esa-sceva/satcom-chunk-collection huggingface dataset:
# Unlicensed Spectrum Allocation for LTE and Wi-Fi Coexistence with HAP
## 4 The Proposed Scheme
### Lower Bound and Upper Bound of \(\delta\)
However, the minimum resource requirement of LTE is based on the sum of the minimum requirements of each LTE user who uses the unlicensed band as in constraint (1c). When the LTE demand is too high, the upper bound of \(\delta\) in (11) may be lower than the lower bound in (9). Therefore, we propose a filtering mechanism to prevent this situation. After calculating the upper and lower bounds of \(\delta\) in each round, if the upper bound is less than the lower bound, the user filtering mechanism will be invoked to confine the number of LTE users who can access the unlicensed band in each round. Then the best time ratio \(\delta\) can be found by the utility function. The concept is illustrated in Figure 7.
## 4 Experiments and Results
### Noise Prediction  
Figure 3 (left) presents the graph of the \(MSE\) with the variation in \(N_{0}\) in the proposed regression methods. It is noticeable that SVR had the worst performance, only achieving good results at an \(N_{0}\) of \(-144\) dBm/Hz. Random forest, extra trees, XGB, LGBM, transformer, and decision tree showed similar performance, as indicated in the graph. However, random forest, XGB, LGBM, and extra trees exhibited better performance, especially at lower levels of \(N_{0}\). XGB was the best model, achieving the lowest level of \(MSE\), 15.53, which indicates that the predicted values are close to the actual ones.  
Figure 3 (right) presents the graph of the \(MAE\) with the variation in \(N_{0}\) in the proposed regression methods. It is also noticeable that SVR had the worst performance, only achieving good results at an \(N_{0}\) of \(-144\) dBm/Hz. Random forest, extra trees, XGB, LGBM, transformer, and decision tree showed similar performance, as indicated in the graph. However, random forest, XGB, LGBM, and extra trees exhibited better performance, especially at levels of \(-164\) dBm/Hz and \(-154\) dBm/Hz. Random forest was the best model, achieving the lowest level of \(MAE\), 1.9473, which indicates that the predicted values are close to the actual ones.  
Figure 4 (left) presents the graph of the \(RMSE\) with the variation in \(N_{0}\) in the proposed regression methods. It is also noticeable that SVR had the worst performance, only achieving good results at an \(N_{0}\) of \(-144\) dBm/Hz. Random forest, XGB, LGBM, extra  
Figure 3: Graphic of the \(MSE\) (**left**) and \(MAE\) (**right**) of proposed methods with \(N_{0}\) between \(-114\) dBm/Hz and \(-174\) dBm/Hz.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Transformer** \\ \hline Head size & \([8,16,32]\) \\ Number of heads & \([2,4,8]\) \\ Filter dimension & \([8,16,32]\) \\ Transformer block & 1 \\ Dense layer & 32 \\ Dropout & 0.25 \\ Batch size & 32 \\ Optimizer & Adam \\ Learning rate & 0.001 \\ Loss & \(MSE\) \\ \hline \hline \end{tabular}
\end{table}
Table 5: Transformer hyperparameter configurations.  
trees, transformer, and decision tree showed similar performance, as indicated in the graph. However, random forest and extra trees exhibited better performance. Random forest was the best model, achieving the lowest level of \(MAPE\), \(1.2534\), which indicates that the predicted values are close to the actual ones.  
In Table 6, the general metrics for all the proposed models are presented for noise prediction. It is worth noting that, in terms of the correlation coefficient, random forest, XGB, LGBM, transformer, and extra trees exhibited the highest and similar performance. However, random forest performed slightly better on the \(MAE\) and \(MAPE\) metrics, while XGB performed better on the correlation coefficient, \(MSE\), \(RMSE\), and \(R^{2}\). SVR presented the worst performance in the general metrics.
## 2 Principle
### Gray Imbalance Correction Method
In this method, by the least square method, an evaluation function, is introduced to compare the similarity of two images, which is expressed as:  
\[E=\sum\limits_{x=1}^{M}\sum\limits_{y=1}^{N}\left[I_{RB0}(x,y)-k*I_{RB1}(x,y) \right]^{2} \tag{10}\]  
where \(E\) is the square of the gray difference value of each pixel of the image between \(I_{RB0}(x,y)\) and \(I_{RB1}(x,y)\). When \(E\) takes the minimum value, \(dE/dk\) is zero, which is expressed as:  
\[\frac{dE}{dk}=\frac{\sum\limits_{x=1}^{M}\sum\limits_{y=1}^{N}\left[I_{RB0}(x, y)-k*I_{RB1}(x,y)\right]^{2}}{dk}=0 \tag{11}\]  
The value of \(k\) is calculated to be:  
\[k=\frac{\sum\limits_{x=1}^{M}\sum\limits_{y=1}^{N}I_{RB0}(x,y)I_{RB1}(x,y)}{ \sum\limits_{x=1}^{M}\sum\limits_{y=1}^{N}I_{RB1}(x,y)I_{RB1}(x,y)} \tag{12}\]  
The half sum of red and blue can be calibrated as:  
\[I_{RBM}=k*I_{RB1} \tag{13}\]  
Thus, Equation (6) can be rewritten as:  
\[\phi_{M}(x,y)=\arctan(\frac{2*(I_{\text{G}}(x,y)-I_{RBM}(x,y))}{I_{\text{R}}( x,y)-I_{\text{B}}(x,y)}) \tag{14}\]  
Due to the arctan function, \(\varphi_{M}(x,y)\) is wrapped and should be unwrapped to be \(\Phi_{M}(x,y)\)[28]. In the same way, the corresponding unwrapped phase of the reference plane is acquired as \(\Phi_{r}(x,y)\) and the phase \(\Phi_{h}(x,y)\) that is caused by the height of measured object can be represented as:  
\[\Phi_{h}(x,y)=\Phi_{M}(x,y)-\Phi_{r}(x,y) \tag{15}\]  
Thus, by the phase-to-height mapping [29; 30] shown as  
\[\frac{1}{h(x,y)}=a(x,y)+b(x,y)\frac{1}{\Phi_{h}(x,y)}+c(x,y)\frac{\Phi_{r}(x, y)}{\Phi_{h}(x,y)} \tag{16}\]  
the measurement of 3D shape is successfully achieved. \(a(x,y)\), \(b(x,y)\), and \(c(x,y)\
# Development of a Predictive Tool for the Parametric Analysis of a Turbofan Engine
## 4 Discussion  
Figures 3-7 showing the effects of the Mach number, FPR, altitude, turbine entry temperature, and BPR on the specific thrust, propulsive efficiency, SFC, and total fuel flow are explained in detail below:  
* Mach number: The specific thrust of a turbofan engine decreases with the Mach number at a specific altitude, as shown in Figure 3. As the turbine inlet temperature is restricted by material temperature restrictions, a rise in the Mach number reduces the potential to add energy to the combustor, and hence the specific thrust.
* Altitude: The fuel consumption per unit of thrust generated is measured via the SFC. Thrust decreases as altitude increases with a similar throttle position owing to a decrease in the air mass flow into the engine. As the air mass flow rate decreases, the engine control system correspondingly cuts the fuel flow. As the altitude climbs, the combustion efficiency drops, necessitating the use of more fuel to produce comparable  
Figure 8: Machine learning models trained for each input variable. (**a**) Regression and Tree models. (**b**) SVM and Ensemble models. (**c**) GPR and neural network models.  
Figure 7: (**a**) Effect of the turbine entry temperature on the specific thrust. (**b**) Effect of the turbine entry temperature on the specific fuel consumption.  
energy at lower altitudes. As a result, SFC increases with altitude, as depicted in Figure 4.
* Bypass ratio: As shown in Figure 5, SFC declines with an increase in the BPR. The declining trend in the SFC indicates that the engine burns less fuel at a given thrust level. In addition, a lower SFC indicates that the turbofan engine produces a higher thrust for a given amount of fuel. The specific thrust drops with a higher bypass ratio due to the enormous quantity of total mass flow through the engine.
* Fan pressure ratio (FPR): Increasing the FPR leads to an initial fall in the SFC followed by an increase due to a rising propulsive efficiency at low FPRs. However, with greater FPRs, there is a rise in the SFC, which is attributable to a drop in propulsive efficiency. Since the fuel intake is set for a particular BPR, the lowest SFC and maximum specific thrust correspond to the same FPR. These trends are evident from Figure 6.
* Turbine entry temperature: The SFC and specific thrust increase with an increase in the turbine inlet temperature, as displayed in Figure 7. The enthalpy of the combustion gases increases as the turbine entry temperature increases, resulting in a greater output power that will improve the gas turbine's total thermal efficiency. It affects fuel consumption since higher temperatures often demand higher fuel flow rates to maintain the necessary amount of power production.
# Development of Intelligent Fuzzy Controller for a Two-Axis Solar Tracking System
## 5 Result Analysis
### Comparison Analysis of Output Power  
Two solar energy systems are set up for the test mentioned above. One is fixed at an elevation angle of 23.5 degrees, while the other tracks the sun. After eight hours of sunshine, the fixed-angle solar energy system has an average output current of 0.58 A, while the intelligent sun tracking solar energy system has an output average current of 0.94 A, indicating a difference of 0.36 A. Thus, the intelligent sun tracking solar energy system has an increased capacity of 36%, an increased power of 4.32 W, and an increase in total energy of 34.56 watt-hours (Table 6).
# A Novel SIW Leaky-Wave Antenna for Continuous Beam Scanning from Backward to Forward
## 4 Conclusions  
In this paper, a novel SIW LWA was presented for use in continuous beam-scanning applications. The unit cell of the LWA consists of a longitude slot and rectangular-shaped H-plane discontinuity and was proposed to suppress open stopband (OSB). The open stopband suppression was performed using an impedance matching technique. A prototype of the proposed antenna was fabricated, with overall physical dimensions of \(7.9\,\mathrm{mm}\times 128\,\mathrm{mm}\), for experimental verification. A comparison of the results demonstrated that an adequate and satisfactory level of agreement between measurement and simulation was achieved. The results confirmed that the proposed SIW LWA can provide continuously scanning in the frequency range of 14.5 to 22.5 GHz between \(-60\) and \(+57.5\) degrees through broadside, with a maximum gain of 16 dBi and radiation efficiency of 71%.  
Conceptualization, S.K. and M.S.; software, S.K.; investigation, S.K.; writing--original draft preparation, S.K.; writing--review and editing, S.K.; supervision, M.S. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.
## 2 Materials and Methods  
### Image Acquisition and Preprocessing  
The image data used in the experiment were collected from the Guangxi Subtropical Agricultural Science New City Experimental Base, located in Liku Town, Fusui County, Chongzuo City, Guangxi Zhuang Autonomous Region. The sugarcane crop was sown in late March 2024 and was scheduled for harvest in mid December 2024. Data acquisition was performed using a DJI Matrice 300 RTK UAV (DJI, Shenzhen, China) at a flight altitude of 30 m, with a forward overlap of 70% and a side overlap of 80%. The survey was conducted in June 2024, coinciding with the early elongation stage of sugarcane growth, which is a critical period for in-field weed management and the removal of redundant tillers. A total of 1540 original images were captured at a resolution of 8192 \(\times\) 5460 pixels, and all images were stored in jpg format. The diversity of the dataset enhances the model's adaptability to complex environments. Due to the high resolution of the original images, direct visualization is not conducive to detailed observation. To more intuitively present the variability among samples, representative regions were cropped from the original images for illustration, as shown in Figure 1.  
To balance training efficiency and maintain the original aspect ratio, the resolution was uniformly resized to 1024 \(\times\) 640 pixels. During the labeling stage, the Labelme tool (version 5.5.0) was employed to annotate sugarcane with rectangular bounding boxes, using a consistent label category of \"sugarcane.\" All information related to the annotated objects, including target categories and bounding box coordinates, was saved in json format (an example is provided in Figure 2). Subsequently, the labeling results were converted to text files in YOLO format for model training. As illustrated by the labeling examples, the annotated bounding boxes are densely packed and exhibit some degree of overlap, which presents a significant challenge for the accurate extraction of sugarcane.  
After completing all data labeling, the dataset was partitioned into training, validation, and test sets in a 3:1:1 ratio. To prevent data leakage that could compromise the reliability of evaluation results, data augmentation was applied exclusively to the training set. To  
Figure 1: Illustration of dataset examples: (**a**) missing rows; (**b**) row interruption; (**c**) inter-row weed interference; (**d**) row closure.  
Figure 2: Data annotation example.  
improve the model's generalization ability across diverse scenarios and effectively mitigate overfitting, various data augmentation techniques were applied to the original training images, including vertical flipping, rotation, brightness adjustment, saturation adjustment, and noise injection. Following augmentation, the proportions of samples in the training, validation, and test sets were adjusted to 8:1:1, with 2321 training samples, 308 validation samples, and 308 test samples, respectively.
# Status of Phenological Research Using Sentinel-2 Data: A Review
## 5 Phenology of Other Land Classes
### Dealing with Mixed Pixels in Urban Areas  
Urban areas are also significant reserves of vegetation that are affected by the differences in microclimate, artificial photoperiod and ground water status in comparison to rural and natural vegetated areas [126]. [PERSON] et al. (2020) [47] used a fused Landsat-8 and Sentinel-2-based NDVI time series dataset to study the impacts of spatial resolution on the differences between urban and rural vegetation spring phenology, i.e., SOS. The authors reported that irrespective of the resolution, urban areas experience early SOS in comparison to rural areas. The coarser datasets however tend to overestimate the earliness in the urban areas, mostly due to higher mixing of different land covers in coarser pixels. The study suggested the use of high spatial resolution data in urban areas to prevent mixing of diverse time series profiles of NDVI. However, even at the highest resolution, pixels are rarely 100% homogenous within an urban fabric. [PERSON] et al. (2020) [48] suggested a solution in this regard. The authors used a double logistic function to fit the time series data, and pixels with very high fit errors and very low VIs during maturity period were accurately identified to be mixed or contaminated with non-vegetation features. Additionally, the use of a local smoothing function i.e., Savitzky-Golay filter, on the identified \"pure\" pixels allowed the authors to correctly identify periods of stress or disturbance both intra- and inter-annually, which were tightly linked to an increase in temperature and decrease in soil water content in the summer season. However, the double logistic function fitted pure pixels completely missed such stress events, since this function results in smoother outputs than Savitzky-Golay and hence should be avoided when analysing such events. The effects of coarse spatial resolution on estimation of phenology in heterogenous landscapes was evaluated by [PERSON] et al. (2017) [127], who suggested the pixel SOS to be driven more by the timing of the earliest vegetation to green-up than the later sub-pixel components. The authors suggested that the SOS at coarse resolutions is detected when around one-third of the pixel has experienced green-up; however, it is unknown whether this threshold is valid across different landscapes and for different methods of LSP estimation, especially in urban areas.
# Understanding the Influence of AST-JS for Improving Malicious Webpage Detection
## 3 Overview of Malicious Webpage Detection
### Malicious Webpages  
Due to the advancement of the Internet, many vulnerabilities and attacks threaten users. Attackers or hackers create malicious websites to perform actions such as stealing credentials or data, or installing malware on the target's computer. Installing malware is still the most effective way to gain unauthorized access to a server, a hosting control panel, or even the admin panel of the content management system.  
A malicious webpage is hosted on a website to attack users. The malicious activity can take many different forms, including extracting data from a user, taking control of a browsing device, or using the device as an entry point into a network. Generally, there are two common types of malicious webpages, phishing webpages and malware webpages. For phishing webpages, the attackers try to make a website that looks legitimate, so the target will not recognize it as a risk. Meanwhile, malware webpages refer to a webpage containing malicious payloads that can direct the user's computer to execute the payload code, which may install malware or any intermediary apps. The webpage can be either made by the attacker or by a legitimate webpage with hidden malicious code.  
We show two examples of malicious webpages. Figure 0(a) is an example of a phishing webpage, and Figure 0(b) is a fake website made with malicious code. A phishing webpage is created to look very similar to a known legitimate site. It tries to imitate every aspect of a legitimate website (e.g., logo, position, and content) so the target users will not recognize that the website is fake. Attackers will make it more promising by sending a fake email recommending that a user access the webpage to fill out a credential information form. Figure 0(c) is a legitimate website that Figure 0(a) tries to imitate.  
Meanwhile, the attacker-created fake webpage will likely build its brand entirely from fictitious information, including the logo, address, and content. It mrakes little attempt to resemble a legitimate webpage, but instead poses as a new legitimate company. Fur  
Figure 1: Some examples of malicious and legitimate webpages: (**a**) a phishing Rakuten website; (**b**) a fake cryptocurrency website; (**c**) a legitimate Rakuten website; (**d**) a legitimate cryptocurrency website.  
thermore, an attacker can inject a malicious payload using a fake webpage on a legitimate website. This is the most difficult malicious page to detect, and it can send information to the original (legitimate) server while also sending it secretly to the attacker's server. Figure 1d is one of the counter examples for the fake website in Figure 1b.
# Smart Building Surveillance System as Shared Sensory System for Localization of AGVs
###### Abstract  
The objective of this study is to extend the possibilities of robot localization in a known environment by using the pre-deployed infrastructure of a smart building. The proposed method demonstrates a concept of a Shared Sensory System for the automated guided vehicles (AGVs), when already existing camera hardware of a building can be utilized for position detection of marked devices. This approach extends surveillance cameras capabilities creating a general sensory system for localization of active (automated) or passive devices in a smart building. The application is presented using both simulations and experiments for a common corridor of a building. The advantages and disadvantages are stated. We analyze the impact of the captured frame's resolution on the processing speed while also using multiple cameras to improve the accuracy of localization. The proposed methodology in which we use the surveillance cameras in a stand-alone way or in a support role for the AGVs to be localized in the environment has a huge potential utilization in the future smart buildings and cities. The available infrastructure is used to provide additional features for the building control unit, such as awareness of the position of the robots without the need to obtain this data directly from the robots, which would lower the cost of the robots themselves. On the other hand, the information about the location of a robot may be transferred bidirectionally between robots and the building control system to improve the overall safety and reliability of the system.  
smart buildings; shared sensory system; surveillance system; localization; marker detection +
Footnote †: journal: Applied Sciences
# Design and Evaluation of Steganographic Channels in Fifth-Generation New Radio +
## 2 Background
### Network Steganography  
Steganographic channels, mostly realized through covert channels, try to conceal the existence of data transmission between a covert sender and a covert receiver by hiding the data in an innocent overt communication or carrier. In contrast, cryptography merely hides the content of a data transmission. The carrier can be anything from text, an image, an audio or a video stream to a network communication. In network steganography, secret data are hidden in a network communication channel that has not been designed for transferring information [14]. Network covert channels are categorized into covert storage channels and covert timing channels. Both exploit network protocols, but the former use protocol bits, e.g., modifying bits in either the header or payload, whereas the latter utilize the temporal behavior of the communication to covertly transfer the hidden information, e.g., by delaying some packets to signal information. Network covert channels are always created based on overt network communication between a legitimate sender and receiver. The covert sender and receiver may be the same or different parties than the legitimate participants [1].  
In order to catch the similarities among the many approaches for network steganography that have appeared in the literature (ranging from using different header fields to different protocols on different layers of the protocol stack), hiding patterns have been introduced [4]. These patterns can also be used to systematically check protocols for weaknesses, which we further explain in Section 3 and conduct in Section 4.