esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Analysis
### The Main \(\alpha\)-DCs Formed  
As can be seen from Table 2, \(\alpha\)-DCs in the experimental simulation system included MGO, BD, GO, PD and OP, and the sum of their relative contents increased with the increase in the steaming time, which was consistent with the trend of the relative content of phenylacetaldehyde increasing with the increase in the steaming time (Figure 2). When \(\alpha\)-DCs were captured by OPD in the experimental simulation system, no phenylacetaldehyde was detected, indicating that the intermediate product of phenylacetaldehyde was \(\alpha\)-DCs. Table 3 shows that the main \(\alpha\)-DCs in simulation system 2 were MGO, BD, GO and PD. It can be seen that the two simulation systems generate relatively high contents of MGO and BD from 0 min, namely, 40 min extraction at 80 \({}^{\circ}\)C, indicating that MGO and BD mainly come from the caramelization reaction of glucose by comparing the reactants of the two simulation systems and the products in Tables 2 and 3. The relative contents of MGO and BD in the experimental simulation system were slightly higher than that in simulation system 2, indicating that only a small amount of MGO and BD came from the Maillard reaction between glucose and phenylalanine. The relative content of GO ranked third among the intermediates, and the relative content of GO in the two simulation systems was similar, indicating that GO mainly comes from the caramelization reaction of glucose. GO is produced after 20 min of steaming, while MGO and BD are produced at 0 min of steaming, indicating that MGO and BD are more likely to be produced in a caramelization reaction. The relative content of PD in the simulation system was about 50% of that in the experimental simulation system, indicating that both the caramelization and Maillard reactions can generate PD, but the relative content of PD in the two simulation systems was small, and the contribution of PD to the generation of the characteristic flavour substance  
Figure 3: The relative content of phenylacetaldehyde produced by different reaction ratios of glucose and phenylalanine.  
Figure 2: The relative contents of phenylacetaldehyde at different steaming times in simulated system 1.  
phenylacetaldehyde was small. OP only existed in the experimental simulation system and was derived from the Maillard reaction between glucose and phenylalanine, but its relative content was small, and it did not contribute much to the formation of the characteristic flavour substance phenylacetaldehyde.  
Therefore, the main \(\alpha\)-DCs in the experimental simulation system were MGO, BD and GO, which mainly came from the caramelization reaction of glucose. Their relative content was high, and they contributed the most to the formation of the characteristic flavour substance phenylacetaldehyde.
# Planetary Gearbox Fault Diagnosis Based on ICEEMD-Time-Frequency Information Entropy and VPMCD
## 3 Fault Classification Based on VPMCD  
In mechanical fault diagnosis, there is an intrinsic relationship between the characteristic values of signals. Their intrinsic relationships will change differently under different operation conditions [27; 28; 29].  
The variable predictive model based class discriminate (VPMCD) method is a pattern recognition method based on the variable predictive model (VPM). According to the relationship between characteristic values, a prediction model is established to classify the test samples [30; 31; 32; 33]:  
Linear model (L):  
\[X_{i}=b_{0}+\sum_{j=1}^{r}b_{j}X_{j} \tag{11}\]  
Linear interaction model (LI):  
\[X_{i}=b_{0}+\sum_{j=1}^{r}b_{j}X_{j}+\sum_{j=1}^{r}\sum_{k=j+1}^{r}b_{jk}X_{j}X _{k} \tag{12}\]  
Quadratic interaction model (QI):  
\[X_{i}=b_{0}+\sum_{j=1}^{r}b_{j}X_{j}+\sum_{j=1}^{r}b_{jj}X_{j}^{2}+\sum_{j=1}^{ r-1}\sum_{k=j+1}^{r}b_{jk}X_{j}X_{k} \tag{13}\]  
Quadratic model (Q):  
\[X_{i}=b_{0}+\sum_{j=1}^{r}b_{j}X_{j}+\sum_{j=1}^{r}b_{jj}X_{j}^{2} \tag{14}\]  
where \(r\) is model order, \(r\leq k-1,\ k\) is number of feature values for each fault type, and \(j\) is number of fault types. \(X_{j}(j\
eq i)\) is the predictive variable, \(X_{i}\) is the predicted variable, and \(b_{0}\), \(b_{j}\), \(b_{jj}\) and \(b_{jk}\) are model parameters.  
Select one of the four models as a predictive model, and its expression is as follows [34; 35]:  
\[X_{i}=f\big{(}X_{j},b_{0},b_{j},b_{jj},b_{jk}\big{)}+e \tag{15}\]where \(e\) is the prediction error.  
Suppose there are \(n\) fault types, and \(k\) prediction models are built for each fault type to obtain \(n\times k\) prediction models. Calculate the sum of squared prediction errors of \(k\) eigenvalues under the same fault type. The fault category corresponding to the model with the smallest sum of squares of the prediction error is the final output fault mode. Therefore, the VPMCD classification algorithm can be applied not only into linear classification, but also into nonlinear classification.
# Case Study of an Integrated Design and Technical Concept for a Scalable Hyperloop System
###### Abstract  
This paper presents the design process and resulting technical concept for an integrated hyperloop system, aimed at realizing efficient high-speed ground transportation. This study integrates various functions into a coherent and technically feasible solution, with key design decisions that optimize performance and cost-efficiency. An iterative design process with domain-specific experts, regular reviews, and a dataset with a single source of truth were employed to ensure continuous and collective progress. The proposed hyperloop system features a maximum speed of \(600\,\frac{\text{km}}{\text{n}}\) and a capacity of 21 passengers per pod (vehicle). It employs air docks for efficient boarding, electromagnetic suspension (EMS) combined with electrodynamic suspension (EDS) for high-speed lane switching, and short stator motor technology for propulsion. Cooling is managed through water evaporation at an operating pressure of \(10\,\text{mbar}\), while a \(300\,\text{kW}\) inductive power supply (IPS) provides onboard power. The design includes a safety system that avoids emergency exits along the track and utilizes separated safety-critical and high-bandwidth communication. With predirelated concrete parts used for the tube, construction costs can be reduced and scalability improved. A dimensioned cross-sectional drawing, as well as a preliminary pod mass budget and station layout, are provided, highlighting critical technical systems and their interactions. Calculations of energy consumption per passenger kilometer, accounting for all functions, demonstrate a distinct advantage over existing modes of transportation, achieving greater efficiency even at high speeds and with smaller vehicle sizes. This work demonstrates the potential of a well-integrated hyperloop system to significantly enhance transportation efficiency and sustainability, positioning it as a promising extension to existing modes of travel. The findings offer a solid framework for future hyperloop development, encouraging further research, standardization efforts, and public dissemination for continued advancements.  
Keywords:design decisions; technical concept; hyperloop; maglev; magnetic levitation; magnetic propulsion; system overview; high-speed transportation +
Footnote †: journal:
## 3 Experimental Process and Analysis
### 3-D Response Surface Analysis  
For a better demonstration of the influence of the interaction between two variables, the 3-D plots were adopted. If the interaction has a significant impact on the response surface, the variation of 3-D plot will be accordingly large.  
Figure 7a shows that when \(R_{1}\) and \(s\) interact, if we fix one of the variables and increase the other variable, the maximum electric field intensity will decrease. As can be seen from the trend of the surface graph, the maximum electric field intensity decreased faster with \(R_{1}\) than with \(s\), indicating that \(R_{1}\) has a greater influence on the response value. Comparing Figure 7a with Figure 7b, the response surface of Figure 7b fluctuated slightly, which means that the change of the interaction term \(hw\) has no significant effect on the maximum electric field intensity. Similarly, \(hs\) and \(sw\) are not significant factors either.  
As can be seen from Figure 7c,d, compared with \(s\) and \(w\), the change of the maximum electric field intensity was larger with the change of \(R_{1}\). The maximum electric field intensity increases with the increase of \(w\), which has negative effects on the model. Therefore, in the structural design process, a smaller starting position \(w\) should be selected as far as possible within a reasonable range. From the variation range of the two figures, the interactions between \(R_{1}\) and \(h\), \(R_{1}\), and \(w\) may be a significant factor affecting the maximum electric field intensity. However, since their \(p\)-values in Table 8 are little larger than 0.05, \(R_{1}h\) and \(R_{1}w\) can be considered insignificant factors.  
\begin{table}
\begin{tabular}{c c c} \hline
**Run** & **Actual Values/(V/mm)** & **Predicted Values/(V/mm)** \\ \hline  
046.17 & 11,138.85 \\ \hline \end{tabular}
\end{table}
Table 9: Comparisons between predicted and actual values.  
Through the above analysis, the insignificant factors, such as \(R_{1}h\), \(R_{1}w\), \(hs\), \(hw\), \(sw\), \(h^{2}\), and \(w^{2}\), are removed, and the final response surface equation with a higher prediction ability is shown in Equation (8).  
\[\begin{split} y=12419.312-371.458*R_{1}-55.687*h-783.092*s+84.888*w \\ -9.701*R_{1}*s+9.667*R_{1}{}^{2}+93.357*s^{2}\end{split} \tag{8}\]
# Change Detection of Water Resources via Remote Sensing: An L-V-NSCT Approach
## 1 Introduction
[11] presented a change detection method for multitemporal synthetic aperture radar images based on PCANet and exploited representative neighborhood features from each pixel using PCA filters as convolutional filters. [PERSON] et al. [12] proposed a difference image analysis approach based on deep neural networks for use on image change detection problems. [PERSON] et al. [13] put forward a novel approach for change detection in SAR images. This approach classified changed and unchanged regions by fuzzy c-means (FCM) clustering with a Markov random field (MRF) energy function. At present, commonly used change detection methods are not accurate enough to detect the edge contours of rivers, lakes, urban roads, and forest vegetation in severely damaged areas and there are too many small areas of debris in the disaster areas, which are judged as image changes. At the same time, the impact of this noise will lead to high error rates and false alarm rates. To dynamically monitor the changes of inland surface resources such as Ocean Lake wetlands and lake swamps, this paper proposes an L-V-NECT approach based on the NsCT and the logarithmic variation function. Firstly, NsCT transform was used to decompose the detected image and the VSSIM algorithm was used to extract the texture information difference at different scales after multi-scale decomposition. Secondly, the texture difference images of rivers, lakes and other objects were fused by utilizing the NsCT inverse transform. Finally, the spectral information, texture information, and space of the objects were fully considered. The fuzzy c-means clustering model (CFCM) was designed to classify the difference features of the above images. The experiments in subsequent chapters can prove the effectiveness of the method. At the same time, the greatest innovation of this method was that it has good practicability and has been applied in the protection of Dongting Lake. On the basis of the GF-2 image, the method regularly provided the relevant departments with test results and reference.
## 1 Introduction
Moreover, in [15], the comparison between nonlinear MPC and proportional-integrative-derivative (PID) controllers was investigated to optimise fuel consumption and guarantee the safety of a class of eight trucks over hilly terrain. Among these different approaches, in this paper, a centralised control system is developed to drive a platoon of vehicles along a straight path, designed to target energy saving, safety, and dynamic performance by investigating two control techniques: a classic one used as a reference, represented by the linear quadratic controller (LQC), and a novel one based on the reinforcement learning (RL) algorithm. The LQC strategy has been selected due to its capability to guarantee system stability as well as its fast tuning process and fast computational time. On the other hand, RL was selected as an emerging technology with high innovative potential. RL is a promising control technique for CACC algorithms because it guarantees adaptability to different situations and the possibility to continue its training online; moreover, it has proven to be particularly effective in handling uncertain and hard-to-predict environments. Generically speaking, RL allows us to directly handle the nonlinear dynamics of physical systems. If the training phase is properly designed, an RL-based CACC system can learn how to adapt to the inherent variability in aspects such as road conditions, traffic patterns, and driver behaviour. Furthermore, the potential of RL solutions has been widely assessed in the scientific literature. The control of a vehicle platoon has a large action and state space, thus making deep RL (DRL)-based solutions suitable for the problem [16; 17; 18; 19]. Moreover, the chosen DRL approach is model-free, i.e., it does not require a model to predict the behaviour of the system as opposed to model-predictive control (MPC) [20]. It is also worth observing that whereas LQC requires a quadratic objective function, DRL does not hold any constraint on the reward function shape, which can also be a non-quadratic cost function. Yet, differently from other analytical control methods, DRL-based control cannot theoretically guarantee convergence or system stability. It is indeed a non-linear control that learns through interaction with an environment, pairing the action evaluated to be the best for a given state with the state itself through the experience gained during training. Thus, its performance cannot be mathematically or theoretically guaranteed since it depends not only on the mathematical formulation but also on the training duration and other parameter settings. Further information can be found in Section 4, where the DRL-based control is described. The paper is divided as follows: in Section 2, the vehicle platoon model is presented; in Sections 3 and 4, the LQC and RL architectures are respectively introduced. In Section 5, the results of platoon simulation, tracking an FTP75 driving cycle, and a cut-in scenario are presented with the aim of comparing the CACC controllers by means of a set of performance, energy, and safety indices. Finally, the conclusions of the research are reported in Section 6.
# Isotropic IoT-Based Magnetic Flux Density Meter Implementation for ELF Field Measurements +
###### Abstract  
This article presents the basic principles for an Extremely Low Frequency (ELF) IoT-based isotropic meter implementation, which can measure magnetic flux density from 100 nT up to 10. The identical sensor probes are used for isotropic field measurements in the X, Y, and Z planes. The prototype has a flat response across the frequency range from 40 Hz to 10 kHz, detecting and measuring several magnetic field sources. The proposed low-cost meter can measure fields from the power supply network and its harmonic frequencies in the operating frequency band. The proposed magnetic flux density meter circuit is simple to implement and the measured field can be displayed on any mobile device with Wi-Fi connectivity. An Arduino board with the embedded Wi-Fi Nina module is responsible for data transferring from the sensor to the cloud as a complete IoT solution, supported by the Blynk application via Android and iOS operating systems or web interface. In addition, an ELF energy harvesting (EH) circuit was also proposed in our study for the utilization of the alternating magnetic fields (50 Hz) derived from the operation of several consumer devices such as transformers, power supplies, hair dryers, etc. using low-consumption applications. Experimental measurements showed that the (DC) harvesting voltage can reach up to 4.2 volts from the magnetic field of 33, caused by the operation of an electric hair dryer and can fully charge the 100  storage capacitor (C_s_) of the proposed EH system in about 3 min.  
Internet of Things (IoT); Extreme Low Frequencies (ELF); magnetic field measurements; magnetic flux density; magnetic field energy harvesting; detection coil; RMS detector; reasonable-gain integrator
# Effect of Fuel Composition on Carbon Black Formation Pathways
###### Abstract  
Differences in lamellae length, stacking, and particularly a difference in the core-to-shell radial structure are observed for carbon blacks produced using different feedstocks. Carbon black (CB) produced using a coal tar (CT) feedstock formed particles with amorphous cores exhibiting a sharp transition to extended lamellae oriented about the periphery of the particle. In contrast, the carbon black produced from fluidized catalytic cracker (FCC) decant oil as feedstock formed particles with a single nucleated core possess a rather uniform radial transition--reflecting the presence of ordered, concentric lamellae across most of the particle radius. Minimal disorder was observed in the core while the undulations in perimeter lamellae were fewer. Our interpretation for these structural dissimilarities is premised on differences in fuel composition, specifically component classes as found by saturate, aromatic, resin, asphaltene (SARA) analysis. These in turn lead to variation in the relative rates of particle nucleation and particle growth by pyrolysis products, moderated by temperature. Electron energy loss spectroscopy reveals radial variation in the sp\({}^{2}\) content between the different feedstocks consistent with observed nanostructures. Collectively these results are interpreted in terms of an offset in nucleation and growth--dependent upon the relative contributions of feedstock aromatic content and pyrolysis processes to particle nucleation and growth. To further test the postulate of different formation conditions for the two carbon blacks pulsed laser annealing was applied. The high temperature heating accentuated the dissimilarities in nanostructure and chemistry--leading to stark dissimilarities. These differences were also manifested by comparing oxidative reactivity.  
carbon black; nanostructure; HRTEM; laser annealing; flame formed carbon; fuel composition +
Footnote †: journal: Applied  
Article
## 5 Analysis of Chatter Stability of Two Boring Bar Models  
To analyze how the shear deformation and rotational inertia affect the chatter stability of the boring bar models, we designed a one-dimensional model to study the critical cutting depth and the corresponding spindle speed, which are calculated according to Equations (31) and (32) [34]:  
\[b_{\lim}=\frac{2\zeta\omega_{1}\omega_{c}M}{K_{c}Y_{1}^{2}(L)\sin(2\arctan \left(\frac{2\zeta\omega_{1}\omega_{c}}{\omega_{c}^{2}-\omega_{1}^{2}}\right))} \tag{35}\]  
\[n=\frac{60\omega_{c}}{(2k+1)\pi+2\arctan\left(\frac{2\zeta\omega_{1}\omega_{ c}}{\omega_{c}^{2}-\omega_{1}^{2}}\right)},k=0,1,2 \tag{36}\]  
where \(b_{\lim}\) is the cutting depth, \(n\) is the spindle speed, \(\omega_{c}\) is the chatter frequency of the boring bar, \(\omega_{1}\) is the first natural frequency of the boring bar, \(\zeta\) is the damping coefficient of the boring bar, \(M=\int_{0}^{L}\rho AY_{i}^{2}(z)dz\) is the mass of the boring bar, and \(K_{c}\) is the coefficient of the cutting force (N/mm\({}^{2}\)). In order to compare the difference of cutting stability of these two boring bars, the damping coefficient \(\zeta\) is set to 0.1358 and the cutting force coefficient \(K_{c}\) is set to 2.3391 \(\times\) 10\({}^{6}\), and other parameters are set according to the comparison requirements (which is illustrated in the following subsections). The natural frequency \(\omega_{1}\) of shear boring bar is calculated by AMDM method. Differently, the natural frequency of non-shear boring bar is calculated by Equation (27). The MATLAB software is used to plot Lobe diagram.  
Figure 6: Natural frequencies of these two models for different composite materials.
## 2 Proposed Algorithm
### Algorithm for the Cluster Head Election  
The election algorithm presented in Algorithm 1 rules the process of every node to select itself as a CH. After receiving any message from the BS, all the nodes update their _skip_ value and it obtains the output of the Type-2 Fuzzy System (\(o_{l}\), \(o_{h}\)). This output is mapped in persistent memory, and it is compared with a random number newly generated depending on the node history as can be seen in Algorithm 1. Then, the node that was operating as a CH continues with this role when the random number is lower than or equal to \(o_{l}\). If this condition does not hold, the node stops operating as CH Alternatively, a node that was not working as a CH in the previous selection will become a new CH only when the random number is lower than or equal to \(o_{h}\). This process is repeated every _skip_ rounds until a new message from the BS is received or the node depletes its battery.  
Once a node selects itself as a CH, it sends a message to the entire network. In each round, it receives data from the nodes that are attached to it. Then, it sends the aggregated data to the BS. The CH adds the number of nodes that have been connected to it in that message. It is important to note that the CH structure is maintained for _skip_ rounds and the CH configuration is only sent at the beginning of this set of rounds. In that way, as was stated before, the expensive CH election process is not carried out in each round and we get a trade-off between energy waste, the optimal selection of CHs, and the messages sent. Overall, with this technique, the proposed algorithm achieves a better performance than other representative examples of the bibliography as it is demonstrated in the next section.  
```
ifnode=CHthen if\(rand\leq o_{l}\)then node=CH else node=only sensor end ifelse if\(rand\leq o_{h}\)then node=CH else node=only sensor end
```  
**Algorithm 1**Election of Cluster Head