esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results
### Characterization of Digital Identity
Regarding the actions carried out on the net and in social networks, it is clear that the gender factor is decisive, given the distribution of responses. When analyzing said distribution, behavioral trends are evidenced rather than monotypic behaviors, that is, they tend towards high or low activity, with no marked behaviors. Table 8 established that the number of followers and the number of published photographs are the activities in which the students declare they have the most knowledge. Women have a greater tendency than men to know the number of published pictures (\(p\)-value = 0.0003). Regarding the knowledge of the metrics, the distribution of women points towards a low knowledge, and men present a distribution that is close to that of a Gaussian bell curve with a slight inclination towards the lowest values. This would imply that men, in general terms, have a greater knowledge of the metrics associated with their activities on the Internet. Knowing the impact of the published pictures and the number of followers is two of the most motivating elements for the students surveyed according to Table 9. The same table also shows that women show greater motivation to the extent that they know the impact of their activity, compared to men, in almost all the situations raised, except to the metrics of their publications, which are not shown as a motivating factor for women. In almost all the cases, women declare a high use of social networks and consulting tools, higher than the use declared by men. In the case of Instagram, women present a response distribution with a greater inclination towards what would be understood as high use (\(p\)-value = 0.0004), compared to men. In the case of YouTube, although it is men who tend to use it more than women (\(p\)-value = 0.0403), the tendency is not as significant as the one observed in the case of Instagram, where the difference between men and women is higher. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Activity** & & **1** & **2** & **3** & **4** & **5** & **6** & \(p\)**-Value** \\ \hline Number of & F & 5.86\% & 11.42\% & 14.81\% & 15.12\% & 20.68\% & 32.11\% & \\ followers/friends/subscribers & M & 5.95\% & 14.05\% & 11.89\% & 21.62\% & 22.71\% & 23.78\% & \\ \hline Amount of retweets/likes/shares & F & 36.39\% & 22.78\% & 11.40\% & 8.23\% & 10.76\% & 10.44\% & \\  & M & 37.28\% & 21.89\% & 11.24\% & 11.84\% & 8.28\% & 9.47\% & \\ \hline Number of groups in which you & F & 17.90\% & 16.36\% & 20.98\% & 13.89\% & 14.20\% & 16.67\% & \\ participate & M & 12.97\% & 22.16\% & 18.92\% & 16.22\% & 16.76\% & 12.97\% & \\ \hline Number of published photos & F & 5.86\% & 11.11\% & 16.67\% & 17.28\% & 18.52\% & 30.56\% & \\  & M & 9.19\% & 16.76\% & 22.16\% & 17.84\% & 14.59\% & 19.46\% & 0.0003 (*) \\ \hline \hline \end{tabular} (*) Significance when the \(p\)-value \(<\) 0.05. Increasing scale, where 1 implies a very low knowledge and 6 the highest degree of knowledge. F = female; M = male.  
\end{table} 
Table 8: Degree of knowledge regarding activities carried out on the Internet.
## 1 Introduction  
Real-time embedded systems represent one of the most important segments of the modern electronics market. They are one of the crucial parts of the safety systems operating in medicine, military, control systems, etc. One can observe the constant growth of the expectations of real-time systems over the years. Users require from such systems the highest reliability and long-lasting, failure-free operation. As real-time systems can now be found in mobile and remote applications, a new challenge arises--low power consumption. Thus, new techniques and design methodologies that allow minimizing the energy required by embedded systems have been intensively investigated for the last few decades.  
The present paper concerns a low-level approach to time-predictable systems. The authors focus on the hardware design of real-time system architectures. The research analyzes different solutions of FRET architectures [1] with respect to the various cost parameters. The main effort has been toward the minimization of the total energy consumed by the system. All investigations and practical experiments have been carried out on original architecture, developed in the Department of Electronics, Electrical Engineering and Microelectronics (formerly, the Institute of Electronics) [2; 3; 4].  
The paper is organized in the following way: the second section discusses the related work and briefly describes alternative solutions concerning hardware, as well as software,elements of time-predictable real-time systems. Section 3 presents different architectures of time-predictable systems, wherein the authors analyze the main building blocks of the solutions that are tested in this research. The fourth section contains theoretical analyses of time-predictable systems and formulates the main quality metrics that are used in the analyses. Section 5 presents selected results of the practical experiments carried out under different multitask systems' configurations. The authors emphasize the analysis of the energy consumed by the solutions that meet timing requirements, i.e., in which the deadlines of all tasks are satisfied. Finally, the last section summarizes the paper, discusses the results, draws conclusions, and formulates areas for further research.
## 1 Introduction
The proposed IMD-EACBR model initially derives an improved Archimedes optimization algorithm-based clustering (IAOAC) technique. In addition, the IAOAC algorithm computes a fitness function that involves multiple parameters, namely energy efficiency, distance, node degree, and inter-cluster distance. Moreover, teaching-learning-based optimisation (TLBO) algorithm-based multi-hop routing (TLBO-MHR) technique was executed for the optimum selection of routes to destinations. The design of IAOAC and TLBO-MHR techniques with multiple input parameters depicts the innovation of the research. The presentation of the IMD-EACBR model has been examined in several aspects. In this paper, we propose the IMD-EACBR, a new routing strategy for WSNs that uses a mix of a clustering methodology and a TLBO-MHR technique to extend network lifetime. The goal of the proposal is to find the best route from the source to the destination by prioritiising the maximisation of remaining battery power, the minimisation of energy consumed in a multi-hop path, and the greatest equity across sensor nodes. The simulation results show that this protocol not only balances the overall network's energy usage, it also delays node death time and provides more dependable data delivery. The core donations and innovations of this investigation are as follows:  
* Multi-hop directing methods are thought to be energy-effective explanations for wireless device systems, which address a problem known as gathering. On the other hand, the cluster-based directing techniques used in conventional wireless networks might not be appropriate to WSN due to factors such as the presence of an underwater present, a limited bandwidth, a high water compression, a prolonged broadcast latency, and an increased error prospect. * In order to tackle these challenges and achieve energy efficiency in WSNs, the primary focus of this research is on the making of a metaheuristic-based gathering along with a directing procedure for WSNs. This protocol is given the name IMD-EACBR. * The IMD-EACBR technique selects the most advantageous CHs and the most direct paths to the BS. IMD-EACBR is a method that employs the teaching-learning-based optimization (TLBO) optimizer-based gathering in order to select the most suitable CHs and construct appropriate collections. * In order to demonstrate the manner in which the IMD-EACBR technique enhances performance, a number of simulations are carried out, and the results are analysed in a variety of different ways. The remaining portion of the paper is divided into five sections that are descriptive in nature. In the second section, an assessment of preceding efforts that have been made in the same context is presented, in order to classify the practical gaps. In Section 3, the plan is broken down into its individual IAOAC phases for clarity. The study is brought to a close with a discussion of the potential directions of IAOAC in the years to come. Section 4 provides an in-depth analysis of the presentation, in order to validate the superiority and effectiveness of IAOAC. Section 5 presents the conclusions.
## 3 Results and Discussion
### ML Model Validation
This suggests that in optimizing the component for stable temperatures, greater attention should be paid to controlling the fuel and air flow rates, while variables like oxygen concentration and catalyst condition play more secondary roles. The plots also reveal that the system reaches equilibrium at certain points where increasing a parameter no longer significantly affects the temperature. These stable regions could be potential targets for process optimization, as they represent conditions under which the system can maintain a consistent output temperature without the risk of overheating. The model capabilities will be further used where different multiphysics is receiving considerable attention in recent research endeavors. To discuss and recap the section, some key outcomes can be summarized as follows:  
The dominant influence of air flow on the fuel outlet temperature, with an estimated contribution of 75%, as depicted in Figure 5a, emphasizes the strong impact and need for careful control of airflow to manage temperature levels effectively during the reforming process. In addition to air flow, air temperature also plays a significant role, contributing approximately 10-12% to the fuel outlet temperature. This demonstrates that while airflow remains the primary driver of thermal behavior, adjusting air temperature can still meaningfully influence the reformer's performance. Other factors, including catalyst porosity and oxygen content, showed more modest impacts, with contributions of around 5-7% each. These parameters, while important for fine-tuning the chemical reactions within the pre-reformer, have less direct influence on the overall temperature profile compared to airflow and air temperature, but would be an important criterion for improving material performance. Fuel flow exhibited the least influence on the outlet temperature, contributing only 2-3%, suggesting that it plays a more secondary role in the thermochemical process. The three-dimensional response surface plots in Figure 5b-d illustrate the non-linear interactions between parameters. For instance, Figure 5b shows that increasing both air and fuel flow rates leads to higher outlet temperatures, with airflow exerting a particularly pronounced effect. Similarly, Figure 5c highlights the interaction between fuel flow and air temperature, confirming that while both variables contribute to temperature control, the effects of fuel flow are more gradual compared to airflow. These findings underscore the importance of optimizing airflow and air temperature to achieve stable and efficient pre-reformer operation, while secondary parameters like catalyst porosity and oxygen concentration offer additional fine-tuning capabilities. The machine learning model trained on these insights provided a reliable predictive framework, closely aligning with the results of traditional CFD simulations, with error margins as low as 0.34% across multiple validation cases, suggesting its significance as an accelerated design and optimization tool that will be utilized in future studies.
# Evolutionary Features for Dynamic Link Prediction in Social Networks
## 4 Results
### Feature Importance  
By considering the improved performances of our dynamic features, our next objective is to compare all dynamic features to assess their relative competency in the dynamic link prediction task. For this purpose, an alternative to ROC curves for models with a large skew in the class distribution. P-R curves can sometimes expose differences between classifiers that are not apparent in the ROC curves. In Figure 3, we present the ROC and P-R curves for the best-performing classification dataset from each group. For this figure, we selected a classification dataset from \(G_{UICI}\) constructed using a daily temporal sampling window, \(G_{Rtxt}\) with an hourly sampling window, and \(G_{th}\) from the co-authorship networks. In regard to the classifier, we have selected RandomForest for the undirected network, logistic regression for the directed network, and bagging for the directed network. In this figure, the left column contains the P-R curves, and the right column presents the ROC curves in the three datasets chosen above. In this figure, the red line denotes the best-performing dynamic feature in the chosen classification dataset of each network, the green line denotes an existing time series of topological similarity metrics-based link prediction strategy, and the orange line denotes the performance of _ResourceAllocation_ metric used in link prediction over the static network. The red lines in the P-R and ROC plots denote the high-performing dynamic feature in the corresponding datasets (i.e., \(sim_{2}(a,b)\) in \(G_{UICI}\) and \(G_{Rtxt}\) and \(sim_{1}(a,b)\) in \(G_{th}\). The green lines denote the metric (i.e., \(sim_{Sources}\)) developed by following the method described by [PERSON] and [PERSON] ([PERSON] and [PERSON], 2013). In this dynamic link prediction strategy, this study first developed a time series of the Jaccard similarity measure for every non-connected actor pair for each SIN generated, considering the sampling window size of the high-performing classification dataset of the corresponding network. For example, the high-performing classification dataset in \(G_{UICI}\) was developed using daily SINs (see Table 3 for performance measures). Then, the ARIMA forecasting method was used to predict the future value of the Jaccard metric, which was also used to train the classifier for positive and negatively labeled dyads. Finally, the orange lines denote the traditional topological similarity metric ResourceAllocation (i.e., \(sim_{RA}\)) that is widely used for higher performance in predicting links in cross-sectional networks. It is noteworthy that, in P-R plots, the curves tend to lie in the bottom left corner of the diagonal line, whereas, in ROC plots, the curves tend to lie in the top-left region of the plots. The higher the curves than the diagonal line, the higher the predictor's performance. Through the curves, it is observable that, apart from the P-R curve in the \(G_{Rtxt}\) network, in every plot, the dynamic features constructed in this study exceeded others in performance.
## 4 Influence of 3D Printing Parameters on the Fatigue Life of FFF Prints
### Build Orientation  
Due to the layer-by-layer nature of 3D printing, FFF constructs possess a degree of anisotropy, depending on the building direction as well as the orientation of deposited filaments in each layer. Filaments act like fibres in a fibre-reinforced composite, exhibiting a directional anisotropy. Changing the build orientation alters the mesostructure of prints as well as the bonding chains between extruded filaments, resulting in different mechanical properties along the X, Y, and Z directions [90; 91]. [PERSON] et al. [58] investigated mechanical properties of ABS prints and concluded that the anisotropic behaviour of FFF prints under tensile fatigue loading originates from the directionality of the polymer molecules and air gaps within the specimens. In the work of [PERSON] and [PERSON] [53], tensile specimens were 3D-printed in different building orientations. X, Y, and Z prints were 3D-printed so that the printer bed was perpendicular to the thickness, width, and length of the specimens, respectively. It was found that Y prints possessed a higher fatigue resistance at the same applied stresses. In a similar study by [PERSON] and [PERSON] [36], Y, X, and Z prints possessed the highest to the lowest UTS and tensile fatigue life, respectively. When subjected to a cyclic tensile load equalling 40% of the tensile strength, Y and X prints sustained around 8300 cycles, which was nearly double the cycles sustained by Z prints.  
The flexural fatigue behaviour of polycarbonate (PC) prints is also reported to be significantly affected by the building orientation. In a study by [PERSON] et al. [14], PC rectangular plots with a length of 80 mm, a width of 10 mm, and a thickness of 4 mm were 3D-printed in X (80 \(\times\) 10 \(\times\) 4 mm\({}^{3}\)), Y (4 \(\times\) 80 \(\times\) 10 mm\({}^{3}\)), and Z (10 \(\times\) 4 \(\times\) 80 mm\({}^{3}\)) orientations, with the first two dimensions representing the dimensions of the object on the print bed. It was shown that the Y prints possessed the longest fatigue life (at least 50% higher fatigue life than that of Z prints), followed by X and Z prints.  
A noteworthy point in assessing the influence of the build orientation is the magnitude of the applied stress. The results achieved by [PERSON] and [PERSON] [36] and [PERSON] et al. [14] revealed that the difference between the fatigue life of different prints at different building orientations is notable at higher stresses but tends to decrease at low stresses. This highlights the influence of anisotropy at higher loads and demonstrates the importance of the build orientation when the FFF print is designed to work under high fatigue stresses.
# An Improved PSO Algorithm and Its Application in GNSS Ambiguity Resolution
## 3 Experiments and Result Analysis  
We conducted several experiments to verify the feasibility of the proposed IPSO-AR method. We first demonstrated the search procedure of IPSO-AR. Then, we evaluated the performance of IPSO-AR through three different experiment schemes. We analyzed the influences of the property of the ambiguity float solution and parameter setting on the performance of IPSO-AR in detail. Finally, we used a known baseline to further validate the feasibility of IPSO-AR.
# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
###### Abstract  
An increase in renewable energy injected into the power system will directly cause a fluctuation in the overall voltage and frequency of the power system. Thus, renewable energy prediction accuracy becomes vital to maintaining good power dispatch efficiency and power grid operation security. This article compares the one-day-ahead PV power forecasting results of three models paired with three groups of weather data. Since the number, loss, and matching problem of weather data will all influence the training results of the model, a pre-processing data framework is proposed to solve the problem in this study. The models used are a deep learning algorithm-based artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU). The weather data groups are Central Weather Bureau (CWB), local weather station (LWS), and hybrid data (the combination of CWB and LWS data). Compared to the other two groups, hybrid data showed a 5-8% improvement in measurements. In addition, when it comes to different weather conditions, the advantages of the LSTM model were highlighted. After further analysis, the LSTM model combined with hybrid data showed the most accurate measurements, which was proved through forecasting results for one month. Finally, the results indicate that when the amount of data is limited, using hybrid data and the five weather features is helpful for training the model. Accordingly, the proposed model shows better one-day-ahead PV forecasting.  
deep learning (DL); forecasting: neural network; renewable energy; solar power generation +
Footnote †: journal:  
* [2022] _Applied sciences_
## 3 Results and Discussion
### Cell Density and Protein Production Assessments  
In order to determine the culture phase coinciding with maximum protein production using _Rhodomonas_ sp. strain, cell growth and protein production were monitored (Figure 1). The obtained data showed a classical sigmoidal growth curve. A lag phase occurred during the first 72 h. Then, a typically exponential phase was reached and lasted 9 days, followed by the stationary phase between the 11 th and 22 nd days.  
In parallel to cell number monitoring, protein production was taken into consideration. It was observed that the protein production was in parallel to the biomass production, reaching its maximum after 21 days of culture. Several studies reported that protein production was correlated with nutrient availability. In fact, the maximum protein accumulation was achieved during the logarithmic phase, and it then decreased according to nutrient concentrations [50]. However, [PERSON] et al., [51] proved that the highest essential amino acid values were obtained at the stationary phase for the _Dunaliella salina_ species.
## 3 Methods  
### Data Preprocessing  
The datasets utilized in this study--comprising GOSIF data (0.05\({}^{\circ}\), monthly), GPP data (0.05\({}^{\circ}\), 8-day), NEP data (0.072727\({}^{\circ}\), daily), MODIS data (1 km/500 m, daily), forest age data (30 m), forest type data (30 m), elevation data (30 m), and land cover data (30 m)--exhibit variations in spatial and temporal resolutions. To ensure consistency, all datasets were spatially resampled to a resolution of 0.072727\({}^{\circ}\) and temporally aggregated to a monthly scale.  
Although high spatial resolution data can more accurately capture dynamic changes in carbon sinks in the Qinba Mountain area, resampling them to lower resolutions may lead to a loss of precision due to inherent estimation biases in the original low-resolution data. In contrast, downscaling high-resolution data through multi-pixel aggregation helps preserve data accuracy effectively. Therefore, in this study, all data were resampled to a unified resolution of 0.072727\({}^{\circ}\) to ensure consistency and reliability.  
Since the Qinba Mountain area encompasses two MODIS tiles (h26v05 and h27v05), mosaicking of the two tiles was conducted prior to transforming their spatial and temporal resolutions to 0.072727\({}^{\circ}\) and a monthly scale, respectively.  
In this study, we selected the period from 2011 to 2018 for carbon sinks estimation, as the 9 th National Forest Resources Inventory of China, completed in 2018, provides the most recent, systematic, and authoritative forest resource data available. Using this dataset ensures the comparability of our results and underscores the contribution of the Qinba Mountain region to China's dual carbon goals. Additionally, using data from 2003 to 2010 to project carbon sinks for 2011 to 2018 inherently involves predictions, and the uncertainty in such projections increases with longer time spans. By limiting the study to 2018, we ensure that only the preceding eight years of data (2003-2010) are used to estimate the following eight years (2011-2018), effectively minimizing prediction errors and uncertainties. This approach enhances the reliability of the results, making 2018 the logical endpoint of the study.