esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Application
#### 3.2.3 SSIs  
[PERSON] et al. (2020) [33] applied SSIs, including SIa, SIv, and SId defined in Equation (12), Equation (13) and Equation (14), respectively, to estimate damage to buildings caused by the potential earthquake in central Taiwan and revealed that these indexes are highly useful for evaluating damage to buildings. Accordingly, we simulated strong near-field motions in the broadband range using the method proposed by [PERSON] et al. (2015) [34] and calculated the SIs distributed around Tainan to offer a reference for disaster management. Because no building taller than 22 floors was damaged by the Meinong event in Tainan, we did not calculate Sid values. As previously stated, SIa and SIv values were used to estimate damage to buildings with 1-7 and 7-22 floors, respectively. The simulated SIa and SIv distributions are presented in Figures 8 and 9, respectively.  
Figure 8: SIv distribution calculated using the inverted source model. The dashed line indicates areas with an SIv value of 30 cm/s; damaged buildings taller than 7 floors were located within the contour line.  
High SIa values of >900 gal and high SIV values of >45 cm/s were observed northwest of the hypocenter, revealing that source effects and rupture directivity markedly influenced the SI values and extent of damages to buildings. As indicated in Figure 9, most of the damaged buildings (represented by white squares) were in the region with SIa values higher than 700 gal and distributed along the northwest of the hypocenter. Evidently, rupture directivity can severely damage buildings. Related studies ([1,3,9]) have demonstrated that directivity effects are among the main factors causing damage to buildings. All damaged buildings were located within the contour line of 400 gal, which is consistent to the findings of [PERSON] and [PERSON] (2012) [35]. From an investigation of the Jiasian earthquake (M\({}_{\text{L}}\) = 6.4), [PERSON] and [PERSON] (2012) [35] revealed that damaged buildings were located in areas with SIa values higher than 400 gal and SIV values higher than 30 cm/s. Figure 8 reveals that three damaged buildings were taller than seven floors, and all were located within the area enclosed by the contour line of 30 cm/s. Earthquake-induced resonance effects may be an important factor contributing to severe infrastructural damage.
# Secure Proxy Re-Encryption Protocol for FANETs Resistant to Chosen-Ciphertext Attacks
## 4 Construction of UPF Model
### Accident Occurrence
#### 4.3.2. Rpt_CC(\(O_{p}\)) Construction  
* Input: \(O_{D}\).
* Output: \(f_{k_{D}}(R_{D}),f_{k_{F}}(R_{F})\).  
According to the order \(O_{D}\), the drone takes some actions. Then, it encrypts and transmits the detailed video file report \(R_{D}\) to the CC in real time, and [PERSON] also reports the processing status \(R_{F}\) to the CC. All communications from this step are encrypted.
## 6 Conclusions  
In this paper, experimental and theoretical analyses of the mode-I fracture toughness and tensile strength of sandstone exposed to different temperatures were carried out. The main conclusions drawn are as follows:  
(1) The mode-I fracture toughness and tensile strength of rock decrease slowly with temperature from 20 \({}^{\circ}\)C to 500 \({}^{\circ}\)C. They reduce much more drastically between 500 \({}^{\circ}\)C and 600 \({}^{\circ}\)C. The change trends of mode-I fracture toughness and tensile strength with temperature are basically consistent with each other. This is because high temperatures damage the interior of the rock and gradually create pores and cracks. When rock is subjected to temperatures above 500 \({}^{\circ}\)C, the cracking of the rock is more serious, and a fragmentation structure forms. These effects cause the two parameters to change significantly as the temperature increases.  
(2) Both theoretically and experimentally, the mode-I fracture toughness of rock is positively correlated with tensile strength; that is, the fracture toughness increases with the increase of tensile strength. At some temperatures, the relationship between mode-I fracture toughness and tensile strength is well suited for description with a proportional function. At other temperatures, an exponential function provides a more accurate description. The relational expressions differ at different temperatures. Therefore, temperature has a significant effect on the relationship between mode-I fracture toughness and tensile strength.  
(3) The relationship between mode-I fracture toughness and tensile strength is affected by the crack propagation radius (\(r_{IC}\)). \(r_{IC}\) is related to the type of rock and is affected by temperature. For the same rock, the \(r_{IC}\) is different after heat treatment at different temperatures.  
(4) Both the SCB and BD samples failed along a main central tensile crack. The features of the failure were the same; that is, they split into two halves. SEM observation of the fractures further shows that, at the same temperature, the fracture morphology of SCB and Brazilian splitting samples after failure is similar. For example, river-like patterns were observed on both BD and SCB samples that had been heated to 300 \({}^{\circ}\)C, and a layered tear-like pattern was observed in samples heated to 600 \({}^{\circ}\)C. This mesoscopic morphological similarity between samples subjected to the two tests confirms they have the same failure properties.  
[PERSON] performed the experiments, data analyses, and manuscript writing; [PERSON] designed the experiments and helped to analyze the results; [PERSON], [PERSON] and [PERSON] analyzed the results and revised this manuscript.  
This study was supported by the National Key Basic Research and Development Program of China (973 Program) (grant no. 2018 YFC0808401) and the National Natural Science Foundation of China (grant no. 51574173).
## 2 System Model
### Model Constraints
#### 2.2.3 Load Constraints  
The paper considers three types of typical loads: (1) critical loads; (2) switchable loads; and (3) controllable loads. Critical loads are associated with various critical tasks whose electricity demands must be met. Switchable loads are non-critical loads whose power can be adjusted based on the actual situation, such as ventilation equipment, air conditioning, etc. The proportion of switchable loads must satisfy the following constraint:  
\[\beta_{cur}^{\text{min}}\leq\beta_{cur}(k)\leq\beta_{cur}^{\text{max}} \tag{8}\]  
Compared to critical loads, controllable loads have more flexible energy requirements and only need to meet their power supply needs within a predefined time range. The energy management system can schedule their power consumption during various time periods. Examples of controllable loads include production tasks in industrial microgrids, various devices awaiting charging, and electric vehicles awaiting charging. The paper considers various controllable loads, and for each controllable load, the following constraints must be satisfied.  
The device must operate within the time range from \(T_{\text{start},i}\) to \(T_{\text{finish},i}\), where \(T_{\text{start},i}\) and \(T_{\text{finish},i}\) represent the earliest start time and the latest stop time for the \(i\)-th controllable load. Additionally, it is necessary to meet the total operating time requirement \(T_{sch,i}\) and the total energy demand \(E_{sch,i}\) for each controllable load:  
\[\sum_{k=T_{\text{start},i}}^{T_{\text{finish},i}}\delta_{sch,i}(k)=T_{sch,i} \tag{9}\]\[\sum_{k=T_{\text{start},i}}^{T_{\text{finish},i}}P_{sch,i}(k)\Delta t=E_{sch,i} \tag{10}\]  
For each operating time period, the device's operating power must satisfy the power upper and lower bounds:  
\[\delta_{sch,i}(k)p_{sch,i}^{\text{min}}\leq P_{sch,i}(k)\leq\delta_{sch,i}(k)p_{sch,i}^{\text{max}} \tag{11}\]  
Furthermore, to prevent frequent start-stop cycles that may damage the device, once the device is turned on, it cannot be stopped during its operation. This constraint is satisfied by:  
\[\sum_{\tau=k}^{k+T_{sch,i}-1}\delta_{sch,i}(\tau)\geq T_{sch,i}(\delta_{sch,i}(k )-\delta_{sch,i}(k-1))\quad k=T_{start,i},\cdots,T_{finish,i}-T_{sch,i}+1 \tag{12}\]
# Satellite and Ground Based Thermal Observation of the 2014 Effusive Eruption at Stromboli Volcano
###### Abstract  
As specifically designed platforms are still unavailable at this point in time, lava flows are usually monitored remotely with the use of meteorological satellites. Generally, meteorological satellites have a low spatial resolution, which leads to uncertain results. This paper presents the first long term satellite monitoring of active lava flows on Stromboli volcano (August-November 2014) at high spatial resolution (160 m) and relatively high temporal resolution (\(\sim\)3 days). These data were retrieved by the small satellite Technology Experiment Carrier-1 (TET-1), which was developed and built by the German Aerospace Center (DLR). The satellite instrument is dedicated to high temperature event monitoring. The satellite observations were accompanied by field observations conducted by thermal cameras. These provided short time lava flow dynamics and validation for satellite data. TET-1 retrieved 27 datasets over Stromboli during its effusive activity. Using the radiant density approach, TET-1 data were used to calibrate the MODVOLC data and estimate the time averaged lava discharge rate. With a mean output rate of 0.87 m\({}^{3}\)/s during the three-month-long eruption, we estimate the total erupted volume to be 7.4 \(\times\) 10\({}^{6}\) m\({}^{3}\).  
volcanic thermal anomalies; lava flow; time averaged lava discharge rate; Stromboli; small satellites; TET-1 2015 7
# EEG-Based Methods for Diagnosing Color Vision Deficiency: A Comprehensive Review
## 5 EEG-Based Approaches for Diagnosing CVD
### ERP Approaches for Diagnosing CVD
However, the study did not attempt to apply this finding for diagnostic purposes, suggesting that additional improvements may be necessary to effectively diagnose CVD. Furthermore, some studies have utilized Ishihara plates as stimulation procedures instead of the color stimuli employed in the studies discussed previously. For example, [PERSON] et al. [81] used ERPs extracted from EEG signals acquired during Ishihara visual stimulus. They analyzed the signals using various methods, including Global Field Power (GFP), ERP Component Analysis, and Brain Topography Analysis. They found that the amplitude of the wave associated with the number that the individual cannot detect did not appear clearly, as if no visual process had occurred. Similar to some previously discussed studies, their participants were within a limited age range. Similarly, [PERSON] et al. [82] used 38 plates of the Ishihara test to evaluate the EEG and determine which Ishihara plates yielded the best results in detecting CVD. They analyzed the signals using false discovery rate (FDR) correction and classified them using the K-nearest neighbor (KNN) classifier. They found that the best electrodes showing differences between CVD and healthy individuals were observed in P4, T6, and O2 in certain frequency bands. It is worth noting that only men participated in this study; therefore, the results might not be representative of both genders, potentially introducing biases. The similarity between the studies using Ishihara plates remains in the fact that both have found significant differences in ERP components in the occipital and parietal regions between subjects with normal vision and those with CVD.
## 6 Major Challenges in Machine Learning and Research Gaps
### Model Selection  
The machine learning models can be very diverse based on the type of problem. It's not like one model fits all. It has been observed that for the same data, different machine learning algorithms yield different accuracy. Moreover, as the complexity of the model increases, there is an exponential increase in training time and hardware resources. Therefore, for more robust machine learning models, the model accuracy, training time, and resources required should be considered.  
In order to find the best model for the problem, prior to model building, data preprocessing and data visualization should be performed in order to see the distribution and other statistical measures. Furthermore, dimensionality reduction and outlier removal should be performed for the most generalized model. Additionally, methods based on information criteria, such as model selection techniques can also be used to compare and choose the best model among a set of candidate models. These criteria balance the goodness of fit of the model with the complexity of the model, penalizing overly complex models that may overfit the data. Some of the widely used information criteria methods are:  
1. Akaike Information Criterion (AIC): AIC is a model selection criterion introduced by [PERSON] [64]. It estimates the relative quality of a set of models and is given by: \[\text{AIC}=2k-2\ln(L)\] (27) where \(k\) is the number of parameters in the model, and \(L\) is the maximized value of the likelihood function for the model.
2. Bayesian Information Criterion (BIC): Also known as Schwarz Information Criterion (SIC), BIC is a model selection criterion introduced by [PERSON] [65]. It is similar to AIC but has a stronger penalty for model complexity: \[\text{BIC}=-2\ln(L)+k\ln(n)\] (28) where \(n\) is the number of samples, \(k\) is the number of parameters in the model, and \(L\) is the maximized value of the likelihood function for the model.
3. Minimum Description Length (MDL): MDL is an information-theoretic model selection criterion based on the idea of data compression [66]. It aims to find the model that can represent the data with the shortest description length (the sum of model complexity and data encoding length). MDL works well for dataset model selection. It is better than the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC) since it does not require assumptions about data distribution or model prior probability.
## 4 Conclusions  
Single particle observation of a QD was performed on SLBs that were formed on GO flakes on a SiO\({}_{2}\)/Si substrate. A single bilayer membrane or double bilayer membranes existed on the GO flakes. SPT measurement of the QD was achieved under the effect of GO quenching, and the fluorescence intensity of the single QD was obtained during the lateral diffusion in the regions with and without GO to evaluate the quenching efficiency. The distance between the QD and GO that was estimated from the quenching efficiency distributed \(\sim\)5 nm and \(\sim\)15 nm, reflecting the layer numbers of the lipid bilayers on the GO  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Number** & \(\mathbf{I_{\text{GO}}}\) **(a.u.)** & \(\mathbf{I_{\text{SiO2}}}\) **(a.u.)** & \(\mathbf{E}\) & \(\mathbf{d}\) **(nm)** \\ \hline
1a & 14.4 & 20.2 & 0.29 & 9.4 \\
2b & 14.5 & 17.8 & 0.19 & 10.9 \\
3b & 22.5 & 29.4 & 0.23 & 10.1 \\
4b & 15.1 & 17.5 & 0.13 & 11.9 \\
5b & 20.2 & 20.9 & 0.03 & 17.4 \\
6b & 13.3 & 12.9 & \(-\)0.03 & - \\ \hline \hline \end{tabular} a
[FOOTNOTE:a]Footnote a: footnotetext: Figure 3c.
[ENDFOOTNOTE]  
b  
Figure 4: (**a**) Relationship between the quenching efficiency (\(E\)) and the distance from GO (\(d\)) based on Equation (1). The values of \(E\) of QDs #1–#5 (Table 1) are indicated with dotted lines. (**b**) Schematics representing \(d\) of a QD on a single lipid bilayer (left) and on double lipid bilayers (right).  
flakes. The results of this study demonstrated the evaluation of the vertical positions of a single molecule in lipid bilayers via SPT by applying the fluorescence quenching of GO.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/app12083733/s1](https://www.mdpi.com/article/10.3390/app12083733/s1)]([https://www.mdpi.com/article/10.3390/app12083733/s1](https://www.mdpi.com/article/10.3390/app12083733/s1)), Figure S1: Time course of the fluorescence intensity of quantum dots; Video S1: Single particle tracking movie.  
Conceptualization, Y.O., S.I. and R.T.; methodology, Y.O. and R.T.; validation, Y.O., S.I. and R.T.; investigation, Y.O.; formal analysis, Y.O.; writing--original draft preparation, Y.O.; writing--review and editing, S.I. and R.T.; visualization, Y.O.; supervision, S.I. and R.T.; project administration, R.T.; funding acquisition, R.T. All authors have read and agreed to the published version of the manuscript.  
This research was funded by JSPS KAKENHI Grant Nos. JP20H02690 and JP20K21125, and the Nagai Foundation for Science and Technology, Japan.  
Not applicable.  
We acknowledge support from the Cooperation Research Project of the Research Institute of Electrical Communication (RIEC), Tohoku University, and the Electronics-Inspired Interdisciplinary Research Institute (EIIRIS) Project of Toyohashi University of Technology.  
The authors declare no conflict of interest.
## 3 Data Collection to Cloud: Automation and Real-Time Processing
### Data Collection to Cloud: IoT Architecture for Smart Irrigation
#### 3.1.2 Data Types and Formats  
When collecting and utilizing data for automated irrigation systems, factors such as volume, frequency, format, and source are relevant [27]. Figure 3 illustrates the primary data types utilized in automated irrigation systems, along with their respective sources and collection methods. This visualization emphasizes the diverse range of information integrated to optimize irrigation management, while clarifying the relationships between data types, their origins, and acquisition techniques. The volume of data generated can be substantial, necessitating efficient data storage, processing, and transmission technologies [63]. Data collection frequency impacts the temporal resolution and the ability to detect rapid changes in plant water status or environmental conditions. [PERSON] and [PERSON] [64] demonstrated that collecting full pulse resolution data from water meters provided more accurate estimates of event occurrence and features compared to aggregated temporal resolutions. Data format determines the compatibility and interoperability with analysis tools and platforms [65]. Standardized data formats, such as JSON, XML, or CSV, facilitate data exchange and integration between different components of the automated irrigation system [65]. Data sources can impact reliability, accuracy, and spatial coverage. In-field sensors provide localized measurements, while remote sensing platforms offer data at larger spatial scales [31]. Combining data from multiple sources allows for a more comprehensive understanding of crop water requirements and optimizes irrigation management. Data quality, accuracy, and reliability are paramount in irrigation management, as they directly impact decision-making and water use efficiency [66]. Researchers have investigated data compression, aggregation, and filtering techniques to reduce bandwidth requirements and improve transmission efficiency [67]. Data standardization and harmonization are also crucial for facilitating integration and interoperability between system components. Metadata provide context and enable better data interpretation and decision-making [68]. By incorporating metadata into the data management and analysis pipeline, decision-makers can make more informed and context-aware decisions, leading to improved water use efficiency and crop productivity [68].
# Investigation of Hydrogen Flux Influence on InGaP Layer and Device Uniformity
## 3 Results and Discussion
### Impact of Total H\({}_{2}\) Flux on the Uniformity of InGaP/GaAs HBTs
#### 3.2.3 Breakdown Voltage Curve Measurements  
Breakdown voltage curves of the emitter-base (EB) junction were analyzed at five specific points across each of the three samples to enable a detailed comparative study of these curves within each sample. The results of this analysis are illustrated in Figure 6.  
Figure 6 provides a detailed comparison of the EB junction breakdown voltage curves across five measurement points in Samples 1 to 3. This breakdown voltage is a critical measure of a transistor's reliability and stability under high-voltage conditions, illustrating the device's robustness. In Sample-2, the breakdown voltage curves for all measurement points display remarkable alignment, unlike Sample-1 and Sample-3, where variations are noticeable. We further quantified this observation by calculating the standard deviation of the breakdown voltage for each sample's measurement points, as detailed in Table 5. The findings reveal that Sample-2 has the lowest standard deviation, signifying exceptional uniformity in breakdown voltage. This consistency with the IV and Gummel curve analyses reinforces Sample-2's superior epitaxial wafer uniformity, highlighting its outstanding performance and reliability as a semiconductor material.  
Our comprehensive analysis highlights Sample-2 as exhibiting the highest degree of homogeneity among the measured samples. This conclusion is drawn not only from the comparative study of the breakdown voltage curves and their standard deviation analysis but also from the corroborative evidence provided by the IV and Gummel curve assessments. Furthermore, building upon insights from earlier sections, we deduce that the uniformity of the InGaP layer plays a pivotal role in achieving overall epitaxial wafer  
Figure 6: Comparison of EB junction breakdown voltage curves at 5 measurement points for the samples.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Measurement**} & \multirow{2}{*}{**1**} & \multirow{2}{*}{**2**} & \multirow{2}{*}{**3**} & \multirow{2}{*}{**4**} & \multirow{2}{*}{**5**} & **Standard** \\  & & & & & & **BVeb (V)** \\ \hline \multirow{3}{*}{BVeb (V)} & 5.8 & 5.8 & 5.7 & 5.7 & 5.5 & 0.12249 \\  & 5.8 & 5.8 & 5.7 & 5.7 & 5.7 & 0.05477 \\  & 5.7 & 5.7 & 5.7 & 5.6 & 5.4 & 0.13039 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The breakdown voltage of the EB junction corresponding to the 5 measurement points for Sample-1, Sample-2, and Sample-3.  
uniformity during the HBT growth process. Thus, for the enhancement of HBT device performance, meticulous attention must be given to optimizing the growth conditions and process parameters of the InGaP layer to ensure its uniformity. This strategic focus is crucial for advancing the reliability and performance of HBT devices.
## 3 Results
Saliva samples from each subject were spotted onto filter paper and developed with the substrate as described in the materials and methods. Results show the least intense blue color in the health samples with progressively increasing color intensity in the gingivitis and the most intense color in the periodontal disease group. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Evaluation** & **Clinical Group** & **Mean** & \multicolumn{2}{c}{**Std.**} & \multicolumn{2}{c}{**95\% Confidence Interval for Mean**} & \multicolumn{2}{c}{**Minimum**} & \multicolumn{1}{c}{**Maximum**} \\  & & & **Deviation** & & **Lower Bound** & **Upper Bound** & **Minimum** & **Maximum** \\ \hline \multirow{2}{*}{Baseline} & Healthy & 64.887 & 20.626 & 3.766 & 57.185 & 72.589 & 21.102 & 98.220 \\  & Gingivitis & 95.395 & 46.557 & 8.960 & 76.978 & 113.813 & 38.051 & 205.000 \\  & Periodontitis & 102.034 & 35.143 & 6.416 & 88.911 & 115.156 & 41.441 & 187.203 \\ \hline \multirow{2}{*}{Day 8} & Healthy & 53.701 & 25.733 & 4.698 & 44.092 & 63.310 & 15.169 & 97.373 \\  & Gingivitis & 99.947 & 41.322 & 7.952 & 83.600 & 116.293 & 28.729 & 202.458 \\  & Periodontitis & 109.407 & 51.491 & 9.401 & 90.180 & 128.634 & 25.339 & 280.424 \\ \hline \multirow{2}{*}{Day 15} & Healthy & 57.768 & 21.710 & 3.964 & 49.662 & 65.875 & 19.407 & 97.373 \\  & Gingivitis & 103.023 & 46.289 & 8.908 & 84.711 & 121.334 & 20.254 & 230.424 \\  & Periodontitis & 113.955 & 50.813 & 9.277 & 94.981 & 132.929 & 43.136 & 282.627 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Summary of sialic acid (\(\mathrm{\mu g}\)/mL) analyses from each clinical group over the study period.