esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Discussion
### Impact of Meteorology on MODIS GPP  
The PAR in the MODIS GPP algorithm together with T\({}_{\text{min}}\) and VPD\({}_{\text{avg}}\), which influence LUE, are all meteorological factors. Thus, the meteorological inputs may be the largest sources of error in the GPP estimates [50]. The NCEP-DOE Reanalysis II meteorology dataset used in the C5.5 version of MOD17A2 product has a low resolution. Although a strict interpolation was used to make the resolution comparable to the MOD15A2 product, the errors caused by the original low resolution could not be eliminated completely. In addition, the interpolation process produces new uncertainties. From comparisons between the meteorological reanalysis data and the site meteorology data, it can be clearly observed that the NCEP-DOE Reanalysis II meteorology data are ineffective for estimating PAR, and the outcome is even worse for VPD (Figure 9c,d). In fact, the MODIS GPP algorithm does not directly take the soil moisture into account. However, soil moisture plays a key role in GPP estimation [51]. To compensate for this defect, the VPD was added to the algorithm as a proxy for the soil moisture; however, errors will be unavoidably introduced. In particular, the estimation error will increase when the deviations between the VPD estimates and measurements are large.  
In this paper, we replaced the NCEP-DOE Reanalysis II meteorology data with the site meteorology data to recalculate the MODIS GPP formula and obtained new GPP values (_i.e._, MOD_Tower GPP). Unexpectedly, the GPP estimates over eight days calculated using the site meteorology data did not result in obvious improvements in the accuracy of the GPP estimates (Figure 2a,b). On the contrary, the estimates of the seasonal and annual GPP calculated using the site meteorology data were not as accurate as those calculated using the meteorological reanalysis data (Table 3). This implies that improved meteorological inputs will not necessarily enhance the estimation effectiveness of the MODIS GPP algorithm, highlighting the need to analyze other sources of error.
# Integrating Cognitive Radio with Unmanned Aerial Vehicles: An Overview
## 4 CR-Based UAVs
### Software Characteristics
#### 4.4.2 Spectrum Handover  
The channel occupied by an unlicensed should ideally be vacant when a PU arrives. This is desirable in order to generate minimal interference to the primary user transmission. The literature generally refers to the process of hoping to another channels as spectrum handover. It involves distinct strategies with regards to its integration with spectrum sensing, such as non-handover, pure proactive, pure reactive, hybrid, and ML approaches, which incorporates the preceding [54].  
In the non-handover strategy, the SU remains idle until the PU leaves the channel, resuming the transmission of data later. Although spectrum sensing is limited solely to current channel monitoring, the drawback of this concept is unveiled during a long PU transmission, which significantly restricts the transmission time available to the SU, in terms of detection of PU arrivals and departures.  
Unlike non-handover, before and after the arrival of the PU, both pure proactive and reactive strategies concentrate on handover to an idle channel, respectively. The pure proactive algorithm tries to predict the arrival of the PU while perceiving the atmosphere to locate a spectrum hole based on the traffic pattern of the channel. On the contrary, after the identification of the PU, the pure reactive approach only senses and switches to an unused channel. A potential disadvantage in the pure proactive method may be created by a poor prediction of traffic, leading to an unnecessary handover, whereas the pure reactive approach may have a greater handover delay just after a PU arrival due to the execution of the spectrum sensing stage.  
The hybrid handover strategy, taking advantage of the advantages of both methods, incorporates both the proactive spectrum sensing phase, perceiving the spectrum holes before the arrival of the PU, and reactive handover action. Therefore, due to the proactive process, the handover delay is reduced, and not every PU arrival needs to be predicted by the algorithm. However, the backup channel can become obsolete before use, as in the proactive method, thus driving the algorithm to perform a supplementary spectrum sensing phase.  
The literature has stressed the utility of ML algorithms to overcome the complexities of spectrum handover. [PERSON] et al. [69] developed a method of negotiating multi-agentsystems that enables SUs to migrate opportunistically to the most adequate spectrum band provided its characteristics, achieving around 97% of spectrum utilization. An investigation on the Hidden Markov Model (HMM) application for spectrum handover and simulated data showed that in detecting transmission opportunities, this technique can give SUs greater accuracy [70]. Finally, in order to achieve dynamic handover management, [PERSON] and [PERSON] [71] suggested a supervised Machine Learning (ML) approach referred to as Spectrum Particle Swarm Optimization (SpecPSO), using Visitor Location Register and Home Location Register databases to train the algorithm.
## 3 The Proposed Model
### System Model
#### 3.1.3 Network Model  
Consider that the n sensor is positioned in the region of M size and the sensor is static. Every sensor is regarded as being aware of the identification and location of other SNs. Also, consider that the advanced node's location is predetermined, while the normal and intermediate nodes are placed randomly. All the nodes transmit information to neighboring CH for data aggregation. The distance between the BS and nodes is \(d_{s}\geq d_{s}^{r}\), where \(d\) indicates a predetermined range (\(d_{s}^{r}=10\) m).
# Carbonyl Composition and Electrophilicity in Vaping Emissions of Flavored and Unflavored E-Liquids
## 4 Conclusions and Implications  
Using GC/EI-MS and LC/ESI-QTOFMS methods, this study characterized 14 carbonyls and levels of seven target carbonyls in vaping emissions of flavored and unflavored e-liquids. The identified carbonyls are formaldehyde, acetaldehyde, acetone, propionaldehyde, acrolein, butylaldehyde, glyoxal, methylglyoxal, dimethylglyoxal, n-butanol, 3-methyl-2-butenal, _trans_-2-methyl-2-butenal, 2-methyl-2-pentenal, _trans_-2-hexenal, and benzaldehyde. This study demonstrated the formation of commonly reported toxic carbonyls (e.g., formaldehyde, acetaldehyde, acetone, propionaldehyde, and glyoxal) and unique carbonyls \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & \multicolumn{4}{c}{\(f_{k}^{*}\) (for Nucleophilic Attack)} \\ \hline
**Compound** & **Structure** & **O\({}_{\text{carbonyl}}\)1** & **C\({}_{\text{carbonyl}}\)2** & **C\({}_{\text{\alpha}}\)3** & **C\({}_{\text{\beta}}\)4** \\ \hline  & & & simple carbonyl & & \\ formaldehyde & & 0.3465 & 0.5923 & n/a & n/a \\ acetaldehyde & & 0.3365 & 0.4817 & n/a & n/a \\ benzaldehyde & & 0.2260 & 0.2426 & n/a & n/a \\  & & 0.2260 & 0.2426 & n/a & n/a \\  & & 0.2,\(\beta\)-unsaturated carbonyl & & & \\ acrolein & 1\({}^{\circ}\)0\(\xrightarrow{}\)2 & 0.2503 & 0.238 & 0.0684 & 0.3016 \\ _trans_-2-hexenal & & 0.2146 & 0.2426 & 0.0563 & 0.2619 \\ \hline \hline \end{tabular}  
* \({}^{1}\): Position of Ocarbonyl on structure. \({}^{2}\): Position of Ccarbonyl on structure. \({}^{3}\): Position of C\(\alpha\) on structure. \({}^{4}\): Position of C\(\beta\) on structure.  
\end{table} 
Table 3: Condensed Fukui function, \(f_{k}^{+}\), of carbonyl compounds calculated using the UCA-FUKUI software. associated with transformation of flavoring chemicals during vaping (e.g., _trans_-2-hexenal and benzaldehyde). Formaldehyde-GSH and _trans_-2-hexenal-GSH adducts formed via 1,2-additon and/or 1,4-conjugated addition reactions between carbonyls and GSH highlighted vaping-associated reactive carbonyl species as an external source of toxicity. The global electrophilicity indices (\(\omega\)) show benzaldehyde, acrolein, and _trans_-2-hexenal as most reactive among selected carbonyls, while the local reactivity parameter, \(f_{k}^{+}\), supports that C\({}_{\text{carbonyl}}\) of simple carbonyls, and C\({}_{\text{carbonyl}}\) and C\({}_{\beta}\) of \(\alpha\),\(\beta\)-unsaturated carbonyls are more favorable for nucleophilic attacks. A wide variety of carbonyl compounds have been detected in vaping emissions, but only a few have been reported. These emitted carbonyls may pose potential health risks upon inhalation depending on their abundance in the vaping emissions as well as their chemical reactivity. Further studies are required to comprehensively profile the emission products from flavoring chemicals to gain a better understanding of potential health effects associated with vaping.
## 3 Channel Estimation in Different Frequency Bands
Mean squared error (MSE) and normalised MSE (NMSE) are commonly used to express the accuracy of channel estimation. The MSE and NMSE can be expressed as Equations (10) and (11), respectively:  
\[\mathbf{G}=\mathbf{H}\mathrm{diag}(\mathbf{h}_{k}) \tag{2}\]1  
\[\mathbf{G}=\mathbf{H}\mathrm{diag}(\mathbf{h}_{k}) \tag{2}\]2  
where \(\hat{\mathbf{G}}\) is the estimated value. The signal-to-noise ratio (SNR) represents the ratio of signal power to noise power. It can be expressed as:  
\[\mathbf{G}=\mathbf{H}\mathrm{diag}(\mathbf{h}_{k}) \tag{2}\]3  
where \(P_{\text{S}}\) is the signal power and \(P_{\text{N}}\) is the power of the noise. The pilot overhead is also one of the important metrics for evaluating channel estimation methods. The bit error rate (BER) and achievable rate can indicate the efficiency of the message delivery after channel estimation and beamforming.
# Mineral Identification Based on Deep Learning Using Image Luminance Equalization
## 2 The Proposed Method
### Laplace Operator Image Enhancement  
The Laplace operator [28] image enhancement is widely used in image processing as a second-order differential algorithm commonly used in the field of digital image processing. It causes the gray contrast to be enhanced, thus making the blurred image sharper. The essence of image blurring is that the image is subject to averaging or integration operations, so the image can be inverse operated. For example, differential operations can highlight image details and make the image sharper. Since Laplace is a differential operator, its application enhances the areas of sudden gray changes in the image and attenuates the areas of slow gray changes. Therefore, the Laplace operator can be selected to sharpen the original image to produce an image describing the abrupt grayscale changes, and then the sharpened image is produced by superimposing the Laplace image with the original image. The basic method of Laplace sharpening can be represented by the following equation.  
\[L(x,y)=\left\{\begin{array}{l}f(x,y)-\
abla^{2}f(x,y),t\leqslant 0\\ f(x,y)+\
abla^{2}f(x,y),t>0\end{array}\right. \tag{2}\]  
where \(f(x,y)\) denotes the two-dimensional image, \(\
abla^{2}f(x,y)\) denotes its Laplace operator, and \(t\) is the neighborhood center comparison coefficient. This simple sharpening method produces the effect of a Laplace sharpening process while preserving the background information. By superimposing the original image to the processing result of the Laplace transform, we can preserve each gray value in the image so that the contrast at the gray abrupt change is enhanced. The final outcome is to bring out small details in the image while preserving the image background. However, this tends to produce a double response to image edges, which will affect the experimental results.
## 5 Case Study
### Scenario 2  
#### 5.2.1 Basic Data in Scenario 2  
To better evaluate the effectiveness of the proposed method, we conduct simulations on different MGs, including differences in the sizes, parametrization, generators, as well as grid topologies. Due to the space constraints, here we choose one typical scenario, whose optimal results are given in detail. Compared with Scenario 1, the load of MGs is smaller, and the parameters of the RESs and BESSs are different. Moreover, the CHP is considered in this scenario, and there is a CHP system in MG1. This scenario is called Scenario 2.  
In Scenario 2, the parameters of the RESs, BESSs and CHP are given in Tables 4-6, respectively.  
Figure 20 shows the net load of each MG and the total net load of the coalition in Scenario 2. The PV power, WT power and load power in each MG are represented in Figure 21.  
Obviously, the net load in Periods 15-16 and 32-60 is negative, so the surplus renewable energy is used for charging. In remaining time slots, the MGs choose to generate power by CHP, discharge by BESS or purchase power from the main grid.
# Resource Management in Cloud Radio Access Network: Conventional and New Approaches
## 4 Challenges and Open Research Issues
### Forecasting Future Demand  
Demand forecasting is a key issue in C-RAN resource management. Efficient forecasting techniques can yield optimal resource allocation strategies to minimize energy consumption. Machine learning techniques can be an appropriate solution for this problem, particularly the utilization of a neural network-based predictor for forecasting the future demands in the network. Another feasible method could be the adoption of reinforcement learning (RL) techniques, where the RL agent would learn the network environment and forecast the future demand and allocate resources based on that learning. The RL agent would allocate resources to the BBUs associated with those RRHs by learning from the user demands of different RRHs in a BBU pool.
# Improved Homotopy Perturbation Method for Geometrically Nonlinear Analysis of Space Trusses
## 4 Results and Computational Examples
### Example 4: 168-Member Space Truss  
The fourth example is related to a dome truss extracted based on the previous studies [29] which is shown in Figure 8. The concentrated force of 820 KN is applied at the apex of 168 element truss with 73 nodes and 147 DOFs. It is noted that the out-of-plane motion is restrained for pinned supports for this structure. The cross-sectional area is 50.431 cm\({}^{2}\) for all the members, and the material constitutive model has an elastic modulus of \(2.04\times 104\) kN/cm\({}^{2}\). The incremental load (\(\Delta\)P) is 20 kN, which is applied at the apex of the structure.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{2}{c}{**Decrease Percentage**} & \multicolumn{2}{c}{**Present Method**} & \multicolumn{2}{c}{**Newton Method**} \\ \hline
**Iteration No.** & **Time (s)** & **Iteration No.** & **Time (s)** & **Iteration No.** & **Time (s)** \\ \hline
31 & 10.43 & 51 & 0.4894 & 74 & 0.5464 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison of the computational cost and iteration number  
Figure 6: Space truss with 120–members, all dimensions are in mm [28].  
Figure 7: Space truss with 120–members, all dimensions are in mm.  
Figure 9 shows the numerical load-displacement comparison with the classic numerical method. It can be concluded that the PM method for this example is able to precisely evaluate the behavior with 8.7% reduction in computational costs and 29.0% in iteration number, which is shown in Table 4.
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 1 Introduction
### Attacks on Blockchain  
Phishing attacks are one such security concern observed in blockchain transactions. Phishing is a malicious technique attackers use to gain sensitive information, such as user-names, passwords, and private keys, from unsuspecting victims [41]. Phishing attacks can be carried out via email, messaging apps, or social media platforms, leading to financial loss, identity theft, and other serious consequences [42]. For example, fraudsters are taking advantage of flaws in major search engines' ad presentations to drive consumers to phishing sites. In contrast to a common strategy of sending emails to everyone, links to phishing sites in search engine advertising may be more convincing, especially if the domain they are targeting is listed as the destination. To guard against phishing, users' credentials are actively protected, which involves storing password hashes rather than passwords. Even though many firms spend large sums of money researching phishing and developing unique tools and algorithms for its detection and blocking, no products provide complete protection from such assaults. Because phishing attacks are typically carried out with the victim's involvement, anti-phishing defense uses technical and social engineering tools [43].  
Phishing detection has been intensively researched in recent decades, with several approaches presented [44, 45]. However, despite the properties of blockchain, there has been little study on phishing fraud detection. [PERSON] [46] classifies the significant types and schemes of phishing attacks on the blockchain project and proposes ways of phishing attack defense from the blockchain project's perspective. Unlike them, we are focusing overall blockchain ecosystem and alerting users to phishing schemes as soon as they appear. AI and deep learning methods can be applied to detect network intrusions [47, 48] and recognize malware and botnet attacks [49, 50, 51]. To improve recognition accuracy, ensemble learning architectures have been successfully adopted [52, 53, 54].
## 2 Materials and Methods
### Vision  
#### 2.2.1 Object Detection, Segmentation and Localization  
Vision-based object detection and localization are accomplished using YOLO [36], a cutting-edge convolutional neural network model. YOLOv8 is renowned for its real-time processing capabilities and its effectiveness in detecting and classifying objects with a single forward pass. This significantly reduces computational demands and enhances speed, making it ideally suited for real-time applications. The architecture is built upon a deep convolutional neural network that is optimized for both speed and accuracy, featuring an advanced backbone for feature extraction and multiple scale predictors designed to detect objects of varying sizes. Below, Figure 3 illustrates a comparison of YOLO models trained on the COCO dataset.  
Figure 2: Experimental setup consisting of Intel RealSense D435i Depth Camera, AR4 Robotic Arm (Annin Robotics, Meridian, ID, USA) and Inspire Robot Dexterous Hand (INSPIRE-ROBOTS, Beijing, China).  
Based on Figure 3, the YOLOv8-n model is both smaller and faster compared to others, making it an ideal choice. To enhance the accuracy and estimate the object size, we opted for the YOLOv8-n-segmentation model. This model has been trained on a custom dataset that incorporates various lighting conditions to ensure robust detection across different environments. The network outputs bounding boxes, segmentation masks, and 3D coordinates for the objects, which are utilized to guide the robotic arm to the appropriate grasping position.
# An Efficient System for Automatic Blood Type Determination Based on Image Matching Techniques
## 4 Experimental Setup and Implementation
### Blood Samples Collection  
A total of 500 blood samples were collected and labeled for the system construction and evaluation. 300 samples were collected and processed by lab technicians at An-Najah National University Hospital. The remaining 200 samples were collected and processed by lab technicians in the college of medicine at An-Najah National University in Nablus, Palestine. Table 3 shows the blood type distribution of the collected samples; the blood types were determined manually by the lab technicians. From Table 3, it can be observed that the number of A\(-\), B\(-\), and AB\(-\) is small. This is because A\(-\), B\(-\), and AB\(-\) are classified as rare blood groups. The results shown in Table 3 are supported by a study conducted by the American Red Cross of blood groups distribution by race/ethnicity [38]. For Asians, only 0.5% carry A\(-\), 0.4% carry B\(-\), and 0.1% carry AB\(-\). The small number of A\(-\), B\(-\), and AB\(-\) blood groups in the 500 blood samples used in this research has no effect on determining the blood type using image matching since the blood type in this research is determined based on the presence or absence of agglutination in an image, not on the statistical distribution of blood groups. The collected data are almost balanced because the 500 blood samples generated a total of 1500 blood spots--799 with positive agglutination and 701 with negative agglutination.  
A mobile phone equipped with 8 MB RGB camera was used to capture photos of the blood samples after mixing them with antibodies A, B, and D, as shown in Figure 2. Each image is partitioned into three patches so that each patch contains a blood spot. The core problem is to recognize the agglutination in these patches. For sake of simplicity, we will use the words samples or images to denote those patches.
## 2 Materials and Methods
### **Ericsson Model 9999, (P\({}_{Eric}\))**  
This model was developed by Ericsson's company [9; 12; 37]. It allows the parameters to be adjusted according to different propagation environments. The parameters are presented in (14) and (15).  
\[P_{Eric}=a_{0}+a_{1}\text{log}(d)+a_{2}\text{log}(h_{b})+a_{3}\text{log}(h_{b })\text{log}(d)-3.2\Big{[}\Big{(}\text{log}11.75h_{r}\Big{)}^{2}\Big{]}+g(f) \tag{14}\]  
\[g(f)=44.49\text{log}(f)-4.78\Big{(}\text{log}(f)^{2}\Big{)} \tag{15}\]  
where \(a_{0}\), \(a_{1}\), \(a_{2}\), and \(a_{3}\) are constants. Table 3 presents the details of the environments and the constants.  
\(f\) is the carrier frequency, 150-1900 MHz;  
\(h_{b}\) is the transmitting base station's tower or mast height, ranging between 20 and 200 m;  
\(h_{r}\) is the receiver antenna's height, and it ranges from 1 to 10 m;  
\(d\) is the distance between the transmitter and the receiver (Tx-Rx), which ranges from 1 to 20 km.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**s/n** & **Environment** & \(\mathbf{a_{0}}\) & \(\mathbf{a_{1}}\) & \(\mathbf{a_{2}}\) & \(\mathbf{a_{3}}\) \\ \hline
1 & Urban & 36.2 & 30.2 & 12 & 0.1 \\ \hline
2 & Suburban & 43.2 & 68.93 & 12 & 0.1 \\ \hline
3 & Rural & 45.95 & 100.6 & 12 & 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Environments and constant definitions for Ericsson model [9; 12; 35].