esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Classic Hellwig's Method and Its Applications
### The Procedure of Selecting Diagnostic Variables  
When applying the TMD method, one critical aspect should be discussed, namely the selection of diagnostic variables [117; 118]. The proper selection of variables determines the final results of the study and influences the accuracy of assessments, the precision of predictions, and consequently, the accuracy of decisions made based on them. The selection of variables encompasses both substantive and formal aspects, including the measurement of variability and the verification of the degree of correlation between variables.  
First of all, the substantive selection of variables involves preparing potential diagnostic features that, in light of the substantive knowledge about the phenomenon under investigation, are deemed most important for conducting a comparative analysis of the subjects. Next, the diagnostic features should be chosen based on the criterion of universality. They should be characterized by substantive suitability from the perspective of the studied phenomenon and possess economic significance. Diagnostic variables should capture the most significant properties of the phenomena being considered and accurately represent them, formulated in a straightforward, clear, and precise manner. The selected features must be measurable, preferably expressed as structural or intensity indicators. When selecting variables, one should also consider the availability of the most recent and up-to-date data.  
Finally, it is sometimes necessary to reduce the dataset by eliminating features with a low diagnostic value, characterized by low variability and high correlation. Variables describing objects in linear ordering should effectively discriminate between objects (exhibit high variability), be weakly correlated with each other, and be strongly correlated with the rejected variables. Selected features should exhibit sufficient spatial variability and differentiate the examined objects. The degree of variability is verified based on the value of the coefficients of variation (Vs) determined for individual characteristics. It is commonly assumed that variables with a coefficient of variation below 10% are eliminated. Generally, it is assumed that strongly correlated variables carry similar information. In the case of multivariate analyses, the goal is to build a set in which the risk of duplicating information by strongly correlated variables is minimized. When faced with two strongly correlated features, the selection of the representative one should be based on substantive premises and the level of variability described above.  
The procedures for determining variables have been refined in several papers, including [73; 83; 95; 115; 119], among others.
# Reliability Model Based Dynamic Multi-Level Trust Analysis
###### Abstract  
Trust assessment is of great significance to related issues such as privacy protection and rumor transmission in online social networks. At present, there are mainly two types of trust models: discrete type and continuous, type. Little work has been done on considering distrust and trust at the same time, especially on the propagation mechanism of distrust. To this end, this paper proposes a multilevel trust model and a corresponding trust evaluation method based on a reliability model called multivalued decision diagrams (MDDs). The proposed trust model combines characteristics of both discrete and continuous trust and considers the dynamic changing mechanism of the multilevel trust. The propagation of distrust and conflicts of opinions are also handled. Experimental results show that the proposed method outperforms other existing methods.  
dynamic trust; multi-level trust; reliability model; multivalued decision diagram
## Appendix B Calculation of Gravitational Load  
Assuming that the load mass of the humanoid manipulator is \(m_{load}\), then the gravity vector of the load in the base coordinate system of the humanoid manipulator is \({}^{0}\mathbf{P}=[0,0,-m_{load}]^{T}\). The gravity vector of the load in the coordinate system of the end effector is calculated as follows:  
\[\left[\begin{array}{c}G_{x}\\ G_{y}\\ G_{z}\end{array}\right]=\overset{g}{0}\mathbf{R}\left[\begin{array}{c}0\\ 0\\ -m_{load}g\end{array}\right] \tag{2}\]  
where \(G_{x}\), \(G_{y}\) and \(G_{z}\) are the components of the gravitational load in the coordinate system of the end effector of the humanoid manipulator; \(\overset{g}{0}\mathbf{R}\) is the transformation matrix of base coordinate system relative to the humanoid manipulator end effector coordinate system.  
The coordinate of the center of gravity of the load relative to the coordinate system of the end effector of the humanoid manipulator is \((x_{G},y_{G},z_{G})\), the torque exerted by gravitational load on the end effector of the humanoid manipulator is shown in Figure A3.  
According to Figure A3, the calculation is as follows:  
\[\left\{\begin{array}{l}T_{gx}=G_{z}y_{G}-G_{y}z_{G}\\ T_{gy}=G_{x}z_{G}-G_{z}x_{G}\\ T_{gz}=G_{y}x_{G}-G_{x}y_{G}\end{array}\right.\] (A3)  
where \(T_{gx}\), \(T_{gy}\) and \(T_{gz}\) are respectively the torque exerted by gravitational load on each direction of the end effector of the humanoid manipulator.
## 3 Methodology
### Data Pre-Processing
#### 3.1.2 Calculation of Received Power  
The received power level was calculated for the entire route and analyzed for each candidate point for installation (Figure 6). To obtain the power level, propagation models can be applied to predict the signal Path Loss (PL), which, when subtracted from the transmitted power, has the received power of the signal at a given point, according to (2):  
\[Pr=Pt+G-PL \tag{2}\]  
where \(Pr\) is the power received at a given point in dBm; \(Pt\) is the power transmitted by the gateway in dBm; \(G\) is the sum of transmission and reception gains, usually in dBi; \(PL\) represents the signal propagation loss in dB, predicted by a given propagation model.
## 3 Results and Discussion
### Carbon Parameters
The colour quotients of humic substances (\(Q_{HS}\)), humic acids (QHA), and fulvic acids (QFA) are measures of the colour intensity of these organic components. These parameters are indicative of the maturity and degree of humification of the organic matter in the soil, providing insights into the decomposition and transformation of organic compounds over time. The humification degree (DH) represents the extent of humification, reflecting the degree of transformation of organic matter into humic substances. This parameter offers valuable information about the stability and maturity of the organic matter in the soil, influencing its interactions with heavy metals and also other soil properties. By analysing these carbon parameters (Table 2), we gained a comprehensive understanding of the soil profile characteristics, which contributed to a more thorough assessment of the soil's fertility and other soil properties. These parameters were essential for understanding the composition and organic matter present in the soil. Figure 2: Colour quotients and humification degrees (\(Q_{HS}\)—colour quotient of HSs, QHA—colour quotient of HAs, QFA—colour quotient of FAs, DH—humification degree). The content of the _TOC_ is a crucial indicator of the soil's organic matter content, representing the quantity of carbon sequestered in organic compounds within the soil. It serves as a key measure of soil fertility, nutrient availability, and the potential for carbon sequestration. The values of _TOC_ in topsoil did not vary between profiles P1-P2 and P4 (Cambisols) and P5 (Planosol) (Table 2). The _TOC_ content provides insights into the soil's ability to support microbial activity, retain moisture, and sustain plant growth. Additionally, changes in _TOC_ content can reflect alterations in land use, management practices, and environmental conditions, making it a valuable parameter for assessing soil health and productivity. A comprehensive picture of the organic composition was obtained by analysing the _TOC_ content in conjunction with other carbon metrics (Table 2 and Figure 2), which aided in a more thorough assessment of the capacity for storing carbon and any potential interactions with heavy metals.
## 2 Materials and Methods
### PVC Identification  
In each of the 14 blocks segmented above, the continuous wavelet transform (CWT) was applied to decompose the ECG signal into different frequency scales, thus obtaining a matrix of coefficients of 153 by 300,000. The 153 values associate the coefficients with frequency values covering a range from 0.0115 Hz to 434.12 Hz. Then, a specific frequency band (2.6-3.2 Hz) was selected, in which the PVCs were proposed to be found, through a logic mask. Subsequently, the accumulated energy in this band was calculated, adding the absolute values of the wavelet coefficients at each instant of time, which yielded an amplitude vector. For the identification of PVCs, an adaptive threshold was established, defined as 40% of the maximum value of the amplitude vector, thus allowing only the most significant events to be detected. Finally, the peaks in the energy signal (amplitude vector) were identified, considering only those that exceed the threshold, which allows for determining the temporal locations of the possible PVCs within the ECG recording.
## 6 Conclusions  
Thus, the conclusion can be made that building of computational channel of the DF operating in the FFA codes without the converter and ADC influence on the input data values allows increasing accuracy of calculation of filter output sample. Similar estimates are valid not only for the impulse response samples of the filter but also for the input signals. In the first case, the impulse response of the filter is changed. In the second case, the output sample will be distorted. For example, if an adaptive DF will be implemented, the error of the output sample will require setting up the filter coefficients. It, in turn, will require high time costs.  
The actual variant of construction and organization of calculations in the computational channel of the DF will depend on the requirements for performance and accuracy of the output sample calculation.  
Finally, we can conclude that the properties of finite field algebra ensure the construction of efficient computational algorithms and structures of DF with high performance and accuracy of the output sample calculation.  
Conceptualization, D.K. and S.A.; bata curation, A.V. and D.B.; formal analysis, E.D. and D.B.; funding acquisition, D.K.; investigation, S.A. and D.B.; methodology, A.V. and P.L.; project administration, D.K., S.A., and P.L.; resources, S.A. and E.D.; software, A.V. and P.L.; supervision, D.K. and S.A.; validation, P.L. and D.B.; visualization, A.V. and E.D.; writing--original draft, D.K., A.V., and D.B.; writing--review and editing, E.D. and D.B. All authors have read and agreed to the published version of the manuscript. This research was funded by the grant of the Russian Science Foundation (Project Ne19-19-00566). The authors declare no conflict of interest.
# An LSTM-Based Method Considering History and Real-Time Data for Passenger Flow Prediction
## 5 Conclusions  
Based on the LSTM structure, this paper proposes a new passenger flow prediction model that considers real-time data. This model combines point of interest data with smart card data to improve the dimension of dataset. Then, we use the date as an index of LSTM structure to produce a history information vector. Furthermore, the time section is used as an index of LSTM structure to produce a real-time information vector. With the help of these two information vectors, we can predict bus  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Model**} & \multicolumn{3}{c}{**Line 56008**} & \multicolumn{3}{c}{**Line 685**} \\ \cline{2-7}  & **RMSE** & **MAE** & **MaxError** & **RMSE** & **MAE** & **MaxError** \\ \hline HRPFP & 7.98 & 7.53 & 12 & 4.69 & 4.24 & 8 \\ LSTM & 8.39 & 8.13 & 12 & 5.32 & 4.46 & 8 \\ Xgboost & 9.16 & 8.62 & 20 & 5.76 & 4.74 & 10 \\ SVM & 9.86 & 9.58 & 43 & 6.09 & 4.90 & 21 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Evaluation values of different models for on-board passenger number prediction.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{2}{c}{**Input Values**} & \multicolumn{3}{c}{**Predicted Values from 8:00 to 8:30**} \\ \hline
**Passenger Number from** & **Passenger Number from** & & & & \\
**7:00 to 7:30** & **7:30 to 8:00** & & & & \\ \hline
235 & 180 & 170 & 152 & 149 & 145 \\
245 & 190 & 181 & 152 & 150 & 146 \\
255 & 200 & 194 & 152 & 150 & 146 \\
265 & 210 & 195 & 152 & 152 & 148 \\
275 & 220 & 212 & 152 & 153 & 148 \\ \hline \hline \end{tabular}
\end{table}
Table 2: A simple case of the 8 th station in line 56008 on the sensitivity of different models.  
passenger number more accurately after a decoding process. By comparing with different models, the HRPFP model has a higher accuracy than other models in passenger flow prediction. Due to the addition of the real-time encoding part, the HRPFP model has a better sensitivity to real-time data. Therefore, when a sudden passenger flow change occurs, the HRPFP model can provide an early warning of this phenomenon based on the real-time collected data, provide more accurate data for the adjustment of the bus plan, and make the bus system have a better stability. In the future, we will try to find a more proper sequence length for both the history data encoding part and real-time encoding part to make our research more complete. We hope this research can help bus operations and improve the bus system stability in the future.  
Conceptualization, Q.O.; Methodology, Q.O.; Software, Q.O.; Formal analysis, J.L.; Resources, J.M.; Project administration, Y.L. All authors have read and agree to the published version of the manuscript.  
This research was funded by National Natural Science Foundation of China grant number 61872036 and National key technologies Research & Development program grant number 2017 YFC0804900. And the APC was funded by National Natural Science Foundation of China.  
The authors declare no conflict of interest.
## 1 Introduction  
### Background and Motivation  
With the increasing scientific exploration and production activities on a global scale, human focus has expanded beyond the Earth's surface to include subsurface domains, underwater environments, and near-Earth space. Attention to global issues now spans phenomena such as internal plate tectonics, crustal movements, atmospheric dynamics, and ionospheric variations in near-Earth space [1]. Global challenges--such as climate change, pollution, and natural disasters--stem from interactions across Earth's spheres. Addressing these challenges cannot rely solely on a single discipline or the study of a single Earth layer;it necessitates in-depth research into multi-layer interactions and a holistic understanding of the Earth system [2;3]. Beyond the efficient organization of surface spatial data, there is an urgent demand for the integrated management of atmospheric and near-Earth spatial information across Earth's spheres.
## 5 Conclusions
Although the measurement results were affected by the specimen bending caused by mounting in the measurement apparatus, which caused unexpected dips in the spectra, the resulting spectra are comparable. Specifically, the resulting \(nTL\) spectra determined in accordance to ASTM and MM correspond to each other rather well. The membrane asymmetrical resonances and anti-resonances (which normally have a dominant effect on the \(nTL\), \(\alpha\) and \(\tau\) spectra) were nicely recognised. LDV gave us the opportunity also to identify other modes of the vibrating membranes. Specifically, in the presented case, when the membrane was not stretched symmetrically (caused by soft bending of the stretching ring--something that would occur often in practice), it was able to determine the reason of occurrence of the additional peaks (dips) in the spectra. The MM would clearly find application in membranes analysis even for more complicated elements, with higher energy dissipation in the structure and possible internal resonances, the \(\alpha\) cannot replace the \(\tau\). For practical applications, the advantage of MM is that the \(nTL\) as well as \(\alpha\) can be determined from a single-sided scan. In case of more complex samples the measurement procedure will be needed to perform from both sides of the specimen. The method may be extended by a single microphone based incident sound pressure determination approach known from standards. The presented work can be considered as an intermediate step for method development applicable in practice. Main disadvantage of the proposed method has only the limited application and is time consuming in comparison to the conventional methods. The absorption measurement approach can be applied only for membrane-based constructions, specially, with membranes with negligible internal losses. Method is also sensitive on the amount of excitation energy (sensitivity of LDV, respectively). Further investigation in the MM is expected. Future experiments in the anechoic laboratory environment and the in situ free field are essential to prove the wider application of the method. The goal is to have a method appropriate for the determination of membrane-based structures acoustic/structural properties from larger distances. Mainly in the cases when the surface of investigation in inaccessible places due to location or extreme conditions. Conceptualization, methodology, software, investigation, resources, writing--original draft preparation, visualization, D.U. ; formal analysis, writing--review and editing, D.U., N.B.R. and O.J. ; validation D.U., V.J., M.B. All authors have read and agreed to the published version of the manuscript. This research was supported by the International Mobility of Researchers in CTU CZ.02.2.69/0.0/0.0/16_027/0008465, CTU project SGS19/166/OHK3/3T/13 Development of modern acoustic measurements and was funded by European Union, grant number H2020-MSCA-RISE-2015 project 690970. Not applicable. All data included in this work are available upon request by contact with the author [PERSON]. The authors declare no conflict of interest.
## 3 Results and Discussion
### CT Tomography Assessment
#### 3.1.5 Assessment of the Test Specimens' Wall Thickness  
The VGStudio MAX 2.2 software enables the assessment of specimens' wall thickness, or its measurement locally, in selected points. As part of the assessment, a wall thickness analysis was performed with the analyzed thickness set to 2 mm. Due to the significant surface irregularities and analysis algorithms, the use of the given module to measure the strut diameter is limited. On the surface, several areas can be seen, the resulting thickness of which is smaller than that of the produced one. The analysis result is shown in Figure 12.  
The second choice is to measure the wall's diameter/thickness in a selected cross-section. To prove the impact of the fit method setting, the gray lattice structure's diameter was assessed using a Gaussian feature, minimum circumscribed feature, and maximum inscribed feature. The results imply a difference between the methods of 0.120 mm in the radius, which is due to the cross-section's shape and surface roughness. Figure 13 shows the creation of individual features.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Specimen Designation** & **A [mm]** & **B [mm]** & **C [mm]** & **Area A \(\times\) B [mm\({}^{2}\)]** & **Percentage Change in Area A \(\times\) B [\%]** \\ \hline Red lattice & 34.687 & 34.506 & 7.051 & 1196.910 & \(-\)2.293 \\ \hline Black lattice & 34.363 & 34.468 & 6.994 & 1184.240 & \(-\)3.312 \\ \hline Gray gyroid & 34.760 & 34.761 & 6.935 & 1208.292 & \(-\)1.364 \\ \hline Red gyroid & 34.563 & 34.662 & 6.990 & 1198.023 & \(-\)2.202 \\ \hline Black gyroid & 34.496 & 34.337 & 6.637 & 1184.489 & \(-\)3.307 \\ \hline \hline \end{tabular}
\end{table}
Table 6: _Cont._  
Figure 12: Assessment of the wall thickness of the lattice gray specimen (SLS method) using the wall thickness analysis module: (**a**) FRONT plane; (**b**) TOP plane.  
Figure 13: Measurement of the diameter in gray lattice specimen (SLS method): (**a**) Gaussian feature; (**b**) minimum circumscribed feature; (**c**) maximum inscribed feature.  
Figure 14 shows the cross-section of a red and black lattice structure. The significant deformation of the cross-section's shape is visible, and no single cross-section measurement methodology provides \"quality\" data.  
The same analyses were performed for gyroid experimental specimens. Figure 15 shows the measurement of the wall thickness at a selected point in all gyroid specimens. In all specimens, the effect of manufacturing is obvious; in the case of the gray specimen, shown in Figure 15a, it is the effect of surface \"roughness\", and in the case of red and black ones, it is the effect of the manufacturing technology (material layering), with significant differences between the two measurements (see Figure 15b,c).
## 4 Evaluation of Model Developments
### Observational Data to Evaluate Model Developments
#### 4.2.8 SYNOP Observations  
SYNOP observations of 2-m temperature and snow depth play an important role in the assessment of the impact of the new snow scheme as well as the urban model. SYNOP observations are exchanged through the WMO Global Telecommunication Service (GTS) and are used everyday for initialisation and monitoring of NWP models. The usage of in-situ SYNOP observations within the ECMWF IFS model is described in [PERSON] et al. [87].
# Joint Task Offloading and Power Allocation for Satellite Edge Computing Networks
## 6 Conclusions  
In this paper, we consider the scenario of a LEO satellite edge computing system with time-sensitive tasks. We formulate an optimization problem to minimize the total computing and transmitting delay of the system, present Theorem 1 theoretically, and propose a joint allocation strategy of transmission power and computing resources to handle the challenging nonconvex problem. The convergence performance is validated in the simulation, and the delay performance is demonstrated under various conditions, both of which demonstrate the significant advantages of the proposed DB-IPDD algorithm by leveraging the feature of distributed balance. In future work, we plan to investigate intelligent computation offloading strategies in integrated space-terrestrial networks.  
[PERSON]. ([PERSON]), [PERSON] and [PERSON] conceived and designed the experiments; [PERSON]. ([PERSON]) performed the experiments; [PERSON] and [PERSON] analyzed the data; [PERSON]. ([PERSON]) contributed analysis tools; and [PERSON] ([PERSON]) wrote the paper. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Spatial Technology Innovation Workstation under grant CX2023030700 and by the Fund Project of National Defense Science and Technology Key Laboratory under Grant 614201003032208.  
**Institutional Review Board Statement**: Not applicable.  
**Informed Consent Statement**: Not applicable.  
**Data Availability Statement**: No new data were created or analyzed in this study. Data sharing is not applicable to this article.  
**Conflicts of Interest**: The authors declare no conflicts of interest.  
Figure 14: The computation delay and transmission delay in each SS.