esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Methodology
### Machine Learning Algorithms
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Group** & **Class Name** & **Labeling Method \({}^{*}\)** & **Physical Explanation** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{Low clouds} & Highest backscatter & Clouds within or close to the \\  & & coefficient; low & boundary layer; mostly water \\  & & depolarization ratio & clouds in the training data set \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{High clouds} & \multirow{3}{*}{CNR threshold; DBSCAN clustering} & Clouds in the free troposphere \\  & & & and at higher altitudes; \\ \cline{1-1}  & & & and at higher altitudes; \\ \cline{1-1}  & & & and separated from other non-noise \\ \cline{1-1}  & & & data; mostly ice clouds, but \\ \cline{1-1}  & & & could be water clouds \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{Rain} & Descending movement; low & \multirow{3}{*}{Rain} \\  & & depolarization ratio & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \cline{1-1}  & & & & \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{Aerosol type I} & High backscatter coefficient; & Dust particles in dry \\  & & high depolarization ratio \({}^{**}\) & atmosphere; relative large size, \\  & & high depolarization ratio & high concentration with a \\  & & & non-spherical shape \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{Aerosol type II} & Medium-high backscatter coefficient; & Dust particles in dry \\  & & coefficient; high & atmosphere; relative small size, \\  & & depolarization ratio & low concentration, with a \\  & & & non-spherical shape \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{Aerosol type III} & High backscatter coefficient; & Dust particles in a humid \\  & & medium to high & atmosphere; relative high \\  & & depolarization ratio & concentration, the shape is \\  & & & more spherical \\ \hline \multirow{3}{*}{7} & \multirow{3}{*}{Others} & \multirow{3}{*}{Unclassified data points} & Other data points except for \\  & & & noise data \\ \hline \multirow{3}{*}{8} & \multirow{3}{*}{Noise} & CNR threshold; DBSCAN clustering & Noise signals \\ \cline{1-1}  & & & \\ \hline \hline \end{tabular} \({}^{*}\)In addition, some data points are manually corrected. ** High/medium/low values used for labeling are relative values with manual correction, which could be varied from case to case.  
\end{table} 
Table 2: The eight classes used in this study and their labeling methods and physical explanation. Figure 2: Labeled lidar data classes on (**a**) June 14–15, (**b**) 31 July–1 August 2019. The vertical blue lines are introduced due to measurement gaps. This data will be used to train and validate the models.
# Multiple Optimizations-Based ESRFBN Super-Resolution Network Algorithm for MR Images
## 3 Experimental Results and Analysis
### Performance Evaluation  
In this section, the proposed multi-optimization ESRFBN super-resolution network algorithm for MR images is compared with the super-resolution algorithm based on interpolation, reconstruction, dictionary learning and deep learning. The algorithms include BIC [15], IBP [16], POCS [5], MAP [6], JDL [7], IJDL [17], RDN [11], EDSR [18], SRGAN [12], ESRGAN [19], SRFBN [14], ESRFBN [20].  
The methods mentioned in this section and the experimental results of each method are shown in Tables 5 and 6. It can be seen from the table that the method proposed in this paper has obtained the best results in four parts of the human body and in two super-division scales, and the evaluation index value improved greatly. Compared with the suboptimal method under the scale \(\times 2\), the PSNR value is increased by 4.93, 5.90, 5.42 and 3.44, respectively; the SSIM value is increased by 0.0019, 0.0191, 0
# LaRoAwan and Urban Waste Management--A Trial
## 2 LoRa Technology--Long Range  
LoRa technology is a long-range, low-power wireless transmission technology that defines the physical layer of communication between devices, operating in an unlicensed band (in Europe it operates in the 863-870 MHz band). This is the underlying technology of the LoRaWAN protocol [2], which specifies the upper layers of communication.  
The main characteristics of this technology are described below.
# Graph-based Method for App Usage Prediction with Attributed Heterogeneous Network Embedding
## 5 Discussion  
This paper presents a network embedding based framework, AHNEAP, which leverages large scale digital App usage records to reveal the underlying patterns, and formulates the App usage prediction as a link prediction problem. Extensive empirical experiments with data from the _LiveLab_ project demonstrated the effectiveness of our framework AHNEAP by comparing it mainly with _Most Recently Used_ model and Bayes model.  
We plan to conduct our future work in the following several aspects. First, we can use all the data records to implement embedding representation of heterogeneous networks with attributes and use users' historical records to obtain the neural network model for personalized prediction, but not focusing on a specific user. Second, there is a lot of information in the dataset that is not used in this paper. For example, this is too monotonous taking the one-hot value of cellular base station as attributes of location nodes. Attributes of time nodes do. We can consider how to use WiFi information and cellular signals to express a location in more detail on the basis of obtaining a more efficient data set. Third, in all experiments, we did not pay attention to the adjustment of the model parameters. We should design some experiments to observe the influence of parameters. Finally, we need to try more combinations of different scales of time granular when training models and making predictions.  
**Author Contributions:** Methodology, S.L.; Writing-original draft, Y.Z.; Software, Y.Z., S.L.; Writing-review & editing, S.L.; Project administration, Y.L. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is supported by the National Key Research and Development Program of China under Grant No. 2018 YFB1003602, and the Key Research and Development Program of Guangdong province under Grant No. 2019B010137005.  
**Conflicts of Interest:** The authors declare no conflict of interest.
## 11 Conclusions  
This survey paper covers the energy optimization techniques for UAV-assisted wireless communication networking by categorizing them in terms of the optimization algorithm employed. On one hand, there are some well-known optimization methods, such as heuristics, game theory, etc., that have been widely used for energy optimization. The implementation of ML for optimization, on the other hand, have been gaining momentum due to its proven capabilities. Thus, we combined both conventional and ML algorithms in this survey paper in order to cover the literature in a comprehensive and inclusive manner. The studies on energy optimization in UAV-assisted wireless networking were investigated thoroughly to reveal the state of the art. Some background information on both the optimization algorithms and power supply/charging mechanisms of UAVs were given in order to cover the topic in a more complete manner. Moreover, different types of UAV deployments were also discussed to highlight how the UAV-assisted communication networks can be divergent, increasing the level of challenge in optimization. As one of the most novel parts of this survey, emerging technologies, such as RIS and landing spot optimization, were presented to capture the latest advancements in the literature. The survey was concluded by the identification of challenges and possible research directions. This will help focus the research efforts on these areas, thus making UAV-assisted wireless networking a complete and mature concept. This, in turn, would result in a feasible and applicable concept for wireless communication networks, which has the potential to mitigate the capacity scarcity issue.  
**Author Contributions:** Conceptualization and Outline, A.I.A., M.O. and K.G.O.; writing--original draft preparation, A.I.A., I.A., K.G.O., M.O., C.O. and A.M.A.-S.; writing--review and editing, A.I.A., I.A., K.G.O., M.O., C.O., A.M.A.-S. and S.H.; visualization, M.S.M.; supervision, Q.H.A., S.H. and M.A.I. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This work is funded in parts by EPSRC IAA award grant number EP/R511705/1. The work of the first author is funded by the Tertiary Education Trust Fund (TETFund) of the Federal Republic of Nigeria.  
**Data Availability Statement:** Not applicable.  
**Conflicts of Interest:** The authors declare no conflict of interest.
approaches require less time and effort compared to traditional SPE, making them more effective [5]. In this context, miniaturized techniques, such as solid-phase microextraction (SPME), microextraction by packed sorbent (MEPS), magnetic SPE (MSPE), pipette-tip solid-phase microextraction (PT-SPE), solid-phase dynamic extraction (SPDE), bar adsorptive microextraction (BA\(\mu\)E), and dispersive micro solid-phase extraction (DuSPE), have gained popularity [5; 6; 7]. These techniques are well known as sorbent-based microextraction techniques, and they have been extensively used instead of traditional techniques. These procedures provide numerous advantages, such as minimizing the solvents' consumption, adsorbents, and sample volumes, reducing processing steps, and reducing waste generation. As a result, these approaches pave the way for the development of cost-effective, highly efficient, user-friendly, and eco-friendly extraction and purification processes [8].  
The sorbent is considered to be the \"heart\" of a sorbent-based microextraction procedure. In this context, various materials have been synthesized and utilized in these procedures including molecularly imprinted polymers (MIPs) or membranes (MIMs), carbon-based compounds (CBCs), metal-organic frameworks (MOFs), covalent organic frameworks (COFs), restricted access materials (RAMs), and liquid-supported particles (i.e., ionic liquids and deep eutectic solvents (DES) embedded onto magnetic nanoparticles (MNPs)) [9; 10; 11; 12]. A comprehensive review article on modern materials that are utilized in sorbent-based microextraction techniques has been recently published by [PERSON] et al. [6].  
Several review articles have been published in the literature covering topics such as green approaches [7], SPME [1], and various microextraction techniques [8]. However, sorbent-based microextraction technologies in bioanalysis have received relatively little attention. The number of published bioanalytical applications using sorbent-based microextraction prior to GC-MS analysis is shown in Figure 1. The present review summarizes the latest GC-MS applications using sorbent-based microextraction technologies covering a timeframe of the last decade. The latest trends and innovative strategies are thoroughly discussed.  
Figure 1: Number of published bioanalytical applications using sorbent-based microextraction prior to GC-MS analysis in the period 2014–2024.
# A Novel Spectrum Scheduling Scheme with Ant Colony Optimization Algorithm
## 2 System Model and Problem Formulation
### Problem Formulation and Measure Functions  
Given the model above, we formulate the spectrum allocation problem by the following optimization function:  
\[\begin{split}&\max_{A\in\Lambda_{n,m}(L,C)}\sum_{n=1}^{N}\sum_{m=1}^ {M}a_{n,m}\times b_{n,m}\\ & s.t.\quad\forall 1\leq n,k\leq N,1\leq m\leq M\\ &\quad a_{n,m}+a_{k,m}\leq 1,if\ c_{n,k,m}=1\\ &\quad A=\left\{a_{n,m}\right\}_{N\times M}\\ &\quad L=\left\{l_{n,m}\right\}_{N\times M}\\ &\quad C=\left\{c_{n,k,m}\right\}_{N\times N\times M}\end{split} \tag{1}\]  
where \(\Lambda_{n,m}(L,C)\) is the set of interference free spectrum assignments for a given set of \(N\) cognitive users and \(M\) spectrum bands and constraints \(C\).  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Name of Matrix** & **Definition of Matrix** \\ \hline Available matrix & \(L=\{l_{n,m}|l_{n,m}\in\{0,1\}\}_{N\times M},\quad 1\leq n\leq N,1\leq m\leq M\) \\ Benefit matrix & \(B=\{b_{n,m}|b_{n,m}\geq 0\}_{N\times M},\quad 1\leq n\leq N,1\leq m\leq M\) \\ Interference matrix & \(C=\{c_{n,n,m}|c_{n,n,m}\in\{0,1\}\}_{N\times N\times M},\quad 1\leq n\leq N,1\leq m \leq M\) \\ Allocation matrix & \(A=\{a_{n,m}|a_{n,m}\in\{0,1\}\}_{N\times M},\quad 1\leq n\leq N,1\leq m \leq M\) \\ Degree matrix for cognitive users & \(Z=\{z_{n}|z_{n}=\{0,1,\ldots,M\}\}_{N},\quad 1\leq n\leq N\) \\ Degree ascending matrix & \(K=\{k_{n,m}|k_{n,m}\in\{0,1\}\}_{N\times M},\quad 1\leq n\leq N,1\leq m \leq M\) \\ \hline \hline \end{tabular}
\end{table}
Table 1: Matrices Definition.  
There is always more than one allocation solution that would satisfy all the constraints. In order to choose the optimum solution in terms of different applications and measure the algorithm thoroughly, we use three different measure functions that already exist in the literature [17] to evaluate the solution.  
1. Max-Sum-Reward-Mean (MSRM): This function is used to measure the average of total spectrum utilization in the system, which is the average of the sum user rewards. \[U_{mean}=\frac{1}{n}\sum_{n=1}^{N}\sum_{m=1}^{M}a_{n,m}\times b_{n,m}\] (2)
2. Max-Proportional-Fair (MPF): The function is to measure the fairness among cognitive users accessing the spectrum in the system, which is driven by \(\sum_{m=1}^{M}a_{n,m}\times b_{n,m}\). \[U_{fair}=(\prod_{n=1}^{N}\sum_{m=1}^{M}a_{n,m}\times b_{n,m}+10^{-4})^{\frac{ 1}{N}}\] (3)
3. Max-Min-Reward (MMR): The function is to maximize the spectrum utilization at the bottleneck cognitive users who receive the lowest reward, which is a simple notion of fairness. \[U_{min}=\min_{1\leq n\leq N}(\sum_{m=1}^{M}a_{n,m}\times b_{n,m})\] (4)
## 1 Introduction
However, many technical challenges still remain, including image co-registration, such that PET radiomics can effectively contribute to personalized medicine [24]. For this reason, seventeen glioblastoma patients who underwent both MET-PET and MRI between a time range of three years (2016-2019) were used for our analysis by extracting radiomics features grouped into shape, first- and higher-order features. Usually, the feature extraction task is one of the five fundamental tasks of a radiomics workflow [25] together with image acquisition, target segmentation, feature selection, and implementation of the classification model to predict the clinical outcome. Nevertheless, our study will omit the final task focusing on the first four steps by newly adding the PET/MRI co-registration prior to the feature extraction process to evaluate its impact in a radiomics study.
## 2 Related Works
### Wireless Sensor Network (WSN) Traffic Modeling Method  
The traffic model in wired and wireless network has been investigated. We can divide the traffic prediction model into two categories: traditional network and WSN. Traditional network traffic prediction models are Markov model, Poisson model, linear regression model and time series forecasting model [21]. Customary, Constant Bit Rate (CBR) traffic model and Markovian model is generally used in WSNs without any discussion as to whether this is appropriate of not [22]. Otherwise, the traffic model in WSNs has not been investigated much. Table 2 shows the previous traffic-related work in WSNs.  
To defend attacks in WSNs, Intrusion Detection [23], Auto-Regressive model (AR) [24], Auto-Regressive Moving Average model (ARMA) [25], Swam intelligence Auto Regressive Moving Average model (S-ARMA) [21], and Anomaly Detection [26] were proposed. Other WSN traffic prediction algorithms include the Poison model [27], Constant Bit Rate (CBR) model [28], and Markov process model [29]. For the traffic analysis and modeling, traffic arrival process, sequence relations among general kinds of packets, and data traffic load distribution were has been introduced. Besides, Mobility-Aware modeling that captures the statistical patterns of the mobility and spatial correlation using mobile agent was presented [22].  
WSNs are difficult to predict due to their dynamic nature. Furthermore, traffic dynamics in WSNs are application dependent. Therefore, WSN, which is dynamic and event-driven system, specific traffic prediction model is the prerequisite for the network management [21] such as network optimization, traffic distribution, load balancing, attack detection, _etc_.
# A Review on Solar Energy Utilization and Projects: Development in and around the UAE
## 5 Solar Energy Projects in the UAE
### Solar Energy Projects in the Dubai Emirate
#### 5.1.2. Rooftop Solar Energy, Dubai  
Recently, the Dubai emirate has announced the largest rooftop solar project in the UAE, which includes Etihad ESCO, with a capacity of 60 MW peak that will see photovoltaic (PV) panels installed on 640 villas in Dubai's mountainous Hatta region. DP World is constructing the country's largest distributed plant, with a total capacity of +20 MW peak with 51 rooftops and 4 parking garages. ALEC Energy Company is now constructing a 6.4 MW peak for Nabooda and a 6.5 MW peak rooftop for Emaar on the Dubai Hills, and Alsa Solar Systems has completed a 2 MW peak for the Mall of the Emirates, with four more MW pending in the MAF group in the UAE. Aramex, Pepsi, ABB, and the Al Tayer company have all declared rooftop ambitions as part of the Dubai Solar Schools Program [93; 94; 95].  
The Sheikh [PERSON] solar project has generated more electricity than other projects. Additionally, the deployment of solar energy plants is impacted by the global market's rising oil prices and lowering solar module prices. Furthermore, it was discovered that processes powered by solar parks have a lesser environmental effect in terms of CO\({}_{2}\) emissions. The rooftop electricity power generation has been used successfully in the residential and commercial sectors, making building-integrated PV an appealing choice for widespread solar energy adoption. PV panels are currently planned to be installed directly on the exteriors of houses in order to save money on construction.
# Channel Estimation for UAV Communication Systems Using Deep Neural Networks
## 2 System Model and Problem Formulation
### Problem Formulation
#### 2.1.2 Enhanced Gradient
[CrossRef]
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* [PERSON] and [PERSON] (2009) [PERSON]; [PERSON] _Probabilistic Graphical Models: Principles and Techniques-Adaptive Computation and Machine Learning_; The MIT Press: Cambridge, MA, USA, 2009. * [PERSON] and [PERSON] (2005) [PERSON]; [PERSON] [PERSON] On Contrastive Divergence Learning. In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, Bridgetown, Barbados, 6-8 January 2005; Volume R5, pp. 33-40. * [PERSON] and [PERSON] (2008) [PERSON] [PERSON]; [PERSON] [PERSON] Representational Power of Restricted Boltzmann Machines and Deep Belief Networks. _Neural Comput._**2008**, _20_, 1631-1649. [CrossRef] [PubMed]
* [PERSON] and [PERSON] (2013) [PERSON]; [PERSON]; [PERSON] Enhanced Gradient for Training Restricted Boltzmann Machines. _Neural Comput._**2013**, _25_, 805-831. [CrossRef] [PubMed]
* [PERSON] et al. (2013) [PERSON]; [PERSON]; [PERSON] [PERSON] Gaussian-Bernoulli deep Boltzmann machine. In Proceedings of the International Joint Conference on Neural Networks (IJCNN), Dallas, TX, USA, 4-9 August 2013; pp. 1-7. * [PERSON] et al. (2017) [PERSON]; [PERSON]; [PERSON] UAV Air-to-Ground Channel Characterization for mmWave Systems. In Proceedings of the 2017 IEEE 86 th Vehicular Technology Conference (VTC-2017), Toronto, ON, Canada, 24-27 September 2017; pp. 1-5. [CrossRef]
## 2 Computational Model
### Constitutive Model and Parameters
#### 2.2.2 Relative Permeability Coefficient of Water  
The relationship between the relative flow of water and water saturation can be described by the relative permeability coefficient of water (Figure 1a). In this research, the van Genuchten-Mualem model was selected because of its broad applicability [19].  
\[k_{rw}=(\frac{S_{r}-S_{rw}}{1-S_{rw}})^{0.5}*\left\{1-[1-(\frac{S_{r}-S_{rw} }{1-S_{rw}})^{1/n}]^{n}\right\}^{2} \tag{4}\]  
where \(k_{rw}\) is the relative permeability coefficient of water and \(n\) is a model parameter that is related to the material.
## 5 Conclusions  
This paper analyzed the changes in contact pressure and shear stress through a three-dimensional finite element analysis focused on whether elastomeric bearings for bridge support were installed around the shear keys of the grouted connection, and the conclusions below were derived.  
First, the contact pressure analysis showed that the contact pressure for all grouted contact areas increased about 2.5-fold when elastomeric bearings were installed. In particular, when the rubber plate material was used as natural rubber, the contact pressure with the shear key was 1.9 times lower than when neoprene was used. Among the contact surfaces with tubular steel, the contact pressure in the area in contact with the bottom of the monopile was the highest.  
Second, the contact pressure increased toward the bottom side of the grouted connection when elastomeric bearings were not installed, but it decreased when elastomeric bearings were installed. In addition, with the installation of elastomeric bearings, the contact pressure was generally low in the central part with respect to the height direction of the grouted connection.  
Third, when the shear key size was increased from 20 to 100 mm under the same conditions, the maximum shear stress was decreased from 63.91 MPa to 19.60 MPa, and, when the shear key height was 60 mm, the value of 25.95 MPa was obtained. In this paper, when the elastomeric bearing was installed, the maximum shear stress was 5.78 MPa for neoprene material and 4.90 MPa for natural rubber material, thus representing a decrease of 77-81% compared to 25.95 MPa.  
Through this paper, we intend to contribute to the research on the load transfer characteristics of the grout joint and the development of design technology for the contact and material failure of the grout.  
Conceptualization, writing, methodology, and software, Y.-S.Y., and M.-Y.S.; validation, writing--original draft preparation, and funding acquisition, M.-Y.S. All authors read and agreed to the published version of the manuscript.  
This research was funded by Korea Western Power Co., Ltd., grant number 2102001542.  
Not applicable.  
Data Availability Statement: Data are available from the authors upon request.  
This paper is the result of a study conducted with the support of the Korea Western Power Co., Ltd., Taean, Republic of Korea. for Advancement of Technology's \"Development of high-efficiency offshore wind farms for the southwest coast of Korea (2102001542)\" project. All authors and this company consented to this acknowledgment.  
Figure 12: Maximum shear stress at grout contact on TP of tension side.