esa-sceva/satcom-chunk-collection huggingface dataset:
# A Machine Learning-Based Method for Modeling TEC Regional Temporal-Spatial Map
## 2 Modeling
### Modeling Determination
#### 2.3.1 Modeling of Temporal Characteristics
\begin{array}{l}\text{TEC}=\widehat{\text{TEC}}(p,r,m,t)\\ =\sum\limits_{k=0}^{K}\sum\limits_{l=0}^{L}\left[c_{k,l}^{p}p^{l}\cdot\cos(2\pi km /12)+s_{k,l}^{p}p^{l}\cdot\sin(2\pi km/12)+,\\ c_{k,l}^{r}l^{r}\cdot\cos(2\pi km/12)+s_{k,l}^{r}l^{r}\cdot\sin(2\pi km/12) \right]\end{array}  \tag{4}\]  
where \(p\) is \(F10.7_{12}\), \(r\) is \(R_{12}\), \(m\) is the month, \(t\) is the given universal time, \(K\) represents the upper bound of the time cycle, with values of 1, 2, 3, and 4 respectively representing annual, semi-annual, seasonal, and monthly cycle. \(L\) represents the periodic variation of the solar activity; that is, the \(l\)-order harmonic of the solar activity index is taken for regression. The values of \(K\) and \(L\) are determined by regression analysis. In addition, the coefficients \(c_{k,l}^{p},s_{k,l}^{p},c_{k,l}^{r},s_{k,l}^{r}\) are determined by the LS regression.
# A Novel Anti-Jamming Decision-Making Algorithm Based on Knowledge Graph Technology
## 4 Decision-Making Algorithm Based on Anti-Jamming Knowledge Graph
### Decision-Making Algorithm
#### 4.1.2 Strategy Fusion Method  
The proposed strategy fusion method optimizes and prioritizes the strategies according to the communication requirements to reduce the communication cost.  
Common communication decision criterion mainly includes the minimum power consumption criterion and the maximum transmission rate criterion.  
* After selecting the best communication channel under the minimum power criterion, we preferentially adopt the lowest order modulation coding mode. The transmission power corresponding to the channel environment and bit error rate is the minimum consumption power.
* After selecting the best communication channel under the maximum transmission rate criterion, we preferentially adopt the highest order modulation coding mode, and the corresponding transmission power is the maximum rate power.  
Under the requirements of these two criteria, JSR threshold of the receiver in Formula (7) need to be determined according to the bit error rate requirements of different modulation coding modes.  
Strategy priorities are classified as follows:  
Frequency domain strategy: adaptive frequency change > adaptive frequency hopping.  
Power domain policy: Adopt the lowest power strategy with the lowest modulation order under the minimum power criterion; Adopt the lowest power strategy with the highest modulation order under the maximum transmission rate criterion.  
Among different domain policies, frequency domain strategy > power domain strategy > time domain strategy.
## 4 Case Study
### DEMATEL Weighting Criteria
Dependability (B\({}_{3}\)) ranks next in importance due to its direct impact on customer trust and confidence. Consistently delivering on promises and meeting delivery deadlines strengthen reliability and credibility, promoting positive customer experiences and long-term relationships. Responsiveness (B\({}_{4}\)) is also significant as it contemplates the ability to address and resolve customer inquiries or complaints promptly. Figure 3 reports the histogram of prominence values showing that, according to the company management, two criteria are associated with approximately the same level of importance, i.e., B\({}_{1}\) (Optimization of lead-time) and B\({}_{5}\) (Affordability). \begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline  & **B\({}_{1}\)** & **B\({}_{2}\)** & **B\({}_{3}\)** & **B\({}_{4}\)** & **B\({}_{5}\)** & **B\({}_{6}\)** & **B\({}_{7}\)** & **B\({}_{8}\)** & **B\({}_{9}\)** & **B\({}_{10}\)** \\ \hline B\({}_{1}\) & NO & H & M & VL & M & NO & VL & L & M & VL \\ B\({}_{2}\) & L & NO & VL & M & H & H & L & NO & VL & L \\ B\({}_{3}\) & H & VH & NO & M & M & H & L & VL & NO & H \\ B\({}_{4}\) & VL & VH & M & NO & L & H & VL & M & L & M \\ B\({}_{5}\) & H & H & M & L & NO & M & M & VL & NO & VL \\ B\({}_{6}\) & M & VH & H & M & M & NO & L & L & L & M \\ B\({}_{7}\) & L & VL & M & VL & M & L & NO & NO & VL & M \\ B\({}_{8}\) & VH & H & M & M & VL & L & M & NO & H & M \\ B\({}_{9}\) & M & M & L & H & VL & M & L & H & NO & VL \\ B\({}_{10}\) & NO & H & L & L & NO & L & VL & M & M & NO \\ \hline \end{tabular}
\end{table} 
Table 2: Input matrix reporting linguistic evaluations for DEMATEL application. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline  & **B\({}_{1}\)** & **B\({}_{2}\)** & **B\({}_{3}\)** & **B\({}_{4}\)** & **B\({}_{5}\)** & **B\({}_{6}\)** & **B\({}_{7}\)** & **B\({}_{8}\)** & **B\({}_{9}\)** & **B\({}_{10}\)** & \(r_{i}+c_{i}\)** &The weights reported in the last column of Table 3 will be used within the set of input data for the following ELECTRE TRI application.
## 2 Related Work
### Key Consideration  
The proposed scheme has several key considerations for our research paper is as vehicle detection and vehicle tracking.  
* Accuracy of vehicle detection: Accuracy is crucial in vehicle detection. Ensure that the detection algorithm/model is able to accurately detect vehicles in various scenarios, including different vehicle types, lighting conditions, weather conditions, and occlusion situations. Evaluate the detection accuracy using appropriate metrics such as the precision, recall, and F1 score.
* Accurate and consistent vehicle tracking: Vehicle tracking should provide accurate and consistent results over time. The tracking algorithm should be able to associate detected vehicles across frames and maintain their identities, even in situations with occlusions or appearance changes. Consider the use of techniques like Deep SORT or Kalman filtering to improve the tracking performance.
* Multi-object tracking: Consider the ability of the system to track multiple vehicles simultaneously. This is especially important in scenarios with heavy traffic or crowded scenes. The tracking algorithm should handle multiple objects and maintain their identities correctly, without confusing or swapping identities.
* Adaptability to different environments: Consider the adaptability of the system to different environments or domains. A robust vehicle detection and tracking system should be able to generalize well and perform effectively in various scenarios, such as urban environments, highways, or off-road situations.
* Limitations and future directions: Discuss the limitations of the proposed system for vehicle detection and tracking and outline potential areas for improvement. Highlight any challenges faced during the development of the system and propose future research directions, such as exploring advanced algorithms, incorporating contextual information, or addressing specific use cases.
## 7 Results  
After experimentation with coupled and data-driven modes of SDS, it was found that varying the size of the population of agents holding a model hypothesis had a large impact on the convergence performance of the technique; there are also implications for the accuracy of the algorithm. For CSDS, a small model population generally allowed for quicker convergence times at the expense of some accuracy; similarly a larger model population converged more slowly, but produced better accuracy measures. Remembering that as the dimensions increase, there is a combinatorial explosion of the number of possible hypotheses available, it stands to reason that a larger number of model hypotheses entertained by CSDS and DDSDS should lead to better solutions being found (i.e., the risk of overlooking an optimal solution is smaller).  
Experimentally, for CSDS, a rule of thumb relationship between the dimensions of the data and the number of model agents was roughly established as: \(agents=dimensions^{3}\). The larger numbers of model agents gives the algorithm a better chance of finding a good solution in the exploding number of possible hypotheses in higher dimensions. Using too many agents results in very long computation times per iteration, while too few cannot cover the search space adequately to converge on a solution quickly.  
Variance in SDS convergence time and accuracy is known to be sensitive to noise and initial state. For example, should a population be instantiated with a high degree of agents at the optimal solution, the convergence time will be shortened. The trends presented are representative of convergence times and accuracy. At high noise and in high dimensions, variance increases. A more detailed analysis of these issues is provided in [14].  
Using a data space size of 1,000 points, results for the following population sizes are presented (in \(n\) dimensions):  
CSDS: \(n^{3}\) hypothesis agents and 1,000 data agents.  
DDSDS: 500 agents.  
Two residual tests were performed during the test phase. This gives a hypothesis two chances of becoming/staying active during the test phase (the agent activates if either of its residual tests fall under the desired threshold).  
Results are presented here in comparison with RANSAC performing the same parameter estimation task.
## Appendix A Mathematical Formulation of the 3D Fuzzy Model for a DPS  
Let the space domain of a distributed parameter system (DPS) be represented by \(\overline{Z}=\{z_{1},z_{2},\ldots,z_{P}\}\), where \(P\) denotes the number of sensors. The system includes \(m\) spatial input variables, denoted by \(\theta(\overline{Z})=\left[\theta_{1}(\overline{Z}),\theta_{2}(\overline{Z}),\ldots,\theta_{m}(\overline{Z})\right]\in\mathbb{R}^{P\times m}\). Each spatial input \(\theta_{s}(\overline{Z})\) is represented as a vector of values \([\theta_{s}(z_{1}),\ldots,y_{s}(z_{P})]\in\mathbb{R}^{P\times 1}\) for \(s=1,2,\ldots,m\). The spatial input vector \(\theta(\overline{Z})\) serves as the input for the 3D fuzzy system.  
The 3D fuzzy system is composed of the following components: A singleton fuzzifier is used for fuzzification, while Gaussian-type membership functions are employed for each spatial input variable. The system operates with a set of fuzzy rules \(R_{l}\) for \(l=1,2,\ldots,NR\), where \(NR\) is the total number of rules. The product t-norm is utilized to combine the fuzzy sets, and a linear defuzzifier is applied to compute the final output. This setup ensures that the system effectively handles spatial and temporal dynamics in distributed parameter systems.  
The output of the system, denoted by \(y(\overline{Z})\), is a weighted sum of spatial functions. The general expression is:  
\[y(\overline{Z})=\sum_{l=1}^{NR}\left(\sum_{j=1}^{P}a_{l}^{l}\prod_{s=1}^{m} \mu_{C_{l}^{l}}(\theta_{s}(z_{j}))\right)\varphi_{l}(\overline{Z}) \tag{1}\]where \(\psi^{l}\) is the spatial fuzzy basis function, which is defined as:  
\[\psi^{l}=\sum_{j=1}^{p}a_{j}^{l}{\prod_{s=1}^{m}\mu_{C_{s}^{l}}\left(\theta_{s} \left(z_{j}\right)\right)} \tag{10}\]  
where \(C_{s}^{l}\) represents the 3D fuzzy set associated with the \(s\)-th spatial input variable \(\theta_{s}\left(\overline{\mathbb{Z}}\right)\), and \(\mu_{C_{s}^{l}}\) is the corresponding membership function. \(a_{j}^{l}\) is the spatial weight associated with the sensor \(z_{j}\). \(\varphi_{l}\left(\overline{\mathbb{Z}}\right)\) is the consequent function associated with rule \(R_{l}\).
# Active Equalization of Lithium-Ion Battery Based on Reconfigurable Topology
## 4 Calculation and Comparison
### Comparison of Balancing Efficiency  
The efficiencies of the three circuits mentioned above are compared under different \(P_{\rm eq}\) and \(\eta\).  
Firstly, assuming that \(\eta\) is constant 85%, the efficiencies of the three circuits versus \(P_{\rm eq}\) are shown in Figure 10. The efficiency of the conventional converter equalization circuit is approximate to that of the ideal circuit and higher than the efficiency of the proposed circuit when the equalization power \(P_{\rm eq}\) is close to zero. In this situation, the proposed circuit performs worse than the conventional converter circuit because of the extra energy loss caused by MOSFETs. However, the efficiency of the conventional converter circuit decreases straightly with the increase of \(P_{\rm eq}\), while the other two circuits maintain the same efficiency. The proposed circuit outperforms the conventional converter circuit when \(P_{\rm eq}\) is larger than 0.5 W in this case. Compared to the conventional converter circuit, the proposed circuit improves the efficiency more significantly when the imbalance gets bigger.  
Assuming that \(P_{\rm eq}\) is constant 10 W, the efficiencies of the three circuits versus \(\eta\) are shown in Figure 11. The efficiencies of the three circuits are close to 100% when \(\eta\) is close to 100%. The total efficiency of the three circuits all decrease with the decrease of \(\eta\). The efficiency of the proposed circuit is always close to that of the ideal circuit, but the efficiency of the conventional converter circuit decreases faster than those of the other two circuits. Compared to the conventional converter circuit, the smaller \(\eta\) is, the greater the efficiency improvement the proposed circuit makes.  
Figure 10: The total efficiency versus the equalization power \(P_{\rm eq}\).
## 5 Experimental Results
### Software Used  
The platforms mentioned above employed the most updated version of TinyOs 2.x (trunk 5535). Additionally, all the platforms used the MAC layer implementation established by the IEEE 802.15.4 standard. This implementation was developed by researchers from the CISTER Research Unit in synergy with the TinyOS 15.4 and ZigBee working groups. We performed some modifications regarding ACK handling, transmission and reception of PANIDs. The Texas Instruments SmartRF Packet Sniffer version 2.13.63.0 was used for packet analysis.
## 3 Ferry Node Ranking Clustering Algorithm (FNRCA)
### Changing the Number of Checkpoints  
By changing the sensing range for the optimized path, the number of checkpoints will be changed as well. We changed the sensing range to be 20 m and the number of checkpoints to be 25 checkpoints, 40 m and the number of checkpoints to be nine checkpoints, 50 m and the number of checkpoints to be four and finally, to be 100 m and the number of checkpoints to be one. Table 3 shows the network performance of changing the sensing range and the number of checkpoints. From Table 4 we can see that the network lifetime increases as the number of the checkpoints increases. This is because the more checkpoints we have, the less distance data will travel which in returns saves cluster heads energy and the overall energy of the network. However, looking into the overall time of one round of data collection of the ferry, we can see from Table 5 and Figure 4 that it is increases as the number of checkpoints increases. Roundtrip travelling time has a direct relationship with the number of checkpoints. This is due to the increase in the length of the traveling path plus the increase in the number of stopping time at each checkpoint. Given a particular application, the number of checkpoints can be chosen for a particular scenario based on the maximum tolerable delay.  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multirow{2}{*}{**\#Checkpoints**} & \multicolumn{2}{c}{**Measurements**} \\ \cline{2-3}  & **Round First Node Dies** & **Round Last Node Dies** \\ \hline Sensing range 20 & 2460 & 4433 \\ \#Checkpoint 25 & 2111 & 4120 \\ \#Checkpoint 9 & 2010 & 4003 \\ Sensing range 50 & 1400 & 3500 \\ \#Checkpoint 1 & \\ \hline \hline \end{tabular}
\end{table}
Table 4: Simulation results for the network life time using different number of checkpoints.  
\begin{table}
\begin{tabular}{c c} \hline \hline \#Checkpoints & **Time in minutes** \\ \hline Sensing range 20 & 18.60 \\ \#Checkpoint 25 & \\ Sensing range 40 & 9.60 \\ \#Checkpoint 9 & 5.40 \\ Sensing range 100 & 3 \\ \#Checkpoint 1 & 3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation results for one round collection.
# Joint Design of Transmit Waveforms for Object Tracking in Coexisting Multimodal Sensing Systems
## 3 Spectrum Sharing Radar and Communications Systems
### Wireless Multiuser Communications Receiver  
#### 3.3.1 Communications Received Waveforms  
For a wireless communications system with \(M\) users, the transmit waveform of the \(m\)th user, \(m=1,\dots,M\), \(s_{\mathrm{c},m}(t)\), is assumed to have duration \(T_{\mathrm{c}}\). Without loss of generality, and before transmission, we assumed that the waveform is modulated using multiple phase shift keying (PSK) of order \(P\) (\(P\)-PSK). The modulated waveform is given by \(s_{\mathrm{c},m}(t)e^{j2\pi(p_{m}-1)/P}\), where \(p_{m}=1,\dots,P\), is the modulation phase shift index used by the \(m\)th user. Each user could transmit up to \(\log_{2}(P)\) bits of information per symbol over the duration of the waveform and \(Q=[\ T_{\mathrm{PRI}}/T_{\mathrm{c}}]\) symbols over one PRI of the radar system.  
Considering the \(k\)th PRI, and assuming a sampling period \(T_{s}\), the continuous train of transmitted symbols by the \(m\)th user is given by:  
\[s_{\mathrm{c},m,k}[n]=\sqrt{P_{\mathrm{c}}}\ \sum_{q=0}^{Q-1}s_{\mathrm{c},m}(nT_{s }-qT_{\mathrm{c}}-kT_{\mathrm{PRI}})\ e^{j2\pi(p_{m,q,k}-1)/P},\quad n=1,\dots, N_{s}. \tag{20}\]  
Here, \(P_{\mathrm{c}}\) is the power of the return signal, and \(p_{m,q,k}\), for \(p_{m,q,k}=1,2,\dots,P\), is the phase shift index of the \(P\)-PSK constellation point representing the \(q\)th symbol, \(q=0,\dots,Q-1\), of the \(m\)th user, \(m=1,\dots,M\), in the \(k\)th PRI, \(k=1,\dots,K\).  
Assuming an AWGN communications channel, the overall signal received at the communications receiver by all users during the \(k\)th PRI is given by:  
\[z_{\mathrm{c},k}[n]=x_{\mathrm{c},k}[n]+x_{\mathrm{r},k}[n]+w_{k}[n]=\sum_{m= 1}^{M}s_{\mathrm{c},m,k}[n]+x_{\mathrm{r},k}[n]+w_{k}[n]\,\quad n=1,\dots,N_{s}\, \tag{21}\]  
where \(s_{\mathrm{c},m,k}[n]\) is provided in Equation (20). Note that signal terms \(x_{\mathrm{c},k}[n]\), \(x_{\mathrm{r},k}[n]\), and \(w_{k}[n]\) are as provided in the radar return in Equations (9) and (10).
# An Improved Detail-Enhancement CycleGAN Using AdaLIN for Facial Style Transfer
## 3 The Proposed Method
### Loss Function
#### 3.3.1 Generative Adversarial Loss  
The generative adversarial loss of CycleGAN is shown in Equation (5):  
\[L_{adv}(G,D_{Y},X,Y)=E_{y\sim p_{data}(y)}[logD_{\gamma}(y)]+E_{x\sim p_{data} (x)}[\log(1-D_{\gamma}(G(x)))] \tag{5}\]  
where \(y\) denotes the sample in domain \(Y\) and \(x\) denotes the sample in domain \(X\). \(D_{Y}(y)\) denotes the score of the sample in the true \(Y\) in the discriminator \(D_{Y}\), and the closer to 1, the more true the discriminator considers this sample. \(G(x)\) is the sample in the same distribution as \(Y\) generated by the generator based on \(x\). \(D_{Y}(G(x))\) is the score of the discriminator based on the generated samples; if \(D_{Y}\) considers the generated samples to be more false, then closer to 0 the score \(D_{Y}(G(x))\) is, the closer \(1-D_{Y}(G(x))\) is to 1. The stronger the discriminator is, the better it will be able to distinguish the real \(y\) from the \(G(x)\) generated by the generator based on \(x\), and the larger this loss value will be.
## 2 Materials and Methods
### Stage III: Study Selection  
The overall inclusion criteria for study selection were based on stage I and II methodologies. The inclusion criteria encompass peer-reviewed journal articles published in English, focusing on studies investigating CAD-CAM resin blocks. These studies should evaluate CAD-CAM resin blocks' clinical and in vitro performance, examining mechanical properties, wear resistance, biocompatibility, and clinical outcomes. The review also includes studies published within a specified time frame to ensure relevance and contemporaneity since the first CAD-CAM resin block came to the market around 2000. Figure 2 summarizes the studies included in the systematic scoping review in the PRISMA flow diagram.  
On the other hand, exclusion criteria were (1) studies that exclusively investigated Glass Ceramic, Zirconia, or Polymethyl methacrylate (PMMA) CAD-CAM blocks without the inclusion of resin in its composition; (2) studies that investigated direct composite restoration instead of indirect Composite Resin CAD-CAM blocks; (3) languages other than English for the ease of retrieving from the database; and (4) any literature published before the 2000s.  
Search results were imported into Convidence (Covidence systematic review software, Veritas Health Innovation, Melbourne, Australia. Available at www.covidence.org), a library web-based screening and data extraction tool recommended by the Cochrane Collaboration. All articles were duplicated into two identical sets to facilitate independent  
Figure 2: PRISMA flow diagram of studies in the systematic scoping review with reasons for exclusion. Based on “Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement”.  
review. The reviewers then assessed full-text articles. If there were disagreements between the reviewers, the paper in question was discussed until a consensus was achieved.  
A literature search was conducted between February and April of 2022. The database search identified 529 manuscripts published between 1984 and 2022. No duplicate studies were identified during this process. The titles and abstracts of the identified manuscripts were screened. Excluded studies (n = 320) primarily fell into four categories: (1) those exclusively investigating Glass Ceramic, Zirconia, or Polymethyl methacrylate (PMMA) CAD-CAM blocks without resin inclusion (N = 272); (2) studies focusing on direct composite restoration rather than indirect restorations using CAD-CAM resin blocks [18; 19; 20] (3) non-English language publications (4); and (4) literature published before the 2000s (13).
## 4 Development of Optical Sensing Techniques for Red Meat Quality Prediction
### Hyperspectral Imaging (HSI)
#### 4.3.3 Spectral Processing  
During the process of capturing hyperspectral images, various undesired effects, such as random noise, variations in the light path length, and light scattering, are inevitable, and to address these issues, spectral pre-processing algorithms are commonly employed [222]. Furthermore, hyperspectral images have high spectral resolution with a very small interval between bands, which can lead to collinearity problems due to the high correlation between independent variables [223]. Hence, selecting optimal wavelengths not only helps to eliminate bands with low signal-to-noise ratio (SNR) but also ensures the extraction of the most representative wavelengths, reducing the spectral dimension [224]. Additionally, another approach to reducing the spectral dimension involves calculating parameters related to the shape of the spectral curve [220]. In summary, spectral processing and image processing have similar objectives, but similar treatments are applied to the different dimensions of hyperspectral images [220].
# Analyzing the Performance of the S3 Object Storage API for HPC Workloads
## 3 Results
### Tests against S3 Compatible Systems
#### 3.2.3 Latency Analysis  
MD-Workbench reports not only the throughput, but also the latency statistics for each I/O operation. The density of the individually timed operations is plotted as shown in Figure 8. A density graph can be considered a smoothed histogram where the x-axis shows the observed runtime and the y-axis represents the number of occurrences.  
The Lustre density figure shows roughly a Gaussian distribution for individual operations, where create is the slowest operation.  
Using MinIO, the creation phase takes substantially longer. The local-gw mode, as already seen from the IO500 results, yields the best performance among the tested S3 implementations; however, far from the Lustre performance, which has a latency of around 10 ms and is extremely slow compared to the network base latency of approximately  
Figure 8: Latency density for the different systems.  
Figure 7: Throughput of Scality Ring and MinIO on 3 nodes.  
10 \(\upmu\)s. The SwiftS3 system changes the overall behavior significantly, leading to less predictable times.  
We conclude that the involved processes behind the S3 implementation are the main cause of latency.