esa-sceva/satcom-chunk-collection huggingface dataset:
# Semantic Web and Knowledge Graphs for Industry 4.0
## 5 Semantic Web and Ontologies for Industry 4.0
### Ontologies for Industry 4.0 Supply Chain Management  
A supply chain is a web of business services and delivery options that play key roles: procurement of raw materials, transforming these raw materials into a product, and delivery of these products to customers [87]. Ordinarily, the raw materials are acquired from distinct vendors and are transformed into the product at one or more production plants. The finished product is shifted to the storage room in the warehouse. According to the characteristics defined by [88], the heterogeneous information flow of the supply chain network creates complex processes between partners that need to be adjusted for business profit. An ontology-based data integration framework is proposed in the same research work that utilizes the relational databases and data in XML format.  
The current advancement in the internet-based technologies pave a way for the task of extended supply chain and new constraints, therefore helping in managing product-related information that comes from product models. The product data framework initially established by PRoduct Ontology (PRONTO) is extended to give the foundations for distributed data management (DPDM) which in turn helps to validate the data aggregation and disaggregation processes needed by the activities of logistic planning [27]. PRONTO modelled the product and its variant set without considering any standard or reference architecture and did not reflect any other part of manufacturing production line [89].  
In the supply chain, several failures such as factory fire, machine failures, acquisition of raw material, etc., have been observed [86]. In [90], researchers proposed a decision support system based on the ontology model to decide optimal recovery action as a high resilience level by applying Particle Swarm Optimization (PSO) approach. Similarly in another study, ontology and multi-agent have been used to propose a decision support framework for the supply chain of prefabricated components [91]. In another study, the problem related to the logistic process was optimized by proposing a framework based on ontology [92].  
There is literature available on ontologies for solving different problems related to supply chain management covering numerous domains [93; 94; 95].
# Developing Machine Learning-Based Models for Railway Inspection
## 6 Discussion  
Based on the preliminary results obtained from the experiments, it is obvious that the feature-based method can detect the joint with high detection rate but requires two independent models for right and left rail respectively. The deep learning-based method can address this limitation with one global model to detect a joint on either the left rail or right rail with the expected performance. Another advantage is deep neural networks can directly deploy the raw time history data as the model input without complex data engineering which makes the model more useful in practice and applicable in real applications. The experimental results shown in Table 3 demonstrate that ResNet and FCN both achieved good performance in joint detection, with ResNet performing slightly better than FCN on the first dataset and similarly on the second dataset. It is worth mentioning that the deep learning-based model will unable to detect the joints that appear simultaneously on the both side since the models were trained only for each side of the rails.  
It is worth noting that there are a few parameters that require special attention in the proposed methods: the window size, the number of data points in the data object, and the length of the joint. These parameter settings or configurations will have a great impact on the model performance and applicability of the developed models. In this work, these parameters are set up based on the requirement of rail joint detection. In the future, if we want to use the model to detect other rail components--such as turning points, crossings, etc.--these parameters have to be reconfigured based on the new requirements.  
Although this preliminary study was focused on detecting rail joints, we believe that the developed approach can be easily extended to other railway inspections such as rail defect detection or rail component identification. The next step of the research is to apply the approach to other rail defects such as localized surface collapse (LSC), rail end batter (REB), or crushed head (CH) prior to rail failure.
## 3 Results and Discussion  
### Hydrometeorological Characteristics  
As shown in Figure 3a, the annual average observed flow during 2006-2018 for the Beijing River Basin showed an annual growth rate of 0.27 m\({}^{3}\)/s, while the annual rate decreased by 13.65 m\({}^{3}\)/s for the Dongjiang River Basin. According to the daily flow duration curves (FDCs) obtained from the discharge observations (Figure 3b), the flows were 134 m\({}^{3}\)/s and 170 m\({}^{3}\)/s in the Beijing and Dongjiang River Basins, respectively, with a 99.98% probability exceedance. For the Beijing River Basin, the flow of 1490 (1750, 2780, 3980, and 6500, respectively) m\({}^{3}\)/s had a 25% (20%, 10%, 5%, and 2%, respectively) probability exceedance. For the Dongjiang River Basin, the flow of 839 (971, 1440, 2030, and 3120, respectively) m\({}^{3}\)/s had a 25% (20%, 10%, 5%, and 2%, respectively) probability exceedance.  
The different exceed-probability floods are necessary elements which can guide the design and construction of multiple water-conservancy projects, including hydraulic power, flood control planning, and water supply and drainage engineering [11].
## 5 Methodology
### Metrics
#### 5.3.3 Energy  
The average energy (\(E\)) of the n-pulse modulation set of symbols (\(S\)), for a given maximum number of pulses (\(n_{\text{max}}\)), in a symbol period (\(T_{\text{s}}\)) is given by  
Figure 9: Communication system pipeline, considering the data encoding system and the event camera modulated light detector, in conjunction with the data decoding.  
\[\overline{E}_{\text{S}}=\frac{1}{n_{\text{max}}}\sum_{i=1}^{n_{\text{max}}}\frac{P \cdot D\cdot i}{n_{\text{max}}}=\frac{P\cdot D}{2}\cdot\frac{n_{\text{max}}+1}{n _{\text{max}}}, \tag{8}\]  
where the electrical power (\(P\)) can be computed as \(P=V\cdot I\). It can be observed that the energy is primarily influenced by the duty cycle, rather than the maximum number of pulses.
# High-Performance Microwave Photonic Transmission Enabled by an Adapter for Fundamental Mode in MMFs
###### Abstract  
Microwave photonic links (MPLs) have long been considered as an excellent way for radio frequency (RF) transmission due to their advantages such as light weight, high bandwidth, low cost and large spurious-free dynamic range (SFDR). The effective mode-field area (A\({}_{\text{eff}}\)) of the single-mode fiber (SMF) used in the traditional MPL is not large, so the MPL based on SMF have relatively strong nonlinearity, which limits the processing power of SMFs to a level of few milliwatts. Few-mode fibers (FMFs) have been applied in MPL as an alternative due to the larger A\({}_{\text{eff}}\), and photonic lanterns are used simultaneously to excite the high-order mode of FMFs for RF signal transmission. However, the photonic lantern could bring additional insertion loss, and the production cost of FMFs is high, so we propose an MPL based on multimode fibers (MMFs) with mode field adapters (MFAs). Since MMFs have larger A\({}_{\text{eff}}\), the nonlinearity of the link can be greatly reduced. And matched MFAs realized by reverse tapering, to excite only the fundamental mode in MMFs to reduce the crosstalk, which are very stable. As a result, the stimulated Brillouin scattering threshold and SFDR are improved by 5 dB and 14.5 dB, respectively.  
microwave photonic link; mode field adapter; reverse tapering +
Footnote †: journal: Applied Sciences  
**Citation:** [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON].  
or placement posture, so a specific fiber mode would not be purely excited, leading to non-negligible multi-path interference (MPI). Besides, considering the availability and cost of FMFs and mode converters, we need to seek a more stable and cost-effective solution.  
Here, we propose a new MPL utilizing commercial multimode fibers (MMFs), which is featured by mode field adapters (MFAs) fabricated using reverse tapering technique and fused to both the ends of an MMF. RF signals are carried only by the fundamental mode, with greatly reduced loss and crosstalk between modes, which would significantly reduce the MPI and improve the system stability. Compared with traditional SMF-based MPLs, the SBS threshold of the MFA-based 17.6-km MMF link is improved by 5 dB, while the SFDR increases by 14.5 dB, with an input optical power of 17 dBm.  
In this paper, we first introduce the background and application fields of our work and propose the novel MPL solution. Section 2 presents the principle of the proposed scheme and the fabrication of the MFAs. Section 3 shows experiments on the measurements of SBS threshold and SFDR, and the corresponding result analysis. Section 4 is the conclusion and outlook.
# A Dynamic Distributed Deterministic Load-Balancer for Decentralized Hierarchical Infrastructures
## 7 Experimental Study
### Performance Evaluation for \(D^{3}\)-Tree
#### 7.1.1 Cost of Node Joins/Departures.  
To measure the network performance for node updates, we conducted experiments for three different value ranges of _node criticality_: [0.25 0.75], [0.35 0.65] [0.45 0.55].  
For a network of \(N\) initial nodes, we performed \(2N\) node updates. In a preliminary set of experiments with mixed operations (joins/departures), we observed that redistributions rarely occurred, thus leading to negligible node update costs. Hence, we decided to perform only one type of update, \(2N\) joins, that are expected to cause several redistributions.  
Figure 5(a) shows the average amortized redistribution cost, while Figure 5(b) depicts the same metrics in the worst case, where the same node (the leftmost leaf) is the host node for all node joins. Note that we have taken into account only the amortized cost of node joins causing redistributions since otherwise, the amortized cost is negligible. Figure 5(c) shows the redistribution rate for \(D^{3}\)-Tree in average and worst case.  
Through experiments, we observed that even in the worst-case scenario, the \(D^{3}\)-Tree node update and redistribution mechanism achieves a better-amortized redistribution cost, compared to that of BATON, BATON\({}^{*}\), and P-Ring.  
We also observed that in the average case, during node joins, the Invariant 4 is rarely violated to invoke a redistribution operation (Figure 5(c)). This also depends on the range  
Figure 5: The \(D^{3}\)-Tree simulator user interface.  
in which node criticality belongs. When the range is narrowed, more redistributions take place during node joins, but the amortized cost is low since the majority of redistributions occur in subtrees of low height, as shown in Figure 6d. This is more obvious in the worst case. When we use wide ranges, more node joins take place before a redistribution occurs, making the redistribution operation more costly since many nodes have been cumulated into the bucket of the leftmost leaf.  
Figure 6d shows in detail the allocation of redistribution height for different node criticality ranges in a network of 10,000 initial nodes. We observe that in the worst case, the number of redistributions is more than twice the number of redistributions of the average case.
# Industrial Dust Explosions. A Brief Review
## 3 A Catastrophic Aluminium-Alloy Dust Explosion in China in 2014
### Probable Ignition Source of the Initial Primary Explosion  
It appeared that it was impossible to identify any obvious ignition source. However, it was observed that the dust collecting barrel below the filter of the production line where the explosion had apparently started had its bottom completely blown out. Careful examination of the damaged barrel revealed a different-looking hole that had most probably been caused by corrosion over a long period prior to the explosion. Both the filter and barrel underneath were located outdoors. It had been raining heavily for two days before the explosion accident, and it still rained lightly at the time of the catastrophe. Rainwater had probably entered the barrel through the corroded hole and moistened the aluminium-alloy dust in the barrel. This water probably initiated the process, leading to self-ignition of the aluminium-alloy dust in the barrel according to the reaction equation:  
\[2\text{Al}+6\text{H}_{2}\text{O}=2\text{Al}(\text{OH})_{3}+3\text{H}_{2}+ \text{heat} \tag{1}\]  
Laboratory experiments that were conducted after the explosion accident gave support to this hypothesis. It was found that the unburnt aluminium-alloy dust that was retrieved from the barrel after the explosion was contaminated by some organic material from the wheel hub grinding/polishing process (polishing waxes etc.). This made the dust \"fluffy\" and gave it a significantly lower bulk density than those of deposits of pure dry aluminium alloy dust. The contaminated dust also had a lower thermal conductivity than would be expected for pure aluminium-alloy dust.  
A significant detail was observed during the laboratory experiments: after the self-ignition process had developed fully inside the wet contaminated metal dust deposit, open flames lasting for about 10 s were observed on top of the deposit surface. It seems reasonable to assume that these flames were due to the burning of the hydrogen released according to equation (1), possibly mixed with pyrolysis gases from the organic matter in the deposit. Such a flame probably ignited the cloud of aluminium alloy dust falling down from the filters in the dust collector during the shaking of the filters. The significant burning of hydrogen inside the dust in the barrel, before the combustion zone reached the powder surface at the top, was probably excluded by the free passage between the dust particles being significantly smaller than the quenching distance of hydrogen in air (about 0.5 mm at atmospheric pressure).  
Figure 6: Destroyed process lines on the second floor. From [PERSON] et al. (2016). Copyright (2016), with permission from Elsevier.
## 3 TR Rule and Its Approximate Combination Rules  
The TR rule is based on engineering experience. It is assumed that the maximum values of the effect components and the combined effect occur simultaneously, but there is no rigorous theoretical basis. In order to provide the TR rule with a rigorous theoretical basis, the simplified combination Equation, combination coefficient of TR1 rule and the numerical integral expression of the TR2 rule are proposed based on the theoretical knowledge of random vibration and mathematical statistics.
## 2 Data and Methodology
### Methodology
#### 2.3.3 Algorithm of SSM Estimation  
The backscattering coefficient is sensitive to the surface dielectric constant and is closely related to SSM. A previous study [41] has shown there is a good linear correlation between the soil backscattering coefficient (\(\sigma^{0}_{soil}\)) and SSM. Thus, the \(\sigma^{0}_{soil}\) can be expressed as follows:  
\[\sigma^{0}_{soil}=d\cdot\text{SSM}+e \tag{10}\]  
where, \(d\) and \(e\) are the parameters of the soil. To build the SSM estimation model, Equations (9) and (10) were substituted into the WCM. Finally, the SSM estimation model was established:  
\[\text{SSM}=l_{1}+l_{2}\sigma^{0}+l_{3}\text{VI}+l_{4}\text{VI}^{2}+l_{5}\text {VI}^{3}+l_{6}\text{VI}^{4}+l_{7}\sigma^{0}\sec\theta+l_{8}\sigma^{0}\text{ VI}\sec\theta+l_{9}\sigma^{0}\text{VI}^{2}\sec\theta \tag{11}\]where SSM is the surface soil moisture, cm\({}^{3}\)/cm\({}^{3}\); \(\sigma^{0}\) is the VV or VH backscattering coefficients, dB; VI is the vegetation index calculated from S2 images; \(\theta\) is the S1 incident angle (\({}^{\circ}\)); and \(l_{1}\)-\(l_{9}\) could be obtained by the method of the best-fitting regression.
## 4 Experimental Results and Analysis
### Analysis of Experimental Results
#### 4.3.2 Wine Quality-White Data Set
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & \(x_{1}\) & \(x_{2}\) & \(x_{3}\) & \(x_{4}\) & \(x_{5}\) & \(x_{6}\) & \(x_{7}\) & \(x_{8}\) & \(x_{9}\) & \(x_{10}\) & \(x_{11}\) \\ \hline MTS1 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 \\ MTS2 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ MTS3 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 & 1 \\ MTS4 & 1 & 1 & 1 & 1 & 0 & 0 & 1 & 1 & 1 & 1 & 1 \\ MTS5 & 0 & 1 & 0 & 1 & 0 & 1 & 1 & 1 & 1 & 1 & 1 \\ MTS6 & 0 & 1 & 1 & 1 & 0 & 1 & 1 & 1 & 1 & 0 & 1 \\ \hline \hline \end{tabular} Note. “1”means using the variable, “0” means not using the variable.  
\end{table} 
Table 8: Feature variable selection results of BT-MTS of the Wine quality-white data set. As can be seen from the table, the method of optimized BT-MTS in this paper outperforms the algorithms of BT-MTS, FCBF-SVM, SMOTE-KNN, and BPNN, indicating that the algorithm in this paper can better adapt to the data environment with a high imbalance ratio. Comparing the method of this paper with RF, we can find that the accuracy, macro-F1, kappa coefficient, and CBA of this paper's algorithm are slightly lower, but the total misclassification cost of the optimized BT-MTS is significantly lower, which also indicates that this paper considers the cost-sensitive joint optimization model to improve the focus on minority class samples in the classification process.
## 3 Results and Discussion
### Characterization of TPCB
#### 3.1.3 Thermal Properties of TPCB  
The thermogravimetric (TG), differential thermogravimetric (DTG), and differential scanning calorimetric (DSC) curves are shown in Figure 4 to study the thermal properties of TPCB. There are two mass loss stages in the TG image of TPCB. In detail, the temperature range of stage one is from 50 \({}^{\circ}\)C to 126 \({}^{\circ}\)C, the mass loss ratio in this stage is 1.42%, and the fastest loss rate can be found at a temperature of 94.3 \({}^{\circ}\)C. The peak value of 102.9 \({}^{\circ}\)C in the DSC curve shows that stage one is an endothermic phase, so it corresponds to the endothermic phase of the evaporation of water by TPCB. The temperature range of stage two is from 126 \({}^{\circ}\)C to 700 \({}^{\circ}\)C. There is a peak value of 416.4 \({}^{\circ}\)C in the DSC curve, which indicates that stage two is a heat-releasing phase and mainly corresponds the combustion of the residual organics in TPCB. The mass loss ratio of 10.38% is relatively larger in this stage, but the heating temperature in the preparation process of TPCB/GF-70 is not higher than 160 \({}^{\circ}\)C and the mass loss ratio at 160 \({}^{\circ}\)C is only 2.76%, which shows that the thermal properties of TPCB can satisfy the requirements for preparing a TPCB-modified asphalt binder.  
Figure 4: TG, DTG, and DSC curves of TPCB in the temperature range of 50 \({}^{\circ}\)C to 700 \({}^{\circ}\)C.  
Figure 3: The Gaussian fit of laser size analysis of TPCB.
## 2 Preliminaries and Problem Description
### A New Integral Barrier Lyapunov Function
Consider the system:_  
\[\dot{\eta}=h\left(\eta,t\right) \tag{8}\]  
_where the system states \(\eta:=\left[\omega,x\right]^{T}\in\mathrm{N}\) and function \(h:R_{+}\times\mathrm{N}\to R^{l+1}\) is piecewise continuous in \(t\) and locally Lipschitz in \(\eta\), uniformly in \(t\), on \(R_{+}\times\mathrm{N}\). Suppose that there are functions \(U:R^{l}\to R_{+}\) and \(V_{x}:\chi\to R_{+}\) are positive definite and continuously differentiable in their corresponding domains of definition, such that:_  
\[V_{x}\left(x\right)\rightarrow\infty\quad\text{as}\quad\left|x\right| \to k(t) \tag{9}\]  
\[\gamma_{1}\left(\left\|\omega\right\|\right)\leq U\left(\omega\right)\leq \gamma_{2}\left(\left\|\omega\right\|\right) \tag{10}\]  
_where \(\gamma_{1}\) and \(\gamma_{2}\) signify class \(k_{\infty}\) functions. Define \(V\left(\eta\right)=V_{x}\left(x\right)+U\left(\omega\right)\) and the initial state satisfies \(x\left(0\right)\in\chi\). If the time derivative of \(V\) in \(\left|x\right|<k(t)\) satisfies:_  
\[\dot{V}=\frac{\partial V}{\partial\eta}h\leq-\mu V+\lambda,\quad\eta\in \mathrm{N} \tag{11}\]  
_where \(\mu\) and \(\lambda\) are positive constant, then we have that \(\omega\) is bounded and \(x\left(t\right)\in\chi,\forall t\in\left[0,\infty\right)\)._  
Proof of Theorem 2.: According to the conditions on \(h\), we know that the existence and uniqueness of a maximal solution \(\eta\left(t\right)\) on the time interval \(\left[0,t_{\max}\right)\) can be guaranteed. On the premise of that \(x\left(0\right)\in\chi\), we have that \(V_{x}\left(x\left(0\right)\right)\) and \(V\left(\eta\left(0\right)\right)\) are existent. Solving the inequality (11) yields \(V\left(\eta\left(t\right)\right)\leq V\left(\eta\left(0\right)\right)+\lambda /\mu,\forall t\in\left[0,t_{\max}\right)\). In light of \(V\left(\eta\right)=V_{x}(x)+U(\omega)\) and the fact that \(V_{x}(x)\) is a positive function, we obtain that \(V_{x}(x)\) is bounded, \(\forall t\in\left[0,t_{\max}\right)\). Based on the fact that \(V_{x}(x)\rightarrow\infty\) only if \(x\to k(t)\) and boundedness of \(V_{x}(x)\), we can deduce that \(x>k(t)\), \(\forall t\in\left[0,t_{\max}\right)\). Therefore, there exists a compact subset \(\mathrm{K}\subset\mathrm{N}\) such that the maximal solution of (8) satisfies \(\eta\left(t\right)\in\mathrm{K}\), \(\forall t\in\left[0,t_{\max}\right)\). However, \(\eta\left(t\right)\) is defined in \(t\in\left[0,\infty\right)\). It is clear that \(x\left(t\right)\in\chi\), \(\forall t\in\left[0,\infty\right)\). The proof of Theorem 2 is completed. Remark 3: _In Theorem 2, the state space consists of the constrained state \(x\) and free states \(\omega\). The state \(x\) needs the integral barrier Lyapunov function \(V_{x}(x)\) to ensure it remains within the limits \(k(t)\) and \(-k(t)\), while the free states may require only quadratic Lyapunov functions such as \(U\left(\omega\right)=\frac{1}{2}\omega^{T}\omega\)._