esa-sceva/satcom-chunk-collection huggingface dataset:
# Prediction of Theoretical Derailments Caused by Cross-Winds with Frequency
## 4 Evaluation of Derailment Safety under Cross-Wind
### Derailment Safety Due to the Cross-Wind Frequencies  
When a constant wind force acts on a vehicle, the ratio of wheel unloading must be within 0.9 not to be derailed on the straight track and the curved tracks (lateral acc. 0.5 [m/s\({}^{2}\)], 1.0 [m/s\({}^{2}\)] in the EN). However, wind speeds in nature are not constant. In addition, the winds generated by wayside structures on the track or other opposite trains make exciting frequencies [38].  
In this section, the ratios of wheel unloading according to the winds with frequency were compared using the theoretical formula derived in Section 2 and the dynamic simulations.  
The conditions to compare the ratios of wheel unloading according to different wind frequencies are shown in Table 12.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Track Condition**} & \multirow{2}{*}{
\begin{tabular}{c} **Wind Speeds** \\ **[m/s]** \\ \end{tabular} } & \multicolumn{3}{c}{**Ratios of**} \\  & & **Wheel Unloading** & **Differences** \\ \cline{3-5}  & & **Theory** & **Simulation** & \\ \hline \multirow{4}{*}{Straight track} & 0 & 0.198 & 0.173 & 0.025 \\ \cline{2-5}  & 30 & 0.318 & 0.304 & 0.014 \\ \cline{2-5}  & 40 & 0.413 & 0.403 & 0.010 \\ \cline{2-5}  & 45 & 0.468 & 0.462 & 0.006 \\ \cline{2-5}  & 0 & 0.323 & 0.305 & 0.018 \\ \cline{2-5}  & 30 & 0.443 & 0.429 & 0.014 \\ \cline{2-5}  & 40 & 0.538 & 0.528 & 0.010 \\ \cline{2-5}  & 45 & 0.593 & 0.587 & 0.006 \\ \cline{2-5}  & 0 & 0.448 & 0.454 & 0.006 \\ \cline{2-5}  & 30 & 0.568 & 0.579 & 0.011 \\ \cline{2-5}  & 40 & 0.663 & 0.681 & 0.018 \\ \cline{2-5}  & 45 & 0.718 & 0.739 & 0.021 \\ \hline \hline \end{tabular}
\end{table}
Table 11: The ratios of wheel unloading (\(\Delta\)Q/Q) between the theoretical formula and the simulation for cross-wind along with dynamic wheel-rail interactions (P/Q = 0.315).  
\begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Parameters**} & **Values** \\ \hline Track & Straight \\ \hline Rail irregularity (P/Q) & & 0.315 \\ \hline Vehicle velocity & 300 & [km/h] \\ \hline \multirow{2}{*}{Wind} & Average speed & 45 & [m/s] \\ \cline{2-5}  & Fluctuation speed & \(\pm\)4.5 & [m/s] \\ \hline \hline \end{tabular}
\end{table}
Table 12: The conditions to compare the derailment coefficients according to cross-wind frequencies.
## 2 Study Area, Dataset, and Methodology
### Meteorological and Human Activity Data Sources and Preprocessing
#### 2.3.2 Nighttime Light Data  
Nighttime light data were sourced from a combination of DMSP-OLS (2000-2013) and NPP/VIIRS (2012-2020) datasets, with a spatial resolution of 1 km, to represent urbanization and human activity intensity [40; 41]. Consistency adjustments were applied to DMSP-OLS data using a pseudo-invariant pixel calibration method to ensure comparability across years. For data after 2012, NPP/VIIRS annual average values replaced DMSP-OLS, with brightness values standardized between the two datasets. This process resulted in a continuous nighttime light dataset from 2000 to 2020, reflecting the spatiotemporal dynamics of human activities.  
The improved DMSP-OLS-like data typically retains the dimensionless digital number (DN) format for compatibility with the original DMSP-OLS data. Although consistency adjustments, such as pseudo-invariant pixel calibration, are applied to make the data comparable across years, these calibrated values do not have direct physical units. Instead, they represent relative brightness intensity.  
For improved DMSP-OLS-like data, standardization or calibration methods are used to align the brightness values between DMSP-OLS and VIIRS data, ensuring consistency in brightness values across datasets. However, the final data are still expressed as DN, typically within the original range of 0 to 63, allowing for long-term temporal analysis.
# Research on Kalman Filter Fusion Navigation Algorithm Assisted by CNN-LSTM Neural Network
## 3 Constructing the Framework for CNN and LSTM Assisted GNSS/INS Navigation System
### Neural Network Structure and Function
#### 3.1.2 Multilayer Feedforward Neural Network  
The multilayer feedforward neural network [33], also known as the multilayer perceptron (MLP), introduces hidden layers between the input and output layers to enhance the performance of single-layer perceptrons. The number of these hidden layers can be one or more, and they function as the \"internal representation\" of input patterns. With this improvement, the original single-layer perceptrons transform into multilayer perceptrons, thereby enhancing their ability to handle complex patterns. The training of multilayer feedforward neural networks often utilizes the error backpropagation algorithm, hence they are also commonly referred to as Back Propagation (BP) networks [34].  
The structure of multilayer feedforward neural networks is hierarchically rich, consisting of an input layer, several hidden layers, and an output layer. Each layer can be viewed as an independent single-layer feedforward neural network, with each one linearly classifying input patterns. However, it is the combination and superposition of these layers that enables multilayer feedforward neural networks to perform more complex and refined classification tasks on input patterns.  
Multilayer feedforward neural networks are renowned for their excellent nonlinear processing capabilities. Despite their relatively simple structure, they have an extremely wide range of applications. These networks can approximate any continuous function and square-integrable function with arbitrary precision. Moreover, they can accurately represent any finite training sample set, making them of significant value in various fields. Figure 4 illustrates the model of a multilayer feedforward neural network.  
Figure 3: Model of an artificial neuron.
## 5 Methodology
### To BIM, or Not to BIM?  
The literature review highlighted the successful applications of BIM for sustainability and during the design phase in particular. However, less investigated topics are the application of digital methods to optimize the planning of the demolition phase of existing buildings and to support the Public Client during the call for tenders phase, including environmentally sustainable criteria.  
Regarding BIM adoption for the demolition phase planning, the creation of a BIM model exclusively for demolition purposes shall be carefully evaluated. The availability of a BIM model of the to-be-demolished building is an undoubted advantage for CDW management [67], supporting deconstruction, and reusing and recycling activities [68]. However, most of the existing buildings do not have a BIM model, and creating one is challenging [79]. In addition, when generating a BIM model that is exclusively for the demolition phase, many benefits and optimizations that justify the effort of producing a BIM model are invalidated [25]. In general, the IMM approach aims to support decision-making processes by focusing on organizing and digitizing data and information in a structured form. Data and information must be available, up-to-date, precise, and provided to the right person, at the proper time, and in the correct format [80,81], in a concurrence with process and information management [82]. Therefore, data should accomplish two fundamental pre-requisites: (I) information readiness and (II) information processability [83]. Databases are comparable to BIM models in fulfilling the two above-mentioned pre-requisites since BIM models can be assimilated to the relational databases that are required for 3D geometrical visualization [73]. In addition, the creation of a digital database is less time and effort consuming than producing a full BIM model.  
Consequently, the presented methodology proposes the adoption of IMM approaches by using a digital database to manage CDW and for the proper planning of selective demolition activities. The creation of a BIM model, on the other hand, is proposed for the design phase, supporting sustainable resource use and waste minimization strategies. Concerning the call for tenders phase, the methodology proposes a framework for the introduction of award criteria regarding sustainability aspects in the tender documents, and for the evaluation of the environmental impact of the bids (Figure 3).  
In summary, the research aims to provide the Public Client with a model to adopt sustainability practices focusing in particular on CDW management and minimization and selective demolition practices, that are supported and managed via IMM approaches during the design and call for tenders phases.
## 3 Results and Discussion
### Characterization of Laminated CNCs-Coated PET Films (P-CNC-P)
#### 3.3.2 Oxygen Permeability of Laminates with CNCs Coated Films (P-CNC-P)  
Table 4 illustrates the values of oxygen permeability expressed in cm\({}^{3}\)/(m\({}^{2}\)\(\times\) 24 h \(\times\) bar) of laminated coated-films measured at 50% and 80% RH at 23 \({}^{\circ}\)C. The lamination of coated films further improved the gas barrier for all the three types of cellulose nanocrystals.  
The lamination technique was very effective in blocking water from the surrounding environment to disrupt or modify the crystallinity network of the cellulose nanocrystal's coatings. In addition, gas barrier properties were not lost after the lamination, and that proved the excellent chemical affinity between the adhesive and the CNCs. The combination of both techniques, i.e., chemical modification and lamination, to mitigate the swelling of the coatings of cellulose nanocrystals surface resulted the most effective approach to strongly enhance the gas barrier properties even at higher relative humidity. It bears observing that sealable and hydro-repellent plastics, such as PP and PE, showed better O\({}_{2}\) under 50% and 80% RH, compared to PLA, which is a water sensitive polymer. To this regard, however, CNC\({}_{COOR}\) proved their effectiveness in reducing the PLA oxygen permeability. In particular, they reduced the permeability by a ten-fold factor with respect to CNC\({}_{SO3H}\) at 50% RH, but positive effects were also recorded with respect to CNC\({}_{COOH}\) even at 80% RH.
## 2 Textural Segmentation in OIA of Coke
### \"Washed out\" IMDC Textural Identification  
In the majority of cases, the IMDC identification methods described above work quite well. However, in certain cokes, some IMDC areas are quite dark in appearance, even darker than epoxy, and cannot be thresholded as a part of the coke matrix. In these cases an extra identification method is needed (Figure 5). Several explanations can be offered for such appearance of IMDC. One possibility is that such IMDC may have very fine porosity which is not impregnated by epoxy during block preparation. Alternatively, some of these areas may have a very weak structure and so are destroyed, plucked out and/or \"washed out\" (the general term used to call these areas) during the polishing of the block surface. The rest of the coke matrix is brighter than epoxy, so these dark areas are not included when coke matrix is segmented.  
Figure 4: The algorithm of “Porous IMDC Identification” of not reacted IMDC: (**a**) the whole porosity identified as binary map; (**b**) fine porosity removed; (**c**) dilation applied; (**d**) map inverted; (**e**) clusters of fine porosity identified; (**f**) strong dilation (**g**) strong erosion; (**h**) compensating dilation; (**i**) map of porous IMDC.  
For segmentation of \"washed out\" IMDC, thresholding of areas with reflectivity higher than that of porosity but lower than epoxy is performed (Figure 6a,b). Next, moderate dilation is performed (Figure 6c) to preserve the washed out IMDC areas during following strong erosion (Figure 6d). After dilation, compensating for that strong erosion, filling holes and scrapping of undersized objects (Figure 6e), the areas of washed out IMDC are determined. Masking those areas with the combination of the map in Figure 6a and the coke matrix gives the actual map of washed out IMDC (see Figures 2b and 6f).  
There exists a possibility that the described method may also identify dark minerals with reflectivity between that of porosity and epoxy. If such minerals are known to be sufficiently present in the sample as relatively large grains, they should be identified texturally prior to IMDC identification in the workflow. If that cannot be achieved, a decision should be made about whether the \"washed out\" identification method should be included in the whole IMDC identification procedure. For the coke shown in Figure 5, it was in fact critically important to include such identification due to the significant presence of \"washed out\" areas.  
Figure 5: Photomicrograph of coke with the significant presence of dark/“washed out” IMDC.
## 2 Theory and Methodology
### Improved Particle Filter Online Recognition Method
And \(l_{k}\) it can be written as in Equation (9):  
\[J_{k}\ =\ \left[l_{k}(1),\cdots,l_{k}(N_{L})\right]^{T}\quad l_{k}(l)\in\{0,1,2 \}\ \ l\ =\ 1,\cdots,N_{L} \tag{9}\]  
where \(l_{k}(l)\) is a Markov chain, transition probabilities is \(p(l_{k}(l)\ =\ \beta|\,l_{k-1}(l)\ =\ \alpha)\). For any \(l\
eq m\), \(l_{k}(l)\) and \(l_{k}(m)\) are independent of each other. \(l_{k}(l)\in\{0,1,2\}\) represents whether the \(l\) measurement at \(k\) time is abnormal and the type of anomaly. For example, \(l_{k}\ =\ \left[0,0,\cdots,0\right]^{T}\ (l_{k,0}\ =\ \left[1,1,\cdots,1\right]^{T})\) indicates no anomalies; \(l_{k}\ =\ \left[1,1,\cdots,1\right]^{T}\) (\(l_{k,1}\ =\ \left[1,1,\cdots,1\right]^{T}\)) indicates random errors; and when \(l_{k}\ =\ \left[2,2,\cdots,2\right]^{T}\) (\(l_{k,2}\ =\ \left[1,1,\cdots,1\right]^{T}\)) it indicates gross errors. It can be seen that \(l_{k}\) and \(l_{k,n}\) can substitute for each other when indicating whether an anomaly exists and its type. To account for multiple types of outliers, an improved particle filtering algorithm is proposed, where the augmented state vector is defined as in Equation (10):  
\[B_{k}\ =\ \left[y_{k}^{T},m_{k}^{T},J_{k}^{T}\right]^{T} \tag{10}\]  
where \(y_{k}\) is the seepage pressure state vector; \(m_{k}\) and \(l_{k}\) are the parameter vectors of the outlier detection model, and they are independent of each other. Use the initial probability density \(p(B_{0})\) to randomly generate an augmented state vector \(M\):  
\[B_{0}^{(i)}\ =\ \left[y_{0}^{(i)\,T},m_{0}^{(i)\,T},J_{0}^{(i)\,T}\right]^{T} \ \ i\ =\ 1,2,\cdots,M \tag{11}\]  
Predicting the next moment particle \(B_{k|k-1}^{(i)}\) at every moment \(k\ =\ 1,2,3,\cdots\):  
\[B_{k|k-1}^{(i)\,T}=\ \left[y_{k|k-1}^{(i)\,T},m_{k|k-1}^{(i)\,T},j_{k|k-1}^{(i) \,T}\right]\ \ i\ =\ 1,2,\cdots,M \tag{12}\]where \(B^{(i)}_{k|k-1}\) it is composed of three particles: \(y^{(i)}_{k|k-1}\), \(m^{(i)}_{k|k-1}\) and \(J^{(i)}_{k|k-1}\), \(y^{(i)}_{k|k-1}\) can be obtained by calculation, and \(m^{(i)}_{k|k-1}\) can also be obtained by prior probability density function, which can be written as in Equation (13):  
\[ \begin{array}{l}p\Big{(}m_{k}\Big{|}m^{(i)}_{k-1|k-1},J^{(i)}_{k-1|k-1}\Big{)} \ =\ \prod\limits_{l\ =\ 1}^{N_{L}}\Big{[}\delta\Big{(}J^{(i),l}_{k-1|k-1}\Big{)}+\delta\Big{(}J^{(i),l}_{k-1|k-1}-1\Big{)}\ \ u(m^{(i)}_{k}\cdot\,a^{l},b^{l})\\ \ +\delta\Big{(}J^{(i),l}_{k-1|k-1}-2\Big{)}\,N(m^{(i),l}_{k}\cdot\,m^{(i),l}_{k- 1|k-1},\sigma^{\Delta^{\beta}}_{k})\end{array}  \tag{13}\]  
where the superscript \(l\) is the \(l\) element of the vector: \(j^{(i),l}_{k-1|k-1}\) is the value of the \(l\) dimension of the particle \(J^{(i)}_{k-1|k-1}\): \(m^{(i),l}_{k-1|k-1}\) is the value of the \(l\) dimension of the particle \(m^{(i)}_{k-1|k-1}\): \(\sigma^{\Delta^{\beta}}_{k}\) is the variance of the \(l\) dimension of the \(\Delta m_{k-1}\): \(u\) is a uniform distribution on the interval \(\Big{[}a^{l},b^{l}\Big{]}\).
# Assessment of IRNSS-Only Data Processing: Availability, Single-Frequency SPP and Short-Baseline RTK
## 5 Results and Analysis
### Availability of Satellite Constellation
The distribution of the average PDOP value over the time span when the positioning service of the IRNSS-only case is available is displayed in Figure 7. The areas where the position determination is always unavailable are also marked in white in Figure 7. The PDOP value in primary service areas is between 3.3 and 6.2 under the IRNSS-only case, basically less than 15 in secondary service areas, and above 20 in some marginal regions beyond the IRNSS service areas. The distribution of the PDOP values of the GPS-only case and the GPS/IRNSS case is shown in Figure 4.0 The average statistic of PDOP value for the GPS-only case over all the latitude and longitude regions is 2.0, and the minimum PDOP value of 1.7 on the equator is achieved. The PDOP value of the GPS-only case increases first and then decreases with the increasing latitudes in middle and low latitude regions, after which it gradually increases toward the poles until reaching the peak value of 2.3. The PDOP value of the GPS/IRNSS case in the primary and secondary service areas of IRNSS varies from 1.3 to 1.7 with an average statistic of 1.5. Thus, the GPS/IRNSS combination can improve the satellite sky distribution for the service areas of IRNSS. Figure 4.1 Average value of PDOP for GPS-only case and GPS/IRNSS case. Figure 4.2 Average value of PDOP for IRNSS-only case.
# Linear Computation Coding: A Framework for Joint Quantization and Computing
## 4 Proposed Scheme for Linear Computation Coding
### General Wiring Optimization  
For given distortion measure \(d(\cdot,\cdot)\) (see Definition 1 for details), given the upper limit on the computational cost \(C\), given wide target matrix \(\mathbf{T}\) and codebook matrix \(\mathbf{B}\), we find the wiring matrix \(\mathbf{W}\) such that  
\[\mathbf{W}=\operatorname*{argmin}_{\Omega:\mathbb{C}[\Omega\mathbf{x}]\leq C} \operatorname*{\mathbb{E}}_{x}d(\mathbf{T}x,\mathbf{B}\Omega\mathbf{x}) \tag{11}\]  
where the operator \(\mathbb{C}[\cdot]\) measures the computational cost.  
For a tall target matrix \(\mathbf{T}\), run the decomposition algorithm (11) with the transpose of \(\mathbf{T}\) and transpose its output. In that case, the wiring matrix is multiplied to the codebook matrix from the left, not from the right. Unless specified otherwise, we will consider wide target matrices in the sequel, without loss of generality.
## 5 Evaluation
### Assessment on Quality of Synthetic anomaly
This indicates that the ICS time-series anomaly generated by the proposed method is not only visually similar to the actual anomaly (as shown in Figure 1) but also statistically does not deviate significantly from the original source data. Nevertheless, it is worth noting from Figure 6 and Table 2 that there is a difference in similarity between the original source time-series and the anomaly depending on the features. For example, in Figure 6, the KDE between the source data and the synthetic anomaly for HAI's P1_B2016 is almost similar, whereas a significant difference is observed in P1_B4022. This observation suggests that the level of realism and indistinguishability may vary depending on the features. **The answer for RQ1. ** Various experimental results presented in this section confirm that the ICS time-series anomaly generated by mutation-based anomaly generation is visually and statistically quite similar to the actual anomaly. However, as shown in the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Variable (HAI)** & **P1\_B4022** & **P1\_PP04** & **P2\_ST01** & **P1\_PIT02** & **P1\_FCV03D** & **P1\_FCV01Z** \\ \hline
**JSD** & 0.13 & 0.075 & 0.065 & 0.037 & 0.022 & 0.02 \\ \hline
**Variable (SWaT)** & **FIT503** & **PIT503** & **PIT501** & **FIT501** & **FIT502** & **LIT401** \\ \hline
**JSD** & 0.138 & 0.08 & 0.077 & 0.034 & 0.015 & 0.004 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Jensen–Shannon divergence between the original source data and the synthetic anomaly. Figure 6: Kernel density estimation between the original source data and the synthetic anomaly. experiments using the SWaT dataset, the proposed method somewhat depends on the reconstruction performance of AVRAE. Therefore, to improve the performance of the proposed method, it is necessary to enhance the model that maps the original data (such as AVRAE) into the embedding space.
## 2 Problem Formulations  
The heterogeneous WSN model is considered and, in this network, the disjoint sets of sensors form a cluster. Each cluster has a gateway associated with it to send the data packets received from the sensor nodes. Later, the information is aggregated, and it is communicated to the base station [6]. In this process, the power consumption of the receiver and transmitter leads to the energy dissipation of the radio due to free space and the multi-path fading channel [44]. Therefore, the lifetime of the networking is majorly affected by the power consumption, which demands load balancing among the cluster heads, i.e., gateways. With symbols having their usual meanings [8,44], the problem of the load balancing of gateways is formulated for minimizing the load of the maximum loaded gateway, which is as follows:  
\[\textit{Minimize}\ G_{TL}=\max\{G_{L}\big{|}\textit{where},\ G_{j}\in G\ \} \tag{1}\]  
The above problem is subject to the following constraints,  
1. **Threshold distance:** The threshold distance of sensors from the CH is determined and used to generate the initial solution space. Here, \(\textit{Max}\_G\) is the maximum loaded CH with \(p\) number of sensors connected and \(\textit{Min}\_G\) is the minimum loaded CH associated with \(q\) number of sensor nodes. \(S\) is the total set of \(p\) and \(q\), and the sensor connected to these CHs is the union of both groups as it is given in (2), \[S\in\left\{\begin{subarray}{c}p+q\\ \cup\\ k=1\end{subarray}S_{k}\right\}\] (2) \[\sum_{s_{j}\in S}\text{dist(i,j)}\ \leq t\_dist\ \Bigg{|}\ \textit{where},\ G_{i}\in G_{s}\] (3)  
In Equation (3), \(t\_dist\) is the distance threshold of the sensor from each CH, and it is calculated as under,  
\[t\_dist=\frac{1}{S}\sum_{i=1}^{S}\sum_{j=1}^{2}dist(i,j)\times b_{ji} \tag{4}\]  
In Equation (4), \(S\) is the number of sensors in the maximum and minimum loaded CH set. \(b_{ji}\) is used for the connection between the sensors and CHs.  
1. **Disjoint set:** The disjoint sets allow each sensor node to connect with only one CH. \[\sum_{G_{i}\in G_{s}}b_{ji}=1\ \Bigg{|}\ \textit{where},\ s_{j}\in S\] (5)  
In Equation (5), if \(b_{ji}\) is 1, then it is the member of the disjoint set; otherwise, it is not a part of it.  
1. **Gateway load:** The traffic load of a CH \(G_{i}\) should be less than or equal to the maximum load \(G_{TL}\) of a maximum loaded CH. \[\sum_{s_{j}\in S}d_{j}\.\ b_{ji}\leq G_{TL}\ \Bigg{|}\ \textit{where},\ G_{i}\in G_{s}\] (6)  
In Equation (6), \(d_{j}\) is the load of sensor \(s_{j}\), and \(s_{j}\) belongs to the disjoint set of \(G_{j}\).