esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Materials and Methods
### Analysis  
Remotely-sensed proxies for terrestrial vegetation activity (SIF and NDVI) and atmospheric reanalysis data were processed via the same methodology. These data were presented as standardized anomalies (z-scores), calculated by removing climatological mean values and dividing by the standard deviation of the mean. The resulting signal was therefore corrected for seasonal variability and the differences in the magnitude of the anomaly among different variables. In this analysis, multi-year means were calculated over as long a time period as the record permitted. For zonally averaged values, the drought focus region was defined as the land area between 1.5\({}^{\circ}\) S to 4\({}^{\circ}\) N and 38.5\({}^{\circ}\) E to 46.5\({}^{\circ}\) E (see black box in Figure 1), modified from [20].  
In our analysis, the 2010-2011 drought was compared to previous droughts in East Africa as recorded in the emergency events database EM-DAT: The International Disaster Database, consistent with [38]. EM-DAT provides global historical drought records. In our study we included all EM-DAT-recorded drought events in Kenya, Ethiopia, and Somalia with the exception of 1987, 1988, and 1989, which were excluded because the drought season could not be determined. Previous regional drought years in the Horn of Africa include 1980 (MAM), 1983 (OND), 1991 (OND), 1994 (MAM), 1997 (MAM), 1998 (OND), 1999 (MAM), 2000 (MAM), 2003 (MAM), 2004 (MAM), 2005 (OND), 2008 (MAM), and 2009 (MAM). We note that 1998-1999 is the only other consecutive set of drought events before the 2010-2011 drought in the past 30 years.
## 3 Sector Capacity Evaluation Method Based on the DNN Algorithm
### Route-Availability Model
The following operation steps were performed:  
1. Input universe U, which contains 12 bad weather-influence coefficients \(W_{\tilde{y},a}\). 2. Fuzzy subset \(A_{1}=\{\text{bad-weather grade is 1}\}\). 3. When the membership degree exceeds 0.65, the bad weather belongs to a. 4. Repeat steps 2 and 3 to calculate \(A_{2}=\{\text{bad-weather grade is 3}\}\). The actual capacity of a route in a sector being influenced by bad weather can be calculated by using the availability model of the route. The simulation parameters can be set in the AIRTOP simulation model, and the flight-restricted area can be delimited to simulate the actual operation in the sector under bad weather conditions so as to identify the flights that need to be rerouted under this condition.
## 2 Results and Discussion
### Molecular Docking and Molecular Dynamics Simulation Analysis
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Protein** & **PDB ID** & **Inhibitor** & \multicolumn{2}{c}{**GOLD Fitness Score**} \\ \cline{3-5}  & & & **Inhibitor** & **Spilanthol** \\ \hline TLR-4 & 2Z65 & CID6912404 & 141.86 & 58.29 \\ NF-kB1 & 1 SVC & CID5280961 & 49.29 & 49.73 \\ IRAK4 & 6 BFN & CID 44449334 & 81.29 & 59.38 \\ IKK\(\alpha\) & 5 EBZ & CID16048085 & 57.99 & 59.70 \\ MD2 & 2E56 & CID11005 & 58.78 & 58.64 \\ iNOS & 1 DD7 & CID16116169 & 93.52 & 73.91 \\ COX-2 & 5 IKR & CID5090 & 66.46 & 62.13 \\ MyD88 & 2 JS7 & ST2825 & 61.55 & 44.96 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: GOLD fitness scores for spilanthol and specific inhibitors of pivotal proteins in the TLR4 signaling pathway. Figure 4: Superimposition and GOLD docking score comparison of reference ligands CID16048085 and CID5280961 for IKK\(\alpha\) and NFkB1, respectively, with spilanthol from molecular docking results. **(A)** Depicts the superimposition for IKK\(\alpha\) and NFkB1. **(B)** The two-dimensional interaction map illustrates the average structure derived from the last 20 ns of MD simulations involving IKK\(\alpha\), NFkB1, and all ligands.
# On the Fault Detection and Diagnosis of Railway Switch and Crossing Systems: An Overview
## 3 FDD of Railway S&C Systems  
In this section, an overview of the existing FDD algorithms for railway S&C systems is presented. Condition monitoring systems (CMSs) are applied to enhance system performance, to ensure safety and reliability, and to reduce downtime and replacement costs [54; 55]. There are two well-known approaches to fulfilling CMS tasks: model-based and data-driven. Thus, in this review paper, the different existing FDD algorithms for railway S&C systems are classified into two groups: model-based methods and data-driven methods. While the first uses both input and output signals to build its mathematical model (reference model) to be used for generating the residuals for detecting and then diagnosing the different faults, the other uses only system output signals (historical data) to detect and diagnose possible faults and is increasing in popularity due to machine learning and big data approaches in the CMS field.
## 3 WD-IWOA-DELM Prediction Framework  
### Data Introduction  
This study employs the publicly available NASA dataset and CALCE dataset to validate the accuracy of the proposed method. In both sets of data, the lithium batteries are lithium cobalt oxide batteries, charged and discharged through the redox reactions between a graphite anode and a lithium cobalt oxide cathode.  
The NASA dataset, provided by NASA PCOE, was chosen considering the significant impact of temperature on lithium battery performance. The optimal temperature range for lithium batteries is 20 \({}^{\circ}\)C to 40 \({}^{\circ}\)C [31]. Therefore, four groups of lithium batteries with a rated capacity of 2 Ah were selected for charge-discharge cycling experiments at room temperature of 24 \({}^{\circ}\)C. Table 1 presents the specified parameters for the experimental processes of the four lithium batteries in the NASA dataset. Figure 2 illustrates the variation of battery capacity with cycle number for the four battery groups in the NASA dataset. It can be observed that transient capacity regeneration occurs in the lithium batteries during cycling, leading to a temporary increase in capacity in the subsequent cycles. This phenomenon results in nonlinear fluctuations in the capacity sequence, which can disrupt the predictive performance of algorithms.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Battery** & **Charging** & **Rated Capacity** & **Temperature** & **(C/D) Current** & **(C/D) Voltage** & **Cut-Off** \\
**Number** & **Protocol** & **Rated Capacity** & **Temperature** & **(C/D) Current** & **(C/D) Voltage** & **Current** \\ \hline B0005, 6, 7, 18 & CC-CV & 2 Ah & 24 \({}^{\circ}\)C & 1.5/2 A & 4.2/2.7, 2.5, 2.2, 2.5 V & 0.02 A \\ \hline \hline \end{tabular}
\end{table}
Table 1: NASA lithium-ion battery experimental specifications.  
Figure 2: NASA’s lithium-ion battery capacity variation curve with the number of discharge cycles.  
The second dataset was obtained from the CALCE CS2 series at the University of Maryland. It comprises cobalt oxide lithium batteries (Battery 35, 36, 37, 38) with a rated capacity of 1.1 Ah and the model INR 18650-20R. These batteries underwent constant voltage charging and constant current discharge cycling experiments. The experiments were conducted at room temperature (24 \({}^{\circ}\)C) and involved charge-discharge cycles. The charge-discharge cycle experiment comprises three stages: constant current charging, constant voltage charging, and constant current discharging [32]. Table 2 presents the specified parameters for the experimental processes of the four groups of lithium batteries in the CALCE study. Figure 3 illustrates the variation of battery capacity over cycling for the four groups in the CALCE dataset. Towards the later stages of charge-discharge cycles, there is a noticeable increase in noise fluctuations, and the degradation rate of lithium battery capacity intensifies in the middle to later cycles.
## 7 Interference Mitigation Strategies
### Step 1: Adjusting MVDS Transmission Power and Receiver LNA Gain  
The first step of the proposed solution involves reducing the MVDS transmitted power significantly. The current transmission power is around 10 dBW. To minimize interference to negligible levels at the GEO satellite while maintaining MVDS performance, the MVDS transmission power gain can be reduced to \(-10\) dBW. To compensate for this substantial reduction in transmission power and preserve the quality of service for MVDS customers, the gain of the low-noise amplifier (LNA) at the MVDS receiver can be simultaneously enhanced from the existing 30 dB to 65 dB (commercially available). This trade-off ensures that the reduced transmission power is offset by increased receiver sensitivity, thereby preserving the necessary signal quality for the MVDS link while significantly decreasing the interference radiated towards the GEO satellite.  
To validate our claim of preserving the quality of service for MVDS customers, the new proposed MVDS system is solely simulated using a radio network planning and optimization tool based on integrated communication systems (ICS) and automated spectrum management solutions (ATDIs). Table 7 illustrates the parameters of both the current and proposed MVDS systems used in the simulation software.  
Table 7 shows the power levels at the output receiver LNA in both cases. These results confirm that the proposed system maintains sufficient signal quality to preserve the expected quality of service for MVDS users. In the second step, we suggest decreasing the off-axis gain of the used parabolic antenna at the MVDS broadcaster.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**System** & **Current** & **Proposed** \\ \hline MVDS G\({}_{\text{Tx}}\) (dBi) & 41.5 & 41.5 \\ P\({}_{\text{MVDS}}\) (dBW) & 10 & \(-20\) \\ MVDS G\({}_{\text{Rx}}\) (dBi) & 41.5 & 41.5 \\ G\({}_{\text{Rx}\_\text{LNA}}\) (dB) & 30 & 65 \\ R\({}_{\text{max}}\) (km) & 50 & 50 \\ Path loss (dB) from (10) & 149.3 & 149.3 \\ P\({}_{\text{Rx}}\) at LNA i/p & \(-29.519\) & \(-59.519\) \\ P\({}_{\text{Rx}}\) at LNA o/p & 0.481 & 5.481 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Current and proposed MVDS system parameters.
## 4 Discussion on Numerical Efficiency
X-ray images of such imperfections are used to classify the casting quality of parts; however, reconstruction of X-ray images may lead to different results [98], which should be related to their local fatigue strength. The introduced methods provide the ability to handle single pores, as well as pore networks, quite efficiently. For this application, the overall pre-processing time was 1313 s, the time for linear-elastic solving was about 540 s, and the post-processing time was evaluated as 3552 s. The evaluated pore network consisted of eight imperfections, which possess quite globular shapes. The depicted fatigue assessment in Figure 29b utilized the Theory of Critical Distances, where an average exceedance stress limit of \(\overline{\sigma}_{\text{TCD.M2}}=2.73\) MPa was observed for defect number two. The corresponding orientation of this most damaging load vector was evaluated as \(\varphi_{95\%\_max}=100^{\circ}\). The maximum TCD stress along the circumference was obtained as \(\sigma_{top}=8.97\) MPa, confirming that exceedance values are more applicable when comparing the defect notch sensitivity of irregularly shaped imperfections. The other defects exhibited a similar fatigue loading, and only defect number three was less stressed. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  
* Computation time calculated based on estimations for a single load vector (compare with Tables 6 and 7), multiplied by the number of load vector increments. Only single-core processing is assumed. ** SP: Single-core processing; MP: Multi-core processing of functions implemented in Python. *** The computing effort to solve the linear–elastic models in Method 2 can be significantly reduced if multiple jobs can be solved in parallel.  
\end{table} 
Table 12: Time effort for pre-processing, solving, and post-processing of three characteristic defect geometries using single- and multi-core processing for load vector step of \(\Delta\varphi=10^{\circ}\).
# Mean Square Consensus of Nonlinear Multi-Agent Systems under Markovian Impulsive Attacks
## 4 Numerical Examples
#### Case 4: Stochastic gains and instants with \(d_{k}\in[-2,0]\)  
In this case, we further assume that \(d_{k}\in[-2,0],k\in\mathbb{N}\) and that gains of the impulsive deception attacks \(\mu_{i},i=1,2,3,4\) take values from \(\mu_{1}=0.9\xi_{1}^{*},\mu_{2}=-0.9\xi_{2},\mu_{3}=0.8\xi_{3}\), and \(\mu_{4}=-0.8\xi_{4}\), where \(\xi_{i},i=1,2,3,4\) satisfy the Bernoulli distribution with expectations \(\mathbb{E}[\xi_{1}]=0.1,\mathbb{E}[\xi_{2}]=0.1,\mathbb{E}[\xi_{3}]=0.1\), and \(\mathbb{E}[\xi_{4}]=0.1\), respectively.  
Then, we can verify that \(v_{1}=0.081,v_{2}=0.081,v_{3}=0.064\), and \(v_{4}=0.064\). In addition, instants of attacks obey the Markovian chain for which the generator is presented as above. Hence, based on the given conditions, one can get that \(\sum_{j=1}^{4}\max_{i\in\Gamma}\{p_{ij}\}\frac{\lambda_{i}p_{ij}}{\lambda_{j}- \gamma}=0.180<1\). Thus, by employing Theorem 1, the consensus of system (22) can be achieved as shown in Figure 8.  
Figure 7: The error trajectories of agents in Case 3 with determined gains and Markovian instants.  
**Remark 7**.: _From Case 4, we can find that impulsive attacks can stabilize an unstable system when gains belong to \([-2,0]\). In fact, we can find its theoretical support from the error system (3). When \(d_{k}\in[-2,0]\), one can get that \(|\eta_{k}|=|d_{k}+1|\leq 1\). The impulses can stabilize an unstable system, as we know, when the gains of impulses are less than one. Thus, the error system (3) can be stabilized with impulsive control._  
**Remark 8**.: _In order to further compare the simulation results with the controller, we give the following simulations with attacks but without control and a system with control but without an attack. For a system (3) affected by the attack and no controller added, the error trajectories of the agents are depicted in Figure 9. From Figure 9, we can find that the error states of system (3) become bigger and bigger, meaning that consensus cannot be reached under attacks without the controller. In addition, for the system that is not attacked, the error trajectories of agents with control are depicted in Figure 10, from which we know that system (22) can achieve consensus under the common controller \(\hat{a}_{i}(t)=K\sum_{j=1}^{N}a_{ij}(x_{j}(t)-x_{i}(t))\) and without the influence of attacks \(\sum_{k=1}^{+\infty}d_{k}x_{i}(t)\delta(t-t_{k})\)._  
Figure 8: The error trajectories of agents in Case 4 with stochastic gains and Markovian instants.  
Figure 9: The error trajectories of agents affected by an attack without the controller.
## 2 Materials and Methods
### Entomopathogenic Fungi
#### 2.1.2 Lab Culture of Fungal Isolates  
SDA was used as a medium for cultivation of EPF isolates in 9 cm Petri dishes for 15 days at 25 \({}^{\circ}\)C and 65\({}^{\circ}\)H relative humidity. To prevent contamination, Petri dishes were sealed with Parafilm(r) (American National Can, Chicago, IL, USA). Fresh conidia were harvested after 15 days by scraping the Petri dish surface with a sterile loop and placing them in a 500 mL glass beaker containing 50 mL of sterile distilled water and 0.05% Tergitol(r) NP9 (Sigma-Aldrich, St. Louis, MI, USA). A magnetic stirrer was used to mix the conidial suspension for 5 min after filtering through sterile cloth layers [35]. A Neubauer hemocytometer was used for the measurement of the fungus conidia concentration (WEBER SCIENTIFIC hemocytometer for cell counting, Hamilton Township, NJ, USA). Dilutions were made by adding 10 mL of the conidial suspension to the required amount of sterile water, resulting in a final concentration of 10\({}^{8}\) conidia per ml for the fungal isolates. This specific concentration was chosen due to its widespread use in numerous relevant studies, and conidial viability exceeded 97% for all fungal isolates.  
The viability of all the tested fungi was determined by spreading a 100 \(\upmu\)L aliquot of a conidia suspension (1 \(\times\) 10\({}^{6}\) conidia mL\({}^{-1}\)), prepared with a sterile surfactant solution (0.1% \(v/v\)) of Tween 80, on SDA medium in Petri dishes (90 \(\times\) 15 mm) and incubating them in the dark at 25 \(\pm\) 1 \({}^{\circ}\)C. SDA plates of the tested fungi were incubated for 18 h, prior to evaluation. Conidia were scored as viable if any germ tube was 2\(\times\) longer that the diameter  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Fungal Species** & **Isolate** & **Insect Bait** & **Collection Site** & **Percent Identity** & **Blast ID Number** \\ \hline _Metarhizium anisopliae_ & MET S & _Galeria mellonella_ & Paramali—Cyprus & 96.7 & 20140422 CS3P4\_C02\_2016- \\ _Metarhizium robertsii_ & MET K & _Stophilus granarius_ & Galia—Crete, Greece & 99.1 & H9 VK23 WY013 \\ _Purpurecolillium_ & 101 & _Stophilus granarius_ & Elo sAjias—Patras, W. Greece & 99.0 & 1E6 TE101P1 GenBank \\ _lavendulum_ & 101 & _Stophilus granarius_ & W. Greece & accession MZ047311 \\ \hline \hline \end{tabular}
\end{table}
Table 1: _Cont._of the spore: a total of 100 conidia per sample under 400\(\times\) magnification. Conidial viability was calculated based on the formula below:  
\[\text{Viability (\%)}=[\text{G1}/(\text{G1}+\text{G2})]\times 100\]  
where G1 refers to the number of germinated conidia, G2 is the number of non-germinated conidia, while the sum of G1 and G2 is equal to 100. Thus, the percentage of viable conidia was determined by counting a total of 100 conidia per fungal sample. Fungal strains presenting \(\geq\) 95% viability were used in the insect bioassays.
## 3 Calculation Method for the Optimized Capacity of the PV and ESSs with 90% Energy Self-Sufficiency
### Calculation Method for Optimized ESS Capacity  
\[E_{0}=\frac{PV_{E0}\times H_{AS}}{P_{L}\times E_{DOD}\times E_{SOC}}-0.61D_{AWh} \ (kWh) \tag{3}\]  
\(\bullet\)\(E_{O}\) = Optimized capacity of ESS  
\(\bullet\)\(PV_{EO}\) = Optimized capacity of PVs  
\(\bullet\)\(P_{L}\) = PCS loss (0.965)  
\(\bullet\)\(H_{AS}\) = Daily average insolation of the area in the previous year (h)  
\(\bullet\)\(D_{anh}\) = Daily average power usage (kWh)  
\(\bullet\)\(E_{DOD}\) = Depth of discharge (0.9)  
\(\bullet\)\(E_{SOC}\) = State of charge (0.9)  
The factors presented above were calculated so that the errors in optimizing the capacity of the ESS would be minimized. The values for each factor were calculated by analyzing data of the target area for eight years.
## 3 Results  
### Etching Ratio and Piezolectric Film Thickness  
We first analyzed the propagation characteristics when the etching ratio and ZnO film thickness change. The variation of \(V_{\rm P}\) and \(K^{2}\) in both modes as functions of ZnO thickness in different etching ratio conditions when the top Al electrode thickness \(t_{1}=0.05\lambda\) was studied and shown in Figure 3. All of the results presented in Figures 3-5 were simulated when the metallization ratio \(2w/\lambda\) was equal to 0.5. The \(d/h=0\) represents the condition of the conventional structure. It can be seen in Figure 3a that regardless of the etching ratio \(d/h\), the \(V_{\rm P}\) value of the quasi-Rayleigh mode decreases monotonically as the ZnO thickness \(h\) increases. This is due to the fact that SiC exhibits a much larger velocity compared total of ZnO. When the ZnO film is totally etched into pillars on the substrate, the \(V_{\rm P}\) value declined to a minimum value compared to other etching ratios at the same ZnO normalized thickness. As for the \(K^{2}\), as Figure 3b shows, when the etching ratio is beyond 50%, the \(K^{2}\) value sees an obvious increment and peaks at \(d/h=1\). When the etching ratio is below 50%, the \(K^{2}\) value improved weakly or was even worse than that of the conventional structure. Since the higher etching ratio and larger ZnO thickness both contribute to the growth of \(K^{2}\), the maximum \(K^{2}\) value is 12.90%, as the \(d/h=1\) and \(h/\lambda=2\) in Figure 3b. However, during these conditions, the \(V_{\rm P}\) is extremely low, only 662.2 m/s. As Figure 3c,d shows, with the increase of ZnO thickness, the velocity continues to decrease, while the \(K^{2}\) of the quasi-Sezawa mode reaches the maximum value in the beginning and then decreases for each branch. The maximum \(K^{2}\) value of the quasi-Sezawa mode is 8.52%, appearing at \(d/h=0.5\) and \(h/\lambda=1\).  
It can be seen from Figure 3 that the combinations of different etching ratios and ZnO thicknesses bring a rich diversity of \(V_{\rm P}\) and \(K^{2}\), providing a variety of design possibilities. However, considering that the overall small aspect ratio \(w/d\) in the actual preparation causes the etching process to be complicated and may even cause the structure to collapse,  
Figure 2: Typical simulated admittance spectra \(|\,Y({\rm S})\,|\) of the partially etched ZnO/SiC configuration at \(h/\lambda=0.6\) and \(d/h=0.7\); the insets show the COMSOL-simulated mode shape of each mode.  
the trade-off between the desired performance and fabrication difficulty must be taken into consideration. Moderate parameters \(d/h=1\) and \(h/\lambda=0.5\) were chosen for the further optimization of quasi-Rayleigh mode in which a high \(K^{2}\) value of 8.16% and \(V_{\rm P}\) of 2309.8 m/s were obtained. Meanwhile, \(d/h=0.7\) and \(h/\lambda=0.6\) were chosen for the further optimization of quasi-Sezawa mode, in which the values of \(V_{\rm P}\) and \(K^{2}\) reach about 5532.0 m/s and 6.40% respectively.
## 1 Introduction  
### Background and Motivation  
The manufacturing sector has progressed from the mechanization of the first Industrial Revolution to the highly connected, data-driven factories of Industry 4.0, where cyber-physical systems, the Internet of Things (IoT), and cloud platforms greatly amplify automation and data use [1]. Industry 5.0 extends this evolution by placing humans back in the loop, that is, keeping skilled operators actively involved alongside intelligent machines to achieve resilient, customized, and sustainable production [2]. To meet these expectations, self-organizing manufacturing systems (SOMSs) have emerged, allowing a decentralized network of autonomous agents to adapt collaboratively to rapidly changing shop floor conditions in real time [3]. SOMSs enable mass-personalization and agile reconfiguration, yet their very decentralization makes it hard to coordinate hundreds of independent resources when large-scale disturbances occur. Communication overhead, distributed scheduling conflicts, and the absence of a global oversight layer often degrade overall performance. Figure 1 illustrates the evolution of manufacturing from mechanized looms to human-centric Industry 5.0 systems.