esa-sceva/satcom-chunk-collection huggingface dataset:
# Numerical Evaluation of Residual Stress Influence on SIF in CT Specimen
## 3 Residual Stress Modeling  
The original stainless-steel plate was pre-strained by rolling at very high ratios (between 30% and 60%). In fact, the pre-straining was not homogeneous through the thickness, which generated a residual stress profile to ensure strain compatibility. In this paper, the focus will be on the plate pre-strained at 40%.  
There is no straightforward method for residual stress application at the numerical model in most FEA codes. It is not always possible to obtain a correct distribution of residual stresses using only external mechanical loading, such as force or pressure. That is why, in this case, thermal loading was used. It was based on the assumption that thermal loading and residual stresses are equivalent to the imposed strain loading conditions. The change in the temperature compared to the base temperature makes the material expand or contract. The amount of expansion is described in the material model by the thermal expansion coefficient. Temperature itself cannot induce stress or strain if the expansion or contraction of the material is not restricted. Only then, if the expansion or contraction is restricted, can the strain in the material appear. The restriction in the model can be applied by the boundary conditions or by the opposite thermal load similar to the forces acting in opposite directions. According to this, we applied the temperature profile to the model in such a way that the induced stress profile was similar to the measured residual stresses. In addition, this obtained stress profile was used for the evaluation of residual stress influence on SIF.  
The main challenge of thermal load application is to find the correct temperature profile distribution through the thickness of the plate which leads to a similar residual stress distribution that was measured experimentally. The next step is to run the CT specimen tension simulation for SIF calculation by applying the defined temperature distribution.  
For the determination of the thermal profile for residual stress application and SIF calculation, the FE code Cast3M was used. This code was developed in DEN/DM2S/SEMT CEA Saclay, France and is used for solving various problems, especially for fracture mechanics [19].
## 2 Presentation of Components, Data Acquisition Methodology and the System
### Presentation of the Complete System  
A raw EEG signal archived by EmotivPRO software may be exported and investigated in any software environment. Figure 3 shows a signal analysis dashboard in the set manufacturer's software  
\begin{table}
\begin{tabular}{c c c c} \hline \(LH-Fp1\) & \(LD-FC5\) & \(RA-C4\) & \(LJ-Pz\) \\ \hline \(RH-Fp2\) & \(LC-FC1\) & \(RB-T8\) & \(RN-P4\) \\ \hline \(LG-F7\) & \(RC-FC2\) & \(LQ-CP5\) & \(RO-P8\) \\ \hline \(LF-F3\) & \(RD-FC6\) & \(LP-CP1\) & \(LM-PO9\) \\ \hline \(RK-Fz\) & \(RE-FT10\) & \(RP-CP2\) & \(LL-O1\) \\ \hline \(RF-F4\) & \(LB-T7\) & \(RQ-CP6\) & \(RJ-Oz\) \\ \hline \(RG-F8\) & \(LA-C3\) & \(LO-P7\) & \(RL-O2\) \\ \hline \(LE-FT9\) & \(LK-Cz\) & \(LN-P3\) & \(RM-PO10\) \\ \hline \end{tabular}
\end{table}
Table 1: Names of electrodes.  
Figure 1: Data analysis and robot control systems.  
Figure 2: Presentation of electrodes configuration and place of research.  
In the case of the investigations carried out, all analyses and classification methods were performed with Matlab, a platform for programming and numerical calculations. The system operation diagram is shown in Figure 1. This program makes proper signal filtering for their further proper analysis and classification possible. In the next stage, the signal is integrated after time for each of 32 electrodes separately. It takes place within one second for the whole period of the signal duration. In this way, one-second integrated samples are obtained. They will make it possible to determine and clearly classify the type of a signal based on the evoked potential subjected to integration (Details in Section 2.2).  
The obtained samples containing the signal integrated after time can be subjected to proper classifying algorithm. Two algorithms classifying integrated samples of the EEG signal collected from the electrodes were suggested. Figure 1 effectively shows the discussed issue. The first of them is a classic algorithm comparing the values of the particular electrodes. The other one is based on a multilayer neural network. A user may choose between the two classifying systems. The recognized signal evokes a move order forward, backward, to the right or to the left. It can be passed to the robot to perform the requested orders (see Figure 1).
# Design and Development of Bladeless Vibration-Based Piezoelectric Energy-Harvesting Wind Turbine
## 1 Introduction
The rigidity of the resonant structure can be modified by applying magnetic forces to it, increasing the operational life and applicable wind speed range. The energy from the rhythmic movement can be converted into electricity using electromagnetic induction. This paper aims to develop a VIV wind energy-harvesting device that converts mechanical energy (vibrations) to electric energy. The proposed device generates energy while utilizing aeroelastic resonance phenomena. The device is bladeless and has an elastic rod that oscillates when the frequencies of both the material of the elastic rod and the airflow become equal. The device uses piezoelectric sensors located at three different locations to harness energy. Different geometrical designs were used to gauge the performance of each and determine the optimal shape/geometrical design that may increase the harvested energy. In the following section, the design methodology of the suggested design is introduced.
# Supramolecular Materials as Solid-Phase Microextraction Coatings in Environmental Analysis
## 4 Covalent Organic Frameworks (COFs)
### COFs as SPME Coatings for Environmental Applications
#### 4.3.3 Extraction of Polybrominated Diphenyl Ethers and Polyhalogenated Biphenyls
This behavior could be ascribed to \(\pi\)-\(\pi\) stacking and hydrophobic interaction between the target molecules and the coating, presenting mesopores able to accommodate PCB molecules. The HS-SPME-GC-MS method exhibited excellent performance for the determination of 17 PCBs, with LOQs in the 0.005-0.029 ng/L range, a linearity over five orders of magnitude, and good precision, with RSDs \(\leq\) 9%. Finally, the method was applied for the analysis of sea, river, and reservoir water, detecting PCB-209 in all samples at sub-ng/L concentration levels. Finally, [PERSON] et al. developed an SPME coating using a 3D COF [99]: four different 3D COFs were synthesized using tetra (\(p\)-aminophenyl) methane (TAM) and aldehyde reagents (Tp, 1,3,5-triformylbenzene, terephthalalaldehyde and 1,1'-biphenyl-4'-dicarbaldehyde) under solvothermal conditions and each coating was deposited onto etched SS fibers using an epoxy glue. The TpTAM coating showed a higher BET surface area (537.2 m\({}^{2}\)/g), larger pore volume (0.32 cm\({}^{3}\)/g), sharper pore size distribution (0.68 nm), and higher EFs (6940-10,305) compared to the other developed materials. The adsorption of both planar and non-planar PCBs was enhanced by \(\pi\)-\(\pi\) stacking, hydrophobic interactions, halogen bonds, and steric complementarity between host and guest. Finally, the selectivity of the material towards the investigated analytes was proved by the achievement of EFs lower than those calculated for PCBs and biphenyl when structural PCBs analogs, including \(o\)-dichlorobenzene, naphthalene, anthracene, and pyrene were considered. An excellent HS-SPME-GC-MS method performance was obtained, with LOQs in the 0.004-0.066 ng/L range, recoveries in the 85-117% and 85-115% ranges for water and soil, respectively, and precision, with RSDs \(<\) 11%.
# PVPBC: Privacy and Verifiability Preserving E-Voting Based on Permissioned Blockchain
## 1 Introduction
### Contributions  
To conceal the link between the voter and ballot during the authentication phase in such a way that a cryptographic action by voters is not required, an authentication protocol which keeps the voter's identity confidential is introduced in this paper. The protocol uses an access model which is based on the revocable-anonymity concept where incorporating identity revocation in an anonymous communication system is achieved using a permissioned distributed ledger (DL) and smart contract. To ensure resilience, trust, and privacy, e-voting requires a framework for access control which matches the decentralized attribute of e-voting.  
To provide E2E verifiability, the Selene e-voting scheme [7] is employed as an e-voting scheme which is designed to support individual and universal verifiability. This means voters and observers can check the validity of votes during the election and post-election phases. Selene simplifies the voter's experience during the vote-casting time by hiding the cryptographic complexity from the voter. The proposed system uses DL technology within Selene to manage cryptographic primitives.  
We summarise our contributions as follows:  
* We propose an e-voting framework, which is called the PVPBC voting system, which preserves crucial e-voting features: (i) voter privacy and anonymity by making use of a permissioned ledger technology and smart contracts; and (ii) E2E verifiability by making use of the Selene voting scheme and a permissioned DL. Importantly, these crucial features are provided without affecting user experience, as the PVPBC framework does not affect the protocol fulfilled by the voter when they are voting.
* We propose a comprehensive architecture for a capability-based authorization protocol, which can be used to authenticate voters in e-voting platforms. The proposed approach supports dynamic voter authentication in e-voting systems based on an access-control model which supports revocable anonymity, as a result of using permissioned DLs. The protocol includes capability management and access-right validation.  
Compared to existing work, the PVPBC voting system we introduce has a number of benefits, which we emphasize here: (i) it supports usability preservation for voters in the sense that voting is the only task required of the voter; (ii) the system integrity and security is supported by a distributed authentication mechanism based on DL technology; (iii) the system is verifiable by all parties; and (iv) the system fulfils vote privacy as well as voter privacy.
# A Review on Solar Energy Utilization and Projects: Development in and around the UAE
## 9 Solar Energy Industry
\begin{table}
\begin{tabular}{p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt} p{14.2 pt}} \hline \hline
**Solar Projects in Abu Dhabi** & **Capacity** & **Panel Type** & **Area** & \begin{tabular}{c} **Reduction of CO\({}_{2}\)** \\ **Emissions/** \\ **Annually** \\ \end{tabular} & **Main Characteristics** \\ \hline \multicolumn{5}{p{14.2 pt}}{Solar PV Farm-Masdar City} & 10 MW & PV & 220 km\({}^{2}\) & 15,000 tonnes & \begin{tabular}{c} This facility generates 17,500 MWh of renewable power /year. Suntech and First \\ Solar Company use a total of 87,777 \\ polycrystalline (silicon) and 69,048 thin-film \\ modules (CdTe). \\ \end{tabular} \\ \hline \multicolumn{5}{p{14.2 pt}}{Srams 1 plant} & 100 MW & CSP & 2.5 km\({}^{2}\) & 175,000 tonnes & \begin{tabular}{c} The world’s first and biggest operational CSP \\ plant in North Africa and the Middle East. The project will cost USD 600 million. It is the first CSP plant to be recognized by the United Nations as a Clean Development Mechanism (CDM) project. This project also incorporates a dry-cooling system. \\ \hline \multicolumn{5}{p{14.2 pt}}{Noor 1 solar plant} & 100 MW & PV & \begin{tabular}{c} This plant is located in Al Ain, Abu Dhabi. The first will consist of around 50 MW of crystalline PV modules, while the second will consist of approximately 50 MW thin-film modules east of Al Ain city, with an annual output projection of 170 GWh. \\ \end{tabular} \\ \hline \multicolumn{5}{p{14.2 pt}}{Um Al Zomul off-grid PV plant} & 100 kW & PV & 130 tonnes & \begin{tabular}{c} The desert off-grid installation plant. This plant’s solar energy penetration is based on \\ 100\% of the farm’s power demand. \\ \end{tabular} \\ \hline \multicolumn{5}{p{14.2 pt}}{Murawah Island} & 500 kW & PV & 710 tonnes & \begin{tabular}{c} On Murawah Island, Masdar built a grid-connected solar PV facility off the coastline of the UAE. \\ \end{tabular} \\ \hline \multicolumn{5}{p{14.2 pt}}{Abu Dhabi-Rooftop solar program} & 2.3 MW & PV & 3220 tonnes & \
\begin{tabular}{c} Installed in 11 municipality engineering houses. Eleven rooftop systems were installed due to this program. The initiative is intended to encourage private individuals, businesses, and other entities to install solar panels. \\ \end{tabular} \\ \hline \multicolumn{5}{p{14.2 pt}}{Sweihan independent power project} & 1177 MW & PV & 780-hectare & 7 million tonnes &
\begin{tabular}{c} Based on the IPP model. The project’s anticipated investment is USD 870 million. The project is being carried out by the DoE in collaboration with the Japanese firm Marueni and the Chinese manufacturer JinkoSolar. JinkoSolar will supply high-efficiency monocrystalline PV modules for the solar power plant. DoE owns 60\% of the joint venture, while Marubeni and Jinko Solar share the remaining 40\%. \\ \end{tabular}
\end{table} 
Table 4: List of solar energy projects in Abu Dhabi emirate with main characteristics.
## 2 Materials and Methods  
### Chemistry  
The NMR spectra were recorded on Bruker Advance spectrometers (\({}^{1}\)H at 600 MHz, \({}^{13}\)C at 150 MHz) in CDCl\({}_{3}\) and DMSO\({}_{66}\). Two-dimensional COSY, ROESY, HSQC, and HMBC spectra of compound **8** were recorded on a Bruker Advance spectrometer at 600 MHz. All NMR analyses were performed at room temperature using the standard TopSpin 3.7.0 software. The HR MS spectra (ESI--Electro Spray Ionisation) were run on a Brucker Impact II. The samples were prepared as solutions in acetonitrile. Melting points were determined in open capillary tubes on a Boetius melting point apparatus. TLC was performed on silica gel 60 F\({}_{254}\) (Merck 1.05735) with CHCl\({}_{3}\)-EtOH (10:1 \(v/v\)) and on Al\({}_{2}\)O\({}_{3}\) 60 F\({}_{254}\) neutral (type E) (Merck 1.05581) with CHCl\({}_{3}\)-EtOH (10:1 \(v/v\)) as eluents.  
The parent starting dipyridothiazines **1**-**4** (Scheme 1) and ludidine derivatives **6a**-**9a** were prepared according to reference [25].
## 6 Discussion
### Spatial Analysis of GPP Datasets  
We noticed nuanced insights into the factors influencing GPP in various regions of Turkishye. Samsun, positioned along the Black Sea coast, emerged as the region with the highest average GPP, consistent with the wetter conditions (Figure 1) and coniferous forests in the area, including the Anatolian black pine (_Pinus nigra_) that has a relatively high photosynthetic capacity [99] and is relatively insensitive to ecoclimate setting [100]. Additionally, Samsun's climate, particularly is relative humidity, is more favorable to plant growth than other regions, resulting in a relatively lower vapor pressure deficit (VPD). GPP is strongly constrained by VPD [101] and the combination of favorable climate and dense forest cover create an optimal environment for high GPP. The Kirklareli region, known for its extensive agricultural activities, displayed the next highest average GPP. This region, situated in the north of Thrace, benefits from the western Black Sea's precipitation patterns, receiving rainfall levels above the national average (Figure 1). The Izmir and Antalya regions, both influenced significantly by their coastal locations, exhibit distinct GPP dynamics. Izmir's annual average precipitation tends to be lower than that of the coastal regions of Samsun and Antalya (Figure 1). However, its average temperature is higher than Samsun's and similar to Antalya's. The warmer temperatures in Izmir favor agricultural practices that depend on warm and humid conditions, and this intensive agricultural management impacts GPP, such that the selected region of Izmir has higher GPP than Antalya, albeit with similar trends (Figures 7 and 9).  
The Konya region is one of the least rainy areas in Turkishye and has a predominantly arid climate (Figure 1). The majority of agricultural activities in this region rely on dry-land farming due to the limited availability of water. Konya has little forest cover, and the vegetation is largely dependent on the diversity of seasonally cultivated agricultural crops. According to data from the Turkish Statistical Institute (TUIK), the cultivated area in the region has increased from 1,326,220 hectares in 2004 to 1,507,451 hectares in 2022, which is consistent with the observed trend of increasing GPP values in the study region (Figures 7 and 9). Similarly, the Sanliurfa region is characterized by low rainfall but has greater irrigation intensity than Konya, which supports large-scale agricultural operations [102]. After Konya, Sanliurfa has some of the lowest average GPP values among the regions studied, but likewise with an increasing trend (Figures 7 and 9). These findings highlight the complex interplay between climatic factors, vegetation types, and topography in determining regional GPP and point to the importance of understanding ongoing GPP trends for ecological management and forecasting the impacts of climate change on regional productivity.
## 2 Basic Theory
### Few-Shot Learning  
Few-shot learning is a type of machine learning problem (defined by the variables experience \(E\), task \(T\), and performance metric \(P\)), where experience \(E\) only contains a small number of examples and supervised data about task \(T\)[20]. The model can be well-extended to new tasks and trained with a relatively small number of samples, or \"learning to learn\" [39], as seen in Figure 2. The majority of the newly proposed few-shot learning techniques pull knowledge from some auxiliary sets from the source domain to train the model with a given small sample support set in the target domain.  
With multiple learning experiences and many applications, it is initially trained using a variety of sample types belonging to the same or distinct categories. The input is a sample pair with the same or different classes (\(x_{1}^{i}\),\(x_{2}^{i}\)). The output is the probability that (\(x_{1}^{i}\),\(x_{2}^{i}\)) two input samples are the same. In contrast to conventional classification techniques, few-shot learning frequently expresses performance in terms of \"\(K\)-way \(N\)-shot.\" In a \(K\)-way test, given a test sample \(\hat{x}\) for classification, the support set \(S\) is shown in Equation (1), which contains \(K\) samples, each with a different label \(y\).  
\[S=\{(x_{1},y_{2}),\ldots,\,(x_{K},y_{K})\} \tag{6}\]  
As stated in Equation (2), the test samples are subsequently classified based on the most similar samples in the support set.  
\[C(\hat{x},S)=\operatorname*{argmax}_{c}(P(\hat{x},x_{c})),\ x_{c}\in S \tag{7}\]  
In the \(K\)-way \(N\)-shot test, the model is given a support set consisting of \(K\) different classes, each with \(N\) samples (\(S_{1}\), \(S_{2}\),..., \(S_{N}\)). The model must then determine which support set class the test samples should belong to, as shown in Equation (3).  
\[C(\hat{x},(S_{1},\cdots,S_{N}))=\operatorname*{argmax}_{c}(\sum_{n=1}^{N}P( \hat{x},x_{cn})),\ x_{cn}\in S_{n} \tag{8}\]  
Even though few-shot learning-based methods can attain the needed accuracy based on a tiny dataset, they require base data for pre-training, and the degree of relevance of the  
Figure 2: General procedure of Few-shot learning.  
base data to the target domain can greatly affect the outcomes. It is crucial to achieving cross-domain few-shot learning for this reason.
## Data and Study Area
### Datasets  
The LPIS data used for the object partitioning of the time-series, along with the declared cultivated crop types, were made available by INTIA. INTIA is a public company, attached to the Department of Rural Development, Environment and Local Administration of Spain, which offers advanced services in the agri-food sector in Navarra. The provided dataset includes farmers' crop type declarations for 2016, family- and season-based grouping of the crop types, and the parcels' geospatial information in vector format. This unique dataset is provided by one of the direct end-users to the proposed scheme's application, which adds to the transparency of the process. INTIA additionally offered a timeline of the phenology stages for key Navarra crop types, which was instrumental in the appropriate selection of the image acquisition dates, but also for the logical assessment and interpretation of the results (Figure 3).  
An analysis was performed (Supplementary Material) on the impact and feasibility of utilizing a dense time-series of Sentinel-2 imagery, regardless of the cloud coverage. The analysis showed that the usage of solely cloud-free imagery (less than 3% cloud coverage over the study site) that covers critical phenological stages was responsible for a near maximum crop type classification accuracy. On the other hand, the additional incorporation of cloudy imagery offered only a marginal increase in accuracy for a disproportionally larger cost in processing time. In this study, it was decided to use only cloud-free and nearly cloud-free imagery (<20% cloud coverage), which guaranteed an even temporal distribution of acquisitions and at the same time filtered out potentially redundant information. This way the data management and computational efficiency considerations, integral to the design of the proposed methodology, are appropriately showcased. This further supports the ultimate objective of the present study to be viewed in the context of an operational monitoring system for the control of farmers' compliance to their CAP obligations.  
Sentinel-2 MSI's 10 m and 20 m resolution bands (B02-B08A & B11-B12) and Landsat-8 OLI's 30 m resolution bands (B02-B08), in the visible, Near Infrared (NIR), and Short-Wave Infrared (SWIR) parts of the spectrum, formed the Sentinel-based and Landsat-based feature spaces, respectively. Figure 3 depicts the nine selected cloud-free Sentinel-2A acquisitions (May to October) and the eight equivalent Landsat-8 scenes (March to September) for 2016.  
Figure 3: Timeline of phenology stages for key crops in Navarra, overlaid with the acquisition dates of Sentinel-2 images for 2016.