esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Materials and Methods
### Specification of ARDL Model  
The ARDL model was estimated by [PERSON] and [PERSON] (1998) [48] to check the analysis of long-run and short-run relationships. It was further protracted by [PERSON] et al. (2001) [49]. [PERSON] et al. (2004) [50] also used this model in a study to check the variables' associations. The order of integration is distributed with variables at I(0) or I(1) apart from the occurrence of I(2). Here we will demonstrate separately the long-run and short-run models, to check the variables' associations. The long-run model is depicted in Equation (5), below:\[\begin{array}{ll}\Delta lnCO2e_{t}&=\phi_{0}+\sum\limits_{i=1}^{Q}\phi_{1i} \Delta lnCO2e_{t-i}+\sum\limits_{i=1}^{W}\phi_{2i}\Delta lnCA_{t-i}\\ &+\sum\limits_{i=1}^{E}\phi_{3i}\Delta lnEN_{t-i}+\sum\limits_{i=1}^{R}\phi_{ 4i}\Delta lnFO_{t-i}\\ &+\sum\limits_{i=1}^{T}\phi_{5i}\Delta lnGDPPC_{t-i}+\sum\limits_{i=1}^{Y} \phi_{6i}\Delta lnSD_{t-i}\\ &+\sum\limits_{i=1}^{U}\phi_{7i}\Delta lnTF_{t-i}+\sum\limits_{i=1}^{I}\phi_{ 8i}\Delta lnWA_{t-i}\\ &+\delta_{1}lnCO2e_{t-1}+\delta_{2}lnCA_{t-1}+\delta_{3}lnEN_{t-1}\\ &+\delta_{4}lnFO_{t-1}+\delta_{5}lnGDPPC_{t-1}+\delta_{6}lnSD_{t-1}\\ &+\delta_{7}lnTF_{t-1}+\delta_{8}lnWA_{t-1}+\varepsilon_{t}\end{array} \tag{5}\]  
In Equation (5), \(\phi_{0}\) indicates the constant intercept. \(\Delta\) is the difference operator. Q, W, E, R, T, Y, U and I illustrate the order of lags. \(\delta\) is the coefficient of the long-run, and \(\varepsilon_{t}\) is the error term. [PERSON] et al. (2001) demonstrated that the two values may be used for the cointegration: first for the lower bound, where the variables are integrated at zero-order, followed by the upper limit bound value, where variables are integrated at order one. The short-run model estimation of the study variables is illustrated by following the error correction model (ECM) in ARDL and is specified as:  
\[\begin{array}{ll}\Delta lnCO2e_{t}&=\phi_{0}+\sum\limits_{i=1}^{A}\phi_{1i} \Delta lnCO2e_{t-i}+\sum\limits_{i=1}^{S}\phi_{2i}\Delta lnCA_{t-i}\\ &+\sum\limits_{i=1}^{D}\phi_{3i}\Delta lnEN_{t-i}+\sum\limits_{i=1}^{F}\phi_{ 4i}\Delta lnFO_{t-i}\\ &+\sum\limits_{i=1}^{G}\phi_{5i}\Delta lnGDPPC_{t-i}+\sum\limits_{i=1}^{H}\phi _{6i}\Delta lnSD_{t-i}\\ &+\sum\limits_{i=1}^{J}\phi_{7i}\Delta lnTF_{t-i}+\sum\limits_{i=1}^{K}\phi _{8i}\Delta lnWA_{t-i}+\alpha ECM_{t-1}\\ &+\varepsilon_{t}\end{array} \tag{6}\]  
Equation (6) show the analysis of the short-run among study variables by the ECM. A, S, D, F, G, H, J and K represent the order of lags in the equation.
## 2 Materials and Methods
### Degradation
#### 2.3.2 Simultaneous Biodegradation of TBNPA and DBNPG with Yeast Extract  
Simultaneous biodegradation of TBNPA and DBNPG by the four-strain consortium was tested to examine whether the community exhibits a preference for one compound over the other. The four-strain consortium was acclimated to DBNPG and TBNPA and was inoculated in experimental flasks after being transferred three times through DBNPG and TBNPA amended media. Experiments were conducted in triplicate, with TBNPA and DBNPG (50 mg L\({}^{-1}\) each) amended to the medium (90 mL of growth medium inoculated with 10 mL of active four-strain consortium in the stationary phase). Two biotic controls consisted of the four-strain consortia with either DBNPG or TBNPA at 100 mg L\({}^{-1}\), and a negative abiotic control was amended with DBNPG and TBNPA 100 mg L\({}^{-1}\) each. The samples (i.e., DBNPG, TBNPA, and bromide concentrations) were analyzed for optical density (OD600).  
3.3 Individual and Simultaneous Degradation of TBNPA and DBNPG with Vitamin Mix or Glucose Amendment  
Experiments were carried out separately with 45 mL of growth medium inoculated with 5 mL of the active four-strain consortium in the stationary phase. When amended together, TBNPA and DBNPG initial concentrations were 50 mg L\({}^{-1}\) each, whereas when amended separately, initial concentrations were 100 mg L\({}^{-1}\). Experiments were conducted in duplicates and accompanied by an abiotic control.
# Principal Subspace of Dynamic Functional Connectivity for Diagnosis of Autism Spectrum Disorder
## 3 Material and Method
### ASD Diagnosis and Performance Evaluation  
To practically evaluate the performance of our proposed CNN models, we compute the metrics given in Equations (10)-(13). Here, the True Positives (TPs) are the ASD patients that are correctly identified by the classifier, while False Negatives (FNs) are the normal cases (NC) that are incorrectly classified as positive. On the other hand, True Negatives (TNs) are negative images that are correctly classified as negative. In contrast, False Positives (FPs) are predictions that are incorrectly classified as positive.  
\[\text{Accuracy (AccU)}=\frac{TP+TN}{TN+FN+FP+TP} \tag{10}\]  
\[\text{Sensitivity (SenS)}=\frac{TP}{FN+TP} \tag{11}\]  
\[\text{Specificity (SpeC)}=\frac{TN}{FP+TN} \tag{12}\]  
\[\text{Precision (PreC)}=\frac{TP}{FP+TP} \tag{13}\]  
In general, AccU in classification problems is the total number of correctly predicted ASD and NC subjects versus the total observed data. SenS measures the effectiveness of the proposed models in correct identification of the WC images of ASD, and SpeC measures the effectiveness of the models in identifying WC images of NC. Moreover, the receiver operating characteristic curve (ROC) and the area under curve (AUC) are also used for better visualization of the performance of a binary classifier. The ROC is a representation of sensitivity to specificity, and the AUC shows how well a method makes positive and negative categorical distinctions. A larger AUC is better and indicates the model's ability to distinguish between ASD and NC.  
Figure 5: Structure of 3L-CNN for ASD diagnosis using FC-images of BOLD rs-fMRI signals.
# Dynamic Graph Representation Learning for Passenger Behavior Prediction
## 4 Methodology
### Temporal Sequence Construction
#### 4.1.1 Extracting Historical Sequences  
The sequence of historical neighbors always reflects the temporal patterns of nodes in the dynamic graphs [18; 21; 23]. To capture the evolving patterns for passengers and stations, we extract a fixed number of first-order neighbors from an infinitely long historical interaction sequence and construct a corresponding interaction sequence. Formally, this can be expressed as follows: Given a potential interaction \(e=(u,s,l,t)\) at time \(t\), we extract the corresponding interaction sequences \(\mathcal{Q}^{l}_{u}=\{(u,s^{\prime},l^{\prime},t^{\prime})\mid t^{\prime}<t\} \cup\{(u,u,0,t)\}\) and \(\mathcal{Q}^{l}_{s}=\{(u^{\prime},s,l^{\prime},t^{\prime})\mid t^{\prime}<t\} \cup\{(s,s,0,t)\}\) for the passenger node \(u\) and the station node \(s\) with label \(l\) before time \(t\). Specifically, we consider that the state of passengers and stations at time \(t\) is related to their characteristics, so we treat the node itself as a neighbor and include it in the model. To accurately represent the current node features, we sort the interactions by time and select the \(N\) recently interacted neighbors up to time \(t\). For nodes with fewer neighbors, we use a zero-padding technique to expand the historical neighbors, ensuring that each node has \(N\) neighbors. This method improves the efficiency of the model without sacrificing accuracy and maintains the stability of model training.
# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation
### Prototypes Implementation  
Two different prototypes were developed in order to evaluate the performance of the framework using various implementation types, network infrastructures and data retrieval mechanisms. Table 5 summarizes the main features of both prototypes. The Prototype I was implemented using the Unity development platform [43], whereas the Prototype II was implemented as a native Android application. Provided that Unity is a cross-layer platform, the Prototype II was deployed for both Android and Windows OS.  
Concerning the network infrastructures, the two prototypes provide distinct methods to create peer-to-peer proximity networks among MASU units. The Prototype I allows a group of units to connect to an existing AP, while the other configures automatically the first unit that joins the activity as AP to provide wireless access for the whole group of units. As a result, AP-based (AP) and Terminal-to-Terminal (T2T) networks are established, respectively. In both prototypes, the first unit that joins the network uses AllJoyn to create the peer-to-peer proximity network. Such a unit will also act as Manager for the overall duration of the collaborative sensing activity. Therefore, we have a Fixed Centralized role architecture of the Control Tier without any mechanism for the dynamic selection and activation of roles in such a tier. For this reason, these prototype implementations were useful only to evaluate the Sensing Tier of the MASU framework.
# An Advanced Path Planning and UAV Relay System: Enhancing Connectivity in Rural Environments
## 3 Results and Discussion  
The proposed approach was tested to verify its effectiveness in maximizing the transmission coverage. The UAV used in this research is a long search and rescue/military/surveying drone.
# Floquet Modification of the Bandgaps and Energy Spectrum in Flat-Band Pseudospin-1 Dirac Materials
## 1 Introduction
The presence of an unusual band structure for the \(\alpha\)-\(\mathcal{T}_{3}\) model can lead to unique and unexpected features, including electronic [58; 59; 60], collective [61; 62; 63], magnetic [64], and optical properties [65; 66; 67; 68; 69], and even a phase transition [70]. These behaviors are very different from those of graphene. The major issue addressed by many researchers has been how these graphene-like properties will be modified by the presence of an additional flat band [71; 72; 73]. Already, a variety of materials, with their electronic properties described approximately by an \(\alpha\)-\(\mathcal{T}_{3}\) model, have already been identified and made by synthesis; meanwhile, the presence of such a stable flat-band in their energy spectrum has been experimentally verified. Particularly, the so-called Lieb lattice, which has already been demonstrated in a number of different systems and wave-guides [74; 75; 76], presents a new type of technologically promising pseudospin-1 Hamiltonian, and its corresponding inverted band structure reveals a finite bandgap as well as a flat band which lies within this bandgap but intersects the upper (valence) band at its bottom [77; 78; 79]. Apart from that, there are multiple applications of Dirac materials, such as a temperature-sensitive multi-band grating metamaterial absorption device [80] and a dual-tunable broadband metamaterial absorber using bulk Dirac semimetal in the terahertz frequency range [81]. The remaining part of this paper is organized as follows. In Section 2, we review the electronic properties, including energy band structure, and electronic states of \(\alpha\)-\(\mathcal{T}_{3}\) materials, a dice lattice, and a Lieb lattice. Specifically, we discuss the properties of an \(\alpha\)-\(\mathcal{T}_{3}\) model with a fixed finite bandgap induced by a dielectric substrate. Moreover, the electron dressed states due to an applied off-resonance circularly and, more generally, elliptically polarized irradiation, including related derivations, equations, and mathematical formalism, are all presented in Section 3. Section 4 is devoted to a detailed discussion on our numerical results and their connections to the band structure of electronic dressed states associated with different materials. Our concluding remarks are made in the final Section 5.
## 3 Results and Discussion
### Determination of Relative Recovery for Dilution Solutions

Table 4: Relative recoveries of four target TSNAs in relation to the type of extraction solution (AA and CAN) and concentration levels of spiking (50, 250, and 500 ng mL\({}^{-1}\)) in L-S2 solution: Here, the dilution factor was fixed at 50 fold. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Order**} & \multirow{2}{*}{**Sample Code a1**} & \multicolumn{5}{c}{**Compounds**} \\ \cline{3-6}  & & **NNN** & **NNK** & **NAT** & **NAB** \\ \hline \multicolumn{6}{l}{(A) Theoretical concentration of TSNA in the spiked sample diluted with two solutions (ng mL\({}^{-1}\)) b  } \\
1 & Spiked sample & 103 & 103 & 101 & 103 \\ \hline \multicolumn{6}{l}{(B) Detected concentration of TSNA in the spiked sample by LC-MS/MS (ng mL\({}^{-1}\)) b  } \\
1 & L-AA-100 & 1.19 & 0.92 & 0.93 & 1.07 \\
2 & L-AA-50 & 2.35 & 1.98 & 1.89 & 2.33 \\
3 & L-AA-20 & 7.00 & 5.83 & 5.46 & 7.04 \\
4 & L-ACN-100 & 1.63 & 1.21 & 1.18 & 1.37 \\
5 & L-ACN-50 & 3.08 & 2.59 & 2.38 & 2.93 \\
6 & L-ACN-20 & 8.61 & 6.97 & 6.60 & 7.80 \\ \hline \multicolumn{6}{l}{(C) Relative recovery, RR (\%) b  } \\
1 & L-AA-100 & 115 & 88.7 & 91.3 & 104 \\
2 & L-AA-50 & 114 & 95.7 & 93.5 & 113 \\
3 & L-AA-20 & 135 & 113 & 108 & 136 \\
4 & L-ACN-100 & 158 & 117 & 117 & 133 \\
5 & L-ACN-50 & 149 & 125 & 118 & 142 \\
6 & L-ACN-20 & 167 & 135 & 130 & 151 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Relative recoveries of four target TSNAs in relation to the type of extraction solution (ammonium acetate (AA) and acetonitrile (ACN)) and dilution factor (20, 50, and 100 fold) using one point spiking of TSNA in L-S1 solution.
## 2 Materials and Methods
### Discussion  
The overall results are presented in Tables 5-8 and they show that it is possible to obtain concretes with a very satisfactory mechanical strength and a low water absorption capability if sand is completely substituted with slag and fly-ashes, or with slag and microsilica.  
The methods used to proportion the mix design, suitably refined through preliminary tests, have proved to be valid. From the results it is evident that the pozzolanic activity of fly-ashes, and especially silica fume, played a key role in reaching a very good compressive strength [51, 52, 53]. In fact, water/cement ratios are very high in both cases: these ratios, if utilized for an ordinary concrete mix without the pozzolanic activity of microsilica and fly-ashes, would have hardly given the same high strength values [54, 55]. For this reason, it is worth considering the water/(fly-ashes + cement) and water/(microsilica + cement) ratios.  
Higher compressive strength is achieved utilizing silica fume instead of fly-ashes [56]. In fact, it is known that the pozzolanic activity of concretes added with SF is higher with respect to other materials, as demonstrated also by [PERSON] and [PERSON] [57]. This is due to the higher pozzolanic activity and the smaller sizes of microsilica granules, even if more quantities of water and super-plasticizer are necessary to get the same consistency of fly-ashes concretes. In fact, the water/cement ratio from the value of 0.65 for concretes with slag and fly-ashes, reaches the value of 0.87 for concretes with slag and microsilica; the water/(fly-ashes + cement) ratio is 0.47, while water/(microsilica + cement) ratio is 0.63.  
From the results of the tests it has been also possible to notice that microsilica concretes, apart from the higher mechanical strength, present a specific weight lower than for slag and fly-ashes concretes. Moreover, it seems that for both concretes the tensile strength is consistent with the compressive strength, while bending strength is rather low, especially for slag and microsilica concretes. Similarly,  
Figure 6: Percentage of water absorbed by the specimens.  
the longitudinal elastic moduli are not very high in comparison to the compressive strength and are lower than in an ordinary concrete of the same strength. It is probably due to the discontinuous composition of the aggregates utilized in the mix design and to the higher amount of mortar.  
Finally, the water absorption test results showed that the mean values of water absorbed by the different concrete specimens were lower for slag and fly-ashes (or microsilica) concretes in comparison with ordinary concrete, and slag and microsilica concretes with respect to slag and fly-ashes concretes (Table 8, Figures 5 and 6).  
In summary, these concretes that use by-products and scoriae of industrial production have mechanical properties comparable with a high strength ordinary concrete. Moreover, they show a lower water absorption capacity and a higher durability with the advantage to improve re-use and recycling of waste materials.
## 3 Results and Discussion
### Effect of Steel Fibers and Steel Slag on the Thermal Parameters of Asphalt Mixtures  
The thermal parameters of asphalt mixtures modified with steel fiber or/and steel slag were shown in Table 3. Compared with BA, the thermal conductivity and thermal diffusion of SF increased by 10.2% and 16.3% respectively, while the volumetric heat capacity decreased by 5.3%. The thermal conductivity and thermal diffusion of SF + SS increased by 6.6% and 18.1% respectively, while the volumetric heat capacity decreased by 9.7%. It means that steel fiber increased the thermal conductivity and thermal diffusion while decreased the volumetric heat capacity of asphalt mixture. Steel slag showed a reverse effect on the thermal parameters of asphalt mixture. The thermal conductivity and thermal diffusion of asphalt mixture decreased by 5.7% and 18.3% after adding steel slag in BA, and the volumetric heat capacity increased by 15.4%. It can be concluded that the addition of steel fiber and steel slag will influence the thermal parameters of asphalt mixture. However, the thermal parameters of SS + SF showed the least difference from BA due to the opposite effects of steel fibers and steel slag.  
The material properties of the mixtures are the main reason for the changes in the thermal constants. As a kind of metal material, steel fibers possess good conductivity and diffusion of heat. Compared with basalt stone and asphalt, the conductivity and thermal diffusion of steel fibers were higher. While the heat capacity of steel fibers is lower than that of basalt and asphalt. In spite of containing some metal elements, a large number of tiny pores within steel slag obstruct the heat transfer and diffusion, decreasing the conductivity and thermal diffusion of the mixture. The tiny pores also have a heat storage capacity, which helps for the increase of the heat capacity of the mixture. Due to the opposite effects of steel fibers and steel slag, the thermal parameters of SS + SF will be strongly related to the ratio of steel fibers and steel slag.
# Combinatorial Optimization Problems and Metaheuristics: Review, Challenges, Design, and Development
## 2 Materials and Methods
### Optimization  
Optimization can be summarized as a collection of techniques and algorithms [6; 8; 16; 21; 22] to find the best possible solution (optimal or near-optimal) iteratively. From a searching approach perspective, they can be organized into three classes of methods: (1) enumerative, (2) deterministic, and (3) stochastic [10; 23; 24; 25; 26].  
The enumerative methods are based on a brute force approach because it inspects all possible solutions in the search space. For an understandable reason, it always will find the optimal global solution. However, computationally speaking, this approach only works in small search spaces. On the other hand, the most challenging problems, with a vast search space, may require an enormous amount of time to list all solutions, something not feasible most of the time. In these cases, different approaches are required.  
The deterministic methods can be seen as an extension of the enumerative approach. They use knowledge of the problem to develop alternative ways to navigate search space. Deterministic methods are excellent problem-solvers for a wide variety of situations, but they are problem-dependent. Consequently, they have difficulties when it is not possible to apply the knowledge of the problem effectively. Deterministic methods receive this classification because their parameters' values and the initialization approach determine their result. In other words, if a particular configuration is used, the output solution will always be the same.  
The stochastic methods are problem-agnostic and include uncertainty. They move in the search space using a sophisticated navigation model, applying a probabilistic approach--random but guided by probabilities--to explore the search space [27; 28]. Since the stochastic methods are based on randomness aspects, the solution returned will almost always be different. Therefore, it is not possible to determine which will be the final solution and its performance [29]. They are an excellent alternative in scenarios where the data are unpredictable, the problem knowledge cannot be used, and the other approaches (enumerative and deterministic) do not have a good performance or are not feasible.
## 2 Materials and Methods  
### The Study Area  
The Metropolitan City of Naples (MCN) in the Campania region, Southern Italy, represents the former Province of Naples, divided into 92 municipalities (Figure 1). The Municipality of Naples is the administrative centre. The MCN covers about 1200 km\({}^{2}\) and has a population of about 3 million inhabitants. The study area includes some of the highest density of population among Italian municipalities (e.g., Portici and Casavatore have about 12,000 inhabitants/km\({}^{2}\)), two volcanic sites (Vesuvius and Phlegraean Fields), a long coastline, and urban areas that extend without interruption also across agricultural fields and green areas, thus presenting several types of land cover. The area falls under a Mediterranean climate zone, therefore characterized by Mediterranean vegetation, with diversified agrarian production (potato, peach, apricot, tomato, fernel, plum, grapes, tomato, cauliflower, broccoli, and strawberry) [50].  
The area accounts for three state-owned forests (Phlegraean Area, Mount Cumae's and Roccaranola's forests), which cover more than 1000 ha mainly with elm, elder, hawthorn, ash, chestnut, alder, and beech tree species [51]. The municipality of Naples includes 53 different public and private parks and green areas, almost entirely managed by municipal services, presenting a tree population of above 60,000 elements (very common _Pinus pinea_ to different kinds of citrus trees, _Ginko biloha, Olea europaea_, _Cupressus_, and _Ficus_), with a large part of trees aged more than 50 years. Their management within the urban context represents a challenge for administrators [52]. Between 2011 and 2015, the number of new plantations exceeded the number of cuts of trees by 1.58%. However, Naples is 88 th among the 105 Italian provincial chief towns, having just six trees per 100 inhabitants [53]. Due to high urbanization, the urban tree stock is minimal.  
The most represented cover classes within the MCN 2018 land cover, assessed by CORINE Land Cover [54; 55], are the broad-leaved forest (\(\approx\)21%), the non-irrigated arable land (\(\approx\)17%), the complex cultivation patterns (\(\approx\)15%), the fruit trees and berry plantations (\(\approx\)12%), the continuous and discontinuous urban fabric (respectively \(\approx\)12% and \(\approx\)8%), and the industrial or commercial units (\(\approx\)2.5%)--confirming the strong agricultural and naturalistic tendency of the investigated area (65% of the territory).