esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Functional Roles of Nicotinamide and Pyridoxine
### Absorption, Transport, and TISSUIE Distribution
#### 3.2.2 Pyridoxine (Vitamin B6)  
Pyridoxine and its vitamers--pyridoxal and pyridoxamine--are absorbed in the jejunum mainly through passive diffusion, although recent evidence suggests that carrier-mediated uptake may also occur. Once absorbed, these forms are transported to the liver, where they are phosphorylated by pyridoxal kinase and oxidized by pyridoxamine-phosphate oxidase to generate pyridoxal 5\({}^{\prime}\)-phosphate (PLP), the active coenzyme form [112; 113; 119; 120]. PLP binds tightly to albumin in plasma and is delivered to peripheral tissues, including skeletal muscle. Entry into cells likely occurs via facilitated transport followed by intracellular dephosphorylation and rephosphorylation. In skeletalmuscle, PLP is involved in over a hundred enzymatic reactions, including those catalyzed by aminotransferases (e.g., alanine aminotransferase - ALT and aspartate aminotransferase - AST), glutamate decarboxylase, and glycogen phosphorylase [54]. These reactions are essential for amino acid turnover, neurotransmitter synthesis, glycogenolysis, and energy homeostasis. PLP's ability to chelate aldehydes and act as a carbonyl scavenger also supports its antioxidant function, particularly under conditions of muscle inflammation and metabolic stress [121; 122].
# LoRa Enabled Smart Inverters for Microgrid Scenarios with Widespread Elements
## 4 LPWAN Systems and MG Control
### Residential MGs Communication Considerations  
Residential MG systems require an M2M communication structure to create a platform for smart devices to communicate and coordinate. WiFi and ZigBee are the leading communication technologies for smart homes. They both use the 2.4 GHz band, and their physical and data link layer protocols are IEEE 802.11 and IEEE 802.15.4, respectively. Although both of these technologies can be used for home automation, their characteristics are quite different. WiFi is a high-power and high-throughput communication system, while ZigBee is the opposite [65; 66; 67]. The coverage of the aforementioned networks is less than 100 m, at best, and that is why they are called short-range technologies. Therefore, if in the MG of interest the separation between communication nodes (DGs) are in terms of hundreds of meters or more, WiFi and ZigBee cannot be used to establish a proper communication framework. In this case, other solutions are required to be considered.
# A Feature Selection Method for Multi-Label Text Based on Feature Importance
## 3 Proposed Method  
In this paper, a feature importance calculation method based on CC is proposed, and based on this method, a feature selection method for multi-label text is proposed. The process of the feature selection method proposed in this paper is shown in Figure 1.  
The purpose of feature selection is to select features with strong category discrimination ability. Calculating the importance of each feature accurately and selecting features with higher importance to construct the feature space are very important for the classification performance. In this paper, the contribution of features to the classification is considered from two aspects of inter-category and intra-category, and a method of feature importance based on CC is proposed. The main steps of the feature importance method are as follows.  
1. The inter-category contribution and intra-category contribution of each feature to each category are calculated respectively.
2. For each feature, the inter-category contribution variance and intra-category contribution variance are calculated respectively based on the inter-category contribution and intra-category contribution to each category calculated in (1).
3. The importance of each feature is calculated by fusing the intra-category contribution variance and the inter-category contribution variance.
# End-to-End Automated Guided Modular Vehicle
## 7 Results and Discussion
### Best Model Results  
CNN model 13 was the one that obtained the best results in the tests, especially in stage 3 of the test, where an automated steering direction of the entire in-campus circuit was achieved with almost no human intervention to modify the steering angle of the vehicle. The architecture of this model is the one presented in Section 5.3. This model was trained with the Adam optimizer for 14 epochs. The duration of training was 28 min and 37 s with a high capacity computer, whose characteristics are mentioned in Section 6.3. This model was trained with 206,726 images obtained by the augmentation of the 103,363 original data points with their respective steering wheel angles.  
An important parameter to consider during training is loss, as shown in Figure 14 for all training epochs.  
The training set efficiency was verified by the ever-decreasing values of loss across the epochs. As for the validation set, a mild improvement was noticed but it had a mostly constant performance all over the training. Although there was a low improvement in the loss for the validation set, the values for the validation set are lower than 0.02 units. As mentioned above, a loss value close to 0 and with a decreasing trend indicates a well-trained model. The loss value of the validation set is higher than the training set is normal. The important thing is to protect the model from overfitting by keeping its value as low as possible with a decreasing trend.  
The platform thus can be expected to develop as planned when the model starts running as a node in the ROS topics, instead of being directed by the remote controller. From the loss plot, it is important to note that the loss value generally declines, since an increasing trend would suggest overfitting the model.  
Figure 15 shows the prediction of the steering wheel angle made by our CNN model against the ground truth obtained from the training data. This prediction was generated with one of the datasets in which it was trained in order to observe the precision obtained by the model.  
Figure 14: Loss plot of model 13 during training.  
A certain \"noise\" can be observed in the prediction, which is reduced by applying a moving average filter when the predicted value is sent to the steering wheel actuator. This filter is expressed by the equation:  
\[y(i)=\frac{1}{M}\sum_{j=0}^{M-1}x(i+j), \tag{3}\]  
where \(y\) is the new output, \(x\) is the predicted output, and \(M\) is the number of data points used. This model, as it is trained on a fixed route, presents excellent precision with the data in the tests. However, if a more generalized prediction for the model is desired, it is necessary to increase the variety and amount of training data. This model obtained a 94.5% accuracy with the training data. Although other models had a higher percent accuracy, this model performed better in the other stages of the evaluation protocol.
# Textile-Based Flexible Coils for Wireless Inductive Power Transmission
## 4 Fabrication
### Interface Layer  
The selected standard 65/35 polyester/cotton textile has a rough surface with an arithmetic mean deviation of 140 \(\upmu\)m [21] so it was necessary to print an interface layer on the textile to create a smooth surface for the printing of subsequent conductive layers with uniform thickness. An UV curing interface ink Fabink-IF-UV4 from Smart Fabric Inks Ltd. (Smart Fabric Inks Ltd., Southampton, UK) [22] was printed and cured to form the interface layer on the textile, to achieve a smoother surface.  
After each print, UV curing is performed for 60 s at an intensity of 50 mW/cm\({}^{2}\). The thickness of each cured layer was then measured using the non-contact method. An acceptably smooth surface was achieved with six sub-layers of interface with a total thickness of 96 \(\upmu\)m \(\pm\) 10%. A comparison of surface roughness is shown in Figure 4 which shows example conductive tracks printed on a smooth interface layer with six sub-layers on the left side compared with a rough surface of the interface layer with two sub-layers on the right side. Based on SEM measurements it can be concluded that the maximum diameter of surface defects is less than 5 \(\upmu\)m with a 6 sub-layer interface having a thickness of 96 \(\upmu\)m as shown in the SEM micrograph in Figure 5.  
Figure 4: Roughness of interface layer comparison. Demo tracks printed on a smooth surface of the interface with 6 sub-layers on the left side, and a rough surface of the interface with 2 sub-layers on the right side.
## 3 Summary and Scientific Performance
Regarding the polarization setup, SKADI features the optimal solution both for long and short wavelengths, as well as the possibility to later upgrade for either even longer wavelength or any other optical component that might be considered to be useful. Together with the spin flipper and the \({}^{3}\)He-analyzer that can be installed at the sample position, this gives SKADI the capability to both investigate magnetic samples, or, more generally, spin dependent scattering, for all orientations, as well as the possibility to use polarization for the suppression of spin-incoherent background, which might prove useful for weakly scattering samples, where simply prolonging the measurement does not necessarily improve the data anymore. In terms of sample environment, there is already now a lively community working of developing their own custom sample environments for SKADI. This takes advantage of the open sample environment system, which allows simple access to the sample cave with predefined kinetic mounts and other interfaces. This will both facilitate the design of custom sample environments by other users, as well as shorten the setup times between beamtimes, even for complex sample environments, since they can be prepared to a very high degree off the instrument, before the actual experiment starts. Finally, the SoNDe detector is a completely new development, which is aimed to allow the use of the high brilliance of the ESS to capacity. The semitransparent beamstop will allow SKADI to measure down to virtually Q = 0 A\({}^{-1}\), only being limited by the pixel size. In addition, no attenuation of the beam is necessary, since the SoNDe detector will be linear, even for the highest intensities available. Furthermore, the free positioning of the detectors will allow an optimal selection of resolution, collimation, and flux for any experimental setup. Taken together, all of these features present SKADI as one of the world leading SANS instruments and an excellent addition to the ESS instrument suite. Conceptualization S.J., S.D., R.H., R.E. and H.F. ([PERSON]); Methodology S.J. ; Technical designs S.J., H.F. ([PERSON]), T.K., R.H., S.D., P.L., A.G., S.B., R.E. and H.F. ([PERSON]); writing--original draft preparation S.J. ; writing--review and editing S.J., A.C., S.D., T.K., H.F. ([PERSON]), P.L., R.H., A.G., S.B., R.H., H.F. ([PERSON]), S.F. and [PERSON]-[PERSON]; supervision S.F. and P.M.-B. ; project supervision S.J. ; funding acquisition S.J. and H.F. ([PERSON]). All authors have read and agreed to the published version of the manuscript. K.K., T.H., P.M.-B. acknowledges funding by the BMBF project \"Flexiprobe\" (grant number 05K16 WOA). The authors declare no conflict of interest.
## 6 Sensitivity Analysis
### Effect of the Variation of the Producer Availability and the Subcontractor Availability  
As shown in the following paragraphs, the main intention of this section was to present the analysis results regarding the influence of varying the subcontractor and producer availability. To make things simple, we defined the availability of the production system \(D_{pro}\) as the fraction of time when the manufacturing unit was available for production, as denoted previously in Equation (7). Additionally, to develop a realistic production model, it was assumed that the subcontractor was unreliable due to random disturbances. Then, the fraction of time where the subcontractor was available was denoted by \(D_{sub}\), as noted earlier in Equation (8). Table 9 presents the effects of the availability of the producer and the availability of the subcontractor on the parameters of the control strategy and also on the total cost.  
From the results presented in Table 9, it was observed that when the availability of the subcontractor \(D_{sub}\) was low (case xv), more protection was needed against shortages, hence the inventory level \(Z^{*}\) increased. Moreover, in this context, it was required to operate the system for longer to compensate for the low availability of the subcontractor, thus major maintenance must be postponed; this measure led to an increased age \(A_{o}^{*}\). With the low availability of the subcontractor, it was normal that its participation was delayed, increasing the age \(A_{sub}^{*}\). When the availability of the subcontractor increased (case xvi), the opposite effects were observed.  
In the case where the availability of the producer \(D_{pro}\) reduced (case xvii), the system required more protection against random failures; thus, the inventory level \(Z^{*}\) increased. Major maintenance was postponed to allow the unit to operate for longer without interruptions and to increase the capacity to satisfy customer demand, thus increasing the age \(A_{o}^{*}\). Since the subcontractor's cost was more expensive than in-house production, when the availability of the producer decreased, then it was preferable to continue with in-house production, thus increasing the age \(A_{sub}^{*}\). When the availability \(D_{pro}\) increased, the opposite effects were observed (case xviii).
# Finite Element Analysis of the Distribution Parameters of a Metal Dot Array in a SAW Gyroscope
## 2 Theoretical Analysis
Therefore, in this study, the difference between the symmetric frequency and the anti-symmetric frequency is used to represent the output signal of the differential SAW gyroscope. When the substrate model is stationary, the output signal of the SAW gyroscope can be expressed as:  
\[f_{0}=f_{sc+}-f_{sc-} \tag{3}\]  
When a rotation vector is applied to the piezoelectric substrate distributed metal dot array, \(f_{sc+}\) and \(f_{sc-}\) will changed accordingly. At this time, the output signal of the SAW gyroscope can be defined as [32]:  
\[f_{\text{c}}=f^{\prime}_{sc+}-f^{\prime}_{sc-} \tag{4}\]  
where, \(f^{\prime}_{sc+}\) and \(f^{\prime}_{sc-}\) are the symmetric and antisymmetric frequencies under rotation, respectively. By subtracting Equation (3) from Equation (4), the magnitude of the SAW gyroscopic effect caused by the rotation can be obtained. Here we define a parameter \(g\), as shown in Equation (5) below, to represent the SAW gyroscopic effect intensity. In this way, the optimum distribution parameters of the metal dot array in the sensor can be determined by comparing the value of \(g\) under the same rotation speed:  
\[g=f_{\text{c}}-f_{0}=(f^{\prime}_{sc+}-f_{sc+})-(f^{\prime}_{sc-}-f_{sc-}) \tag{5}\]
## 4 Results and Discussion
### Bradening Coefficient, H\({}_{2}\)  
Pure H\({}_{2}\) was utilized as the buffer gas in this measurement of the NO-H2 broadening coefficient (purity 5.0, Linde). Figure 7a provides an illustration of a measured NO in O\({}_{2}\) absorption spectra at total pressures of 167.9 mbar and 671.4 mbar. The Voigt profile was used to fit the NO in the H\({}_{2}\) spectrum after it had been averaged over 20 scans. The optical density (spectral fitting residuals) was close to (1\(\sigma\)) 3 \(\times\) 10\({}^{-3}\). According to the slope of the linear regression of the Lorentzian widths vs. pressures, the measured NO-H\({}_{2}\) broadening coefficients for Peaks 1 and 2 are \(\gamma_{\rm H2}\) = 71.67(43) \(\times\)10\({}^{-3}\) cm\({}^{-1}\)/atm and 62.77(38) \(\times\) 10\({}^{-3}\) cm\({}^{-1}\)/atm.
## 3 Experimental Results and Discussion
### Security Analysis  
The goal of applying an encryption algorithm is to obtain an indecipherable result, i.e., a hacker must not be able to recognize the plain image behind the ciphered image; thus, the security analysis in this section demonstrates that the proposed encryption algorithm is highly secure.
## 2 Background and Related Works
### Background
#### 2.2.1 Cypher Query Language  
Cypher is the declarative query language used by Neo4j [22], one of the most widely adopted graph databases. Cypher queries operate on nodes, relationships, and their properties, reflecting the structure of a graph.  
Cypher queries typically follow a MATCH-WHERE-RETURN structure. The MATCH clauses define the graph pattern to search for, WHERE applies filters to nodes or relationships based on properties, and RETURN specifies which values or structures to retrieve.  
An example of the Cypher query:  
MATCH (d:Director)-[:DIRECTED]->(m:Movie)  
WHERE d.name = \"[PERSON]\"  
RETURN m.title  
This query retrieves the titles of movies directed by [PERSON]. In the MATCH clause, the node labels \"Director\" and \"Movie\" represent the types of entities, while [:DIRECTED] defines the relationship between them. The pattern (d:Director)-[:DIRECTED]->(m:Movie) is the triple relationship. The WHERE clause filters results using the key value, \"[PERSON]\". The RETURN clause specifies that the titles of the matched movies should be included in the result.  
Due to the structure of Cypher queries, accurately specifying triple relationships in the MATCH clause and key values in the WHERE clause is crucial for generating precise queries that retrieve the desired information from the graph database. Utilizing semantic information for matching between a natural language query and SQL query has been exploited in Text2 SQL [5; 6]. But, in research on Text2 Cypher so far, semantic information such as key-value and triple relationships has not been fully considered. In this paper, we show that key-value extraction and triple relationship extraction are essential tasks in converting natural language into Cypher queries. Previous research [21] has also shown that incorporating key-value and triple relationship features can significantly improve model performance in text-to-Cypher generation.
## 4 Conclusions  
The findings of this work show a more complex profile of phenolic compounds in blueberries than in sweet cherries, mainly dominated by anthocyanins. Without surprise, this composition is clearly related to the radical scavenger and antidiabetic activities observed. Among the obtained results, blueberry and sweet cherry extracts were several times more active in reducing NO\({}^{\bullet}\) and in inhibiting the \(\alpha\)-glucosidase enzyme than the positive controls ascorbic acid and acarbose, respectively. These results reinforce the idea that the consumption of berries is an excellent choice for basal health maintenance, reducing the incidence of chronic diseases, such as diabetes. Additionally, blueberry extracts present activity against bacterial pathogens. Among the alternatives, phenolic compounds derived from natural products appear to be promising alternatives since they have significant health-promoting effects. The current study adds to the evidence that phenolics from red and purple fruits, including blueberries and sweet cherries, have a high potential for incorporation into pharmaceuticals, food supplements, and nutraceuticalials. Nonetheless, further research is required to fully understand their potential and determine safe dosage in the control of bacterial development.  
Conceptualization, A.C.G., A.R.N., J.D.F.-F., A.F., G.A. and L.R.S.; methodology, A.C.G., S.M., A.R.N. and J.D.F.-F.; software, A.C.G., S.M. and J.D.F.-F.; validation, A.C.G., J.D.F.-F., A.F., G.A. and L.R.S.; formal analysis, A.C.G., A.R.N., S.M. and J.D.F.-F.; investigation, A.C.G., M.A.-C., R.R.-C. and J.D.F.-F.; resources, J.D.F.-F., G.A. and L.R.S.; data curation, A.C.G., A.R.N., S.M. and J.D.F.-F.; writing--original draft preparation, A.C.G., A.R.N. and J.D.F.-F.; writing--review and editing, A.C.G., A.R.N., A.F., R.R., G.A. and L.R.S.; visualization, all authors; supervision, A.F., G.A. and L.R.S.; project administration, G.A.; funding acquisition, J.D.F.-F. and G.A. All authors have read and agreed to the published version of the manuscript.  
The authors are grateful to the Foundation for Science and Technology (FCT), the Ministry of Science, Technology and Higher Education (MCTES), the European Social Fund (EFS), and the European Union (EU) for the Ph.D. fellowships of [PERSON] (2020.04947.BD) and [PERSON] (SFRH/BD/139137/2018). [PERSON] was supported by the European Union's Horizon 2020 research and innovation programme under the [PERSON] grant agreement no. 101003373, and the European Union NextGenerationEU and the Portuguese Government under the project PRR-C05-i03-I-000143 (RedFruitHealth).  
Not applicable.  
Data Availability Statement: Data are contained within this article.  
The authors declare no conflict of interest.
## 3 System Model and Problem Formulation
### Communication Model  
In this system, communication between M-IoT devices and UAVs uses FDMA (frequency division multiple access). In a three-dimensional Cartesian coordinate system, the position of M-IoT device \(i\) can be expressed as \(\mathbf{p}_{i}(t)=\left[x_{i}(t),y_{i}(t),0\right]\), and UAVs are always flying at a fixed altitude \(H\). The position of the \(j\)-th UAV in time slot \(t\) can be expressed as \(\mathbf{q}_{j}(t)=\left[x_{j}(t),y_{j}(t),H\right]\). Therefore, the distance between the \(i\)-th M-IoT device and the \(j\)-th UAV is:  
\[\begin{gathered} d_{ij}=\sqrt{H^{2}+\left[x_{i}(t)-x_{j}(t) \right]^{2}+\left[y_{i}(t)-y_{j}(t)\right]^{2}}\\ =\sqrt{\left\|\mathbf{p}_{i}(\mathbf{t})-\mathbf{q}_{j}(\mathbf{t })\right\|^{2}}\end{gathered} \tag{1}\]  
Assuming the height of the UAV is sufficient for line-of-sight transmission, a typical composite channel containing large and small fading can be employed. Therefore, at each time slot \(t\), the channel gain between the \(i\)-th M-IoT device and the \(j\)-th UAV can be expressed as:  
\[h_{ij}=g_{ij}\hat{h}_{ij} \tag{2}\]  
\(g_{ij}\) and \(\hat{h}_{ij}\) represent the path loss and Rice fading during information transmission, respectively. In particular, the path loss can be expressed as \(g_{ij}=h_{0}d_{ij}^{-\alpha}\), where the \(h_{0}\) reference channel gain at the distance \(d_{0}=1\) is represented, \(\alpha\) is the path loss index, which indicates the rate at which the signal attenuates with distance, and the specific value is \(\alpha=2\). Meanwhile, the Rice fading can be given by the following formula.  
\[\hat{h}_{ij}=\sqrt{\frac{M}{M+1}\overline{h}_{ij}+\frac{1}{M+1}\widetilde{h}_{ ij}} \tag{3}\]  
Figure 1: System model diagram.  
Here, \(M\) is the Rice fading factor, which indicates the power ratio between the line-of-sight (LoS) component and the non-line-of-sight (NLoS) component, where a higher M value indicates that the LoS component dominates, resulting in smaller channel fading, \(\overline{h}_{ij}\) represents the LoS component satisfying \(\left|\overline{h}_{ij}\right|=1\), and \(\widetilde{h}_{ij}\) represents the NLoS component following the \(\widetilde{h}_{ij}\sim\mathcal{CN}(0,1)\) distribution.  
To avoid serious co-channel interference, the M-IoT device offloads its computing tasks to the UAV in FDMA mode. If the \(i\)-th M-IoT device decides to offload its tasks to the \(j\)-th UAV, the offloading rate of the \(i\)-th M-IoT device to the \(j\)-th UAV can be expressed as:  
\[R_{ij}=B_{ij}\left(1+\frac{P_{ij}^{u}\left|\hat{h}_{ij}\right|^{2}g_{ij}}{N_{0 }}\right) \tag{4}\]  
Here, \(B_{ij}\) represents the bandwidth allocated to the M-IoT device for the unloading task, \(P_{ij}^{u}\) represents the transmission power from the \(i\)-th M-IoT device to the \(j\)-th UAV, and \(N_{0}\) is the noise power.