esa-sceva/satcom-chunk-collection huggingface dataset:
# Simplified Welch Algorithm for Spectrum Monitoring
## 5 Laboratory Tests and Real-Time Validation
### BEEcube SDR  
The NanoBEE device by National Instruments is used as a SDR receiver. This SDR is equipped with a programmable FPGA to implement the algorithm as well as a register bank module to store samples and result data [27].  
Once the data is processed, the results are stored inside the register bank, then sent via Ethernet cable to a computer equipped with MATLAB for spectral representation. Figure 14 explains the reception structure of the BEEcube used in this setup. The modified design resides in the Welch block marked in red. It should be mentioned that all the PSD estimation steps are completed in the heart of SDR FPGA, and MATLAB just presents the results stored in the register bank as figures for visualization purposes.  
Figure 14: Nano-BEE reception structure.  
Figure 13: (**a**) FFT block from the Xilinx Digital Signal Processing (DSP) Generator library; (**b**) Random Access Memory (RAM) block from the Xilinx DSP Generator library.
###### Abstract  
Although texturing of the transparent electrode of thin-film solar cells has long been used to enhance light absorption via light trapping, such texturing has involved low aspect ratio features. With the recent development of nanotechnology, nanostructured substrates enable improved light trapping and enhanced optical absorption via resonances, a process known as photon management, in thin-film solar cells. Despite the progress made in the development of photon management in thin-film solar cells using nanostructures substrates, the structural integrity of the thin-film solar cells deposited onto such nanostructured substrates is rarely considered. Here, we report the observation of the reduction in the open circuit voltage of amorphous silicon solar cells deposited onto a nanostructured substrate with increasing areal number density of high aspect ratio structures. For a nanostructured substrate with the areal number density of such nanostructures increasing in correlation with the distance from one edge of the substrate, a correlation between the open circuit voltage reduction and the increase of the areal number density of high aspect ratio nanostructures of the front electrode of the small-size amorphous silicon solar cells deposited onto different regions of the substrate with graded nanostructure density indicates the effect of the surface morphology on the material quality, i.e., a trade-off between photon management efficacy and material quality. This observed trade-off highlights the importance of optimizing the morphology of the nanostructured substrate to ensure conformal deposition of the thin-film solar cell.  
solar cells; photon management; nanostructured substrate; photovoltaics 2018
# Performance Analysis of Hard-Switching Based Hybrid FSO/RF System over Turbulence Channels
## 4 Conclusions  
In this work, performance availability and reliability of hybrid FSO/RF system based on hard-switching are analyzed. The analysis is done based on three significant metrics for the communication systems known as outage probability, BER and ergodic capacity. The performance of the proposed hybrid FSO/RF model shows significant improvement and better performance in terms of availability and reliability as compared to conventional single FSO model. RF link with higher SNR further studies the hybrid FSO/RF system and gives much better results for the whole link.  
Conceptualization, H.K. and S.S.M.; methodology, H.K. and S.S.M.; software, H.K.; validation, H.K., S.S.M. and H.E.N.; formal analysis, H.K. and S.S.M.; investigation, H.K., S.S.M., H.E.N. and G.S.T.; resources, H.K., S.S.M., H.E.N. and G.S.T.; writing--original draft preparation, H.K. and S.S.M.; writing--review and editing, H.K., S.S.M. and H.E.N.; supervision, S.S.M.; funding acquisition, H.E.N., G.S.T. This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement No: 777596.  
The authors declare no conflict of interest.
# Skin Depigmenting Agents in Anti-Aging Cosmetics: A Medicinal Perspective on Emerging Ingredients
## 3 Results and Discussion
### Medicinal Chemistry Perspective of New Skin Depignetting Ingredients in Anti-Aging Products
#### 3.2.1. Tranexamic Acid
There is also one study reporting the efficacy of a formulation containing 3% tranexamic, 1% kojic acid, and 5% niacinamide tested for the treatment of melasma, post-inflammatory pigmentation, and hyperpigmentation in Brazilian volunteers with Fitzpatrick skin types I-IV. After 12 weeks, there was a significant improvement in skin tone and skin homogeneity with good tolerance [22]. Despite these studies, tranexamic acid's sole application for the treatment of hyperpigmentation due to skin aging is yet to be established. Over the last decade, numerous efforts have been made to find efficient methods to synthesize tranexamic acid [23,24,25,26,27,28]. [PERSON] et al. [28] reported a seven-step synthetic route from the readily available starting material dimethyl terephthalate **1**, obtaining tranexamic acid with 99.6% purity in 59.2% overall yield (Scheme 1). The reported process represents an optimization regarding those previously reported methodologies since it avoids the use of toxic reagents (CrO\({}_{3}\), Cl\({}_{2}\)) and solvent (CCI\({}_{4}\)). These have a detrimental impact on the environment and human health, and also avoid the use of expensive metal catalysts (e.g., PtO\({}_{2}\)) which renders the process economically nonviable and industrially unattractive [28]. The synthesis of this synthetic \(\omega\)-amino acid consists of a direct and efficient method for the preparation of the key intermediate methyl 4-(acetamidomethyl)benzene (**6**) by one-pt hydrogenation and acylation in acetic anhydride using Ni/Al\({}_{2}\)O\({}_{3}\) as a catalyst [28]. **Scheme 1. Synthesis of tranexamic acid by [PERSON] et al. \"Reprinted (adapted) with permission from [28]. Copyright 2021 American Chemical Society\". **  
Later, the synthesis of tranexamic acid was achieved by a new synthetic route starting from _E_-4-oxocyclohexane-1-carboxylate (**8**) using simple and commonly available reagents and under mild conditions (Scheme 2) [29]. This synthetic process was also aimed to avoid all the toxic and costly reagents and high-pressure reactions previously reported [23; 24; 25; 26; 27]. One of the latest reports on the synthesis of this acid relates to an efficient, greener preparation process of the pure tranexamic acid in higher purity using milder conditions and reduced cycle times. This environmentally friendly procedure involves effective recycling of all the minor products and the reagents used, making the process industrially applicable and cost-effective (Scheme 3) [30]. Hence, halogenation of the starting material _trans_-1,4-cyclohexane dimethanol (**14**) and additional oxidation gives 4-bromomethyl cyclohexane-1-carboxylic acid (**18**). Further esterification of **18** followed by azidation, hydrolysis to the corresponding potassium salt **21**, and final reduction yields tranexamic acid [30].
## 5 Optimization Algorithm  
Based on the above finite element and sensitivity analyses, the topology optimization problem in Equation (1) can now be solved using a gradient-based optimization algorithm. In this research, the optimality criteria update optimization solver proposed by [PERSON] et al. [13] and the parameters recommended by [PERSON] et al. [13] were adopted. The flowchart of the proposed iterative solution algorithm is shown in Figure 5.  
Figure 4: Convergence curves for compliance and volume fraction.  
Step 1 Parameter setting: Define the number of finite elements in the design domain and the material properties, such as the Poisson's ratio, elastic modulus, \(\beta\), \(\eta\), filter radius, volume constraints, and buckling constraints.  
Step 2 Finite element preparation: Prepare the element stiffness matrix, node number of the geometric stiffness matrix, preparation matrix for density filtering, etc.  
Step 3 Boundary condition setting: Set the boundary, load, and other conditions.  
Step 4 Initialize the density field: Calculate the physical density by using linear density filters and projection filters.  
Step 5 Boundary search: Based on the DRLSE loading surface search algorithm, search for the position where the force is applied and obtain the force vector.  
Step 6 Finite element analysis: Perform finite element analysis.  
Step 7 Sensitivity analysis: Calculate the sensitivities of the objective function, the K-S functions for the buckling constraints, and the material volume constraint.  
Step 8 Update of the density field: Update the density field.  
Step 9 Judgment of convergence condition: After judging whether the convergence condition is reached, the convergence automatically changes the buckling constraints to continue the optimization.  
Step 10 Repeat or End: Repeat steps 4-10 and continue to iterate if not converged. Otherwise, end the loop and post-process the optimization results.  
Figure 5: Algorithm flow chart of topology optimization method considering stability constraints.
## 3 False Data Injection Attacks on RL-Based Charging Coordination
### Evaluations of FDI Attacks
#### 3.2.4 Charging Power Usage  
As discussed earlier, one of the key objectives of the RL charging coordination system is to minimize the power used for charging home batteries while achieving its other goals. Figure 5a,b show the power usage for Attack 1 under scenarios with no malicious consumers, two malicious consumers, and four malicious consumers, with \(C_{\max}=6\) and \(C_{\max}=10\), and attack severity (\(\alpha=0.1\) and \(\alpha=0.7\)). Tables 2 and 3 provide the change in the power usage when launching the four FDI attacks.  
Figure 3: SoC of honest consumer #1 and malicious consumer #3 with \(C_{max}\) of 6 when there are no FDI attacks.  
The results indicate that power usage is significantly higher in scenarios with malicious consumers compared to those without any malicious influence. This increase is due to the excess power allocated to satisfy the artificially lowered SoC values reported by malicious consumers. Additionally, the results show that more power is consumed as the number of malicious consumers increases and as \(\alpha\) decreases, meaning the attackers report lower SoC values, leading to an over-allocation of power.  
Figure 4: Fairness degradation under Attack #1 for two and four malicious consumers. (**a**) Fairness in power distribution with two malicious consumers under Attack #1. (**b**) Fairness in power distribution with four malicious consumers under Attack #1.
# SACT: A New Model of Covert Communication Based on SDN
## 5 Experimental Results
### Anti Attack Ability
#### 5.3.1 Distributed Simulation Test  
In the experiment, the normal distribution of data flow is \((\sigma=4,\mu=20)\). We compare the SACT system with the Time Lord model proposed in [PERSON] et al. (2017). In the experiment, 3000 interval arrival delays are sent through the Time Lord model. The step size between the arrival delays of adjacent packet is \(\Delta=20\) ms, and the minimum interval arrival delay is \(T_{\min}=30\) ms. The interval arrival delay distribution of carrier data stream is shown in Figure 12. It can be found that its distribution law is completely different from normal data flow, and it is easy to be attacked.  
SACT sends the same number of interval arrival delay at the sender, and the step size and minimum interval arrival delay between adjacent packets are consistent with Time Lord model. It is transformed into the distribution state \((\sigma=4,\mu=20)\) of normal data stream and embedded into data stream for transmission. After receiving the data stream, the receiver controller extracts the delay sequence and decodes it to obtain the watermark information.  
Figure 12: Time delay distribution before simulation of distribution.  
After simulating and modulating the delay sequence of interval arrival through the distributed simulation mechanism, the delay distribution is shown in Figure 13. It can be seen that it basically matches the distribution state of delay sequence of normal data stream. SACT improves the anti detection ability of watermark information and realizes the covert tracking of anonymous communication.
## 5 Simulation of a 40-Channel WDM-PON
### Protection Scheme for ROA\({}_{1}\)  
The simulation setup and parameters for the protection state where ROA\({}_{3}\) is used to backup ROA\({}_{1}\) are similar to those shown in Figure 8 with additional SSMF, AWG at the RN, and ONUs. Based on the results in Section 5, the EDFA the in ROA\({}_{3}\) has 7.5 m long EDF and 200 mW pumping power. In the simulation scenario, the 40-channel signals bypass the failed EDFA in ROA\({}_{1}\) and are amplified by ROA\({}_{3}\). The amplified signal is then transmitted via the SSMF and de-multiplexed by a 1 \(\times\) 40 AWG (De-MUX) at the RN to distribute to the ONUs for bit error rate (BER) analysis. Each transmitter consists of a CW laser diode and an MZM, and the optical power of each channel is \(-\)3 dBm. The output optical power of the EDFA is 22.05 dBm. Furthermore, the receiver mainly consists of a PIN-TIA photodetector and a Bessel filter, and the received optical signal is analyzed using a BER analyzer.  
Figure 14 shows the output optical spectrum after ROA\({}_{3}\). The gain flatness (\(\Delta\)G) is 6.9 dB because ROA\({}_{3}\) does not contain any gain flattening filter (GFF). The gain difference may be acceptable for the single-stage amplification since the crosstalk effect between adjacent channels is negligible in our simulation. A GFF can be added to improve gain flatness if needed.  
Figure 13: Experimental results for the EDFA in ROA\({}_{3}\): curve (**a**) backward-pumping configuration and curve (**b**) forward-pumping configuration.  
The BER curves of CH1 (190.8 THz, 1571.24 nm), CH20 (192.7 THz, 1555.75 nm), and CH40 (194.7 THz, 1539.77 nm) are shown in Figures 15-17, respectively. For a BER of \(10^{-9}\), the difference in the Rx sensitivities of these three channels is not obvious on a back-to-back (B2B) condition; the Rx sensitivity is approximately \(-22.8\) dBm. After transmitting via the SSMF, fiber dispersion has the most significant effect on CH1; the longest transmitted distance of CH1 is limited to 85 km, whereas that of the remaining two channels is up to 95 km. Moreover, fiber dispersion has the lowest influence on CH40, with it having lower the minimum required received optical power (ROP) of approximately \(-20.08\) dBm for a BER of \(10^{-9}\).  
Figure 14: Gain flatness of the post-amplifier in ROA\({}_{3}\).  
Figure 15: BER curve of CH1 (190.8 THz).  
Figure 16: BER curve of CH20 (192.7 THz).  
Figure 17: BER curve of CH40 (194.7 THz).
# Optimizing Investment Portfolios with Bacterial Foraging and Robust Risk Management
## 3 Portfolio Management and Integration of BFO
### Integrating BFO into Portfolio Management
#### 3.3.1 The Rationale for Integrating BFO  
The traditional methods of portfolio management often rely on statistical models and historical data, which may struggle to adapt to the ever-changing financial landscape. Integrating BFO into portfolio management offers several compelling advantages:  
BFO's nature-inspired approach enables it to adapt to dynamic market conditions. Bacteria within the swarm adjust their strategies in response to changing nutrient concentrations, much like investors adapt to evolving market trends.  
The BFO search algorithm explores a broad solution space efficiently, resulting in a more comprehensive examination of potential investment opportunities.  
BFO's capacity to optimize while considering risk aligns with the fundamental goal of portfolio management: achieving an optimal balance between risk and return.
# Evaluation of Coupling PV and Air Conditioning vs. Solar Cooling Systems--Case Study from Jordan
## 3 Results and Discussions
### Coupled PV and Air Conditioning  
The continual, significant decline in the cost of electricity produced by utility-scale PV extended into 2018, with a decline in the worldwide weighted-average levelised cost of energy (LCOE) of PV technology to $0.085/kWh, which represents a 13% decrease compared to projects implemented in 2017. Consequently, the decrease in the worldwide weighted-average LCOE of PV technology between 2010 and 2018 amounted to 77%. A further 94 GW of capacity was added in 2018, which amounted to 55% of the overall renewable power generation capacity expansion. In 2010, the worldwide weighted-average LCOE of the utility scale was $0.371/kWh, which had decreased to $0.085/kWh by 2018, a decline of 77% compared to 2010 and an annual decrease of 13%. The reductions in cost in 2018 were complemented by decreases in the price of crystalline silicon modules ranging from 26 to 32%. As of December 2018, the European module prices for high-efficiency modules varied between $216/kW and $400/kW. The LCOE of utility-scale PV technology had an estimated learning rate for the period between 2010 and 2020 that was greater than the International Renewable Energy Agency (IRENA) estimate of 35% reported in January 2019. Recent data indicates that the cost of electricity generated using PV technology could decrease to $0.048/kWh by 2020 [50].  
The original cost of the PV system and air conditioning units was approximately \(\epsilon\)3M and has saved around $655K / year (minus the maintenance and cleaning ($5K)); the expected life for the PV system is around 20 years and the interest rate for this calculation was 6% per year with no salvage value for the system after 20 years. The present value profit minus the expenses will be approximately $7,455,450 across the lifetime of the project. The cooling capacity of a total floor area of around 7500 m\({}^{2}\) with 200 occupants will need to be approximately 118 tons (1,415,200 BTU or 415 kW), which can be achieved via the installation of a number of central air conditioners (6 units \(\times\) 20 tones), which is less expensive than the existing price of $100k that is available on the market. As demonstrated by Figure 5 shown below, the PV system installed on the campus of GJU had an LCOE of under $0.05/kWh ($0.045/kWh), which was attributed to the wide accessibility of solar radiation on the campus, which is exposed to 300 days of sun on an annual basis.
# A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection
## 3 Preliminaries
### Example of BRB Making Predictions  
BRB can be applied in many classification tasks. Here, a virtual dataset is used as an example to illustrate how BRB can be used to predict fraud detection.  
The following example illustrates the prediction mechanism of the BRB model.  
\[M=\{m1,m2,m3,m4\}=\{V1,V2,V3,V4\}\]  
\[D=\{d1,d2\}=\{NotFraud,Fraud\}\]  
Due to the sensitive nature of fraud detection data, feature names are presented here in an anonymized form. Take the BRB model with two rules as an example.  
\[R_{1}:IF(m_{1}=-10)\wedge(m_{2}=10)\wedge(m_{3}=0)\wedge(m_{4}=5)\]  
\[THEN\;D=\{(d_{1},0.1),(d_{2},0.9)\}\]  
\[R_{2}:IF(m_{1}=-2)\wedge(m_{2}=-20)\wedge(m_{3}=-20)\wedge(m_{4}=-6)\]  
\[THEN\;D=\{(d_{1},0.9),(d_{2},0.1)\}\]  
Take a data sample with attributes \(\{x_{1},x_{2},x_{3},x_{4}\}\) and corresponding values as follows.  
\[attr_{1}=-3.2,\;attr_{2}=-10.4,\;attr_{3}=-14.0,\;attr_{4}=-5.0\]  
According to Equation (2), the matching degrees can be calculated as follows.  
\[\alpha_{1,1}^{1}=\frac{-2-(-3.2)}{-2-(-10)}=0.15\]  
\[\alpha_{1,2}^{2}=\frac{-3.2-(-10)}{-2-(-10)}=0.85\]  
\[\alpha_{2,1}^{1}=0.32,\alpha_{2,2}^{2}=0.68\]\[\alpha_{3,1}^{1}=0.3,\ \alpha_{3,2}^{2}=0.7\] \[\alpha_{4,1}^{1}=0.0909,\ \alpha_{4,2}^{2}=0.9091\]  
Given that \(\theta_{1}=\theta_{2}=1\) and \(\delta_{1}=\delta_{2}=\delta_{3}=\delta_{4}\), the activation weights of rules are generated based on Equations (3) and (4).  
\[\begin{array}{l}\omega_{1}=\frac{0.15\times 0.32\times 0.3\times 0.0909}{0.15 \times 0.32\times 0.3\times 0.909+0.85\times 0.68\times 0.7\times 0.9091}\\ =0.0035\end{array}\]  
\[\omega_{2}=0.9965,\]  
The belief degrees of consequents are calculated according to Equations (5)-(7).  
\[\mu=1.0029,\ \beta_{1}=0.8997,\ \beta_{2}=0.1003.\]  
These results indicate that the possibility of this sample being not fraud is 0.8997 and of being fraud is 0.1003. For the classification mission, the class that has the maximal belief degree will be the final result. Therefore, in this example, the final prediction result is not fraud.
## 2 Platform Scheduling Model
### Platform Task Model  
DAG is usually used to describe the task scheduling model in heterogeneous platforms, where each node represents a task component and each directed edge represents a communication link between components. The platform task scheduling model is constructed as \(\mathbf{G}=\{\mathbf{V},\mathbf{E},\mathbf{W},\mathbf{C}\}\), where \(\mathbf{V}=\{\mathbf{v}_{1},\mathbf{v}_{2},\mathbf{v}_{3},\cdots,\mathbf{v}_{n}\}\) is the set of all tasks in the scheduling processing system; \(\mathbf{E}=\{\mathbf{e}_{12},\mathbf{e}_{23},\cdots,\mathbf{e}_{ij}\}\) represents the set of communication links between associated tasks, \(\mathbf{e}_{ij}=(\mathbf{v}_{i},\mathbf{v}_{j})\) represents the communication edge between tasks \(\mathbf{v}_{i}\) and \(\mathbf{v}_{j}\), and \(\mathbf{v}_{i}\) is the parent task of \(\mathbf{v}_{j}\); \(\mathbf{W}=\{\mathbf{w}_{1},\mathbf{w}_{2},\cdots,\mathbf{w}_{n}\}\) represents the set of computing costs for any executed task; \(\mathbf{C}(\mathbf{v}_{i},\mathbf{v}_{j})\) represents the communication cost of data transmission between tasks. If any two tasks (\(\mathbf{v}_{i}\) and \(\mathbf{v}_{j}\)) are assigned to the same node (on the same processor), the communication cost \(\mathbf{C}_{ij}\) is 0. Figure 2a shows a DAG with 10 components. Numbers in nodes represent tasks' serial numbers, orange numbers next to nodes represent tasks' computing overheads, and blue numbers on communication edges represent tasks' inter-task communication overheads.  
Figure 1: The software architecture of the heterogeneous signal processing platform.  
Figure 2: Platform scheduling model: (**a**) task scheduling model; (**b**) processing system model.  
The hardware resource architecture is abstracted as \(\mathbf{N}~{}=~{}\{\mathbf{P},\mathbf{H},\mathbf{R},\mathbf{T}\}\) by an undirected graph, where \(\mathbf{P}~{}=~{}\{p_{1},p_{2},\cdots,p_{k}\}\) is the set of processors; \(\mathbf{H}\) is the feature of the processor, including the main frequency, cache, communication bandwidth of the processor; \(\mathbf{R}~{}=~{}\{r_{1},r_{2},\cdots,r_{g}\}\) is the processing ability of processors; \(\mathbf{T}\) is the communication bandwidth between processors. This paper simplifies the communication bandwidth between processors to be 1. Figure 2b shows a processing system model with four heterogeneous processors.
## 4 Experiment
### Evaluation Metric: Earth Mover's Distance (EMD)  
The Earth Mover's Distance (EMD), also known as the Wasserstein distance, is an effective measure for comparing two probability distributions. In the context of our study, where we aim to evaluate the similarity between the reconstructed 3D point cloud and the ground truth, EMD provides a nuanced understanding of the differences in terms of both geometry and density.  
The primary reason behind selecting EMD as our evaluation metric is its ability to provide a more holistic view of the discrepancies between distributions, as opposed to simpler metrics that might only measure point-wise differences. EMD measures the minimum cost to transform one distribution into the other, which resonates well with the geometric nature of our task.  
Mathematically, for two discrete distributions \(P\) and \(Q\), the EMD is defined as follows:  
\[EMD(P,Q)=\frac{\sum_{i,j}w_{i,j}d(p_{i},q_{j})}{\sum_{i,j}w_{i,j}} \tag{14}\]  
where \(w_{i,j}\) represents the flow between points \(p_{i}\) from \(P\) and \(q_{j}\) from \(Q\), and \(d(p_{i},q_{j})\) is the Euclidean distance between these points.