esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Multivariate Analysis of Studied Parameters
This preliminary study serves as a starting point for further research, allowing for a broader perspective on how \(\gamma\)-PGA interacts with bioactive compounds across diverse plant species. By choosing Brassicaceae (kale) and Fabaceae (lupine), we aimed to explore the effects of \(\gamma\)-PGA on two nutritionally valuable plant groups--one rich in glucosinolates and polyphenols, the other abundant in isoflavenes and proteins. Investigating whether \(\gamma\)-PGA modulates secondary metabolite production differently in Brassica and legumins plants will provide valuable insights into its potential applications in our future study. Additionally, this approach allows for a better understanding of how \(\gamma\)-PGA influences plant metabolism across different functional groups, potentially leading to new strategies for improving food quality, nutritional value, and agricultural sustainability. The findings from this study lay the groundwork for further analyses and the development of optimized applications of \(\gamma\)-PGA in various plant-based food systems. However, at this stage of research, any mechanistic explanations would be mere speculation.
###### Abstract
An advanced strategy in this area features the combination of molecularly imprinted polymers (MIPs) with ceramic-based materials, yielding hybrid systems with tailored molecular recognition capabilities and improved durability, offering significant  
Figure 1: (**A**) Different types of catalysts. (**B**) Schematic illustrating the diverse applications of catalysis and (**C**) a generic example of a supported heterogeneous catalyst. potential for sustainable catalytic applications [8; 9; 10; 11; 12]. MIPs showcase remarkable versatility, functioning as enzymatic catalysts by mimicking the active sites of natural enzymes, participating in catalytic processes of chiral molecules, and playing crucial roles in environmental chemistry [11; 12; 13; 14]. In environmental applications, MIPs selectively recognize and bind target molecules, thereby allowing the selective adsorption and recovery of valuable contaminants [15] or the adsorption and further degradation by (photo)catalytic processes of specific toxicants [15; 16; 17]. Recent developments have demonstrated that ceramic-supported MIPs are highly effective in removing organic pollutants, enabling selective adsorption, photodegradation, and even sensor-based detection of environmentally hazardous substances. These hybrid systems have shown exceptional potential for use in water purification, air quality monitoring, and the degradation of persistent contaminants, thanks to their tunable selectivity, high surface area, and stability under operational conditions. Additionally, computational modeling has begun to play a key role in the rational design of these systems, allowing prediction and optimization of their structure and catalytic behavior in environmentally relevant contexts. In the degradation of compounds, MIPs can function as catalysts, sorbents, or sensor components [18; 19; 20]. Their ability to mimic the natural recognition properties of biological receptors makes MIPs highly valuable in several catalytic applications [21; 22]. The prospect of designing novel catalysts using computational methods has long been a \"Holy Grail\" in the field [23]. While computational tools have advanced significantly, allowing for calculations that were once highly time-consuming to be performed rapidly, the de novo design of catalysts remains a formidable challenge [24; 25; 26]. Therefore, this review article aims to address the main characteristics of advanced ceramic-based catalysts supported by MIPs, focusing on their structural properties, performance metrics, and potential applications in various chemical processes of environmental interest. It will also cover computational methodologies, particularly those based on Density Functional Theory (DFT), used in order to investigate the structural, electronic, and catalytic properties of these novel catalytic systems. Additionally, the present work aims to delve into the advantages and limitations of MIP/ceramic catalytic systems compared to traditional catalysts, providing a comprehensive overview of current research trends and future directions in this field.
## 2 Sensors
### Camera
#### 2.3.3 Automatic Image Registration and Image-Based Point Cloud Generation  
Automatically connecting two or more images is a fundamental photogrammetry and computer vision problem [80]. Two-dimensional image-point connections are called tie points. Automatic tie-point extraction is a process involving finding optimum image point positions that are less susceptible to affine or projective transformations (localization) and finding correct matches between subsequent or overlapping images [81]. Multi-modal image registration is a more demanding task that has recently received attention [82]. Most photogrammetric processing steps require robust image registration; therefore, the existence of blunders could significantly affect the subsequent processing steps. In image registration, filtering of false matches is performed by employing techniques such as random sampling consensus (RANSAC), where a wide variety of kernel functions could be selected to adapt the solution to a specific situation of a problem (see, e.g., [83; 84]).  
On the other end of the photogrammetric processing chain lies image-based point clouds that are considered the foremost output of this process. A chain of analysis (e.g.,applying classifiers) is expected to be followed afterwards. Point-cloud generation requires dense matching between all image pairs that have some level of overlap. Therefore, the quality of a photogrammetric point cloud directly depends on factors such as the geometric strength of intersections [85] and the correctness of image matches. Several technologies could be used to produce image-based point clouds, e.g., global and semi-global matching techniques [86] use network optimization strategies to find matches between stereo pairs, or ML-based methods use different approaches such as hidden Markov model (HMM) to address this problem. This problem could also be cast into a semi-supervised classification problem that could be addressed by employing neural networks [87].  
Despite the clarity of the photogrammetric intersection concepts, extraction from a single image without using the intersection principle is also possible, e.g., techniques such as shape from shading [88] or deep learning could be employed to approximate building heights in aerial photos. Similar research outputs, such as cross-domain diffusion [89], try to use the same principle to create neural networks that can generate 3D information by employing highly trained non-linear classifiers.
## 4 Experiment Setup and Evaluation Metrics
### Hardware  
In this section, the experiments are based on the first architecture of IoT-Fog-Cloud which was proposed in [6], as shown in Figure 1. First, we describe, in detail, the types of devices that were used in the IoT-Fog-Cloud architecture as shown in Figure 6. The devices used to perform the experiments in the IoT-Fog-Cloud architecture were a DHT11 sensor [14] and the Raspberry Pi 3 Model B [15]. The DHT11 sensor is a low-cost device that produces real-time data for measuring the temperature degree and humidity degree of the surrounding air. The Raspberry Pi is a single-board computer with integrated Wi-Fi and computing capabilities that is used in a variety of applications, including smart health care, weather monitoring, and smart homes. The Raspberry Pi was used in our work to provide light computational capabilities for data generated by the DHT1 sensor. In addition, the Raspberry Pi was used to provide light storage for the configuration performed by the DHT11 sensor. One of the advantages of using the Raspberry Pi is that it can be readily transferred from one location to another. Table 1 shows a complete list of the hardware utilized in the IoT-Fog-Cloud architecture. Figure 6 shows the hardware used in the IoT-Fog-Cloud architecture.  
Figure 6: Hardware used in the IoT–Fog–Cloud architecture.
# DeepGOMIMO: Deep Learning-Aided Generalized Optical MIMO with CSI-Free Detection
## 3 Detection Schemes for GOMIMO Systems
### CSI-Based ZF-DNN Detection  
To efficiently address both the high computational complexity issue of joint ML detection and the noise-amplification and error-propagation issues of ZF-ML detection, a ZF-DNN detection has been proposed for GOSMP systems in [27]. The key idea of the ZF-DNN detection scheme is to employ a feedforward DNN module to directly and simultaneously estimate the transmitted spatial and constellation bits by taking the ZF equalized signal vector \(\mathbf{\hat{x}}_{\text{ZF}}\) as input. For more details about the implementation of the ZF-DNN detector, please refer to [31; 32]. In a word, the feedforward DNN module can fulfill the tasks of spatial index vector estimation, constellation symbol vector estimation, spatial symbol demodulation, and constellation symbol demodulation at the same time. Simulation results in [27] clearly show that, by selecting a proper training signal-to-noise ratio (SNR), the ZF-DNN detector can achieve nearly the same BER performance as the optimal joint ML detector, but with a significantly reduced computational complexity.  
Despite the near-optimal BER performance and low computational complexity of the ZF-DNN detector, it takes the ZF equalized signal vector \(\mathbf{\hat{x}}_{\text{ZF}}\) as the input of the feedforward DNN module. As per (8), \(\mathbf{\hat{x}}_{\text{ZF}}\) is obtained by multiplying the received signal vector \(\mathbf{y}\) with \(\mathbf{H}^{\dagger}\), i.e., the pseudoinverse of the channel matrix \(\mathbf{H}\). Generally, the CSI (i.e., the channelmatrix) can be efficiently estimated by transmitting training symbols [28]. Nevertheless, the use of training symbols for accurate CSI estimation inevitably reduces the achievable data rate of GOMIMO systems, especially for low SNR scenarios. Furthermore, because the channel matrix is highly related to the specific location of the MIMO receiver, i.e., the PD array, channel estimation needs to be executed instantaneously with the change of receiver location. In consequence, instantaneous channel estimation inevitably introduces additional communication time delay and computational complexity in practical GOMIMO systems.
# Survey on Power-Aware Optimization Solutions for MANETs
## 3 Analysis of Power-Aware Optimization Solutions in MANETs
### Reactive (Source-Initiated-Based) Routing Protocols
IRAODV is based on the calculation of the distance of the packet transmission by the individual nodes with other nodes. For this calculation, it uses the RSSI (Received Signal Strength Indication) parameter. The authors simulated IRAODV and compared its performance with AODV. They found that IRAODV outperforms AODV in terms of PDR, throughput, end-to-end delay, and residual energy. [PERSON] [170] proposed another protocol, called Dynamic Power-AODV (DP-AODV). DP-AODV adapts the AODV protocol to dynamically adjust transmission power usage. To achieve this improvement, the DP-AODV protocol uses the dependence of a transmission range on density. Simulation results demonstrated that, as density increases, DP-AODV shows a decrease in delay, compared to AODV, and offers better performance for highly-populated networks exceeding 200 nodes. The simulation results showed that DP-AODV increases network throughput whilst reducing node interference in a dense region, as well as enhancing the overall network performance concerning the increased packet delivery fraction, reducing the control overheads and jitter, enhancing overall throughput, reducing interferences and finally, shortening the end-to-end delay in medium-high-density conditions. In summary, energy-efficient reactive routing protocols have better scalability than the link-state-based (proactive) routing protocols. Nevertheless, in reactive routing schemes, the overall time delay is high since the node needs extra time to wait for the route discovery process after the node tries to deliver a packet. Another interesting category of power-aware optimization solutions follows. It includes routing protocols that are based on transmission power regulation methods. Such methods can improve the overall MANET performance by increasing throughput and simultaneously reducing power consumption.
# Crude Oil Price Forecasting Model Based on Neural Networks and Error Correction
## 3 Experiments
### Experimental Evaluation of the Proposed Model
#### 3.3.2 Comparison with Models Utilizing Diverse Ensemble Strategies  
To verify that the ensemble method had optimal prediction accuracy, model 4 was compared with the TCN, GRU and ESN. In addition, to fully verify the superiority of the ensemble strategy employing RL over traditional heuristic ensemble methods, the SARSA was compared with several classical heuristic algorithms, including the BHA, GA, and GWO. Table 5 visually shows the evaluation indicators of experimental models. Table 6 presents the promoting percentages of ensemble learning methods. Figure 5 is the  
\begin{table}
\begin{tabular}{c c c c c} \hline
**Series** & **Predictors** & **MAE (USD)** & **MAPE (\%)** & **RMSE (USD)** \\ \hline \multirow{8}{*}{\#2} & TCN & 2.2485 & 4.7553 & 3.2635 \\  & GRU & 2.1264 & 4.6699 & 2.8677 \\  & ESN & 2.1818 & 4.7152 & 2.9101 \\  & BPNN & 2.8466 & 6.3487 & 3.6113 \\  & GRNN & 3.5657 & 7.9179 & 4.6912 \\  & RBFNN & 3.5109 & 7.8512 & 4.8379 \\  & LSTM & 2.3411 & 5.0723 & 3.1779 \\  & CNN & 2.3602 & 5.1736 & 3.2895 \\  & GMDH & 2.9150 & 6.5246 & 3.8918 \\ \hline \multirow{8}{*}{\#3} & TCN & 5.6431 & 12.0686 & 7.7571 \\  & GRU & 6.0148 & 13.9647 & 8.5027 \\  & ESN & 5.5273 & 12.0189 & 7.4268 \\  & BPNN & 7.1119 & 14.7113 & 9.2021 \\  & GRNN & 7.7206 & 18.4653 & 10.3241 \\  & RBFNN & 7.3445 & 18.0054 & 10.1406 \\  & LSTM & 6.0466 & 14.1694 & 8.4556 \\  & CNN & 6.2123 & 14.1316 & 8.6501 \\  & GMDH & 7.6269 & 18.3832 & 11.0227 \\ \hline \end{tabular} Note. MAE (mean absolute error), MAPE (mean absolute percentage error), and RMSE (root mean square error) measure predictive accuracy. Lower values indicate better performance. Series #1, Series #2, and Series #3 refer to three WTI crude oil price datasets with different time spans.  
\end{table}
Table 4: _Cont._  
Figure 3: Prediction errors for different predictors across the WTI crude oil price series #1.
## 4 Discussion
### Vortex Radius (\(R_{c}\))  
Interestingly, when examining the vortex radius for different MVG heights, the data show that the radius generally decreases as the MVG height increases, as shown in Figure 11, indicating a more concentrated vortex core. Low periodicity values are a significant factor in increasing the vortex radius. Both effects are related to the previously described influence of the wall as well as the adjacent vortex pairs on the vortex velocity profile. When vortex pairs are very close together, the velocity peaks shift, increasing the vortex diameter, showcased in Figure 7. Considering both the asymmetry and radius values is crucial as the Batchelor vortex fit assumes an axisymmetric vortex and does not account for strong asymmetrical effects. This limitation can result in outliers, particularly at the edges of the investigated domain. One of these outliers is analyzed in Figure 17, where the high asymmetry also results in an \(R_{c}\) value that does not follow the general trend, meaning that the asymmetry plays a large role for these outlier cases. A clear correlation exists between the vortex radius and drag reduction: a lower radius tends to be more effective in achieving larger downstream drag reduction. The vortex radius significantly influences the streak generation and amplitude, which are critical factors in the attenuation of TS waves.
## 3 Developments of CIM
### Problems Encountered in CIM Development
#### 3.2.3. CIM Data Integration Issues  
In the development of CIM, data integration plays a pivotal role in enabling the comprehensive analysis, visualization, and decision support for urban data. This process involves the systematic amalgamation of heterogeneous urban data from diverse sources, formats, and structures into a unified and cohesive model. By dismantling data silos, it enhances data interoperability and provides holistic, precise, and actionable insights for urban planning, management, and decision making [16].  
Nevertheless, data integration continues to encounter significant challenges. First, the integration of heterogeneous data from multiple sources presents a formidable obstacle. Urban data are derived from diverse entities, including government agencies, businesses, and the public, with variations in data formats, standards, and storage methodologies, exacerbating the complexity of integration and complicating the maintenance of data consistency. Second, the mechanisms for cross-departmental data sharing remain under-developed, particularly in fostering collaboration between the public sector and private enterprises. The lack of a unified legal framework and standardized protocols restricts data circulation and interoperability. Furthermore, data security and privacy concerns constitute critical barriers to effective data integration. The existing data governance frameworks necessitate further refinement to ensure compliance and safeguard security throughout the data-sharing process [53].  
To solve the above problems, it is recommended to take the following measures:  
* Develop unified data standards: leverage ISO 19157 [54] Geographic Information Data Quality Standards and OGC (Open Geospatial Consortium) standards to standardize data formats, semantics, and storage methods, thereby enhancing the cross-platform data interoperability.
* Establish a cross-departmental data sharing mechanism: Create a City Data Sharing Committee to clarify data ownership and responsibilities. Implement blockchain and smart contract technologies to enforce data access control and traceability, strengthening data security.
* Introduce intelligent data processing technologies: Utilize AI-driven data cleaning and fusion algorithms to improve the data quality and consistency. Optimize the data flow through ETL (Extract, Transform, Load) processes to increase the integration efficiency.
* Build a CIM data integration platform: Adopt cloud computing architecture to support large-scale data storage and processing. Integrate knowledge graph technology to enhance the correlation and analytical capabilities of multi-source data, thereby constructing an efficient and intelligent data integration system.
# Design and Application of a Distribution Network Phasor Data Concentrator
## 4 The Test of the Key Performance Indicators of DPDC
### Access Capacity  
The number of connected uMPMUs is the basic parameter to the access capacity of DPDC. Meanwhile, the amount of data sent by uMPMU should be considered. The connection diagram of this test is shown in Figure 7. The two network ports of the PC 1 are connected to the two network ports of the DPDC via the network cable 1 and the network cable 2, respectively. The PMU Emulator and the Main station Emulator are run on the PC1, and the simulated PMUs transmit data to the DPDC through the network cable 1. After the DPDC collects the data, the data is transmitted to the simulated main station through the network cable 2.  
Figure 6: DPDC test environment.  
The single line measurement configuration of the \(\upmu\)MPMU is referenced in the test. Configure each simulated PMU data volume into 12 phasors (three-phase voltage, three-phase current, voltage and current symmetrical components), 20 analog quantities (active power P, reactive power Q, 3 rd, 5 th, 7 th harmonic voltage and current) and 4 digital quantities (external signal lights, relays). A data frame length is 114 bytes. Test at 10, 25, 50, and 100 frame/s reporting rates. Each test increments the number of simulated PMUs, observes whether the data is correct through the simulated master station, and monitors the CPU and RAM utilization of the PDC process through the top command. The test results are shown in Table 3.  
As can be seen from Table 2, as the number of PMUs or the reporting rate increases, the CPU Utilization also increases, and the RAM Utilization is only related to the number of PMUs. In terms of CPU Utilization, the network card driver calls the DMA engine to copy the data packet to the kernel buffer. After the copy is successful, it initiates an interrupt notification interrupt handler. As the number and frequency of input streams increase, message queues trigger the CPU more frequently through interrupts, so CPU Utilization will rise. The DPDC program's internal mechanism also affects CPU Utilization. In terms of RAM Utilization, a buffer of a fixed depth has been allocated according to the configuration file when the DPDC was started. After PMUs are connected, the length of the buffer is also determined by the data length of each PMU. In the absence of other data processing tasks, the RAM Utilization of the DPDC program does not change. The data aggregation of DPDC in this test is normal, and the test result shows that the DPDC meets the access capability requirement.
## 2 Data and Methodology
### Methodology  
#### 2.3.1 Feature Extraction  
Four kinds of features were extracted to discriminate between different types of land cover in the study area: spectral features, vegetation indexes, a water index and a radar backscattering coefficient. Four bands of GF-2 data were selected as the spectral features: blue, green, red and NIR. The normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) served as the vegetation indexes. The normalized difference water index (NDWI) is the water index in this investigation. These indexes are defined as follows [25; 26]:  
\[NDVI=\frac{\rho_{NIR}-\rho_{red}}{\rho_{NIR}+\rho_{red}} \tag{1}\]  
\[EVI=2.5\times\frac{\rho_{NIR}-\rho_{red}}{\rho_{NIR}+6\rho_{red}-7.5\rho_{ blue}+1} \tag{2}\]  
\[NDWI=\frac{\rho_{green}-\rho_{NIR}}{\rho_{green}+\rho_{NIR}} \tag{3}\]  
where \(\rho\) denotes the reflectance in different spectral bands: NIR, red, green or blue.  
Figure 3: The flowchart of preprocessing for GF-2/GF-3 data.  
The NRCS, extracted from the calibrated GF-3 SAR image, on the other hand, can be used as the measurement of the radar backscattering from targets on the ground. The following equation [27] demonstrates the extraction of NRCS (\(\sigma_{dB}^{0}\)) in the GF-3 product of L1A.  
\[\sigma_{dB}^{0}=10\times\lg\Bigl{[}P^{I}(QualifyValue/32767)^{2}\Bigr{]}-K_{dB} \tag{4}\]  
where \(P^{I}=I^{2}+Q^{2}\) and \(I\) and \(Q\) are the real and imaginary parts of the GF-3 SAR data, respectively, in the product of L1A. The _QualifyValue_ and \(K_{dB}\), on the other hand, are constants that can be found in the field of <_QualifyValue_> and <_CalibrationConst_>, respectively, in the *.meta.xml file of the GF-3 L1A product.