esa-sceva/satcom-chunk-collection huggingface dataset:
# Hybrid RFSVM: Hybridization of SVM and Random Forest Models for Detection of Fake News
## 1 Introduction
### Propagation-Based Study of Fake News  
This study gives information about the spreading of rumors and the process of spreading it. In this propagation-based study, we deal with the following:  
* How are the propagation patterns of false news represented?
* What will be the measuring parameters for the characterization of dissemination of false news?
* How do we differentiate the dissemination of fake news from news that is verified?
* How do we analyze the pattern of fake news in various domains like politics, economy, and education?
* How does fake news propagate differently for topics like presidential elections and health, for various platforms like Instagram, Facebook, and X (Twitter), in different languages like English, Hindi, Mandarin Chinese, and Spanish?  
The sections that make up this document are as follows: The research methods, motives, problem description, and objectives deduced from the literature review and the proposed architecture and proposed algorithm for measuring various parameters are  
\begin{table}
\begin{tabular}{c c c} \hline \hline  & **Topics Covered** & **Content Analysed** \\ \hline PolitiFact & American Politics & Statements \\ \hline FactCheck & American Politics & TV ads, Debates, Speeches, Interviews and News \\ \hline Snopes & Politics and other Social Issues & News Articles and Videos \\ \hline TruthorFiction & Politics, Religion, Nature, Food and Medical & Email Rumours \\ \hline HoaxSlayer & Ambiguity & Articles and Messages \\ \hline FullFact & Economy, Health, Education, Crime, Immigration, Law & Articles \\ \hline \hline \end{tabular}
\end{table}
Table 1: Expert-based fact-checking websites.  
Figure 1: Automatic fact-checking process.  
discussed in Section 2. A summary of the process implementation and outcomes is given in Section 3. Conclusions in Section 4 along with the scope of future work.
# SDebrisNet: A Spatial-Temporal Saliency Network for Space Debris Detection
## 5 Experimental Results and Analysis
### Comparison with Other Space Object Detection Methods  
We compare the proposed SDebrisNet with some representative space object detection methods, as mentioned in Section 4. However, they are not saliency-based object detection methods, so the saliency detection metrics cannot be used for comparing these methods. The detection probability \(P_{d}\) and false alarm rate \(P_{f}\) as described in Section 4.2 are introduced to evaluate the different space debris detection methods. Table 4 presents the detection results of all the space debris detection methods in the proposed SDD dataset. In Table 4, the bold denotes the best result under the current metric. It can be seen from Table 4 that the multi-frame-based method outperforms the single-frame-based method because it can utilize the interframe motion information. The video-based methods achieve better detection results than both the single-frame-based and multi-frame-based methods. This is because satellite video images have strong interframe correlations and their motionless backgrounds have great redundancy under the sidereal tracking mode. However, the multi-frame satellite images have weak correlations due to the long exposure times between two adjacent frames. As a whole, the proposed SDebrisNet achieves the best performance. Compared with the NODAMI method, SDebrisNet improves by 3.5% and 1.7% in terms of \(P_{d}\) and \(P_{f}\), respectively.  
The computational efficiencies of different space debris detection methods over the image size of \(960\times 576\) pixels are also listed in Table 4. On average, our method requires a processing time of 0.013 seconds/frame, which is less than the other three methods. The proposed method could process a frame for the video satellite with a frame rate of 25 frame/s.
## 4 Discussion
### Impacts of Drought on GPP  
This study investigated the MODIS GPP product accuracy by employing the monthly observed GPP datasets from the Qianyanzhou flux tower. The overall accuracy of MODIS GPP product data is good. Further studies should also combine with other GPP products [43; 44] (e.g., the Global LAnd Surface Satellite (GLASS) GPP [45] and the Fluxcom GPP [46]) to explore the regional GPP variation accurately. Furthermore, it is notable that in Figure 12, the negative standardized anomalies areas for GPP during November and December in 2019 decreased significantly, which was consistent with that of GOSIF, while the standardized anomalies areas of VIs are still significant (Figure 10). This phenomenon indicated that the responsibility of NDVI to short-term precipitation is limited, which may lead to delayed expression of vegetation production recovery. As shown in Figure 7d, the most severe meteorological drought occurred in November 2019; while the reduction in GPP was not noticeable (Figure 12), this phenomenon could be explained by the difference between meteorological drought and agricultural drought. Although the VIs can indicate the agricultural drought potentially [47; 48; 49], considering the reduction in VIs was significant in November and December, which was not found for GOSIF, the actual agricultural drought that emphasizes yield reduction, or GPP losses may be less severe in the MLRYR [15]. Moreover, [PERSON] et al. [24] also reported that the well-known 2019 drought event that occurred in the MLRYR had a much less severe impact on vegetation than expected; this is mainly due to the lack of Pre, leading to an increase in sunshine and heat resources, which in turn improved crop growth in most areas; therefore, compared with  
Figure 14: Pearson correlation coefficients between the standardized anomalies of drought indices and that of SIF as well as VIs during July–December 2019.  
VIs, SIF can better monitor the vegetation's physiological state changes (e.g., senescence) and GPP variations even when suffering from agricultural drought. In addition, due to the fact that the crop water demand (e.g., rice and wheat) in MLRYR can be satisfied by irrigation when drought occurs [24; 50], the mitigation of drought effects on crop GPP by irrigation and groundwater storage [15] and the impacts of climatic impacts of irrigation on crop growth [50; 51] deserve more attention and need further research in this region.
# Heterogeneous Network Switching Strategy Based on Communication Blind Area Dwell Time
## 3 VLC Heterogeneous Networking Technology Research
### Cbdhvio Scheme  
In this article, we utilize Wi-Fi for uplink data transmission. Meanwhile, we employ both VLC and Wi-Fi for downlink data transmission. Furthermore, we analyze three situations of communication blind areas based on the light source layout: (1) The visible light coverage overlapping area. (2) The visible light coverage tangent area. (3) The visible light has no coverage area. The flow chart of the specific handover strategy is shown in Figure 6.  
Figure 5: Visible BER distribution.  
Figure 6: CBDHVHO scheme flowchart.  
In the above flowchart, the communication blind area dwell time _Ts_ refers to the time from the mobile receiving device entering the communication blind zone to the time of leaving. The carrier-to-noise ratio is normal, and the case of BER \(>10^{-4}\) is chosen as the communication blind area in this paper. \(T_{threshold}\) refers to the threshold time, which follows a random distribution in the interval [\(T_{res}\), \(T_{max}\)]. \(T_{res}\) is the time limit of the response time for the user's thought stream to remain uninterrupted [18], and \(T_{max}\) is the maximum waiting time for the visible channel interruption. When the \(T_{S}\) is greater than \(T_{threshold}\), the user receiving equipment in the communication blind area will be affected by the channel interruption. In order to maintain normal communication, the vertical handover is accessed at this time, and in order to prevent the switching handover cost from being too high, the execution of the vertical handover will be maintained until the end of the mobile process. When the \(T_{S}\) is less than the \(T_{threshold}\), the user's receiving device is basically not affected by the channel response interruption. The implementation of horizontal handover can ensure high-quality user communication.
## 4 Discussion
The choice between ULA plates and StemFit depends on the specific research objectives and cell types employed, with ULA plates offering a broader versatility in dental 3D culture applications. The proliferation rate of the 3D PDLSC aggregates declined over time. The CCK-8 assay results of a previous study [37] revealed an increase in cell proliferation over time in two-dimensional cultures and a decrease in 3D cultures. Our experimental results also revealed a decrease in cell proliferation in 3D cultures. In particular, the live/dead assay confirmed that the outer surface and center of the sPDLSCs were quickly filled with dead cells. A study on DPSCs revealed opposing results, as the proliferative ability of ST cells was reported to be significantly better than that of permanent teeth [38]. This result was observed in the culture system and ST. Scaffold-free 3D culture is more physiologically relevant with improved cell-cell interactions and is simpler to set up; however, owing to weak structural support for proliferation and differentiation, it results in limited scalability. Recently, PDLSCs were demonstrated to have a high potential for multilineage differentiation into osteogenic, chondrogenic, and adipogenic cells [39]. PDLSCs close to the bone have a stronger capacity to differentiate into bone tissue. This process is induced by various growth factors and signaling molecules, such as bone morphogenetic proteins (BMPs), transforming growth factor-beta (TGF-\(\beta\)), and Wnt signaling [40]. Our finding revealed that sPDLSCs and pPDLSCs displayed similar osteogenic differentiation. Chondrogenic differentiation, which has a similar inducing growth factor as osteogenic differentiation (ex, TGF-\(\beta\) and BMPs), was superior to pPDLSC. In contrast, the adipogenic differentiation tendency was higher for ST than for permanent teeth. Similar reports by [PERSON] et al. [38], who compared DPSC from ST and permanent teeth, revealed that osteogenic and chondrogenic differentiation were stronger in pDPSCs while adipogenic differentiation was superior in sDPSCs. Further studies on stem cells originating from the ST are needed. This study had some limitations. First, due to the spontaneous spheroid formation technique used in this study, it was challenging to control the composition of the spheroids and their size and establish the right ratio of two different cell types in the spheroid for co-culture. Second, the assays were not optimized for cells cultured in a 3D environment. For example, the CCK-8 assay requires a media change, which increases the risk of loss of floating cells. Therefore, further research is necessary to establish a suitable method for analyzing 3D cultured cells. Third, this evaluation was a pilot study comparing 3D cultured sPDLSCs and pPDLSCs. The study also had an insufficient sample size; thus, additional studies are required to determine the genetic function using RT-PCR. In the future, it will be essential to determine the expression characteristics of the sPDLSCs. Despite these limitations, we confirmed that sPDLSCs could be an alternative source of pPDLSCs.
# Phishing Detection in Blockchain Transaction Networks Using Ensemble Learning
## 5 Conclusions  
In conclusion, this research article proposed a deep learning-based approach for detecting phishing attacks in blockchain transaction networks. The study used long short  
\begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**References** & **Method** & **Scam** & **Accuracy** & **Recall** & **Precision** & **F-Score** \\ \hline
[75] & XGBoost &
\begin{tabular}{l} Malicious users’ \\ detection \\ \end{tabular} & 96.54\% & — & — & — \\
[76] & Multilayer & Cryptocurrency & 98.00\% & — & 98.98\% & — \\  & perception &
\begin{tabular}{l} Beginning \\ detection \\ \end{tabular} & 98.00\% & — & 97.00\% & 73.00\% \\
[77] & Graph2 Vec & Phishing & — & 77.00\% & 69.00\% & 73.00\% \\
[78] & GCN & Phishing & — & 14.53\% & 72.94\% & 23.57\% \\
**Proposed method** & **Ensemble** & **Phishing** & **99.72\%** & **99.86\%** & **99.86\%** & **99.86\%** \\ \hline \hline \end{tabular}
\end{table}
Table 6: Comparative analysis with existing studies.  
term memory (LSTM), bi-directional LSTM (Bi-LSTM), and convolutional neural network LSTM (CNN-LSTM) to detect phishing attacks in real-time on blockchain networks. The proposed system integrated ensemble learning techniques, such as convolutional and recurrent neural networks, with blockchain and deep learning to address low precision, latency, stability, and privacy issues. The evaluation results demonstrate the proposed approach's effectiveness in detecting phishing attacks, which can contribute to developing more secure and trustworthy blockchain-based systems. The novelty of this study lies in the proposal and evaluation of a deep learning-based approach to detect phishing attacks in blockchain transaction networks, a new and innovative application of deep learning techniques.  
Future research can focus on developing more effective and knowledgeable deep learning algorithms to improve the system's performance. Additionally, an improved architecture that employs deep learning principles, such as feature extraction, scaling, and classification, can be suggested in a decentralized medium to address these concerns.  
Conceptualization, R.D., R.O.O. and S.M.; Formal analysis, R.O.O. and M.O.A.; Investigation, R.O.O., M.O.A. and R.D.; Methodology, R.D. and S.M.; Software, R.O.O. and M.O.A.; Supervision, S.M.; Validation, R.O.O. and M.O.A.; Visualization, R.D.; Writing--original draft, R.O.O. and M.O.A.; Writing--review and editing, R.D. and S.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The dataset used in this study is available from: [[https://github.com/MyEtherWallet/ethereum-lists](https://github.com/MyEtherWallet/ethereum-lists)]([https://github.com/MyEtherWallet/ethereum-lists](https://github.com/MyEtherWallet/ethereum-lists)) (accessed on 1 February 2023).  
The authors declare no conflict of interest.
## 5 Conclusions and Future Work  
A new void avoidance algorithm for geo-routing that forms a void-free wireless sensor network is presented in this paper. Simulation results show that VAA can evade the communication voids with small routing overheads. VAA increases the distance and decides the next hop node via a cost function that has three tuples to give efficient resolution for the dead end or concave node. Energy consumption is low and it selects an efficient communication path.  
Our study is limited to [PERSON] wireless position-based sensor networks. In the future, it can be extended to limited mobile or highly mobile wireless position-based sensor networks. Moreover, we simulated this algorithm only for a single BS. It can be extended to multiple BSs. We also left unresolved some important areas, such as efficient positioning, maximum utilization of resources, energy conservation and localization for the future research purpose.
## 2 Background and Related Work
### Architectural Concepts
#### 2.1.1 CPU Caches  
CPU caches are small and fast memories located between the CPU and main memory, specially designed to hide main memory access latencies. As the cost of memory is related to the speed, the cache memory is structured in a hierarchy of typically three levels. Level 1 is the smallest, fastest cache memory and similar to level 2 is usually core private. Level 3, the biggest and slowest cache memory, is commonly shared among processing cores. They hold a copy of recently used data, which will likely be requested by the processor in a short period of time. If the data requested by the processor are not present in any level of the cache, they will be loaded from main memory and stored in the cache. If a cache level is completely filled at that point, some of the data currently contained in it will be replaced (evicted) to store the loaded data instead. The selection of the evicted data depends on the processor architecture. Processors, including AMD, Intel and some ARM high-performance processors, typically use a policy related to least recently used (LRU). However, the replacement policy of most ARM processors is based in random replacement or round-robin replacement.  
In Intel processors, such as the one used in our experiments, caches are inclusive memories. This means that high level caches, such as level 3 (L3) cache, have a copy of the data of lower level caches. In addition, what is more important and relevant to most attacks, L3 cache is shared among all cores. Consequently, a process being executed in a core, related to L3 cache data, may produce side effects in other core processes. Other processors use exclusive caches, where a memory block is present in only one level of the cache hierarchy.  
Caches are usually set associative: they are organized as \(S\) sets of \(W\) lines (also called ways) each holding \(B\) bytes. It is common to name them as _W-way set associative_ cache. Given a memory address, the less significant \(log_{2}B\) bits locate the byte on the line, the previous \(log_{2}S\) bits do the same for the set and the remaining high-order bits are used as a tag for each line. The tag will be used to discern whether a line is already loaded or not in the cache. In modern processors, the last level cache (LLC) is divided into slices. Thus, it is necessary to know the slice, and the set in which a datum will be placed. The slice is computed as a hash function of the remaining bits of the address. If the number of cores is a power of two, the hash will be a function of the tag bits. Otherwise, it will use all the bits of the address [22; 23] Note that, as main memory is much larger than CPU caches, multiple memory lines map to the same cache set.
## 3 Results and Discussion
### Models Comparison
Moreover, Figure 4c illustrates that the distribution of the difference between two consecutive sampling points of wind is almost the same for all regions with variations between 0 and 3 m/s. While the largest fluctuation of POA irradiance occurred in Tri An, and the smallest fluctuation of that happened in Ha Noi. This means that the effect of thermal inertia on the thermal prediction in Tri An is greater than for the other regions due to its high intensity and frequent fluctuation of POA irradiance. Consequently, our proposed model with wind obtained the best result in Tri An where the on-site conditions fluctuated most significantly, while the proposed model without wind gained better performances in Tri An and Ha Noi, where the wind was less important than in Da Nang. Figure 2: A comparison of the difference between predicted and measured temperature of PV module in three different locations, resulting in changing of incident POA with 5-min time steps: (**a**) Tri An; (**b**) Da Nang; and (**c**) Ha Noi. Figure 4: The distribution of POA irradiation and wind in three locations in Vietnam (1-min time step): (**a**) average monthly distribution of POA irradiation level; (**b**) average monthly distribution of wind speed; and (**c**) the variation of POA irradiance and wind speed between two consecutive sampling points. Figure 3: Correlation between measured and calculated module temperature for different POA levels obtained with the studied models for Tri An, Vietnam (time recording = 5 min): (**a**) correlation factor \(R^{2}\); (**b**) \(RMSE\). The back-surface temperature of the PV module that is calculated for three regions by our proposed model with wind effect (Equation (13)) using optimized values of \(\Delta T_{ref}^{*}\) and \(W_{ref}\) coefficients is presented in Table 6. By applying these best-fitted coefficients, the obtained \(RMSE\) values were decreased significantly to below 2 \({}^{\circ}\)C for all regions.
# Uniform Page Migration Problem in Euclidean Space
## 1 Introduction
This algorithm can be viewed as a natural extension of the \(\left(2\ +\ 1/2D\right)\)-competitive algorithm under \(L^{1}\) norm of [3]. We note that if there exists a \(\left(2\ +\ 1/2D\right)\)-competitive algorithm under \(L^{2}\) norm, then it would have to behave on any line in the same way as the \(\left(2\ +\ 1/2D\right)\)-competitive algorithm of [3]. The setting of the unit page size, i.e., \(D=1\) is called the uniform model in the context of data management problems including the page migration problem [1; 5; 6; 7]. Other previous results for the page migration problem are as follows. [PERSON], [PERSON], and [PERSON] [8] proposed a 4.086-competitive deterministic algorithm for general networks. For trees and uniform networks, [PERSON] and [PERSON] [2] presented 3-competitive deterministic algorithms, together with the lower bound of 3 even at two points. The first deterministic lower bound larger than 3 for general networks was 3.148 [3], which was improved to 3.168 by [PERSON] [5]. Although these lower bounds, larger than 3, were proved only for \(D=1\), the first lower bound of \(3+\Omega\left(1\right)\) was recently proved [9], where \(\Omega\) notation is with respect to \(D\). For three points, 3-competitive deterministic algorithms with \(D\in\left\{1,2\right\}\)[3; 10] and a \(\left(3+1/D\right)\)-competitive deterministic algorithm with \(D\geq 3\)[10] were proposed. The latter algorithm asymptotically matches the lower bound of \(3+\Omega\left(1/D\right)\) on three points for every \(D\geq 3\)[10]. As for randomized algorithms against adaptive online adversaries, a 3-competitive algorithm for general metric spaces was proposed in [4] and the upper bound of 3 is also a lower bound on two points [11]. As for a randomized algorithm against oblivious adversaries, [PERSON], [PERSON], [PERSON], and [PERSON] [12] proved that the optimal competitiveness of \(2+1/2D\) can be achieved even on uniform networks. The rest of this paper is organized as follows. Some preliminaries are given in Section 2. We define our algorithm on \(\mathbb{R}^{n}\) under \(L^{2}\) norm and prove its 2.75 competitiveness in Section 3. In Section 4, we prove a lower bound of 2.732 for our algorithm. We conclude the paper with remarks and future works in Section 5.
## 4 Performance Evaluation and Result Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline
**Ref** & **Decoder** & **Parameters** & \multicolumn{5}{c}{**Performance Indicators**} \\ \cline{4-9}  & & & & **BLER/FER/BER** & **Latency** & **Throughput** & **Energy Efficiency** & **Computational Complexity** \\ \hline \multirow{3}{*}{[42]} & Successive & AWGN, BPSK, PC & BLER \(\approx 10^{-4}\) at SNR & NA & NA & NA & Average number of queries \(\approx 100\) at SNR \\  & cancellation list & (64, 41), R = 0.65 & 5 dB & NA & NA & NA & NA \\  & (SCL)-GRAND & \multicolumn{5}{c}{Rayleigh fading} \\  & & channel, maximum & & & & & & 1.5 queries at \\  & & Hamming weight = 4, & & & & & \(E_{k}/N_{0}=26\) dB for target FER of \(10^{-7}\) \\ \hline \multirow{3}{*}{[44]} & \multirow{3}{*}{Fading-GRAND} & AWGN, BPSK, FP & \multirow{3}{*}{FER \(\approx 10^{-4}\)} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} & \multirow{3}{*}{NA} \\  & (ALF) decoder & & & & & & & \\  & & & & & & & \\  & & & & & & & \\ \hline \multirow{3}{*}{[45]} & Improved belief & Polar code & \multirow{3}{*}{BLER \(\approx 10^{-4}\) at} \\  & propagation list & length = 128 bits, & 3.8 dB & NA & NA & NA & NA \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \cline{1-1}  & & & & & & & \\ \hline \end{tabular}
\end{table} 
Table 3: _Cont._