esa-sceva/satcom-chunk-collection huggingface dataset:
# Nano- and Micropatterning on Optical Fibers by Bottom-Up Approach: The Importance of Being Ordered
## 3 Bottom-Up Fabrication of Periodic Structures on OFs
### Other Self-Assembly Methods  
Besides colloidal self-assembly, a few other approaches guided by an auto-organization phenomenon are capable of generating highly ordered patterns: breath figure formation and directed self-assembly.
## 2 Coding Metamaterials for RCS Reduction
### Development of Unit-Cell Metamaterial Structures
#### 2.1.2 Square Split-Ring Resonator  
Three types of simple square metamaterial designs were constructed, and performance analyses were carried out for each design structure. Meanwhile, three similar types of substrate materials, namely FR-4, Rogers RT6002, and Rogers RO3006, were adopted in this work. Based on the circular unit-cell metamaterial design, the designs each exhibited a similar performance in terms of scattering patterns and monostatic RCS values. However, a square unit-cell design exhibited bistatic RCS values at 8 GHz with the maximum and minimum values ranging from \(-53\) dBm\({}^{2}\) to \(58.9\) dBm\({}^{2}\), as demonstrated in Table 2. The FR-4 substrate material exhibited the lowest value of bistatic RCS as well. Moreover, the square metamaterial design enabled slightly lower monostatic RCS values that reached less than \(-40\) dBm\({}^{2}\), as illustrated in Figure 2. As discussed earlier, the thickness of the substrate material also influenced the RCS values for this type of unit cell.  
Figure 2: _Cont._Figure 2: Monostatic RCS values for square metamaterial unit-cell design with various substrate materials: (**a**) Design 1; (**b**) Design 2; and (**c**) Design 3.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Designs** & **FR-4** & **Rogers RT6002** & **Rogers RO3006** \\ \hline Design 1 & & \(\underline{d8(m^{*}2)}\) & \(\underline{d8(m^{*}2)}\) & \(\underline{d8(m^{*}2)}\) \\ \hline Design 2 & & \(\underline{d8
## 4 Experimental Results
### Building Function Identification Results  
From the results as shown in Figure 7, the accommodation services including the residential and hotel are relatively large in the town of Moxi, and the commercial services are also more developed, which is in line with the functions of the tourism industry in Moxi.  
Figure 6: Building extraction and vectorization results.
# Multitask-Based Trajectory Planning for Redundant Space Robotics Using Improved Genetic Algorithm
## 4 Numerical Simulations
### Comparisons
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**No.** & **C1 AET** & **C1 WF** & **C1 BF** & **C1 AF** & **C2 AET** & **C2 WF** & **C2 BF** & **C2 AF** \\ \hline
5 & 17.5387 & 8.7288 & 8.7288 & 8.7288 & 40.7669 & 28.8014 & 28.8014 & 28.8014 \\
7 & 19.4081 & 16.2850 & 16.2850 & 16.2850 & 45.7278 & 38.2449 & 38.2449 & 38.2449 \\
10 & 24.9153 & 22.2850 & 16.4822 & 19.0011 & 53.4793 & 105.4628 & 90.2001 & 97.1588 \\
15 & 35.6762 & 45.6080 & 34.0422 & 38.5686 & 70.6147 & 160.8218 & 138.5077 & 148.4189 \\
20 & 46.5072 & 67.4052 & 58.3376 & 60.9117 & 86.0894 & 179.2049 & 167.3822 & 174.0670 \\
30 & 68.3224 & 120.4871 & 98.7836 & 103.3427 & 130.1552 & 229.6452 & 200.9169 & 212.8989 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Average execution time and fitness values of AL2. Figure 11: Optimal fitness values of IGA, AL1, and AL2 in two cases. (**a**) MTTP for FFSR1 and (**b**) MTTP for FFSR2. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**No.** & **C1 AET** & **C1 WF** & **C1 BF** & **C1 AF** & **C2 AET** & **C2 WF** & **C2 BF** & **C2 AF** \\ \hline
5 & 29.6717 & 2.9690 & 2.4773 & 2.5280 & 87.2751 & 3.2721 & 2.6073 & 2.6871 \\
7 & 41.7781 & 3.7754 & 3.7439 & 3.7680 & 102.3258 & 4.6112 & 4.1563 & 4.3424 \\
10 & 62.6735 & 5.6198 & 5.3880 & 5.5059 & 130.2154 & 6.0007 & 5.5044 & 5.7854 \\
15 & 88.4186 & 9.4253 & 8.6173 & 9.0766 & 165.2741 & 11.0144 & 9.5132 & 10.3125 \\
20 & 118.4722 & 13.2953 & 12.0814 & 12.7159 & 201.4602 & 15.8477 & 14.0814 & 14.5411 \\
30 & 168.7083 & 19.8873 & 16.9274 & 18.6520 & 268.4956 & 23.3607 & 21.0884 & 22.0983 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average execution time and fitness values of IGA.
# Simultaneous Quantitation of Isoprenoid Pyrophosphates in Plasma and Cancer Cells Using LC-MS/MS
## Materials and Methods
### Preparation of Stock, Calibration Standard and Quality Control Sample Preparation  
Aliquots from the original stock solutions of GPP, FPP and GGPP were mixed to prepare spiking solution mixtures and stored at \(-\)20 \({}^{\circ}\)C until used. The stripped plasma and phosphate buffer were used to construct calibration curves for the plasma and culture cell sample analysis, respectively.  
The calibration curve (CC) of the analytes ranged from 0.04 to 20 ng/mL. The charcoal-stripped blank plasma or buffer (300 \(\upmu\)L) was spiked with mix spiking analyte (10\(\times\)) solutions to achieve a final standard concentration of 30 \(\upmu\)L each, and then vortexed for 30 s. QC samples at four different concentrations (0.04, 1, 5 and 15 ng/mL), the lower limit of quantification (LLOQ), low quality control (LQC), middle quality control (MQC) and high quality control (HQC) were prepared separately in five replicates independently of the calibration standards. A synthetic analog, RAM1147, was used as the internal standard (IS). The IS (10 \(\upmu\)L) was added to all CC, QCs and study samples which were then extracted. All the main stocks, intermediate stocks, spiking calibration, and QCs stock solutions were stored at \(-\)80 \({}^{\circ}\)C.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Analytes** & **MRM Transition \(\boldsymbol{m/z}\) (Q1\(\rightarrow\)Q3)** & **Q1 (V)** & **CE (V)** & **Q3 (V)** & **Retention Time** \\ \hline
**GPP** & 313.1\(\rightarrow\)79.1 & 11 & 25 & 10 & 3.6 \\
**FPP** & 381.2\(\rightarrow\)79.1 & 14 & 35 & 26 & 5.8 \\
**GGPP** & 449.2\(\rightarrow\)79.1 & 15 & 25 & 22 & 7.5 \\
**RAM1147** & 356.2\(\rightarrow\)79.1 & 27 & 55 & 24 & 4.6 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Mass spectrometric parameters for individual isoprenoids (IsoPs): precursor to fragment ion transition, voltage potential (Q1), collision energy (CE) and voltage potential (Q3).
# A Review: Thermal Stability of Methylammonium Lead Halide Based Perovskite Solar Cells
###### Abstract  
Perovskite solar cells have achieved photo-conversion efficiencies greater than 20%, making them a promising candidate as an emerging solar cell technology. While perovskite solar cells are expected to eventually compete with existing silicon-based solar cells on the market, their long-term stability has become a major bottleneck. In particular, perovskite films are found to be very sensitive to external factors such as air, UV light, light soaking, thermal stress and others. Among these stressors, light, oxygen and moisture-induced degradation can be slowed by integrating barrier or interface layers within the device architecture. However, the most representative perovskite absorber material, CH3 NH3 PbI3 (MAPbI3), appears to be thermally unstable even in an inert environment. This poses a substantial challenge for solar cell applications because device temperatures can be over 45 \({}^{\circ}\)C higher than ambient temperatures when operating under direct sunlight. Herein, recent advances in resolving thermal stability problems are highlighted through literature review. Moreover, the most recent and promising strategies for overcoming thermal degradation are also summarized.  
photovoltaic; perovskite solar cells; thermal decomposition; thermal stability +
Footnote †: journal: Applied Physics Letters
## 2 Materials and Methods
### Characterization of AgNPs  
Silver nanoparticles, obtained by both green and chemical methods, were characterized using different techniques to determine their physical characteristics. UV-vis spectroscopy was performed using a METASH 5000M spectrophotometer. The spectra were recorded between 800 and 200 nm, with a step of 1 nm, using quartz cells. The nanoparticle samples were diluted with distilled water in a 1:10 ratio. X-ray diffraction was used to determine the crystalline nature and crystal size of the AgNPs-S and AgNPs-C, using a a Rigaku Ultima IV diffractometer. The radiation used was Cu K\(\alpha\) and the 29 degree range was from 20 to 80\({}^{\circ}\).  
The morphology and particle size of the AgNPs-S and AgNPs-C were evaluated using a Hitachi SU8230 cold-field emission scanning electron microscope with a voltage of 1 kV. The particle size distributions were calculated by dynamic light scattering (DLS), using a Litesizer 500 from [PERSON]. The measurements were taken with a semiconductor laser diode (\(\lambda\) = 658 nm) with a fixed side angle (90\({}^{\circ}\)) and a disposable cell in automatic mode, with the instrument optimizing the number of runs, the optical filter, and the focus position. The data were collected with a measurement duration of 10 s and the temperature of the sample at 25 \({}^{\circ}\)C. The colloidal stability was determined using zeta potential with the Litesizer 500 as an average of three measurements. For this analysis, the samples AgNPs-S and AgNPs-C were placed into an [PERSON] Paar Univette reusable cuvette. The temperature was set at 25 \({}^{\circ}\)C and the pH value of the dispersed silver nanoparticles was adjusted by adding aqueous 1 M HNO\({}_{3}\) or 1 M NH\({}_{3}\)OH. The amount of water, organic compounds, and, consequently, the silver concentration in the AgNPs-S and AgNPs-C samples were determined by thermogravimetric analysis (TGA), using a Mettler Toledo TGA/DSC 2+ thermal analyzer. The analysis was performed with a heating rate of 10 \({}^{\circ}\)C/min between 30 and 700 \({}^{\circ}\)C. Finally, Fourier transform infrared (FTIR) spectroscopy was used to determine the functional groups present in the organic compounds of the _Sargassum_ extract. The compounds involved in the reduction and stabilization of the AgNPs were synthesized with the _Sargassum_ extract, using a Perkin Elmer Spectrum Two FTIR spectrometer. On dried samples, the FTIR spectra were recorded using a wavelength from 400 to 4000 cm\({}^{-1}\).
# GSFedSec: Group Signature-Based Secure Aggregation for Privacy Preservation in Federated Learning
## 5 Performance Analysis
### Computation Overhead for [PERSON] et al. [5]
#### 5.2.2 Computational Cost on the Server Side  
1. Decryption Cost: Assuming that the authors are using the same algorithm for decryption, the cost of decryption is \(C_{DEC}\).
2. Verification Cost: To verify the signature, we must decrypt it using the sender's public key. Although the costs of normal decryption and signature verification are not identical, we can assume they are similar for a large calculation. Hence, for our calculation, \(C_{VER}\approx C_{DEC}\).
3. Aggregation Cost: FedAvg can be calculated using \(\Sigma_{k=1}^{K}p_{k}w_{t+1}^{k}\), which is equal to \(C_{AGG}=1.612\).  
Hence, the computational cost of the server is computed as the formula given in Equation (24):  
\[C_{Server}=C_{DEC}+C_{VER}+C_{AGG}. \tag{24}\]
# Design of a Device Based on an LED Matrix for Water Sterilization
## 1 Introduction  
Today, it is common to add chemical disinfectants such as bromine, chlorine, iodine, ozone, and chloramines to drinking water. Chlorine, due to its low cost and high sterilization effectiveness, is often the most popular choice. However, chlorine can react with organic matter and form byproducts that may be carcinogenic, mutagenic, cytotoxic, and teratogenic [1, 2]. Hence, it is crucial to develop new alternative strategies to eliminate pathogenic microorganisms present in water.  
The use of ultraviolet (UV) radiation can be an excellent solution for removing microorganisms from water. It offers several advantages, including the absence of harmfulsterilization byproduct formation, the elimination of the need to add chemicals, and the prevention of the emergence of bacteria resistant to disinfectants [3].  
UV radiation spans wavelengths in the electromagnetic spectrum between 200 and 400 nm. This radiation is divided into three regions: UV-A, UV-B, and UV-C. UV-C produces the most efficient sterilization, as its wavelength range corresponds to the peak absorption of nucleic acids (260 nm), resulting in molecular damage leading to cell death [4; 5].  
Mercury lamps have been widely employed in water sterilization systems, yet they are not deemed the optimal choice, owing to mercury being a toxic element and these lamps having a short lifespan [3]. Alternatively, light-emitting diodes (LEDs) are smaller, stronger, less polluting, and have greater durability [6]. Moreover, they can emit in a narrower wavelength range, allowing us to select the best LED for each application and combine them to treat multiple microbial targets [5].  
This study compares the antimicrobial properties of various UV-C LEDs obtained from different manufacturers, with a wide range of prices and different emission wavelengths. To enable their use as water sterilizers, an innovative encapsulation of these LEDs in flexible and transparent polymeric materials was carried out. The device with the best properties was selected to conduct studies on disinfection efficiency against a wide variety of pathogens, including bacteria, yeasts, and bacterial resistance forms (endospores). The influence of parameters such as bacterial concentration, sample volume, or exposure time and conditions on disinfectant capacity was analyzed. This information was used to construct a matrix of encapsulated LEDs whose efficiency was tested in laboratory cultures and environmental samples. The efficiency of this device paves the way for its expansion to practical applications in water treatment.
# Grey-Box Fuzzing Based on Reinforcement Learning for XSS Vulnerabilities
## 5 Evaluation
### Result and Analysis
#### 5.2.3 Answering RQ3: Resource Consumption  
The system resource consumption of fuzz testing consists of two parts, one is resources consumed by the target web application, and the other is resources consumed by our proposed method. The resources consumed by the target web application depend on its developer and are generally considered to be constant. However, as shown in Section 5.2.1, we found that providing more system resources to the target web application can reduce page loading time to a certain extent, thereby reducing false positives. The method proposed in this paper consumes fewer CPU resources but requires at least 64G memory to run. We think this is an acceptable cost of resources.
# Optical and SAR Image Registration Based on the Phase Congruency Framework
## 2 Proposed Method  
This section first introduces the PC algorithm [17] and then illustrates the proposed PC-Harris detector and the proposed PCLG descriptor. Figure 1 shows the framework of the proposed algorithm.
# On Microservice Analysis and Architecture Evolution:
## 5 Results and Discussion
### RQ1: Methods and Techniques Used  
There were several interesting things to note about the common trends among the types of methods. Of the two main methods of analysis, dynamic analysis is used more often than static analysis. Using dynamic techniques, several different approaches become feasible that cannot be done via static techniques, for example, performance analysis and optimization techniques as well as other metrics-based analyses [12; 32; 36; 39; 40]. A specific subset of dynamic techniques is commonly applied to fault analysis and root cause analysis: log analysis and execution trace analysis are perfect for this task, as they examine traces directly related to program execution [35; 40; 42; 43; 44].  
Most papers that used static analysis as their main method tended to do so to gather information specifically about the architecture. These techniques depend on analyzing statically defined artifacts to reconstruct an architectural view of a system, mainly analyzing source code [16; 26; 28; 30], but also other artifacts, such as OpenAPI specifications [15]. Static analysis is applicable to other goals as well, such as anti-pattern or code smell detection [26; 28; 29].  
An approach used less often is that of model-based analysis, in which a specific model is built to represent the microservice system. This can range from modeling dependencies and architecture of microservices [56; 57; 62] to developing security models [59] or performance [60] and resilience models [58; 61].  
Like model-based techniques, graph-based analysis depends on representing the microservice system as a graph and then analyzing that structure, exploiting microservices' natural graph-like connections. Graph-based techniques are commonly used for detecting faults or performing root cause analysis [47; 52; 54; 55], as well as performing monolith-to-microservice migration by representing an existing monolith as a graph that can be segmented into microservices [23; 24; 48; 49]. However, it can also be used in tracing patterns [27; 28]. Finally, graph-based methods are also used in monitoring and visualization systems [26; 53; 2].  
Another approach is pattern-based analysis that mostly employs static analysis to recognize specific patterns in the source code [27; 28; 29; 31] and architectural patterns [30]. However, it can use graphs as well [27; 28]. This approach also involves mathematical model checking techniques to evaluate microservice systems [30; 61; 62].  
The summary of our findings relating to RQ1 is shown in Table 5.
## 6 Defenses against Model and Data Attacks
### Model Pruning  
Pruning a neural network is a technique used to enhance the efficiency of the model by eliminating redundant or insignificant parameters, such as weights, neurons, or entire layers, without substantially compromising the network's performance [94]. This process involves identifying and removing those components that have minimal impacts on the network's output, thereby reducing the model's size and computational complexity [95]. The block diagram of model pruning is presented in Figure 8.  
There are two main types of pruning: structured and unstructured pruning. Structured pruning involves removing entire structures within the network, such as neurons, filters, or even layers [96]. This method simplifies the network architecture and leads to more efficient computation and memory usage as the remaining network can be more easily optimized for parallel processing and hardware acceleration. In contrast, unstructured pruning targets individual weights, removing the least important ones based on criteria such as magnitude [97]. While unstructured pruning can yield a highly sparse network, it often requires specialized hardware or libraries to fully capitalize on the sparsity as the remaining network structure is irregular.  
Pruning is widely used in FL [98; 99] and can significantly enhance the defense of local models by reducing the attack surface and improving model robustness [100]. By eliminating redundant or less important weights and neurons, pruning minimizes the impact of adversarial manipulations [101]. This reduction in model complexity makes it harder for attackers to inject malicious updates without detection [102]. Additionally, pruning improves the generalization ability of local models, making them less sensitive to minor perturbations introduced by attacks [103]. With fewer parameters, pruned models also reduce communication overhead during parameter sharing, enabling faster and more secure aggregation processes [17; 98]. Punning can be implemented on both the server and client sides.
## 4 Results and Discussion
### Distribution of DSM Services by Communication Protocol  
Based on the results obtained from individual LoRaWAN and LoRaMESH networks, the scenarios where each network can act and in which scenarios the hybrid network can be a more viable option have been collected in the Table 7 considering that DAP proposes this possibility of joining both topologies in only one AMI. As can be seen in the table, depending on the scenario to be implemented AMI, LoRaWAN or LoRaMESH topologies can be used either in a restricted or in a hybrid way. In the case of LoRaWAN, it is more suitable when the distribution of SMs is unregular (near or long the gateway), as well as more used when the SMs are in positions geographically distant from the gateway, and can reach up to approximately 2.35 km. LoRaMESH is best suited for scenarios with SMs distributed on a regular basis and with distances of up to 600 m between nodes, such as condomiums of houses and residential buildings. Hybridization will be recommended when the distance between the nodes exceeds the range distances of each restricted network, in this case, a LoRaWAN hybridization will be performed to reach greater distances and the use of LoRaMESH to connect several SMs that are nearby without overloading the LoRaWAN network. In the case of Smart Home at greater distances, LoRaMESH can be an option in houses with distances of up to 600 m, LoRaWAN would be an option in cases with distances of up to 2.35 m and hybrid if the maximum distance between the houses is approximately 3 km. For the cases of condominiums of houses and buildings, a mesh topology can be used inside the condominium and the condo for the EEC a LoRaWAN network, thus making the hybrid network and implementing the DAP for the condominium. Another option could be LoRaWAN, if the entire condominium is within the gateway's coverage area. In case of public buildings and industry 4.0, two factors should be considered (A) it can be a building with many devices or a very large area with the distributed SMs, (B) Generally these scenarios are more distant or distant from the urban area, so LoRaWAN can be an option but the hybrid will make more possibilities either of distance as of quantity of devices present in the scenario. The LoRa technology has by default the LoRaWAN protocol, however the Brazilian company Radioenge developed a new routing algorithm mesh for the LoRaMESH protocol based on RPL so that LoRa devices can operate under a mesh network. For reasons of industrial secrets this routing code is not open source, so it was not possible to make improvements or present a model that could become a standard to be used, however this work was limited only to apply it in practice in order to compare Radioenge's LoRaMESH protocol with the world standard LoRaWAN protocol, and analyze its viability for the proposed scenario. Based on the results obtained, it is possible to analyze the LoRaWAN and LoRaMESH protocols in the urban scenario considering all the specifications that HyDSMaaS requires so that it can function in a complete way and without further losses in