esa-sceva/satcom-chunk-collection huggingface dataset:
###### Abstract  
Based on tunable diode laser absorption spectroscopy (TDLAS), two-dimensional (2D) distribution reconstructions of gas concentration and temperature are realized using an algebraic reconstruction technique (ART). The influence of the beam distribution and grid size on combustion field reconstruction is investigated to attain optimal reconstruction results with a limited number of beams. Under limited optical-path numbers, it shows that a better spatial resolution is attainable only when the laser beam paths are vertical and parallel to the symmetry axis of the combustion field. Furthermore, experiments with 16 beam paths using one and two flat flame combustion fields are carried out in different fuel-air equivalence ratios under room temperature. The results are in agreement with the simulation results, and the time resolution is less than 1 s.  
absorption; spectroscopy; diode lasers; temperature; combustion diagnostics; tomographic imaging +
Footnote †: journal: Applied Sciences
###### Abstract  
Magnesium-dissolved oxygen seawater batteries have open structures and flow seawater as electrolytes. These two features attract much attention. The cathode electrode is one of the key components that affect the performance of seawater batteries. In this study, seawater batteries with carbon cathodes made from three commercial carbons were investigated and discussed. The porous structure of the cathode was adjusted by changing the mass ratio between polytetrafluoroethylene (PTFE) and carbon materials. The binder ratios range from 10% to 50%. The structure of the different porous carbon cathodes was characterized, and the discharging performance was analyzed. Results showed that the number of pores with diameters of 2-10 nm decreased as the PTFE ratio increased. Meanwhile, as the PTFE ratio increased from 10% to 50%, the seawater battery discharging voltage and capacity were first inhibited when the PTFE ratio was less than 20% but then promoted. It revealed that a balance should be achieved between the number of reaction sites and the paths for oxygen transfer. Moreover, the oxygen transfer in the porous electrode is more important for batteries working in seawater. This study practically investigates seawater batteries with various PTFE binder ratios and provides a reference for the design of magnesium-dissolved oxygen seawater batteries.  
binder ratio; porous structure; mass transfer; seawater battery +
Footnote †: journal: Applied Sciences
## 4 Method Validations Using Simulations and Experiments
### Simulation Signal Analysis
Shown in Figure 13a is raw simulation signal waveform in first 11 carrier cycles. No apparent fault symptoms can be observed because of existence of heavy noise. The envelope order spectrum suggests that planet gear is damaged. However, the amplitude at the fault characteristic orders is not significant. Shown in Figure 14 is CVS signal and its envelope order spectrum. Comparing time domain waveform of raw signal and CVS signal, noises within the signal are effectively depressed. Additionally, periodical impulses can be observed in CVS signal though not very significant. The envelope spectrum shown in Figure 14b indicates that the periodical impulses are fault-related impulses. Amplitude peaks at planet gear fault characteristic order and its multiples are more significant than that in raw signal envelope spectrum. Shown in Figure 15 is MED signal and its envelope order spectrum. Signal waveform in Figure 15a has more dominant impulses than CVS signal. Here, 11 equally spaced periodic impulses can be easily observed since MED enhances the impulsive signals using a maximum kurtosis criterion. In Figure 15b, amplitude peaks at planet gear fault characteristic order are more significant. Figures 13-15 indicate that planet gear fault diagnosis method using CVS and MED is effective to detect planet gear fault under heavy noises.
## 3 Roadway Surrounding Rock Deformation Patterns
### The Impact of Roadway Depth on Surrounding Rock Deformation  
Taking the example of the total displacement D of the surrounding rock at various roadway depths for SRC = III, H = 6 m, and W = 6 m, as shown in Figure 7, the study investigates the impact of roadway depth on surrounding rock deformation.  
From Figure 7, it can be observed that although there are some minor nonlinear factors, the deformation of the surrounding rock generally increases linearly with the increase in depth. The analysis yields the following reasons for this:  
(1) Within these specific depth ranges, if the stress changes caused by excavation do not exceed the elastic limit of the rock, the deformation response may appear linear. Also, under ideal conditions, as the depth increases, the vertical stress (caused by gravity) theoretically increases linearly. If the horizontal stress also increases linearly with depth, then the overall stress state may lead to a relatively uniform and predictable deformation response [24].  
(2) The boundary conditions and material properties set in FLAC3D remain consistent or change little at these depths, including the rock's elastic modulus, Poisson's ratio, internal friction angle, cohesion, etc. If these parameters do not vary significantly with depth, the response of the surrounding rock in the simulation appears to be linear.  
(3) In the fluid-solid coupling model, the pore water pressure is effectively controlled, and its variation has a relatively uniform impact on surrounding rock deformation, also leading to a linear increase in deformation. In situations where pore water pressure is relatively stable, the deformation response of the rock may be more uniform and predictable.
## 3 V2X System Architecture When Integrating Blockchain Technology and MEC
### Application of Blockchain Technology between Road Users and RSUs
#### 3.2.2 Receiving and Synchronization of Primary Data  
After the necessary parameters are assigned and the user is verified, the data are synchronized between the certification centers and the user, with some data automatically synchronized to the blockchain (rating, device ID, etc.) and to the device.
## 2 Materials and Methods
### Methods
#### 2.2.2 Mathematical Model
\begin{array}{c}\forall s_{n}\in S,\forall c_{k}\in C^{n},\forall l_{i},t_{j} \in T,i\
eq j:\\ \sum\limits_{i=1}^{N_{T}}e_{ob}\cdot d_{i}\cdot x_{ki}^{n}+\sum\limits_{i=1}^{ N_{T}}\sum\limits_{j=2}^{N_{T}}\left(e_{u}\cdot u_{ij}^{nk}+e_{st}\right)\cdot y _{ij}^{nk}\leq E_{n}\end{array} \] (9)  
The attitude transition activities in agile imaging satellite are a temporal constraint problem. During imaging acquisition activity, after a ground target is selected, the starting time will be a continuous variable within a visible time window, and different starting time corresponds to different attitude angles, furthermore, different attitude angles mean different imaging quality. During attitude transition activity, for contiguous targets, the satellite needs to complete attitude transition, and the transition duration depends on the ending and starting time of previous and next targets, different transition duration means different energy consumption. Consequently, attitude angle, imaging quality, attitude transition duration, energy consumption are closely related to time. In previous work, we have studied the temporal constraint model and designed calculation methods [34]. And we used the model and methods in this paper. This mathematical model includes the optimization objective of maximizing the planning rewards of all selected tasks and takes operational constraints into consideration, such as the time window, the attitude transition, and onboard memory and energy constraints, meanwhile, it is characterized by high complexity.
###### Abstract
For example, the placement of sensors to monitor leaks in drinking water networks [39] or to achieve the target coverage of the wireless sensor network based on the selected coverage metric [40]. Most of the current research uses one-dimensional approaches [35; 36; 38] that usually simulate structural beams. Other researchers use two-dimensional simulated environments to determine the optimal placement of sensors [37; 39; 40], usually with additional constraints. Arising from the aforementioned AGP, there is no general analytical solution to the sensor placement problem. The optimal solution for discrete search spaces can be determined using exhaustive search, but this involves a high computational cost. By using other, mostly meta-heuristic approaches, the problem with the high computational costs could be alleviated, but the selected sensor placement could not be guaranteed to be optimal. The aim of the present study is two-fold. First, a variant of the exhaustive search approach was proposed to address the problem of two-dimensional and three-dimensional sensor placement. This modified exhaustive search method is used to establish a ground truth for the single sensor placement problem. As shown in Section 3, exhaustive search is not feasible for three-dimensional environment models and the proposed approach mitigates some computational costs. Second, following the same idea of finding approaches that lead to (near) optimal solutions, we explore nature-inspired genetic algorithms and the impact of the number of evaluations of the optimization function for these algorithms on both accuracy and computational cost. In addition, the effects of the rasterization of the environment are analyzed. This enables users to select the right parameter values and balance coverage and computational costs. To summarize, the following contributions are presented in this manuscript:  
* An exhaustive search-based approach that serves as ground truth for sensor placement in two-dimensional and three-dimensional environments. * Analysis of the influence of the number of evaluations of the optimization function and the rasterization of the environment on the performance of stochastic optimization sensor placement algorithms. * Evaluation of three stochastic optimization algorithms for sensor placement in two-dimensional and three-dimensional environments with selected parameters and comparison with the ground truth values. This manuscript is structured as follows. Section 2 describes the used environment and sensor models, optimization function and algorithms as well as the newly proposed recursive exhaustive Search. In Section 3, the results are then presented, analyzed and discussed. Finally, concluding remarks are given in Section 4.
# Side-Channel Vulnerabilities of Unified Point Addition on Binary Huff Curve and Its Countermeasure
## 3 Vulnerabilities of Unified Point Addition
### Vulnerabilities of Unified Point Addition  
We describe the vulnerabilities of unified point addition considering ROSETTA and HCCA. Both are analyses using the correlation between the input data and operations. ROSETTA can determine whether the operands of a field multiplication are equal (squaring) or different (multiplication). HCCA can determine whether two field multiplications have the same or different operands. We defined the two types of vulnerabilities exposed by these analyses.  
**Type 1. (Vulnerability by ROSETTA)**: The unified point addition operation can compute the point doubling and point addition with the same formula. However, depending on the input points of unified point addition, field multiplications can be performed as squaring or multiplication. For example, let \(P_{1}=(X_{1}:Y_{1}:Z_{1})\) and \(P_{2}=(X_{2}:Y_{2}:Z_{2})\) be the two input points of the unified point addition formula. Note that there exists the operation \(X_{1}\cdot X_{2}\) in unified point addition. If \(P_{1}=P_{2}\), then this operation computes to \(X_{1}\cdot X_{1}\). If \(P_{1}\
eq P_{2}\), then this operation computes to \(X_{1}\cdot X_{2}\). Then, this operation becomes a vulnerability that is exploitable by ROSETTA.
**Type 2. (Vulnerability by HCCA)**: Considering two field multiplications, if they have at least one common operand, they can be distinguished by HCCA. In unified point addition, the two different multiplications can be identically computed according to the inputs. For example, the operations \(X_{1}\cdot Y_{1}\) and \(X_{2}\cdot Y_{2}\) exist in unified point addition. If \(P_{1}=P_{2}\), then \(X_{1}\cdot Y_{1}\) will be computed twice. If \(P_{1}\
eq P_{2}\), then \(X_{1}\cdot Y_{1}\) and \(X_{2}\cdot Y_{2}\) will be computed. Then, these operations become a vulnerability that is exploitable by HCCA.
## 3 Results and Discussion
### Adsorption Mechanism
In this state, the adsorption of NAC occurs predominantly through the sulphur atoms of the thiol (-SH) group [50; 64], which could coordinate with the vacant d-orbitals of the copper atoms to form stable covalent Cu-S bonds. This results in the formation of a chemisorbed layer that effectively blocks the active sites on the bronze, thereby reducing the corrosion rate. In addition to chemisorption, the partial deprotonation of the carboxyl group might allow some physical interactions [80] that further stabilize the adsorbed inhibitor film and improve the surface coverage. In neutral saline environments, NAC exists as a zwitterionic form, and it bonds to the bronze surface through the thiol and deprotonated carboxyl groups [50]. However, this increased electron density on the carboxylate group could enhance its nucleophilicity, which might facilitate the coordination with the copper ions from solution [81; 82]. As previously reported [83], the formation of a soluble complex between the inhibitor molecule and hydrated Cu\({}^{2+}\) ions would help increase the metal dissolution. This might explain the slightly lower corrosion resistance experimentally observed in the saline environments compared to in the simulated acid rain. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Electrolyte** & **Slope** & \
\begin{tabular}{c} **K** \\ **(L mol\({}^{-1}\))** \\ \end{tabular} &
\begin{tabular}{c} \(\Delta G_{\text{ads}}^{0}\) \\ **(kJ mol\({}^{-1}\))** \\ \end{tabular} \\ \hline
3.5 wt. \% NaCl & 1.18 & 1044.9 & \(-27.2\) \\
0.2 g L\({}^{-1}\) NaHCO\({}_{3}\) + 0.2 g L\({}^{-1}\) Na\({}_{2}\)SO\({}_{4}\) + 0.2 g L\({}^{-1}\) NaNO\({}_{3}\), pH = 3.4 & 1.19 & 25,839.8 & \(-35.1\) \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Thermodynamic parameters for the adsorption of NAC on the bronze surface in the two tested electrolytes at 298 K.
## Materials and Methods
### Adsorption Measurements  
To prepare 1 L of the stock dye solution, 1 g of RR120 (R-0378 Sigma-Aldrich, molecular weight = 1469.98 g mol\({}^{-1}\)) was weighed accurately and dissolved in deionized water. To obtain the desired solution, the stock solution was then diluted. Then, 0.1 M HNO\({}_{3}\) or 0.1 M NaOH solution was used to adjust the pH to the desired value.  
A 250-mL Erlenmeyer flask, which contained 0.6 g of the respective adsorbent (clay, chitosan or MCC), together with 0.1 L of the working solution with an initial RR120 dye concentration of 100 mg L\({}^{-1}\) at pH 5.5, was shaken for a given time (15-420 min). The dye concentration before and after adsorption was analyzed by UV-Vis at a wavelength of 512 nm. The percentage of dye removal was calculated from Equation (1):  
\[\%\text{Removal}=(C_{\text{i}}-C_{\text{f}})\times 100/C_{\text{i}}, \tag{1}\]where C\({}_{\rm i}\) and C\({}_{\rm f}\) are the dye concentration (mg L\({}^{-1}\)) before and after adsorption, respectively.  
In order to determine the optimal condition for maximum dye removal, the adsorbent that showed the highest performance at a given condition was selected for further study of the influence of different initial dye concentrations (10-300 mg L\({}^{-1}\)), pH (3-10), and adsorbent dosage (0.3-1.2 g). Moreover, the reusability of the adsorbent was investigated over 11 successive adsorption cycles. After separating the adsorbent from the water, the spent adsorbent without washing was used in the next cycle.  
To gain insight into the adsorption process, three adsorption isotherm models (Langmuir, Freundlich, and Koble-Corrigan model) were used to estimate the adsorption behavior at a given temperature. To determine the nature of the adsorption process, the thermodynamic parameters were evaluated. For kinetic studies, pseudo-first-order and pseudo-second-order kinetic models were selected to study the rate of the adsorption. Finally, the rate-limiting step of the adsorption on the MCC was also determined.
## 4 Implementation Details
### Direct Measurement Array  
In addition to the in situ time-based sensors, standalone layouts of \(R_{metal}\) and \(C_{metal}\) structures have been fabricated alongside the sensors, resulting in a direct measurement array (DMA). The structures of DMA resistor array and DMA capacitor array are depicted in Figure 11, and detailed in [30]. Notably, since external equipment is used to measure the DMA, the precise outcomes from the DMA are utilized as a benchmark for the validation of the accuracy of the time-based sensors.  
The structure of the DMA resistor array is illustrated in Figure 10(a). A single cell is enabled at a time by activating its corresponding \(EN\) signal. An accurate external current source is then utilized to measure the resistance values of \(R_{metal}\). Notably, owing to the high input impedance of the voltage meter, the current \(I_{s}\) exclusively flows through \(R_{metal}\) and the parasitic resistance, \(R_{p}\), without being drawn into the voltage meter. Concurrently, the voltage meter measures the voltage across the \(R_{metal}\), and the resistance vale of \(R_{metal}\) can be determined by: \(R_{metal}=\Delta V/I_{s}\). It is noteworthy that the measurement accuracy may be influenced by the leakage of the MOSFET switches. To mitigate this issue, thick gate devices are adopted in this structure. Simulations indicate that the leakage current of thick gate device is at a pA level.  
The structure of the DMA capacitor array is depicted in Figure 10(b). Similar to the resistor DMA, a single cell is activated at a time through the utilization of an \(EN\) signal. The operation involves the input of a clock signal, \(CLK\), into a non-overlapping clock circuit, which generates two distinct clock phases, \(\phi_{P}\) and \(\phi_{N}\). These clock signals ensure the PMOS and NMOS do not turn on simultaneously. Specifically, when \(\phi_{P}\) is in its low state, the PMOS is in an ON-state, while the NMOS is in an OFF-state, thereby allowing a voltage source, \(V_{P}\), to charge the \(C_{metal}\) capacitor. Conversely, when \(\phi_{N}\) is in its high state, the PMOS turns off, and the NMOS turns on, leading to the discharge of \(C_{metal}\) to a ground state, \(V_{N}\). The value of \(C_{metal}\) can then be determined by measuring the average current, \(I_{arg}\), drawn from the voltage source \(V_{P}\), utilizing the formula \(C_{metal}=I_{arg}\times T_{CLK}/\left(V_{P}-V_{N}\right)\)[31]. Note that even though the clock, \(CLK\), has a 50% duty cycle, the on-time of the PMOS and NMOS are not equal to half of the clock period due to the propagation delay of the inverter chain. This discrepancy can introduce a measured error in the determination of \(C_{metal}\). To mitigate this  
Figure 11: Direct measurement array structure (**a**) Resistor array. (**b**) Capacitor array.  
error, half of the clock period needs to be significantly larger than the delay of the inverter chain. Simulations show that the delay of the inverter chain is about 130 ps.
## 4 Experiments and Results
### Neuron Classification Process Analysis Based on LCCDNNs and FCCDNNs  
Figures 9 and 10 show the neuron classification accuracies of the training processes using the LCCDNN and FCCDNN models, where the red and green curves represent the average values of neuron classification, and the red and green areas were obtained by adding and subtracting the standard deviation values. Figure 9 shows the training process accuracy curves for classifying two kinds of neuron datasets with LCCDNNs. As shown in Figure 9a, it can be seen that the accuracy of the test set of the C. elegans dataset increased slowly during the whole process, and the fluctuation was larger before 40 learning epochs before it decreased. At the end of the training, the classification accuracy was 87.44% \(\pm\) 4.09%. As shown in Figure 9b, it can be seen that the accuracy of test set of the zebrafish dataset increased slowly during the whole process, the fluctuation decreased gradually, and the final average accuracy was 81.16% \(\pm\) 9.2%. Figure 10 shows the training process accuracy curves of the two neuron datasets with FCCDNNs. From Figure 10a, we can see that the accuracy of the C. elegans training set increased rapidly before 20 learning epochs, then reached a stable value, and the fluctuation of the est set was violent during the whole training process. As shown in Figure 10b, the classification accuracy fluctuation of the test set was concentrated to before 40 epochs in the zebrafish dataset, and the classification accuracy was 72.33% \(\pm\) 6.05%.  
According to the above results, it can be seen that when LCCDNNs and FCCDNNs are used to solve the neuronal morphology classification problem, the constructed 3D neuron dataset has a greater impact  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Parameter**} & \multirow{2}{*}{**Dataset**} & \multicolumn{2}{c}{**Deep Neural Network Model**} \\ \cline{3-4}  & & **LCCDNNs** & **FCCDNNs** \\ \hline \multirow{2}{*}{Number of intermediate layers} & C. elegans & 10 & 1 \\  & Zebrafish & 5 & 10 \\ \multirow{2}{*}{Number of building blocks} & C. elegans & 3 & 3 \\  & Zebrafish & 5 & 5 \\ \multirow{2}{*}{Mini-batch size} & & 64 & 64 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The base building block parameter settings of the LCCDNNs and FCCDNNs.  
Figure 9: The change curve of accuracy during training by LCCDNNs. (**a**) C. elegans dataset, (**b**) zebrafish dataset.  
on the classification accuracy. For the zebrafish dataset, the morphological structures of the different types of neurons were more similar, so it is more difficult to classify neurons by the deep learning model. In addition, the neurons in the dataset had more types and more complex morphological structures, so the deep neural network model obtained a lower accuracy of neuron classification.
## 4 Simulation Analysis
### Consideration of the Minimum Battery Capacity for the Stabilizing Control  
In this section, the minimum necessary power rating of the battery in the proposed coordinated control system is evaluated since the power rating of the battery should be reduced to as small as possible because of its very expensive cost. Simulation analyses of Case 1 and Case 2 shown in Table 2 for the same network fault condition explained in the previous section were performed, in which the ASG or FSG was in the pumping operation and the power rating of the battery was changed from 0 MW (no battery) to 10 MW.  
Figure 22 shows the system frequency responses, in which \"No Control\" means Case 1 (the FSG was in operation), and its response was the same as that in Figure 18; and the six cases of \"Battery 0 MW\" to \"Battery 10 MW\" mean Case 2, in which the ASG and the battery were in operation (in the case of \"Battery 0 MW\", the battery was not connected to the grid). It is observed in Figure 22 that the grid frequency could return to the rated frequency in Case 2 even if there was no battery (0 MW). However, the system frequency reached 48.5 Hz or below when the battery rating was 2.5 MW and  
Figure 19: ASG and FSG output (Pumping Operation).  
Figure 20: Generator rotor speed (Pumping Operation).  
As a result, it was concluded that the battery was indispensable to the proposed coordinated control between the ASG and battery to keep the stability of the power system operating effectively during a disconnection of the WF due to a network fault.