esa-sceva/satcom-chunk-collection huggingface dataset:
# GIS-Based Site Suitability Analysis for Solar Power Systems in Mongolia
## 1 Introduction
Finally, a GIS tool was used to generate compound site indexes, which were then applied to create a location compatibility map, with the country divided into four potential location groups: good, fair, low, and poor. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Reference** & **Plant Type** &
\begin{tabular}{c} **MCDA** \\ **Method** \\ \end{tabular} & **Country** & **Scale** \\ \hline [PERSON] [9] & PV, CSP & AHP & Turkey & Regional \\ \hline [PERSON] et al. [10] & CSP & AHP & Zimbabwe & National \\ \hline Watson and Hudson [11] & PV, CSP & AHP & England & Regional \\ \hline [PERSON] et al. [12] & PV, CSP & AHP & Tanzania & National \\ \hline [PERSON] and [PERSON] [5] & PV & AHP & Saudi Arabia & National \\ \hline [PERSON] et al. [13] & PV, CSP & AHP & West Africa & Continental \\ \hline [PERSON] et al. [2] & PV & AHP & Morocco & Regional \\ \hline [PERSON] et al. [14] & PV & AHP & Morocco & Regional \\ \hline [PERSON] et al. [15] & PV & Fuzzy AHP & Iran & Regional \\ \hline [PERSON] et al. [16] & PV & Fuzzy AHP & Iran & National \\ \hline [PERSON] and [PERSON] [17] & PV & Fuzzy AHP & Korea & Regional \\ \hline [PERSON] et al. [18] & PV & Fuzzy AHP & Spain & Regional \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of previous studies that analyzed site suitability for solar power plants using geographic information systems (GIS) and the analytic hierarchy process (AHP).
## 5 The Use of AI for the Creation of SRE
### Recent Developments  
AI and ML have been in the spotlight for quite some time now and they have started to decline from the over-hyped phase into a more stable one, solidifying the research in the field. The same holds for the employment of AI and ML, combined with RISs, in wireless communications. As research solidifies, the following directions draw strong interest.  
Traditional approaches, such as RL, keep developing [62; 63] in parallel with more innovative schemes such as the use of Large Language Models [64]. Security issues in RIS-enabled 6G systems still concern the research community and especially for frequencies as high as THz [65] or for Ground-to-Air IoT [66]. In addition, channel estimation in RIS-assisted communication systems has been achieved by employing a methodology that integrates principal component analysis (PCA)-based clustering with FL, tailored for heterogeneous users. The proposed technique has led to a significant reduction in the communication overhead, while enhancing the system security [67].  
Furthermore, the feasibility of a RIS-aided Federated Edge Learning system has been investigated under the realistic supposition where only the statistical channel state information is available among devices [68]. FL has also been applied on the Internet of Vehicles; a RIS-assisted FL framework has been introduced in order to enhance communication reliability and diminish the straggler effect caused by heterogeneous computing and communication resources of vehicles [69]. A protocol that combines RISs, FL, and edge computing in order to enhance network performance in IoT-enabled mobile ad hoc networks with cell-free massive MIMO has been proposed in [70] with promising results as regards packet delivery ratio, routing delay, and control overhead. Recently, reconfigurable intelligent metasurfaces (RIMs) have emerged as a revolutionary technology, which, when combined with distributed learning mechanisms and especially FL, may offer a controllable means for the solution of key problems in RIM-aided wireless networks such as channel estimation, passive beamforming, and resource allocation [71].
# Novel Sparse-Coded Ambient Backscatter Communication for Massive IoT Connectivity
## 3 Sparse-Coded Load Modulation
It is noteworthy that the previous works [2; 23] in AmBC assumed \(\widetilde{\mathbf{h}}_{k,n}^{+}=\mathbf{H}_{n}^{+}\mathbf{s}_{k}\) and \(\widetilde{\mathbf{h}}_{k,n}^{-}=\mathbf{0}_{L}\) by simply discarding \(L^{+}-1\) samples affected by ISI, inevitably leading to SNR reduction by factor \(1-(L^{+}-1)/L\). On the other hand, the proposed channel estimation based on the Algorithm 2 enables exploitation of these interferences without SNR loss. In the following section, we discuss iterative detection scheme for superposition signals using dyadic channels as well as codebooks. ```
1:input the composite forward-backward channels \(\left\{\mathbf{h}_{n}\right\}_{n\in\left\{1,\cdots,N\right\}}\) and the ambient source signals \(\left\{\mathbf{s}_{k}\right\}_{k\in\left\{1,\cdots,K\right\}}\)
2:for\(n\in\left\{1,\cdots,N\right\}\)do
3:initialize\(f_{n}(1)=\sqrt{h_{n}(1)}\)
4:for\(l^{\prime}=2:(L^{\star}+1)/2\)do
5: solve (20) for the variable \(f_{n}(l^{\prime})\)
6:endfor
7: estimate the channel vector \(\hat{\mathbf{f}}_{n}=[\hat{f}_{n}(1),\cdots,\hat{f}_{n}((L^{\star}+1)/2)]^{T}\)
8: estimate the Toeplitz matrices \(\hat{\mathbf{F}}_{n}^{+}\) and \(\widetilde{\mathbf{F}}_{n}^{-}\) from the Equation (3)
9:for\(k\in\left\{1,\cdots,K\right\}\)do
10: estimate the dyadic forward and backward channels \(\widetilde{\mathbf{h}}_{k,n}^{+}\) and \(\widetilde{\mathbf{h}}_{k,n}^{-}\) from (26) and (27)
11:endfor
12:endfor
13:output the dyadic forward channel \(\left\{\widetilde{\mathbf{h}}_{k,n}^{+}\right\}_{\forall k,n}\) and the dyadic backward channel \(\left\{\widetilde{\mathbf{h}}_{k,n}^{-}\right\}_{\forall k,n}\)
```  
**Algorithm 2** Dyadic channel estimation algorithm (D-CEA)
## 2 The Optimization Procedure
### Principle of the TFM-Based Optimization  
Based on a single acquisition of the inter-element impulse responses with a linear array, the optimization procedure enhances the quality of a TFM image with an iterative algorithm that modifies the weld model parameters accordingly to increase the amplitude of the defect echo [9]. Only the propagation of Quasi-Longitudinal (QL) waves is considered to form the images. The procedure is divided into four main steps:  
1. Computation of N propagation times for each pixel of the image, where N is the number of array elements;
2. Computation of the TFM image by coherent summation of the N\({}^{2}\) inter-element signals at each pixel;
3. Extraction of the image quality criterion (maximum amplitude of the defect echo--Amax);
4. Modification of the weld model parameters.  
The computation of the propagation times is achieved with the semi-analytical ray-tracing algorithm of the CIVA software [10; 11]. We encourage the readers to refer to [9] for more information on the procedure.  
The main assets of our approach are that, unlike other approaches, it does not rely on any foreknowledge on the nature nor on the position of an artificial reflector located near the weld [12; 13], and it does not need a dedicated experimental setup to estimate the material properties before the weld inspection [14; 15; 16; 17; 18; 19].
## 4 Construction of Octree-Based Shape Information Model  
### Test-Bed and Equipment  
Laser scanning was performed on the spillway left wall of a concrete dam structure in South Korea. Figure 8 shows the panoramic picture of the TLS measurement setup, and the scan was performed on the second wall surrounded by red lines. The left and right heights of the wall were 11.741 m and 10.118 m, respectively, and the width was 10.00 m. Figure 9 and Table 2 show the enlarged view of the target structure and the information of main crack pattern map through visual inspection. It should be noted that the cracks do not represent the stains, sediments, efflorescence, and microcracks on the surface.  
In this study, we used the Leica ScanStation C5 model as specified in Table 3 to scan the test-bed. The minimum and maximum measurement distance was 31.907 m and 31.918 m, respectively. The left and right angles were \(11^{\circ}\) and \(11.5^{\circ}\) and the up and down angles were \(90^{\circ}\) and \(-45^{\circ}\) as presented in Table 4. The scan mode was set to high-resolution mode, the scanning time was about 28 min and 12 s, and the scan data (PTS format) size was 19.56 MB. The 3D scan data had x, y, and z coordinates at each point. Linux Ubuntu 12.04.2 LTS and C++ were used to configure the octree shape information model and development programming language, respectively.
## 1 Introduction
Based on simulation studies, we perform inter-beam interference analysis of the 5G massive MIMO system for modified and non-modified models under LOS and NLOS conditions. 5. We make a recommendation to use selected modified models for different propagation conditions. 6. We make a recommendation to use selected modified models under different propagation conditions for fast engineering calculations at the initial research stage. The simulation results show that modifying the simple antenna pattern models allows us to obtain an SIR error of no more than 3 dB and 0.1 dB under LOS and NLOS conditions, respectively. Compared with non-modified models, the SIR error is up to 10 dB or 12 dB for the LOS or NLOS scenarios, respectively. On the other hand, the utilization of the non-modified or modified simple models allows for a significant reduction in computation time compared to real patterns. We show that the generation time of simple patterns is about 300 to 520 times shorter compared to 3 GPP. Our research also illustrates that, in assessing interference, the directivity of the main lobe becomes the most important parameter (especially under NLOS conditions) and not the shape of the side lobes. The paper is structured as follows. A short description of the reference model of the multi-beam antenna system is presented in Section 2. Section 3 shows the simple models of antenna patterns in analytical and graphic form. The modification of simple pattern models in relation to a reference antenna is described in Section 4. In Section 5, the MPM-based interference assessment procedure is shown. This section includes a short survey of the MPM, simulation study assumptions, and the obtained results confirming the developed solution's correctness. The paper finishes with a summary and final remarks in Section 6.
## 2 Literature Review
### The HMM as the Superior Choice  
The HMM is particularly suited for this research due to its ability to address the limitations inherent in standard Markov Chains. Unlike a conventional Markov Chain, which only considers observable states, the HMM incorporates a layer of hidden states--such as Novice, Intermediate, and Expert--which evolve over time according to defined transition probabilities. These states are not directly measurable but are inferred from observable data streams that represent an individual's proficiency.  
* Probabilistic Linking: The HMM probabilistically connects these hidden proficiency states to observable data, such as physiological metrics (e.g., HRV), behavioral metrics (e.g., TCT), and subjective feedback (e.g., responses on the NASA-TLX). This linkage allows a comprehensive understanding of proficiency that is informed by real-world indicators [59].
* Latent Proficiency Assessment: The structure of the HMM aligns with the challenge of proficiency assessment by acknowledging that true proficiency is a latent construct that cannot be measured directly. Instead, it infers proficiency through related, observable behaviors and states, thus enriching the assessment process. This capability to capture temporal learning rates and state transitions can be instrumental for understanding dynamic human performance [19].
* Temporal Dynamics: HMM retains the ability to model temporal dynamics and state changes effectively, providing valuable insights into how an operator's skills develop over time. For instance, using established algorithms such as the Viterbi Algorithm, an HMM can analyze sequences of multimodal observations and discern the likeliest underlying sequence of proficiency states, making it a promising approach for this type of analysis [60].
* Real-World Applications: HMMs have been successfully applied in diverse fields, such as speech recognition, bioinformatics, and human-computer interaction, demonstrating their versatility in modeling sequences where hidden states play a crucial role [60]. The adaptability and robustness of HMMs makes them particularly advantageous forcapturing the fluctuating nature of human proficiency as influenced by various factors be they learning, fatigue, or task complexity.  
The HMM framework not only effectively models the transitions between observable behaviors and unobservable proficiency states, but also potentially provides the interpretability needed for practical applications. By capturing hidden states and facilitating a nuanced understanding of operator performance, HMMs offers prospects of empowering researchers and practitioners to assess and support human operators more effectively.
## 1 Introduction
The optimization method can be briefly described as: for a specific mode, two demodulated orthogonal signals can be iteratively updated through two filters with the estimated instantaneous frequency; then, the estimated instantaneous frequency is further updated by the arc-tangent demodulation method which obtains phase of the two orthogonal signals. Firstly, we use the novel method on three artificial signals to evaluate its performance. Then, applying it to pipeline defect detection, the results show that has significantly improved calculation efficiency and accuracy compared with the EMD, VMD, and EWT method. The methods mentioned above can be taken as different filters, and have their respective characteristics, as summarized in Table 1.  
The article is organized as follows. In Section 2, the signal model is defined and related concepts are introduced. We propose the VSME method, including the main ideas and algorithm flow in Section 3. In Section 4, the experiment design is presented, and this method of reliability was verified by simulation and laboratory experiments. Finally, Section 5 concludes the paper. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Contents** & **EMD** & **EWT** & **VMD** & **VSME** \\ \hline Basis & Self-adaption & Prior & Prior & \\  & & determination & determination & Self-adaption \\ Frequency & Difference: Local & Convolution: Local & Difference: Global & Convolution: Global \\ Characterization & Energy-Time & Energy-Time- & Energy-Time- & Energy-Time- & Frequency \\ Nonlinear & Yes & Yes & Yes & Yes \\ Nonstationarity & Yes & No & No & Yes \\ Feature Extraction & Yes & Discrete: No & & Yes \\  & & Continuous: Yes & & Yes \\ Theoretical Basis & Empirical & Complete theory & Complete theory & Complete theory \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of EMD, EWT, VMD and VSME.
# Machine Learning-Based Adaptive Synthetic Sampling Technique for Intrusion Detection
## 2 Literature Review
ReLU was used as the non-linear learning algorithm, the ADAM function served as an algorithm, and cross-entropy was employed as the logistic regression to train the DNN. The approaches for output classification used both binary and multiclass categorization. Figure 2 illustrates the two primary categories of machine learning established by researchers: unsupervised and supervised. The availability of labeled data is a critical success factor in supervised learning, especially for classification tasks. Supervised learning algorithms may struggle to generalize to new, unlabeled data if there are insufficient labeled data. Obtaining labeled data, on the other hand, can be a difficult and time-consuming process. Unsupervised learning approaches, conversely, provide a solution to the problem of obtaining labeled data. Unsupervised learning can simplify the process of gathering training data by extracting useful feature information from unlabeled data. While unsupervised learning has shown promise in some applications, it typically fails to outperform supervised learning methods in detection tasks. As a result, the choice between supervised and unsupervised learning methods is determined by the task at hand as well as the availability of labeled data. In some cases, unsupervised learning may be sufficient for certain applications, whereas supervised learning may be required to achieve the desired performance. Table 1 provides a thorough summary of the methodology and results, as well as a list of previous reference research. It is a powerful tool for quickly grasping the research's essential details, providing a clear and concise snapshot of the study. Figure 2: Machine learning algorithm in IDS. It is important to note that our contribution extends beyond accuracy alone. The proposed approaches incorporate advanced deep learning architectures, attention mechanisms, and synthetic sampling techniques to address the limitations of existing methods, such as low detection rates and the need for extensive feature engineering. This study's proposed approach fills several research gaps in the field of IoT network security. First, it addresses the shortcomings of traditional intrusion detection techniques, which struggle with low detection rates and necessitate extensive feature engineering. Second, for traffic anomaly detection, it employs a deep learning model that combines an attention mechanism with an LSTM network to extract the sequence properties of data flow via a CNN. Third, it uses ADASYN to increase the size of minority-class samples, which improves the model's performance. Finally, the proposed approach outperforms previous comparable algorithms in terms of accuracy and F1 scores, indicating its ability to improve overall performance by combining the advantages of both models.
## 3 Results and Discussion  
### CeO\({}_{2}\) Morphologies and Crystal Structures  
XRD and the crystal model of CeO\({}_{2}\) revealed the crystal structure of CeO\({}_{2}\) used as a catalyst in both polishing slurry and photocatalysis. As shown in Figure 1a, the diffraction peaks of CeO\({}_{2}\) are sharp and intense, indicating its high crystallinity. No impurity peaks were detected within the resolution range of the equipment, confirming the high purity. The main diffraction peaks at 28.60\({}^{\circ}\), 33.04\({}^{\circ}\), 47.55\({}^{\circ}\), 56.26\({}^{\circ}\), 59.12\({}^{\circ}\), 69.41\({}^{\circ}\), 76.78\({}^{\circ}\), 79.16\({}^{\circ}\), and 88.49\({}^{\circ}\) correspond well to the (1 1 1), (2 0 0), (2 2 0), (3 1 1), (2 2 2), (4 0 0), (3 3 1), (4 2 0), and (4 2 2) planes of the cubic fluorite structure of CeO\({}_{2}\) (JCPDS Card 43-1002), respectively. The cubic fluorite structure of CeO\({}_{2}\) belongs to the Fm-3m space group, as shown in Figure 1b[17]. After the addition of hydrogen peroxide, it can be observed from the figure that there was almost no change in the XRD pattern of H-CeO\({}_{2}\) powder, indicating that the lattice structure and crystalline phase of the CeO\({}_{2}\) material remain unchanged.  
Figure 1: (**a**) XRD diffraction pattern of CeO\({}_{2}\) and H-CeO\({}_{2}\) powders, (**b**) crystal model of CeO\({}_{2}\), (**c**) SEM image of CeO\({}_{2}\), (**d**) particle size distribution of CeO\({}_{2}\), (**e**–**g**) TEM images of CeO\({}_{2}\).  
TEM and SEM characterized the internal lattice and external morphological structures of the nano-CeO\({}_{2}\). As shown in Figure 1c, the SEM image displays the nano-CeO\({}_{2}\) as predominantly spherical with a rough surface. The size distribution shown in Figure 1d indicates that the nano-material has a wide size distribution consisting of polydisperse nano-CeO\({}_{2}\) particles. The catalytic performance of a catalyst is largely influenced by its surface area and the number of catalytically active sites available. The small size and the extensive surface area of nanoparticles facilitate rapid charge transfer between the catalyst and redox couples, leading to enhanced catalytic activity [18]. The approximately spherical nature of the nano-CeO\({}_{2}\) material increases the number of reactive sites and facilitates charge transfer between the catalyst and redox molecules, achieving high catalytic activity [19].  
The size characteristics of nano-CeO\({}_{2}\) are also clearly demonstrated in TEM images (Figure 1e-g) showing the particle-like, nearly spherical distribution consistent with the SEM results. Additionally, the lattice fringe of CeO\({}_{2}\) is 0.314 nm, which matches well with the (111) peak at around 28.60\({}^{\circ}\) in XRD.
# An Optimal Resource Allocation Scheme with Carrier Aggregation in 5G Network under Unlicensed Band
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Definition** \\ \hline CA & Carrier aggregation \\ \hline
5G-U & 5G in Unlicensed band \\ \hline QoS & quality of service \\ \hline CSMA/CA & carrier sense multiple access with collision avoidance \\ \hline RBs & resource blocks \\ \hline CCs & component carriers \\ \hline UE & user equipment \\ \hline PCC & primary CC \\ \hline SCC & secondary CC \\ \hline
**G** & a graph represent the network \\ \hline MBS & macrocell base station \\ \hline
**M** & set of M MBSs \\ \hline AP & access point \\ \hline
**S** & set of S small cell APs \\ \hline
**W** & set of W Wi-Fi APs \\ \hline
**U** & set of U UE \\ \hline \(\delta_{m}\) & intensity of MBSs \\ \hline \(\delta_{s}\) & intensity of small cell APs \\ \hline \(\delta_{w}\) & intensity of Wi-Fi APs \\ \hline \(\delta_{u}\) & intensity of UE \\ \hline \(D_{t}\) & delay threshold for delay-sensitive service \\ \hline \(C_{t}\) & rate threshold for rate-sensitive service \\ \hline \(\psi_{d}\) & association bias with small cell \\ \hline \(\psi_{\tau}\) & association bias with Wi-Fi \\ \hline \(\psi_{u}\) & association bias \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Notation definition. The rest of this paper is organized as follows. In Section 2, we review the related literature. In Section 3, we describe the system model for the 5G-U network with a CA feature. In Section 4, we present the problem formulation to maximize system satisfaction with performance guarantee for a Wi-Fi system in 5G-U. In Section 5, we evaluate the effectiveness of the proposed algorithm by showing numerical results, followed by the discussion and presentation of future work in Section 6.