esa-sceva/satcom-chunk-collection huggingface dataset:
# Failure Mechanism of Multilayer Ceramic Capacitors under Transient High Impact
## 2 Experimental Study on MLCC Parameter Fluctuation Failure
### Transient Variation of Capacitance  
The capacity of a ceramic capacitor changes during the impact process. Figure 3 shows that the capacity of the ceramic capacitor (type: 16V-22 uF) increased with increasing impact acceleration. Compared with the ceramic capacitors with different withstand voltages and differently rated capacities, when the peak value of impact acceleration exceeded 5000 g, the ceramic capacitance of them all have evident changes and the change value increased with the increasing impact peak value. The measured data of the changes are shown in Table 2. Among them, the capacitance value change reached 0.18 uF (absolute change); therefore, it cannot be ignored. In addition, the relationship between the MLCC capacitance change and impact acceleration is nonlinear, but the amount of change increases sharply after the threshold acceleration (>4000 g) is exceeded. This indicates that the higher acceleration the penetration fuz is subjected to, the more serious the reliability problem regarding capacity fluctuation can be. Besides, the larger the rated capacity and withstand voltage, the greater the capacitance fluctuation.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Capacitor Type**} & \multicolumn{4}{c}{**Capacitance Variation/μF**} \\ \cline{2-5}  & **5026 g** & **5608 g** & **6305 g** & **8297 g** \\ \hline \({}^{\prime}\)J\({}^{\prime}\)CT41G-1210-X5R-16V-22 uF & 0.022 & 0.030 & 0.106 & 0.146 \\ \({}^{\prime}\)J\({}^{\prime}\)CT41G-1210-X5R-25V-22 uF & 0.027 & 0.033 & 0.109 & 0.151 \\ \({}^{\prime}\)J\({}^{\prime}\)CT41G-1210-X5R-16V-47 uF & 0.031 & 0.056 & 0.122 & 0.174 \\ \({}^{\prime}\)J\({}^{\prime}\)CT41G-1210-X5R-25V-47 uF & 0.037 & 0.060 & 0.127 & 0.181 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Capacity change of the MLCC under a different impact acceleration.  
Figure 3: Change in ceramic capacitance with impact acceleration. **(a)** The impact peak value is 5209 g. **(b)** The impact peak value is 5608 g. **(c)** The impact peak value is 6305 g. **(d)** The impact peak value is 8297 g.  
The experimental results show that, although the capacitance value changes at the moment of mechanical impact, it can restore to its original rated capacity after the impact, which verifies our view that even if the MLCC and other fuze devices do not have structural failure, various transient functional failures may still occur at the impact moment.
## 4 Experiment Details and Results
### Coverage Solution  
The coverage constrained problem was solved for discrete values of \(x\) (specifically 0 and 1) because an item is either recommended or not recommended to a user. For the solution of the problem, the cvxpy [33] python library was utilized. Although the new lists for different values of \(K\) are different from the baseline lists, \(Lu\), the results of the total rating for different coverage thresholds found with the discrete solution are the same for different values of coverage. In particular, the total rating is the same for \(K\) = 1, 1.5, 2, 2.5, while for \(K\geq 3\) there is no solution. This fact occurs because there are many excellent ratings (rating = 5) in the predicted rating matrix, and the items of the new lists \(Lu^{\prime}\) are different from the ones of the baseline recommendation list \(Lu\), but the new recommended items are rated perfectly, the same as the old ones (5 stars). However, the distribution of coverage changes per item provider (category), as shown in Figures 1 and 2:  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c} \hline
**Category** & **1** & **2** & **3** & **4** & **5** & **6** & **7** & **8** & **9** & **10** & **11** & **12** & **13** & **14** & **15** & **16** & **17** & **18** & **19** & **20** \\ \hline Items & 101 & 91 & 105 & 115 & 105 & 107 & 90 & 121 & 101 & 101 & 90 & 114 & 94 & 120 & 115 & 104 & 114 & 108 & 107 & 118 \\ \hline \end{tabular}
\end{table}
Table 1: Different providers or categories and their number of items.  
Figure 1: Coverage per provider for \(K\) = 1 and \(K\) = 1.5.  
As observed from the figures above, as \(K\) increases, the coverage tends to be more uniform.
## 1 Introduction
Thus, evaluating the impacts of drought on terrestrial NPP in Mongolia, particularly for the rangelands, is a critical challenge. Historical records show a high precipitation variability during June-August in Mongolia, which makes up the majority of the annual total rainfall. Therefore, a drought may have long-lasting effects on vegetation production in the growing seasons. Droughts especially have a far-reaching effect on growing season NPP (NPP\({}_{\text{gs}}\)) dominant vegetation. The response of NPP to drought conditions is one of the notable indicators to understand the effects of climate change on regional or global carbon cycling. This study intends to evaluate the impacts of droughts on rangelands in Mongolia. In the light of the anticipated increase in drought frequency and severity in the future (5-15%) [26], it is also imperative to evaluate the status of vegetation NPP over the next century. Previously, in situ experiments [27], remote sensing imageries [28], and ecological modeling [29] have been employed to assess the responses of NPP to drought conditions. The station-based drought index (e.g., the standardized precipitation evapotranspiration index, SPEI [30]; the Palmer drought severity index, PDSI; [31], and the standardized precipitation index, SPI; [32]) has been used. However, such indicators have only considered temperature and precipitation to assess the impacts of drought on NPP. Moreover, these drought indices can effectively evaluate the impact of drought on NPP. However, sparse distribution of field and station data limits the assessment of the impact of drought on terrestrial NPP at the broader spatial and multi-temporal coverage. Remote-sensing indices and methods, such as NPP and NDVI, have made it possible to understand vegetation response to drought from a broader perspective and larger spatial extent [33; 34; 35; 36; 37]. We used the MODIS NDVI and a process-based ecosystem model, namely the Boreal Ecosystem Productivity Simulator ((BEPS), [PERSON] et al. [38]) to determine the response of NPP on drought events in Mongolia during 2003-2018. The objectives of this study were to (1) quantify the spatial and seasonal (growing season) variability of drought events, (2) examine the spatial and temporal variations in terrestrial NPP, (3) assess the impact of drought on NPP change, and (4) identify uncertainties and knowledge gaps associated with NPP estimation in Mongolia.
## 3 Results and Discussion
### Post-Test Cell Analysis  
The results of the post-test diagnostics showed the distinct advantages of the new CV step and the adaptive step rapid charging methods compared to the conventional CCCV step method. In addition, cycle life evaluation was complete at 80% (20% degradation) relative to the initial capacity under all charging conditions. Interestingly, 400 cycles were achieved for the CCCV step charging method, the new CV step charging reached 650 cycles, and over 780 cycles were carried out for the adaptive step rapid charging method, thereby indicating the improved performances of the new CV and adaptive step rapid charging methods when compared to the conventional CCCV step method.  
The gas analysis results shown in Figure 8 were consistent with the measurement data regarding the cell thickness. More specifically, the total amount of gas extracted from the expanded cell and the quantities of methane and ethane produced were significantly higher in the cell that employed the  
Figure 7: DVA plots of ICR18650-33F showing the variations in cell cycle lives upon the implementation of different charging methods. (**a**) The capacity of the battery; and (**b**) The direct current internal resistance of the battery.  
CCCV step pattern. Interestingly, methane and ethane are typically formed in the cell due to electrolyte decomposition and SEI layer formation [34; 35; 36].  
The residual electrolyte extracted from the dissembled separator was analyzed by GC-MS, and a higher level of propylene carbonate (PC) decomposition was found to take place for the cell subjected to CCCV step charging (Figure 9). This was consistent with the gas analysis results, and it should be noted that the difference in additive consumption was negligible.  
Figure 10 shows the thickness of each electrode following disassembly of the cell. Upon comparison with the slight difference in cathode thickness, the CCCV step charging cell exhibited a greater increase in anode thickness than the CV step charging cell. Based on the rapid charging pattern design methodology, it was assumed that the novel CV step pattern allows the cell to self-regulate the charge current and that the adaptive step rapid charging method adjusts the charge current at each RPT, thereby alleviating the accumulation of resistance-increasing factors, and affecting the anode to a greater extent. Minimizing the increase in resistance is thereby expected to minimize cell degradation  
Figure 8: (**a**) The total volume of gas extracted; and (**b**) the composition of the extracted gas after reaching the end of the cell lives. Data provided for the fresh cell, the step charging cell, the CV step charging cell, and the adaptive step charging cell.  
Figure 9: Standardized residual electrolyte propylene carbonate components of the 3.3 Ah Cell.  
and both inhibit electrolyte decomposition and unwanted side reactions. We then validated these hypotheses using various analytical techniques and surface characterization methods.
## 4 Conclusions  
An efficient experimental method was developed to investigate the deformation and breakup of hundred-micron droplets. This method utilizes a combination of shock wave-driven droplet experiments and double-pulse laser holography to obtain large field-of-view high-resolution images. The proposed approach provides an opportunity to simultaneously acquire high spatial and temporal resolution data on the deformation and breakup modes of a hundred-micron droplet under different loading conditions, as well as the distribution of Weber numbers associated with the droplet's breakup modes. Based on the obtained experimental data, the following results were obtained:  
Figure 10: (**a**) The Windward displacement of low _We_, (**b**) the windward displacement of high _We_.  
1. At low Weber numbers within the critical range (15 \(<\) _We \(<\) 19), reducing the droplet diameter impacts the count of Rayleigh-Taylor (R-T) waves and triggers a shift in droplet breakup mode. On the other hand, under high Weber numbers (_We \(=\)_ 184 and _We \(=\)_ 696), the curvature of the droplet increases with decreasing diameter leading to greater difficulty in droplet breakup. Consequently, at the same Weber conditions, the droplet experiences prolonged deformation time resulting in increased deformation behavior.  
2. At high Weber numbers, the front end of the windward side of the droplet shows K-H instability-induced folds that become more pronounced as Weber number and time increase. Meanwhile, the leading edge of the droplet experiences R-T instability, which can result in significant droplet mass stripping.  
3. The dimensionless windward displacement of a hundred-micron droplet will increase with the increase of this dimensionless time, the droplet's windward is not uniformly accelerated under the action of gas flow, and its acceleration will change as the deformation and breakup. At high Weber numbers, the effect of gas velocity on the windward displacement of the droplet is small. Instead, the ambient gas flow plays a much more significant role in shearing than piercing the droplet's windward side and overall propulsion.  
Conceptualization, J.L.; methodology, Y.Z. and J.L.; investigation, Y.Z. and J.L.; writing--original draft preparation, Y.Z.; writing--review and editing, Y.Z.; supervision, J.L., R.D. and H.S. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the National Natural Science Foundation of China (grant number: 11772309), Basic Public Welfare Research Program Project of Zhejiang Province (grant number: LGG19A020002) and China Academy of Engineering Physics Innovation Development Foundation (grant number: CX2019004).  
Not applicable.  
The authors declare no conflict of interest.
# Analysis of SNR for High-Orbit Target Detected by Ground-Based Photoelectric System
## 3 Parameters for Simulation  
Using MODTRAN, we simulated six form data from short infrared to long infrared, and the specific band division is shown in Table 1. The atmospheric transmission and radiation are shown in Figure 2. The excellent atmospheric transmission lines in the wavelength region were in the H and K bands, while the M band had a very low transmission. Radiances were lower than \(5.0\times 10^{-5}\) W\(\cdot\)Sr\({}^{-1}\cdot\)cm\({}^{-2}\cdot\)um\({}^{-1}\), except in the J and H bands.  
The model was run for an observer at 43.85 latitude and 125.4 longitude in the Changchun area. The phase angle of the sun was set at 60 degrees, and the target-included angle (the sun-target-Earth angle, where the vertex is the target) was larger than 100 degrees [12]. In this case, the direct light of sun exposure to the telescope could be neglected.  
Ground-based detection systems are mainly composed of optical telescopes (which collect the radiation of the target) and photoelectric sensors (which convert photons into electrons). Optic transmission included the transmission of telescope mirrors and Dewar windows, and the parameters are listed in Table 2.
# Automatic Hybrid Attack Graph (AHAG) Generation for Complex Engineering Systems +
## 4 Hybrid Attack Graph (HAG)  
The states of Attack graph reflect the logical evolution of system parameters (e.g., system stability and services are either true or false) during attacks until reaching the final states where the security property \(\varphi\) is violated and hence are the worst states. As multiple attacks can reach the same final state, it is essential to identify the worst attack scenario. A Hybrid Attack Graph (HAG) associates real values to all attack scenarios terminating in these final states. These real values correspond to the Level-of-Resilience parameters determined from the system dynamical response. The detailed computations of LoS\({}_{\text{R}}\), eventual LoP\({}_{\text{R}}\) and RT of networks CN\({}_{1}\) and CN\({}_{2}\) under \(A_{G}\) scenarios are given in [43], and [15]. We formally define HAG as follows.  
**Definition 3**.: _A Hybrid Attack Graph (HAG) of a system model \(M\) is a data structure representing a union of all attack paths comprising \(A_{G}\) annotated with the associated Levels-of-Resilience._  
\[HAG=A_{G}\cup\text{LoR($M$, $A_{G}$)}.\]  
Algorithm 1 compares the Levels-of-Resilience, associated with the attacks comprising an Attack graph, and alerts the worst-case scenario.  
```
INPUT: Attack graph (\(A_{G}\)) Comprising Attack Scenarios (S \(\equiv\)\(\cup\)S\({}_{i}\), i \(\in\)\(\{1,\ldots,\)\(z\}\)) and associated LoR values OUTPUT: Alert Worst Case S\({}_{[i]}\) Procedure: forCase [LoS\({}_{\text{R}}\), LoP\({}_{\text{R}}\), RT] ifS\({}_{[i]}\)[LoS\({}_{\text{R}}\)] \(>\) S-S\({}_{[i]}\) [LoS\({}_{\text{R}}\)]: Alert Worst Case S\({}_{[i]}\) elseifS\({}_{[i]}\)[LoP\({}_{\text{R}}\), Flp] \(>\) S-S\({}_{[i]}\) [LoP\({}_{\text{R}}\), Flp]  Alert Worst Case S\({}_{[i]}\) elseifS\({}_{[i]}\)[LoP\({}_{\text{R}}\), Video] \(>\) S-S\({}_{[i]}\) [LoP\({}_{\text{R}}\), Video]  Alert Worst Case S\({}_{[i]}\) elseifS\({}_{[i]}\)[LoP\({}_{\text{R}}\), Email] \(>\) S-S\({}_{[i]}\)[LoP\({}_{\text{R}}\), Email]  Alert Worst Case S\({}_{[i]}\) elseifS\({}_{[i]}\)[RT] \(>\) S-S\({}_{[i]}\) [RT]: Alert Worst Case S\({}_{[i]}\) else no Alert
```  
**Algorithm 1** Alerting Worst-Case Attack Scenario
## 1 Introduction
Thus, the production planning based on an efficient energy use strategy, following ISO 50001, should consider monitoring the energy consumption and measures for increasing the energy efficiency. In these conditions, SMEs have needed software products to monitor and control the consumption and to identify solutions that allow the adoption of measures to increase energy efficiency. Thus, a decision support platform is proposed, having the modules with characteristics that differentiate it and offer more advantages compared to the other approaches which treated the energy management at the SMEs, whatever the industrial activity branch:  
1. _The data management module_ is based on the data mining technique that extracts the technical details on energy consumptions used to develop production scheduling strategies. It allows the analysis of large size databases regarding the daily, monthly, and yearly energy consumption or technical and operating characteristics for equipment and installations. In addition, it ensures the interface between the application modules, having a query that follows the correlation between energy consumption, economic indicators (the turnover, production value, personnel costs, and total purchases with goods and services), and costs. 2. _The profiling module_ integrates an original approach based on clustering techniques to determine the typical energy consumption profiles (TECPs) assigned to different activities necessary for establishing the type of tariffs that will lead to the reduction of energy bills and optimal planning of the activities. 3. _The forecasting module_ contains a new approach that uses an expert system based on the rules _If-Then_ to forecast the energy consumption of each installation and equipment. The total energy consumption at the SME level is obtained through the aggregated process of all individual energy consumptions associated with each equipment and installation. 4. _The production scheduling module_ includes a novel approach based on the combinatorial optimization for daily consumption programming, which uses a fast searching algorithm to find the optimal solutions in the admissible set. The algorithm uses the status (on or off), type (programmable or non-programmable), working period, the number of operating cycles, and dependence (within the activities) of the equipment and installations as input data, determining the optimal solutions regarding the flattening of daily energy consumption profile with the benefits on the cost of energy bills. The remainder of the paper is structured as follows. Section 2 presents a review on the treated topic highlighting the advantages of the proposed decision support platform. Section 3 details the characteristics of the developed decision support platform, highlighting the advantages of the proposed approaches inside of each module. The obtained results in testing of the platform at a small enterprise, from a representative field (the trade and repair of vehicles), are presented in Section 4. Section 5 highlights the conclusions and future works.
## 2 The Relaxed Flexible Flow Shop Problem Model with Sequence-Dependent Setup Times (RFFSP) Methods
### Mathematical Model  
The mathematical model introduced by [23] is used and adapted to the problem presented in this work. This adapted model uses inter-stage infinite buffers, which serve as temporary storage to place completed operations from an earlier stage, as machines cannot be blocked if other machines in later stages have not finished processing a job. The machine blocking constraint presented in [23] does not apply in this model.  
The objective of the mathematical model, represented by the Equation (1), minimizes the maximum completion time (makespan, \(c_{max}\)) found in the sequence of jobs generated by the system in study. The makespan \(c_{jm}\) of the \(j\)-th job in the last stage \(m\), is when it finishes its processing in the system (\(c_{jm=s_{jm}+ST_{jm}+p_{jm}}\)) and its value is the sum of its start time \(s_{jm}\), its processing time \(p_{jm}\), and its setup time \(ST_{jm}\). Constraints in (2) indicate that for each \(j\) there is only one job \(l\) that immediately precedes \(j\) (\(l\prec j\)) which is only assigned to a single machine \(i\) in stage \(k\). Constraints in (3) point out all jobs are processed in a well-defined sequence by each machine in each stage. These complement the restrictions in (2), guaranteeing that jobs are executed in a well-defined sequence on each machine at each stage. It is only allowed to process the \(j\)-th job once in one machine for each stage.  
\[Minc_{max}\Big{(}s_{jm},p_{jm},ST_{jm}\Big{)}=Min\big{[}max\big{(}c_{jm}\big{)} \big{]} \tag{1}\]  
s.t.  
\[\sum_{i=1}^{m_{k}}\sum_{l=0J\
eq j}^{n}x_{iljk}=1\hskip 28.452756 pt\forall k,j \tag{2}\]  
\[\sum_{l=0J\
eq j}^{n}x_{iljk}=\sum_{q=0J\
eq j}^{n}x_{ij\
eq k}\hskip 28.452756 pt \forall k,i,j \tag{3}\]\[c_{jk}\geq s_{jk}+p_{jk}+\sum_{i=1}^{m_{k}}x_{ijk}\varsigma T_{ijk} \forall j,l,k \tag{4}\] \[c_{jk}-c_{j(k-1)}\geq p_{jk}+\sum_{i=1}^{m_{k}}\sum_{l=0}^{n}x_{ ijk}\varsigma T_{ijk} \forall k>1,j\] (5) \[c_{max}\geq c_{jm} \forall j\] (6) \[x_{ijk}\in\{0,1\} \forall i,j,l,k \tag{7}\]  
Constraints in (4) indicate that if a slack time between \(l\)-th and \(j\)-th jobs processed on the \(i\)-th machine of the \(k\)-th stage occurs, the makespan \(c_{jk}\) of the \(j\)-th job of the \(k\)-th stage can be increased [24]. The start time \(s_{jk}\) of the \(j\)-th job is taken when it begins your setup time. Constraints in (5) define the precedence restrictions of each \(j\)-th job's operations and guarantee that each job is processed in all stages. Constraints in (6) indicate that each job has a makespan less than or equal to the maximum makespan. Finally, relations in (7) restrict the \(x_{ijk}\) to be binary variables only: \(x_{ijk}=1\), if job \(j\) is assigned to machine \(i\) in stage \(k\) where job \(l\) is its predecessor job, otherwise \(x_{ijk}=0\).
## 4 Multi-Objective Scheduling Optimization Model  
### Multi-Objective Scheduling Model  
To satisfy different operational requirements and consider different benefits, the maximum operation revenue, the minimum abandoned energy cost, and the minimum output fluctuations were chosen as the MECS operation objectives in this study.