esa-sceva/satcom-chunk-collection huggingface dataset:
# Can Self-Similarity Processes Be Reflected by the Power-Law Dependencies?
## 2 Confirmation of the SSP for the Weierstrass-Mandelbrot Function and in Other Models
### Detection of the Hidden Self-Similarity
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & _Jmn(X)_ & _Jup(X)_ & _Jdn(X)_ \\ \hline \(w_{2}\) & 1.43606 & 2.37 & 1.65648 \\ \hline \(w_{1}\) & \(-5.9006\) & \(-13.172\) & \(-11.2695\) \\ \hline \(w_{0}\) & 5.46453 & 11.802 & 10.6131 \\ \hline \(\ln\hat{\varepsilon}_{\text{opt}}\) & 1.75812 & 2.19726 & 1.79376 \\ \hline \(\mid r_{1}\mid\) & 2.33763 & 3.43541 & 3.25777 \\ \hline \(\arg(\varphi)\) & 1.47739 & 1.37006 & 1.46987 \\ \hline \(\
u_{1}\) & 0.48298 & 0.56167 & 0.65842 \\ \hline \(\text{RelErr}(Yn(X))\) & 1.04105 & 1.33646 & 1.70876 \\ \hline \(\text{RelErr}(\ln[Yn(X)]\) & 3.84489 & 4.55068 & 5.14187 \\ \hline \(A_{0}\) & \(-4.98628\) & 171.163 & \(-0.52067\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The set of the fitting parameters for the normalized integral curves for Signal 2. Figure 18: Numerical verification of [PERSON]’s inequality for all three functions _Jmn(X)_, _Jup(X)_ and _Jdn(X)_ obtained for Signal 2.  
where the decomposition coefficients in (25) (\(w_{l}\)) are related to the \"roots\" \(r_{l}\) (\(l=1,\,\ldots,\,L\)) in (45) as  
\[ \begin{array}{l}w_{L-1}=(-1)^{L-1}(r_{1}+r_{2}+\ldots+r_{L}),\\ \cdot\\ \cdot\\ \cdot\\ w_{0}=(-1)^{L-1}r_{1}r_{2}\ldots r_{L}.\end{array}  \tag{47}\]  
If the scaling operator's link in Equation (44) is successful, Equation (45) immediately provides all the potential \"scalings\" that may be applied to the system under consideration. In our example, analysis of the integrated curves generated in various scales supports Equation (44) (see Figures 1, 2, 6 and 15). As seen above, the generalized geometric mean defined by (32) finds the global fitting minimum in perfect compliance with inequality (33) if one of the roots in (46) coincides with the unit value. Equation (45) admits the further generalization for the 3D case. In this case, one can define a more general scaling operator  
\[\prod_{k=1}^{3}\hat{E}_{\xi_{k}}S(z_{1},z_{2},z_{3})=S(z_{1}\xi_{1},z_{2}\xi_{ 2},z_{3}\xi_{3}) \tag{48}\]  
\[\prod_{k=1}^{3}\prod_{l_{k}=1}^{L_{k}}\big{(}\hat{E}_{\xi_{k}}-r_{l_{k}}\big{)} S(z_{1},z_{2},z_{3})=F(z_{1},z_{2},z_{3}) \tag{49}\]  
Here \(F(z_{1},z_{2},z_{3})\) is an arbitrary given function. Equation (48) represents the most general case of the SSP for the 3D case. It indicates that the three integral curves shown in the previous image are self-similar and are characterized by a linear combination of the power-law exponents. One can note that in [15], a similar algorithm was applied to real acoustic data (sounds captured from \"a wind\" and these data are available at \"soundspunos.com\" site), demonstrating that the envelopes of these \"noisy\" data totally confirm the self-similarity principle and can be fitted by functions containing complex-conjugated power-law additions.
## 3 Results and Discussion
### Analysis of Normal Probability Plot  
To understand the nature of fitting, a normal probability plot could be applied to the residuals, i.e., the difference between the predicted and experimental values [32]. Figure 4a revealed that the residuals appeared to follow a straight grade, indicating that neither response transformation was required, nor was there any apparent problem with normality [33], thus affirming the analytical hypothesis.  
Figure 3: Contour plots and response indicating the interactive effects of two factors on the extraction yield of RES: pressure and time (**a,b**); pressure and temperature (**c,d**); time and temperature (**e,f**).  
To ascertain the most satisfactory fit of the developed models, we used residuals runs for analysis, as shown in Figure 4b. It was particularly helpful to determine whether or not the order of observations influences the results. Our results confirmed that all data points lay within acceptable limits. Figure 4c shows the relationship between the actual and predicted values of RES yield, which indicated that the developed model was adequate, due to the small residual and the fact that the residual related closely to the diagonal line [21]. From the results in those figs, there appeared no evidence of error terms correlating with each other.
# Quantitative Description for Sand Void Fabric with the Principle of Stereology
## 6 Determination and Application Analysis of Void Fabric
### Application of Orthogonal Fabric  
When the amplitude parameters on the orthogonal plane are determined, the orthotropic void tensor or transversely isotropic tensor of sand fabric is obtained by Equation (18). According to the definition of void fabric, fabric tensor describes the probability and statistical law of the void spatial distribution. Therefore, the measured tensor is substituted directly into the constitutive model to describe the effect of micro-voids on the macro-mechanics response of the material. According to the measured values in Figure 9, \(b_{1}=0.012\), \(b_{2}=0.128\), and substitute \(b_{1}\), \(b_{2}\) to Equation (19), the orthotropic fabric tensor of orthotropic materials are as follows:  
\[M_{ij}=\left[\begin{array}{ccc}0.3637&0&0\\ 0&0.2812&0\\ 0&0&0.3551\end{array}\right] \tag{28}\]  
According to the measured value in Figure 9b and Equation (19), the transversely isotropic fabric tensor of transversely isotropic materials is as follows, when \(b_{1}=b=0.128\).  
\[M_{ij}=\left[\begin{array}{ccc}0.3928&0&0\\ 0&0.3036&0\\ 0&0&0.3036\end{array}\right] \tag{29}\]  
Combined with the analysis results of Section 6.1, it can be seen that in the 3D orthogonal space, the value of a plane void fabric can be quantitatively and uniquely determined, while the measured value of the other two planar fabrics is hardly determined uniquely. However, the measurement values of the other two plane fabrics are difficult to determine uniquely, so the orthotropic fabric is not unique. This also fully illustrates the complexity of orthogonal spatial fabrics. For fabrics projected on the same plane, the spatial void distribution is not unique. It also affects the application of orthogonal fabrics greatly. However, under specific conditions or test lines, the fabric is unique. For example, under  
Figure 10: Relationships of amplitude parameters \(b\) and \(\beta_{m}\).  
transversely isotropic conditions, the definition in this article shows that the fabric expressions on two independent measurement surfaces are unique and can be directly applied to macro-mechanical equations.
## 2 Principles and Methods
### Solution Method of Model  
Based on the calculated values, the model created by Equation (6) can be converted into a non-homogenous linear equation, with the construction coefficients \(\mathbf{A}\), \(\mathbf{x}\) and \(\mathbf{b}\), making \(\mathbf{A}\mathbf{x}=\mathbf{b}\), building a broad matrix \(\overline{\mathbf{A}}=(\mathbf{A}|\mathbf{b})\) based on Equations (7) and (8).  
\[\mathbf{x}=\left[\begin{array}{c}x_{1}\\ x_{2}\\ x_{3}\end{array}\right],\mathbf{b}=\left[\begin{array}{c}1\\ 0\\ 0\end{array}\right]\] (7  
\[\mathbf{A}=\begin{bmatrix}1&1&1&1&1\\ \varphi(\mathrm{CH}_{4})_{1}(\delta^{13}\mathrm{C}(\mathrm{CH}_{4})_{mix}-\delta ^{13}\mathrm{C}(\mathrm{CH}_{4})_{1})&\varphi(\mathrm{CH}_{4})_{2}(\delta^{13} \mathrm{C}(\mathrm{CH}_{4})_{mix}-\delta^{13}\mathrm{C}(\mathrm{CH}_{4})_{2})& \varphi(\mathrm{CH}_{4})_{3}(\delta^{13}\mathrm{C}(\mathrm{CH}_{4})_{mix}- \delta^{13}\mathrm{C}(\mathrm{CH}_{4})_{m})&0\\ \varphi(\mathrm{CO}_{2})_{1}(\delta^{13}\mathrm{C}(\mathrm{CO}_{2})_{mix}- \delta^{13}\mathrm{C}(\mathrm{CO}_{2})_{1})&\varphi(\mathrm{CO}_{2})_{2}( \delta^{13}\mathrm{C}(\mathrm{CO}_{2})_{mix}-\delta^{13}\mathrm{C}(\mathrm{CO }_{2})_{2})&\varphi(\mathrm{CO}_{2})_{3}(\delta^{13}\mathrm{C}(\mathrm{CO}_{2}) _{mix}-\delta^{13}\mathrm{C}(\mathrm{CO}_{2})_{m})&0\end{bmatrix} \tag{9}\]  
The broad matrix \(\mathbf{\overline{A}}\) is solved by using the Gaussian method commonly used in linear algebra; the primary transformation of the line is transformed into a step-by-line; the equation system is solved with the original equation, and the extraction ratio in each group of mixed CBM is solved. For \(\mathbf{A}\mathbf{x}=\mathbf{b}\) matrix operations, the construction matrix \(\mathbf{A}\) and \(\mathbf{b}\) can also be obtained by editing code from the MATLAB software with version number R2015a, and the coefficient matrix \(\mathbf{x}\) is obtained by dividing \(\mathbf{x}=\mathbf{A}\backslash\mathbf{b}\), which in turn obtains the extraction ratio in each group of mixed CBM. Since a non-homogenous linear equation can solve up to three unknowns, the model created by Equation (6) is suitable for the ratio calculation of three groups.  
In the field application, the volume fractions and carbon isotope values of \(\mathrm{CH}_{4}\) and \(\mathrm{CO}_{2}\) in three groups of CBM and mixed gas were tested, and the extraction ratio of a target coalbed from the three groups was calculated according to Equation (9).  
Since the mathematical models including Equation (9) are complex, although they can be calculated using numerical calculation software such as MATLAB, it may be difficult for non-professional coal mine technicians to use these models, so our next step will be to develop simple and easy-to-use software based on the mathematical models for their use.
### Related Previous Works
This new concept of decentralization permits to execute a full node on an IoT device with smaller capacity. The idea of running an entire network on devices with low memory and processing power is supported by this approach. Hence, this eliminates the requirement of clusters of powerful computing machines to solve cryptographic puzzles. Several related studies linking blockchain concepts to robotics and drones/unmanned aerial vehicles (UAVs) are described in subsequent paragraphs. In this study, use of blockchain in groups of robots covering interest and issues has been discussed [16]. Moreover, interests of blockchain for robotic swarm systems have been proposed. Moreover, it discusses the use of blockchain in robotic swarm systems and the advantages it offers in terms of transparency, consensus and security. For the purpose of awards in case of a market-based coordination strategy, this study highlights the utilization of the financial side of blockchain for implementation in such scenarios [17]. Alongside, this study discusses applications that are blockchain-based robotics. It also records shortcomings, such as a system's overall enhanced complexity and difficulty in connection with its implementation on small card computers. A proposal for using a blockchain smart contract for employment of robots for work is also discussed in a comprehensive manner in the stated study. The methodology described in the present study can be classified as one of the applications explained in two previous studies. Security is one of the most vital challenges in the distributed system as specific categories of attacks on groups of robots/ UAVs may paralyze the entire network. In this study [18], the main advantages in possible blockchain-embedded UAVs with the perspective of security are the focus. In this study [19], a comprehensive analysis on communication between robot to robot is presented. Embedding blockchain into a UAV 6G network could result in network fragmentation, that is not typical in conventional blockchains, and is mainly dependent on wired networks. This study [20] focuses on the challenges arising from swarms' divisions. In order to archive stable partitions, the Swarm DAG protocol was proposed to correctly manage the splits and merges of the network during the partitions. This study [21] presents an evaluation on joint effects of 6G, blockchain and IoT; however, a thorough analysis with respect to unmanned aerial vehicles is missing. In this study [22], a comprehensive review on 6G-based systems is presented. The study in [23] presents analysis on implementation of blockchain and edge computing based 6G that can be consequently extended to UAV network. In connection with the above stated previous works, it is ascertained that the security intrinsic to the blockchain provides this kind of service to 6G networks. Therefore, we aim to provide a comprehensive review of blockchain-based implantations, requirements, challenges and futures trends in 6G networks; moreover, we also cover the limitations of previous studies as summarized in Table 1.
# Automatic Detection of Airway Invasion from Videofluoroscopy via Deep Learning Technology
###### Abstract  
In dysphagia, food materials frequently invade the laryngeal airway, potentially resulting in serious consequences, such as asphyxia or pneumonia. The VFSS (videofluoroscopic swallowing study) procedure can be used to visualize the occurrence of airway invasion, but its reliability is limited by human errors and fatigue. Deep learning technology may improve the efficiency and reliability of VFSS analysis by reducing the human effort required. A deep learning model has been developed that can detect airway invasion from VFSS images in a fully automated manner. The model consists of three phases: (1) image normalization, (2) dynamic ROI (region of interest) determination, and (3) airway invasion detection. Noise induced by movement and learning from unintended areas is minimized by defining a \"dynamic\" ROI with respect to the center of the cervical spinal column as segmented using U-Net. An Xception module, trained on a dataset consisting of 267,748 image frames obtained from 319 VFSS video files, is used for the detection of airway invasion. The present model shows an overall accuracy of 97.2% in classifying image frames and 93.2% in classifying video files. It is anticipated that the present model will enable more accurate analysis of VFSS data.  
deplution disorders; aspiration; videofluoroscopy; deep learning Article
# A Combined Elevation Angle and C/N0 Weighting Method for GNSS PPP on Xiaomi MI8 Smartphones
## 5 Conclusions  
The observation weighting method is vital for smartphone-based GNSS PPP. In this study, a combined elevation angle and C/N0 weighting method was proposed by taking advantage of the two indices. The obtained weight values were more stable than the C/N0-only derived weight values. Simultaneously, it also instantaneously responded to the observation accuracy variation. The proposed weighting method was fully evaluated in two GNSS PPP kinematic experiments using a Xiaomi MI8 smartphone.  
Two kinematic PPP experiments were carried out in different satellite visibility conditions. Using the combined elevation angle and C/N0 weighting method, the RMS improvement of the 3D position for PPP reaches 22.7% and 24.2% over the elevation-angle-only and C/N0-only weighting methods, respectively in the open sky environment, and 52.0% and 26.0% over the elevation-angle-only and the C/N0-only weighting methods, respectively in the constrained visibility environment. It should be noted that all conclusions were drawn based on the Xiaomi MI8 GNSS kinematic experiments. Further smartphone experiments should be conducted in future research.  
Conceptualization, Y.L. and C.C.; methodology, Y.L.; software, Y.L.; validation, Y.L. and Z.X.; formal analysis, Y.L.; investigation, Y.L. and Z.X.; resources, C.C.; data curation, Y.L. and Z.X.; writing--original draft preparation, Y.L. and C.C.; writing--review and editing, Y.L. and C.C.; visualization, Y.L.; supervision, C.C.; project administration, Y.L. and C.C.; funding acquisition, C.C. All authors have read and agreed to the published version of the manuscript.  
Figure 14: Quad-constellation PPP errors using three different weighting scenarios.  
\begin{table}
\begin{tabular}{c c c c} \hline  & **ELE** & **CN0** & **ELE/CN0** \\ \hline East (m) & 1.24 & 0.83 & 1.00 \\ North (m) & 0.71 & 1.28 & 0.62 \\ Up (m) & 5.03 & 3.03 & 2.22 \\
3D (m) & 5.23 & 3.39 & 2.51 \\ \hline \end{tabular}
\end{table}
Table 3: RMS statistics of the PPP errors in the east, north and up directions.  
**Funding:** This research was funded by the National Key Research and Development Program of China, grant number 2020 YFA0713501; the National Natural Science Foundation of China, grant number 42174040; the Hunan Natural Science Foundation, grant number 2020J4111; the Hunan Provincial Innovation Foundation for Postgraduate, grant number CX20200241.  
**Acknowledgments:** The authors wish to acknowledge the Centre National d'Etudes Spatiales (CNES) for providing open access real-time orbit/clock and ionosphere corrections products.  
**Conflicts of Interest:** The authors declare no conflict of interest.
# Reputation-Based Trust Approaches in Named Data Networking
## 3 Reputation-Based Approaches in NDN
### Motivation
As involving routers in the specifics of trust management is cumbersome, routers have little defense means to mitigate cache poisoning attacks. In this light, reputation-based trust schemes have been leveraged as an alternate method to cope with cache poisoning. In addition, due to their light-weight design, reputation-based models have also been investigated as a means for mitigating other attacks, such as IFAs.
## 2 Applications of Artificial Intelligence and Machine Learning
### Industrial Applications  
An Incremental Grey-Box Current Regression Model for Anomaly Detection of Resistance Mash Seam Welding in Steel Mills [1], presents an application of grey-box models, which can be regarded as algorithms that combine both theoretical knowledge about the behaviour of a system and a data-driven approach to the characterization of said models, to the problem of the automated detection of anomalies in the industrial process of wielding on steel mills. [PERSON] et al., presents the results of the use of this technique, including the corresponding details about the steel processing facilities, the data acquired and the evaluation of the model, both in a batch and incremental approaches to the problem, making this a very interesting demonstration of soft-computing techniques for industrial processes.  
Influence of Environmental Noise on Quality Control of HVAC Devices Based on Convolutional Neural Network [2], presents a system that tackles a similar problem to the second one but using a different technique to do so. In this case, [PERSON] et al., aimed at assessing the quality of several industrial HVAC (Heat, Ventilation, and Air-Conditioning) devices at the end of the assembly line, to determine the capacity of the models to do so by using only the sound expression of the devices. Authors [PERSON] et al., propose the use of deep learning techniques to train a system that can handle sound information, translated into images by means of a spectrogram, to discriminate between working and defective devices, without human intervention. A comprehensive testing on how the surrounding noise from the industrial facility can affect the capabilities of the system is included as proof of the capabilities of the system.  
Development of Ground Special Vehicle PHM with Case-Based Reason Model [3], also focuses on the prognostics and evaluation of machine components. In this case, the automated analysis is based on the Case-Based Reasoning (CBR) scheme, supported by a data-driven approach. [PERSON] et al., applied this methodology for the assessment of the functioning state of a ground special vehicle, used in satellite transportation. In this case, previous cases or states registered on the sensors, attached to the vehicle, provide the system with good insight into its current condition. The contribution complements theoretical explanations with the comparison of three different variants of the CBR architecture, depending on the calculation of the weights to the different parameters registered on each case, providing a good insight into the capabilities of each.
# Cooperative Downloading for LEO Satellite Networks: A DRL-Based Approach
## 5 Conclusions  
In this paper, we proposed DRL-based cooperative downloading for LEO satellite networks. We first modeled the satellite networks as a graph to capture the generalized network states and formulated the MDP problem for cooperative downloading. To solve the formulated problem, we adopted the SAC-based training algorithm for discrete action space and design neural networks. Evaluation results show that the proposed DRL-based cooperative downloading scheme can guarantee higher utilization of satellites' contact time by up to 17.8% compared with other schemes. We adopted a centralized reinforcement  
Figure 6: Number of satellites vs. average contact-time utilization.  
learning assuming all the network state information (i.e., channel state information of all the links and satellites states) can be observed at the centralized agent. However, it is challenging to collect network-wide observations, as LEO satellites have high mobility and follow their own orbits. To address this issue, in our future work, we will extend our scheme to include multi-agent reinforcement learning in which each satellite operates as an agent, trains the optimal policy with few or minimal data exchanges, and makes decisions only with locally observable network state information. In addition, IRS is a promising technology to improve routing, link performances, and energy efficiency in multi-layer satellite networks. Thus, we will consider IRS-assisted download services in our future work.  
Conceptualization, S.P.; methodology, S.P. and H.C.; software, H.C.; validation, H.C.; investigation, S.P. and H.C.; writing--original draft preparation, H.C.; writing--review and editing, S.P.; supervision, S.P. All authors have read and agreed to the published version of the manuscript. This work was supported by the Future Combat System Network Technology Research Center program of Defense Acquisition Program Administration and Agency for Defense Development (UD190033 ED). Not applicable. Not applicable. Not applicable. Not applicable. Not applicable. The authors are grateful to the anonymous reviewers for their comments and valuable suggestions. The authors declare no conflict of interest.