esa-sceva/satcom-chunk-collection huggingface dataset:
# Quantitative Study on the Impact of Energy Consumption Based Dynamic Selfishness in MANETs +
## 4 Evaluation Methods
### Evaluation Metrics  
Data forwarding is the fundamental network function in MANETs. In order to estimate the impact of node selfishness on the network performance based on packet forwarding efficiency, three metrics are used, namely average packet loss rate, average round-trip delay, and average throughput.  
(1) Average Packet Loss Rate (APLR): This metric is calculated as the ratio of the number of lost data packets to the number of all packets sent by the source nodes.  
\[APLR=\frac{P_{\mathit{lost}}}{P_{\mathit{sent}}}*100\% \tag{2}\]  
In Equation (2), \(P_{\mathit{sent}}\) and \(P_{\mathit{lost}}\) represent the number of packets sent by the source node and the number of packets that did not reply, respectively.  
(2) Average Round-Trip Delay (ARTD): This metric is calculated as the average sum of the round-trip delay of each data packet that the source node sent.  
\[ARTD=\frac{\sum_{i=1}^{P_{\mathit{sent}}-P_{\mathit{lost}}}[T_{r}(i)-T_{s}(i)] }{P_{\mathit{sent}}-P_{\mathit{lost}}} \tag{3}\]  
In Equation (3), \(T_{r}(x)\) represents the time when the \(i\)th corresponding reply packet is received by the sender, \(T_{s}(x)\) represents the time when the \(i\)th data packet is sent out of sender \(x\).  
(3) Average Throughput (AT): This metric is calculated as the sum of successfully received packet sizes divided by the simulation time.  
\[AT=\frac{\sum_{i=1}^{P_{\mathit{sent}}-P_{\mathit{lost}}}P_{\mathit{size}}}{T _{b}-T_{e}} \tag{4}\]  
Figure 3: Evaluation algorithm scenario.  
In Equation (4), \(P_{size}\) represents the packet size, and \(T_{b}\) and \(T_{e}\) are the simulation start time and the end time, respectively.
# Validating the Use of Smart Glasses in Industrial Quality Control: A Case Study
## 2 Object Detection
### Evolutionary Trends
In [50], a multi-stage approach that fuses features extracted from different orientations and refines detections iteratively is presented, achieving improved performance for rotated object detection. Reference [51] explores the intricacies of visual inspection within assembly processes, leveraging state-of-the-art techniques rooted in contrastive and transfer learning. The authors address the challenges associated with inspecting small components, a critical aspect of assembly quality control, and propose innovative approaches to enhance the precision and efficiency of the inspection process. With a focus on leveraging advanced visual methodologies, this paper is poised to enrich our understanding of contemporary strategies in small component inspections. [PERSON] et al. [52] introduced an innovative approach to online assembly inspection, merging a lightweight hybrid neural network with precise positioning box matching techniques. Navigating the complexities of real-time assembly assessments, the authors delivered a comprehensive and integrated methodology promising to enhance the accuracy and efficiency of product assembly inspections significantly. These additional studies underscore the continuous progress in rotated object detection research, with researchers continuously exploring innovative techniques to address the challenges of detecting objects with arbitrary orientations in real-world environments. The development of robust and versatile rotated object detection systems has the potential to revolutionize various applications, including autonomous navigation, video surveillance, and medical imaging analysis.
# Research on Short Video Hotspot Classification Based on LDA Feature Fusion and Improved BiLSTM
## 3 Models and Improvements
### Feature Fusion Method Based on LDA Topic Model
#### 3.2.2 Fusion Method of Word Vector and LDA Topic Model  
[PERSON], [PERSON], et al. [28] fused LDA document-topic distribution features with weighted Word2 Vec word vector features to construct short text features in Weibo and performed  
Figure 1: LBSA model structure diagram.  
topic clustering based on the K-means algorithm, which exploited the LDA document-topic distribution features. [PERSON], [PERSON], et al. [29] used the LDA topic model word-topic distribution to calculate the topic similarity between words and their contextual words, which was incorporated into the word vector as topic semantic information. The above papers all use the documents generated by the LDA topic model to improve the word vector. In this paper, based on the above papers, the document-topic distribution and word-topic distribution of the LDA topic model are used, combined with the Word2 Vec word vector to obtain the topic extension features, as follows.  
First, the document-topic distribution characteristics can be obtained according to the trained LDA topic model, and then the document \(d_{i}\) under the maximum probability topic \(Z_{max}\) is selected. According to the topic \(Z_{max}\) under the topic word distribution, the top \(k\) subject words \((c_{1},c_{2},c_{3},\cdots,c_{k})\) and their probability \((p_{1},p_{2},p_{3},\cdots,p_{k})\) can be obtained, and the probability value should be normalized to calculate the weight of \(k\) words. The selected \(k\) in this paper is 30, as shown in Formula (6):  
\[q_{i}=\frac{p_{i}}{\sum_{c=1}^{k}p_{c}} \tag{6}\]  
where \(q_{i}\) denotes the normalized value of \(p_{i}\) and \((q_{1},q_{2},q_{3},\cdots,q_{k})\) is the weight size of the previous \(k\) words.  
Second, the Word2 vec model is used to obtain the word vector of each word \((X_{1},X_{2},X_{3},\cdots,X_{n})\), with the maximum probability topic \(Z_{max}\) in each document \(d_{i}\) as the key, and if the words in the document match the words under topic \(Z_{max}\), the word vector of n words is weighted and summed to obtain the topic extension feature, as shown in Formula (7):  
\[Y_{i}=\sum_{j=1}^{n}q_{j}\times X_{j} \tag{7}\]  
where \(Y_{i}\) is the topic extension feature of document \(d_{i}\). Finally, the Word2 vec word vector features are stitched with the topic extension features as the input of the BiLSTM model. The specific process is shown in Figure 2 below. The method solves the problems of inconspicuous topic boundaries and sparse features caused by short comment contents and improves the precision of topic classification in this paper.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
### Failure Modes of Railway S&C Systems
#### 2.4.1 Permanent Way  
These elements, which consist of sleepers, stretcher bars, rails, and ballast to name a few, of a railway S&C system are exposed to the environment and guide the rail vehicles over it. Failures in these elements can be considered in two categories: the rails and the elements attached to them.  
In linearly actuating switches, the switch rails slide over the sleepers (or on the slide chairs). These rail components can experience a considerable amount of wear due to friction during the switching movement, interaction with the wheels, or environmental conditions [26]. Wear of the switch rails due to friction is reduced by using roller baseplates, which reduce the friction between the rails and the baseplates [50]. Wear of the rail elements is also expected to be reduced in case of lift-hop-drop  
Figure 9: Railway S&C system failure modes/sources.  
actuation [51]. Failure of the slide chairs due to friction is one of the causes of full S&C system failure [52], and these faults can be reduced by the new plate designs. Due to inappropriate actuation and repeated operation, the switch rails may bend or buckle from their original profile. This can lead to misalignment of the switch system or complete failure of the railway S&C system. Misalignment of the layout within an allowable range can be rectified using a closed-loop self-adjusting controller [40].  
The different elements attached to the rail components such as the sleeper, ballast, stretcher bars, etc., also contribute to a large number of failures. Regular maintenance is needed for ballast tamping [9; 52] to allow secure movement of vehicles over the switches. Railway run-throughs often damage the permanent way severely and can lead to derailment of the vehicle over it [20]. Failures may occur if the stretcher bars, which are responsible for holding the switch rails together, fail. Accidents such as Potters Bar and Grayrigg were caused by failure of a permanent way. Failures in the stretcher bars, and an improper inspection and maintenance process led to failure of the systems [48; 53].
## 10 Future Directions and Emerging Trends
Big data analytics could also play a crucial role by analyzing transaction patterns to develop credit scoring models for individuals with no traditional credit history, allowing them to access loans and build financial stability. Furthermore, blockchain and big data can be leveraged to enhance transparency and accountability in governmental and non-governmental organizations. Research could be conducted on implementing blockchain-based systems for tracking and auditing the distribution of aid and development funds, ensuring that resources reach their intended recipients, and reducing corruption. Additionally, big data can be used to analyze social and economic trends in real-time, allowing policymakers to address issues such as poverty, healthcare access, and education more effectively. By exploring these avenues, researchers can identify ways in which blockchain and big data can drive social good and promote sustainable development, particularly in regions that have historically been underserved by traditional systems. Nonetheless, blockchain also presents several challenges to its widespread adoption, even if it has huge potential. Technical challenges include energy consumption and the complex integration with present systems, which must be addressed first. Furthermore, cultural opposition and unclear laws might hinder acceptance of it. Moreover, there is a growing need for qualified professionals with strong knowledge of blockchain technology to properly enable its implementation across several sectors. The general acceptance and prosperity of blockchain technology depend on their ability to overcome these challenges.
## 4 Results and Discussion
### Ann Performance Measures
A side-by-side evaluation of the ANN results with the reference solution is shown in Figure 1(b), indicating a high degree of agreement between the two solutions, with MSE errors in the order of \(1\times 10^{-10}\), showcasing the viability of the suggested method. Figure 4 illustrates the fluctuation in MSE and the regression analysis for Scenario 2: case 6, and for Scenario 3: case 6. From Figure 3(a),b, it can be seen that the best training results are \([2.7067\times 10^{-10},2.6283\times 10^{-10}]\) at epochs [499,109] for solving \(Re_{\omega}\) and \(Re_{\Omega}\) for \(G(\zeta)\), and \(M\) for \(\theta(\zeta)\), respectively. These charts illustrate the effective validation of the training data across various scenarios. The regression graphs are shown in Figure 3(c),d.  
Figure 4: \(Cont\). Figure 3: Plots for solving \(M\) for \(f^{{}^{\prime}}(\zeta)\).
## 3 Results  
### Breakdown Strength  
The positive breakdown voltage and time to breakdown of pure oil and nanofluids are shown in Figure 4. Six repeat breakdown measurements for each sample were performed to generate the breakdown voltage and time to breakdown. The breakdown voltages of nanofluids are improved by the addition of three types of nanoparticles. It is worth noting that the improvements for breakdown voltage of Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) nanofluids are up to 41.3% and 29.8% respectively, much higher than that for Al\({}_{2}\)O\({}_{3}\) nanofluid of 7.4%. Especially, the times to breakdown in Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) nanofluids are significantly increased by 76.3% and 69.6%, respectively. As for Al\({}_{2}\)O\({}_{3}\) nanofluid, the time to breakdown is almost the same as that of pure oil. These indicate that the average propagation velocities of positive streamers are greatly slowed down due to the addition of Fe\({}_{3}\)O\({}_{4}\) and TiO\({}_{2}\) nanoparticles, which is of great importance since it is closely related to the breakdown strength.
## 2 Materials and Methods
### Datasets for Model Evaluation
#### 2.4.2 MTE Product  
The 0.5\({}^{\circ}\) gridded GPP and ET from MTE data-driven model are used to calculate LUE and iWUE* [41]. The data-driven model integrates site-level flux tower measurements of GPP and ET fluxes to global scale using the statistical approach of MTE. The MTE approach is based on a combination of TRee Induction ALgorithm (TRIAL) and Evolving tRees with RandOm gRowth (ERROR) technique [37]. The trained MTE at site level is used to generate global GPP (MTE-GPP) and ET fluxes (MTE-ET) [41]. The estimation of MTE-GPP and MTE-ET from extrapolation of local FLUXNET measurements is based on 29 explanatory variables, including precipitation and temperature (both measured _in situ_) and remote sensing indices of monthly fAPAR from GIMMS during the 1982-1997 period, SeaWiFs sensor during 1998-2005, and MERIS since 2006 [41]. Because the MTE data-driven model integrates several measurements, it is taken as a reference to evaluate the ORCHIDEE results. Note that the fAPAR3g (15-day resolution) and MTE products (monthly resolution) are available from 1982 to 2010.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Model Version** & **Transient CO\({}_{2}\)** & **Climate Forcing** \\ \hline S1 & 11-layer & Yes & WFDEI \\ S2 & 2-layer & Yes & WFDEI \\ S2-FIXCO2 & 2-layer & 336.5 ppm & WFDEI \\ S2-WFD & 2-layer & Yes & WFD \\ \hline \hline \end{tabular}
\end{table}
Table 1: Overview of the ORganizing Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) simulations analyzed in this study.
## Appendix A Numerical Simulation of Data Set Using Simulation Example
### Matrix Pencil Method (MPM) [58]  
The MPM approximates time series data by a sum of complex exponentials as  
\[y(t)=x(t)+n(t)\approx\sum_{i=1}^{M}R_{i}\mathrm{e}^{S_{i}t}+n(t); \tag{1}\]  
where  
\(y(k)=\) observed time response data  
\(x(k)=\) original signal  
\(n(k)=\) noise in the system and measurement  
\(R_{i}=\) coefficient and  
\(S_{i}=-\sigma_{i}+j\omega_{i}=\) poles.  
Due to noise in the data, we used a total-least-squares MPM. In this, we formed a data matrix,  
\[[Y]=\begin{bmatrix}y(0)&y(1)&.....&y(L)\\ y(1)&y(2)&.....&y(L+1)\\ \vdots&\vdots&\vdots&\vdots\\ y(N-L-1)&y(N-L)&.....&y(N-1)\end{bmatrix}_{(N-L)\times(L+1)},\]  
where \(L\) is noise filtering parameter and for efficient filtering, which should be between \(N/3\) to \(N/2\). Next, we found a singular value decomposition (SVD) of the matrix [Y]  
\[[Y]=[U][\Sigma][V]^{H}, \tag{2}\]where the superscript \(H\) denotes the conjugate transpose. To select \(M\), we compared the ratio of the various singular values to the largest one. Generally, we considered the singular values \(\sigma_{c}\) such that \(\frac{\sigma_{c}}{\sigma_{max}}\approx 10^{-p}\), where \(p\) is the number of significant decimal digits in the data. Therefore, \(M\) is the value at which the above ratio is greater than equal to \(10^{-p}\). Next, we constructed a matrix \([V^{\prime}]\), which is called a \"filtered\" version of \([V]\) and contains only M dominant right-singular values of \([V]\). Therefore,  
\[[Y_{1}]=[U][\Sigma^{\prime}][V^{\prime}_{1}]^{H}\quad\text{and}\quad[Y_{2}]=[U ][\Sigma^{\prime}][V^{\prime}_{2}]^{H}, \tag{10}\]  
where \([V^{\prime}_{1}]\) is obtained by deleting the last row of \([V^{\prime}]\) and \([V^{\prime}_{2}]\) is obtained by deleting the first row of \([V^{\prime}]\). \([\Sigma^{\prime}]\) is obtained from \(M\) column of \([\Sigma]\) corresponding to M dominant singular values. Finally, the poles \((z_{i})\) can be obtained by solving the generalized eigenvalue problem of the following matrix  
\[\{[V^{\prime}_{2}]-\lambda[V^{\prime}_{1}]^{H}\}\Rightarrow\{[V^{\prime}_{1}] ^{H}\}^{\intercal}\{[V^{\prime}_{2}]^{H}\}^{\intercal}-\lambda[I]. \tag{11}\]  
Once \(z_{i}\) are values known, the residual \(R_{i}\) values are solved from the following least-square problem  
\[\begin{bmatrix}y(0)\\ y(1)\\ \vdots\\ y(N-1)\end{bmatrix}=\begin{bmatrix}1&1&\ldots&1\\ z_{1}&z_{2}&\ldots&z_{M}\\ \vdots&\vdots&&\vdots\\ z_{1}^{N-1}&z_{2}^{N-1}&\ldots&z_{M}^{N-1}\end{bmatrix}\begin{bmatrix}R_{1}\\ R_{2}\\ \vdots\\ R_{M}\end{bmatrix}. \tag{12}\]
## 3 Research Method
### Model Introduction
#### 3.2.1 SARIMA Model  
The SARIMA (Seasonal Autoregressive Integrated Moving Average) model is a mature statistical time series analysis tool that is particularly suitable for data with seasonal fluctuations [77]. The model combines the three components of autoregression, difference, and moving average, and effectively captures the variation pattern of PM\({}_{2.5}\) concentration by modeling the seasonal characteristics of the time series.  
In our study, the non-seasonal parameter is set to non_seasonal_order = (1, 1, 1), corresponding to (p, d, q). The seasonal parameter is seasonal_order = (1, 1, 1), S = 12. The mathematical formula and the meaning of each parameter are as follows:  
\[\text{ SARIMA}(\text{p},\text{d},\text{q})(\text{P},\text{D},\text{Q})_{\text{S}}\]  
p is the number of autoregressive terms. In our study, p = 1, which means that the current PM\({}_{2.5}\) concentration is affected by the concentration at the previous moment, which can help the model capture the short-term dependence trend of the data.  
d is the non-seasonal difference order, and d = 1 means that the original data are processed by first-order difference, the purpose of which is to make the data stationary so that the model can better identify the patterns in the data.  
q is the number of moving average terms. q = 1 indicates that the current PM\({}_{2.5}\) concentration is related to the previous error term, and the previous error term can be used to correct the current prediction.  
P is the number of seasonal autoregressive terms, P = 1 and the seasonal period S = 12, indicating that the current concentration is affected by the concentration in the previous period (i.e., 12 months ago), which helps capture the annual seasonal dependence of the data.  
D is the seasonal difference order, and D = 1 is used to seasonally differenciate the data to remove seasonal trends and make the data more stable, which is convenient for model analysis of other potential patterns.  
Q is the number of seasonal moving average terms, and Q = 1 means that the previous error term within the seasonal cycle is used to adjust the current forecast to improve the accuracy of the seasonal forecast.  
S is the length of the seasonal cycle, which is set to S = 12, which is consistent with the possible annual periodic variation of the monthly PM\({}_{2.5}\) data.
# Epitaxial Graphene on 4H-SiC (0001) as a Versatile Platform for Materials Growth: Mini-Review
###### Abstract  
Material growth on a dangling-bond-free interface such as graphene is a challenging technological task, which usually requires additional surface pre-treatment steps (functionalization, seed layer formation) to provide enough reactive sites. Being one of the most promising and adaptable graphene-family materials, epitaxial graphene on SiC, due to its internal features (substrate-induced \(n\)-doping, compressive strain, terrace-stepped morphology, bilayer graphene nano-inclusions), may provide pre-conditions for the enhanced binding affinity of environmental species, precursor molecules, and metal atoms on the topmost graphene layer. It makes it possible to use untreated pristine epitaxial graphene as a versatile platform for the deposition of metals and insulators. This mini-review encompasses relevant aspects of magnetron sputtering and electrodeposition of selected metals (Au, Ag, Pb, Hg, Cu, Li) and atomic layer deposition of insulating Al\({}_{2}\)O\({}_{3}\) layers on epitaxial graphene on 4H-SiC, focusing on understanding growth mechanisms. Special deliberation has been given to the effect of the deposited materials on the epitaxial graphene quality. The generalization of the experimental and theoretical results presented here is hopefully an important step towards new electronic devices (chemiresistors, Schottky diodes, field-effect transistors) for environmental sensing, nano-plasmonics, and biomedical applications.  
eptiaxial graphene; SiC; deposition; metals; insulators; growth mechanism +
Footnote †: journal: Applied Physics Letters  
1
## 4 Mathematical Formulation of the Optimization Problem
### Link-State Advertisement  
LSA is produced in the space segment and the near space segment, _i.e._, the HAPs and the satellite. During the periodic LSA broadcast, each of the HAPs and the satellite broadcasts its address, geographic position (longitude \(\psi_{j}\) and latitude \(\phi_{j}\)), transmission power \(P_{s,j}\) and antenna gain \(G_{s,j}\) to the users. Although the terminal has no requirement to sense the wireless radio environment, the reception and processing of the LSA still uses additional energy. Therefore, the size of the advertisement packets should be kept to a bare minimum to ensure no unnecessary energy is consumed during the advertisement receiving periods.  
When the LSA of the \(j\)-th HAP or satellite is received by the \(i\)-th terminal, by combining this with its own location (longitude \(\psi_{i}\) and latitude \(\phi_{i}\)), the distance between the \(i\)-th terminal and \(j\)-th node is given in [36] by:  
\[d_{i,j}=\sqrt{\left(l_{i}+R\right)^{2}+\left(l_{j}+R\right)^{2}-2(l_{i}+R)(l_{ j}+R)\cos\theta_{i}} \tag{4}\]  
where \(R\) denotes the Earth's radius, \(l_{i}\) and \(l_{j}\) denote the altitude of \(i\)-th terminal and \(j\)-th nodes and \(\theta_{i}=\arccos(\cos(90-\phi_{i})\cos(90-\phi_{j})+\sin(90-\phi_{i})sin(90- \phi_{j})\cos(\psi_{i}-\psi_{j}))\) are the azimuth angles.  
When \(d_{i,j}\) is obtained, for an S-R link, \(L_{m}(d_{i,j})\) will be calculated, and the temporal gain \(\beta\) of fading can be estimated by computing the received power level of the reporting packet and using the knowledge about the LSAs (e.g., transmission power \(P_{s,j}\) and antenna gain \(G_{s,j}\)).