esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction  
Composite materials are increasingly used in ultralight aerospace designs. Composite load-bearing structures allow design and manufacture unmanned aerial vehicles (UAVs) that weigh no more than a dozen kilograms and have wingspans exceeding 20 m. The strength of the composite materials is no longer a noticeable barrier. High-strength composite materials that significantly reduce weight are already widely available. However, other problems have not been solved so far. One of the most important is related to the loss of stability [1; 2], which is the primary criterion for shaping such design structure. Among the other problems are the use of technologies to produce large-size structures from materials with thicknesses much less than 1 mm, and the challenge of designing large-size structures with extremely thin walls [3]. Forming a highly flexible structure, especially in aviation operating conditions--i.e., significant changes in geometry under varying load conditions--and aeroelastic phenomena associated with the operation of such an arrangement are issues that must be considered [4]. Structural shaping to meet the abovementioned requirements is becoming a fundamental issue. It is essential to analyze the spectrum of possible solutions at the concept and preliminary design stages in such conditions [5; 6; 7; 8; 9].  
Time-consuming analyses and geometric modelling of structures make this process ineffective. Therefore, in such a particular case, using highly flexible thin-walled aircraft designs, knowledge-based engineering methods, and specific generative modelling was proposed [10; 11]. This should allow the rapid generation of solutions and automate the support structure's modelling process based on simplified design criteria.  
The generative model's operation is combined with a set of simplified verification methods based on Finite Element Method (FEM) used for a simplified structure model based on integrated load states. This method allows one to quickly evaluate individual solutions and choose a solution for further optimization analysis, which is carried out in the following design stages [10; 11].  
To solve the problem mentioned in the introduction, the authors propose a design methodology based on the use of the generative modelling technique, and the principles of forming highly flexible aircraft structures and thin-film composites with special attention focused on the FEM validation of the preliminary geometry [12; 13].  
The background of these issues is presented in Section 2 (state of the art) and the methodology proposed by the authors is described in Section 3--developed methodology. For the methodology verification, the example of a HALE UAV built by the authors was used, and verification details are presented in Section 4--use case. The article ends with conclusions and an assessment of the effectiveness of the methodology used.
# Highway Microgrid Project Evaluation under Energy Transportation Integration
## 4 Case Study
### Evaluation Result Analysis
#### 4.3.3 Comparison of Evaluation Results under Different Methods  
In order to declare the availability of the evaluation method, the evaluation results calculated in this paper are compared with those calculated by the Delphi method and fuzzy comprehensive evaluation method, as shown in Table 4.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Evaluation Method**} & \multicolumn{2}{c}{**Scheme 1**} & \multicolumn{2}{c}{**Scheme 2**} & \multicolumn{2}{c}{**Scheme 3**} \\ \cline{2-7}  & **Score** & **Ranking** & **Score** & **Ranking** & **Score** & **Ranking** \\ \hline Textual method & 0.9127 & 1 & 0.7891 & 3 & 0.8264 & 2 \\ Delphi method & 0.7821 & 1 & 0.5743 & 3 & 0.6347 & 2 \\ Fuzzy comprehensiveevaluation method & 0.8443 & 1 & 0.7578 & 2 & 0.6951 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Evaluation results of different evaluation methods.  
Figure 3: Results of scheme evaluations.  
Figure 2: Results of the weights by three methods.  
Table 4 shows the results obtained by different evaluation methods. As can be seen from Table 4, the score of Scheme 1 is 0.7821, Scheme 2 is 0.5743, and Scheme 3 is 0.6347. The score calculated by the Delphi method is poor. The primary cause is that the Delphi method relies on the evaluator's experience, but ignores some objective factors. However, the comprehensive evaluation index system of the expressway microgrid in this paper involves many attributes and indicators, and the Delphi method cannot truly reflect the performance level of the expressway microgrid planning scheme. The evaluation method proposed in this paper combines the weight determination methods. The calculated comprehensive weight of indicators can reflect not only the knowledge and experience of evaluators, but also the objective information. In addition, it can be seen from Table 4 that the ranking by the fuzzy evaluation method is Scheme 1 > Scheme 2 > Scheme 3, which is different from the ranking results obtained by other methods, because the fuzzy comprehensive evaluation method can only evaluate the system containing model factors, and the evaluation information is repeated due to the correlation between evaluation indicators, which cannot be solved. The grey comprehensive evaluation method is a multi-attribute decision-making method, which can make up for the disadvantages of the fuzzy comprehensive evaluation method and make the evaluation results closer to the actual situation.
## 3 Materials and Methods
### GC/MS Analysis  
On an Agilent 5975C GC/MS (Agilent Technologies, Santa Clara CA, USA) with a HP-5 MS column (5% phenyl methyl siloxane, 30 m \(\times\) 0.25 mm, 0.25 \(\upmu\)m), the volatiles were subjected to GC analysis. At a flow rate of 1 mL/min, helium was utilized as the carrier gas. After being set at 45 \({}^{\circ}\)C for 1 min, the temperature was elevated to 250 \({}^{\circ}\)C at 10 \({}^{\circ}\)C/min, maintained at 250 \({}^{\circ}\)C for 50 min, and then programmed to hold at 280 \({}^{\circ}\)C for 1 min. A split ratio of 40:1 was used with a 1 \(\upmu\)L sample injection in split mode. The quadrupole was heated to 220 \({}^{\circ}\)C, and the ion source temperature was adjusted to 280 \({}^{\circ}\)C. The multi-channel plate voltage of 70 eV was applied, and the detector was set to operate in EI mode with a \(m/z\) range of 50-500.  
All compounds identification was performed using the mass spectra, linear retention index (LRI), and Kovats retention index (KRI) data. The retention indices (RI) of every compound were calibrated with C5-C30 alkanes, comparing them to the mass spectra in the NIST libraries. By using peak area normalization [37; 38; 39], the relative amounts of the identified volatile compounds were quantified.
## 4 Nanostructured Materials for Uranium Removal
### Application of Carboxyl-Functionalized Graphene Oxide (COOH-GO) Material for U(VI) Removal  
The effect of increasing the presence of specific chemical functional groups, such as the carboxyl groups, on the selectivity of uranium sorption was investigated by using a carboxyl-functionalized graphene oxide (COOH-GO) modified material, which was studied in comparison with the simple graphene oxide (GO) and with graphite [84]. According to [PERSON] et al. (2018), the modified COOH-GO demonstrated superior performance as a sorbent material for the selective removal of uranyl ions from aqueous solution with distribution coefficient value, \(K\), \(3.72\pm 0.19\times 10^{3}\) mL g\({}^{-1}\) in comparison to \(3.97\pm 0.5\times 10^{2}\) and \(2.68\pm 0.2\times 10^{2}\) mL g\({}^{-1}\) values for GO and graphite, respectively. Moreover, COOH-GO presents a higher sorption capacity for U (\(Q_{\text{max}}=169\) mg g\({}^{-1}\)) and shows a greater selectivity towards U with \(65.9\pm 2.7\%\) retained in the presence of competing ions in comparison to \(38.9\pm 1.2\%\) value, observed for GO, at the optimum pH 4. These enhanced values are most probable due to the effect of selective surface groups presence, such as the carboxyl.
## 2 Materials and Methods
### Preprocessing  
Firstly, image normalization was performed, changing the pixels' value range to [0; 1; 2] to stabilize the gradient descent step during training. Secondly, centre cropping or padding was performed to homogenize the shape of the images to 256 \(\times\) 256 according to the model's input requirements. The last pre-processing step is the minimum filtering used to discard the potential noise in the background (pixels close to zero). MR images often exhibit regions with pixels that are near or reach values close to zero. The aim is to isolate and preserve these pixels inside the body, as they represent regions with minimal signal and are potentially crucial for maintaining the overall structure of the image. This threshold determination is grounded in a histogram-based examination and utilization of the anatomical information from MRI images. The visualization of the minimum filtering is depicted in Figure 2. Data augmentation was also performed for the training process, thereby contributing to overfitting deterrence by increasing the number of samples. More specifically, the data augmentation was random vertical flipping, random rotation with a limit of \(\pm\)15\({}^{\circ}\) and arbitrary scaling with a full scale of 10% of the original image size.
## 3 Results
### Antibacterial and Antifungal Activities and MIC  
The antimicrobial activity and minimum inhibitory concentration of seven different snail protein was performed and tabulated in Tables 1-4. In the investigation of [PERSON] and [PERSON] (2002) [4] on the antibacterial activities of ethanol extracts of gastropod _Babylonia spirata_ and _Turbo brunneus_, they observed highest activity against _E. coli_, _K. pneumoniae_, _P. vulgaris_ and _S. typhi_. Antibacterial activity was evaluated for seven different snail proteins against various pathogenic cultures. Chloramphenicol (100 \(\mathrm{\SIUnitSymbolMicro mL}\)) was used as a positive control and it showed a maximum 25 mm zone of inhibition in tested pathogens. The snail _A. fulica_ protein in high concentration (100 \(\mathrm{\SIUnitSymbolMicro mL}\)) has antibacterial activity but the land snail _C. bistrialis_ protein showed the maximum zone of inhibition in at low concentration (25 \(\mathrm{\SIUnitSymbolMicro mL}\)) (Figure 3). The snail _P. globosa_ protein showed activity only against _S. aureus_. The remaining snail proteins such as _P. virens_, _B. dissimilis_, _B. (Digoniostoma) pulchella_ and _M. tuberculata_ did not show activity in antibacterial and MIC against any of the selected bacterial cultures. In antifungal activity the snail _C. bistrialis_ protein also showed potential activity against _C. albicans_, _P. chrysogenum_, _A. fumigatus_, _M. racemosus_, as compared to other snail proteins. Antifungal and MIC results also showed that the land snail _C. bistrialis_ protein inhibits the microbial growth at low concentration (Figure 3). Figure 3: _Cont_. Figure 2: A 12% SDS-PAGE analysis of protein extracts from seven different snail species. Lane M, medium-range protein molecular weight markers (labelled in kDa); lane 1, _Achatina fulica_; lane 2, _Cryptozona bistrialis_; lane 3, _Pila globosa_; lane 4, _Pila virens_; lane 5, _Bellamya dissimilis_; lane 6, _Bithynia (Digoniostoma) pulchella_; lane 7, _Melanoides tuberculata_. _Appl. Sci._**2018**, \(8\), 1362
## 5 Results and Discussion
### Length Measurement Results and Discussion
The deviation of Joint2 is between \(-\)0.133 and +0.042, and the mean deviation is \(\pm\)0.056. By comparing the deviations of Joint2 to Joint1, the model consistently performs well, despite weak signals and complex defects in Joint2. Figure 10: Comparisons of the lengths measured by the proposed method and the designed values: (a) comparison of Joint1; (b) comparison of Joint2; (c) deviations from the designed values of Joint1; (d) deviations from the designed values of Joint2. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Welded Joint Number** & **Defect Number** & **Designed Value** & **Measured Value** \\ \hline \multirow{5}{*}{Joint1} & D1 & 25 & 23 \\  & D2 & 25 & 24 \\  & D3 & 25 & 26 \\  & D4 & 25 & 27 \\  & D5 & 25 & 24 \\ \hline \multirow{5}{*}{Joint2} & D1 & 15 & 17 \\  & D2 & 15 & 16 \\ \cline{1-1}  & D3 & 20 & 21 \\ \cline{1-1}  & D4 & 25 & 27 \\ \cline{1-1}  & D5 & 25 & 24 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparisons of the lengths measured by proposed method and designed values.
# Wave-Shaped Microstructure Cancer Detection Sensor in Terahertz Band: Design and Analysis
## 3 Detail Design of the Proposed HC-PCF  
A HC-PCF has tremendous potential to increase sensitivity thanks to its greater volume in the core area. Additionally, a hollow core significantly minimizes the amount of background material used during manufacture, which in turn lowers material absorption loss. We chose to design our proposed sensor in the Terahertz band because of its high sensing characteristics in this regime [31; 32] and the Electromagnetic Interference [33] is very low at the Terahertz band. The Terahertz band also offers low attenuation loss according to the fiber loss curve. The suggested HC-PCF's inter-section is shown in Figure 3. This article uses the FEM as a computational tool to investigate mode propagation characteristics. The simulations were run using the program COMSOL Multiphysics 5.5.  
The wave pattern cladding consists of six-layer ring-shaped air holes labeled as C1, C2, C3, C4, C5, and C6, having radii of R1, R2, R3, R4, R5, and R6, respectively, with an equal width of \(T\). The diameter of the core air holes is labeled as \(D_{c}\). The structural parameters for the proposed PCF are \(R1~{}=~{}510~{}\upmu\)m, \(R2~{}=~{}710~{}\upmu\)m, \(R3~{}=~{}910~{}\upmu\)m, \(R4~{}=~{}1110~{}\upmu\)m, \(R5~{}=~{}1310~{}\upmu\)m, \(R6~{}=~{}1510~{}\upmu\)m, \(T~{}=~{}190~{}\upmu\)m. The thickness of the solid cladding layers is called the strut. An optimal strut thickness is retained to prevent manufacturing difficulty during the whole process. A variety of strut widths, \(S\), of \(10~{}\upmu\)m and \(20~{}\upmu\)m are considered, In current manufacturing methods, a \(6.5~{}\upmu\)m strut width is bearable [27; 34; 35]. The PML has a radius of \(1710~{}\upmu\)m with a thickness of \(185~{}\upmu\)m. Any change in the form or dimensions of the cladding might affect CL but have absolutely no effect on the core analyte.  
As an anti-reflective layer to block incoming PCF waves, a PML is positioned at the cladding's edge, according to [8]. The breadth of PML is significant because of its superior impact on numerical analysis. As a consequence of this test, it was discovered that the best results were obtained when the PML was thicker than 10 percent of the whole diameter. It should be pointed out that the RI of various cancer cells and their corresponding normal cell, such as the breast, blood, skin, cervical, and adrenal glands are shown in Table 1.  
\begin{table}
\begin{tabular}{c c c c} \hline \multirow{2}{*}{**Cancer Type**} & \multirow{2}{*}{**Cell Type**} & **RI of Normal Cell** & **RI of Cancerous Cell** \\  & & **(Concentration** & **(Concentration** \\  & & **Range 30-70\%)** & **Range 80\%)** \\ \hline Breast (type 1) & MBA-MD-231 & 1.385 & 1.399 \\ Breast (type 2) & MCF-7 & 1.387 & 1.401 \\ Blood & Jurkat & 1.376 & 1.39 \\ Skin & Basal & 1.36 & 1.38 \\ Cervical & HeLa & 1.368 & 1.392 \\ Adrenal glands & PC-12 & 1.381 & 1.395 \\ \hline \end{tabular}
\end{table}
Table 1: RI and concentration level of various cancer cell (breast, blood, skin, cervical, and adrenal glands) and corresponding normal cell [36].
# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 5 A Case of Use of Meteorological Information Service Modeling
### Platform Configuration  
In this section, it will be shown how we can model and configure a device within the Thinger.io platform. After registering on the platform Thinger.io, it will be necessary to register two resources  
Figure 17: Thinger.io physical interface.  
Figure 18: Integrated sensors in Thinger.io.  
within the administration panel. In this case, we will have to register a device, which will be the one that connects to the platform to provide the information of the sensors (in our case, the ClimaStick), and we will also have to register a data bucket that allows the information to be saved over time. The management of these resources is documented in more detail at [[http://docs.Thinger.io/console/](http://docs.Thinger.io/console/)]([http://docs.Thinger.io/console/](http://docs.Thinger.io/console/)), but here the basic steps necessary for the development of the case study are provided. To create the device, we will access the _Devices_ section of the side menu of the platform, from which we can manage the devices associated with our user account.  
Once we are in this section, it will be necessary to register the device by clicking the _Add Device_ button in the list of devices, which will open a section to record basic information about the device, such as its identifier, a description, and its access credentials. It is important to keep the information of the identifier and the access credentials, because then it will be necessary to use it in the code that will be programmed in the device. For this practical example, the identifier of the device is _Climastick_, and its credentials are h4 Pxg!a1 aDEK, as can be seen in Figure 19.  
To create the data bucket, analogously, we must go to the corresponding section of the administration console, called _Data Buckets_ in this case.  
Once we are in this section, it will be necessary to register the data bucket by clicking the _Add Bucket_ button in the data bucket list, which will open a section to configure the information store. In the same way as with the device, it will be necessary to assign an identifier of the data bucket, i.e., a name and a description, if we want to activate it, or the source of data from which the information will be taken. In this case, we will create a data bucket with the identifier _MeteoBucket_, which will be activated, and the data source will be _From Write Call_, that is, the device will start writing the information when it deems necessary. Figure 20 depicts an example of the mentioned data bucket configuration.  
Figure 19: Adding a device in Thinger.io.  
With these two resources created, we can now prepare our device so that, on the one hand, it connects to the platform using the generated credentials, and on the other, it sends weather information to the data bucket.
## 5 Future Scope and Applicability of the Proposed Model in Seaports
### Safety Concerns or Challenges in Seaport Operations  
An alarming trend in seaport operations is the escalating number of accidents, particularly since 2016 [50]. Figure 4 shows that the number of deaths and injuries at port container terminals is increasing every year. The Korea Port Logistics Association's 2022 Port Unloading Disaster Statistics and Cases report underscores a concerning reality--fatalities and injuries within port container terminals are on the rise [51]. These accidents primarily involve interactions between port equipment and workers, indicating a pressing need for heightened safety awareness and the application of intelligent technologies. Integrating smart solutions, such as IoT-based sensors and real-time monitoring systems, can substantially enhance the safety protocols in seaports. By leveraging these technologies, the ports can proactively identify the potential risks, create safer work environments, and significantly reduce accidents, thereby safeguarding the well-being of both the workers and the equipment.  
Implementing an innovative framework in real-world seaport environments requires comprehensive assessment and validation. Pilot studies, such as those analyzed in the Tanjung Intan Seaport, can determine the performance under actual conditions [52]. Quantitative metrics, such as those proposed for the Port of Rotterdam, should be established in order to measure the throughput, congestion, and efficiency [53]. A comparison of the new framework against the existing methodologies could utilize user perception metrics for a more rounded evaluation [54]. The adaptability of the framework to various seaport sizes can be guided by a scalable management platform approach [55]. User feedback is essential for qualitative validation, ensuring that the usability and effectiveness meet the stakeholder expectations. An environmental impact assessment is necessary in order to evaluate sustainability efforts, a key aspect of modern port management. An economic analysis should determine the cost-benefit and return on investment in order to justify implementation. Safety, security, and data integrity are paramount to protect against threats and ensure reliable decision making. Lastly, adherence to industry standards and regulations will ensure compliance and facilitate broader acceptance of the framework [56].
## 2 Solar-Hydrogen Storage System
### Components
#### 2.2.4 Fuel Cells  
Fuel cells serve an important role in completing the energy cycle while providing significant efficiency and environmental benefits. These electrochemical systems transform hydrogen's chemical energy directly into electricity, with only water vapor and heat as byproducts. Fuel cell efficiency is a significant advantage over traditional combustion-based energy conversion technologies [43]. Traditional power production systems, such as internal combustion engines, sometimes entail many energy conversion processes, each of which results in energy losses. Fuel cells, on the other hand, are more efficient, turning more than 50% of the energy content in hydrogen into electricity. This simpler energy conversion process improves overall system efficiency by minimizing energy waste and boosting the energy cycle's sustainability [44].  
Furthermore, the environmental benefits of fuel cells derive from their clean and emission-free operation. Unlike combustion-based power generation, which emits pollutants and greenhouse gasses, fuel cells generate energy via an electrochemical reaction that does not involve combustion [45]. The principal byproduct of fuel cell operation is water vapor, making them an environmentally favorable option for power generation. This quality is especially important in the context of mitigating climate change and lowering air pollution [46]. As the demand for clean energy solutions grows, fuel cells emerge as a potential technology to address both efficiency difficulties and the environmental consequences associated with traditional energy cycles. Fuel cells' adaptability, from small-scale applications like portable electronics to larger-scale deployments in cars and stationary power systems, positions them as a crucial element in developing a sustainable and low-emission energy future [47].
## 3 Proposed Procedure
### Algorithms and Optimization Procedures for Efficient Processing of Vector Data
#### 3.4.4 Data Validation and Standardization  
The final stage involves validating and standardizing the generated data to ensure accuracy and consistency. The process verifies compliance with CityGML 3.0 standards to guarantee interoperability while comparing vector and raster data to evaluate speed, capacity, and accuracy, thereby demonstrating the advantages of the vector-based approach. The quality assessment of interpolated textures using the WIT technique confirmed improvements in visual continuity and boundary processing performance compared to traditional raster-based texture mapping methods, which rely on fixed-resolution pixel grids. These conventional methods often suffer from aliasing artifacts, resolution-dependent blurring, and distortions when applied to curved or non-rectangular surfaces.  
Experiments conducted using OpenGL 2.21.0 and CesiumJS 1.123 demonstrated that the WIT (Wise Interpolated Texture) implementation maintained rendering quality while reducing texture discontinuities and enhancing visual coherence.