esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
\begin{table}
\begin{tabular}{l l} \hline \hline
**Item** & **Descriptions** \\ \hline Greenhouse gas emissions & 20\% reduction of GHG emissions compared to the projected \\  & BAU (business-as-usual) target in 2030 \\ \hline Global average temperature increase & Below 2 celcius degrees \\ National renewable energy targets to & 30\% of total energy consumption from renewable energy \\ respond the Paris agreement & resources in 2036 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Key points which are applied to Thailand in the Paris Agreement [6; 7]. \begin{table}
\begin{tabular}{l l l} \hline \hline
**Regions** & **Year** & **Configuration** & **Cost and Renewable Fraction** \\ \hline Maldives [11] & 2018 & PV-diesel generator-battery & 50.245 per kWh of COE (cost of energy) and 30\% of renewable fraction \\ Indonesia [12] & 2013 & PV-wind turbine-battery & 50.751 per kWh of COE and 100\% of renewable fraction \\ Myanmar [13] & 2018 & PV-diesel generator-battery & 50.193-51.830 per kWh of COE \\ Thailand [14] & 2002 & PV-diesel generator-battery & 50.589 per kWh of COE and 36.9\% of renewable fraction \\ Cambodia [15] & 2017 & PV-diesel generator-battery & 50.377 per kWh of COE and 13.0\% of renewable fraction \\ Malaysia [16] & 2017 & PV-battery & 51.220 per kWh of COE and 100\% of renewable fraction \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Examples of the suggested configuration of renewable energy production systems in Southeast Asia. Therefore, the current study introduces the optimal configuration of renewable energy generation systems for Chiang Mai University, which is one of the largest public universities in Thailand. Using HOMER software (Hybrid Renewable and Distributed Generation System), the possible components of the configuration are introduced by reducing the environmental pollution and the dependence on the national grid system. Although there are notable limitations of HOMER software in exploring the feasibility of renewable resources including the needs of time-series datasets, notable time consumption, and certain criteria on converge, HOMER software can consider multiple combinations of different energy-related technologies, provide relatively precise results, and present optimized configurations of energy production systems [19]. That is, the current study aims to respond to the following research questions. * **Research question 1** What is the optimal renewable electricity production system for Chiang Mai University in Thailand? * **Research question 2** How much are the amount of greenhouse gas emissions reduced by as a result of using the optimal system for the university?
## 2 Methodology
### Central Difference for Time Partial Derivative Approximation  
The displacement at time \(t+\Delta t\) and \(t-\Delta t\) can be expanded at time \(t\) via the Taylor series:  
\[u(t+\Delta t)=u(t)+\Delta t\frac{\partial u(t)}{\partial t}+\frac{(\Delta t)^ {2}}{2!}\frac{\partial^{2}u(t)}{\partial t^{2}}+\frac{(\Delta t)^{3}}{3!} \frac{\partial^{3}u(t)}{\partial t^{3}}+\ldots \tag{2}\]  
\[u(t-\Delta t)=u(t)-\Delta t\frac{\partial u(t)}{\partial t}+\frac{(\Delta t)^ {2}}{2!}\frac{\partial^{2}u(t)}{\partial t^{2}}-\frac{(\Delta t)^{3}}{3!} \frac{\partial^{3}u(t)}{\partial t^{3}}+\ldots \tag{3}\]  
Truncating the 2M-order obtains the central difference expression:  
\[u(t+\Delta t)=2u(t)-u(t-\Delta t)+2\sum_{m=1}^{M}\frac{\Delta t^{2m}}{2m!} \frac{\partial^{2m}u(t)}{\partial t^{2m}}+O\Big{(}\Delta t^{M}\Big{)} \tag{4}\]  
When \(M=1\), then:  
\[u(t+\Delta t)=2u(t)-u(t-\Delta t)+\Delta t^{2}\frac{\partial^{2}u(t)}{\partial t ^{2}}+O\Big{(}\Delta t^{2}\Big{)} \tag{5}\]  
The time partial derivatives \(\partial^{2}u_{x}(t)/\partial t^{2}\) and \(\partial^{2}u_{z}(t)/\partial t^{2}\) in Equation (1) can be converted using Equation (5) into the following form:  
\[\left\{\begin{array}{l}u_{x}(t+\Delta t)=2u_{x}(t)-u_{x}(t-\Delta t)\\ +\Delta t^{2}\left[\frac{\lambda+2\mu}{\rho}\frac{\partial^{2}u_{x}}{\partial x ^{2}}+\frac{\mu}{\rho}\frac{\partial^{2}u_{x}}{\partial^{2}z}+\frac{\lambda+ \mu}{\rho}\frac{\partial^{2}u_{z}}{\partial x\partial z}\right]+f_{x}(t+ \Delta t)\\ u_{z}(t+\Delta t)=2u_{z}(t)-u_{z}(t-\Delta t)\\ +\Delta t^{2}\left[\frac{\lambda+2\mu}{\rho}\frac{\partial^{2}u_{z}}{\partial x ^{2}}+\frac{\mu}{\rho}\frac{\partial^{2}u_{z}}{\partial^{2}z}+\frac{\lambda+ \mu}{\rho}\frac{\partial^{2}u_{x}}{\partial x\partial z}\right]+f_{z}(t+ \Delta t)\end{array}\right. \tag{6}\]
## 3 Results
### Satellite Accuracy Comparison  
For Landsat-8 imagery, the algorithm's kappa coefficient was recorded as 78, 80, and 74% for KD-Tree, RF, and K-NN, respectively (Figure 8). For Sentinel-2A imagery, the same algorithms recorded considerably increased kappa coefficient values, i.e., 2.5, 10, and 10.8% for the KD-Tree, RF, and K-NN algorithms, respectively. Similarly, the average kappa coefficient was 83.3% for the Sentinel-2A, while the average was 77.3% for the Landsat-8.  
The random forest classifier's overall accuracy was recorded as 92 and 85% for Sentinel-2A and Landsat-8 satellites, respectively (Figure 9). The average accuracy of the KD-Tree classifier for both satellites was recorded to be 84.5%. The K-NN achieved 86 and 81% overall accuracy for the Sentinel-2A and Landsat-8 satellites, respectively. A slightly lower average overall accuracy of 83.5% was recorded for the K-NN algorithm in comparison with the KD-Tree classifier (Figure 9).  
Figure 8: Kappa coefficient comparison of two satellites using different classifiers.
## 3 Results
### Genotoxic Effect
#### 3.4.2 Comet Assay  
The Comet assay was performed to complement the results obtained by the SMART assay. It was performed in duplicate, and 100 cells per gel/condition were visually scored each time. After fluorescence microscopy analysis of the slides, the results obtained showed an increase in basal damage at all concentrations when compared with the control. However, statistical analysis performed using Student's \(t\)-test for independent samples later revealed that these changes were indeed significant for all concentrations, except for the 1 mM concentration (Figure 8). The results from both genotoxic assays thus seem to prove that BPA is potentially genotoxic, but only causes genetic damage in Drosophila cells at environmentally unrealistic exposure doses, way above the LOAEL.  
\begin{table}
\begin{tabular}{c c c c} \hline
**BPA Concentration** & **Observed Spots/Analyzed Eyes (n)** & **Number of Spots per 100 Eyes (\%)** & **Results Obtained by X\({}^{2}\)** \\ \hline Control & 22/748 & 2.941 & \\ \hline
1 mM & 25/710 & 3.521 & i \\ \hline
10 mM & 19/364 & 5.220 & - \\ \hline
20 mM & 45/810 & 5.556 & w\({}^{+}\) \\ \hline
50 mM & 31/732 & 4.235 & i \\ \hline \end{tabular}
\end{table}
Table 3: The results of the SMART \(w/w^{+}\) assay, obtained by the double-decision \(\chi^{2}\) test. The number of spots per number of analyzed eyes and the percentage of female eyes with spots observed at each BPA concentration. Legend: (i)—inconclusive; (-)—negative; (w\({}^{+}\))—weakly positive; (+)—positive, according to acceptance or rejection of H\({}_{0}\) (the condition does not increase the number of spots when compared with the control) and/or H\({}_{A}\) (the condition induces m (calculated multiplication factor) more spots when compared to the control). The number of analyzed eyes (n) varied between 364 and 748 per treatment.  
Figure 7: Examples of the presence of white clones in the ommatidia observed during the SMART assay, after chronic exposure of the flies to 10 mM of BPA throughout larval development.
# Translating Speech to Indian Sign Language Using Natural Language Processing
## 8 Performance Evaluation  
The performance of our system is measured based on the net promoter score system. The net promoter score measures the willingness of customers to recommend products or services to their friends and family. A survey was taken with 30 disabled people that asked them to give a rating to the system in terms of how likely they were going to recommend our system to their friend or family who is hearing-impaired.  
* Promoters: responses from 9 to 10.
* Passives: responses from 7 to 8.
* Detractors: responses from 0 to 6.
* Total number of people who participated in the survey: 30.
* Total number of promoters: 26.
* Percentage of promoters: 86.6%.
* Total number of passives: 3.
* Percentage of passives: 10%.
* Total number of detractors: 1.
* Percentage of detractors: 3.33%.
* Net promoter score = total percentage of promoters \(-\) total percentage of detractors.
* Net promoter score = 86.6 \(-\) 3.33 = 83.27\(-\)83.
* A net promoter score above 50 is considered excellent by the creators of the NPS score.
# Low-Power Rectennas in Microwave Wireless Power Transmission
## 3 Low-Power-Density Rectenna
### Multi-Wideband Rectenna Configurations  
Ambient RF energy exists across multiple frequency bands; designing multiband or wideband rectennas can help harvest more energy. Reference [82] proposed a triple-band high-gain antenna covering the GSM-1800, UMTS-2100, and Wi-Fi bands. Under an input power of \(-\)30 dBm, the rectenna achieves 7.6%, 8.6%, and 4.3% efficiencies at these three bands, respectively. A seven-band rectenna with even broader frequency coverage was first introduced in [83], capable of harvesting RF energy from GSM1800, LTE, Wi-Fi, and various 5G bands. The design process and topology can be seen in Figure 14. This rectenna operates over an ultra-wideband from 1.67 to 5.92 GHz, achieving rectification efficiencies of 49% @ 1.84 GHz, 51% @ 2.04 GHz, 52% @ 2.36 GHz, 53% @ 2.54 GHz, 46% @ 3.3 GHz, 45% @ 4.76 GHz, and 35% @ 5.8 GHz when the input power is around 3 dBm. Furthermore, it can function effectively even under an input power of \(-\)20 dBm. In [84], coverage of five commercial frequency bands was achieved using only a single diode, with an average efficiency of 23.2% under \(-\)20 dBm input power. Notably, when a five-tone signal was input, an efficiency of 43.7% was attained at \(-\)20 dBm input power. Figure 15 introduced a transversely connected folded, tightly coupled ultra-wideband dipole array. By employing capacitive-loaded metallic walls to eliminate the common-mode resonance of the array and broaden the operational bandwidth, the designed 16 \(\times\) 16 array can operate within the 0.41-3.3 GHz frequency range [85].  
Figure 14: (**a–d**) Design process and topology of the seven-band rectenna [83].  
Figure 13: Prototype of 4 \(\times\) 4 antenna array fed by SIW network: (**a**) Front view, (**b**) Bottom View [79].
# Pas: Privacy-Preserving Authentication Scheme Based on SDN for VANETs
## 2 Relate Works
### Group Signature-Based Authentication Schemes  
In group signature-based authentication schemes, VANETs contain multiple groups, where each group manager is trustworthy. All group members are able to participate in authentication and communication as a group without revealing their real identities. Ref. [18] provides a threshold anonymous authentication protocol for VANETs. The proposed scheme employs a decentralized group model to release the burden of vehicle certificate generation and maintenance for the TA. Meanwhile, Ref. [18] supports traceability and message linkability. The tracking manager (TM) can easily trace the true identity of illegal vehicles. However, in mutual authentication protocol, the computational cost and authentication delay are too high for vehicles, which makes it difficult to build a trusting relationship between vehicles. In [19], RSUs, as the group leaders, are required to support vehicles' anonymous authentication. As an anonymous certificate is cancelled, Ref. [19] can effectively reduce the transmission and communication costs in certificate issuing and anonymous authentication. However, the RSU is vulnerable to adversaries. Once the RSU is compromised, the information of all vehicles in the group will be exposed. Ref. [20] proposes a group signature-based secure VANETs communication scheme (GIGS). GIGS adopts a group signature to achieve the secure communication among vehicles, where messages are able to be anonymously signed by vehicles to hide their identities. On the other hand, in [20], as a certificate is canceled, the risk of certificate leakage is solved and the computational cost of certificate verification is reduced. However, there is no effective mechanism to revoke illegal vehicles. In [21], a regional group manager is added into VANETs to update vehicle identifiers and group keys periodically, which is able to solve the authorisation disputes. Nevertheless, for vehicle signature and verification, a large number of point multiplications and bilinear pairings seriously affects the communication efficiency. In addition, if a vehicle is considered as the illegal node, it is difficult to trace the vehicle's historical communication records. NECPPA [22] adopts a group signature to support conditional privacy-preserving. In order to protect the security of VANETs, all vital information is stored in RSUs and TPDs. In addition, NECPPA achieves batch verifications to improve efficiency. However, the pseudonyms are randomly generated by vehicles; once a vehicle misbehaves, it is difficult to trace the vehicle. Ring signature, a special group signature mechanism, is employed in [23]. Vehicles can generate a ring signature independently; thus, RSU is able to centralize computation and communication capabilities to provide services for vehicles. However, in order to revoke illegal vehicles, other vehicles have to traverse all the information of the ring members, which leads to a low efficiency of revocation.
## 3 Major Bioactive Compounds and Potential Applications
### Mycosporine-like Amino Acids  
MAAs are small, water-soluble secondary metabolites produced by marine microalgae, cyanobacteria, and macroalgae as a defence against ultraviolet radiation. These compounds absorb UV-A (320-400 nm) and UV-B (280-320 nm) radiation with high molar extinction coefficients (typically 28,000-50,000 M\({}^{-1}\) cm\({}^{-1}\)), dissipating the energy as heat without generating free radicals, thus providing effective photoprotection [48]. Common MAAs include shinorine, porphyra-334, mycosporine-glycine, and polythine, with concentrations in microalgae ranging from 0.1% to 1.2% of dry weight, depending on species and light exposure [49].  
In marine cyanobacteria and green microalgae, MAA production is upregulated under environmental stress, including high solar radiation, nutrient limitation, or salinity changes. For example, engineered _Nanochloropsis salina_ strains expressing genes from red algae can produce up to 25 mg g\({}^{-1}\) dry weight of porphyra-334 under UV exposure [50]. This has led to increasing industrial interest in scalable production for use in photoprotective skincare formulations.  
In addition to UV absorption, MAAs exhibit broad-spectrum antioxidant and anti-inflammatory properties. Mycosporine-2-glycine (M2G), for example, has demonstrated strong radical-scavenging activity and reduced nitric oxide (NO) production by 2-3-fold in lipopolysaccharide-stimulated macrophages, outperforming other MAAs such as shinorineand porphyra-334 [51]. These effects are linked to NF-kB pathway inhibition and downregulation of pro-inflammatory mediators such as cyclooxygenase-2 (COX-2) and inducible nitric oxide synthase (iNOS).  
MAAs also contribute to anti-ageing and skin-regenerating effects in cosmetic formulations. In vitro studies using HaCaT keratinocyte cells showed that MAA treatment after UV exposure restored levels of elastin and procollagen C proteinase enhancer, both of which are essential for skin elasticity and repair [52]. Additionally, increased expression of involucrin, a marker of epidermal stress, was suppressed by MAA treatment, further confirming their role in photoproduction and dermal homeostasis.  
In the nutraceutical and functional food domains, MAAs are being explored for oral photoprotection, antioxidant supplementation, and anti-inflammatory support. Their water solubility, low toxicity, and resistance to photodegradation make them suitable for inclusion in beverages and dietary supplements. Several MAAs are also being investigated for antiviral and anticancer activities, although these applications remain largely preclinical [53].  
In summary, MAAs are multifunctional metabolites produced by microalgae and cyanobacteria with diverse applications in medicine (anti-inflammatory, anti-ageing), cosme (natural sunscreens, skin protection), and functional foods (antioxidant and immune support). Their stability, specificity, and biosafety profile make them attractive candidates for integration into next-generation bioproducts.
## 5 Conclusions  
The proposed dual-band antenna for PCL systems, comprising internal and external dipoles, can derive the multi-frequency matching characteristics by using an electromagnetically coupled feed mechanism. The simulated VSWR was less than 2.5 from 88 to 217 MHz, and the 3-dB gain bandwidth ranged from 83 to 250 MHz in the boresight direction. The proposed dipole antenna element was extended to an eight-element UCA to form the reference and surveillance beams in the T-DMB and FM  
Figure 9: Location estimation for the target with the proposed antenna array element: (**a**) location map for three illuminators, the target, and the PCL system; (**b**) ARD maps from each illuminator.  
bands. The resulting multi-band performance is suitable for PCL systems by estimating the target location from ARD maps in the Seoul-Gyunggi urban area.  
Conceptualization, H.C. and S.W.; methodology, H.S.; software, S.W. and H.S.; validation, H.C. and S.W.; formal analysis, S.W.; investigation, S.W.; data curation, S.W.; writing--original draft preparation, S.W.; writing--review and editing, H.C.; visualization, S.W.; supervision, H.C.; project administration, H.S.; funding acquisition, H.S. All authors have read and agreed to the published version of the manuscript.  
This work was supported by a grant-in-aid from HANWHA SYSTEMS.  
The authors declare no conflict of interest.
## 3 Results
### GOS Production Kinetics  
Samples taken during the enzymatic reaction of the E-CWP at 30% lactose, were analyzed for galacto-oligosaccharides DP 2 to DP 8. For GOS DP 2, the following molecules were counted as GOS fiber: Allo-lactose (Gal-[1->6]-Glc), Gal-[1->3]-Gal, Gal-[1->3]-Glc, Gal-[1->2]-Glc. Figure 1 presents the GOS yield, expressed as g GOS/g lactose, at the different time points where samples were taken.  
Results presented in Figure 1 indicate that maximum GOS yield in the enzymatic conversion of lactose in concentrate cheese whey was reached at 0.5 h, and after that time, a decrease in GOS fiber is observed at 1, 1.5 and 2 h. Even though the statistical analysis did not detect any significant difference in GOS yield during the reaction, it is evident that, under the reaction conditions defined in this experiment, the reaction time that maximize GOS production is 0.5 h. From time 0.5 h to 2 h, the GOS yield decreased by 16%, which can be explained by the double enzyme activity present in \(\beta\)-galactosidase enzymes, that include transgalactosidase activity, responsible for GOS formation, but also able to hydrolyze lactose and GOS according to the equilibrium of components in solution [28].  
The distribution of the carbohydrates present in the syrup at each sampling point is presented in Figure 2. The results are expressed in mg of the carbohydrate per gram of the syrup. The composition presented correspond to the syrups at a soluble solids content of 75% \(w/w\).  
\begin{table}
\begin{tabular}{c c c} \hline
**Component, \% wb** & **CWP** & **E-CWP** \\ \hline pH & 6.1 & 6.1 \\ Soluble solids & 25 & 37 \\ Protein & 0.4 & 0.6 \\ Lactose & 20.6 & 30.5 \\ Ash & 1.2 & 1.8 \\ Potassium & 0.28 & 0.41 \\ Sodium & 0.08 & 0.12 \\ Calcium & 0.15 & 0.22 \\ Phosphorous & 0.03 & 0.04 \\ Others & 2.2 & 3.26 \\ Fat & 0 & 0 \\ Water & 75 & 63 \\ \hline \end{tabular}
\end{table}
Table 2: Composition on a wet basis (wb) of the concentrated whey permeate.  
Figure 1: GOS yield reached at different time points during the enzymatic reaction. Bars with different letters represent statistical difference, with a \(p\)-value of 0.05.  
As observed in Figure 2, concentrations of galactose and glucose constantly increase in solution, because of the hydrolysis of lactose necessary for GOS production. Glucose concentration is significantly higher than galactose since the former molecule is released from lactose, but at the same time, it is used for GOS formation, while glucose molecules remain free in solution. Lactose concentration decreases over time as result of the hydrolysis process needed for GOS formation. It is evident that after the first 0.5 h, most of the GOS molecules are formed, with a high concentration of GOS DP4, compared to DP2 and DP3. Over time, the concentration of GOS DP4 remains constant, with a slight decrease from 1.5 h to 2 h; however, GOS DP3 content decreases over time, while that of DP2 slightly increases over time, but in a lower concentration compared to DP3 and DP4.
# Rain Removal of Single Image Based on Directional Gradient Priors
## 4 Experiments
### Experimental Parameters
#### 4.3.2 Regularization Parameter  
Four regularization parameters exist in the proposed rain removal model, in which \(\alpha\) is used to limit the sparsity constraint on the background layer, \(\beta_{1}\) and \(\beta_{2}\) limit two gradient prior terms, and \(\beta_{3}\) is used to limit the convolutional sparse coding coefficient for the rain layer.  
In the experiments, we limit the values of \(\alpha\) and \(\beta_{3}\) to [0.001, 0.01] and [0.001, 0.12], respectively. By self-adapting the values of \(\beta_{1}\) and \(\beta_{2}\), \(\alpha\) and \(\beta_{3}\) are sampled at intervals of 0.001 and 0.002, respectively, that is, \(\alpha\) = [0.001, 0.001, 0.01] and \(\beta_{3}\) = [0.001, 0.002, 0.012]. Different combinations of \(\alpha\) and \(\beta_{3}\) are used in the model to test the Rain 12 dataset. The average PSNR and SSIM values of the rain removal results are shown in Figure 4 (the marked values of \(\alpha\) and \(\beta_{3}\) on the coordinate axis are enlarged by 1000 times for convenience). From Figure 4, it can be seen that the values of \(\alpha\) and \(\beta_{3}\) have a significant influence on the experiment results, and combined with the extremum points of the PSNR and SSIM, we finally choose \(\alpha\) = 0.005 and \(\beta_{3}\) = 0.01.  
Here, \(\beta_{1}\) and \(\beta_{2}\) are used to limit the gradient sparse prior terms, which constrain the reconstruction of the rain layer by using the horizontal and vertical gradient information  
Figure 4: The average PSNR and SSIM of the 12 images in the experiment increased with \(\alpha\) and \(\beta_{3}\) respectively on the exponential function.  
Figure 3: The average PSNR, SSIM values, and running time on the Rain 12 dataset in the experiment increased with the number of iterations.  
of the rain layer. When the information of the rain layer is more abundant, the gradient value of the rain layer will be larger. Therefore, we think that the more abundant the rain layer information is, the stronger the constraint of the gradient sparse prior terms should be. On the basis of this consideration, this paper defines \(\beta_{1}\) and \(\beta_{2}\) as monotone increasing functions of \(\|\
abla_{y}R\|_{2}^{2}\) and \(\|\
abla_{x}R\|_{2}^{2}\), and thus \(\beta_{1}\) and \(\beta_{2}\) can be adaptively determined on the basis of the intensity of the rainfall gradient information. In the experiments, we define two exponential functions to obtain the values of \(\beta_{1}\) and \(\beta_{2}\). The definitions of \(\beta_{1}\) and \(\beta_{2}\) are as follows: \(\beta_{1}=\exp(a_{0}*||\
abla_{y}R||_{2}^{2})\) and \(\beta_{2}=\exp(a_{0}*||\
abla_{x}R||_{2}^{2})\), where \(a_{0}\) is a small constant. For these two definitions, we also carried out experiments on the Rain 12 dataset to determine the choice of \(a_{0}=0.004\) according to the average PSNR value, as shown in Figure 5.