esa-sceva/satcom-chunk-collection huggingface dataset:
# Empowering WBANs: Enhanced Energy Efficiency Through Cluster-Based Routing and Swarm Optimization
## 1 Introduction
### Contributions of the Research  
* The key contribution of this study is the design and implementation of an energy-efficient framework for WBANs, which integrates sensor node (SN) data collection within the system model and applies optimization techniques to minimize power consumption during data transmission, as detailed in the energy model. This approach enhances energy efficiency by dynamically adjusting the routing and clustering strategies based on real-time network conditions.
* Two methods are available: route selection by MSSOA and clustering and CH selection by employing ABBSOA.
* Establishing the closest route between the base station (BS) and CH through biological data aggregation based on MSSOA is provided.
* To illustrate the effectiveness of our ABBSOA and MSSOA methodology, a comparison has been established between the proposed and conventional approaches.  
The basic outline of the paper is given in the following section: In Phase 2, related works are provided. Phase 3 explains the proposed ABBSOA and MSSOA methodology. The discussion and results have been determined in Phase 4. Phase 5 presents conclusions.
# Evaluation on Radiometric Capability of Chinese Optical Satellite Sensors
## 3 Results and Discussion
### Radiometric Capability Evaluation of Huan Jing 1/Charge-Coupled Device (H)-1/CCD
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Band**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Mean\_a\({}^{*}\)**} & \multirow{2}{*}{\
\begin{tabular}{c} **Mean\_a\({}^{*}\)** \\ **Mean\_M\({}^{*}\)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Standard** \\ **Deviation** \\ \end{tabular} } \\ \hline \multirow{8}{*}{2009} & Blue & \(2.67\times 10^{-7}\) & 0.1548 & 0.1408 & 0.1475 & 0.1679 & 0.0279 & 0.0054 \\  & Green & \(8.88\times 10^{-6}\) & 0.1777 & 0.1621 & 0.1674 & 0.1847 & 0.0187 & 0.0052 \\  & Red & \(7.04\times 10^{-6}\) & 0.2249 & 0.2032 & 0.2122 & 0.2171 & \(-\)0.0059 & 0.0067 \\  & NIR & \(1.16\times 10^{-5}\) & 0.2472 & 0.2280 & 0.2401 & 0.2400 & \(-\)0.0340 & 0.0062 \\ \hline \multirow{8}{*}{2010} & Blue & \(5.76\times 10^{-5}\) & 0.1950 & 0.1792 & 0.1884 & 0.2144 & 0.0744 & 0.0073 \\  & Green & \(1.73\times 10^{-5}\) & 0.2076 & 0.1949 & 0.2011 & 0.2218 & 0.0558 & 0.0053 \\  & Red & \(-1.96\times 10^{-6}\) & 0.2629 & 0.2388 & 0.2533 & 0.2592 & 0.0362 & 0.0091 \\  & NIR & \(2.34\times 10^{-4}\) & 0.2939 & 0.2407 & 0.2667 & 0.2666 & \(-\)0.0074 & 0.0222 \\ \hline \multirow{8}{*}{2011} & Blue & - & 0.2193 & 0.1585 & 0.1795 & 0.2043 & 0.0643 & 0.0184 \\  & Green & \(2.98\times 10^{-5}\) & 0.2137 & 0.1805 & 0.1956 & 0.2158 & 0.0498 & 0.0109 \\ \cline{1-1}  & Red & \(5.56\times 10^{-5}\) & 0.2731 & 0.2264 & 0.2475 & 0.2533 & 0.0303 & 0.0129 \\ \cline{1-1}  & NIR & \(-\) & 0.3006 & 0.2500 & 0.2717 & 0.2716 & \(-\)0.0024 & 0.0149 \\ \hline \multirow{8}{*}{2012} & Blue & \(-2.04\times 10^{-4}\) & 0.2051 & 0.1516 & 0.1743 & 0.1984 & 0.0584 & 0.0188 \\ \cline{1-1}  & Green & \(-1.13\times 10^{-4}\) & 0.2024 & 0.1712 & 0.1879 & 0.2073 & 0.0413 & 0.0097 \\ \cline{1-1}  & Red & \(-8.56\times 10^{-5}\) & 0.2468 & 0.2167 & 0.2328 & 0.2382 & 0.0722 & 0.0097 \\ \cline{1-1}  & NIR & \(-1.06\times 10^{-4}\) & 0.2832 & 0.2386 & 0.2647 & 0.2646 & \(-\)0.0094 & 0.0143 \\ \hline \hline \end{tabular} \({}^{*}\) Mean of the TOA reflectance after spectral matching with MODIS, and the same below. \({}^{*}\) Mean of the TOA reflectance of MODIS, and the same below.  
\end{table} 
Table 8: The statistics from the time series of the TOA reflectance of HJ-1A/CCD1. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Sensor**} & \multirow{2}{*}{**Year**} & \multirow{2}{*}{**Band**} & \multirow{2}{*}{**Slope**} & \multirow{2}{*}{**Maximum**} & \multirow{2}{*}{**Minimum**} & \multirow{2}{*}{**Mean**} & \multirow{2}{*}{**Mean\_a\({}^{*}\)**} & \multirow{2}{*}{ \begin{tabular}{c} **Mean\_a\({}^{*}\)** \\ **Mean\_M\({}^{*}\)** \\ \end{tabular}
# Fault Prediction and Reconfiguration Optimization in Smart Grids: AI-Driven Approach
## 5 Evaluation
### Machine Learning Techniques
#### 5.3.3 Dimensionality Reduction  
After analyzing the best combinations of hyperparameters for each ML technique, the application of some dimensionality reduction methodologies on the dataset was studied. This aimed to eliminate irrelevant or redundant information from the dataset to achieve more efficient model training. Three different techniques were tested to later compare the performance of the RF and SVM models in Section 6:  
* Recursive Feature Elimination with Cross-Validation (RFECV) [49]: This method iteratively discards the least influential features until the model's performance no longer significantly improves. With 5-fold cross-validation, it can be seen in Figure 14 that the maximum accuracy for the optimal RF model was achieved with eight features. Additionally, it was confirmed that the identified features (_cap_, _dist_, _origen_id_, _dest_id_, _len_origen_tag_, _len_dest_tag_, _total_balance_, _abs_flux_) matched those with the highest scores in Section 5.3.1. However, in the case of SVM, the classifier class did not have the necessary attributes for implementing RFECV (_coef_, _feature_importances_), so this technique was only applied to RF.
* Univariate Feature Selection [50] uses the _SelectKBest()_ method, which requires prior scaling and the specification of a fixed number of features to work with. In this case, values of \(k\) equal to five and eight features were selected.
* PCA [51] reduces the dimensionality of the dataset using the Singular Value Decomposition (SVD) technique. It works by finding the principal directions of variation (\(k\) vectors) in the dataset to construct a projection matrix, which establishes a new \(k\)-dimensional feature space. To define the optimal number of components, the elbow method was used. Figure 15 shows the variance leveling off at three components, so for comparative purposes, the performance of RF and SVM was studied for two and four components.
## 1 Introduction
### Polystore  
The numerous data types and data sources available warrant an architecture that can leverage these data across multiple databases [10]. This has given rise to the concept of Polystores, which support transparent access across a variety of backends which have the disimilar data models. Polystores enable uniform querying across many data models and are necessary to handle data across numerous data models fast and efficiently. Polystores are a type of database management system that consists of a collection of interconnected heterogeneous database engines that communicate via an Application Programming Interface (API) [11]. Polystore systems, sometimes referred to as multistore systems, enable integrated access to a variety of heterogeneous cloud data storage, including NoSQL and RDMS. The taxonomy of a Polystore system was examined in a work [12] that categorized it as either loosely or tightly coupled. The mediator or wrapper concept is akin to the loosely coupled multistore architecture. When it comes to the data store, it has a common user interface and may be operated independently of the multistore architecture on a local level. Through an API, the wrapper interfaces with the data store to create queries, transform them, and execute them, and it delivers results to the operator engine. The tightly coupled multistore system enables local user engagement and task sharing across several systems, resulting in increased performance. Additionally, it enables the aggregation of data from several data sources. The hybrid system combines the benefits of a loosely coupled multistore with the advantages of a tightly coupled system. It optimizes querying many cloud-based data storage sources by using native subqueries and an operator order.  
In order to manage capacity to access data/information between applications, polysources must link and be integrated to be processed. The changes in types of models and variety of data over the past few decades have given rise to extensive research on managing heterogeneous data. The data available in different scientific domains present challenges in integrating data with different models. Data integration is a process of integrating data from multiple sources [13]. It should provide a single view over all the sources. Generally, it features two types of architecture. Data Warehousing is a type of physical model in which data from multiple sources are copied and stored in a warehouse. The other type of architecture is the virtual architecture. The virtual architecture basically consists of Federated Database Systems (FDBS), a mediator, and the newly proposed Polystores.
# Efficient Intrusion Detection Algorithms for Smart Cities-Based Wireless Sensing Technologies
## 1 Introduction
The source receives a number of RREP packets from its neighbors and selects one node with the least number of hops to send the message. In a sinkhole scenario, the compromised node sends RREP with the minimum number of hops to the sink node. This scenario is repeated with every node that sends the RREQ message. In this case, the compromised node blocks all of the traffic to the sink node. It might perform a denial of service (DoS) attack or reply attack, changing the content of received messages and sending them back to the sink node. As can be seen, such a scenario either interrupts the operation of the smart city networks or provides false information to the decision-makers. Therefore, there is a need for a simple and efficient solution to the sinkhole  
Figure 2: Sinkhole attack solutions classifications [18; 19; 20; 21; 22; 23; 24; 25; 26; 27; 28]. problem. However, by studying the previously mentioned solutions, the following common issues have been found, and the proposed algorithms in this paper try to solve some of them:  
* Memory network overhead,
* Network communication cost is high,
* The message loss due to sinkhole attack is not considered,
* Collision may impact the proposed solutions,
* Dummy messages are used to detect intrusion. This paper is motivated to find elegant solutions to the sinkhole problem. The focus of this paper is on the sinkhole attack and its effect on network performance. For instance, collision and dummy messages are not considered in this paper. However, it tries to reduce the communication cost, giving that it is a tradeoff between the network communication and security. Besides, the proposed algorithms try to free the sensor nodes from excessive computation and offload most of the overall computation to the sink node. The paper is organized as follows: The next section presents the most related work to the research proposed in this paper. The system design is described in Section 3. The proposed intrusion detection techniques are described in Section 4. Section 5 presents the conducted experiments and analysis, while Section 6 contains the discussion. The paper has the conclusion and future work in Section 7.
# Energy-Aware Forwarding Strategies for Delay Tolerant Network Routing Protocols
## 4 Evaluation and Analysis
### Simulation Parameters  
Our selection of simulation parameters was motivated by an existing system deployment for the flying fox monitoring program [29]. All nodes include batteries with 300 mAh capacity corresponding to 4000 J of stored energy. Each GPS reading consumes energy of 0.75 J. We use a low-power listening scheme set with 1% radio duty cycle [32] that uses 0.052 J to transmit a packet and 0.002 J to receive a packet or channel scanning for presence of another node. The radio range is 500 m, transmission speed is 250 kbps, and each node has 5 MB packet buffer space. The packet buffers are sufficient to store all messages generated during simulation. Sensor nodes can harvest energy up to the maximum battery capacity. The summary of the simulation parameters is listed in Table 1.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Number** & **Simulation Parameter** & **Value** \\ \hline
1 & Simulation time & 7 days (3 h per day) \\
2 & Movement model & External Movement \\
3 & Message size & 100 b \\
4 & Buffer size & 5 Mb \\
5 & Update interval & 0.01 \\
6 & Battery capacity & 300 mAh–4000 J \\
7 & Transmit energy & 0.052 J \\
8 & Scan energy & 0.001 J \\ \hline \hline \end{tabular}
\end{table}
Table 1: Simulation parameters for both flying fox and pigeon scenarios.
# Benefit Optimization of Short Food Supply Chains for Organic Products: A Simulation-Based Approach
## 2 Materials and Methods  
### Research Gaps and Questions  
We identified research gaps related to both theoretical and empirical aspects, with the use of computer simulation to support the introduced theses. The SFSC strategy is rarely described in the literature as a new trend or an area of scientific interest. The deficiencies in terms of theoretical foundations and support for future directions of research are visible. The existing studies refer to practical examples, without focusing on or defining a strong theoretical basis for the study, including the identification of SFSC solutions as a new supply chain strategy that can be part of global trends, related to sustainable development and the functioning of sustainable supply chains.  
The novelties of this research include the application of simulation-based optimization in the search for the proportion of organic products supplied by a local farmer. The use of computer simulation with an optimization module allows the local farmer to find the best balance of supply for each type of market. From a theoretical viewpoint, we systematize the scientific knowledge and identify motivators,barriers, and drivers, and relate SFSCs to organic products in sustainable supply chains (SSCs) in order to search for profit and benefit opportunities for individual links serving different types of local markets and short food supply chains.  
We raised the following research questions: How does a change in a market within the SFSC affect the achievement and change in producers' profits? Which of the presented solutions are optimal for the selected organic products? How can local producers be encouraged to take interest in the new supply chain strategy, i.e., the SFSC, for organic products?  
The research methods included the following: (1) a critical analysis of the literature showed that extensive theoretical foundations related to the discussed issues are lacking. (2) Computer simulation was used to identify possible scenarios of optimization of changes in the profits of producers servicing different markets within the SFSC, with the example of three selected products. (3) The data for empirical research were collected on the basis of interviews with farmers producing organic goods in the West Pomeranian Voivodeship in Poland. The selected products included goods produced in the studied area, which met the organic products requirements, with producers that served various markets, including those that were identified as a part or element of an SFSC.
## 5 Identification Experiments with Different Sensors in the Same Area  
In this section, to compare the performance of the EAEND model-assisted CEM method in identifying Arctic sea ice images at different spatial resolutions, we conduct experiments on the same area using different sensors for Arctic sea ice identification. To further validate the effectiveness of the identification method, we compare the identification performance of EAEND model (10) with other traditional classification methods, which are the support vector machine (SVM) method, Otsu thresholding method, and Mahalanobis distance (MD) method.
## 3 The Proposed Imputation Method
### Iterative Feature Optimization
#### 3.2.2 Regression Model and Dataset Update  
There are several types of regression models, such as linear, logistic, polynomial, stepwise, and random forest regression, that could be used in our proposed model to estimate the missing data. In our proposed method, Random Forest Regression (RF) is chosen due to its versatility and power as a machine learning algorithm that is robust, scalable, and flexible [62; 63]. This model combines multiple decision trees and incorporates randomness, resulting in accurate predictions while reducing the risk of overfitting, and is therefore widely favored in data science projects. The inherent randomness of RF regression effectively mitigates overfitting [64; 65]. It can also handle large datasets characterized by high dimensionality and mixed numerical and categorical features. RF also provides scores on the importance of features, which can help in the selection of features and improve the interpretation of the data [66; 67; 68].  
The proposed imputation approach based on informative features or feature selection (denoted by FS) uses the RF regression model, which is trained on the training set using the selected informative features for each feature \(f_{i}\). The trained model is then used to estimate the missing values for that feature \(f_{i}\) on the test set. The dataset is updated by replacing the original feature with the imputed values once the imputation for feature \(f_{i}\) is complete. This is repeated for all features until a complete missing values dataset is obtained.
# Non-Contact Dermatoscope with Ultra-Bright Light Source and Liquid Lens-Based Autofocus Function
## 1 Introduction
### State-of-the-Art Commercially Available Non-Contact Systems  
A few non-contact devices for skin examination have already been reported. They can be categorized in (i) non-contact dermatoscopes, which have a high magnification (see for example [4; 5]), and (ii) non-contact skin screening devices for overview images of the skin (see for example [14]).  
So far, different designs for non-contact dermatoscopes are utilized. One type is realized as a handheld magnifying glass while another is configured as an adaptor for mobile phones. The latter use the camera of the mobile phone to take the images. Other devices rely on built-in camera and illumination sources. However, all these systems are applied at short working distances of a few centimeters. An important aspect in this context is (auto)focusing, which has to be considered for all non-contact devices. For example, non-contact devices with more than 2 cm working distance use spacers to avoid the need of autofocusing. Furthermore, the illumination conditions are more difficult to control for non-contact devices as environmental influences are more relevant. Thus, sufficiently bright light sources are needed to ensure reproducible illumination conditions [4; 5].  
Further concepts rely on imaging systems at large working distances, which provide only overview images of the skin with lower resolution compared to dermatoscopes. They usually make use of standard cameras with passive autofocusing techniques that employ phase or contrast detection. The cameras are mounted on a movable holder so that images of the whole body can be taken. As room light is used for illumination it is difficult to compare images taken under varying lighting conditions [14].
## 4 Simulation Results
### Quadrature Phase Shift Keying (QPSK)  
#### 4.2.1 Coherent QPSK  
The MATLAB code simulates a QPSK system of \(10^{4}\) bits and SNR in the range of \(-3\) to \(7\) in dB. An AWGN is generated with variance equal to \(1/(10^{(\text{SNR}/10)})\) and is added to the transmitted signal. The same AdaBoost algorithm is used of \(T=15\), where it is the number of trials after which the desired result is achieved, and \(K=4\) because in the case of the QPSK, we are only interested in dividing the data into four classes. The algorithm separates the data into four classes achieving the QPSK concept as in Figure 8.  
Figure 7: BER vs. SNR plot for noncoherent BPSK modulation over the Rayleigh fading channel.  
Figure 9 illustrates the BER against the SNR of the coherent QPSK system. The figure depicts both simulated and theoretical results. At a low SNR, it is recognized that the BER increases, and at higher values of SNRs, the BER decreases. The simulated BER appears to almost coincide with the theoretical one except at the SNR value of 7 dB, where the simulated curve appears to dip slightly under the theoretical. The reason for this result is the same as that previously mentioned in discussion of Figure 6. Hence, this verifies the improving effect of the AdaBoost algorithm on the used modulation technique.  
Figure 8: QPSK modulation technique.  
Figure 9: BER vs. SNR for coherent QPSK modulation over the AWGN channel.
# Estimating Parameters of Photovoltaic Models Using Accurate Turbulent Flow of Water Optimizer
## 4 Simulation Results and Discussion
### Statistical Analysis for KC200 GT Solar Module  
#### 4.3.1 Single Diode Model  
The comparison of the results for the SDM is explained in Table 9; this table includes the best _RMSE_ and the parameters extracted from each algorithm. From Table 9, it can be observed that the best _RMSE_ value (0.000636657) is achieved by the TFWO algorithm, while the second-best _RMSE_ (0.002888472) is achieved by EO, followed by MRFO, BSDE, BSA, MPA, CSO, and GWO. Based on TFWO, the photo-generated current is 8.216747428 A; the dark saturation current of the SDM is 0.0262486 \(\mu\)A; the diode ideality factor is 1.212957711; the series resistance is 0.004825464 \(\Omega\); the shunt resistance is 6.284632281 \(\Omega\). Figure 17 describes the convergence rates of the algorithms which show that the capability of the proposed TFWO in finding the minimum _RMSE_ is the fastest. Added to that, the P-V and I-V curves for the SDM based on the estimated data from TFWO at the best _RMSE_ are explained in Figure 18, which illustrates the high coincidence of the simulated with the experimental data.