esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Design and Simulations
### Electric Tubular Heater Design  
The designed tubular heater is adaptable to be run in inclined wells for oil-shale in situ resorting. The geometric model of the novel electric downhole tubular heater is presented in Figure 1a. The electric heater mainly consists of the following parts: (1) power cables (\(L_{1}\), \(L_{2}\), \(L_{3}\), and \(N\)) for delivering the electrical energy; (2) heating tubes for heating the gas flow to achieve the pyrolysis temperature of the oil shale; (3) an enclosure for withstanding the downhole operation pressure; (4) an insulation layer to reduce the heat loss and to avoid the creep deformation of the enclosure [19]; (5) supporting plates, which are used to align the tubular heaters to prevent the short circuit; and (6) ceramic tubes, which are used to insulate the support plates from the tubular heaters. Figure 1b,c present the left (inlet part of the flow) and right (outlet part of the flow) cross-sectional views of the layout of heating tubes. The tubular heaters are arranged in a three-phase electricity system. In each phase, the heating current passes through six tubular resistors that are connected in a series. The nitrogen gas with ambient temperature is fed into the heating tubes and flows from the left to right sides of the tube arrays. The thermal performance of the heater is evaluated by the numerical calculation of the temperature, velocity, and pressure fields. A control-variable scheme is applied by changing the heater geometrical parameters, such as tube lengths (\(L\)) and diameters (\(d\)), operating conditions (mass flow rate \(G\)), etc.  
In the initial thermal design, the outer diameter of the tubular heater is set to 250 mm to fit the downhole well dimension. The upper limit of the tube length is 7 m to save the manufacturing cost. The thickness of the insulation layer is no less than 50 mm to ensure that more than 95% of the electric heating can be absorbed by the nitrogen gas. In the restricted space, the corresponding inner-tube diameter should be less than 8 mm.
# Cloud Computing Based on Computational Characteristics for Disaster Monitoring
###### Abstract  
Resources related to remote-sensing data, computing, and models are scattered globally. The use of remote-sensing images for disaster-monitoring applications is data-intensive and involves complex algorithms. These characteristics make the timely and rapid processing of disaster-monitoring applications challenging and inefficient. Cloud computing provides a dynamically scalable resource over the Internet. The rapid development of cloud computing has led to an increase in the computational performance of data-intensive computing, providing powerful throughput by distributing computation across many distributed computers. However, the use of current cloud computing models in scientific applications using remote-sensing image data has been limited to a single image-processing algorithm rather than a well-established model and method. This poses problems for the development of complex disaster-monitoring applications on cloud platform architectures. For example, distributed computing strategies and remote-sensing image-processing algorithms are highly coupled and not reusable. The aims of this paper are to identify computational characteristics of various disaster-monitoring algorithms and classify them according to different computational characteristics; explore a reusable processing model based on the MapReduce programming model for disaster-monitoring applications; and then establish a programming model for each type of algorithm. This approach provides a simpler programming method for programmers to implement disaster-monitoring applications. Finally, some examples are given to explain the proposed method and test its performance.  
programming model; disaster monitoring; computational characteristics; remote-sensing data +
Footnote †: journal: Applied Sciences  
16 August 2020; 21 September 2020; Published: 24 September 2020
## 6 From Single-Point to Multi-Point Equalization
### Clustering Methods  
We can exploit the similarities between different spatially distributed room responses by clustering them according to a chosen distance measure. In [138], the _\"extremely large set\"_ of possible RTFs within an enclosure was grouped together and equalized by a smaller number of equalizers. The RTFs were modeled with all-pole filters using LPC analysis, and thus minimum-phase equalizers were designed. Then, vector quantization was performed to optimally classify the all-pole filters. The classification can be used as a spatial equalization library, achieving reduction in reverberation over a wide range of positions within the enclosure, depending on the actual position of the listener. The main limitation of this method is the necessity to extract and memorize a large set of room responses and equalizers and to track the position of the listener.  
A fuzzy c-means clustering method is applied in [30; 70; 71; 72; 121; 139; 140]. In the approach of [121; 139], _\"representative prototypical room responses\"_ are derived from several measured room responses that share similar characteristics using the fuzzy c-means unsupervised learning method. The prototypical responses are then combined to form _\"a general point response\"_ based on the fuzzy standard additive model of [PERSON] [141; 142]. The method employs a weighting according to _\"the level of activation\"_ of a prototype, depending upon the degrees of assignment of the room responses to the cluster containing the prototype. The equalizer is then computed from the inverse of the general point response using LPC analysis, _\"obtaining a significant improvement in equalization performance over the spatial averaging methods\"_ with the suppression of the peaks in the room magnitude spectra [139]. The method was further improved in [70; 71; 140] by applying the fuzzy c-means clustering to warped impulse responses, thus taking advantage of the perceptual properties of the ear. The approach was also combined with multirate filtering in [72] to allow effective filtering of the low frequency response at low sampling rates with computational savings.  
The approach of [70; 139] was later improved by applying frequency warping and fuzzy c-means clustering to the magnitude room responses [73; 75], with a strong improvement in terms of computational complexity. A weighted fuzzy c-means clustering was also proposed in [143], where the RIR samples were weighted in a different manner to account for the different effect they have on RRE.
# Exploratory Data Analysis and Searching Cliques in Graphs
## 3 The Auxiliary Graph Based on Covariance Coefficients  
In this section we describe how to construct an auxiliary graph \(G=(V,E)\) associated with a given data set. Let \(O_{1},\ldots,O_{m}\) be the objects in the given data set and let \(c(i,j)\) be the Pearson covariance coefficient between the objects \(O_{i}\) and \(O_{j}\). Here, \(1\leq i<j\leq m\). The numbers \(c(i,j)\) are the entries in the \(m\)-by-\(m\) covariance matrix of the objects. The nodes of \(G\) are the unordered pairs \(\{O_{i},O_{j}\}\) for each \(i\), \(j\), \(1\leq i<j\leq m\); that is,  
\[V=\{\{O_{i},O_{j}\}:\,1\leq i<j\leq m\}.\]  
Two distinct nodes \(\{O_{p},O_{q}\}\) and \(\{O_{r},O_{s}\}\) will be adjacent in \(G\) if  
\[-\varepsilon\leq\det\begin{pmatrix}c(p,r)&c(p,s)\\ c(q,r)&c(q,s)\end{pmatrix}\leq\varepsilon,\]  
where \(\varepsilon\) is a predefined small positive threshold.  
Lemma 2: _Locating cliques in the covariance-based auxiliary graph \(G\) can be used to locate approximately \(1\)-dimensional linear manifolds formed by objects in a given data set._  
Proof: Suppose for a moment that the rank of the covariance matrix of the objects \(O^{\prime}_{1},\ldots,O^{\prime}_{t}\) is equal to one. In this situation there are numbers \(a_{1},\ldots,a_{t}\) and \(b_{1},\ldots,b_{s}\) such that the covariance coefficient \(c^{\prime}(i,j)\) between the objects \(O^{\prime}_{j}\) and \(O^{\prime}_{j}\) is equal to the product \(a_{i}b_{j}\) for each \(i\), \(j\), \(1\leq i,j\leq t\). Using this information we obtain  
\[\det\begin{pmatrix}c^{\prime}(p,r)&c^{\prime}(p,s)\\ c^{\prime}(q,r)&c^{\prime}(q,s)\end{pmatrix}=\det\begin{pmatrix}a_{p}b_{r}&a_{ p}b_{s}\\ a_{q}b_{r}&a_{q}b_{s}\end{pmatrix}=0.\]  
This means that the two distinct nodes \(\{O^{\prime}_{p},O^{\prime}_{q}\}\) and \(\{O^{\prime}_{r},O^{\prime}_{s}\}\) are adjacent in \(G\). Therefore, the nodes \(\{O^{\prime}_{i},O^{\prime}_{j}\}\), \(1\leq i<j\leq t\) are nodes of a \([t(t-1)/2]\) clique in the graph \(G\).  
Similarly, when the covariance matrix of the objects \(O^{\prime}_{1},\ldots,O^{\prime}_{t}\) can be well approximated by a rank-one matrix, then the nodes \(\{O^{\prime}_{i},O^{\prime}_{j}\}\), \(1\leq i<j\leq t\) in \(G\) are nodes of a \([t(t-1)/2]\) clique in the graph \(G\). Finally, if the nodes \(\{O^{\prime}_{i},O^{\prime}_{j}\}\), \(1\leq i<j\leq t\) are nodes of a \([t(t-1)/2]\)-clique in the graph \(G\); then, the covariance matrix of the objects \(O^{\prime}_{1},\ldots,O^{\prime}_{t}\) can be well approximated by a rank-one matrix.
## 3 System Overview
### Hardware Architecture  
The hardware is based on six main components, including a laser ToF distance sensor, ESP32 SoC [29], and light emitting diode (LED) indicator, as shown in Figure 1a. A hardware block diagram is provided in Figure 2. The ESP32 communicates with the distance sensors using the I2C protocol. The LDO regulator adjusts the voltage from the battery to be no more than 3.3 V, because a full 3.3 V battery outputs 4.2 V, which is more than the recommended supply for the ESP32 SoC. The LDO regulator's output is connected both to the SoC power input and to its analog input pin in order to measure the battery voltage level over time. The charger module allows the system to operate continuously when the battery voltage drops below 3.1 V. The battery is connected to the charger module. The final component is the LED indicator. The LED shows the active status of the device. A list of all components and essential technical details is shown in Table 1.  
Figure 1: System components. (**a**) Hardware components. (**b**) Paper supply monitoring dashboard. (**c**) Summary of web application.  
Figure 2: Hardware block diagram.
## 6 Conclusions  
In this study, numerical simulations have been performed to study the entire steam ejector system. A wet steam model was implemented to describe the condensation and evaporation processes of the water droplets within the system. The numerical model was validated against experimental results which were found to agree well with the experimental data in terms of the static pressure distribution along the steam ejector. In addition, the model was able to provide an accurate prediction of the changes in water droplets, axial pressure, temperature, and pressure distribution within the entire system. The investigation into different wetness of inlet flows showed that in the whole working process, the wetness of secondary flow influences the pumping efficiency more than the primary flow. Within a certain range (from 0 to 0.1), the wetness of the secondary flow has an inverse proportion relationship with the entrainment ratio. The nucleation rate of water droplets is improved by the decrease of the wetness of the secondary flow at the front half of the mixing chamber, leading to better performance of the steam ejector. On the other hand, the wetness of the primary flow plays a domain role on the spontaneous condensation at the diffuser part. In summary, the following are the key findings from this study:  
1. Based on the ideal gas model, the realizable k\(-\varepsilon\) turbulence model with enhanced wall functions showed a better performance of simulating the working process of the steam ejector.
2. Compared with the ideal gas model, the wet steam model showed a better agreement against the experimental data, especially in the mixing chamber section with approximately 23% improvement.
3. The spontaneous condensation happened in the nozzle and during the mixing chamber, the condensation effect had an influence on the entrainment ratio.
4. By reducing the wetness of the secondary fluid or improving the wetness of the primary fluid, the condensation effect lasted longer and improved the entrainment ratio, which means the performance of the steam ejector is enhanced.  
Conceptualization, G.H.Y. and A.L.; methodology, A.L. and G.H.Y.; software, A.C.Y.Y. and T.B.Y.C.; data curation, A.L. and H.L.; formal analysis, A.C.Y.Y. and C.W.; numerical simulations, A.L. and A.C.Y.Y.; validation, T.B.Y.C. and C.W.; writing--original draft preparation, A.L. and A.C.Y.Y.; writing--review and editing, C.W. and W.Y.; visualization, R.C. and T.B.Y.C.; supervision, G.H.Y. and V.T.; project administration, A.L. and W.Y.; funding acquisition, G.H.Y.  
This research was funded by the Australian Research Council (ARC Industrial Transformation Training Centre IC170,100,032) and the Australian Government Research Training Program. All financial and technical support are deeply appreciated by the authors.  
The authors declare no conflict of interest.
## 5 Sensing Methods for Formaldehyde
### Vegetable and Fruit
The authors investigated the possible interferences, NH\({}_{4}\)+, Na+, K+, Cl-, SO\({}_{4}\)\({}^{-2}\), CH\({}^{2}\)COO\({}^{-}\), CO\({}_{3}\)\({}^{-2}\), NO\({}_{3}\)\({}^{-}\), NO\({}_{2}\)\({}^{-}\), and urea: these interferences were evaluated not to exceed 5% of the current response change, showing that they did not interfere with the FA determination by PdNC-GNF/WS\({}_{2}\) electrode. Finally, three papers, just discussed above, report the FA determination in fruits, cereals, and vegetables by means of conventional techniques, i.e., spectrophotometric technique, SPE-GC-MS, and HPLC, respectively [52; 53; 63]. The first paper describes a UV-Vis procedure using Nash reagent (4-amino- 3-penten-2-one) [68] and reading the absorbance at 415 nm [52]. The authors built the linear regression equation (r\({}^{2}\) 0.9209 in the range 0-10 ppm) and after analyzing different samples (bananas, carrots, tomato, radish, lemon, pineapples, mangos, pomegranates, grapes, cabbage, onion, potato, cucumber) but they did not show any other analytical parameters for evaluating the goodness of their procedure. [PERSON], et al., reported an SPME-GC-MS procedure: they used an SPME fiber coated with 65 um polydimethylsiloxane/divinylbenzenebe (PDMS/DVB) for adsorbing volatile compounds (over FA they also analyzed acetaldehyde) after derivatization with O-(2,3,4,5,6-pentafluoro-benzyl)-hydroxylamine hydrochloride (PFBHA) [53]. They analyzed different samples (both fruits and alcohol-free beverages) achieving very good analytical conditions: LODs ranging between 5.74 and 175 ng g\({}^{-1}\) according to the matrix, linearity \(<\) 0.998 in the range 5-1000 ng g\({}^{-1}\), recoveries in the range 68.37 and 128.22% and RSD \(<\) 14.53. The third paper by [PERSON], et al., showed the optimization and validation of an HPLC method for determining FA in mango, rice, leafy, and fish and milk as well [63]. The authors' intent was to harmonize the methods present in the literature regarding the HPLC-DAD (diode array detector) analysis. The determination was carried out after derivatization with 2,4-dinitrophenylhydrazine (2,4-DNPH) for forming HCHO-2,4-DNPH which is detectable at 355 nm. Particularly, r\({}^{2}\) 0.99 in the range 1.0-100 m L\({}^{-1}\), LOD 0.32 mg L\({}^{-1}\), and LOQ 1.08 mg L\({}^{-1}\) with recoveries ranging between 99.8 and 115.6% and RSD \(<\) 10.59% were the analytical parameters reached by authors under the optimal conditions: mainly, the temperature was a critical factor for the elution (at 33 \({}^{\circ}\)C FA co-eluted with other compounds present in the matrices, so the temperature was set up at 35-40 \({}^{\circ}\)C) whereas the pH solution and methanol% in the mobile phase did not affect the peak areas significantly.
# Enhancement of ECE SuperPin Curved Reflex Reflector by the Use of Double Pins with Corner Cubes
## 2 Principles
In this study, we used a hollow corner cube; the reflections can be done for all six possible sequences of reflections by taking the incident beam,given by the vectors x\" = -1, y\" = z\" = 0, and reflecting it from each of the normals to the faces in the double-primed coordinate system. The y\" and z\" coordinates of the reflected beam give the deviations from the incident direction. The beam spread at normal incidence when all dihedral angles are offset by the same amount is given by the formula \(\varepsilon_{1}=\frac{4}{3}\sqrt{6}\) n \(\delta_{1}\), \(\varepsilon_{2}=\frac{4}{3}\sqrt{6}\) n \(\delta_{2}\), \(\varepsilon_{3}=\frac{4}{3}\sqrt{6}\) n \(\delta_{3}\), where \(\delta\) is the angle by which the dihedral angles exceed \(90^{\circ}\) and \(\varepsilon\) is the angle between the incident and the reflected rays. When the light passes through CCRs, there will be a change in the direction of reflection. The direction of the incident beam is determined by \(\theta^{\prime}\) and \(\phi^{\prime}\) in the primed coordinate system. Based on the above mathematical theory, the optical simulations through the TracePro software (Lambda Research Corporation of Littleton, Massachusetts, USA) show that CCRs with the same dihedral angle can reflect six beams with the same reflection angle \(\varepsilon\) with respect to the incident beam. When the dihedral angle is \(\delta_{1}=0.095^{\circ}\); (\(\alpha=90.095^{\circ}\)); \(\delta_{2}=0.095^{\circ}\); (\(\beta=90.095^{\circ}\)); \(\delta_{3}=0.12^{\circ}\) (\(\gamma=90.12^{\circ}\)), the reflected beams can be found that \(\varepsilon_{1}\) = 0.33\({}^{\circ}\), \(\varepsilon_{2}=\varepsilon_{3}=0.31^{\circ}\), which fit EU ECE regulation requirements. The simulation result is shown in Figure 5c. By using the different angles between each pair of facets, we can move the three retro-reflected rays on the top into one direction and the other three into another direction, as shown in Figure 5c. In this way, we dramatically improve the optical performance of a corner cube, because the retro-reflected energy in a given direction is tripled. We call this techique SuperPin technology. The experiment beam pattern retro-reflected from the SuperPin corner is shown in next section.
## 5 Results
### LSTM Network  
Another classification model, based on Long Short-Term Memory (LSTM) networks, was developed to predict the driver's state more accurately. Unlike the previous models, the LSTM model uses the full time series of measured values rather than rely on calculated metrics such as average speed. It was anticipated that using time series data would lead to better classification accuracy by preserving more of the dynamic information inherent in the signals.  
The model was trained with approximately 90% of the participants' data, while the remaining 10% were used for testing. This high training proportion was chosen due to the relatively small sample size. Each scenario was analyzed individually, and the model was trained and tested with the respective datasets. The model was trained over 100 epochs, with training improvements generally stabilizing around 60-70 epochs. In line with previous results, the fusion of multiple data sources tended to yield better results than using a single dataset, though the accuracy varied across scenarios:  
* The average testing accuracy in urban scenarios ranged from approximately 50% to 70%.
* The best testing accuracy was observed on rural roads, with maximum values exceeding 80%.
* A poor testing accuracy was noted on the highway, with values around 50%.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Scenario** & **Model** & **Data** & **Accuracy** \\ \hline City & Random Forest & Fusion Interpretation Level & 70 \\ \hline Rural Road & Random Forest & Fusion Data Level & 73 \\ \hline Highway & Logistic Regression & Fusion Data Level & 61.67 \\ \hline Complete Track & Logistic Regression & Camera & 68.3 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Best model performance in scenarios.  
During training and testing, it became evident that classification accuracy was more influenced by driving behavior than by eye-tracking data. This explains why the test accuracy was higher on rural roads, where driving behavior showed the most pronounced differences between sober and intoxated states.  
However, the small sample size posed a significant challenge for the LSTM model. Overfitting was a persistent issue during training, and attempts to mitigate this by augmenting the data with synthetic samples did not yield any meaningful improvement in classification performance.
###### Abstract  
In traditional structural disaster prevention design, the effects of various disasters on structures are usually considered separately, and the effects of multi-type hazards are rarely considered. The traditional Tuned Mass Damper (TMD) and Active Mass Damper/Driver (AMD) are ineffective for the control of swing vibration. The Tuned Rotary Inertia Damper (TRID) system has the problems of being ineffective under multi-type hazard excitation and exhibiting a limited robustness. The Active Rotary Inertia Driver (ARID) system is proposed to solve these problems and the robustness of such an active control system is investigated in this paper. Firstly, the equations of motion corresponding to the in-plane swing vibration of the suspended structure with the ARID/TRID system are established. The control algorithm for the ARID system is designed based on the Linear Quadratic Regulator (LQR) algorithm. Next, numerical analyses carried out using Simulink are presented. Then, numerical analyses and experimental investigations corresponding to five working conditions, i.e., free vibration, forced vibration, sweep excitation, earthquake excitation, and sea wave excitation, are introduced. Lastly, the numerical analyses and experimental results of the ARID system, and numerical results of the TRID system, are compared to demonstrate the effectiveness and robustness of the ARID control system. It can be concluded that the ARID system is effective and feasible in structural swing vibration control and it exhibits a better control robustness than the TRID system. Furthermore, the feasibility of applying the ARID control system to multi-type hazard excitations is validated.  
swing vibration control; active control; Active Rotary Inertia Driver; multi-type hazards; shaking table experiment +
Footnote †: journal: Applied  
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