esa-sceva/satcom-chunk-collection huggingface dataset:
## 1 Introduction
To reduce repetitive synthesis/experimental testing of compounds and chemical waste [19], computational (_in silico_) methods such as density functional theory (DFT) and its time-dependent extension (TD-DFT) are suitable tools for accurately reproducing conformational, stacking, chemical, and optical features of anthocyanins, alone or involved in non-covalent interactions [20]. For the TD-DFT method, a wide class of functionals and different basis sets are used to theoretically calculate the excitation energies, structures, and UV-vis absorption spectra of polyphenols up to approximately 200 atoms [21; 22]. In particular, DFT calculations based on hybrid functionals are considered a valuable tool in the case of medium-sized molecules having \(\pi\)-conjugations such as anthocyanins. Of the hybrid functionals, B3 LYP with the 6-31+G(d,p) double-\(\zeta\) basis set is one of the most adopted for assessing the thermodynamic and photophysical properties of flavonoids due to its favorable accuracy and reasonable computational cost ratio [23; 24]. For anthocyanins involved in copigmentation systems, the use of classical hybrid functionals (uncorrected) is not sufficient to reproduce non-covalent interactions (mainly dispersive effects) and their electronic and optical properties properly. The theoretical investigation of the binding and thermodynamic parameters of anthocyanin complexes provides useful information for predicting if complexation processes are favored, the nature of the interactions, and their thermodynamic stability under different simulated conditions [24]. In addition, predicting the color changes of anthocyanins due to structural factors and molecular interactions is very useful for developing strategies aimed at modulating and stabilizing anthocyanins. Despite the recent advances in this field, knowledge of the suitability of _in silico_ methods for accurately predicting the perceived color of anthocyanins (rather than its spectral features) is still scarce, representing one of the challenges for current theoretical approaches. Nevertheless, further studies are needed to gain insight into the modulation and prediction of the color of important natural pigments such as anthocyanins. Therefore, the purpose of this study was to propose an _in silico_ colorimetric protocol based on a combination of computational tools (docking and molecular dynamics), TD-DFT calculations (using the dispersion-corrected hybrid functional B3 LYP with the double-\(\zeta\) Pople-type basis set), and tristimulus colorimetry to predict color variations of anthocyanin pigments in the CIELAB color space. The main interest was focused on studying the effect of conformational changes on the flavylium cation structure of malvidin 3 glucoside (torsion of the dihedral angle of B-ring, Figure 1) and its molecular interaction with a grape seed peptide (GSP).
## 2 Materials and Methods
### Data Processing  
Data processing was performed using the Excel 2016 software program. Correlation line plots were produced from fungal and bacterial OTUs in addition to fungal/bacterial OTU ratios in association with in situ CO\({}_{2}\) fluxes for the 8 sampling dates. Histograms of fungal and bacterial OTUs numbers, in addition to soil respiration rates, were also established for the 8 and 14 sampling dates, respectively. The above were all using Origin 2017 software. Classes with greater than 1% relative abundance were designated as dominant classes, and the Origin 2017 software established relative abundance histogram. OTUs with greater than 1% relative abundance were designated as dominant OTUs, and the CoNet plug-in for the Cytoscape 3.7.1 software was used to construct a bacterial-fungal co-occurrence network [37]. Four statistical algorithms were used: Pearson's correlation, Spearman correlation, Bray-Curtis dissimilarity and Kullback-Leibler dissimilarity. The Brown method was used to integrate the \(p\) values. The data with significant correlation (\(p<0.05\)) were selected for subsequent analysis. The Benjamin-Hochberg method was used as the multiple test correction. The MCODE plug-in for Cytoscape 3.7.1 was used to analyze network modularity using default criteria, whereas the Network Analyzer for Cytoscape 3.7.1 was used to identify the number of nodes, number of connected edges, and the connectivity within the network. Functional predictions based on the dominant fungal and bacterial OTUs were inferred using the FUNGuild and FAPROTAX databases, respectively, and the resulting functional profiles were visualized as heat maps using the R Studio 5.3.1 program. All statistical analyses were carried out with SPSS 24.0. Correlation analysis was conducted using Pearson correlation (trigonometric function converted to normal distribution) and Spearman correlation [38]. The abundance difference was conducted with independent sample \(t\)-test [39].
## 2 Method
### Synthesizing the Required Pressure Field by Finite Control Points
As each control point can be regarded as a monopole source, the acoustic field to be built is then expressed as the summations of all the monopole radiation effects  
\[p\big{(}\mathbf{r}_{j}\big{)}=\sum_{m=-\infty}^{+\infty}\frac{i}{2}\pi\alpha_{m} (k)H_{m}^{(1)}\big{(}k\|\mathbf{r}_{q}\|\big{)}J_{m}\big{(}k\|\mathbf{r}_{j}\| \big{)}\,\exp\big{(}im\varphi_{j}\big{)}, \tag{7}\]  
where \(\alpha_{m}(k)\) is a set of harmonic coefficients indicating the contribution of all the control points to \(m\)th item, \(H_{m}^{(1)}(\cdot)\) and \(J_{m}(\cdot)\) are the \(m\)th order Hankel function of the first kind and Bessel function of the first kind, respectively, and \(\varphi_{j}\) is the local azimuth of point \(r_{j}\). On the other hand, the desired sound field can also be written in terms of cylindrical harmonics  
\[p_{desired}\big{(}\mathbf{r}_{j}\big{)}=\sum_{m=-\infty}^{+\infty}A_{m}(k)J_{ m}\big{(}k\|\mathbf{r}_{j}\|\big{)}\,\exp\big{(}im\varphi_{j}\big{)}, \tag{8}\]  
where \(A_{m}(k)\) is the \(m\)th order harmonic coefficients. For small \(kr\), the low-order spherical Bessel functions overweight the high-order terms. So Equations (7) and (8) can be truncated to \(M=[kr]\) items, where \(r\) is the radius of the target region and \([\cdot]\) denotes the operator of rounding up to the nearest integer. The truncation error associated with \(M\) terms is less than 10%, which is tolerable for practical applications [46]. By matching each order of the spherical harmonics in Equations (7) and (8), the corresponding coefficient \(\alpha_{m}(k)\) can be calculated  
\[\alpha_{m}(k)=\frac{2A_{m}(k)}{i\pi H_{m}^{(1)}(k)},\ m=-M,\ldots,M. \tag{9}\]  
The pressure of each control point can be obtain by Equation (7), consequently. It should be noted that satisfying the Nyquist condition in this case requires the total number \(Q\) of control points to be larger than \(2M\).
## 2 System Model
### Channel Model
The relay \(R\) is able to decode the interference, when both nodes 1 and 2 are active, if the following conditions are satisfied  
\[R_{2}\leq\log_{2}\!\left(1+\frac{|h_{2R}|^{2}d_{2R}^{-a}p_{2}^{t }}{1+|h_{1R}|^{2}d_{1R}^{-a}p_{1}^{t}}\right), \tag{6}\] \[R_{1}\leq\log_{2}\!\left(1+|h_{1R}|^{2}d_{1R}^{-a}p_{1}^{t} \right), \tag{7}\]  
which are equivalent to  
\[\gamma_{2}=2^{R_{2}}-1\leq\frac{|h_{2R}|^{2}d_{2R}^{-a}p_{2}^{t}}{1+|h_{1R}|^ {2}d_{1R}^{-a}p_{1}^{t}}\triangleq\text{SINR}_{2R}\text{ and }\gamma_{1}\leq\text{SNR}_{1R}. \tag{8}\]  
The event \(\mathcal{D}_{1,R}^{\{1,2\}}\) is given by \(\mathcal{D}_{1,R}^{\{1,2\}}=\{\text{SINR}_{2R}\geq\gamma_{2}\}\cap\{\text{SNR}_{1R }\geq\gamma_{1}\}\), and the probability that \(R\) can decode the transmitted information from 1 (given that both 1 and 2 are active) is given by (9) [20]. \[\text{Pr}^{SIC}\Big{(}\mathcal{D}_{1,R}^{\{1,2\}}\Big{)}=\text{Pr}\{\{\text{ SINR}_{2R}\geq\gamma_{2}\}\cap\{\text{SNR}_{1R}\geq\gamma_{1}\}\} \tag{9}\] \[\Pr\!\left(\mathcal{D}_{j,i}^{\{j\}}\right)=\Pr\!\left\{\text{SNR}_{ji}\geq \gamma_{j}\right\}=\exp\!\left(-\frac{\gamma_{j}d_{ji}^{a}}{p_{j}^{t}}\right). \tag{2}\]0  
\[\Pr\!\left(\mathcal{D}_{j,i}^{\{j\}}\right)=\Pr\!\left\{\text{SNR}_{ji}\geq \gamma_{j}\right\}=\exp\!\left(-\frac{\gamma_{j}d_{ji}^{a}}{p_{j}^{t}}\right). \tag{2}\]1 An indicative topology of the considered system. For the rest of the paper, for reasons of brevity, the probability that node \(i\) is able to decode the packet transmitted from node \(j\), given a set of active transmitters denoted by \(\mathcal{M}\), will be denoted by \(p_{j/\mathcal{M}}^{i}\).
## 3 Results and Discussion
### Input Selection  
The BFS was applied to evaluate the significance of inputs for predicting the AOp. In the beginning, the suggested approach examined nine inputs for the final selection of the inputs, and 100 iterations were used to execute the BFS. The authors did not notice any variations in the research results exceeding 100 runs. The findings of the BFS-based technique are presented in Figure 5.  
Box plots in Figure 5 explain the significance of the inputs assessed through BFS. The green plots show those inputs that have more prominent predictability than those indicated by the blue colors. All inputs were classified as significant. Therefore, in developing various input mixtures for the AOp forecast, all nine inputs will be employed. Based on the suggested structure, nine predictive models will be proposed. This approach aims to determine the minimal optimal variable's collection for overcoming the issues of underfitting and overfitting. Furthermore, the inputs indicated in red in the BFS results possess smaller informational potential compared with shadow traits. Hence, these inputs are eliminated from the final collection. Moreover, the yellow inputs show tentative ones. As a result, no inputs appeared tentative or unimportant.  
Figure 5: The results of the input selection study on the data.  
The RQD, MCPD, and PF inputs were confirmed to be the most significant inputs, graded in the same descending form of value for the data obtained from the site. Following these inputs, the HD placed fourth in significance. These findings confirm that using the three characteristics of blasting improves the effectiveness of AOp predictability. Therefore, this study suggests that prospective scholars use these variables as inputs in their models. This algorithm is strong and can produce an unbiased and firm choice of significant and insignificant inputs from a dataset. Since combining more inputs can induce overfitting issues, the novel BFS's capacity to prioritize inputs in decreasing sequence of values can assist scholars in deciding which inputs apply to the AOp forecast. Hence, dropping unnecessary or less correlated inputs may reduce calculation complications and time linked with enhancing the suggested hyperparameters of the scheme.
## 6 Enabling Technologies in Healthcare
### Internet of Things  
The IoT is a technology that allows medical devices to collect, transmit, and analyze data in real-time [55]. This helps healthcare providers make real-time, data-driven decisions and improve the quality of patient care. It is used for remote patient monitoring, allowing patients to receive care and monitoring without the need to visit the doctor's office. Using IoT, medical devices and specialized care services are identified, detected, and authenticated. Globally, the term 'IoT' refers to a network of physical devices that are connected to the internet in order to collect and share data. With the help of wireless sensors, the IoT network gathers and shares data. IoT enables the delivery of personalized, affordable, and cost-effective services in digital healthcare. Using a wireless system, IoT streamlines data collection and communication, helping doctors and nurses save time and effort in accessing and analyzing patient information. This enables medical practitioners to have real-time access to comprehensive patient information, empowering them to make timely and well-informed decisions. In addition, remote patient monitoring reduces the need for frequent in-person visits and manual data collection, resulting in cost savings for the healthcare system.
## 4 Empirical Strategies for Improving TIA Production  
### Empirical Optimization of the Culture Medium and Culture Conditions  
The composition of the culture medium as well as the physical environmental conditions, and the addition of specific compounds to the culture medium have been proved to increase TIA yields in _C. roseus in vitro_ cultures, sometimes in a dramatic way. Thus, reducing or removing nitrate and/or phosphate ions from the medium leads to a significant decrease of cell growth but remarkably, an increase in TIA accumulation [64]. On the other hand, the addition of KCl (4 g/L) did not inhibit cell growth and increased threefold the production of ajmalicine and serpentine in comparison to the levels found in control compact callus clusters cultures of _C. roseus_[65] (Table 1). Similarly, the addition of NaCl (1.7 g/L) caused a rise in catharanthine levels (around 90%), while the same KCl concentration resulted in an increase of catharanthine around 300%. Moreover, the effect of a higher NaCl concentration (2.9 g/L) increased the content of vindoline, catharanthine, vinblastine and vincristine in _C. roseus_ seedlings [66]. The highest levels of TIAs were obtained by adding 250 mM mannitol (four- to five-fold higher in comparison to the controls) to compact callus clusters cultures of _C. roseus_[65] (Table 1). The carbon source concentration is another limiting factor for the TIA biosynthesis. In fact, [PERSON] _et al._[67] observed that the presence of a low glucose concentration correlated with a high ajmalicine production level. [PERSON] _et al._[68] observed an increase of ajmalicine, serpentine and catharanthine (a total alkaloid yield of over 43 mg/L), when the concentration of sucrose increased in the culture medium of compact callus clusters of _C. roseus_ (Table 1). Similarly, [PERSON] _et al._[69] observed an increase in the levels of catharanthine when the carbon source changed (fructose for sucrose) in _C. roseus_ hairy root cultures. Several authors have showed that the production of ajmalicine was significantly lower when a low _C. roseus_ cell _inoculum_ density was used although catharanthine levels were nearly the same at high and low cell densities [70, 71]. The immobilization of plant cells provides resistance to the diffusion and it could increase the levels of TIAs, specially using a high cell density. In fact, several carriers have been used to immobilize cells of _C. roseus_[1]. In this sense, an increase of ajmalicine levels and its excretion to the medium when _C. roseus_ cells were immobilized with calcium alginate was observed by [PERSON] and [PERSON] [72]. Also, [PERSON] and [PERSON] [71] observed that high inoculum density in _C. roseus_ cultures in which cells are immobilized on alginate beads produced a high ajmalicine concentration (120 mg/L) [71] (Table 1).
# Variable Speed Limit and Ramp Metering for Mixed Traffic Flows: A Review and Open Questions
## 3 Research and Implementation
### VSL in Mixed Traffic Flows
#### 3.4.1 Optimization-Based Methods for VSL
Two scenarios were tested. The first contained only C-VSL, and the second used both C-VSL and P-VSL. In both scenarios, the AV penetration rate was changed, ranging from 0% to 100%. In the first scenario, the best results were obtained at a 40% AV penetration rate by reducing the delay by 49.5%. In the second scenario, the best results were obtained at a 90% AV penetration rate by reducing the delay by 47.9%. An MPC-based VSL system was analyzed in [93]. In this approach, the future state is predicted and the control strategies are proactively updated in the system. The proposed MPC approach consists of four main components: data input and traffic state estimation, traffic state prediction, optimization using an objective function based on a rolling horizon, and a control action. The control time step used in this study was set to 1 min. A control horizon was used to account for the complexity and performance of the proposed system. The VSL control algorithm was based on a multi-objective function formulated with \(TTT\) as a measure of network efficiency, \(TTC\) as a measure of the instantaneous safety, the emission, and fuel consumption measures. This approach outperformed the scenario without VSL and resulted in \(TTT\) reductions of about 20%, safety improvements of 6-11%, and overall fuel consumption reductions of 5-16% with 100% CAV penetration rate. The results also suggest that when the CAV penetration rate is 100%, optimization of only one component performs better. However, in scenarios with lower penetration rate, multi-objective optimization performs better in terms of mobility, safety, and sustainability simultaneously. Another MPC strategy for VSL control of mixed traffic flows with CAVs was implemented in [104]. CAVs were modeled to have the same driving behavior as HDVs under no control condition and to fully comply with a set speed limit. A discrete first-order model that accounts for the capacity drop of shock waves was extended in this study to address the proposed VSL control. The proposed control structure of MPC consists of data processing and prediction based on traffic measurements, followed by the optimal control layer, control application layer, and intelligent motorway system. The used three-lane motorway model was 10 km long with a traffic demand of 5400 veh/h and a 20% CAV penetration rate. A disturbance speed lasting 2 min was generated to simulate shock waves, which was set to 5 km/h in the VSL-controlled segment. The free-flow speed was set to 100 km/h. The proposed strategy achieved total delay reduction of 3.7% from 35.3 veh \(\cdot\) h to 34.0 veh \(\cdot\) h.
## 4 Performance Measures  
The first performance metric is the spectral separation coefficient (SSC) [19]  
\[\text{SSC}(G_{1},G_{2},B)=\frac{\int_{-B/2}^{+B/2}G_{1}(f)G_{2}(f)\text{d}f}{ \int_{-\infty}^{+\infty}G_{1}(f)\text{d}f\int_{-\infty}^{+\infty}G_{2}(f)\text{ d}f}\,, \tag{16}\]  
where \(G_{1}(f)\) and \(G_{2}(f)\) denote the power spectral densities (PSDs) of two signals to test. The second performance measure was the multipath error envelope assuming one line-of-sight (LOS) tap and one non-line-of-sight (NLOS) tap with a 3 dB attenuation for varying multipath tap delays [20]. The last investigated performance measure is the unsmoothed TOA estimation error stdev.  
\[\centering\sigma=\sqrt{\frac{1}{J}\sum_{j=1}^{J}(\hat{\tau}_{0}(j)-\mu)^{2}}\@add@centering \tag{17}\]  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Candidate** & **Modulation** & \(f_{\text{cE1D}}\)**/MHz** & \(f^{\Delta}\)**/MHz** & \(f_{0}+f^{\Delta}\)**/MHz** & \(f_{\text{IE}}\)**/MHz** \\ \hline E1D-a & BPSK-R & 1.0230 & 4.092 & 1579.512 & 1577.466 \\ E1D-b & BPSK-R & 0.5115 & 4.092 & 1579.512 & 1577.466 \\ E1D-c & CBOC(6,1,1/11,-) & 1.0230 & 4.092 & 1579.512 & 1577.466 \\ E1D-d & BPSK-R & 1.0230 & 20.460 & 1595.880 & 1585.650 \\ E1D-e & BPSK-R & 0.5115 & 20.460 & 1595.880 & 1585.650 \\ E1D-f & CBOC(6,1,1/11,-) & 1.0230 & 20.460 & 1595.880 & 1585.650 \\ E1D-g & BOC(8,1) & 1.0230 & 12.276 & 1587.696 & 1581.558 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Parameters of investigated Galileo E1D signal candidates forming a meta-signal with the Galileo E1C signal.  
Figure 4: Spectral allocation of individual signals and potential Galileo meta-signal ACFs options. (**a**) PSDs of broadcast GNSS L1/E1/B1 signals and potential E1D signal candidates in upper L-band. (**b**) Absolute part of E1C+E1D meta-signal ACFs assuming a centrally placed intermediate frequency.  
of the code phase, where \(\hat{\tau}_{0}(j)\) denotes the \(j\)-th TOA estimate based on the \(j\)-th realization of \(\bar{\mathbf{q}}\) of, in total, \(J\) Monte-Carlo realizations, and \(\mu=(1/J)\sum_{j=1}^{J}\hat{\tau}_{0}(j)\) denotes the sample mean of the TOA estimates, which are derived either by coherent early-late processing (CELP) or noncoherent early-late processing (NELP), as explored in [21].
## 3 \(\mathcal{H}_{\infty}\)-Based Control Strategies  
The main objective of this paper is to define a procedure for the synthesis of the controller \(\mathbf{K}(z)\) in (6) that improves the robustness of the system. Four controller designs are considered:  
* **Proportional-Resonant Voltage Control tuned using Root Locus**: This standard control procedure has been included as a benchmark for the other techniques (this design is included above as the benchmark controller);
* **Cascaded Voltage and Current Control based on \(\mathcal{H}_{\infty}\) synthesis**: The outer voltage loop and the inner current loop are individual controllers based on \(\mathcal{H}_{\infty}\) synthesis;
* **Proportional-Resonant Voltage Control tuned using \(\mathcal{H}_{\infty}\) synthesis**: The parameters of the regulators have been tuned using \(\mathcal{H}_{\infty}\) synthesis;
* **Voltage Control based on \(\mathcal{H}_{\infty}\) synthesis**: A single \(\mathcal{H}_{\infty}\) voltage controller, i.e., without internal current loop.  
The design alternatives \(I\) and \(2\) follow a cascade control structure as the benchmark controller. One uses \(\mathcal{H}_{\infty}\) synthesis for the PR-based control parameter tuning, while the third calculates state-space voltage and current controllers based on \(\mathcal{H}_{\infty}\) synthesis. The last design alternative calculates a single-loop voltage controller based on \(\mathcal{H}_{\infty}\) synthesis.  
Design Alternative 1: Cascaded Voltage and Current Control Based on \(\mathcal{H}_{\infty}\) Synthesis  
To improve the robustness of the controller with a cascaded structure, a voltage control and a current control have been obtained using \(\mathcal{H}_{\infty}\) synthesis to replace each of the two root-locus PR controllers designed above. Considering the plants described in (9) and (10), the first step is the zero-order hold (ZOH) discretisation of both plants, using a sampling rate of 0.25 ms. Additionally, the PWM delay is considered by adding a sample-time delay (i.e., \(G_{Iw}(z)=z^{-1}\)). Then, the generalised plant shown in Figure 5 is obtained.  
Be aware that optimal \(\mathcal{H}_{\infty}\) design does not explicitly include uncertainty. Instead, this uncertainty needs to be introduced through the assignment of weights. For example, a widely acknowledged practice involves assigning weight to the output of the generalised plant associated with control action, thereby ensuring robustness against unstructured additive uncertainty. However, the favourable computational aspects of \(\mathcal{H}_{\infty}\) synthesis (convex optimisation) compared to the non-convex iterative procedures for structured uncertainty in, say, \(\mu\)-synthesis [17; 18] made \(\mathcal{H}_{\infty}\) the choice in this work.
# Nomological Deductive Reasoning for Trustworthy, Human-Readable, and Actionable AI Outputs
## 5 Nomological Deductive Reasoning (NDR): A Proposed Solution
### NDR Framework Architecture and Operational Integration with Machine Learning
#### 5.2.2 Knowledge Encoding: Symbolic Rule Definition  
The knowledge base in NDR is composed of two components: **domain-specific laws (L)** and **antecedent conditions (C)**. These are hand-encoded as logical rules formalized using first-order logic and operationalized as IF-THEN statements. The following are example rules (symbolic format) in a financial-use case:  
* IF checking_account = None AND savings < 100 THEN LiquidityRisk = TRUE;  
Figure 7: Proposed architecture for an NDR-based explanation approach.  
* IF employment_duration < 1 year AND age < 30 THEN IncomeInstability = TRUE;
* IF LiquidityRisk = TRUE AND IncomeInstability = TRUE THEN CreditRisk = High.  
Each rule maps observed conditions to semantic risk categories. These rules are stored in a structured ontology that defines the hierarchy and relationships between classes (e.g., ApplicantProfile, RiskIndicator) and properties (e.g., hasCreditBehavior, leadsToRecommendation).