esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Results and Discussion  
### Lens Performance Verification  
Three lenses, one 10 th AF lens, one second lens, and one 14 th lens, were used as the aspheric surfaces in the design. The total comprised of 14 lenses, including two extra low dispersion (ED) lenses. The aspherical surface of the AF lens was used for spherical aberration correction in the basic design process. Additionally, to correct residual aberrations such as coma aberration, astigmatism, and distortion aberration, the object side and the upper lens side was used. Figure 9 is a collection of graphs illustrating the species difference between the infinity and the principal shooting distance. The pitch angle difference is generally stable at infinity and the principal shooting distance, and the distortion aberration is \(-5\%\), which includes some barrel distortion but still meets the design goal.  
Figures 10 and 11 illustrate the TV distortion and MTF graph of the final design. In infinity, the TV distortion has a Barrel type distortion of \(-1.40\%\) and \(+1.57\%\) at the furthest object distance, which has a pincushion type of distortion. The MTF satisfies 70% or more at 30 lp/mm with respect to the center of the screen, and more than 30% at the periphery up to the 0.85-field.  
Figure 8: Diagram of the final design.  
Figure 10: TV distortion of the final designs at the infinite and nearest distance.  
Figure 9: Spherical aberration degree of the final design at the infinity and principal shooting distance.  
As shown in Figure 11, as the image height increases, the MTF gradually decreases. This is because aberrations such as coma and astigmatism increase as the image height increases, as shown in the astigmatic field curve in Figure 9. Most camera manufacturers disclose MTFs with 10 lp/mm and 30 lp/mm spatial frequencies for camera optics [21,22]. Therefore, this paper also identified MTF based on the same criteria.  
Figure 12 shows the MTF as the spatial frequency changes. Figure 12a shows the object at infinity and Figure 12b shows the magnification of \(-\)1/40 times. The maximum spatial frequency in each graph is 135 lp/mm, which is the Nyquist frequency of the digital sensor with the largest pixel of the currently available camera body.  
Figure 11: Image heights vs. MTF plots of the final designs at the infinite and main imaging distance.  
Figure 12: Frequency vs. MTF plots of the final designs at the infinite and main imaging distance.
## 4 Results and Analysis  
### Evaluation Metrics  
To evaluate the predictive performance of our model, we utilized a range of evaluation metrics to assess the performance of the machine-learning-based models. Considering the data imbalance, six evaluation metrics were employed: accuracy (\(acc\)), area under the receiver operating characteristic curve (\(AUC\)), F1 score (\(F1\)), precision (\(PRE\)), recall (\(REC\)), and the kappa coefficient (\(kappa\)). Table 2 represents a confusion matrix for the binary target recognition problem. In the table, \(TP\) refers to the number of samples belonging  
Figure 11: The t-SNE visualization results for the 5-fold cross-validation of features after SMOTE oversampling.  
Figure 12: Stacking ensemble model construction diagram.  
to the positive class and correctly predicted as positive-class samples by a classifier. \(FN\) indicates the number of samples from the positive class that are incorrectly predicted as negative-class samples. \(FP\) represents the number of samples from the negative class that are incorrectly predicted as positive-class samples. Lastly, \(TN\) signifies the number of samples from the negative class that are correctly predicted as negative-class samples.  
The evaluation metrics can be calculated based on a confusion matrix, and the corresponding calculation formulae are as follows:  
\[acc=\frac{TP+TN}{TP+TN+FP+FN} \tag{11}\]  
\[PRE=\frac{TP}{TP+FP},REC=\frac{TP}{TP+FN} \tag{12}\]  
\[F1=\frac{2\times PRE\times REC}{PRE+REC} \tag{13}\]  
\[kappa=\frac{p_{0}-p_{e}}{1-p_{e}} \tag{14}\]  
\[p_{0}=\frac{TP+TN}{TP+TN+FP+FN} \tag{15}\]  
\[p_{e}=\frac{(TP+FP)\times(TP+FN)+(TN+FN)\times(TN+FP)}{(TP+TN+FP+FN)^{2}} \tag{16}\]  
The form of the ROC curve is also a metric that can be used to evaluate the prediction accuracy of classifiers. The ROC curve plots the true positive rate (TPR, i.e., recall) versus the false positive rate (FRP = FP/(TN + FN)). \(AUC\) is referred to as the area under the ROC curve. The \(AUC\) values vary from 0.5 to 1, indicating the discrimination accuracy, which can be divided into five categories: no discrimination (0.5-0.6), poor discrimination (0.6-0.7), fair discrimination (0.7-0.8), good discrimination (0.8-0.9), and excellent discrimination (0.9-1). The \(AUC\) calculation formula is as follows:  
\[AUC=\int_{0}^{1}TPR(FPR^{-1}(t))dt \tag{17}\]  
where \(FPR^{-1}(t)\) represents the inverse function of the FPR, and \(t\) varies within the range of 0 to 1.
# 10 Gb/s Bidirectional Transmission with an Optimized SOA and a SOA-EAM Based ONU
###### Abstract  
We investigated the application of a semiconductor optical amplifier (SOA) and an SOA electro-absorption modulator (SOA-EAM) as attractive, low-cost solutions in passive optical networks (PONs). The main characteristics of an SOA with optimal performance for phase and amplitude modulation were tested. Additionally, a 10 Gb/s bidirectional transmission with an optical network unit (ONU) transmitter integrated with a distributed feedback (DFB) laser, electro-absorption modulator (EAM), and SOA was designed. The upstream (US) and downstream (DS) receiver sensitivities at the forward error correction (FEC) level reached \(-29.5\) dBm and \(-33.5\) dBm for back-to-back (BtB) fiber and \(-28.9\) dBm and \(-33.1\) dBm for 20 km fiber. For multichannel transmission, the US receiver sensitivities reached \(-28.8\) dBm and \(-28.2\) dBm for BtB and 20 km fibers, and the DS receiver sensitivities reached \(-33\) dBm and \(-32.6\) dBm for BtB and 20 km fibers, respectively.  
semiconductor optical amplifier (SOA); semiconductor optical amplifier electro-absorption modulator (SOA-EAM); phase modulation (PM); amplitude modulation (AM); optical network unit (ONU)
# Efficient Parallel Processing of Big Data on Supercomputers for Industrial IoT Environments
## 4 Experimental Evaluation
### Experimental Results
#### 4.2.2 Varying the Number of Processing Nodes and the Size of the Datasets  
The second metric we considered in our evaluation was the scale-up, sometimes also called scaled speedup. This metric represents how large a problem can be solved in the same amount of time by a larger number of parallel processes. Hence, in this case, we considered the same three reference datasets, but as we increased the number of machines that execute the algorithm in parallel, we also increased the size of the datasets. Specifically, we considered a half-sized version of the reference datasets when using one node, the reference datasets when using two nodes, and a double-sized version of the reference datasets when using eight nodes. Figure 5 shows the results. We observe that the best behavior, i.e., the most linear, is reported for the datasets with the least number of rows. In line with the results of the previous experiment, the reason can be attributed to the less demanding load of the system when dealing with smaller records.
# Dynamic Energy Management Strategy of a Solar-and-Energy Storage-Integrated Smart Charging Station
## 5 Simulation Inputs and Results
### Simulation Inputs
#### 5.1.3 Others  
Regarding the input tariff for the analysis of EVCS operational benefits, the existing costs and pricing structures in Taiwan are referenced. A cost of 3.1 TWD per kWh is considered for solar energy, while a feed-in tariff of 3.9 TWD per kWh is utilized. The ESS incurs a fixed and operational cost of 55,000 TWD per month. The EVCS generates revenue of 9 TWD per kWh of electricity sold. Construction costs are temporarily excluded from consideration. The grid tariff (electricity cost of the EVCS) is in line with the rates outlined in Table 1, \"Taipower Electricity Tariff of EV Charging and Swapping Facility\".  
In order to verify the effectiveness of implementing EMS in reducing the contracted capacity of the EVCS, this study examines and analyzes the operational scenarios for four different contracted capacities: 499 kW, 400 kW, 300 kW, and 250 kW. A comparative analysis is conducted. The scenario without EMS, PV, and ESS is also simulated and compared simultaneously.
## 3 A Prototype System for Pedestrian Positioning
### 3D Matching Algorithm
This is an arithmetical average of depths divided by the \(\cos\alpha_{j}\) factor to obtain the Euclidean distance--\(cf\)., Equations (26) and (27) that show the difference between the depth of a point and distance to the camera's \(Y\) axis. \[d_{Sj}(\alpha_{j})=\frac{1}{\cos\alpha_{j}}\frac{1}{W_{W}^{2}}\sum_{x_{ \mathrm{pix}}}\sum_{y_{\mathrm{pix}}}\mathbf{L_{S}}(x_{\mathrm{pix}},y_{ \mathrm{pix}});\ (x_{\mathrm{pix}},y_{\mathrm{pix}})\in\mathbf{W}_{j} \tag{44}\]  
**Ad. 3e. ** Given the coordinates and orientation of a virtual camera in the 3D environment, the \(d_{Vj}\) distance from the virtual camera to the nearest object at \(\alpha_{j}\) angle can be easily obtained. **Ad. 3f. ** The corresponding distances \(d_{Sj}\) and \(d_{Vj}\) are compared by the error function \(f_{Lj}\). For the integrity sake of Equation (47), the function \(f_{Lj}\) should return \(1\) if window \(\mathbf{W}_{j}\) was rejected from comparison, _i.e._, \(k_{j}\leq k_{\mathrm{thr}}\). The function \(f_{Lj}\) reflects the correspondence between \(d_{Sj}\) and \(d_{Vj}\), whereby the comparison should be more tolerant for greater distances, according to formula (32)--the accuracy of determining \(d_{Sj}\) drops with its square value. Following the reasoning in the previous chapter, the function \(f_{Lj}\), expressed by Equation (45), was proposed. The parameter \(a\) introduces some tolerance in case of unexpected objects registered by the stereo camera (e.g., cars, humans), _c.f._ Figure 14. Experimentally, \(a\in<0.95,0.98>\) gives desired results. \[f_{Lj}(k_{j},d_{Sj},d_{Vj}) = \begin{cases}1&k_{j}\leq k_{\mathrm{thr}}\\ a+(1-a)\exp\left(-\frac{(Bf\Delta z)^{2}}{2\sigma_{L}^{2}(d_{Lj}+\Delta z)^{2} }\right)&k_{j}>k_{\mathrm{thr}}\end{cases}  \tag{45}\] \[\Delta z =d_{Sj}-d_{Lj} \tag{46}\]  
**Ad. 4. ** Finally, the function \(f_{L}\) comparing the two environments, _i.e._, real and virtual, can be defined by Equation (47). \[f_{L}(\mathbf{L_{S}},\mathbf{L_{V}})=\prod_{j=1}^{n_{S}}f_{Lj}(k_{j},d_{Sj},d_ {Vj}) \tag{47}\]  
The 3D match algorithm is evoked 10 times a second.
# Green Extraction of Valuable Compounds from Rubber Seed Trees: A Path to Sustainability
## 6 Valuable Compounds in Rubber Seeds
### Glyceride Components of Rubber Seed  
The glyceride components of rubber seeds refer to the various forms of glycerol esters found in the seed oil. Glycerides are the main constituents of plant oils and fats, and they play a crucial role in determining the properties and applications of the oil [62]. In the context of rubber seed oil, glyceride components include different triglycerides which are formed by the esterification of glycerol with fatty acids. Rubber seed oil typically contains a diverse array of glycerides, each composed of glycerol and three fatty acid chains [63]. The specific composition of glycerides in rubber seed oil can vary based on factors such as the plant's genetics, environmental conditions, and the extraction method used [64]. Triglycerides are the predominant glyceride form, and the types of fatty acids linked to glycerol contribute to the oil's characteristics, such as its viscosity, stability, and potential applications [65].  
The potential of rubber seed oil (RSO) as a feedstock for biodiesel production in Bangladesh was explored by [PERSON], [PERSON], [PERSON], [PERSON], [PERSON] and [PERSON] [62]. Various extraction methods, including mechanical press with and without solvent and cold percolation, were employed to extract RSO from locally collected rubber seeds. The highest oil content, reaching 49%, was achieved using a mechanical press with a periodic addition of solvent. The physico-chemical properties of the oil were extensively investigated. The impact of seed storage time on the free fatty acid (FFA) content revealed an increase from 2 wt.% in fresh seeds to 45 wt.% after 2 months of storage at room temperature. The biodiesel production process involved a three-step method, encompassing saponification of the oil, acidification of the soap, and esterification of FFA. The overall yield of FFA from RSO was determined to be approximately 86%. The final esterification step yielded fatty acid methyl ester (FAME), confirmed by the analysis of 1H NMR spectra of both RSO and biodiesel samples.
###### Abstract  
Active optics is a key technology in ground-based large-aperture telescopes. The active correction of the surface shape of the primary mirror is used to reduce the surface shape error and improve the imaging quality. At present, the structure of the active optics support system is not standardized. Therefore, to ensure the imaging quality of a telescope using a 4m SiC (silicon carbide) primary mirror, this article designed an active support system for the primary mirror and comprehensively evaluated the performance of the system. The system used pneumatic actuators to correct the surface shape of the primary mirror and a six-hardpoint positioning mechanism to correct the pose of the primary mirror. A method for compensating for the force on the hardpoints that causes protrusions and dents on the primary mirror surface was proposed, which effectively improved the accuracy of the primary mirror surface. The bending-mode method was used to determine the correction force. To achieve better results in the surface shape correction based on the bending mode, the relationship between the order of the bending modes used in the correction and the correction effect was studied, enabling the system to achieve a higher surface shape accuracy with a smaller correction force. Finally, the performance of the system was evaluated under various conditions, such as under gravity, thermal load, and wind load. The results indicated that the system had good correction effects on the deformation of the primary mirror under various operating conditions and could meet the requirements of optical design for surface accuracy. In conclusion, this study not only verified the application of active optics technology based on the bending mode in large-aperture SiC mirrors, but also improved on the relevant theoretical research on active optics.  
active optics; primary mirror support; six-hardpoint positioning mechanism; force on hardpoint; bending mode
# Performance Evaluation for China's Planned CO\({}_{2}\)-Ipda
## 2 Materials and Methods
### CO\({}_{2}\)-IDPA Configuration  
Table 3 lists the parameters used in the simulation in this study. The load-related parameters are consistent with those of China's CO\({}_{2}\)-IPDA, which is under development at the Shanghai Institute of Optics and Fine Mechanics, the Chinese Academy of Sciences. The default values of reflectance and AOD are also presented here. In the experiments described below, we analyzed the model's sensitivity to different parameters separately. Each subsection below describes the variation of one target parameter. Settings for other parameters are as given in Table 3. In the experiment described in Section 3.4, the default values of reflectance and AOD were replaced by measurements to provide a more realistic simulation. Moreover, the horizontal resolutions of the retrievals over land and over ocean are different, as shown in Table 3.  
\begin{table}
\begin{tabular}{c l c c} \hline \hline
**Category** & \multicolumn{1}{c}{**Parameter Name**} & **Value** & **Unit** \\ \hline \multirow{8}{*}{Laser Transmitter} & Pulse length & 15 & ns \\  & On-line wavelength & 6361.225 & cm\({}^{-1}\) \\  & Off-line wavelength & 6360.981 & cm\({}^{-1}\) \\  & Fluctuation of pulse energy & 1 & \% \\  & Fluctuation of ratio of on-line and off-line pulse Energy & 0.1 & \% \\  & Linewidth & 50 & MHz \\  & Stability of on-line wavelength & 0.6 & MHz \\  & Spectral purity & 99.9 & \% \\  & Energy per pulse & 75 & mJ \\  & Repetition frequency(a pair of on-line and off-line) & 20 & Hz \\  & Divergence angle & 100 & urad \\ \hline \multirow{4}{*}{Telescope} & Time interval of contiguous pair & 0.2 & ms \\  & Diameter & 1 & m \\  & Overall optical efficiency & 51.8 & \% \\  & Optical filter bandwidth & 0.45 & nm \\ \hline \multirow{4}{*}{Detector} & Field of view & 0.2 & mrad \\  & Electronic bandwidth & 3 & MHz \\  & Dark current(noise equivalent power) & 64 & fW/\(\sqrt{\mathrm{Hz}}\) \\  & Quantum efficiency & 73 & \% \\  & Internal gain & 9 & mV/W \\  & Excess noise factor & 3.2 & - \\ \hline \multirow{4}{*}{Other} & Orbit altitude & 705 & km \\  & Orbit type & 1 h/13 h sun-synchronous & - \\  & Viewing geometry & Nadir & - \\ \cline{1-1}  & Reflectance over lands & 30 & \% \\ \cline{1-1}  & Reflectance over seas & 5 & \% \\ \cline{1-1}  & AOD & 0.3 & - \\ \cline{1-1}  & RESOLUTION & 50 km (land)/100 km (ocean) & - \\ \hline \hline \end{tabular}
\end{table}
Table 3: Parameter configuration for China’s CO\({}_{2}\)-IPDA.
# Enhancements and Challenges in CoAP--A Survey
## 4 Enhancements in CoAP
### Congestion Control Mechanisms in CoAP  
This section covers congestion control mechanisms presented in default CoAP and a numerous other advance congestion control mechanism for CoAP based on RTT measurements, gradients of RTT measurements, bottleneck bandwidth of network link, traffic rates or packet loss ratio.
## 5 Water Treatment Employing Bioadsorbents
### Bioadsorbents Based on Grains and Seeds
#### 5.1.2 Industrial Contaminants  
Although a wide variety of industrial contaminants exist in water sources, the main substances that have been removed by employing adsorbents derived from grains and seeds are dyes, bisphenol A, and industrial proteins such as BSA and \(\alpha\)-Lac (Table 3). [PERSON] et al. (2014) used peanut shell AC to adsorb the azo dye metanil yellow in a spontaneous and endothermic process at 35 \({}^{\circ}\)C by adding biochar at an adsorbent/contaminant ratio of 20:1 at pH 2. The chemical association of the dye molecules with the nitrogen and sulfur groups present in the adsorbent material contributed to the highest removal efficiency (98.55%). The Langmuir, Freundlich, and pseudo-second kinetics models were chosen to represent the adsorption dynamics. In the same year, [PERSON] et al. (2017) reported interaction between moringa residues (amino, carbonyl, and amide groups) and reactive red 120 dye, reaching the maximum adsorption equilibrium of 413.32 mg/g (Langmuir isotherm) in 30 min at 50 \({}^{\circ}\)C and pH 1. Thermodynamic analysis revealed that the adsorption process was spontaneous. Other organic dyes such as MB and methyl orange have been retained with high adsorption capacity (q\({}_{\text{e}}\) = 678 mg/g and 612 mg/g, respectively) in biochar from waste coffee grounds activated by CO\({}_{2}\)([PERSON], 2010) or pyrolysis to remove acid orange 7 (q\({}_{\text{m}}\) = 665.9 mg/g) ([PERSON], 2010). Interestingly, this last material can be recycled and used for at least seven cycles at a temperature of 20 \({}^{\circ}\)C. [PERSON] and [PERSON] (2011) modeled acid blue 15 and MB adsorption behavior in a sunflower seed hull-based bioadsorbent, obtaining the best result by implementing the Langmuir model (q\({}_{\text{m}}\) = 430.3 mg/g and 473.4 mg/g respectively), and suggested that the adsorbed layer on the AC is only one molecule thick. It is worth mentioning the advantage of the capacity to simultaneously retain anionic (acid blue 15) and cationic (MB) dyes at 30 \({}^{\circ}\)C.  
The high toxicity of the chemical substances used in the synthesis of plastics and resins (e.g., bisphenol A) has drawn the attention of researchers seeking to eliminate them from water matrices; one alternative is an adsorbent based on coffee waste. Although no removal efficiencies have been reported, sulfonated coffee waste showed a biosorption capacity of 271 mg/g for bisphenol A at room temperature ([PERSON], 2010). [PERSON] et al. (2014) studied the capture of BSA and \(\alpha\)-Lac by employing AC based on siriguela seeds and cacao shells. Although the adsorption of both whey proteins was evaluated under the same conditions (200 mg/L protein solution, 5 mg AC, room temperature, pH 7) the retention capacity remained constant over different times (16 h and 3 h, respectively), presenting removal efficiencies that varied between 70.3-96.6% for \(\alpha\)-Lac and 21.2-92.2% for BSA. The [PERSON], Freundlich, Toth, and Langmuir models most efficiently represented the adsorption data for coffee waste, siriguela seeds, and cacao shells.
# Machine Failure Prediction Using Survival Analysis
## 2 Related Work
All models were tested on five datasets, and their accuracy and AUC-ROC scores were measured. The authors concluded that the best performing machine learning method was random forest, with an average accuracy of 0.964 and an average AUC_ROC score of 0.948 across all datasets. The other notable methods were the decision tree model, with an average accuracy of 0.959 and AUC_ROC score of 0.944, and KNC, with an average accuracy and AUC_ROC score of 0.942 and 0.930, respectively. In the proposal of [12], the authors suggest a new approach for predicting the remaining service life of water mains by combining machine learning and survival statistics. The authors developed a machine learning algorithm that uses a combination of historical failure data and pipe-specific characteristics to predict the probability of failure at any given time. They then applied survival statistics to estimate the remaining service life of the water main based on the predicted failure probability. The study utilized two distinct machine learning models--specifically, a random forest model and a random survival forest model--and additionally incorporated the Weibull proportional hazard survival model to assess and compare their respective abilities, in order to accurately predict the remaining useful life of water mains. The results showed that the RSF model achieved superior performance (C-index = 0.880) compared to the Weibull proportional hazard survival model (C-index = 0.734) and the random forest machine learning model (C-index = 0.807), indicating the potential of machine learning in predicting the remaining service life of water mains. The literature reviewed in this study indicates that many of the methods for predicting remaining useful life either solely employ survival analysis [16; 18; 23] or only use machine learning techniques [10; 11; 20]. However, combining both approaches can be beneficial, as demonstrated by the papers [12; 19], which use a combination of survival analysis and machine learning to make their predictions. Furthermore, most of the papers using survival analysis rely on the use of the COX model for their feature evaluation [18; 19; 23; 24]. By relying solely on the COX model for feature selection, important non-linear or time-dependent relationships between predictor variables and survival time may be missed or obscured, leading to a potentially incomplete or inaccurate understanding of the underlying data. Similarly, in the case of [12], only using feature ranking from a RSF model may not provide information about the direction or magnitude of the relationship between predictor variables and survival time. That is where the combination of different feature ranking/selection techniques can prove to be an advantage in our model. By not only using the standard COX and RSF feature ranking/selection methods, we can achieve a more comprehensive and accurate understanding of the data, as well as increased confidence and validation of the results, while mitigating some of the limitations and biases of each individual method.