-1557 & Communications & 0.0627666 deg/s & 0.0001709 & 53.0575 deg & 117.008 deg & 336.223 deg & 243.109 deg \\
6 & STARLINK-1569 & Communications & 0.0627667 deg/s & 0.0001654 & 53.0574 deg & 108.432 deg & 336.473 deg & 251.684 deg \\
7 & KOMPSAT-2 & Earth Observation & 0.060932 deg/s & 0.0018448 & 97.9422 deg & 95.7576 deg & 43.5438 deg & 264.572 deg \\
8 & Proba-V & Earth Observation & 0.0592918 deg/s & 0.0005443 & 98.4077 deg & 91.3034 deg & 212.541 deg & 268.8877 deg \\
9 & RADARSAT-2 & Earth Observation & 0.0595828 deg/s & 0.0001296 & 95.8743 deg & 92.0516 deg & 171.152 deg & 30.1928 deg \\
10 & Globalstar M083 & Communications & 0.0525945 deg/s & 0.000275 & 51.9838 deg & 225.309 deg & 186.696 deg & 305.39 deg \\ \hline \hline \end{tabular}
\end{table}
Table 3: Satellites parameters in the simulation scenario.  
Figure 7: Spatial information network computing environment simulation platforms. (**a**) Satellite management. (**b**) Observation scene.  
'sko.GA' and'sko.PSO'. The parameter settings for the PSO and GA algorithms are shown in the following Table 4, where ID denotes the parameter number, _size_pop_ indicates size of population, and \(max\_iter\) denotes max iteration.
## 2 Related Work
### Advances in Open Information Extraction  
Open information extraction (OIE) systems aim at converting the unstructured information expressed in natural language into a more structured representation, in the form of relational triples consisting of a set of arguments (subject, object) and their semantic relation (predicate), e.g., <subject, predicate, object> [40]. Unlike closed information extraction approaches that are limited to a narrow set of predefined target relations, OIE systems are able to extract any kind of relation, providing increased scalability and usability over heterogeneous corpora [41]. In order to extract OIE triples, most approaches try to identify linguistic extraction patterns, which may be either hand-crafted or automatically learned from annotated data. Rule-based approaches that rely on hand-crafted extraction rules focus on syntactic constraints expressed as part-of-speech (POS)-based regular expressions [42; 43]. Self-supervised learning approaches usually leverage annotated data sources (e.g., Wikipedia infoboxes) to train classifiers [44; 45] or bootstrap a large training set over which they learn a set of extraction POS pattern templates [46]. Some recent OIE systems are clause-based, using linguistic knowledge about the grammatical and syntactic properties of the language to identify clause constituents, thus restructuring larger complex sentences to many simple ones [47; 48]. The emergence of annotated corpora for OIE evaluation paved the way for supervised neural-based models that further pushed the state-of-the-art in this domain [49; 50]. Latest approaches are extending the use of deep BIO taggers used for semantic role labeling (where B is assigned to the beginning of named entities, I is assigned to the interior, and O is assigned to other) by leveraging the word embeddings of the processed sentences in deep neural networks (e.g., bi-LSTMs) to produce probability distributions over possible BIO tags [51; 52].
# Predicting the Life of Varistors via a Nonlinear Coefficient Based on a Small-Scale Data Model
## 3 Experiment and Analysis  
### Experimental Setup and Data Processing  
The experimental sample used is a certain brand of LKD 22S181K R03 model ZnO, with a breakdown varistor voltage of U\({}_{\text{1 mA}}\) = 180 V, a nominal discharge current of In = 12 KA and a maximum discharge current of I\({}_{\text{Max}}\) = 25 KA. We selected these types of varistors from the same batch for the surge impact test. The experimental equipment is shown in Figure 11, including a test bench, a surge generator and a data acquisition terminal.  
The test sample is impacted with a current of 8/20 \(\upmu\)s waveform, which is stipulated by national standards and railway industry standards, The theoretical impact waveform and the actual impact waveform of the experimental device are shown in Figure 12.  
The interval between each surge impact is 5 min, and its state parameters are recorded until it is completely damaged. Complete damage often takes more than 40 impacts. After complete damage, the appearance clearly changes, one corner of the epoxy resin layer bursts and granular black powder appears, as shown in Figure 13.  
After each impact test, the value of the nonlinear coefficient is read and recorded from the experimental terminal. The nonlinear coefficients of each varistor from initial to complete failure were recorded as a data set. The data set is processed as described in Section 2.3 and becomes the input to the lifetime prediction model.  
Figure 11: The experimental equipment. (**a**) Test bench, (**b**) surge generator, (**c**) data acquisition terminal.  
Figure 12: Diagram of 8/20 \(\upmu\)s impact waveform. (**a**) Theoretical impact waveform; (**b**) actual impact waveform.