esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Discussion on Challenges and Open Issues
This proposed framework serves as a foundational step toward integrating edge devices and AI-driven technologies into ASD detection. While it provides a structured process for data collection, preprocessing, analysis, and clinician interaction, it remains a conceptual model, and numerous challenges may arise during implementation. These challenges could include managing the complexity of multimodal data integration, ensuring interoperability among diverse devices, addressing potential biases in AI models, and navigating privacy and ethical considerations. Despite these hurdles, the framework offers a promising starting point for advancing ASD diagnostics, fostering collaboration between technology and clinical expertise, and paving the way for future iterations to refine and optimize its application.
# Dual-Band MIMO Antenna with Enhanced Isolation for 5G NR Application
## 3 Results and Discussion
### Radiation Pattern  
To study the radiation mechanism of the antenna in-depth, Figure 7 presents the measured far-field radiation patterns in the H-plane and the E-plane at 2.5 GHz and 4.7 GHz, respectively. During the measurement and simulation, one port is excited, while the other one is matched with a 50 \(\Omega\) load terminal. The proposed antenna has nearly stable omnidirectional radiation patterns in the H-plane at 2.5 GHz and 4.7 GHz. At 2.5 GHz, the E-plane is shown as a bidirectional model, while the radiation patterns appear deformed in the E-plane at 4.7 GHz. There is no effect on the radiation performance.  
Figure 6: Simulated and measured S-parameters of the proposed MIMO antenna system: (**a**) S\({}_{11}\) and (**b**) S\({}_{21}\).  
Figure 7: _Cont_.
## 4 Discussion
### Accuracy of Neural Network Predictions  
To quantify the superior generalization ability and computational accuracy of our model compared to others, we compiled the loss functions for different configurations during HOS generation, as shown in Figure 7. Here, \(F\) represents the number of free fiber parameters. In scenarios where only the fiber length varies (\(F=1\)), the root mean square error (RMSE) is 0.01764, the lowest recorded value, indicating high accuracy in pulse prediction throughout the evolution process. We also tested scenarios where pulse width, input peak power, and fiber length varied randomly. When the model width parameter \(n=1\), the accuracy significantly dropped, converging at 0.156. However, increasing the model size to \(n=2\) significantly improved the model's learning ability, achieving a validation loss of 0.0248 within only 320 epochs, demonstrating excellent scalability and performance under complex conditions. The ability to handle three fiber parameter variables simultaneously, with changes in both fiber parameters and propagation distance, represents a broader range of fiber output variability and increased learning difficulty, in which our model performs exceptionally well.  
As shown in Figure 8, the relationship between RMSE and distance reveals gradually accumulated errors during transmission. Training beyond this length was not conducted to test the generalization ability. The results indicate that the model maintained continuous accuracy within any generated distance included in the training set, attributed to its ability to incorporate distance into the learning scope. Unlike other models that accumulate prediction errors over long distances due to progressively accumulated nonlinear effects, our model's prediction error does not accumulate with increasing propagation distance. However, beyond the untrained 13 m distance, the model's error size fluctuates and increases due to enhanced nonlinear effects caused by increased propagation distance. These enhanced nonlinear effects can lead to complex pulse shape changes, making them difficult for the model to accurately capture, resulting in increased errors. Nevertheless,  
Figure 6: Comparison of generation times: SSFM vs. DeepONet.  
the error remains reasonably low compared to the CNN and RNN [16]. Unlike RNN and CNN, our model considers different combinations of input parameters, thus eliminating the need for the special optimization of different parameters, significantly reducing training difficulty and expanding its applicability.
## 1 Introduction
### Active Distribution Network Management
#### 1.1.1 Decentralized Management Schemes
\begin{table}
\begin{tabular}{c c c c} \hline
**County** & **Based on Thermal Limits Considerations** & **Based on Short-Circuit Capacity Considerations** & **Based on Loading/Generation Percentage** \\ \hline \multirow{4}{*}{Canada} & Reverse power flow occurred due to DC & & The total DG ratings should be lower than 50–100\% \\  & integration should not exceed 60\% of the & & N/A & of the feeder capacity or the substation annual \\  & transformer rating at the substation and the & & minimum load \\  & minimum substation load & & minimum load \\ \cline{1-1}  & At the 110 kV networks, the total DG ratings & & \\ \cline{1-1}  & should be lower than the HV transformer rating & N/A & N/A \\ \cline{1-1}  & and the minimum substation load & & \\ \cline{1-1}  & \(\bullet\) The total DG ratings should be lower than 65\% & & \\ \cline{1-1}  & of the LV/LV transformer rating & & \\ \cline{1-1}  & \(\bullet\) The total DG ratings should be lower than 60\% & & \\ \cline{1-1}  & \(\bullet\) The total DG ratings should be lower than 60\% & & \\ \cline{1-1}  & of the thermal limit of the affected feeders & & \\ \cline{1-1}  & \(\bullet\) For LV networks, the total DG ratings should be & & \\ \cline{1-1}  & lower than 25\% of the MV/LV transformer rating & & \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._In Reference [9], the authors propose a decentralized control scheme, based on active and reactive power control algorithms to perform voltage control of the distribution network with a high penetration level of DG. The authors state that the proposed control scheme has higher performance in the mitigation of voltage deviations than more traditional control schemes, increasing the grid's RES hosting capacity from 35.65% up to 66.7%. In Reference [10], a detailed analysis of the main operation modes and control structures for power converters is carried out. In Reference [11], an overview of the state-of-the-art decentralized control techniques is performed. Different control techniques and classification of inverter modules are investigated, detailing the pros and cons of each. Among this, it is possible to find inverters capable of parallel operation that, through the addition of communication links between the DG units' inverter modules, are able to increase the reliability of this type of control and reduce the total amount of generation curtailment.
## 3 Results and Discussion
### Removal Efficiency of Micropollutants from Water
#### 3.5.3 Removal of Pb\({}^{2+}\) and Cd\({}^{2+}\)  
A comparison of the efficiency of Pb and Cd ions for all tested samples, after 120 h (Figure 7), suggested that the sample type influenced the removal efficiency. Thus, both _R. mucilaginousa_ strains presented higher Pb\({}^{2+}\) removal efficiencies compared to the samples without biosurfactants: _R. mucilaginousa_ CMGB-G1 (P1)--66% in _Bio Y_ vs. 61%, respectively; _R. mucilaginousa_ CMGB188 (P2) 59% vs. 58%, respectively. In the case of synthetic wastewater with Cd\({}^{2+}\), the removal efficiency was lower compared to Pb\({}^{2+}\), ranging between 41 and 42% for all the samples.  
Figure 7: Cd and Pb ions’ removal efficiency at the end of the experiment.  
In our previous study [31], we determined the removal efficiency of heavy metals form wastewater in the presence of a crude biosurfactant from _Y. lipolytica_ CMGB32, but using different yeast strains. The results showed a maximum reduction of 99% in Pb\({}^{2+}\) with the _Saccharomyces cerevisiae_ CMGB234 strain, and a reduction of 56% in Cd\({}^{2+}\) ions with _Kluyveromyces marxianus_ CMGBP16, being a proof that the removal efficiency is also dependent on the yeast strain used.
## 2 Results and Discussion
### Headspace and Volatile Oil Composition of T. atomaria
[29] where germacrene D resulted active against human breast adenocarcinoma (MDA-MB 231 and MCF-7), human ductal carcinoma (Hs 578T) and human hepatocellular carcinoma (Hep G2). Epi-bicyclosesquiphellandrene could be connected with antidermatophytic activity [30]. Gleenol exhibited the following biological activities: termiticidal, antihelminitic and growth regulation effects on plant seeds [31]. In comparison with previous research of _T. atomaria_, it is only possible to partially compare the results, since different extracts were investigated previously (not obtained by HS-SPME or HD, but with other methods targeting non-volatile compounds). The extracts mostly contained high-molecular compounds and researches were directed toward the isolation of individual components, not toward obtaining full chemical profiles. The compounds 4-cadinene, cadinane-4(14),5-diene, (\(-\))-germacrene D, axenol, (\(-\))-cubebol, and 4-epi-cubebol were previously identified in petroleum extract (after chromatographic purification) of dried _T. atomaria_ collected in the North Adriatic sea [5]. Therefore, partial similarity with current research data is notable (the presence of cadinane type sesquiterpenes). Less similarity was noticed with respect to recent research on MeOH/CH\({}_{2}\)Cl\({}_{2}\) extract of _T. atomaria_[14]. Germacra-4(15),5,10(14)-trien-9-ol was identified together with five other sesquiterpenes (\(-\))-gleenol, \(\alpha\)-cadinol methyl ether, (\(-\))-_trans_-calamene, (15,5E,75) 1-acetoxygermacra-4(15),5,10(14)-triene, and 4-peroxymuuurol-5-ene. Moreover, two lipidic compounds, polyunsaturated fatty acid, (5Z,8Z,11Z,14Z,17Z)-eicosa-5,8,11,14,17-pentaenoic acid, and glycerol derivative, sn-3-O-(geranylgeranyl)glycerol were also isolated. However, in comparison with the mentioned results of other researchers, current research presents a complete chemical profile of the headspace and volatile oil composition, providing new data: epi-cyclosesquiphellandrene as a dominant compound was found for the first time in _T. atomaria_ as well as numerous other sesquiterpenes with lower abundance (Table 1), not previously reported.
## 4 Results and Discussion
### Lithology Modeling  
The results from the clay volume and porosity perturbation modeling (Figures 7-9) illustrate how the elastic properties changed as the porosity, clay volume, and fluid changed. With increasing \(V_{clav}\) and reducing porosities, although the sensitivity is lowered, the sands are still suitable for lithology/fluid discrimination, due to their good thickness (Figures 7-9). Figure 7 shows the modeling results of reducing the porosities by 10 porosity units, and we see that good separation from the background trend is still observed. Although the fluid sensitivity is reduced, but there is still a relatively fair separation between the fluid scenarios and in the AVA domain, the wet scenario shows a class IIP response, while the hydrocarbon scenario shows a class II response. Overall, the sands' good thickness makes them relatively suitable for lithofluid discrimination, even at reduced porosity (although the sensitivity is reduced).  
Figure 6: (**a**) Log plot showing the modeling results (in situ scenario). (**b**) Cross plots showing the modeling results (in situ scenario). Hydrocarbon sands displayed lower \(V_{p}/V_{s}\) and P-wave impedance (AI) values than wet sands. Strong ability to discriminate fluids, particularly in the AVA domain.  
Figure 8: Cross plots showing the modeling results (positive V\({}_{\rm clay}\) 0.2 scenario). The sands’ good thickness makes them relatively suitable for lithofluid discrimination, even at reduced porosity (although the sensitivity is reduced).  
Figure 7: Cross plots showing the modeling results (negative 10 p.u. scenario). The sands’ good thickness makes them relatively suitable for lithofluid discrimination, even at reduced porosity (although the sensitivity is reduced).  
Figure 8 shows the cross plots depicting the modeling results when the reservoir condition is made shalier by increasing the clay volume by 20%. Here, what we see is that, even at the shalier reservoir condition, there is still a reasonable separation between the reservoir sands and the background; furthermore, there is a good discrimination between the wet and hydrocarbon scenario, especially in the upscaled domain. The sands' good thickness makes them relatively suitable for lithofluid discrimination, even at a reduced clay content (although the sensitivity is reduced when compared to the in situ reservoir condition).  
Figure 9 shows the result of increasing the clay content by 20% and reducing the porosity by 0.1 porosity unit, which is the worst reservoir scenario expected in this field. The sands are still fairly separable from the background trend in the \(V_{p}/V_{s}\) ratio and P-impedance domain, although separating our wet reservoirs and hydrocarbon sands might be a bit challenging in this domain. In the AVA domain, the wet sands show a class IV AVA response, while the hydrocarbon scenario shows a class II AVA response.
# Advances in the Metabolic Engineering of _Escherichia coli_ for the Manufacture of Monoterpenes
## 5 Controlling the Flux Distribution of Essential Intermediates
### Decrease the Flux of Essential Intermediates into Irrelevant Endogenous Pathways  
In metabolic engineering, the irrelevant endogenous pathways utilizing the building blocks of introduced heterologous pathway in host could be the essential factor hindering the target monoterpene production. The loss could be avoided through blocking or rerouting irrelevant flux [76]. [PERSON] et al. introduced recombinant GPPS and the bottom portion of the MEV pathway into _E. coli_ to yield geraniol up to 13.3 mg/L. With the combination of geraniol synthase (GES) heterologous expression, the geraniol production reached 105.2 mg/L. However, endogenous dehydrogenization and isomerization of geraniol into other geranoids restrained the production. After engineering the strain via deleting the microbial gene that is highly homologous to plant geraniol dehydrogenase, the conversion of geraniol significantly reduced and its productivity reached 129.7 mg/L. This titer could be further increased to 182.5 mg/L by the whole MEV pathway integration [37]. [PERSON]'s group explored an inverse conversion approach to acquire an approximate industrial productivity of geraniol. They identified that the acetylesterase (AES) from _E. coli_ could transform geranyl acetate into geraniol; simultaneously, _E. coli_ could reuse acetate as carbon source in the absence of glucose. By stopping glucose supply for the engineered strain after 48 h incubation, the geraniol production under fed-batch fermentation increased up to 2.0 g/L [39]. Thus, accompanied by the engineering of the MEV pathway, identification and deletion of the endogenous metabolic routes interfering the target product synthesis pathway will be a broadly applicable approach for controlling the metabolic flux and improving the production of various terpenoids.
## 3 Materials and Methods
### Trend Analysis Methods
#### 3.2.1 Man Kendall Test  
The Mann-Kendall test (MK) is a nonparametric statistic test [43; 44] that used to examine whether a time series has a monotonic rising or descending trend. It does not require that the data be normally distributed.
## 4 Data Processing  
### Measurement Uncertainties and Missing Data  
Each plant species typically follows its own phenological development, itself dependent on the locally prevailing conditions. For their part, spaceborne EO instruments acquire measurements (usually over  
Figure 2: Time series of daily values for the Gross Primary Productivity (GPP) of the unmanaged vegetation around the Skukuza Eddy Covariance (EC) flux tower (in red) and the daily precipitation estimated by the ERA-Interim reanalysis data accompanying the FLUXNET2015 archive.  
areas large with respect to the dimensions of plants, hence containing many plant species), over periods of time which may or may not coincide with the specific observational needs at any given location. Individual observations are associated with measurement uncertainties, which result from systematic, as well as random, errors, due to incorrect or biased calibration, and intrinsic limitations on the precision of the instrument, respectively. In addition, optical instruments measure the combined contributions of the planetary surface and the overlying atmosphere: clouds and aerosols can perturb or prevent observations of the surface. Measurements actually useful to characterize the phenology of plant canopies are therefore scattered in space and time, especially during rainy seasons [72].  
Models, such as the ones described below, are required to provide a reliable representation of the phenology of plant canopies despite these challenges. Once they have been adjusted to optimally fit the available empirical evidence, they can be used to provide a simplified stable characterization of the evolution of plant canopies. The combined empirical evidence on phenology derived from remote sensing and ground-based observations has been shown to improve the accuracy of models simulating surface-atmosphere interactions (in [73; 74; 75]).
## 3 Results and Discussions  
The preliminary orbital data (injection) given by the launch provider team were used to propagate the orbit (Simplified General Perturbations-4 (SGP4)) and predict the first passages over the command and control station. The first access report provided the estimates presented in Table 4.  
Figure 8: AlfaCrux kinematic reconstruction DT.  
As a pre-launch setup, the ground station equipment, configuration, and procedures were verified. The mission control software was updated, and the control sequences loaded. The pass procedure for the first days based on the access report was revised and ready to be executed.  
In the first passage over the command and control ground station, we were able to receive beacons from the AlfaCrux, in a first moment not able to demodulate, but as soon as the orbit position became more precise, the quality of the tracking, and consequently of the operation, were improving. During the first days, the operation team was focused on the correct identification of the AlfaCrux spacecraft in cooperation with the 18 th Space Control Squadron of the United States Space Force. As an operator organization, the AlfaCrux team started using the TLEs from the Space-Track ([[https://www.space-track.org/](https://www.space-track.org/)]([https://www.space-track.org/](https://www.space-track.org/)), accessed on 20 September 2022) platform, exchanging information about the AlfaCrux operation, checking, and analyzing safety information provided by the conjunction data message, and after some days, the AlfaCrux was properly identified with the international designator 2022-033D.
## 2 Materials and Methods
### Mechanical Tests  
Hydrogel mechanical properties were measured by performing uniaxial tensile tests through an ad hoc setup. This includes two poly(lactic acid) (PLA, K Kentstrapper, Florence, Italy) plates fabricated using fused-deposition modeling (Verve, Kentstrapper, Florence, Italy). Each plate consists of a base and a protrusion with dimensions of (30 \(\times\) 30 \(\times\) 10) mm and (10 \(\times\) 10 \(\times\) 20) mm, respectively (Figure 2a). The protrusions were designed to be inserted and secured into the jaws of a testing machine (Synergie 200, MTS Systems, Eden Prairie, MN, USA) by tightening, thus preventing any slipping of the plates. Hydrogel samples (\(n\) = 5) obtained as described in Section 2.2 were tightly glued to the bases of the plates by using a biocompatible tissue adhesive (3M\({}^{\text{TM}}\) Vetbond\({}^{\text{TM}}\), Thermo Fisher Scientific, Waltham, MA, USA). Subsequently, the plates along with the sample were mounted onto the testing machine, which was equipped with a 100N loading cell (Figure 2b). Samples were pulled until failure at a displacement rate of 0.1 mm/s at room temperature (25 \({}^{\circ}\)C). The displacement rate was selected based on previous tensile tests on retinal samples [27].  
For the analyses, force (N)\(-\)elongation (mm) data were elaborated to obtain the stress [kPa]\(-\)strain [mm/mm] relationship for each sample. The stress and the strain were computed as indicated in Equations (3) and (4), respectively. The elastic modulus (E) was extracted from the stress-strain plots as the slope of the initial linear region of the stress-strain curve.  
\[\sigma=\frac{\text{F}}{\text{A}} \tag{3}\]  
\[\varepsilon=\frac{\text{\Delta L}}{\text{L}_{0}} \tag{4}\]  
where \(\sigma\) and \(\varepsilon\) are the stress and the deformation, respectively. F is the force applied to the original cross section A, and \(\Delta\)L is the change in length with respect to the original length L0.
## 3 Ecotoxicity
### Aquatic Biomodels  
For a correct assessment of toxicity, tests in different biomodels are required in order to ensure a complete representation of the trophic chain in the environment. For this reason, bacteria, algae, crustaceans, plants, and fish should be studied [80]. It has been seen that, depending on the used organism, there is different sensitivity to the tested compounds [81]. Despite the lack of previous comprehensive ecotoxicological studies on eutectic mixtures, there are some data available concerning the ecotoxicological effects of these moieties in bacteria, algae, crustaceans, and fish. In all cases, toxicity depended on the biomodel used and the components of the deep eutectic mixtures. Furthermore, there are also several additional factors that directly affect the behavior, which are detailed in the next sections.  
Ability to cross biological barriers: The ability to enter organisms is strongly related to lipophilicity. Thus, it can be affected by many factors, such as the pH [82] or the length of the alkyl chain. Thus, the toxicity can be modified depending on the charge, and on the number of carbons in the chain [56].  
Presence of water: The presence of water in these compounds modifies their toxicity. It has been seen that the formation of hydrogen bonds between water (can act both as donor and acceptor) and DES components leads to the formation of a complex, of which water molecules form a part [83]. A disruption of the structure of these compounds may occur at a certain limit of the water content. This interaction can increase or decrease the toxicity, depending on the used biomodel, the water content, and the composition of the mixture [84].  
Starting materials: Although a clear trend has not been observed, the starting materials of DESs present different toxicity than the eutectic mixtures [82; 84; 85; 86]. The physicochemical properties of the eutectic mixtures are different from those of the starting materials, mainly due to the extent of the hydrogen bonds. Any different molar proportion may give different properties to the DESs composed of the same starting materials. The interaction of these compounds with the bacterial membrane seems to cause the toxic effect. However, the full mechanism of action is still unknown. On the one hand, it is known that compounds that exhibit charge delocalization are more toxic [87]; thus, it is suspected that eutectic mixtures that present a delocalized charge in their structure (HBA) interact more with the membrane, causing its disruption. Depending on the type of HBD of these mixtures, the charge will be delocalized in the HBA and, therefore, the toxicity will vary. On the other hand, the formation of hydrogen bonds between the components of the eutectic mixtures  
Figure 4: Biomodels used in the ecotoxicity evaluation of DESs, and the relationship with the trophic chain.  
seems to prevent the formation of these bonds with elements of the plasma membrane [88]. However, other theories maintain that HBDs cause denaturation of the membrane proteins, activating enzymatic mechanisms that cause bacterial death [89].