esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
That means the control variables have to be calculated faster than the dynamics of the disturbance inputs, with a limited computation and memory resources. \"Safety\" is necessary because the EMS must respect the physical limits of the system (boundaries), such as the maximal current of the battery or the maximal voltage of the super capacitors. \"Performance\" is necessary because the EMS has to be as close as possible to the global optimal solution. These three criteria are not independent. That is why many studies have been investigated on this topic to solve state-constrained problems in real time. Among the different methods, the so-called equivalent cost minimization strategy (ECMS) is the most commonly studied. ECMS aims to minimize an equivalent fuel consumption, which corresponds to the weighted sum of power of each power sources [14]. For a hybrid electric vehicle with an internal combustion engine and a battery, it means that the engine fuel consumption is summed with an equivalent fuel consumption from the battery. Derived from the co-state of the Pontryagin's Minimum Principle (PMP), an equivalent factor is used to convert battery power to equivalent fuel power. Subsequently, this principle has been extended to other types of electrified vehicles, such as FC-BAT-SC-based electric vehicles [15]. Another method, called \(\lambda\)_-control_, consists of adapting the PMP in real time by applying a feedback control to the co-state, which allows to have an adaptable co-state for different driving cycles and uncertainties of the system [16]. The main advantage of the strategies derived from PMP is easy to implement in real time due to a low computational burden. The \(\lambda\)_-control_ has already been successfully used for conventional hybrid electric vehicles with an engine and a battery [16; 17; 18] and FC-BAT-based electric vehicles [19], but not for FC-BAT-SC-based electric vehicles. The objective of this paper is to validate a real-time optimization-based EMS with \(\lambda\)_-control_ for a FC-BAT-SC-based electric vehicle. The contribution of this paper are summarized as follows: (1) a multi-level control is proposed to formulate the optimal control problem and to deduce the reduced (quasi-static) model, which is required for the control problem resolution; (2) the developed EMS avoids over-charge and discharge of the secondary energy storages sources, while guarantying suboptimal solutions. The strategy is first designed and validated through simulations tests on a standard driving cycle with a worldwide harmonized light-duty vehicles test cycle (WLTC) class 2. Subsequently, the proposed strategy is assessed by experimental tests. The remainder of the paper is organized as follows. Section 2 is devoted to the modelling and the control organization of the studied electric vehicle. Energetic macroscopic representation (EMR) formalism [20; 21] is used throughout the paper to organize both modelling and control of the system. Section 3 deals with the design of the \(\lambda\)-_control_ strategy. Section 4 depicts the experimental validation of the proposed strategy.
# Comparative Transcriptomics for Pepper (_Capsicum annuum_ L.) under Cold Stress and after Rewarming
## 3 Results
### Identification of DEGs in CT and CS by RNA-Seq  
To investigate genes associated with the response to cold stress and rewarming stage in pepper, the transcriptome profiles in pepper leaves were examined. Using the BGISEQ-500 platform to sequence the RNA of 30 samples, the clean reads produced from each sample varied from 42.31 to 46.71 million. In this project, more than 83.03% reads were aligned to the pepper reference genome, and at least 77.7% reads could be aligned to the unique locations of the reference genome. Additionally, Q30 of each sample exceeded 86.25%, and all GC contents of each sample exceeded 40.07% (Supplementary Table S2). Moreover, total of 30,490 known genes were detected, among which the DEGs were selected on the basis of DEG-seq method with the following parameters: \(\mid\)log2 FC\(\mid\)\(\geq\) 1.5 and Q-value \(\leq\) 0.01.  
The DEGs specific to each cold-rewarm treatment were studied by comparing the stage with the previous one. The DEGs detected from cold stress treatment (T1, T2, and T3 groups) and rewarming treatment (T4 group) in CT were 1425, 2315, 547, and 6339, respectively (Figure 2a). In CS, the total DEGs were 258, 322, 921, and 5231, respectively (Figure 2a), which increased following cold exposure. More DEGs were found in T1 and T2 groups and T4 treatment, while fewer DEGs were observed in T3 group were observed. Of particular note, more genes are upregulated than downregulated in T1 group, while more genes are downregulated in T2, T3, and T4 groups (Figure 2a).  
Figure 1: Phenotype and physiological indexes of pepper under cold–rewarm treatments. (**a**) Phenotype of CT in control, cold stress, and rewarming stage. (**b**) Phenotype of CS in control, cold stress, and rewarming stage. (**c**) Changes in malondialdehydedehyde, soluble sugar (SS), and chlorophyll (Chl) content in the pepper leaves of both cultivars. ** indicate significant differences (\(p\leq 0.01\)).  
Figure 2: Differentially expressed genes (DEGs) under cold–rewarm treatments. (**a**) The DEGs specific to each cold treatment by comparing with the previous stage. (**b**) Venn diagram for total DEGs of two cultivars. (**c**) Venn diagram for DEGs in CT. (**d**) Venn diagram for DEGs in CS.  
after recovering treatment in CT (Figure 2c). Hence, these 31 common DEGs could be crucial for the cold-rewarm response, and the 3 DEGs not exhibited in the recovering treatment were responsible for cold stress only. In CS, 42 DEGs were shared in the process of cold stress, and only 14 DEGs of them acted together during the rewarming treatment (Figure 2d), which is less than CT. In total, 7333 and 5953 DEGs responded to one or more of the four treatments in CT and CS, respectively. The 4391 DEGs were shared between the two cultivars (Figure 2b), which were the basic response of peppers to cold treatment. In addition, the 2942 and 1562 DEGs were regulated in only one cultivar, which may be the main reason for the difference in cold resistance of the two cultivars.
# An ANN Model Trained on Regional Data in the Prediction of Particular Weather Conditions
## 4 Results
### Training Variables Selection  
The selection of the most suitable subset of training variables was performed by predicting different targets by using the same variables coming from the 3 previous days for the forecasted day [84]. The results of this procedure as performed for prediction of the maximum daily temperature are contained in Table 6. The values of the MSE are ranked in ascending order. The largest error value was obtained for the MLP training and testing with the daily sum of the precipitation, mean daily wind speed, and mean daily general cloud cover. These variables were, therefore, excluded from the predictive model for the maximum daily temperature.  
The results of the variable selection for other predicted weather conditions are included in Tables 11-12 in Appendix E, and the excluded variables are presented in Table 7. Variables that were not excluded became inputs of the models.  
\begin{table}
\begin{tabular}{l c c} \hline \hline
**Variable** & **Training MSE** & **Testing MSE** \\ \hline Maximum daily temperature & 7.6154 & 8.0929 \\ Mean daily temperature & 8.7251 & 9.4972 \\ Minimum daily temperature & 20.34 & 23.781 \\ Min. daily temp. at ground level & 25.0103 & 28.7659 \\ Daily dew duration & 44.6071 & 48.8083 \\ Mean daily relative humidity & 55.5796 & 54.0218 \\ Mean daily atm. pressure & 58.2674 & 60.8849 \\ Mean daily general cloud cover & 60.6825 & 56.0763 \\ Mean daily wind speed & 69.7158 & 68.5501 \\ Daily sum of precipitation & 73.4619 & 77.2748 \\ \hline \hline \end{tabular}
\end{table}
Table 6: The results of the selection of the most suitable subset of the training variables for the prediction of the daily maximum temperature.  
\begin{table}
\begin{tabular}{l r r r r r} \hline \hline
**Parameter** & **Max. Temp.** & **Min. Temp.** & **Pressure** & **Wind** & **Precip** \\ \hline Hidden neurons & 80 & 80 & 90 & 40 & 50 \\ Learning rate & 0.001 & 0.001 & 0.001 & 0.001 & 0.001 \\ Number of epochs & 500 & 750 & 250 & 500 & 250 \\ L2 & 0.001 & 0.001 & 0.01 & 0.001 & 0.001 \\ Decrease const & 0.00001 & 0.0001 & 0.00001 & 0.0001 & 0.00001 \\ Alpha & 0.001 & 0.0001 & 0.001 & 0.01 & 0.0001 \\ \hline \hline \end{tabular}
\end{table}
Table 5: The MLP hyperparameters as determined using a grid search.
# A Performance-Consumption Balanced Scheme of Multi-Hop Quantum Networks for Teleportation
## 2 Preliminary
### Entanglement Swapping  
In a multi-hop quantum network, entanglement swapping can establish the entanglement between nodes without being directly connected. So, it is also an important technology to realize a quantum relay [13].  
Node B has shared entanglement both with nodes A and C, as shown in Figure 3. Entanglement swapping can help to establish entanglement between node A and C. It is described in detail in Appendix B.
# Advances in the Application of Microcapsules as Carriers of Functional Compounds for Food Products
## 7 Release of Encapsulated Bioactives from Microcapsules
The microparticles were produced by a complex coacervation method using chitosan and xanthan gum as wall materials, and were then incorporated into yogurt and bread. Food samples with microencapsulated bioactive compounds were placed under conditions simulating the gastrointestinal tract. For the bread, 38.9% of the carotenoids were released,while for the yogurt 50.1% were released. Additionally, according to the results obtained in the study, before incorporation into food, the bioactive materials from the microparticles had greater release during the simulation of the gastrointestinal tract, with greater degradation. The authors inferred that the interaction of food matrices with microcapsules may protect the encapsulated bioactives, suggesting that the loaded microcapsules produced have great potential to be incorporated into foods, mainly yogurt.
## 2 Pathophysiology of Perinatal Hypoxia-Ischemic Brain Injury
### Inflammation  
The inflammatory response following HIE has been extensively reviewed by [49; 50] and is therefore briefly discussed. In the immature brain following HI, an innate immune response occurs within minutes of the injury [51]. Initially, microglia become activated within the cerebral parenchyma, followed by a systemic inflammatory response, mediated by the infiltration of circulating monocytes, neutrophils and T-cells into the brain. Activated microglia develop macrophage-like attributes, such as phagocytic properties, cytokine production, antigen presentation and matrix metalloproteinase (MMP) release. The release and activation of MMPs by microglia can compromise BBB integrity, further promoting the invasion of peripheral leukocytes into the cerebral parenchyma; exacerbating ensuing cerebral injury. A hallmark of HIE is the aggregation of amoeboid microglia within the periventricular white matter, resulting in the production of inflammatory cytokines (TNF-\(\alpha\) and IL1-\(\beta\)), NO and other ROS [52]; collectively potentiating the toxic effects of the ischemic cascade in neurons, glial progenitor cells and the cerebral vasculature.  
While astrocytes attempt to provide neuroprotective support to neurons and glia via the release of glutathione, superoxide dismutase (SOD), extra-synaptic uptake of glutamate and the maintenance of ion channel gradients, these protective mechanisms can be quickly overrun. As with microglia, astrocytes become hyper-activated due to the effects of pro-inflammatory cytokines, ROS, and dying neurons and glia. Activated astrocytes also release pro-inflammatory cytokines (IL-6, TNF-\(\alpha\), IL-\(1\alpha/\beta\) and INF-\(\gamma\)), further exacerbating HI-induced cell death pathways (e.g., apoptosis). In addition, astrocytes secrete chemokines that attracts systemic immune cells to infiltrate affected tissue, further aggravating tissue injury.
# AERQ--A Web-Based Decision Support Tool for Air Quality Assessment
## 5 Conclusions  
This study highlights the critical role of advanced technological frameworks in addressing urban air quality challenges. By developing a modular and scalable decision support system (DSS), this research delivers a robust solution for air quality monitoring and analysis. The integration of the AERMOD modeling system with a high-performance computing infrastructure enables precise, high-resolution assessments of air pollutant concentrations while significantly reducing computational time.  
AERQ is designed with a flexible structure comprising independent modules for consuming and exposing web services. The platform leverages geographical datasets and models to facilitate data mapping, querying, sharing, processing, and distribution. It also provides users with the flexibility to develop new content, analyses, and applications, making it adaptable to various use cases.  
The proposed DSS bridges the gap between data acquisition and actionable insights, empowering stakeholders such as policymakers, urban planners, and public health officials to make informed decisions. Its innovative features--including a job-on-demand application, scenario analysis, and user-friendly interfaces--position it as a transformative tool for urban air quality management.  
By demonstrating its application in the Cagliari metropolitan area, the system under-scores its practical value in addressing localized pollution issues while maintaining the flexibility to scale to broader regions. Through discussions of the model, datasets, calibration, and validation, this study provides a clear example of how the DSS can integrate and process available data for environmental modeling. This approach ensures that the methodology and results are transferable to other timeframes or regions with similarly complete datasets.  
Emphasizing security, scalability, and accessibility, the DSS is well suited to the demands of modern urban governance. This study lays the groundwork for future research to enhance urban air quality systems by incorporating additional pollutants, improving spatial and temporal resolutions, and addressing interactions among pollutants.  
As cities grow and face increasing environmental challenges, the development of innovative, data-driven solutions like this DSS will be pivotal in promoting sustainable urban living and improving public health outcomes. Future steps will involve ensemble simulations, running multiple simulations with varied inputs or model configurations, to quantify uncertainties and provide probabilistic forecasts within the DSS framework.  
Conceptualization, P.C.; methodology, P.C. and G.S.; software, D.M. and C.M.; supervision, C.C.; writing--original draft, P.C.; writing--review and editing, D.M., G.S., C.M. and C.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The original contributions presented in this study are included in this article. Further inquiries can be directed to the corresponding author.  
The authors declare no conflict of interest.
###### Abstract  
Soft bioelectronic systems with a unique mechanical property, namely modulus matching between human skin (or tissue) and the device, have gained widespread attention. This is because of their closed-loop strain-insensitive electrical performance ranging from application in the long-term stable measurements of physiological signals and feedback modulation to human skin (or organs). Various materials and integration/fabrication strategies such as buckled, rigid islands, and wavy designs addressed for soft bioelectronic systems require complex device fabrication with time-consuming packaging and integration processes. In this study, we developed a soft bio-integrated multifunctional device (SBMD) fabricated through the simple thermal evaporation and transfer processes. The intrinsically stretchable Au-SEBS film composed of thermally evaporated gold (Au) nanomembranes and an elastomeric substrate was applied to various functional modules that are capable of sensing the strain (up to \(\sim\)300%), temperature (with a thermal sensitivity of \(\sim\)0.6 \(\Omega/^{\circ}\)C), chemicals (at a concentration of NaCl of \(\sim\)0.5 wt%), and even electrophysiological cardiac/muscle signals and showing thermal actuations (80 \({}^{\circ}\)C at 9 V). Specifically, such multifunctions of the SBMD were stably performed even on skin. Thus, we believe the SBMD would be a promising candidate for realizing soft bioelectronic systems.  
Keywords:nanomembrane; crack-based sensor; stretchable electrode; wearable device; biomedical device +
Footnote †: journal: Applied Sciences
## 2 Materials and Methods
### Energy Calculations  
Segments and Segment Properties  
After labelling and filling gaps in trajectories with the built-in options (rigid body, kinematic, or pattern fill), marker trajectories were smoothed using a Woltring quantile-spline routine with a mean square error (MSE) of 5 mm\({}^{2}\). Next, the built-in _Process Dynamic Plug-in Gait Model_ operation was executed within Nexus. Plug-in Gait processing (PIGP) creates a three-axis system for each body segment and modelled markers at joint centres and segment centres of mass, as well as a virtual marker at the body's centre of mass (CoM). As PIGP does not create a three-axis system for the body's CoM, CoM axes were created manually as a parallel-shifted copy of the thorax segment's axes.  
Segment lengths were calculated as follows (italicized terms designate markers or modelled markers from the plug-in gait model):  
* Head: mid forehead (mean of '_RFHD_' and '_LFD_') to 'C7';
* Thorax: '_PelvisCoM_' to 'C7';
* Pelvis: '_PelvisO_' (origin of pelvis coordinate system, approximately the lower edge of the pelvis in the sagittal plane) to '_PelvisCoM_' (approximately the upper edge of the pelvis in the sagittal plane);
* L/R Humerus: shoulder joint centre to elbow joint centre;
* L/R Radius: elbow joint centre to wrist joint centre;  
Figure 2: Arrangement of the 14 Vicon Vantage Cameras (C1 to C14) for 3-dimensional motion capture of vaults.  
* L/R Hand: wrist joint centre to hand coordinate system origin (approximately the most distal point of the fingers);
* L/R Femur: hip joint centre to knee joint centre;
* L/R Tibia: knee joint centre to ankle joint centre;
* L/R Foot: ankle joint centre to foot coordinate system origin (approximately the distal end of the 5 th metatarsal).  
Segment lengths were calculated as described above for each frame separately. However, for further calculations, the median of all valid frames (i.e., those where PIGP was possible) was taken and assumed to be the constant for a given segment.  
Segment masses and radii of gyration were determined by assuming these to be a given percent of total body mass or segment length, respectively, according to the plug-in-gait reference guide [12]. Each segment was considered to be a cylinder; therefore, these radii of gyration were assumed to apply for rotation about segments' transverse and sagittal axes, whereas for rotation about its longitudinal axis, an estimate of the segment's cylindrical radius was made (Appendix A). Thereafter, using these parameters, classical  
Figure 3: Positioning of the reflective markers used for 3-dimensional motion capture analysis according to the Vicon Plug-In Gate model ([PERSON], 2010).  
physics, and the approaches of [PERSON] et al. (2013), [PERSON] (2014), [PERSON] et al. (2015), and [PERSON] (2016), the total energy, translational kinetic energy (TKE), potential energy (PE), and angular kinetic energy (AKE) were calculated. Further details to these calculations are presented in Appendix B.
# A Compact Monopole Wideband Antenna Based on DGS
## 4 Fabrication, Measurements, and Comparison
### The Measured Radiation Patterns  
The measured 2D radiation patterns of the antenna at 6 GHz, 7.5 GHz, 9.5 GHz, and 11 GHz reveal its radiation characteristics and performance (Figure 15(a)-d). The wideband operation and ground plane slots significantly shape these patterns. At 6 GHz, Figure 15(a), the pattern features a broad main lobe, indicating effective energy distribution and good directivity with low sidelobes. At 7.5 GHz, in Figure 15(b), the main lobe is prominent but slightly shifted, showing increased gain and improved radiation efficiency. At 9.5 GHz, in Figure 15(c), the pattern is more defined with a narrower main lobe, suggesting enhanced directivity due to optimal slot operation. At 11 GHz, Figure 15(d), the well-focused main lobe indicates strong performance at the center frequency, with minimized sidelobes for excellent radiation efficiency. Overall, the analysis shows that the antenna performs well across the tested frequencies. The L-slot and circular slot enhance radiation characteristics, contributing to better directivity and gain, validating the design choices and ground plane modifications.
# Stochastic Models and Control of Anchoring Mechanisms for Grasping in Microgravity
## 3 Stochastic Models for Grasping
### Adhesion Model Based on Desorption  
As mentioned by [PERSON], in addition to the spatial distribution of asperities, the contact strength also affects the strength of adhesion [33]. On the surface with low contact strength, the microspine has a greater chance of desorption from the surface. Through observation of the experimental process, it can be seen that with the increase in tension, the microspine can grasp the surface on the rough surface. For the sandpaper surface, with the increase in tension, the microspine desorbs and reengages many times.  
The experiment was carried out by slowly pulling a microspine along a rough surface. After the microspine engaged to the asperity, the tension was continuously applied until the microspine desorbed or reached 6 N. Figure 6 shows the relationship between the tensile force and adhesion probability. For the surface of the brick, in most cases, the microspine could withstand a tensile force of 1.2 N. Thus, the probability curve was almost horizontal if the force was less than 1.2 N. With the increase in tension, the probability curve was no  
Figure 4: Influence of surface properties on grasping probability.  
Figure 5: Experimental results and prediction curves. (**a**) The microspine travels on the surface of the brick, \(\alpha=0.62\), \(\lambda=0.2\). (**b**) the microspine travels on sandpaper, \(\alpha=0.65\), \(\lambda=0.14\).  
longer parallel to the x-axis, but could be approximately regarded as a first-order function. When the force was greater than 1.2 N, the grasping probability could be approximated by a straight line. For the surface of brick, the adhesion probability can be expressed by a piecewise function:  
\[P(F)=\left\{\begin{array}{c}1\;if\;F\leq 1.2\;\text{N}\\ kF+b\;if\;1.2\;\text{N}<F<6\;\text{N}\\ 6k+b\;if\;F\geq 6\;\text{N}\end{array}\right. \tag{4}\]  
where \(k\) is the slope of the first-order function, and \(k\) is less than 0; \(b\) is the intercept of the first-order function from the y-axis. The influence of tensile force on adhesion probability can be obtained by force probability curve. So far, the tangential adhesion of the microspine can be expressed as:  
\[F_{S}=FP(F)P(x) \tag{5}\]  
In practical application, the robot can scrape along the surface with its spine array foot equipped with instruments, so as to understand the surface-asperity distribution [34]. On a rough surface, the tangential force is a function of the pull and travel distance. The longer the travel distance, the more likely the microspine is to be engaged to asperities. However, the microspine may desorb after continuous application of tension. In the follow-up, the adhesion of the microspine will be studied in combination with the stiffness of the microspine.