esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Results and Discussion
### Potential Use of FEYNF Self-Assembling Aggregates in Polyplex Formation
Additionally, Figure 9 also highlights cation-\(\pi\) interactions involving the positively charged amino group at PHE5 (chain 1 of the docked dimer) and the \(\pi\)-electron cloud of aromatic rings in adenine (A6) and thymine (T7). All these noncovalent interactions significantly contribute to the stabilization of the polyplex. The supramolecular assembly of FEYNF peptides demonstrates the potential to compact nucleic acids, transforming B-form DNA into a more condensed sequence specific to polyplexes. This plays a pivotal role in gene therapy. This compaction process serves to safeguard nucleic acids, shielding them from premature degradation, and facilitates their transportation across the plasma membrane, particularly through cellular processes like endocytosis. Consequently, this enhances cell absorption. Figure 9: Best conformation of the polyplex resulting from molecular docking simulation showing the noncovalent interactions between the protonated dimer (FEYNF@FEYNF) as ligand and the Drew–Dickerson dodecamer d(CGCGAATTCGCG)2 as receptor (atom colors: cyan—carbon, blue—nitrogen, red—oxygen, and gray—hydrogen).
# Cognitive Frequency-Hopping Waveform Design for Dual-Function MIMO Radar-Communications System
## 5 Simulation
### Comparison with Other Methods  
We have assess the performance of the new approach in terms of the Bit Error Rate (BER) and compare it with the method in [29]. Note that the latter approach uses a single sequence in tandem with 4 sidelobe levels towards the communication direction to deliver 2 bits of information. On the other hand, we use a PM-based FH waveform to deliver two bits per pulse. To test the BER, a sequence of symbols unencoded (two bits each) is used. Furthermore, a convolutional encoder of rate 2/3 is used in the original waveform leading to encoded waveform. Both the unencoded and the encoded waveforms are embedded independently using the approach of [29] and the new approach. The received encoded signal is decoded using a Viterbi decoder. The BERs versus the signal-to-noise ratio (SNR) for the two approaches is presented in Figure 10 for both the unencoded as well as the encoded data sequences. Obviously, the proposed approach achieves better BER performance compared to the method of [29]. Note that the latter approach embeds 25% of the information via each  
Figure 9: Target-scattering signal profiles at (**a**) iteration 1, (**b**) iteration 5, (**c**) iteration 10, and (**d**) iteration 20.  
of the four beams. Thus, intersymbol interference is a non-negligible source of detection error leading to capability degradation. The behavior can be expected to be worse if longer pulses are transmitted. Note that, for two approaches tested, the BER with respect to the encoded waveform outperforms the BER with respect to the unencoded waveform. However, this BER superiority comes at the price of slower data transmission rate.  
In Figure 11, we compare the performance for adaptive waveform provided by the proposed method to the performance for a static waveform [32] over multiple snapshots. As the proposed method selects specific waveforms, which generate target returns having low correlation over time, the system adapts its transmit waveform better to the fluctuating target Radar Cross-Section (RCS). On the other hand, the static waveform [32] is unable to match the time-varying target response. Therefore, the performance of the static waveform [32] is worse than the proposed adaptive waveform.
## 3 Experiment and Analyses  
GSG-L1 pseudolites have been used in the experiments, and five satellites have been set up in the laboratory. The ranging signals with C/A codes are transmitted on the GPS L1 frequency. The RF front end of the receiver is a USRP, and a full series of GNSS signals can be captured with a suitable sub-board. The pseudolite system is reloaded in the USRP with two DBSRX sub-plates, which can capture a signal of 800 MHz-2.4 GHz. Taking a certain mark as the origin point, the two horizontal directions of the ground are X and Y axis, respectively. The Z axis is perpendicular to the ground, and then the indoor independent coordinate system is established. The location of the pseudolites and some control points are accurately measured by a total station in the room. For convenience sake, all experiments are carried out on the plane (on the ground). In the program, the direction of Z is strongly constrained, and the precision of the plane coordinates is mainly explored. Additionally, the interval of data sampling is 0.1 s. The pseudolite positioning system is set up in a large laboratory (10 \(\times\) 7 \(\times\) 4 m\({}^{3}\)). Five pseudolites are placed on the ceiling of the room, as shown in Figure 3.  
Three groups of experiments are carried out. The zero baseline static test is used to verify the feasibility of our system and an antenna is shared between the base station and rover station in the test. The short baseline static test is used to explore the effect and feasibility of each step in our algorithm and the precision of static positioning. The length of the baseline in the short baseline static test is about 1 m due to the small indoor space. Based on the results of static tests, the short baseline dynamic experiment is used to verify the positioning effect of DAFV-LAMBDA in dynamic environment and a special strategy to exclude incorrect peaks is also applied, which has been mentioned in Section 2.4. The length of the baseline changes from 0.5 m to several meters in the short baseline dynamic experiment.
## 5 Bench-Marking Models
### Linear Regression  
LR allows knowing the relationship between the response variable (energy consumption) and the return variables (the other variables). As a causative technique, regression analysis predicts energy demand from one or more reasons (independent variables), which might include things such as the day of the week, energy prices, the availability of housing, or other variables. When there is a clear pattern in the previous forecast data, the LR method is applied. Due to this, its simple application has been used in numerous works related to the prediction of electricity consumption. [PERSON] et al. (2009) used a LR model to conduct a study on the projection of Italy's electricity consumption [40], while [PERSON] et al.  
Figure 6: Illustration of the \(i\)th LSTM cell at the layer \(m\).  
(2001) looked into various univariate modeling approaches to project Lebanon's monthly electric energy usage [41]. With the help of our statistical model, this research has produced fantastic outcomes.  
The LR model works by fitting a line to a set of data points with the goal of minimizing the sum of the squared differences between the predicted and actual values of the dependent variable. The slope of the line represents the relationship between the dependent and independent variables, while the intercept represents the value of the dependent variable when the independent variable is equal to zero. The LR model describes the linear relationship between the previous values \(\mathbf{y}_{t}\) and the estimated future value \(\hat{y}_{t+1}\), formulated as follows:  
\[\hat{y}_{t+1}=\sum_{i=t-(k-1)}^{t}w_{i}\cdot y_{i} \tag{8}\]
## 5 Conclusions  
In this paper statistical approaches to assess water availability in the context of hydropower generation and adequacy analysis on a seasonal basis are applied, two Italian locations are considered for the numerical analysis, one in Sicily and the other in Sardinia, where major hydroelectric plants are present. First, an empirical relationship between soil moisture content (SMC) and potential evapotranspiration (ET0) is evaluated through linear regression analysis. Linear regression carried out with first-degree polynomials yielded fitting lines whose coefficients have the consistency of magnitude and sign in both case studies. This fact, in addition to the high values obtained from [PERSON]'s correlation coefficient and the R\({}^{2}\) coefficient, suggests that the fitting lines evaluated in this work can be used to estimate ET\({}_{0}\) from SMC data (publicly available), for geographic areas affected by the Mediterranean climate.  
Analyses of precipitation trends showed that at both locations there has been a reduction in cumulative precipitation over the past twenty years, with decreasing trends in winter and summer. It is expected that these trends will be confirmed in the years to come, and prolonged periods of drought in critical seasons for energy demand such as winter and summer may lead to major reductions in water availability, and thus hydropower production, with consequent adequacy risks for the Sicily and Sardinia market zones.  
Finally, Monte Carlo algorithms, applied to generate synthetic profiles of precipitation, direct runoff, and SMC, have proven successful in capturing the statistical variability of historical data of these meteorological quantities. Thus, through the synthetic profiles of water availability obtained in this work, stochastic analyses of hydropower production can be conducted for the purpose of the assessment of the adequacy of an electric power system.  
Future research should continue in this direction, including modeling snow as a natural reservoir of water availability for hydropower production, and conducting adequacy analyses on a seasonal basis, since weather quantities have marked inter-seasonal variability that impacts electricity generation and demand in different ways depending on the season.  
Conceptualization, G.M.T. and C.F.N.; methodology, G.M.T. and C.F.N.; software, G.M.T.; validation G.M.T. and C.F.N.; formal analysis, G.M.T. and C.F.N.; investigation, C.F.N. and G.M.T.; data curation, C.F.N.; writing--original draft preparation, C.F.N. and G.M.T.; writing--review and editing, G.M.T.; visualization, C.F.N.; supervision, G.  
Data Availability Statement: The data used for this study are available online (open access) at the following site of a NASA project: [[https://power.larc.nasa.gov/](https://power.larc.nasa.gov/)]([https://power.larc.nasa.gov/](https://power.larc.nasa.gov/)), accessed on 20 December 2022.  
The authors declare no conflict of interest.
## 3 Results
### Research Questions' Answers
#### 3.2.9 RQ8  
What Types and Sources of Data Were Used to Parametrize the Models?  
The main approach utilized in the analyzed articles of the current systematic literature review is the hybrid method, where the flood model output is utilized as one of the inputs for the traffic model. Additionally, the physical characteristics of the network and traffic demand information are crucial to successfully run the traffic models. Depending on the analysis' objectives and the desired level of accuracy, various transportation information and databases may be necessary.  
Figure 7 categorizes the different variables utilized in flood and traffic models, as identified in the literature. These variables are divided into two categories: flood data and traffic data. Various techniques, such as remote sensing, GIS, hydrological models, volunteered geographical information (such as social media posts, pictures and videos), and crowd-sourced data collection using mobile applications, have been used for mapping the flood vulnerability of road networks. However, the analyzed articles in this systematic literature review mainly focus on the indirect impact of flooding, i.e., traffic disruptions, and hence, have mostly selected pilot zones with a complete historical flooding database. Therefore, in addition to demographic data, flood hazard maps that provide information on flood extent, depth, velocity, and duration are usually applied for running the flood model. Some articles, such as [PERSON] et al. [58] and [PERSON] et al. [64], also use discharge data.
## 3 Methodology
### GitHub Modeling Using DTMC
#### 3.2.3. Property Specification Using PCTL
\begin{table}
\begin{tabular}{c l} \hline \hline
**Label** & **Property Specification** \\ \hline x\_processing & \(filter(state,P=?[X\)_pLabel \& (”(accepted\"|\"rejected\"), pLabel \& \"\(\"pullRequest\"))\) \\ act\_level & \(R\{\"r\_Steps\"\}=?[\)_pLabel_] \\ merge & \(S=?[\)_pLabel \& “merge\"_] \\ p1\_merge & \(filter(avg,P=?[X^{\prime}p1^{\prime}\) \& “\(merge\"],pLabel \& \"accepted\") \\ create\_to\_pr & \(filter(avg,R\{\"r\_Steps\"\}=?[\)_pLabel \& “pullRequest\"), pLabel \& “create\") \\ pr\_to\_processing & \(filter(avg,R^{\prime}r\_Steps\"=?[\)_pLabel \& (“accepted\"|\"rejected\")], pLabel \& “\(pullRequest\")\) \\ pr\_to\_create & \(filter(avg,R\{\"r\_Steps\"\}=?[\)_pLabel \& “create\"], pLabel \& “\(pullRequest\")\) \\ alone & \(filter(avg,R\{\"r\_Steps\"\}=?[\)_pLabel_], pLabel) \\ commit\_to\_commit & \(filter(avg,P=?[X\)_pLabel \& “\(commit\"],pLabel \& “commit\") \\ prRate & \(S=?[\)_pLabel \& “\(pullRequest\")\) / \(S=?[\)_pLabel \& “create\"] \\ acceptRate & \(S=?[\)_pLabel \& “accepted\"_] / \(S=?[\)_pLabel \& “\(pullRequest\")\) \\ rejectRate & \(S=?[\)_pLabel \& “rejected\"_] / \(S=?[\)_pLabel \& “\(pullRequest\")\) \\ \hline \hline \end{tabular}  
* x\_processing: The probability that a PR submitted by a participant will be processed immediately. act_level: A variable indicating how often the participant is active in the project, with smaller values indicating more frequent activity.
* merge: A variable that indicates how often a participant engages in the activity of merging PRs on GitHub. Merge activity plays an important role in project maintenance and management. A variable that indicates how often a participant submits PRs. participants who submit PRs more frequently are more likely to be active participants to the project and stay active for a longer period of time.
* p1\_merge: A variable representing the probability that the first participant (p1) will merge another participant’s PR. The first participant is often the leader of the project, and their active participation in merging PRs can be interpreted as an important contribution to the overall management of the project.
* create\_to\_pr: The average time step it takes for a participant to submit a PR after creating a branch.
* pr\_to\_processing: The average time step it takes for the PR to be processed (accepted or rejected).
* pr\_to\_create: The average time step it takes to create a new branch after submitting a PR.
* alone: A variable that measures the amount of time a participant has been working alone, with a high amount of alone time indicating a lack of collaboration.
* commit\_to\_commit: The probability of a participant performing consecutive commits. prRate: The percentage of branches created that actually lead to a PR.  
\end{table} 
Table 1: Metrics and Corresponding PCTL Specifications. acceptRate: The probability that a submitted PR is accepted. * rejectRate: The probability that a submitted PR is rejected.
## 2 Experimental
### Photocatalytic Tests  
The photocatalytic efficiency of the ZnO samples was evaluated by monitoring the change in optical absorption of a methylene blue (MB) solution, at \(\sim\)662 nm, during its photocatalytic decomposition process, under UV-light irradiation. Methylene blue (C\({}_{16}\)H\({}_{18}\)N\({}_{3}\)SCI, Color Index No. 52015) exists as a solid, odorless, dark green powder that yields a blue solution when dissolved in water, at room temperature. It is a common contaminant in wastewater released from industrial (e.g., textile) dyeing processes, which is toxic to human health. All the photocatalysis experiments were carried out in a customized photocatalytic reactor, with a 250 W GYZ (HWL-125, Osram Lighting Co., Ltd., Berlin, Germany) as the UV light source (\(\lambda\leq\) 365 nm), as shown in our previous work [5]. In a typical experiment, 50 mg each of Sample A and Sample B were dispersed, respectively, into 100 mL of MB aqueous solution (4 mg/L), in 250 mL glass beakers. Before exposing the solutions to UV-light, they were ultrasonicated for 30 s, magnetically stirred for 20 min, and then kept in darkness for 0.5 h, to establish an adsorption-desorption equilibrium.  
The system was then exposed to UV-light. At 10 min intervals, 5 mL aliquots were taken out and centrifuged (5000 rpm, 5 min) to remove the catalyst from the suspension. The absorbance of MB was monitored by a UV-vis spectrophotometer. We used the following equation to calculate the degradation rate:  
\[X=(A_{0}-A_{t})/A_{0}\times 100\% \tag{1}\]  
where \(A_{0}\) is the absorbance of dyes before illumination and \(A_{t}\) is the absorbance of dyes at time, \(t\).  
For multi-cycle performance tests, once the photocatalytic reaction of a testing cycle is complete, the subsequent cycle is then started, after an aliquot of 5 mL of MB mother liquor (80 mg/L) and deionized water are added to the glass beaker, to bring the concentration of the solution to approximately 4 mg/L. The length of each cycle was 100 min.
# Development of A Loss Minimization Based Operation Strategy for Embedded BTB VSC HVDC
## 4 Case Study  
### System Information  
For case studies, we used the Korean metropolitan power system in 2021, the features of which are as follows.  
Figure 4: Algorithm of the system total loss minimization (STLM) control.  
Figure 5: Algorithm of the security-constrained loss minimization (SCLM) control.  
* The load in the metropolitan area is about 40% of the total load, and a large amount of power is supplied from the non-metropolitan area.
* In order to improve the system stability, it is configured as a looped system, and thereby the problem of fault current is serious.
* The 154 kV Yangju bus (1410) will be separated and connected to VSC BTB HVDC to solve the fault current problems in the metropolitan area and to improve system stability.
* Detailed information on the system is shown in Table 2; detailed specifications and system configuration of HVDC are shown in Table 3 and Figure 6.
## 1 Introduction
### Sources of Silicon of Mineral Origin Used in Functional Foods and Food Supplements  
Although silicon is present in nutritionally significant amounts in many foods, the increasing domination of processed foods in the diet makes usual dietary intake insufficient. This may be remedied by supplementation. Silicon can be added to functional foods and supplements in various forms, with varying degrees of bioavailability. Products commercially recommended as concentrated sources of silicon, produced industrially with the best-known health-promoting effects, use OSA solutions and its stabilized forms. During the manufacture of functional foods and dietary supplements, the polymerization of silicic acid to polysilicic acid is often prevented to aid bioavailability. To prevent polymerization, several methods of stabilization have been developed, for example, introducing a methyl group to obtain monomethylsilanetriol (MMST) or \"organic silicon\" [39; 40], stabilization with choline to form choline-stabilized orthosilicate acid (ch-OSA) [39; 41], or combining with vanillin to form orthosilicic acid vanillin complex (OSA-VC) [41; 42]. In addition, amorphous diatomaceous earth is of great interest both as a source of organic silicon and as an additive with multidirectional health-promoting effects [1]. Silicon dioxide and silicates are of lesser importance as a source of silicon due to their low bioavailability. However, they are widely used in food as the additive E551. Extracts of silicon compounds from silicon-rich plants, such as from horsetail (_Eguisetum arvense_ L) and nettles (_Urtica dioica_ L.), have been used for a long time in cosmetics and natural medicine, and nowadays, they are produced in various forms (concentrated liquid, powders, capsules, and tablets) using various carriers and as part of combination products with other added vitamins and minerals. They are marketed mainly as dietary supplements to strengthen the hair, skin, and nails [39].  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Product Group** & **Portion** & **Silicon Content (mg/100 g or mg/100 mL)** \\ \hline Cornflakes & 100 g & 2.42 \\ High-bran cereal & 100 g & 10.17 \\ Lady’s fingers & 2.78 g & 2.78 \\ White rice & 200 g & 2.48 \\ Brown rice & 200 g & 4.14 \\ Pasta & 250 g & 1.50 \\ Wheat bread & 200 g & 3.38 \\ Whole-grain bread & 200 g & 8.94 \\ Brown bread & 200 g & 4.50 \\ Croissant & 100 g & 1.67 \\ \hline Fruit & & \\ Peeled banana & 250 g & 13.60 \\ Peeled orange & 210 g & 0.67 \\ Strawberries & 200 g & 0.24 \\ \hline Vegetables & & \\ Carrots (peeled, raw) & 200 g & 4.58 \\ Potatores (young, cooked) & 200 g & 0.58 \\ Iceberg lettuce & 250 g & 1.03 \\ Green beans (cooked) & 250 g & 6.10 \\ \hline Mineral water & 0.5 L & 3.44 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Meals, portion sizes, and silicon content [34].
## 4 Design Architecture
### Distance Comparison Unit
#### 4.1.2 Use Case: Hamming Distance Processing Element  
In this design, Hamming distance calculation was implemented inside the processing elements (see Figure 3). While the _xor_ comparison between two signatures can be evaluated on the FPGA in one clock cycle, the more complex part is the counting of the ones bits in the _xor_ result, which gives the resulting Hamming distance. The implementation of this population count was inspired by the LUT-based Hamming weight counter, developed by [PERSON] and [PERSON] in [PERSON] and [PERSON] (2018). Therefore, the calculation is performed in a generic adder tree structure, which is automatically adjusted to any given input signature length. The first tree stage was optimized to use the 6-Input LUTs of the Xilinx 7-Series FPGAs. In the next tree stages, two results are added together and transmitted to the subsequent tree stage; this flow is repeated until the final result is calculated. Pipeline registers are used between all tree stages to ensure optimal timing. In total \(\lceil\log_{2}(\lceil SIGNATURE\_LENGTH/6\rceil)\rceil\) adder stages are used.
## 3 Results and Discussions
### Effect of Data Augmentation before Splitting Data into Training and Test Set  
As mentioned in Section 2.1, enhancing images prior to splitting the data into training and test data results in certain images to appear in different partitions with slight variations in brightness or angle, which reduces the effectiveness of validation and testing. To investigate this further, a random image was selected and image enhancements such as rotation (15 and 45 degrees) and flipping of this image were added to the training dataset. Figure 13 shows the result of the model with and without adding an enhanced version of the image and ground truth. As can be seen in Figure 13, the trained model with enhanced copies of the image as part of the training dataset produces identical results to the ground truth, but the model can identify even more trunks on the image when the enhanced versions are not used in the training. Consequently, either the process of data augmentation should be performed after splitting the dataset into a training and a test dataset, or care  
Figure 12: mAP of the SSD MobileDet model, trained on the images without leaves and tested on the images with leaves.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**mAP\%**} & \multicolumn{3}{c}{**Inference Time (ms)**} \\ \cline{2-6}
**Backbone** & \multicolumn{1}{c}{**PC**} & \multicolumn{1}{c}{**RaspberryPi**} & \multicolumn{1}{c}{**RaspberryPi**} \\ \cline{2-6}  & **GPU** & **CPU** & **TPU** & **CPU** & **TPU** \\ \hline MobileDet Edge TPU (640 \(\times\) 480) & 89 & 84.4 & 84.6 & 456.98 & 47.75 \\ \hline MobileDet Edge TPU (320 \(\times\) 320) & 77 & 67.4 & 67 & 1235.73 & 27 \\ \hline MobileDet Edge TPU (640 \(\times\) 480) & 81 & 77 & 77.5 & 456.98 & 47.75 \\ (trained on 20\% of images) & & & & & \\ \hline MobileDet Edge TPU (640 \(\times\) 480) & 87 & 82.6 & 83 & 456.98 & 47.75 \\ (without thermal images in training set) & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Performance of the SSD MobileDet under different parameters.  
should be taken to ensure that an enhanced version of the image from the test dataset is not used in the training dataset.