esa-sceva/satcom-chunk-collection huggingface dataset:
# The Linac Coherent Light Source: Recent Developments and Future Plans
## 4 Future Developments and Science Opportunities--LCLS-II-HE
### LCLS-I-HE Science Opportunities
#### 4.2.4 Materials Heterogeneity, Fluctuations, and Dynamics  
Heterogeneity and fluctuations of atoms and charge-carriers--spanning the range from the atomic scale to the mesoscale--underlie the performance and energy efficiency of functional materials and hierarchical devices. Conventional models of ideal materials often break down when trying to describe the properties that arise from these complex, non-equilibrium conditions. Yet, there exists an untapped potential to enhance material performance and create new functionality if we can achieve a much deeper insight into these statistical atomic-scale dynamics. Important examples include structural dynamics associated with ion transport in materials for energy storage devices and fuel cells; nanostructured materials for manipulating nonequilibrium thermal transport; two-dimensional materials and heterostructures with exotic properties that are strongly influenced by electron-phonon coupling, light-matter interactions, and subtle external stimuli; and perovskite photovoltaics where dynamic structural fluctuations influence power conversion efficiency. LCLS-II-HE will open an entirely new regime for time-domain coherent X-ray scattering of both statistical (e.g., XPCS) and triggered (pump-probe) dynamics with high average coherent power and penetrating capability for sensitive real-time, in situ probes of atomic-scale structure. This novel class of measurements will lead to new understanding of materials, and, ultimately, device performance, and will couple directly to both theory efforts and next-generation materials design initiatives.
## 2 Persisters of Foodborne Pathogens
### Persister Types and Mechanisms of Formation
#### 2.3.3 Persisters in a Biofilm  
Biofilms are major contributors to cross-contamination in food processing because they are tolerant to several stresses and extremely difficult to eliminate [85]. Within a biofilm, cells protected by self-produced matrix material are less sensitive to antibiotics, high salinity, sanitizer, and a host immune system compared to planktonic cells [86]. Biofilms can stick on surfaces of stainless steel of storage and pipe systems. In such a physiological state, these bacteria can pose persistent food safety issues in the food industries [87]. Biofilm-related infections have led to increased morbidity and mortality, and cost nearly $450 million per year [88]. As a small part of the biofilm population, persister-cells (including bacterial spores) are believed to be the essential explanation of biofilm antibiotic tolerance [89]. This is also of importance should a host become infected with (foodborne) pathogenic microorganisms. While the majority of sensitive cells in a biofilm are killed by antibiotics, persister-cells can survive. Once the lethal condition for the bacteria is relieved, persisters within a biofilm can revert into toxin-secreting vegetative cells that can repopulate industrially relevant biofilms and/or medically relevant biofilms causing human infection. For instance, biofilms formed on gallstones act as reservoirs of _S. typhi_ persisters, contributing to the spread of typhoid fever [90].  
Within the biofilm microenvironment, an activated SOS stress response and accumulated ppGpp are important inducers of persister formation [91; 92]. This makes biofilms of _E. coli_, _Salmonella_, _Bacillus_, and _Staphylococcus_ populations contain more persisters than liquid cultures, and these persisters can maintain their phenotype for up to four weeks on antibiotic-containing nutrient-rich medium [93]. For _E. coli_, the TA system MqsR/MqsA directly regulates biofilm formation and the persistence level through toxin MqsR and CspD as well as through the quorum sensing signal autoinducer-2 (AI-2) [94]. In _S. aureus_ and _Salmonella_ populations, the master virulence regulator Agr QS has been proven to serve as a repressor of persister formation by regulating a family of virulence factors named phenol soluble modulus (PSMs) [95]. The latter play an important role in biofilm formation [96]. In general, persister formation and biofilm formation reinforce each other with respect to (environmental) stress resistance.
## 2 Materials and Methods
### NDVI Climatology vs LSP  
The NDVI climatology is defined as a \"historical\" average NDVI for a given time period. Historical is defined as the average of at least 5 years of NDVI data. Such data is available from AVHRR and MODIS for global applications. In this study, LSPs are simply represented by temporally smoothed (3-period moving average) climatology NDVI data sets. Smoothed NDVI climatology data sets are preferred to produce Kcp functions in order to avoid data noise from yearly NDVI data sets. Currently, NDVI climatology is available for global applications from different data sources such as the Global Inventory Monitoring and Modeling Studies (GIMMS) at a monthly scale at 8-km resolution. In this study, the U.S. weekly maximum composite at 1 km is used [15]. This data set is created by first compositing cloud-free weekly maximum NDVI values for each pixel from 1989 to 2004. The climatology was then created by averaging weekly maximum NDVI values over the 16 years. The weekly climatology NDVI were further interpolated to represent dekadal time periods to conform with other modeling procedures and availability of similar (dekadal) NDVI data sets for other regions of the world. A dekad is nominally composed of 10-day time periods in a month, except for the 3\({}^{\rm rd}\) dekad of a month where it may assume values of either 8, 9, 10, or 11, depending on the month and year. Depending on the context of the discussion, the terms LSP and NDVI climatology are used interchangeably.
## 4 Case Study
### Data Description
\begin{table}
\begin{tabular}{c r r r r r r r r} \hline
**O/D** & **Seoul** & **Busan** & **Daegu** & **Incheon** & **Gwangju** & **Daejeon** & **Ulsan** & **Gyeonggi** \\ \hline Seoul & 16 & 10,480 & 7938 & 237 & 4493 & 7529 & 2865 & 1418 \\ Busan & 10,030 & 15 & 1849 & 712 & 0 & 1724 & 656 & 4220 \\ Daegu & 7953 & 2021 & 0 & 593 & 0 & 1461 & 956 & 3311 \\ Incheon & 729 & 737 & 585 & 0 & 295 & 570 & 243 & 2 \\ Gwangju & 4511 & 0 & 0 & 295 & 0 & 30 & 0 & 1761 \\ Daejeon & 7597 & 1925 & 1527 & 577 & 38 & 0 & 681 & 2527 \\ Ulsan & 2939 & 741 & 844 & 251 & 0 & 678 & 55 & 1329 \\ Gyeonggi & 1324 & 4468 & 3404 & 3 & 1763 & 2619 & 1292 & 304 \\ Gangwon & 3406 & 0 & 0 & 128 & 0 & 0 & 0 & 573 \\ North & 2467 & 339 & 250 & 185 & 179 & 220 & 113 & 754 \\ Chungcheong & & & & & & & & \\ South & 4766 & 905 & 742 & 364 & 329 & 1241 & 423 & 1538 \\ Chungcheong & & & & & & & & \\ North Jeolla & 3882 & 0 & 0 & 286 & 500 & 94 & 0 & 1393 \\ South Jeolla & 3886 & 0 & 0 & 328 & 324 & 66 & 0 & 1386 \\ North & 3914 & 547 & 1244 & 340 & 0 & 992 & 119 & 1437 \\ Gyeonggang & & & & & & & & \\ South & 2482 & 55 & 817 & 261 & 0 & 593 & 0 & 868 \\ Gyeongsang & & & & & & & & \\ Sejong & 2642 & 363 & 268 & 198 & 191 & 40 & 121 & 808 \\ \hline
**O/D** & **Gangwon** & **North** & **South** & **North** & **South** & **North** & **South** & **Sejong** \\  & & & & & & & & \\ Busan & 0 & 320 & 801 & 0 & 0 & 517 & 47 & 343 \\ Daegu & 0 & 244 & 679 & 0 & 0 & 1318 & 781 & 262 \\ Incheon & 118 & 187 & 411 & 287 & 333 & 338 & 256 & 200 \\ Gwangju & 0 & 169 & 299 & 509 & 438 & 0 & 0 & 181 \\ Daejeon & 0 & 10 & 1231 & 128 & 93 & 1049 & 559 & 275 \\ Ulsan & 0 & 114 & 403 & 0 & 0 & 117 & 0 & 122 \\ Gyeonggi & 594 & 782 & 1787 & 1373 & 1388 & 1496 & 871 & 838 \\ Gangwon & 531 & 0 & 0 & 0 & 0 & 0 & 0 & 0 \\ North & 0 & 208 & 216 & 194 & 127 & 83 & 0 \\ Chungcheong & 0 & 144 & 101 & 390 & 397 & 355 & 312 & 64 \\ North Jeolla & 0 & 213 & 317 & 702 & 816 & 0 & 0 & 228 \\ South Jeolla & 0 & 194 & 326 & 755 & 257 & 0 & 0 & 208 \\ North & 0 & 137 & 334 & 0 & 0 & 97 & 84 & 147 \\ Gyeongsang & & & & & & & & \\ Sejong & 0 & 58 & 222 & 0 & 0 & 78 & 239 & 62 \\ Sejong & 0 & 10 & 0 & 232 & 208 & 136 & 89 & 0 \\ \hline \end{tabular}
\end{table} 
Table 2: Inter-region O/D trips in KTDB data. Here,  
\(i\): zone (\(\forall i\in Z\));  
\(s\): HSR station (\(\forall s\in\) S);  
\(d_{is}\): Euclidean distance from zone \(i\) to station \(s\);  
\(x\), \(y\): \(x\)-coordinate and \(y\)-coordinate. Sets  
\(\bullet\)\(Z\): The set of zones;  
\(\bullet\)\(S\): The set of HSR stations. Figure 9: HSR network data in the KTDB. The train frequencies at the HSR stations were calculated by extracting the stop stations for each HSR train from the transit line data in the KTDB. Although both the skip and stop stations were presented in the transit line data, skip stations were excluded in the train frequency calculation, and the daily train frequency by line at the stations was calculated based on information for the line to which the train belonged.
# Unsupervised Anomaly Detection and Segmentation on Dirty Datasets
## 5 Ablation Study
### Effect of Mahalanobis Distance Threshold on Performance  
We performed experiments to investigate the effect of hyperparameter \(\eta\) in Equation (15), which relates to the slope threshold \(v*Slope_{mean}\). The \(v*Slope_{mean}\) is used to determine the target window. With \(k=10\), \(s=4\) in Equation (3), we tested the performance of image-level detection for different \(\eta\). The experiments are based on the MVTec dataset.  
As shown in Figure 15, The x-axis represents the different hyperparameter \(\eta\), and the y-axis represents the mean value of image-level AUROC at 5%, 15%, and 25% noise ratio. In Equation (15), when \(\eta\) is small, the Mahalanobis distance threshold is large. It leads to anomaly samples not being effectively excluded, and the estimated Gaussian distribution deviates from the distribution of normal samples. When \(\eta\) is large, the Mahalanobis distance threshold is small, leading to a small number of selected clean samples, and the estimated Gaussian distribution cannot accurately match the distribution of normal samples. The ablation experiment shows that the average performance is highest when \(\eta=2.3\).  
Figure 14: Comparison of the number of clean samples with and without the iteration module.  
Figure 13: Trimming and iteration module performance testing.
## 8 Conclusions  
The application of CFD in food technology has significantly enhanced the understanding and control of thermal processes, such as pasteurization, drying, and cooking. Key findings from this research indicate that CFD allows for the precise simulation of fluid flows, heat transfer, and temperature distribution in complex food systems. CFD has proven especially useful in optimizing process parameters, reducing energy consumption, and ensuring food safety and quality. The simulations conducted demonstrate that CFD can accurately predict the behavior of food products under various processing conditions, offering a reliable, cost-effective, and efficient tool for innovation in the food industry. CFD holds considerable promise for the food industry as it addresses critical challenges in process optimization and product quality control. Its capability to simulate and optimize processes before scaling up to industrial production helps reduce experimental costsand product development time. As the food industry continues to adopt digitalization and Industry 4.0 principles, CFD will become an integral part of digital twins and smart manufacturing systems. The future of CFD in this field will likely see more advanced simulations incorporating biochemical changes and texture transformations, contributing to safer, more sustainable, and higher-quality food production methods. To fully leverage CFD in the food industry, professionals should integrate it with real-time monitoring and control systems to enable dynamic process adjustments. Further research should focus on refining CFD models to include multiphase and multi-component flows, as well as biochemical transformations that occur during processing. Additionally, the interdisciplinary collaboration between CFD specialists, food technologists, and engineers will be essential to develop customized, industry-specific models. Expanding the use of CFD to smaller food businesses could democratize access to these tools, leading to broader innovations across the sector.  
Conceptualization, A.S. ([PERSON]); methodology, A.S. ([PERSON]); software, A.S. ([PERSON]) and A.S. ([PERSON]); validation, A.S. ([PERSON]) and K.P.; formal analysis, A.S. ([PERSON]) and W.B.; investigation, A.S. ([PERSON]); resources, A.S. ([PERSON]); data curation, A.S. ([PERSON]); writing--original draft preparation, A.S. ([PERSON]), W.B., I.W.-K. and J.P.-S.; writing--review and editing, A.S. ([PERSON]), W.B., A.S. ([PERSON]), K.P., I.W.-K., M.Z. and J.P.-S; visualization, A.S. ([PERSON]) and W.B.; supervision, A.S. ([PERSON]) and A.P.; project administration, A.S. ([PERSON]). All authors have read and agreed to the published version of the manuscript.  
This research was financed by the Polish Ministry of Science and Higher Education using the funds of the Faculty of Human Nutrition and Consumer Sciences, Warsaw University of Life Sciences (WULS).  
Not Applicable.  
The authors declare no conflicts of interest.
## 3 Results and Discussion
### Effect of Filler Concentration and Combinations of Nanofililers on the Electrical Conductivity
The morphological analysis carried out (Figure 2a,b) indicated that the hybrid bi-filler composites incorporating a small amount of GNPs exhibit quite well-distributed small nanoplatelet aggregates, indicating that CNTs can prevent GNP aggregation and thus improve their dispersion in the PLA matrix. The strong \(p\)-\(p\) interactions and large Van der Waals forces between GNPs can cause their aggregation and stacking in nanocomposites. However, the long MWCNTs acting like arms and bridging GNPs may inhibit their aggregation, thus creating a strong inter-connected hybrid nanofiller network in the polymer matrix [40], as described in the recent literature for thermoplastic and thermosetting matrices incorporating different hybrid fillers [41; 42; 43]. Hybrid systems also exhibited improved nanomechanical properties after nanoindentation tests as shown in our previous publication [44]. Synergistic effects were observed in GNP/MWCNT/PLA hybrid composites, when combining GNPs and CNTs at a ratio of 3% GNP/3% CNT and 1.5% GNP: 4.5% CNT. The hybrid filler composites have shown a higher Young's modulus of elasticity and hardness properties with respect to the mono-filler systems incorporating individual CNTs and GNPs at the same overall filler concentration. This improvement was attributed to a synergetic combination of CNTs and GNPs, hypothesizing that the long tortuous CNTs limit graphene aggregation and bridge adjacent graphene platelets creating a more efficient network that has better reinforcing effects in the PLA matrix. This means that the application of MWCNTs as a nanofiller--with geometry different to the layered structure of graphene nanoplatelets--has great effect on the mechanical properties of rubber composites.
# A Bayesian Control Chart for Monitoring Process Variance
## 5 Process Simulations
Given \(\alpha_{0}\), \(\beta_{0}\), \(n\), \(\lambda\), \(k_{1}\), and \(k_{2}\), then \(UCL_{EWMA}\) and \(LCL_{EWMA}\) can be calculated by Equations (31) and (32), respectively;
5. Let \(EWMA_{0}\) be equal to the mean of \(EWMA\), i.e., \(n\alpha_{0}/(\alpha_{0}+\beta_{0})\);
6. Simulate random samples of size \(2n\), \(X_{t1}\), \(X_{t2}\),..., \(X_{t,2n}\) from \(Normal(0\), \(d^{2})\) (\(Exponential(1/d)\), or equally-weighted mixture of \(Normal(0\), \(d^{2})\) and \(Exponential(1/d)\)), compute \(Y_{t1}\), \(Y_{t2}\),..., \(Y_{tn}\) by Equation (1), obtain the statistic \(M_{t}\) by counting the number of instances in which \(Y_{tj}>1\), and compute \(EWMA_{t}\) by Equation (25) until \(EWMA_{t}>UCL_{EWMA}\) or \(EWMA_{t}<LCL_{EWMA}\), then record the run length;
7. Repeat step 6 1000 times and obtain the average run length, \(ARL_{1}\). While simulating random samples of size \(2n\) from the equally-weighted mixed process, we assume that observations within one sample come from the same distribution; that is, the skewness of distribution varies only between samples. Table 7 shows the simulation results of the \(ARL_{1}\)s of the proposed Bayesian EWMA variance chart under a normal process, exponential process, and the equally-weighted mixed process of normal and exponential distributions. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Panel A:** & **Normal** & **Process** & & **Panel B:** & **Exponential Process** & \\ \hline \(d\)1  & \(p\) & \(d\) & \(p\) & \(d\) & \(p\) \\ \hline
1 & 0.3173 & & & 1 & 0.2431 & & \\
1.1 & 0.3633 & 0.9 & 0.2665 & 1.1 & 0.2765 & 0.9 & 0.2078 \\
1.2 & 0.4047 & 0.8 & 0.2113 & 1.2 & 0.3077 & 0.8 & 0.1707 \\
1.3 & 0.4418 & 0.7 & 0.1531 & 1.3 & 0.3369 & 0.7 & 0.1326 \\
1.4 & 0.4751 & 0.6 & 0.0956 & 1.4 & 0.3642 & 0.6 & 0.0947 \\
1.5 & 0.5050 & 0.5 & 0.0455 & 1.5 & 0.3895 & 0.5 & 0.0591 \\
1.6 & 0.5320 & 0.4 & 0.0124 & 1.6 & 0.4132 & 0.4 & 0.0291 \\
1.7 & 0.5564 & 0.3 & 0.0009 & 1.7 & 0.4352 & 0.3 & 0.0090 \\
1.8 & 0.5785 & 0.2 & 0.0000 & 1.8 & 0.4558 & 0.2 & 0.0008 \\
1.9 & 0.5987 & & & 1.9 & 0.4751 & & \\
2.0 & 0.6171 & & & 2.0 & 0.4931 & & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The relationship between the shift factor \(d\) and \(p\). \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Panel A:** & **Normal process: \(E_{0}\)(\(p\)) = 0.3173 and (\(\alpha_{0}\), \(\beta_{0}\)) = (317, 683)** & \\ \hline \multirow{2}{*}{\(d\)} & \multirow{2}{*}{\(E_{1}\)(\(p\))} & \(n\) = 2 & \(n\) = 10 & \(n\) = 25 \\  & & **(3.16, 2.77) b** & **(2.98, 2.94)** & **(2.96, 3.05)** \\  & & **381.68 c** & **375.94** & **375.94** \\ \hline  
\hline \hline \end{tabular}  
* denotes the in-control process; b denotes the coefficients of the control limits (\(k_{1}\), \(k_{2}\)); c denotes the preset \(ARL_{0}\).  
\end{table} 
Table 7: The simulation results of the \(ARL_{1}\)s of the proposed Bayesian EWMA variance chart under three different processes (\(\lambda\) = 0.05).
## 4 Numerical Results and Discussion
### Temperature and Magnetic Field Dependence of the Polarization in CBCO  
The strong electric polarization, \(P\), of CBCO below the critical temperature (\(T_{C}\)) is induced by the displacement of oxygen atoms surrounding bonds that couple the triangular and kagome layers. The calculation of the spin-induced polarization, \(P\), along the \(c\)-axis in CBCO is derived from Equation (3), with the resulting data presented in Figure 4. Notably, the polarization, \(P\), exhibits a diminishing trend with rising temperature, \(T\), ultimately reaching nullity at the critical ferrimagnetic temperature (\(T_{C}\)) = 70 K. Additionally, augmentation of polarization is observed with the increasing magnetic field, \(h\), as illustrated in the inset of Figure 4, providing compelling evidence for the multiferroic nature of CBCO. This observation aligns with the findings by [PERSON] et al. [9] and [PERSON] et al. [6], who similarly reported an enhanced polarization, \(P\), in response to the external magnetic field, \(h\). A peak around the ferrimagnetic phase transition temperature (\(T_{C}\)) is reported in the dielectric constant \(\epsilon\) along the \(c\) axis in CBCO [6; 18; 24], which is also evidence for the multiferroic behavior of CBCO.  
Figure 4: Temperature dependence of the polarization, \(P\), of CBCO. Inset: Magnetic field dependence of the polarization, \(P\), of CBCO for \(T\) = 60 K.  
Figure 3: Temperature dependence of the specific heat \(C_{p}\) of CBCO for different magnetic field, \(h\), values: (1) 0, (2) 1, (3) 10 kOe.
###### Abstract  
An accurate description of the transition corridor is of great significance for the flight process of the vertical take-off and landing (VTOL) fixed-wing unmanned aerial vehicle (UAV). To study the transition flight process of vertical take-off and landing fixed-wing UAVs, the dynamic model and transition corridor model of this type of UAV are established in the current article. The method for establishing the model is based on a reasonable match of the power and aerodynamic force of this type of UAV. From the perspective of flight dynamics, the ducted lift-increasing system's deflection angle-speed envelope is studied with the maximum lift coefficient of the wing and the system's available power. The influence of the overall parameters and energy parameters of the UAV on the deflection angle-speed envelope of the ducted lift-increasing system is analyzed, and a method is proposed to expand the vertical take-off and landing fixed-wing UAV's transition corridor. Taking the UAV as the object, using the established model, the transition flight corridor of the UAV is obtained, the influence of the control parameters on the transition flight is studied, and the appropriate transition flight control strategy is determined. At the same time, the influence of the overall parameters and energy parameters on the transition corridor is calculated. According to the calculation results, the effect of expanding the flight corridor of the UAV is more obvious when increasing the available power than when increasing the aerodynamic parameters by the same proportion.  
VTOL fixed-wing UAV; flight envelope; transition corridor; available power; stall angle of attack; force analysis Article  
Study on Ducted Vertical Take-Off and Landing Fixed-Wing UAV Dynamics Modeling and Transition Corridor  
[PERSON], [PERSON], [PERSON], and [PERSON]  
School of Aeronautics, Northwestern Polytechnical University, Xi'an 710072, China; [EMAIL_ADDRESS] (Z.Z.); [EMAIL_ADDRESS] (R.W.); [EMAIL_ADDRESS] (Y.D.)  
* Correspondence: [EMAIL_ADDRESS]
## 3 Towards Autonomous SC with Tenderfone
### An Agent-Oriented Language for SC
#### 3.1.1 Computational Autonomy  
Computational autonomy, in the most basic acceptation of \"encapsulating the flow of control\", is actually enabled by the interplay between a few distinct mechanisms, among which the Tenderfone Interpreter plays a crucial role, as clarified in Section 3.2. Nevertheless, a first autonomy enabler is the init(+Args) callback, which the developer defines to specify what the smart contract should do as its very first action. In other words, such callback dictates what a SC should do right after its deployment transaction completes successfully.  
Within the body of the init/1 callback (for the sake of brevity, we also rely on the standard Prolog notation when referring to rules: rule_name/N indicates a rule named rule_name which accepts N arguments), the developer may decide to take advantage of time-awareness and, possibly, asynchronous message passing to specify a pro-active behavior in which _(i)_ the smart contract periodically executes tasks, _(ii)_ there including reception of messages and _(iii)_ deliberation about whether and when to actually process them. This is technically enabled by a clever combination of the receive/2 callback and the time-aware built-in predicate, both described in the following.  
The semantics enforced by the Tenderfone Interpreter guarantees that deployment of a valid smart contract can only succeed if goal init/1 can be proven using arguments Args provided by the transaction issued by end user \(U\). In case of success, the new smart contract \(SC\) is actually deployed, and its KB is stored on the blockchain. In case of failure, no smart contract is deployed and an error description is returned to the deploying user \(U\).