esa-sceva/satcom-chunk-collection huggingface dataset:
# Exploring Metalloprotease from _Dunaliella_ sp.: Production, Regulation, and Structural Insight
## 3 Results and Discussion
### Protein Production Optimization
Furthermore, the growth of _Limnospira platensis_ (formerly _Arthospira platensis_) (Cyanobacteria) and its biochemical compound content increased concomitantly with the NaHCO\({}_{3}\) concentration in the culture medium. The microalgae biomass grown using NaHCO\({}_{3}\) in the culture medium also contained higher amounts of protein [101]. [PERSON] et al. [67] investigated the growth of _Scenedesmus_ sp. at concentrations of sodium bicarbonate in the culture medium ranging from 4 to 20 mM. Their results demonstrated a dose-dependent increase in protein content, with microalgal cells grown in 16 mM sodium bicarbonate showing the highest protein content. These results are consistent with those of this study. [PERSON] and [PERSON] [102] proved that the concentration of CO\({}_{2}\) significantly influenced the growth and biochemical profiles of _Tetradesmus bajcalifornicus_ (formerly _Scenedesmus bajcalifornicus_) (Chlorophyta) BBKLP-07, with maximum biomass productivity and protein content observed at 15% of CO\({}_{2}\). A review by [PERSON] and [PERSON] demonstrated the significant impact of CO\({}_{2}\) supply on both biomass productivity and protein content, highlighting the relations between CO\({}_{2}\) amount and microalgal species [103]. To achieve maximum protein production, identifying and maintaining an optimal CO\({}_{2}\) concentration in the culture medium specific to each species is essential.
## 3 Results and Discussion
### Model Validation  
To verify the validity of the current work, simulation was first performed for a full-DWC surface when the drop contact angle was \(90^{\circ}\) without the generation and sweeping of drops. Only drop growth and coalescence were demonstrated, and a run was executed whenever a drop reached or exceeded \(r_{max}\). Runs were performed for different starting configurations such as drop size and location. Random and uniform distribution of drops were applied in the initial state. In some starting conditions, all drops had the same radius size, and in other conditions, drops had a random radius size. In addition, different packing was considered, where different initial numbers of drops are used. The average area fraction (\(f\)) for full DWC and the corresponding exponent \(n\) were 0.547 and 0.329, respectively. These results were close to 0.55 and 1/3 [12], with an error of less than 0.6%. This serves as a benchmark for predicting the effect of surface renewal within the DWC surface on drop size distribution.
## 3 Results
### Effect of Nano-Silica Additive under Different Ambient Pressures And Constant Pressure Drop  
Figure 9 shows the influence of ambient pressure on the rock erosion performance of the jets under a constant nozzle pressure drop, \(\Delta P\). In this group of experiments, \(\Delta P\) was set at 30 MPa, the ambient pressure increased from 5 MPa to 25 MPa, and the fluid temperature was set at 340 K. The standoff distances were three and five for the pure and nano-silica SC-CO\({}_{2}\) jet, respectively. The mass fractions of the nano-silica additive were 0 wt %, 3 wt %, 6 wt %, 12 wt %, and 18 wt %, respectively.  
As is illustrated in the figure, it is obvious that the ambient pressure significantly affects the erosion ability, even though the \(\Delta P\) value is constant. The curves increase at first and then decrease, for both the pure SC-CO\({}_{2}\) jet and nano-silica SC-CO\({}_{2}\) jet with a mass fraction of 3 wt %, which is much like that in the rock erosion experiment using the SC-CO\({}_{2}\) jet, conducted by [PERSON] et al. [(2015)]. Additionally, the curves of the other nano-silica SC-CO\({}_{2}\) jet show some deviations, which seems to be attributed to the erosion effect of the nano-silica particles in the jet flow. Moreover, when the ambient pressure is below the critical pressure of 7.38 MPa, the erosion volumes of all the jets show an increasing trend  
Figure 8: Rock erosion characteristics under various ambient pressures and a constant inlet pressure.  
with the increase of ambient pressure, which is attributed to the increasing diffusivity and reducing viscosity of the CO\({}_{2}\) fluid with increasing ambient pressure [27]. Moreover, the erosion ability of the jet with a comparatively low nano-silica mass fraction of 6 wt %, is the largest at ambient pressures of 5 MPa and 7 MPa. Then, with a further increase of the ambient pressure, the mass fraction of 9 wt % becomes the optimal condition, at an ambient pressure of 10 MPa, and the jet with a mass fraction of 12 wt % shows the best erosion performance, when compared to those with an ambient pressure of 15 MPa, 20 MPa, and 25 MPa. In addition, the further increase of ambient pressure can obviously enhance the erosion capabilities of the jets with mass fractions of 15 wt % and 18 wt %. These phenomena indicate that the optimum mass fraction shows an increasing trend with the increase of ambient pressure, under constant \(\Delta P\).
# Hidden Dangerous Object Recognition in Terahertz Images Using Deep Learning Methods
## 3 Proposed Model
### YOLOv5 and Variants  
The framework architecture of YOLOv5 is composed of three main parts: backbone, neck, and predict head. Primarily, the backbone is a convolutional neural network that aggregates and forms image features at different granularities. It extracts feature information from input pictures. On the other hand, the neck is a series of layers to mix and combine image features to pass them forward to prediction. Typically, the neck combines the gathered feature information and creates three different scales of feature maps. The prediction head consumes features from the neck and takes box and class prediction steps. This is completed by detecting objects based on the created feature maps. In fact, the YOLO model was the first object detector to connect the procedure of predicting bounding boxes with class labels in an end-to-end differentiable network.  
It is worth mentioning that YOLOv5 utilizes the CSPDarknet53 framework with an SPP layer as the backbone, the PANet as the neck, and the YOLO detection head. The best anchor frame value is calculated in YOLOv5 by adapting the clustering algorithm in different training datasets. The several activation functions tried by YOLOv5 include sigmoid, leakyReLU, and SiLU.  
The five derived models for YOLOv5 include YOLOv5n, YOLOv5s, YOLOv5m, YOLOv5l, and YOLOv5x. Although they share the same model architecture, inherent in each model is different model widths and depths. Noteworthy is the fact that smaller models are faster; hence, they are usually designed for mobile deployment, whereas larger models, although more computationally intensive, have a better performance.  
In other variants of YOLOv5, CSP-Darknet is used (for instance, in YOLOv5-P7). Such an architecture often has seven stages (or block groups) commonly referred to as [P1, P2, P3, P4, P5, P6, P7] with strides [2, 4, 8, 16, 32, 64, 128] relative to the input image, respectively. Stacks [P1, P2, P3, P4, P5, P6, P7] by design consist of multiple CSPDark blocks with Cross Stage Partial (CSP) connections (example: CSP-Darknet in YOLOv5-P7 has [1, 3, 15, 15, 7, 7, 7] CSPDark blocks).  
In the case of YOLOv5-P6, an additional large object output layer P6, following the EfficientDet example of increasing output layers for larger models is added. However, unique to this case is the fact that it is applied to all models. Note that current models have P3 (stride 8, small) to P5 (stride 32, large) outputs, but the P6 output layer is stride 64 and designed for extra-large objects. Notice that the architecture changes made to add a P6 layer here are key. The backbone is extended down to P6, and the PANet head goes down to P3 (consistent with state-of-the-art) and back up to P6 now instead of stopping at P5. However, new anchors are also added, which are evolved at img 1280. For brevity, we show in Figure 8[40] the generalized architecture for Yolov5.  
Figure 8: Architecture of Yolov5.
## 3 Inter-Satellite Laser Communication Model Based on Simulink
### Signal Scheme Design  
To increase the data-transmission rate under a fixed PRN code rate, multiple data codes are incorporated into one complete PRN code cycle during the XOR operation. This process alters the autocorrelation properties of the resulting communication ranging code compared to the original PRN code. Therefore, it is essential to analyze how different spreading factors, corresponding to the number of data codes incorporated within a single PRN code cycle, affect the autocorrelation properties. The normalized autocorrelation function values of the PRN code at various spreading factors are shown in Figure 9.  
As illustrated in Figure 9, reducing the spreading factor leads to poorer autocorrelation performance of the spread PRN code. To ensure that the signal scheme meets the inter-satellite communication rate requirements while maintaining satisfactory communication and ranging performance, a spreading factor of 16 is adopted for this study. Additionally, the higher the sampling rate, the better the inter-satellite ranging resolution. At the same time, a higher sampling rate also benefits signal retrieving, thereby aiding in the achievement of low bit error rates in communication. However, due to performance limitations in actual hardware, the maximum achievable sampling rate in current hardware development is 100 MHz. Therefore, a sampling rate of 100 MHz is selected for this case. The designed signal scheme is depicted in Figure 10.  
In this scheme, the sampling rate is set to 100 MHz, with a PRN code rate of 1 Mbps and a data code rate of 62.5 kbps. The full code rate is approximately 976.6 Hz. Using a spreading factor of 16 and a PRN code length of 1024, each full code cycle corresponds to 1024 PRN codes and 64 data codes, with each data code encompassing 16 PRN codes.  
Figure 9: Normalized autocorrelation function values of PRN codes with spreading factors of 64, 32, 16 and 8.
# Improving the Efficiency of Energy Consumption in Buildings: Simulation of Alternative EnPC Models
## 3 International Benchmark of Energy Performance Contracting Cases
### Koper-Slovenia  
The Municipality of Koper occupies an area of 303.2 km\({}^{2}\) along the coast of the Adriatic Sea, and is located in the Coastal-Karst region, which despite being one of the smallest regions in Slovenia, is classified as one of the most advanced regions concerning economic development [42].  
In order to improve energy performance, reduce energy costs, and achieve a significant reduction in the use of primary energy in 31 public buildings, the municipality of Koper launched a public tender to select an ESCO to perform the necessary interventions. Under a 15-year shared savings contract, in which a 3% reduction in the final energy used for heating was expected to be achieved, the investment was fully financed by the contractor, and it was stipulated in advance that the municipality of Koper was entitled to receive 10% of the total savings achieved. If in any period the guaranteed savings are exceeded, the municipality receives 50% of the value of the additional savings [17].  
The implementation of all the necessary interventions, good energy management, and the application of some additional technical measures has enabled the municipality of Koper to achieve an annual reduction of EUR 100.000 in energy costs [17].  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Contract Type** & **Contract Value** & **Contract Duration** & \begin{tabular}{c} **Annual Reduction of** \\ **Energy Consumption** \\ \end{tabular} & \begin{tabular}{c} **Reduction of CO2** \\ **Emissions** \\ \end{tabular} \\ \hline \begin{tabular}{c} **Guidonia** \\ **Montecelio** \\ **(Public Lighting)** \\ \end{tabular} & Shared Savings & \begin{tabular}{c} EUR 25,877.281 \\ (plus VAT) \\ \end{tabular} & 20 years & 45\% & - \\ \hline \begin{tabular}{c} **Guidonia** \\ **Montecelio** \\ **(Public Buildings)** \\ \end{tabular} & Shared Savings & \(\approx\)EUR 1250.000 & 15 years & 57\% & 410.9 t/year \\ \hline \
\begin{tabular}{c} **Koper (Public Buildings)** \\ \end{tabular} & Shared Savings & - & 15 years & 24\% (100.000 EUR/years) & - \\ \hline
\begin{tabular}{c} **Saint Gulat des** \\ **Valles (CAR)** \\ \end{tabular} & Shared Savings & EUR 3184.347 & 10 years & 39\% (296.000 EUR/years) & 1.036 t/year \\ \hline \hline \end{tabular}
\end{table}
Table 2: Main results from the study of the application of CDEs.
## 3 Results and Discussion
### Influence of Other Parameters
#### 3.5.2 Temperature of Humid Air  
The temperature of humid air affects the temperature field around the frost crystal surface, which is a major parameter affecting the frost crystal morphology. The frost crystal morphologies under different air temperatures (\(T_{\text{a}}\) = 15 \({}^{\circ}\)C, 20 \({}^{\circ}\)C, 25 \({}^{\circ}\)C) at 10 min are shown in Figure 10. With a cooler air temperature, the motion of water vapor is so less violent that there is enough time to develop the frost crystal. Therefore, the growth of the frost crystals is promoted with a dendritic growth on the crystal prism, and the frost crystals become stronger. The frost crystal morphologies show a needle-columnar-feather shape transition with a cooler air temperature. In addition, it is difficult to distinguish individual frost crystals at a higher air temperature. The growth of frost crystals is also enriched with a lower working pressure around the cooling surface.  
The frost thickness, at 60 min, with working pressure around the cooling surface under different temperatures of humid air is shown in Figure 11a. The frost thickness decreases  
Figure 10: Frost crystal morphologies with working pressure around cooling surface under different temperatures of humid air at 10 min.  
Figure 9: (**a**) Frost thickness; (**b**) frost accumulation; (**c**) frost density with working pressure around cooling surface under different partial pressures of water vapor at 60 min.  
with higher pressure and warmer air temperature. The surface temperature of the frost layer rises in direct proportion to the air temperature, which hinders the frost growth. The frost accumulation, at 60 min, with working pressure under different temperatures of humid air is shown in Figure 11b. The frost accumulation increases with higher air temperature. When the air temperature rises, the heat transfer is enhanced due to the expansile temperature difference. According to the [PERSON] heat and mass transfer analogy theory, mass transfer is positively related to the amount of heat transfer. From Figure 11b,c, the growth rate of frost accumulation and frost layer density are augmented with higher air temperature. This enhancement is reduced as the working pressure increases. Comparing the absolute differences of frost thickness, frost accumulation and density from low to high pressure (0.25 \(\times\) 10\({}^{5}\) Pa to 2.00 \(\times\) 10\({}^{5}\) Pa), they lessen from 0.292 mm to 0.272 mm, 0.134 g to 0.009 g and 108.73 kg/m\({}^{3}\) to 31.49 kg/m\({}^{3}\), respectively, when the temperature of humid air increases from 15 \({}^{\circ}\)C to 25 \({}^{\circ}\)C.
## 3 Results  
### Modeling of Daily Gross Primary Productivity  
The performance indices, including \(R^{2}\), NSE, Bias and RMSE, are used to evaluate the accuracy of the employed models in predicting the daily GPP, \(R\) and NEE in three different forest stands in the present study. Table 4 shows the estimated accuracy of the applied models in the prediction of daily GPP in the testing period at the three sites. It is clearly seen from Table 4 that the models with full environmental variables (\(T_{a}\), \(R_{n}\), \(R_{h}\), \(T_{s}\)) have the best accuracy for each method and site. Therefore, the following comparisons of the used models are mainly based on the inputs using these four variables. The developed machine learning models are consistently superior to the corresponding MLR models at the three sites. Regarding the comparisons of the machine learning models at CA-Obs site, the ANN model is superior to the other models in the testing period. However, the GRNN model generally provides the worst performance. The overall model efficiency of the used models can be ranked as follows: ANN, ANFIS, SVM, GRNN and MLR according to the \(R^{2}\), NSE, Bias and RMSE metrics. Similar to CA-Obs site, the GRNN model also gives the worst performance among the four machine learning models in the testing period at CA-Oas site, while the ANFIS model generates the best estimates. The overall ranks of the used models in the testing period can be summarized as follows: ANFIS, SVM, ANN, GRNN and MLR. Unlike CA-Oas site, the ANN model in the testing period performs the best at CA-Gro site. Additionally, the ANFIS and SVM models have the similar model efficiency and they are both slightly superior to the GRNN model. In order to examine the efficiency of each model in predicting the GPP, \(R\) and NEE in all the three stands, the overall mean accuracy of each model for all the stands in the testing period is provided in Table 5. It is apparent from the table that the MLR model in the prediction of GPP generally has the worst accuracy in the testing period, whereas the ANN, ANFIS and SVM models yield the similar performance with the higher values of \(R^{2}\) ranging from 0.922 to 0.927 and NSE from 0.901 to 0.909. Furthermore, to provide deeper insight into the responses of different forest stands to the applied models in the prediction of daily GPP, \(R\) and NEE, the mean performance of all machine learning models for each site in the testing period is summarized in Table 6. As demonstrated in the table for GPP prediction, the models yield the best precision at CA-Obs site, and give similar results at both CA-Oas and CA-Gro sites.
###### Abstract  
Delay in data transmission is one of the key performance indicators (KPIs) of a network. The planning and design value of delay in network management is of crucial importance for the optimal allocation of network resources and their performance focuses. To create optimal solutions, predictive models, which are currently most often based on machine learning (ML), are used. This paper aims to investigate the training, testing and selection of the best predictive delay model for a VoIP service in a Long Term Evolution (LTE) network using three ML techniques: Multilayer Perceptron (MLP), Support Vector Machines (SVM) and k-Nearest Neighbors (k-NN). The space of model input variables is optimized by dimensionality reduction techniques: RReliefF algorithm, Backward selection via the recursive feature elimination algorithm and the Pareto 80/20 rule. A three-segment road in the geo-space between the cities of Banja Luka (BL) and Doboj (Db) in the Republic of Srpska (RS), Bosnia and Herzegovina (BiH), covered by the cellular network (LTE) of the M:tel BL operator was chosen for the case study. The results show that the k-NN model has been selected as the best solution in all three optimization approaches. For the RReliefF optimization algorithm, the best model has six inputs and the minimum relative error (\(RE\)) \(RE\) = 0.109. For the Backward selection via the recursive feature elimination algorithm, the best model has four inputs and \(RE\) = 0.041. Finally, for the Pareto 80/20 rule, the best model has 11 inputs and \(RE\) = 0.049. The comparative analysis of the results concludes that, according to observed criteria for the selection of the final model, the best solution is an approach to optimizing the number of predictors based on the Backward selection via the recursive feature elimination algorithm.  
delay; dimensionality reduction; LTE; VoIP; Multilayer Perceptron; Support Vector Machines; k-nearest neighbors; Feature Selection; Pareto 80/20 rule 2023
## 3 Dynamic Analysis
### Result of Dynamic Analyses
#### 3.3.2 Effect of Axle Load  
Figure 10 shows the calculated variation of the vertical peak values of the acceleration and dynamic stress on the top surface of a subgrade bed with \(F\) varying from 200 to 300 kN. With an increase in \(F\), the vertical peak values of the acceleration and dynamic stress exhibit different linearly upward tendencies, which can be fitted based on the calculated data as follows:  
\[A_{rs,\max}=0.0152F\;R^{2}=1 \tag{9}\]  
\[\sigma_{rs,\max}=0.649F+0.021\;R^{2}=1 \tag{10}\]  
where \(F\) represents the axle load (unit: kN). The abovementioned linear fitting formulations indicate that \(A_{rs,max}\) and \(\sigma_{rs,max}\) increase with the increasing \(F\). Furthermore, the increment in the \(A_{max}\) is identical to that of the peak dynamic stress. For example, as \(F\) increases from 200 to 300 kN, both the acceleration and dynamic stress increase by 50%. The amplifications for higher axle loads can also be inferred from these results as following a trend similar to the material response; however, only a limited range of axle loads have been considered in this study. This linear relationship is related to the fact that the kinematic energy transmitted to the system, which increases with the mass of the axle is proportional to the vertical peak values of the acceleration and dynamic stress.  
Figure 9: Variation of vertical peak acceleration and dynamic stress at the center of the top layer surface with the train speed.
# Enhancing Sensor Data Imputation: OWA-Based Model Aggregation for Missing Values
## 4 Conclusions  
Determining an aggregation strategy with missing variables is intrinsically difficult since it is subjective to the nature of the data. In this paper, we propose a robust approach based on an OWA algorithm to estimate the measurement when the observations contain missing inputs. Different numbers of missing variables for various types of datasets are investigated and evaluated as an alternative validity metric. As shown in the results, learning with the OWA approach has significantly good performance in data aggregation where the missing data point has a different value in the observation. Therefore, this approach can be utilized as an efficient alternative method for data aggregation based on the learning of OWA operators. In general, we draw two major concluding factors in this paper. Initially, we have demonstrated the procedure for building data models in situations where there are missing inputs in the observed data. It has been argued for certain learning techniques to be applied to large databases because the cost is directly proportional to the number of variables. Then, it can also be seen that the aggregation operators are an efficient method to substitute missing sources in a real environment by learning OWA's operators. For future work, one could investigate optimization methods to identify the most effective parameters for the OWA operator when aggregating missing data. This could include analyzing multiple optimization algorithms, such as particle swarm optimization and the water cycle algorithm, to determine the settings that improve the imputation error.  
Conceptualization, D.T.A.; methodology, M.A.I. and M.A.-A.; software, M.A.-A.; validation, M.A.I. and M.A.-A.; formal analysis, M.A.-A.; investigation, M.A.-A.; resources, L.A.; data curation, M.A.I. and M.A.-A.; writing--original draft, M.A.-A. and L.A.; writing--review and editing, L.A.; supervision, D.T.A. and M.A.I. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The data can be found in [33].  
Author [PERSON] was employed by the company Akunah Company. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.