esa-sceva/satcom-chunk-collection huggingface dataset:
# A Survey of Defensive Measures for Digital
## 5 Metrics for Consideration  
### Security Technology and the Global South Context  
Many security tools are developed anticipating that their users have some amount of savviness, are sufficiently educated, or have access to adequate resources. This is decidedly not the case for many people in the Global South where widespread technology adoption faces many challenges. First, there is a often a lack of requisite education, and UNESCO notes there is a significant gap in schooling between the Global North (where these tools are usually made) and many Global South nations [31]. Second, governments in the Global South rarely make investments in IT. When they do, they often rely on the assistance of Global North nations who do not sufficiently understand the countries they are helping. Thus, their assistance is flawed, and their aid is made ineffective. Third, most Global South countries lack telecommunication infrastructure. There may only be a few telephone lines or limited power supply. Internet cafes are the sole source of internet for many, and those tend to be only found in urban areas. Fourth, well-trained technology professionals are uncommon in these areas. It is very difficult to create and maintain IT infrastructure without skilled IT personnel. Despite these challenges, the growth of internet penetration in the Global South is simply staggering. For example, internet penetration in Africa grew over 3606% between 2000-2012. That is a rate of growth six times larger than North America and Europe combined. Still, only a fraction of Africans have internet access [32; 33].  
Case studies in Saudi Arabia [8] and Ghana [32] highlight common issues that surround user adoption and perception of technology in the Global South. Even among citizens who use internet technology, most are not aware of the full capabilities of their devices. Citizens may be hesitant to trust technology due to criminal hackers, as well as government intrusion. There is also a gender savviness gap in countries where men dominate professions using computers. Economics also play a role, as internet technology is relatively expensive in Global South countries. After these external factors, the ease of use appears to be the biggest impediment to adoption. The technology acceptance model posits that the two factors that immediately influence a person's decision to use new technology are the perceived usefulness and ease of use of technology. This model is illustrated in Figure 1. Generally, people in the Global South already perceive internet technology to be useful. Therefore, when creating a security product, the focus should be on improving perceived ease of use and lowering barriers imposed by external factors.
## 3 Results
### Determination of the Structures of the Allelopathic Substances  
The aqueous fraction and the ethyl acetate fraction of _Albizia richardiana_ extracts arrested the growth of the cress seedlings in a concentration-dependent manner (Figure 2). In the concentration 0.6 g DW equivalent extract/mL, the aqueous portion restricted shoot growth to 8.3% of the control and the growth of the roots to 10%, whereas the ethyl acetate portion completely suppressed the growth of the cress seedlings compared with the control. Hence, the ethyl acetate fraction was selected for the subsequent purification steps using a silica gel column, a Sephadex LH-20 column (in the Sephadex LH-20 purification step, fraction 2 (F2) showed the highest inhibitory activity, which is displayed in Figure 3), and reverse-phase C\({}_{18}\) cartridges to check the allelopathic effects of all the fractions. Two active phytotoxic substances were isolated by using reverse-phase HPLC, and these substances were identified by comparing with earlier reported spectral data.  
The molecular formula of compound **1** was measured as C\({}_{13}\)H\({}_{19}\)O\({}_{3}\) through ESIMS at \(m/z\) 223.1407 [M+H]\({}^{+}\) (calcl. for C\({}_{13}\)H\({}_{19}\)O\({}_{3}\), 223.1334). The 1H NMR (400 MHz, CD\({}_{3}\)OD) spectrum of this compound was 8H 6.99 (d, J = 16.2 Hz, 1 H, H7), 6.43 (d, J = 16.2 Hz, 1 H, H8), 5.93 (brs, 1 H, H4), 2.59 (d, J = 17.8 Hz, 1 H, H1), 2.30 (s, 3 H, H10), 2.28 (d, J = 17.8 Hz, 1 H, H2), 1.90 (d, J = 1.4 Hz, 3 H, H13), 1.06 (s, 3H, H11), 1.02 (s, 3 H, H12). The distinct rotation of this substance was [\(\alpha\)]\({}_{\mathrm{D}}\)\({}^{32}\) = +50 (c = 0.14, CH\({}_{3}\)OH). Comparing this spectral data with earlier recorded data led to identifying this compound as dehydrovomifoli (Figure 4) [36; 37].  
The molecular formula of compound **2** was measured as C\({}_{11}\)H\({}_{17}\)O\({}_{3}\) through HRESIMS at \(m/z\) 197.1193 [M+H]\({}^{+}\) (calcl. for C\({}_{11}\)H\({}_{17}\)O\({}_{3}\), 197.1178). The 1H NMR (400 MHz, CD\({}_{3}\)OD) spectrum of this compound was 8H 5.75 (s, 1 H, H7), 4.22 (m, 1 H, H3), 2.42 (ddd, J = 14.2, 2.8, 2.8 Hz, 1 H, H4b), 1.99 (ddd, J = 14.6, 3.2, 2.6 Hz, 1 H, H2b), 1.76 (s, 3 H, H11), 1.75 (m, 1 H, H4a), 1.53 (dd, J = 14.6, 3.8, Hz, 1 H, H2a), 1.47 (s, 3 H, H9), 1.28 (s, 3 H, H10). The distinct rotation of this compound was [\(\alpha\)]\({}_{\mathrm{D}}\)\({}^{26}\) = \(-\)101 (c = 0.07, CHCl\({}_{3}\)). Comparing this spectral data with earlier recorded data led to identifying this compound as lolio  
Figure 2: The phytotoxic effects on cress seedling growth of aqueous and ethyl acetate fractions obtained by the partitioning of _Albizia richardiana_ extracts at the concentrations of 0.3 and 0.6 g DW equivalent extract/mL. The means \(\pm\) standard errors for every treatment from the two separate experiments with 10 seedlings are displayed. Different letters denote the significant differences according to [PERSON]’s HSD test at a 0.05 probability level.
# Removal of Metal Ions in Spin-on Hardmask Using Functionalized Porous Silica Adsorbents
## 2 Experimental
### Adsorption Experiment  
To evaluate the metal ion adsorption performance, 30 mL of each prepared metal ion solution was placed in a 50 mL vial. Each prepared adsorbent was then dispersed in the solution at a ratio of 10 mg mL\({}^{-1}\) (adsorbent/solution), and the mixture was stirred at room temperature for 24 h. Afterward, the adsorbent was separated from the solution using a syringe filter. The initial and final concentrations of metal ions were measured by ICP-OES. The metal ion removal efficiency (%) of the adsorbents was calculated using the following equation:  
\[\%R=\frac{C_{0}-C_{e}}{C_{0}}\times 100\% \tag{1}\]  
where \(C_{0}\) and \(C_{e}\) represent the initial and equilibrium concentrations of metal ions, respectively.
## 3 Results and Discussion
### Tip Leakage Flow Pattern and Vortex Trajectory
The vortex core trajectory in Figures 8 and 9 only quantifies the relative position relationship between the vortex core and the blade in the axial direction, and it is not possible to observe the changes in the vortex in the radial direction. Based on this, Figure 10 provides the distance from the vortex core to the shroud, which can further clarify the impact of the vortex on the mainstream. As can be seen from Figure 10, under different flow rates, there are differences in the initial and final coordinates of the vortex core for the front and rear impellers. Therefore, the horizontal coordinate range of the curves in Figure 10 is inconsistent. Since the counter-rotating axial flow fan rotates in the opposite direction, the initial positions of the vortex core for the front and rear impellers are opposite. In Figure 10, when the horizontal coordinate \(\theta=100^{\circ}\), it represents the starting point of the vortex core for the front impeller, and when \(\theta=55^{\circ}\), it represents the starting point of the vortex core for the rear impeller. Figure 10a shows that when the flow rate is greater than 1.2 \(Q_{\text{BEP}}\), along the vortex core trajectory line, the distance from the vortex core to the shroud increases slowly and eventually becomes consistent. As the flow rate decreases to 0.9 \(Q_{\text{BEP}}\), along the vortex core trajectory line, the distance from the vortex core to the shroud will significantly increase. At \(\theta=57^{\circ}\), the distance from the vortex core to the shroud is nearly twice that of the 1.0 \(Q_{\text{BEP}}\) to 1.2 \(Q_{\text{BEP}}\) flow rates. When the flow rate decreases to 0.8 \(Q_{\text{BEP}}\), the distance from the vortex core to the shroud shows exponential growth. At \(\theta=77^{\circ}\), the distance is close to 23 mm. Figure 10b shows that the distance from the vortex core to the shroud of the rear impeller is significantly different from that of the front impeller, especially under low flow rates. The distance at the flow rate of 1.0 \(Q_{\text{BEP}}\) is notably greater than that at 1.2 \(Q_{\text{BEP}}\). It should be pointed out that under low flow rates, the increase in distance is similar to that at 1.0 \(Q_{\text{BEP}}\), but the vortex core trajectory line is significantly reduced, resulting in a smaller distance at the end of the vortex. Figure 9: Tip leakage vortex trajectory in the rear impeller: (**a**) 1.2 \(Q_{\text{BEP}}\); (**b**) 1.0 \(Q_{\text{BEP}}\); (**c**) 0.9 \(Q_{\text{BEP}}\); (**d**) 0.8 \(Q_{\text{BEP}}\).
# VHF Omnidirectional Range (VOR) Experimental Positioning for Stratospheric Vehicles
## 3 Results
### SDR Data Positioning
The importance of multiple stations can be seen in Figure 8 which shows the mean error and the standard deviation of positions estimations depending on the amount of stations that were used in the estimation process. The diminishing return effect is attributed to the fact that the VOR stations are spread out, and a higher number usually means that the additional stations are further away. Figure 8: Trend of the mean error and deviation of the position estimation depending on how many VOR stations were used. Figure 7: Error between the VOR-based track and the GPS data.
# Assessment and Evaluation of the Intake of Sugars in European Countries
## 3 Results
### Intake of Total Sugars
\begin{table}
\begin{tabular}{c c c c} \hline
**Country** & **Guideline** & **Endpoints** & **Reference** \\ \hline Europa & Insufficient data to set an upper limit & Several endpoints & EFSA, 2010 [8] \\ \hline \multirow{4}{*}{France} & \(<\)100 g total sugars per day & \multirow{4}{*}{Fasting triglycerides} & \multirow{4}{*}{ANSES, 2016 [15]} \\  & (Excluding lactose and galactose & & \\ naturally present in milk and & & & \\ dairy products) & & & \\ \hline \multirow{4}{*}{Germany} & \multirow{4}{*}{Free sugars \(<\) 10 en\%} & \multirow{4}{*}{Adopted from WHO} & German Obesity Society (DAG), \\  & & & German Diabetes Society (DDG) \\ \cline{1-1}  & & & and German Nutrition Society (DGE), 2019 [21] \\ \hline Ireland & Avoid excessive sugar consumption & Unknown & FSAI, 2011 [16] \\ \hline Netherlands & Minimize SSB consumption & Weight gain, T2 DM & HC, 2015 [17] \\ \hline \multirow{3}{*}{Nordic Countries *} & \multirow{3}{*}{Added sugars \(<\) 10 en\%} & T2 DM, Weight gain, \\  & & Nutrient density, \\  & & Dental caries & NNR, 2012 [19] \\ \hline \multirow{3}{*}{Spain} & Occasional consumption Sweets & \multirow{3}{*}{Dental caries} & \multirow{3}{*}{SSCN, 2001 [18]} \\  & \(<\)4 times per day & & \\ \hline \multirow{3}{*}{UK} & \multirow{3}{*}{Mean population intake of free sugars} & Energy intake & \multirow{3}{*}{SSAN, 2015 [20]} \\  & & SSB: T2 DM, \\ \cline{1-1}  & 5 en\%, SSB should be minimized & Increase BMI (children) & \\ \cline{1-1}  & & Dental caries & \\ \hline \multirow{3}{*}{World (including Europe)} & Reduce intake of free sugars to \(<\)10 en\% & \multirow{3}{*}{Dental caries} & \multirow{3}{*}{WHO, 2015 [6]} \\ \cline{1-1}  & Conditional: Reduce intake of free & & \\ \cline{1-1}  & sugars to 5 en\% & & \\ \hline \end{tabular}  
* Denmark, Iceland, Finland, Norway, Finland, Norway, Sweden. ** EFSA = European Food Safety Authority; GNS = German Nutrition Society; FSAI = Food Safety Authority of Ireland; HC = Health Council; NNR = Nordic Nutrition Recommendations; SSCN = Spanish Society of Community Nutrition; SACN = Scientific Advisory Committee on Nutrition; WHO = World Health Organization.  
\end{table} 
Table 1: Overview of the guidelines for the dietary intake of sugars that apply to European countries. women in the Netherlands have an intake of total sugars that is 1.5 en% higher, and in Portugal this difference is 4.3 en%.
## 5 Conclusions  
This paper dealt with the aeroelastic characteristics and vibration suppression of simply supported functionally graded GPLs reinforced composite plates bond with a piezoelectric patch in a supersonic environment.  
According to [PERSON]'s principle, the motion equation of the GPLs plate with a piezoelectric patch was derived. The supersonic piston theory was applied to obtain the aerodynamic pressure. The effect of graphene nanoplatelet on the vibration characteristics of the plate was studied by changing the weight fraction of graphene. The displacement and acceleration feedback control strategies were utilized to improve the flutter characteristics of the GPLs plate. Based on the numerical results, the following conclusions can be drawn:  
1. Adding a small content of GPLs nanofillers can raise the critical flutter Mach number. The reinforced effect of X-GPLs distribution is better than O-GPLs distribution.
2. The displacement and acceleration feedback control can enhance the aeroelastic flutter characteristics of GPLs reinforced composite plate by attaching active stiffness and active mass.
3. The displacement and acceleration feedback control can cause the vibration response to reduce faster before the critical flutter point. The displacement and acceleration feedback control can effectively suppress the occurrence of flutter at the critical flutter point.
4. The combined control effect of active stiffness **Kp** and active mass **Mp** is better than considering only one of them.  
Conceptualization, J.C. and W.Z.; methodology, R.H. and D.L.; software, R.H. and D.L.; validation, J.C. and R.H.; formal analysis, D.L. and W.Z.; investigation, J.C. and R.H.; resources, J.C. and W.Z.; data curation, J.C. and R.H.; writing--original draft, R.H.; writing--review and editing, J.C. and D.L.; visualization, R.H.; supervision, W.Z.; project administration, J.C.; funding acquisition. J.C. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Program of Shenzhen Municipality, grant number JCYJ20170818094653701, the General Program of Science and Technology Development Project of Beijing Municipal Education Commission, grant number KM202110005030, and the National Natural Science Foundation of China (NNSFC), grant number 12102015.  
Figure 15: The comparison of the response of the GPLs plate after introducing the combination of the displacement feedback control and acceleration feedback control.
# Chaotic Time Series Forecasting Approaches Using Machine Learning Techniques: A Review
## 6 Conclusions
### Future Directions  
This comprehensive review helped open up new scopes in the field of chaotic time series forecasting approaches in various applications and is highlighted underneath.  
* Chaotic time series analysis and SVM can estimate short-term wind speeds while considering weather conditions and more complex scenarios of wind farm operations [16].
* To the dispersed power resource system, the wind power generation unit can be connected to the grid of this system through high-quality forecasting of the parameters using the Jacobian matrix estimate method and weather data optimal points using deterministic chaos [104].
* EMD-based forecasting approaches can increase short-term wind power prediction accuracy based on their behavior characteristics. Furthermore, the relationship between different scale subsequences and numerical weather forecasting can improve the accuracy of this short-term wind power forecasting [108].
* The hybrid neuro evolutionary approach, i.e., adaptive variational mode decomposition-AOA-LSTM proposed for wind farms, has employed multiple outlier identification methods with optimization and decomposition procedures to improve forecasting outcomes [175]. This method can also be adaptable to other geographies.
* The independent component analysis method can be extended for real-time monitoring and controlling the power system's fundamental frequency with an appropriate time delay between observed data frames [94].
* The precision accuracy of the response surface-based Bayesian inference method proposed for the power systems to predict the dynamic parameters has to be improved when there is a substantial outrageous deviation in the boundaries [149].
* The coupled quantity pattern similarity model proposed for the prediction of monthly precipitation can also be applied to the time series with different lead time scales [18].
* The hybrid algorithms proposed using CNN and wavelet transforms for predicting the chaotic time series of Chen, Lorenz, Mackey-Glass, and sunspot numbers can also be used for real-time series, such as geomagnetic, network traffic, and weather systems [13, 170].
* The forecasting accuracy of an online vehicle velocity prediction approach proposed using adaptive RBFNN can be enhanced using additional data, such as driving time, climate, gas, and brake pedals [176].  
Conceptualization, B.R. and K.B.; formal analysis, B.R.; investigation, B.R.; resources, K.B.; data curation, K.B.; writing--original draft preparation, B.R.; writing--review and editing, K.B.; visualization, K.B.; supervision, K.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
# Making Automotive Radar Sensor Validation Measurements Comparable
## 1 Introduction
### Research Questions  
The following research questions arise from the validation measurement campaigns in the literature for radar sensors.  
_Research Question 1: How reproducible are radar measurements and identical measurement setups?_  
Identical measurements in this case refer to a comparison of unchanged measurement setups in less than 10 min consecutively where the sensor is not switched off. Furthermore, it is investigated how dismantling and reconstruction of the measurement setup behave on the radar measurements at constant position with precise markings on the test track.  
_Research Question 2: How do different objects and object positions affect radar measurements?_  
Different objects here denote a corner cube reflector (CCR), which is an ideal point target, and extended targets, which differ in shape and size (Volvo XC90, Mercedes Vito). Furthermore, the poses of the vehicles to the sensor are varied to analyze this influence.  
_Research Question 3: How do environmental conditions such as rain affect radar validation measurements?_  
Due to the stochastic behavior of rain, it is challenging to quantify the effect and make different rain intensities comparable.
## 5 Results and Discussion
### Performance Metrics  
To assess the efficiency of the presented model, we used performance metrics such as Accuracy, F1-Score, Recall, and Precision. Performance metrics shown in Table 5 are  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Batch size & 128 \\ \hline Number of epochs & 25 \\ \hline Number of iterations & 294 \\ \hline Optimizer & Adam \\ \hline Optimizer parameters & Lr = 0.00001 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Hyper parameters for training the model.  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Layer** & **Output Shape** & **Parameters** \\ \hline Input Layer & [(None, 28, 28, 3)] & 0 \\ \hline Convolution 2D\_1 & (None, 28, 28, 32) & 896 \\ \hline MaxPooling2D\_1 & (None, 14, 14, 32) & 0 \\ \hline Batch Normalization\_1 & (None, 14, 14, 32) & 128 \\ \hline Convolution 2D\_2 & (None, 14, 14, 64) & 18,496 \\ \hline Convolution 2D\_3 & (None, 14, 14, 64) & 36,928 \\ \hline MaxPooling2D\_2 & (None, 7, 7, 64) & 0 \\ \hline Batch Normalization\_2 & (None, 7, 7, 64) & 256 \\ \hline Convolution 2D\_4 & (None, 7, 7, 128) & 73,856 \\ \hline Convolution 2D\_5 & (None, 7, 7, 128) & 147,584 \\ \hline MaxPooling2D\_3 & (None, 3, 3, 128) & 0 \\ \hline Batch Normalization\_3 & (None, 3, 3, 128) & 512 \\ \hline Convolution 2D\_6 & (None, 3, 3, 256) & 295,168 \\ \hline Convolution 2D\_7 & (None, 3, 3, 256) & 590,080 \\ \hline Batch Normalization\_4 & (None, 1, 1, 256) & 0 \\ \hline Flatten & (None, 256) & 0 \\ \hline Dropout & (None, 256) & 0 \\ \hline Dense\_1 & (None, 256) & 65,792 \\ \hline Batch Normalization\_5 & (None, 256) & 1024 \\ \hline Dense\_2 & (None, 128) & 32,896 \\ \hline Batch Normalization\_6 & (None, 128) & 512 \\ \hline Dense\_3 & (None, 64) & 8256 \\ \hline Batch Normalization\_7 & (None, 64) & 256 \\ \hline Dense\_4 & (None, 32) & 2080 \\ \hline Batch Normalization\_8 & (None, 32) & 128 \\ \hline Classifier & (None, 7) & 231 \\ \hline \hline \end{tabular}
\end{table}
Table 3: CNN Model Summary.  
calculated from a confusion matrix and are given by Equations (7), (8), (9) and (10), respectively. Performance measurement of the deep learning model comprises the following terms: (a) True Positive (_Tp_), (b) True Negative (_Tn_), (c) False Positive (_Fp_), and (d) False Negative (_Fn_) [36].