esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results: Sensitivity Study
### CO\({}_{2}\) Emissions Comparison between TW and BW  
In Figure 7, the results of the sensitivity analysis are reported in terms of block fuel, for both the TW and BW configurations datasets. These results, taken from the first part of this research [29], highlight that increasing MTOW is beneficial to achieve reduction in block fuel reductions.  
Figure 8 shows the total mass of CO\({}_{2}\) emissions varying the parameter CO\({}_{2}\). The first outcome from these results is that, even if the block fuel reduces increasing MTOW, hence a part of the energy to accomplish the mission is provided by batteries, the \(m_{CO_{2}}\) increases for both BW and TW if the current production of electricity is taken into account; see the left part of Figure 8. The trend becomes neutral if we consider an increase in renewable sources (see the middle part of Figure 8), whereas sensible improvements in \(m_{CO_{2}}\) can be gained if the whole production of electricity is renewable-based (see the right part of Figure 8); in this case, the \(m_{CO_{2}}\) follows the trend of the block fuel. The second outcome provided by this analysis is that BWs configurations, in any CO\({}_{2}\), scenario, produce less \(m_{CO_{2}}\) than the corresponding TW, for the same reasons presented in Section 1 when discussing the block fuel as FoM. The statistical analysis of the datasets of Figure 8 is shown in Figure 9.  
Figure 8: \(m_{CO_{2}}\) vs. MTOW for BW and TW configurations varying CO\({}_{2e}\).  
Figure 7: Block fuel vs. MTOW for the BW (**left**) and TW (**right**) datasets [29].  
It is apparent from this scenario that, if an effective impact from the introduction of hybrid-electric propulsion is to be achieved on a global level, the technological advancement introduced on aircraft must necessarily be accompanied by a system-wide complete transition. In particular, if electricity production does not reduce its reliance on fossil sources, even disruptive aircraft technology developments would not be effective in reducing the climate impact of transport aviation. Thus, it is necessary to provide a significant conversion of electricity production based on renewable sources at the same time as the development of electric propulsion technologies.
## 3 Results  
The results of the simulations detailed in Sections 2.3.2-2.3.5 are here presented. Each subsection refers to a specific set of simulations. For each set of simulations, the angular and linear dispersion parameters, as well as the rms distance values between the exact and estimated AoRs, were calculated. This process was repeated for each algorithm tested, that is, the IHA, with three different weighting factors (Table 2), and SARA. In the following, the main results of these analyses are presented, while the complete sets of results are reported in Appendix A for the sake of conciseness.
# Development of Security Rules and Mechanisms to Protect Data from Assaults
## 5 Testing
### Time and Space Complexity  
The time and space complexity of the tool while encrypting and decrypting the data is determined, as shown in Table 3. First, different plain texts are taken, and then, the length of different plain texts is identified. After identifying the length of the plain text, all the plain text is encrypted with the tool, and the encoding time is determined. When the plain text is converted into cipher-encoded text, the length of the encoded text is 20% more than the plain text length.  
After determining the ciphertext length, each ciphertext is encrypted with different keys using different key sizes. These keys are implemented on the text with the help of the tool, and a secure ciphertext is obtained. When the encoded text is encrypted with the help of the key, the time complexity is also determined when the cipher text is generated. After converting plain text to ciphertext, memory allocation of plain text and ciphertext is performed, as shown in Table 3.  
After encrypting the data, time and space complexity was identified while decrypting the data, in which different ciphertexts were first taken, the ciphertexts were decrypted using different cases, and the decryption time was determined. The memory allocation of the ciphertext and plain text was then determined by identifying the difference between the length of the plain text and the ciphertext when converted to plain text, as shown in Table 4.
# Monitoring Spatial and Temporal Variabilities of Gross Primary Production Using MAIAC MODIS Data
###### Abstract  
Remotely sensed vegetation indices (RSVIs) can be used to efficiently estimate terrestrial primary productivity across space and time. Terrestrial productivity, however, has many facets (e.g., spatial and temporal variability, including seasonality, interannual variability, and trends), and different vegetation indices may not be equally good at predicting them. Their accuracy in monitoring productivity has been mostly tested in single-ecosystem studies, but their performance in different ecosystems distributed over large areas still needs to be fully explored. To fill this gap, we identified the facets of terrestrial gross primary production (GPP) that could be monitored using RSVIs. We compared the temporal and spatial patterns of four vegetation indices (NDVI, EVI, NIR, and CCI), derived from the MODIS MAIAC data set and of GPP derived from data from 58 eddy-flux towers in eight ecosystems with different plant functional types (evergreen needle-leaved forest, evergreen broad-leaved forest, deciduous broad-leaved forest, mixed forest, open shrubland, grassland, cropland, and wetland) distributed throughout Europe, covering Mediterranean, temperate, and boreal regions. The RSVIs monitored temporal variability well in most of the ecosystem types, with grasslands and evergreen broad-leaved forests most strongly and weakly correlated with weekly and monthly RSVI data, respectively. The performance of the RSVIs monitoring temporal variability decreased sharply, however, when the seasonal component of the time series was removed, suggesting that the seasonal cycles of both the GPP and RSVI time series were the dominant drivers of their relationships. Removing winter values from the analyses did not affect the results. NDVI and CCI identified the spatial variability of average annual GPP, and all RSVIs identified GPP seasonality well. The RSVI estimates, however, could not estimate the interannual variability of GPP across sites or monitor the trends of GPP. Overall, our results indicate that RSVIs are suitable to track different facets of GPP variability at the local scale, therefore they are reliable sources of GPP monitoring at larger geographical scales.  
GPP; seasonality; interannual variability; trends; forests 2019, 11, 874; doi:10.3390/rs11070874
## 3 Congestion Prediction Model
### Data Transmission Time  
The time \(g_{i}\) taken to pass through zone \(i\) can be defined with a BPR function. Since the BPR function is easy to manipulate and is not asymptotic to any particular value, it has the advantage that the travel cost can be calculated for any traffic volume. In this paper, data modeling may be performed by applying the BPR function to the congestion of the network.  
The BPR function developed by the United States Bureau of Public Roads shows how the traversal time changes depending on the ratio of traffic volume to the road capacity (Equation (12)):  
\[g_{i}=g_{i}(1+\alpha\frac{D_{i}}{C_{i}})^{\beta} \tag{12}\]  
\(\bullet\)\(t=\) transmission time  
\(\bullet\)\(t_{0}=\) transmission time for free path  
\(\bullet\)\(D_{i}=\) traffic generation per area  
\(\bullet\)\(C_{i}=\) network capacity (bps)  
\(\bullet\)\(\alpha\), \(\beta=\)parameters  
\(D_{i}\) is traffic generation per area and \(C_{i}\) is network capacity. Thus, \(g_{i}\) means transmission time.  
The BPR function has the advantage of fast-paced calculated results depending on the value of parameters \(\alpha\) and \(\beta\) as shown in Figure 6. This may cause excessive calculation. So, it is important to determine the precise values of parameters \(\alpha\) and \(\beta\) for the particular network types.  
Figure 6: Transmission Time Changes by parameter \(\alpha\)[40].
# Circulating Tumor Cells in Metastatic Breast Cancer: Clinical Applications and Future Possibilities
## 4 Therapy Selection Based on CTCs
### Therapy Interventions Based on CTC Counts  
Given that CTCs seem to reflect the features of dominant metastatic cell populations, evaluation of their counts as well as geno-and phenotypes may provide insight into current dynamics of the disease and inform treatment decisions. The first positive study on CTC-based therapy interventions was the STIC CTC trial initiated in France by the Institut Curie [12]. A total of 778 patients with metastatic hormone receptor (HR) positive HER2-negative breast cancer received first-line therapy within this phase III study. CTC numbers were assessed using CellSearch before start of treatment and were elevated in 27% of patients. Patients were randomized into two groups. In the standard arm, they received therapy of physician's choice (chemotherapy or endocrine therapy). In the CTC-arm, the therapy was guided by CTC counts: patients with \(\geq\)5 CTCs received chemotherapy, patients with \(<\)5 CTCs endocrine therapy. After a median follow up of 30 months, the progression-free and overall survival were similar in both groups, i.e., CTC counts stratified patients into low-risk and high-risk groups about the same as an experienced oncologist. Moreover, in case of discrepant risk estimation (i.e., clinically low-risk patients with \(\geq\)5 CTCs or clinically high-risk patients with \(<\)5 CTCs) chemotherapy led to significantly longer overall survival than endocrine therapy. The results of the STIC CTC trial have been extensively discussed after their publication at the San Antonio Breast Cancer Symposium 2018 [12]. While the study has demonstrated the value of liquid biopsy-based treatment selection, it remains open how it might be implemented in the clinical routine. Since the trial has begun before the approval of CDK4/6 inhibitors, endocrine-based combination therapy has not been incorporated in the study design. However, currently most patients with HR+ HER2- metastatic breast cancer receive CDK4/6-based therapy and it is unclear how CTC diagnostics may guide treatment decisions in this setting.
## 2 Concepts of Clustering Approaches  
The clustering process works to divide the sensor network into many groups. Each group elects its cluster head, which is considered as a leader to represent the cluster in the process of sending data. Cluster head election can be set by the network designer or by sensor nodes. Clustering techniques could be used to solve many constraints in wireless sensor networks by authorizing the sensor's architecture differently. It is performed by aggregating the sensors into clusters, and giving a specific task to each sensor, then transferring data to upper levels. The main goals of clustering techniques are to gain high energy efficiency, reuse bandwidth, target tracking, data collection, and guarantee a long lifespan of the network. Furthermore, clustering is considered as one of the common techniques used to face the power consumption and redundancy phenomenon caused during the service access and transmission process. Thus, optimized clustering reduces redundancy within the network and saves energy significantly [11]. Finally, clustering is the key routing technology to economize energy consumption [12]. Figure 1 illustrates the basic and common schemes used to reduce energy consumption, including the cluster-based architecture under study.
## 2 Materials and Methods  
### Measurement Scheme  
The measurements were made in a substation of the railway transportation network. The measurement scheme is shown in Figure 1. The supply was produced by means of a voltage transformer of 110 kV/27 kV. Acquisition of currents and voltages was achieved at the frequency of 5 KHz. The currents were acquired by means of a current transformer with a clamp meter that had the role of a current transformer (Figure 1). An adaptation block for high currents and voltages was used at values supported by data acquisition systems. In the same way, voltage was measured through the same adaptation block. The adaptation block also contained a numeric analogue converter, so the acquired data were in numeric format.  
Figure 1: The measurement scheme.  
The variation in the current and voltage measured in a railway substation is shown in Figure 2a,b. Figure 2a depicts these measurements when the electric locomotive was passing very close to the railway station's measuring point at the station, while Figure 2b depicts the variations in current and voltage when the locomotive was 50 m from the station. The current variation was distorted, as may be seen. Additionally, there was some distortion in the voltage variation. The aforementioned side effects, such as reactive power, harmonic currents, and low-power factor, were caused by these distortions.  
The variations in active, reactive, and distorted forces at the measurement point, i.e., at the station, were calculated using data acquired with a DAQ board. Figure 3a,b illustrate these variations. As would be expected, the reactive power was very high. This indicated that the power output was compromised as a result of these distortions. To determine the actual effect of reactive power on power quality, power modelling was performed using intelligent computational techniques, specifically fuzzy neuro ANN. To validate the models, they were compared to the measurement results. In general, models aid in the analysis of structures, assist in the better understanding of a system's behaviour, and contribute to the prediction or simulation of a system's behaviour. Additionally, the models make it easier to apply and validate advanced control techniques. Power variation modelling is critical because excessive reactive and distorted power results in poor power quality and power loss.
# On Flexural Performance of Girder-To-Girder Wet Joint for Lightweight Steel-UHPC Composite Bridge
## 3 Comparative Model Test: The First Generation T-Shaped Joint and the Traditional I-shaped Joint
### Test Results
#### 3.2.3. Shrinkage in UHPC  
The shrinkage strain of the UHPC panel was obtained by embedded gauges. The test lasted for 2112 h (88 days, from March to June), the temperature was in the range of 10.5-26.5 \({}^{\circ}\)C. Part A adopted natural curing, and the test condition was the same as the actual construction site. While Part C adopted steam curing, the curing lasted for 48 h with a temperature of 98 \({}^{\circ}\)C. After 48 h, the specimen was exposed to the environment when the temperature decrease to normal. The strains were recorded every 2 h during the steam curing period and changed to every 24 h in the first month, then every 48 h in the second month. The test results are shown in Figure 7.  
As can be seen in the figure, for the natural curing part (Figure 7a), the shrinkage increased rapidly in the first 250 h, then fluctuated until the end of the test. The strain was in the range of 87-173 \(\mu\epsilon\), and the average value was 121 \(\mu\epsilon\). While for the steam curing part (Figure 7b), the strain increased rapidly during the curing period (48 h), then showed a slight decrease; the maximum value was 164 \(\mu\epsilon\). Also, 2000 h (83.3 days) later, the shrinkage strain was in the range of 30-76 \(\mu\epsilon\). The maximum shrinkage strain of the reinforced UHPC deck was 173 \(\mu\epsilon\) under natural curing, and was 164 \(\mu\epsilon\) under steam curing. Compared to past research [1; 20; 21; 22; 23; 24; 25], it is seen that the reinforcement has a significant effect on UHPC material shrinkage.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Section Item** & **P\({}_{\text{cr}}\)/kN** & **M\({}_{\text{cr}}\)/kN\(\cdot\)m** & **\(\sigma_{\text{cr}}\)/MPa** & **Section No.** \\ \hline T-shaped joint left interface & 941 & 638 & 5.48 & Section A \\ T-shaped joint right interface & 845 & 564 & 4.42 & Section A \\ I-shaped joint left interface & 845 & 564 & 3.57 & Section B \\ I-shaped joint right interface & 788 & 526 & 3.21 & Section B \\ Mid-span of the specimen & 2190 & 1462 & 12.88 & Section C \\ UHPC matrix (part A) & 2190 & 1462 & 12.77 & Section A \\ \hline \hline \end{tabular}
\end{table}
Table 3: Durability-based nominal stress of critical sections.  
Figure 7: Shrinkage test results: (**a**) shrinkage strain of natural curing part A; (**b**) shrinkage strain of steam curing part C.
to enable the efficient handling of data and feature extraction with limited computational resources [12]. Machine learning and multivariate analyses are gaining popularity in other basic and applied neuroscience fields. The application of a convolutional neural network (CNN) to EEG spectrograms is also a notable example [13; 14]. Currently, these lines of research primarily focus on the practical use of EEG, such as the development of assistive technology for clinical diagnosis and novel educational interventions. It is also possible to simultaneously gain novel insights into neural and physiological functions and how information is represented in EEG patterns by scrutinizing and interpreting the resultant model using explainable AI tools.  
Bioelectric signals other than EEG have been utilized as valuable sources of information in various fields of research and clinical practice. At first glance, the signal pattern is simple compared to EEG, with a relatively small amount of useful information contained in peripheral signals, such as electrocardiograms, galvanic skin conductance responses, and electromyography. However, close attention should be paid to recent findings obtained by the application of machine learning to peripheral biological signals [15; 16; 17], which indicate that seemingly simple signals can sometimes contain far richer information than expected. For example, a recent study [15] showed that a participant's emotional state could be inferred based on the waveform of a single-pulse wave peak. Moreover, peripheral bioelectric signals are generated through a complex web of interactions between central nervous system activation and the functioning of peripheral effectors and could thus potentially contain rich information on the function of both the periphery and central nervous systems. Basic methods to exploit information from peripheral signals have been used for many years and are generally considered well established [18; 19; 20]. However, considering the potential richness and complexity of the information in peripheral signals, the application of machine learning has opened up the possibility of extracting further information from the patterns of peripheral signals.
## 5 Comparative Analysis Results and Discussion
### Hyperparameter Finetuning Results
#### 5.3.2 Max Pooling  
In our model, the convolutional layer is followed by a pooling layer. The primary aim of the pooling layer is to decrease the size of the convolved feature map, and therefore reduce the computational costs. It involves dividing the output layers into subsections and calculating the value that best represents the output. In addition to reducing the number of parameters, this helps the algorithm learn higher-order representations of the data [64]. Max pooling reduces the dimensionality of the input by reducing the number of parameters in the output from the previous convolutional layer. This can be seen in the input and output shapes, shown in Table 1.  
Figure 7 shows an example of max pooling in CNN, which reduces the dimensionality and also represents the highest value in each kernel window.  
The final pooling layer then is flattened. The flattening layer involves taking the pooled feature map that is generated in the pooling step and transforming it into a one-dimensional vector. An example visual representation of the flattening process is illustrated in Figure 7. The flattened layer then is connected to a fully connected neural network for the classification task.  
Figure 6: Filter size effect on training time and accuracy when fine-tuning BERT with different number of convolution and dense layers.  
Figure 7: An example of max pooling in CNN with a \(2\times 2\) kernel size and the flattening function.
## 5 Conclusions  
This article studied the simulation of a hybrid power plant that coupled coal with concentrating solar energy (solar power tower) and presented its evaluation under different conditions. Exergy and economic analyses of the hybrid power plant were conducted to examine the viability of the plant from these two points of view. The most important conclusions drawn from the study are the following:  
* Coal consumption and pollutant emissions can be reduced by 4.6% with the introduction of solar energy, implying an annual reduction of CO\({}_{2}\) emissions of 325,273 tons.
* The efficiency of the hybrid power plant can be increased by 1.6%, when compared to the same plant fueled only by coal. This is associated with the lower fuel consumption of the plant due to the use of solar energy.
* Given the higher purchased equipment cost, the net investment required to operate the hybrid power plant would be 8050.32 $/kW, while the coal-fired thermal power plant would cost 5979.69 $/kW.
* The LCOE of the proposed hybrid power plant would be 0.19 $/kWh, and the value for the same plant fueled by coal would be 0.12 $/kWh.
* Operational stability of renewable power plants, the output of which depends on weather conditions, may be improved by coupling them with conventional fossil fuel plants.  
The relatively high costs of solar technology may somewhat reduce its applicability, when compared to conventional thermal power plants. Nevertheless, hybrid power plants can decrease the supply instability of renewable plants, the fossil fuel consumption of conventional coal-fired power plants, and, consequently, the pollutants emitted during electricity generation.  
An improvement of the plant proposed in this paper could be realized through the examination of ways to minimize the CO\({}_{2}\) emissions of the process using different hybrid power plant configurations. This would most probably imply a higher contribution of renewable energy, taking into account, however, the increase of the associated investment costs. In order to broaden the reach of this study, an examination of the viability of hybrid coal/solar power plants in different regions with different solar resources than southern Spain could also be made. Moreover, a discussion on the most appropriate renewable energy source for the design of a hybrid power plant depending on location and site resources could be a possible drive of future works.  
[PERSON] and [PERSON] collected data and performed the analyses and [PERSON] conceived the study and supervised the work.  
This research was funded by the Spanish Ministry of Science, Innovation and Universities and the Universidad Carlos III de Madrid (Ramon y Cajal Programme, RYC-2016-20971).  
The authors declare no conflict of interest.