esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Data Acquisition and Processing
### Data Processing and Analysis
#### 3.2.4 Exploratory Factor Analysis  
Principal component analysis was used to perform a maximum variance rotation on the measurement data matrix of the nine observed variables. After five iterations, the rotation converged, and the rotated component matrix is shown in Table 3. It can be seen that the grouping of each indicator is consistent with the previous hypotheses, and the cumulative variance contribution rates for each factor were 77.775% and 77.975%, respectively, meeting the basic requirement of being greater than 50%. Therefore, no adjustments to the measurement model are necessary.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Moderator Variable** & **Category** & **Dimension** & \begin{tabular}{c} **One-Way ANOVA Test** \\ **F-Value** \\ \end{tabular} \\ \hline \multirow{3}{*}{hospital type} & general hospital & \begin{tabular}{c} hospital size \\ medical level \\ traffic accessibility \\ traffic attraction \\ \end{tabular} & 36.369 & 0.000 \\  & \begin{tabular}{c} specialized \\ hospital \\ \end{tabular} & \begin{tabular}{c} medical level \\ traffic accessibility \\ traffic attraction \\ \end{tabular} & 4.056 & 0.052 \\  & \
\begin{tabular}{c} hospital \\ traffic accessibility \\ traffic attraction \\ \end{tabular} & 14.022 & 0.002 \\  &
\begin{tabular}{c} traffic attraction \\ \end{tabular} & 6.06 & 0.041 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Analysis of differences in latent variables.  
Figure 3: Correlation analysis of influencing variables.  
Based on the rotated component matrix, the factor loadings for each group of measurement variables were ranked to reflect the degree of influence each measurement variable has on the latent variables. From the analysis results, it can be observed that the factor loadings for each dimension variable are all above 0.5, indicating that the measurement variables effectively reflect the latent variables and that the variable selection is scientifically and rationally done.
# Effect of Magnetic and Electrical Fields on Yield, Shelf Life and Quality of Fruits
## 1 Introduction
The strength of fruit tissues, and thus the stability of the whole fruit, is influenced by the mechanical properties of the cell walls, the presence of intercellular fluid, and intercellular interactions in the middle lamina [29]. These factors are subject to changes and constant modifications during the ripening of the fruit. The cell wall is responsible for the physical endurance and shape of the cell [30]. The properties of the cell wall are determined by cell size, inter-cell adhesion, packing and turgor, wall thickness, wall composition, and cell response to shear stress [29]. Most often, plant cell walls on a dry weight basis contain 15-40% cellulose, 30-50% pectin polysaccharides, 20-30% xyloglucan, and smaller amounts of other hemicellulose and structural proteins [27]. The structure of the cell walls is constantly changing during the growth and storage of the fruit. The main factor responsible for the properties of the cell wall, i.e., stiffness, porosity, and interactions between cells, are pectins [31]. Fruit texture involves a series of alterations in the cell wall, which are genetically programmed, but is also influenced by environmental factors. Pectinase (PE) is involved in the degradation of pectins [32], and thus leads to the degradation of the cell wall. This has a negative effect on fruit firmness [33; 34]. Fruits are classified by their respiration profile and ethylene production as climacteric and non-climacteric ones. Climacteric fruits include tomato (_Solanum lycopersicum_ L.), banana (_Musa acuminata Colla_), apple (_Malus domestica_), mango (_Mangifera indica_), and pear (_Pyrus amygdiformis_), while grape (_Vitis vinifera_), citrus (_Citrus medica_), and watermelon (_Citrullus lanatus_) belong to the non-climacteric class [35]. Ethylene is closely associated with these changes in climacteric fruits [36; 37], but its role cannot be excluded in the softening of non-climacteric fruit [38; 39; 40; 41]. The purpose of this paper is to review scientific results and summarise the emerging topic of the effects of magnetic and electrical fields on the growth, yield, ripening, and shelf life of fruits and on fruit quality, as well as the potential application of MF and EF in agricultural production. The characteristics of the data presented take into account the following variable factors: variety, method used, field intensity, and time of exposure. Treatment with magnetic and electrical fields has been applied in the whole plant production system, namely on the pre-treatment of seeds and the treatment of seedlings, plants, and fruit and of irrigation water.
## 2 Materials and Methods
Each grid cell was divided into fractions of PFTs, with a distribution prescribed by yearly-varying PFT maps derived from the ESA Climate Change Initiative (CCI) land-cover products [24]. ORCHIDEE global simulations were forced with 0.5\({}^{\circ}\) and 6-hourly CRUJRA reanalysis [25]. The monthly near-surface atmospheric COS concentrations used for the computation of vegetation and soil COS fluxes were obtained from LMDz simulations forced with optimized COS surface fluxes [17]. ORCHIDEE global simulations were performed by running a 340-year spin-up simulation in fixed conditions to stabilize all carbon pools and to equilibrate the net biome production [26], followed by a transient phase simulation to introduce increasing COconcentrations, varying climate, and PFT distribution. Next, vegetation and soil COS fluxes were computed from 2000 to 2019 at a 0.5\({}^{\circ}\) spatial resolution following [27; 28], respectively. Of central importance in the following is the plant COS uptake, which is based on [29], where the flux in COS uptake (F\({}_{\text{COS}}\)) is the product of the background atmospheric COS mixing ratio (COS) and gT_COS, where gT_COS is the total of three conductances to COS, i.e., the boundary layer, stomatal, and internal conductances to COS. All fluxes from the different COS source and sink components are listed in Table 1; this global budget of COS is almost in balance (i.e., +19 Gg yr\({}^{-1}\)). In addition, we conducted a sensitivity study of atmospheric COS to the spatio-temporal distribution and magnitude of the oceanic source of COS by comparing two datasets of oceanic fluxes, one from [31; 32] and the optimized fluxes from [17]. After optimization, the total oceanic emissions account for 445 GgS yr\({}^{-1}\) of the global budget of COS [17]. By jointly assimilating the surface measurements of COS and CO\({}_{2}\) into LMDz, the authors of [17] were able to optimize some of the CO\({}_{2}\) and COS budget components. In particular, [17] decreased the seasonal amplitude of the total COS oceanic fluxes in middle and high latitudes to match the observed NH concentrations at the NOAA-ESRL stations ALT, BRW, SUM, MHD, LEF, THD, and HFM. This amounts to stating that the oceanic emissions of COS presented in [31; 32] during the boreal summer are overestimated in the middle and high latitudes. Finally, because atmospheric transport is considered linear with respect to the surface fluxes in the model, each flux can be transported separately by the LMDz atmospheric transport model to simulate individual signal components, as shown later in dealing with the seasonality of COS. All simulated concentrations can be summed together to obtain the simulated concentrations of COS at sites GIF and MHD, represented by the \"net\" curves in those.
## 6 Software Architecture of the Multi-Sensor Platform
### Smart Device Handling  
In addition to the data handling, the software also controls the hardware efficiently via its GPIO controls. Hardware peripherals such as the SARA-R5 module, which consume a lot of power, can be turned off via the GPIO pins to conserve the battery. In case of overcharge or low power detected from the LTC2942 module, an interrupt triggers the software to alert the system for maintenance of the device module. The other onboard sensors periodically check the sensor platform environmental data for water intrusion or overheating. This is significant, as the sensor platform is deployed in a harsh industrial environment and can potentially be destroyed in case of coolant leakage into the box and overheating of the sensor platform. The sensor platform can be switched off remotely over LWM2M to protect the device in case of abnormalities. In addition, this information can be logged and analyzed to enable predictive maintenance of the device. With this software architecture, a powerful smart sensor system is developed to cater to the needs of an Industry 4.0 wireless robust data acquisition system with intelligent control and management.
# Future Generation 5G Wireless Networks for Smart Grid: A Comprehensive Review
## 2 Overview of Wireless Communications Technologies  
Over the past few decades, the world has seen a gradual and steady growth of communication networks, starting from the first generation and moving towards the fourth generation. Currently, there are many advancements occurring in wireless technologies, such as orthogonal frequency division multiplexing [23; 24] with access to many brand new frontrunners with effective frequency. By this growth, there has been a parallel increase of smart devices growing rapidly in day to day life. At this onset, new applications and new business market providers with wireless technology are also increasing at a larger scale. Table 1 depicts the wireless communication technology era for the past few decades and its benefits.  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Features** & **1G** & **2G** & **25G** & **3G** & **3.5G** & **4G** & **5G** \\ \hline
**Deployment** & 1980 & 1990 & 2000 & 2001 & 2006 & 2010 & 2020 or later \\  & & & & & & Digital & & \\  & & & & Digital & & Broadband & \\  & & & & & & & \\
**Data** & Analog & Digital narrow & Packet & broadband & Packet & packet & \\  & & band data & data & packet & data & data & & Not yet defined \\  & & & & & & data & Very high & \\  & & & & & & & & throughput & \\  & & & & & & & & \\  & & & & & & & & \\
**Frequency** & 800 MHz & 850/ & 850/ & 850/ & 850/ & & \\  & & 900/ & 900/ & 900/ & 900/ & 2600 MHz & 3 to 90 GHz \\  & & 1800/ & 1800/ & 1800/ & 1800/ & 1800/ & & \\  & & & 1900 MHz & 1900 MHz & 190/ & 1900/ & & \\  & & & & & & & & \\
**Speed** & 2.4 kbps & 64 kbps & 144 kbps & 2 Mbps & 5-30 Mbps & 100 Mbps & 10 Gbps \\  & & AMPS, & & GPRS, & WDDMA, & HSPA, & Wi Max & \\
**Technology** & NMT & GSM, CDMA, & EDGE, & UMTS, & EVDO & LTE & Had to be defined \\  & & TACS & IS-95 & CDMA & CDMA2000 & [22; 23; 24] & Wi-Fi & \\
**Multiple access** & Frequency division & Time division & Time division & & Code division & \begin{tabular}{} \end{tabular} & \
\begin{tabular}{} \end{tabular} &
\begin{tabular}{} \end{tabular} \\
**Core network** & Public switched & Public switched & Public switched & Packet & Packet & \\  & telephone network & telephone network & network & & Internet & Internet \\  & & Secure, Mass & & Better & & \\
**Advantages** & Mobility & Longer lasting & – & Internet & & & \\  & & of battery & Experience & – & & \\  & & [25; 26; 27] & & & & & \\  & & & & & & & \\  & Very poor & Data rates are & & Failure of & & \\  & spectral & very less and & performance & & & more, so \\  & efficiency and & difficult to & – & for & & found to \\  & poor handoff & match the & internet & & & be \\  & & demand & [31; 32] & & & expensive \\ \hline \hline \end{tabular}
\end{table}
Table 1: Wireless communication technologies era.
## 3 Results and Discussion
### Neurological Mechanisms: AChE Activity  
AChE is the serine hydrolase involved in the regulation of the acetylcholine-mediated neuronal impulse and is one of the most effective biomarkers of neurologic alterations induced by toxicants in aquatic species [55; 74; 75]. As shown in Figure 4 a significant decrease of AChE activity was observed in mussels exposed to 0.1 mg/L CHR compared to mussels exposed to control conditions and 0.73 ng/L CHR; mussels exposed to MP and CHR-MP showed no change compared to each other and to any other performed treatment.  
Overall, these data show that mussel AChE activity was not influenced by either the exposure to virgin or CHR-loaded MP at our experimental conditions. As to the former, this was quite expectable based on our recent findings obtained comparing the effects of both 3-\(\mu\)m and 50 nm PS particles in _M. galloprovincialis_[19]; notably, we found only nano-sized plastics were able to inhibit AChE activity after 21 days of exposure, corroborating evidence comparatively obtained in other aquatic organisms (reviewed by [PERSON] et al. [76]). Further evidence suggests that, once ingested, POPs sorbed on MP might undergo metabolic processes or gut biota interactions, thus eventually hampering their toxic action toward neurological targets. Accordingly, previous studies using mussels showed that MP loaded with other PAHs, such as benzo[a]pyrene and pyrene, do not exacerbate the neurotoxic disorders singularly induced by plain micro/nanoplastics or tested chemicals [18; 32]; therefore, there is some consistency to suggest that regardless of POPs sorption, the structural and physical properties of ingested MP are the main factors characterizing the associated risks for neurological effects. The AChE inhibition observed after mussel exposure to 0.1 mg/L CHR indicates adverse effects of relatively  
Figure 4: AChE specific activity (mean \(\pm\) SEM, \(n\) = 3) measured in mussel gills after 7 days of exposure to \(5\times 10^{3}\) units/L of polystyrene microplastics (MP) and chrysene-loaded microplastics (CHR-MP), 0.1 mg/L and 0.73 ng/L of freely dissolved CHR (without MP). Different letters denote statistically significant differences (_p_ < 0.05, ANOVA—Bonferroni _post-hoc_ comparison) among treatments.  
high concentrations of CHR on cholinergic neurotransmission, which might translate into nervous and neuromuscular dysfunctions. To our knowledge, there are no previous data about CHR toxicity on AChE in mussels and marine invertebrates in general. Field investigations using deployed mussels suggest that the exposure to PAH-contaminated environments may be responsible for AChE inhibition [51], although the mechanisms underlying these changes remain largely unknown. A possible explanation may be the involvement of the CHR-mediated oxidative stress, highlighted by both LF and CAT modulations observed in CHR (0.1 mg/L)-exposed mussels. Evidence supporting this hypothesis was obtained by [PERSON] et al. [77], who found H2O2 to act as a potent AChE allosteric inhibitor, confirming that cholinergic pathways may effectively be altered in conditions of ROS overproduction.
# Service for Deploying Digital Twins of QKD Networks
## 1 Introduction
These tests show that the service can deploy, configure, and run an 8-node QKD network in approximately 7 min, demonstrating the suitability of the platform to agilely perform research and experimentation activities on moderately complex QKD environments without incurring the capital and operational expenditures of a physical QKD ecosystem. This document is structured as follows. Section 2 gives some background on the context of the developed service, reviewing basic concepts about QKD and describing the state-of-the-art in topics related to the service presented. In particular, it discusses digital twins, the difference between bits and qubits, reviews the main ideas of QKD and some of the standards already published on QKD networks, and gives a brief introduction to NFV technologies. Later, the design, functioning, and capabilities of the service are explained in Section 3. Technical and precise details about the software implementation are provided in Section 4. Section 5 presents the validation and performance evaluation results of the service. Lastly, Section 6 outlines the conclusions and future lines of our work.
## 3 Materials and Methods
### Characterization of the Catalysts
Thermogravimetry (TG), derivative thermogravimetry (DTG), and differential scanning calorimetry (DSC) curves were obtained on a TA Instruments SDT Q600 (New Castle, DE, USA) from room temperature to 1000 \({}^{\circ}\)C (20 \({}^{\circ}\)C min\({}^{-1}\)), using synthetic air (99.999%) as the purge gas (flow rate of 60 mL min\({}^{-1}\)). The analysis was performed on alumina crucibles and the external standard employed for calibration was sapphire. N\({}_{2}\) physisorption measurements were performed at \(-196\)\({}^{\circ}\)C on a Quantachrome Instruments NOVA 4200e apparatus (Boynton Beach, FL, USA). All samples were outgassed at 200 \({}^{\circ}\)C under vacuum for 24 h prior to the analysis. Surface area and porosity data were obtained from N\({}_{2}\) adsorption/desorption isotherms. Total specific surface area (\(S_{total}\)), external specific surface area (\(S_{ext}\)), and mesopore volume (\(V_{meso}\)) were determined using a t-plot curve obtained from analytical functions for each portion of the \(P/P_{0}\) domain (see Equations (5)-(8) and Figure 10a) [86]. Mesoporous surface area (\(S_{meso}\)) was calculated by subtracting \(S_{ext}\) from \(S_{total}\). Total pore volume (\(V_{total}\)) was calculated using the penultimate point from the adsorption isotherm. Average pore diameter (\(PD\)) was calculated according to Equation (9). Wall thickness (\(WT\)) was calculated according to Equation (10):  
\[t\ (nm)=A(P/P_{0})^{a}+B(P/P_{0})^{b}\text{ for }0.03\leq P/P_{0}<0.25 \tag{5}\]  
\[t\ (nm)=\{C/[D-log(P/P_{0})]\}^{c}\text{ for }0.25\leq P/P_{0}<0.60 \tag{6}\]  
\[t\ (nm)=\{E/[F-log(P/P_{0})]\}^{c}\text{ for }0.60\leq P/P_{0}<0.90 \tag{7}\]  
\[t\ (nm)=G(P/P_{0})^{g}+H(P/P_{0})^{h}\text{ for }0.90\leq P/P_{0} \tag{8}\]  
\[PD\ (nm)=4V_{total}/S_{total} \tag{9}\]  
\[WT\ (nm)=a-PD \tag{10}\]  
where \(A=0.5675647\), \(a=0.199735\), \(B=0.4116168\), \(b=2.00834\), \(C=0.1423566\), \(D=0.1078\), \(c=0.4888\), \(E=0.08309076\), \(F=0.02995\), \(e=0.369\), \(G=1.268066\), \(g=1.931\), \(H=0.76934\), and \(h=51.09\). Low-resolution transmission electron microscopy (TEM) images were obtained on a JEOL-1011 microscope (Tokyo, Japan) at 120 kV. Before analysis, each sample was dispersed in ethanol (\(0.1\) mg mL\({}^{-1}\))through sonication. Subsequently, 5 \(\upmu\)L of this suspension was deposited to a carbon-coated copper grid and dried overnight at room temperature. The mean particle size was determined using the software, ImageJ (version 1.52a), counting at least 100 particles for each sample. High-resolution transmission electron microscopy (HRTEM) images were obtained on a JEOL JEM-2100 microscope (Tokyo, Japan) at 200 kV under vacuum and N\({}_{2}\) purge. Before analysis, each sample was dispersed in ethanol and subsequently deposited to a carbon-coated copper grid and dried at room temperature.
## 4 Smart Home Connectivity for New Elders
### Physical Connectivity
#### 4.1.3 Environment Connectivity  
Building connectivity in the home environment is mainly to help the elderly improve the comfort and safety of home life. Including the following five aspects:  
* **Temperature and humidity control system:** This system aims to control the temperature and humidity of air and water in the home environment through temperature and humidity sensors to help older adults enhance the perception of environmental changes and do intelligent adjustments according to their needs. The relevant equipment and products include smart air conditioner, smart electric water heater, smart bathtub, etc.
* **Lighting control system:** It aims to realize intelligent control of all the home lights, including energy-saving lamp, spotlight, table lamp, LED light, chandelier, etc. For example, if an old man wants to go to the bathroom in the middle of the night, there is a risk that he may easily fall without light at the beginning, but with the lighting control system he can realize induction and sound control, or the system could even detect his behavior and help with the illumination automatically.
* **Door and window control system:** The system mainly includes automatic curtains, smart door locks, smart cat eyes, etc. The automatic curtains can help the elderly control natural light of the whole home environment intelligently, while smart door locks and smart cat eyes can be controlled by fingerprint recognition, face recognition, or iris recognition to control the entry and exit of personnel. It is helpful if the elderly forgets the key and can prevent unfamiliar strangers from entering the house.
* **Environment monitoring system:** The environment monitoring system could help elders ensure the safety of life, mainly including personal safety and property safety. For personal safety, the relevant functions of this system mainly include air quality monitoring, water leakage monitoring, smoke sensors, gas alarms, etc. With regard to property safety, surveillance cameras can be added into the home environment around the balcony, garage, doors, windows, etc. Additionally, the system can have connections and interaction with community security department or a police station to protect the home safety for elderly people.
* **Energy-saving system:** This system aims to monitor water and power consumption of the whole home system and use smart switches to realize relevant management and control. For example, when the elderly forgets to turn off some electrical appliances, the system can give intelligent reminders or cut off related equipment automatically to avoid waste of resources and prevent any possible accidents.
## 4 Results
### Eye Movement and Visual Processing Analysis and Virtual Reality
The model's effectiveness was measured through precision and recall metrics, and results showed that children with ASD displayed distinctive gaze patterns, particularly in reduced focus on socially relevant areas. These unique gaze patterns set ASD participants apart from neurotypical controls with high accuracy. The study concluded that attention heatmaps offer a valuable tool for detecting early signs of ASD, presenting a user-friendly approach that may aid early intervention efforts. In the first study, [PERSON] et al. (2023) [4] developed the Virtual Reality Data Analysis Tool (V-DAT) to facilitate self-awareness and improve expert analysis of ASD behaviors. The V-DAT system was designed to collect and integrate data across four sensor modalities: head position and rotation, eye movement, audio, and physiological signals. Specifically,20 participants wore a VR headset equipped with advanced sensors to monitor head movements, gaze direction, auditory inputs, and physiological responses, such as heart rate. Within the virtual environment, they engaged in interactive scenarios, during which the system collected comprehensive multimodal data. These data were later analyzed by experts to gain insights into participants' behaviors and emotional responses during the VR activities. The results showed accuracy and the reliability of the data captured and strong correlations between observed behaviors and quantitative data metrics. This study concluded that V-DAT not only supports self-awareness in individuals with ASD by providing them with visualizations of their interactions but also enhances the diagnostic process for experts. The findings underscore VR's potential as an accessible, interactive tool for diagnostic and therapeutic applications in ASD. The potential of eye-tracking, visual processing analysis, and VR as effective, non-invasive methodologies for identifying attentional and gaze patterns related to ASD is demonstrated by these studies jointly. Each study contributes to a burgeoning toolkit for early, accessible ASD screening by highlighting the distinctive visual processing characteristics of individuals with ASD through the examination of scanpath trends, fixation data, and attention heatmaps. These methodologies not only offer high diagnostic accuracy but also facilitate the integration of scalable, real-time assessments into a variety of contexts, including educational environments and clinics.