esa-sceva/satcom-chunk-collection huggingface dataset:
# Metal-Coordinated Polymer-Inorganic Hybrids: Synthesis, Properties, and Application
## 1 Introduction
This review highlights synthetic polymer-inorganic Class II hybrids, emphasizing the importance of strong chemical interactions, particularly coordination bonds, in defining the properties and applications of these materials with an emphasis on non-aqueous systems. Polymers with functional groups containing lone electron pairs, such as nitrogen, hydroxyl, or sulfur, can form coordination bonds with metal ions. Nitrogen-containing polymers, including polyamides, poly(ethylenimine) (PEI), and poly(vinylpyridine) (PVP), have been widely used for metal ion adsorption [19; 20; 21], catalyst resins [22; 23; 24], and biosensors [25; 26]. The organic polymer component not only imparts structural support but also enhances the material's mechanical properties and processability. For example, longer linkers provide greater flexibility, while shorter linkers create more mechanically robust frameworks, making them ideal for applications in gas separation and catalysis [27; 28; 29]. Functional groups on these linkers, such as carboxylates, amines, or hydroxyl groups, influence metal coordination, affecting stability, reactivity, and properties like conductivity and catalytic efficiency [30; 31]. Additionally, the solubility and polarity of organic linkers are crucial for synthesis, as the homogeneity of the coordination complexes impacts the morphology and growth of the material [5]. The modularity between organic polymers and inorganic components in OIHs enables the design of materials that are highly adaptable for various applications. For example, by incorporating polymer coatings, these hybrids achieve enhanced solubility and colloidal stability, facilitating cost-effective processing [5; 32]. These materials are particularly valuable for applications requiring long-term stability, such as magnetic nanoparticles for MRI contrast enhancement or ceramic nanocontainers used in drug delivery systems [32], nanopatterning [33, 34, 35], and microelectronic applications [36, 37].
## 2 Literature Review  
### E-Participation Platforms in Taiwan  
The e-participation platforms Join and iVoting, while complementary in purpose, differ significantly in their scope, functionality, and deployment. Join, introduced in February 2015 by the National Development Council, was designed to foster whole-citizen participation by facilitating comprehensive civic engagement [13]. It allows users to propose, discuss, and vote on policy issues, as well as provide feedback on draft legislation. In contrast, iVoting--launched during the 2012 Legislative Yuan elections--focuses specifically on enhancing the electoral process through informed voting. As Taiwan's first Voting Advice Application (VAA), iVoting helps users make more deliberate and informed decisions by offering issue position diagnostics.  
Despite its initial success--drawing over 1400 members and 40,000 visitors between 2011 and 2012--iVoting has seen a significant decline in activity in recent years [10]. A critical factor contributing to this drop was the platform's controversial use in 2016 for a local redevelopment project in Taipei's Shezidao, where the city government offered only pre-selected options for public voting. This limited democratic choice led to widespread criticism, ultimately eroding public trust in the platform [15].  
Former research on e-petitions in Taiwan found out that political participation has been largely influenced by party mobilization, creating a \"mobilization of bias\" that is difficult to eliminate [16]. However, our study suggests that e-participation, despite being limited by the digital divide, may counterbalance traditional political mobilization by enabling participation from individuals outside established party networks, potentially making deliberation and petition results more representative of public opinions. Therefore, the large volume of unstructured textual data generated by Join and iVoting presents both an opportunity and a challenge for policymakers. Understanding citizen preferences and identifying dominant themes within proposals and discussions is essential for effective governance.
## 1 Introduction
### Literature Review  
Several studies, such as [12; 13; 24; 25], suggest that, with the introduction of massive MIMO technologies in 5G systems, the potential gain from an accurate estimation of channel characteristics will considerably increase, with channel sounding becoming a critical issue. Moreover, as the authors of [12; 13] point out, the most-used approaches to reduce the feedback overhead, based on codebooks or vector quantization, process feedback quantities whose dimensions scale with the number of antennas, making these techniques unsuitable in massive MIMO scenarios. These criticisms have stimulated new studies aimed at overcoming the difficulties associated with traditional approaches with DL-based solutions.  
The authors of [12] designed an autoencoder based on neural networks to compress and then reconstruct the channel state information, called CsiNet. This paper stimulated significant interest among researchers who sought to expand the applicability and improve the performance. In [26], the authors present two different structures, ConvCsiNet and ShuffleCsiNet, both able to outperform CsiNet from an NMSE point of view. Even though the latter has reduced complexity with respect to the first structure, it is still more complex than CsiNet. One of the most recent advancements in DL is the concept of attention, which is exploited in [27; 28]. In the former, long short-term memory (LSTM) is adopted to learn the temporal correlation of channels, while an attention mechanism is developed to perceive local information and automatically weight feature information. This kind of solution can further improve the reconstruction capabilities, but comes at a higher cost that could not be supported in terms of user equipment. A solution to effectively decrease the complexity of CsiNet was proposed in [29], which identified a lighter, fully connected (FC) layer as the key to encoder compression. The network parameters are transformed from a 32-bit floating point number to a 1-bit binary number, optimizing a positive scale factor that minimises the distance between standard weights and binary weights. In [30], a DL-based CSI feedback framework is described for beam-forming (BF) design, CsiBFNet. Its goal is to maximize the BF performance gain rather than the feedback accuracy, including with respect to single-cell or multi-cell scenarios. Another interesting solution is the CQNet described in [31], which focuses on the impact of quantisation on the channel estimation coefficients and exploits radial coordinates to improve quantisation scheme efficiency.
## 3 Ambient Parameter Monitoring and Sensor Technology
### Temperature  
Temperature is the most important ambient parameter in the context of quality retention and shelf life because it strongly influences all spoilage mechanisms of fresh fruit and vegetables as discussed in Section 2. For instance, [PERSON] and [PERSON] [41] found that the shelf life of raspberries was reduced by 50% if the storage temperature was 10 \({}^{\circ}\)C instead of 0 \({}^{\circ}\)C. For blueberries, a shelf life shorter by 3 days was observed for storage at 10 \({}^{\circ}\)C compared to storage at 0 \({}^{\circ}\)C [42]. Also for arugula, increasing the storage temperature from 0 \({}^{\circ}\)C to 7 \({}^{\circ}\)C reduces the shelf life by several days, depending on the season and the harvest batch [43]. For products that are sensitive to chilling injury, such as mangoes, avocados, bananas, papayas and pineapples, not only a temperature increase, but also a lower than optimum temperature can negatively impact their quality. [PERSON] et al. [44] reported first symptoms of chilling injury in mango after 10 days of storage at 5 \({}^{\circ}\)C, whereas at 10 \({}^{\circ}\)C the onset of symptoms only started after 20 days. Another study on papayas showed that chilling injury symptoms were much more severe in fruit stored at 5 \({}^{\circ}\)C for 7 days than in fruit stored at 10 \({}^{\circ}\)C for 7 or even 14 days [45]. These examples illustrate that particularly for very perishable products that require strict refrigeration and for chilling sensitive products, real-time temperature monitoring could be beneficial to prevent accelerated quality losses. Technologically, this is already possible. Temperature sensors are widely available and account for up to 80% of the global sensor market [46]. Although traditionally thermocouples were common, in most IoT temperature sensors resistive temperature devices or thermistors are used [6]. Both types are inexpensive and measure the temperature accurately over a wide range [6]. Given the availability of cost-efficient IoT temperature devices, an application for optimized product quality and shelf life can be profitable for all fruit and vegetables.
###### Abstract  
This work is aimed at addressing surface modification of berea sandstone by silica nanofluids (NFs). Three types of nanofluids were used: silica/deionized water (DIW), silica in DIW with a stabilizer fluid (3-Mercaptopropyl Trimethoxysilane) and sulfonate-functionalized silica in DIW. Core flood studies showed that application of silica nanoparticles (NPs) improved water injectivity in sandstone. The change in the measured zeta potential indicated surface modification of sandstone by application of NPs. Computation of the surface forces showed that the modified berea sandstone has net attractive potential with fines (obtained from water/rock interaction) leading to reduction of fines migration, hence improvement of water injectivity. It was also observed that the silica NPs have greater affinity to adhere/adsorb on quartz surfaces than kaolinite in berea core. This was confirmed by scanning electron microscope imaging and isothermal static adsorption tests. Although the stabilizing of NFs almost did not reduce the fine migration, as was qualitatively indicated by the pressure drop, it enhanced the NPs adsorption on the minerals as obtained by isothermal static adsorption tests. The reduction of fines migration due surface modification by silica NP suggests that NPs can be utilized to overcome the problem of formation damage induced during low salinity flooding in sandstones.  
Keywords:silica nanoparticle; nanofluid stabilization; surface modification; adsorption; fines migration; berea sandstone; low salinity +
Footnote †: journal: Applied Sciences
## 3 Methodology
### GPP Mapping Process  
First, we applied the Frequency Ratio (FR) method to calculate the spatial relationships between groundwater presence and related topographical factors. In this study, we analyzed for correlation between the groundwater wells locations and 18 factors related to groundwater productivity using frequency ratio of each factor. The relationship analysis is the ratio of the area where groundwater productivity in the total study area. So, a value of 1 indicates an average. In other words, if the average value of FR is greater than 1, it has a higher correlation with groundwater. Also, the logistic regression coefficient correlates with the potential productivity of groundwater, and the higher the value, the higher the correlation. The details of FR calculations are described in more detail in [6]. To analyze the GPP, the results of the three models were compared based on the FR model results. The results of the RF and BRT models were calculated using the STATISTICA software and there were classified and re-summed over regression values at all nodes to calculate the importance of the predictor. The LR model was calculated using SPSS 21 statistical software. Groundwater productivity data (T and SPC) were randomly separated for each model. They were used as training (50%) and verification (50%) data.  
Most of the maps used in GPP analysis were generated using the ArcGIS 10.5 software. A number of groundwater-related factor maps, including geological maps, were regenerated in the ASCII grid format at a 30 m resolution. The groundwater productivity values (T and SPC) were set as independent variables and used as training data. In the following step, all data were classified as categorical or continuous data. The continuous variables included the Terrain Ruggedness Index (TRI), slope length (LS) factor, hydraulic slope, depth of groundwater, lineament density, slope gradient, relative slope position, drainage basins, valley depth, distance from faults, Topographic Wetness Index (TWI), and distance from lineament. The categorical variables included the hydro-geology, soil texture, convergence index, plan curvature, and land cover.  
To validate the algorithms, 86 T data points were divided into two different groups and randomly selected. Verification was performed using the previously segregated verification data. In the final verification process, a Receiver Operating Characteristics (ROC) curve was implemented. ROC is an index for the performance of models [13]. To quantitatively determine the accuracy of the models' verification, Area Under the Curve (AUC) of the ROC curve was recalculated for the total area and the correct prediction accuracies were obtained. Typically, the accuracy of the validation of the model is measured by the area under the ROC curve, and it can lie between 0.5 and 1. High AUC values indicate the superior performance of an algorithm.
# Classification of Different Winter Wheat Cultivars on Hyperspectral UAV Imagery
## 2 Materials and Methods
### Data Acquisition  
UHD185 hyperspectral camera and DJI M600 multi-rotor UAV were integrated for data collection of the study area, as shown in Figure 2. UHD185 hyperspectral camera was developed by the Cubert company in Germany. It is a frame type, non-scanning, and real-time imaging storage hyperspectral imager, and is easy to integrate with the UAV system. Its main parameters are as follows: spectral range, 450-950 nm; spectral resolution, 8/nm; spectral sampling interval, 4/nm; the number of spectral channels, 125; Cube resolution, 1000 \(\times\) 1000; and digital resolution, 12/bit. DJI M600 Pro multi-rotor UAV was used as the sensor platform in this study. It has sufficient power and excellent stability. Data collection was performed on 1 May 2023 (at the booting stage of winter wheat) from 11:30 to 12:10 at noon when there was no cloud and wind. After several attempts, the flight height, heading overlap, and side overlap were designed to be 100 m, 90%, and 85%, respectively. Before collecting data, it was necessary to use a black and white board to calibrate the camera radiation. The control points made of A4 paper were evenly distributed in the study area, and their locations were measured by RTK.
# Molecular Dynamics Study on the Crystallization Process of Cubic Cu-Au Alloy
## 3 Results and Discussion
### Factors Affecting the Structure and Crystallization Process of Cu-Au Alloy  
#### 3.2.1 Time of Each Move Steps  
The structural crystallization process of Cu-Au alloy was carried out after increasing the time of each heating step. The obtained results are shown in Figure 2.  
Figure 2: The structural shape (**a1,b1,c1,d1,e1**), radial distribution function (**a2,b2,c2,d2,e2**), and number of structural units (**a3,b3,c3,d3,e3**) of Cu–Au alloy at temperature T = 300 K, heating rate 4 \(\times\) 10\({}^{12}\) K/s with a different time per heating step.  
The results show that, Cu-Au alloy at temperature (T), T = 300 K with a calcination rate of 4 \(\times\) 10\({}^{12}\) K/s, time each move step (h), h = 1.0 fs has the structure shape (Figure 2(a1)), the radial distribution function with r\({}_{\rm Cu-Au}\) = 2.58 A, the height of the RDF was g\({}_{\rm Cu-Au}\)(r) = 4.62, size (I), l = 8.33 nm, E\({}_{\rm tot}\) = \(-\)193 eV (Figure 2(a2)) and the structural units number was 63 FCC, 100 HCP, and 2753 Amor (Figure 2(a3)). When the time for each move step, h, was increased from h = 1.0 fs to 1.5, 2.0, 2.5, and 3.0 fs, the shape of the structure changes (Figure 2(a1,b1,c1,d1,e1)), the length link of Cu-Au increased from r\({}_{\rm Cu-Au}\) = 2.58 A to r\({}_{\rm Cu-Au}\) = 2.62, 2.62, 2.64, and 2.66 A, respectively, the height of the RDF increased from g\({}_{\rm Cu-Au}\)(r) = 4.62 to g\({}_{\rm Cu-Au}\)(r) = 4.98, 5.03, 5.14, and 5.22, respectively, the material size increased from l = 8.33 nm to l = 8.34, 8.35, 8.35, and 8.36 nm, respectively, total the energy of the system increased a little from E\({}_{\rm tot}\) = \(-\)193, \(-\)193, \(-\)193, and \(-\)194 eV, respectively (Figure 2(a2,b2,c2,d2,e2)), and the structure units number changed: FCC increased from 63 FCC to 63, 160, 334, and 537 FCC; HCP increased from 100 HCP to 103, 195, 191, and 256 HCP; Amor decreased from 2752 Amor to 2751, 2561, 2391, and 2123 Amor (Figure 2(a3,b3,c3,d3,e3)). The reason for this phenomenon was that increasing the time of each heating step leads to increased in the displacement transition time of each MD step, giving the Cu and Au atoms bonding time. This shows that increasing l leads to increased crystallization, shown by increasing the length of Cu-Au links, increasing the number of FCC and HCP structural units, and decreasing this number for Amor. These results show that there was a very great influence on the time of each heating step, but the increase in the time of each heating step leads to a strong increase in total simulation time. The time of each heating step is not workable in the current time of computer speed configuration, and we choose the time of each heating step as h = 3.0 fs as a standard to study for other influence cases.
# Task Offloading Scheme Based on Proximal Policy Optimization Algorithm
## 4 Model System
### Task Offloading Model  
The time period \(T\) for the task offloading of terminal devices is divided into several small time segments, \(t\), assuming that the user's location does not change during each small time segment, \(t\). In the time segment \(t\), the service request of the terminal device \(n\) is  
\[j_{n}(t)=(F_{n}(t),S_{n}(t),D_{n}(t)),t\in T \tag{11}\]  
These, respectively, represent the number of CPU cycles required for user-requested tasks, the service cache type on which task execution depends, and the data volume of user tasks.  
As shown in Figure 3, within each time slot, each computation task generated by the terminal user is offloaded to the edge node device with the best channel quality associated with it. If the edge device associated with the terminal user lacks the service cache required for task execution, then the edge device will further offload the computation task to other collaborative edge node devices or download the required service cache from the central cloud.
## 7 Integrated Localization and Communication
### Localization for RISeUAVs
#### 7.3.1 Positioning-Based Beamforming  
As highlighted in Remark 5, beamforming optimization and CSI estimation in dynamic environments are computationally prohibitive. Notably, it is difficult to retain beam alignment for moving vehicles as the channel state is changing not only due to the vehicle movement but also due to changing the signature of the environment. Therefore, the CLoS links in the BS-RISeUAV-UE paths (e.g., in an indirect V2V link through RISeUAV) are fragile and prone to instability. Therefore, positioning-based beamforming (i.e., directional beam-steering) through codebook design has been suggested [63; 201].
## 4 Stock Price Prediction Model Methodology and Research Design
### Data Preparation  
As mentioned above, the data used in this research experiment are high-frequency stock market data. Before feeding these data to the neural network, we need to perform three main steps:  
* Data sourcing;
* Feature selection;
* Normalisation.  
Each of the above-mentioned steps is described in detail in the respective subsection below.  
**Data Collection**: Limit order book and order flow data were collected from the Moscow Stock Exchange and NASDAQ for six stocks spanning different time periods with over 2 million records.  
**Data Normalisation**: Numerical features were normalised using z-score normalisation to account for different scales across features.  
**Feature Engineering**: Features included price and volume from the top 10 bids/asks (40 features), and price, volume, and direction of the latest 10 transactions (30 features) across the 100 most recent timestamps.  
**Data Splitting**: Data were split into 60% training, 20% validation, and 20% test sets for each stock.  
**Model Architecture Design**: A hierarchical model (TFF-CL-GRU) was designed, combining convolutional layers to learn spatial patterns and max-pooling and GRU layers to capture temporal dependencies from input features.  
**Model Training**: The model was trained for 200 epochs on the training set using categorical cross-entropy loss.  
**Model Validation**: Model performance was evaluated on the validation set after each epoch to fine-tune the model.  
**Model Testing**: Final model performance was evaluated on an unseen test set.  
**Model Performance Benchmarking**: Final model performance was compared with the state-of-the-art baseline model using the F1 score, accuracy, precision, and recall metrics.  
**Trading Simulation**: A trading simulation was conducted to test the practicality of the model.  
**Figure 1. Schematic diagram of the methodology.
# Enhanced Solar Photovoltaic System Management and Integration: The Digital Twin Concept
## 6 Conclusions  
This work presented an overview of the current trends in PV-DT critical challenges and future applications. Several aspects of PV-DT implementation were considered, starting with DTs for solar cell development and prototyping, solar cell manufacturing, and, most importantly, operation and maintenance. The importance of the operation and maintenance of DTs is highlighted due to their interconnectivity with the cyber-physical connected energy systems vital to smart utility and energy management.  
Implementation of PV-DTs as smart utility and energy management systems for urban areas in the near future will promote massive development of application domain extensions (ADE) for PV systems. This is because of the high integration of PV systems on urban and residential buildings and the existence of versatile building information management (BIM) and city geography markup language (CityGML) platforms. This would create a new dimension in integrated energy systems life cycle management and simulation.  
A sync of the new robotic technologies, advanced 6G communication networks, and urban systems management will allow for real-time adaptive maintenance of energy systems, high levels of reliability, and safety while increasing system longevity. Furthermore, the refinement of these technologies will ensure alignment with the broader goals of sustainability, efficiency, and urban and rural development.  
Finally, it is considered that this work would provide substantial information to researchers within the field of PV digital twins and enable the identification of core research areas toward developing a full PV-DT.  
Conceptualization, O.O. and M.E.; methodology, O.O.; formal analysis, O.O.; investigation, O.O.; resources, M.E.; data curation, O.O.; writing--original draft preparation, O.O.; writing--review and editing, O.O., M.E., U.C. and P.Y.; visualization, O.O.; project administration, O.O.; funding acquisition, M.E. and P.Y. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Institute of Safe Autonomy, University of York, under the Research England UKRPIF Net-Zero funding and in part by EPSRC and DSIT TMF-uplift: CHEDDAR: Communications Hub For Empowering Distributed Cloud Computing Applications And Research (EP/X040518/1), and (EP/Y037421/1).  
The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.