esa-sceva/satcom-chunk-collection huggingface dataset:
# Financial Optimization of a Solar-Driven Organic Rankine Cycle
###### Abstract  
The objective of this work is the financial optimization of a solar-driven organic Rankine cycle. Parabolic trough solar collectors are used as the most mature solar concentrating system and also there is a sensible storage system. The unit is examined for the location of Athens in Greece for operation during the year. The analysis is conducted with a developed dynamic model in the program language FORTRAN. Moreover, a developed thermodynamic model in Engineering Equation Solver has been used in order to determine the nominal efficiency of the cycle. The system is optimized with various financial criteria, as well as with energy criteria. The optimization variables are the collecting area and the storage tank volume, while the nominal power production is selected at 10 kW. According to the final results, the minimum payback period is 8.37 years and it is found for a 160 m\({}^{2}\) collecting area and a 14 m\({}^{3}\) storage tank, while for the same design point the levelized cost of electricity is minimized at 0.0969 \(\upcels\) kWh\({}^{-1}\). The maximum net present value is 123 k\(\upcels\) and it is found for a 220-m\({}^{2}\) collecting area and a 14-m\({}^{3}\) storage tank volume. Moreover, the maximum system energy efficiency is found at 15.38%, and, in this case, the collecting area is 140 m\({}^{2}\) and the storage tank volume 12 m\({}^{3}\). Lastly, a multi-objective optimization proved that the overall optimum case is for a 160-m\({}^{2}\) collecting area and a 14-m\({}^{3}\) storage tank.  
Keywords:solar concentrating power; parabolic trough collector; financial analysis; sustainable design; organic Rankine cycle +
Footnote †: journal:
## 4 Results
### Comparison of Findings with Existing Works  
The findings from our study highlight the effectiveness of SVM-RFE in handling high-dimensional EEG data for depression detection, achieving an accuracy of 93.54%. This result aligns with the broader trend in the literature emphasizing the importance of targeted feature selection in improving model accuracy. The iterative nature of RFE systematically removes irrelevant features, enhancing predictive capability, which is consistent with previous works highlighting the importance of feature selection in clinical prediction accuracy [58].  
Our results demonstrate that simpler models with effective feature selection can achieve competitive performance compared to more complex methods. For example, Ref. [59] reported 94.72% accuracy using a sophisticated model incorporating spatiotemporal information and weighted feature fusion, while [60] achieved 89.02% with an ensemble approach combining Deep Forest and the SVM. Compared to both methods, our SVM-RFE approach delivered comparable or better outcomes, suggesting that RFE can bridge the gap between traditional machine learning techniques and more complex approaches.  
Comparatively, ref. [61] reported that feature extraction methods such as Principal Component Analysis (PCA), combined with Logistic Regression, yielded 89.04% accuracy for depression prediction. While feature extraction can be beneficial for dimensionality reduction, our findings suggest that targeted feature selection techniques like RFE may offer a more tailored approach to handling high-dimensional data, thereby achieving higher accuracy.  
When compared with other studies, our results demonstrate a competitive edge. For instance, the authors of [62] reported classification accuracies ranging from 80% to 95% across various classifiers trained on linear and non-linear EEG features. In addition, the technique described in [63] achieved 88.33% accuracy using the SVM with features such as alpha2 and theta asymmetry, applying Multi-Cluster Feature Selection (MCFS). While the study in [63] highlighted the importance of specific EEG features, our higher accuracy suggests that SVM-RFE's iterative feature elimination process may offer a more refined  
Figure 10: ROC curves for the six models using features selected by SVM-RFE.  
approach for optimizing model performance by focusing on the most relevant features. Similarly, the method described in [64] achieved 91.07% accuracy on a training set and 84.16% on an independent test set using CK-SVM with coherence-based features selected through sequential backward selection.  
Furthermore, tree-based models in this study demonstrated performance gains with certain feature selection methods (e.g., mutual information, mRMR), which aligns with the findings by the authors of [65], who highlighted the importance of using selection techniques that cater to non-linear relationships in EEG data.  
Overall, the comprehensive evaluation of machine learning models and feature selection techniques in our study underscores the value of targeted selection methods like SVM-RFE for achieving strong performance in EEG-based depression detection.
## 3 Results and Analysis  
### Basic Statistical Analysis of Soil Salt Content  
In order to understand the overall variation of soil salinity in the study area and observe whether the distribution law of the measured sample data meets the normal distribution, descriptive statistical analysis should be conducted on the data of the sampling points in the study area before geostatistical analysis. The statistical results are shown in Table 1.  
It can be seen from Table 1 that the average soil salinity of layers between 0 m and 0.25 m, between 1 m and 2 m, and between 2 m and 3 m was 1.27%, 0.87%, and 0.79%, respectively. According to the classification standard of saline soil in Xinjang [24], the layer between 0 m and 0.25 m was severely salinized, while the layers between 1 m and 2 m and between 2 m and 3 m were moderately salinized. The coefficient of variation of soil salinity in the layers between 0 m and 0.25 m, between 1 m and 2 m, and between 2 m and 3 m was 1.59, 1.07, and 1.34, respectively, showing a trend of first decreasing and then increasing. According to the classification of the coefficient of variation [3], the coefficient of variation of the three layers maintained strong variability. At the same time, the difference between the maximum value and the minimum value of soil salinity of the three layers was large, indicating that the horizontal distribution of soil salinity was uneven. The reasons are as follows: (1) the evaporative precipitation ratios is large in the arid area, and the groundwater rises to the surface in the form of capillary water under the action of water potential gradient, and under the strong evaporation, the soil salt \"surface accumulation\" phenomenon is caused. (2) Due to the poor anti-seepage effect of the canal system, 59% of the water is lost in the process of water conveyance, which raises the groundwater level and promotes the formation of salinization. (3) The imbalance of diversion and drainage, or \"only irrigation without drainage\" and \"upper drainage and lower irrigation\" accelerate soil salinization. Through a K-S test, it is found that \(p\) values are all 0, which does not conform to normal distribution. Therefore, it is necessary to take the logarithm of the measured sample data to eliminate the scaling effect. The frequency distribution after  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Depth/m**} & \multirow{2}{*}{**n**} & **Min** & **Max** & **Mean** & \multirow{2}{*}{**SD**} & **Skewness** & **Kurtosis** & **CV** & **Converted** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & & & & **\(p\)-Value** \\ \hline
0–0.25 m & 92 & 0.04 & 10.35 & 1.27 & 1.46 & 2.72 & 7.74 & 1.150 & 0.2 \\
1–2 m & 92 & 0.01 & 5.64 & 0.87 & 0.89 & 2.56 & 7.23 & 1.023 & 0.2 \\
2–3 m & 92 & 0.06 & 5.11 & 0.79 & 0.92 & 2.73 & 6.46 & 1.265 & 0.052 \\ \hline \hline \end{tabular} Note: n is the number of samples; SD is the standard deviation; CV is the coefficient of variation.  
\end{table}
Table 1: Descriptive statistical analysis of soil salinity.  
conversion is shown in Figure 5, and its significance is greater than 0.05, which conforms to normal distribution.
# Analysis of the Channel Influence to Power Line Communications Based on ITU-T G.9904 (PRIME)
## 5 Methodology  
The study of the influence of the channel distortions on ITU-T G.9904 (PRIME) technology is based on simulations of the described PHY layer. Different transmission and channel parameters are analyzed in order to relate these parameters with the resulting FER.  
The simulation tool is able to configure a physical transmitter and receiver and modify the parameters of the communication and channel effects, and then evaluate the communication quality by measuring the resulting FER.
## 4 Optimal Design and Simulation of Antenna
To mitigate transmission line losses in the L- and S-bands, which cause progressive power attenuation along the coaxial feed, the element spacing was judiciously optimized to enhance radiation performance. Figure 6 compares the reflection coefficient (S11) of two configurations, an equidistant array (element spacing = 65 mm,  
Figure 4: Comparison of tangential axis ratio at the end of the gap: (a) Comparison at 1.4–2.8 GHz; (b) non-equidistant axis ratio at 1.56–1.64 GHz; (c) non-equidistant axis ratio at 2.36–2.54 GHz. corresponding to a half-wavelength at the target frequency) with a non-equidistant array (terminal-to-middle element spacing H2 = 65 mm and adjacent spacing H3 = 75 mm). Both configurations achieved \(\mid\)S111\(\mid\leq-\)10 dB over 1.61-1.6265 GHz. However, the equidistant array failed to cover the S-band operational range (2.4835-2.5 GHz) and exhibited a narrower impedance bandwidth in the L-band compared to the non-equidistant design, which fully satisfied the predefined specifications. Critically, the non-equidistant array successfully covered both the uplink and downlink frequency bands of the BeilDou-1 navigation system, demonstrating superior adaptability.
## 3 Accuracy Verification of the Analytical Solution
### Establishment of a Numerical Calculation Model  
According to the model diagram in Figure 3 and characteristic parameters in Table 1, six two-dimensional steady-state temperature field numerical models are established using COMSOL Multiphysics [38]. The Heat Transfer in Porous Media module is used for calculation, and the correctness as well as the accuracy of the analytical solution are verified by comparing the results.  
Taking the first group of parameters in Table 1 as an example, a sector of a period (\(2\pi/n=18^{\circ}\)) is selected to build a model as shown in Figure 7a. The model is divided by triangular mesh elements. To ensure the calculation's accuracy, select the \"Extra fine\" option for the mesh element size, and increase the mesh density in the freezing tube area, as shown in Figure 7b.
# Multi-Hazard WebGIS Platform for Coastal Regions
## 2 Materials and Methods
### Multi-Hazard Framework for Flood and Erosion Risk in Coastal Zones
#### 2.1.3 Multi-Source Monitoring Framework  
Evaluating the extent of inundation and overtopping during energetic events requires a combination of monitoring and prediction tools. Taking advantage of the results of the modeling and forecasting infrastructure and the availability of freely available field data, a monitoring framework for coastal inundation prediction was developed for application in multiple coastal typologies. This framework aims to gather information from multiple monitoring sources (e.g., conventional and low-cost fixed stations, remote sensing from satellites and unmanned aerial vehicle-UAV) and model-based forecast systems. The data are used sequentially, aiming to build a comprehensive timeline representation of coastal dynamics from a few days before to a few days after the emergency event.  
Figure 1: Model Chain Workflow.  
Each source provides an added layer of information and a new source for global data reliability evaluation. This procedure will be conducted in continuous mode, in a similar fashion as forecasts systems are operated and hence building a database of data on coastal evolution, supported by the several monitoring sources.  
The procedure is closely linked to the temporal window of interest and the availability of data sources during events (Figure 2). First, a complete set of information for a period of 48 h is built based on the application of the hydrodynamic and morphodynamic forecast models. Then, in-situ sensor data are assimilated using data fusion and data quality evaluation techniques [26]. The next step is to integrate processed near-real time Sentinel satellite data for inundation line detection, using the algorithms from the Worsica service ([[https://www.eosc-synergy.eu/thematic-services/worsica/](https://www.eosc-synergy.eu/thematic-services/worsica/)]([https://www.eosc-synergy.eu/thematic-services/worsica/](https://www.eosc-synergy.eu/thematic-services/worsica/)) (accessed on 10 May 2021)), at the times these images are available. This integration will provide a new set of products, based on reliable remote sensing data assimilation. Finally, the spatial scale of satellite images may be insufficient for fine scale and short-lived coastal features. UAV surveys are executed after the events, targeting the most critical hotspots, defined by the faster dynamics areas.
## 7 Evaluation
### Performance and Security Tradeoff
We find the optimum rekeying rate for the best security and performance tradeoff at \(\lambda_{1}=0.4348\), when no random delays are added. However, after reaching the optimum value, the tradeoff measure decreases much slower as the rekeying rate is getting larger. When the rekeying rate has a large value, the system tradeoff metric decreases because of the degrading system throughput at large rekeying rates. As Weibull random delays are padded, the tradeoff metric is greater than that with no random delay padding. This shows that the random delay countermeasure can improve the security and performance tradeoff effectively. For a specific value of the rekeying rate \(\lambda_{1}\), the trade measure always improves as random delays are padded. From Table 2, it can also be seen that the  
Figure 5: \(\Theta\) over rekeying rate \(\lambda_{1}\) and weighting parameter \(w\). optimal rekeying rate decreases when the variance of the Weibull distributed delay is growing. As a consequence, the system needs to make less effort to launch the rekeying process, which leads to less system cost. The command node can adjust the rekeying rate for the gateway node to obtain the minimum cost or maximum security performance tradeoff of the WSN.
# A Comparative Evaluation of Transformers and Deep Learning Models for Arabic Meter Classification
## 5 Discussion
### Limitations and Future Work  
While the proposed study employs half-verse data to achieve a higher level of accuracy in Arabic meter classification, it has certain limitations. One of the restrictions is its reliance on a specific dataset which may not adequately represent the diversity of Arabic poetry. Future research should explore the potential for expanding the dataset to encompass a larger diversity of poetic styles and emotions to assess the generalizability of the models.  
Additionally, exploring multimodal approaches that combine textual and audio features could provide deeper insights into Arabic poetry. Such interdisciplinary research would bridge computational linguistics, phonetics, and literary analysis, potentially leading to more nuanced and human-like poetic classification systems.
## 1 Introduction  
The deployment of Distributed Generation (DG) technologies, driven by several factors of political, social, economical, environmental and technical character has led to an increased complexity of distribution network operation. In particular, excessive DG penetration can sometimes negatively impact the performance of the system and may lead to voltage fluctuation problems, overvoltages, thermal overloading of network equipment, increased risk of exceeding equipment short circuit capacity as well as maloperation of protection equipment [1].  
In deregulated energy markets, a conflict of interest has been found among the DG owners/ investors and Distribution System Operators (DSOs), as investors are looking forward to a greater integration of DG to the distribution network, while operators are primarily concerned with the management of their network in scenarios of excessive DG penetration levels. While in the past this conflict of interest was traditionally mitigated by defining new grid codes as a fair and transparent solution to decide when to accept or reject new DG connection requests [2], as the distribution networks move towards a more active concept, with the integration of controllable Distributed Energy Resources (DER), these methodologies become inefficient. In practice this limits the hosting capacity of DG connection, compromises the secure and efficient operation of the network, and can lead to curtailment of DG in high voltage scenarios. Voltage control in distribution networks is rapidly becoming a concern for DSOs, particularly in situations where high levels of Renewable Energy Sources (RES)--such as solar photovoltaic (PV) panels--are connected directly at the end-user's premises [3].  
This phenomenon is more prevalent in weak Low Voltage (LV) networks, that have long lines usually with low reactance/resistance (X/R) ratios, and where the injection of large amounts of renewable energy may lead to voltage magnitude fluctuations that may fall out of the range defined by the EN-50160 standard [4].
# Airglow Observation and Statistical Analysis of Plasma Bubbles over China
## 2 Processing Method for Airglow Images
### Image Projection  
To further analyze the main features of plasma bubbles, image projection is required to obtain the final projected image. According to the geographic mapping algorithm, the airglow image can be projected to the corresponding geographic latitude and longitude. In this study, plasma bubbles were observed using airglow emissions at 630 nm wavelength, generally located at an altitude of 250-300 km; thus, the airglow altitude was assumed to be 300 km [21], and the airglow image would be projected to the geographic coordinates at 300 km, and the projection rules can be obtained through the parameter information of the stars in the image. The image projection used in this study is similar to the projection method from [PERSON]'s study, the airglow image was also projected to the altitude of 300 km [14]. In the process of image projection, multiple stars that were evenly distributed in an airglow image were selected, and their elevation angle, azimuth angle, and positions were recorded.  
Above all, it is necessary to find the relationship between the incident angle and the image radius of the stars, the incident angle (the zenith angle) is the complement of the elevation angle, and the image radius stands for the pixel distance from the stars to the zenith, and their relationship can be obtained by polynomial fitting, as shown in Equation (2).  
\[R\_img=f(\text{zenith})=b+k_{1}\alpha+k_{2}\alpha^{2}+k_{3}\alpha^{3}+\ldots \tag{2}\]  
The image projection is finally completed by projecting airglow images to the corresponding geographic coordinates. Figure 3a shows the corrected image at 19:39:20 UT on 2 September 2014, and Figure 3b is the obtained projected image. For the projected image in Figure 3b, the range of latitude and longitude is (13.5\({}^{\circ}\)-25.48\({}^{\circ}\) N, 103.22\({}^{\circ}\)-115.2\({}^{\circ}\) E), with per-pixel 0.02\({}^{\circ}\). Fuke station (19.5\({}^{\circ}\) N, 109.2\({}^{\circ}\) E) is located at the center of the projected image, which also represents the zenith position, and its geomagnetic latitude is 9.9\({}^{\circ}\) N.
# Generating Network Intrusion Detection Dataset Based on Real and Encrypted Synthetic Attack Traffic
## 2 Review of Existing Datasets
### Nsl-Kdd  
NSL-KDD is an updated dataset that tries to solve some of the inherent problems in the KDD99 dataset [28]. The NSL-KDD dataset contains features and labels indicating either normal or an attack, with specific types of attacks. Every instance in the training set contains a single connection session, which is divided into four groups, such as basic features from the network connection, content-related features, time-related features, and host-based traffic features. Each instance is labeled either as normal or attack. These attacks are categorized into four groups: Denial of Service (DoS), User to Root (U2R), Remote to Local (R2L), and Probing. Many researchers use it as a benchmark to help them to compare their intrusion detection systems performance. Several groups of researchers used different scopes, such as IoT-based networks [29] and Vehicular Ad Hoc Network (VANET) [30]. The former is for SYN flood, UDP flood, and Ping of Death (PoD) detection, while the latter is mostly for DDoS detection. Other researchers used different methods and switched from conventional machine learning to deep learning based methods [31; 32; 33].
# Autonomous Robotics for Identification and Management of Invasive Aquatic Plant Species
## 2 Methods
### Machine Learning for Aquatic Vegetation Classification
#### 2.2.3 DNN Training  
\"Alexnet,\" an advanced CNN supported by MATLAB's neural network toolbox, was used in the algorithm for plant species classification. Figure 13 shows the layer-wise structure of Alexnet. Due to a limited availability of preliminary data, transfer learning was implemented on the pretrained CNN. Initial algorithm training focused on only two training classes: the target species (_Hydrilla_: 466 images; Figure 14a) and non-target species (other: 1751 images including some with no vegetation; Figure 14b). Images of each class were randomly divided into training and validation sets (with 420 and 46 images, respectively). The DNN achieved 100% training accuracy, indicating sufficient network complexity. However, significant differences between training and validation accuracies indicated overfitting.  
Figure 12: Geo-tagged DownScanTM image with GPS coordinates in the top left corner.