esa-sceva/satcom-chunk-collection huggingface dataset:
## 2 Syntheses of \(\alpha\),\(\omega\)-Dialkoxyfluoropolyethers
### 2.3. Preparation of DA-FPEs by DAF-PFPE Alkylation  
Finally, hydrofluoropolyethers DA-FPEs are prepared through the reaction of diacyl fluoride PFPEs (DAF-PFPEs) with electrophiles in the presence of a source of fluorine ions, generally, metal fluorides. This process actually comprises two steps (Figure 4): Firstly, in aprotic polar solvent, DAF-PFPEs react with fluoride ions released from metal fluorides to form metal perfluoroalkoxides M\({}^{+}\)OCF\({}_{2}\)RFCF\({}_{2}\)O\({}^{-}\)M\({}^{+}\) as intermediates; secondly, metal perfluoroalkoxide is alkylated by a suitable alkylating agent X-R\({}_{\mathrm{H}}\) leading to the introduction of alkoxy groups at the end of the perfluoropolyether chain.  
Figure 3: Deperoxidation of P-PFPEs by chemical reduction.  
Figure 2: Deperoxidation of P-PFPEs by thermal treatment.  
Metal perfluoroalkoxides are known as compounds characterized by low nucleophilicity [15] and they decompose into acylfluorides and metal fluorides, generally around room temperature. In order to increase the stability and the nucleophilic strength of these species, polar aprotic solvents (e.g., diglyme, tetraglyme) are generally required. The stability of these species depends on the nature of metal fluoride [16], temperature, and the solvent [17]. Metal perfluoroalkoxides are also important industrial intermediates in the synthesis of hypofluorites like CF\({}_{3}\)CF\({}_{2}\)OF [18] and fluorinated vinyllethers [19, 20]. Completely anhydrous reaction conditions are required, because in presence of water the hydrolysis of acyl fluoride groups generates hydrofluoric acid, HF, which can be absorbed by the solid metal fluoride giving MHF\({}_{2}\)[16]. Because of the low nucleophilicity of perfluoroalkoxides, strong alkylating agents with high electrophilicity are needed to effectively react with metal perfluoroalkoxides and form DA-FPEs.
## 3 Results
### Study of a Frame-Type Structure
#### 3.2.3 Case Study 3--Frame-Type Structure with Different Square Cross-Sections
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Cluster** & **Runs** & **b1 [mm]** & **b2 [mm]** & **b3 [mm]** \\ \hline
1 & 1, 4, 5, 6, 7, 8, 10 & 88.9381 & 79.7473 & 27.1625 \\  & & (85.66–93.17) & (69.55–92.51) & (19.38–32.92) \\
2 & 2, 3, 9 & 96.5968 & 51.4528 & 20.2318 \\  & & (93.17–99.34) & (26.64–75.86) & (19.38–21.21) \\ \hline \hline \end{tabular}
\end{table} 
Table 18: Cluster centroids for the optimization process with mass constraint, using 30 individuals and 100 iterations in each run. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Run** & \begin{tabular}{c} **Best Solution** \\ **(b1) [mm]** \\ \end{tabular} & \begin{tabular}{c} **Best Solution** \\ **(b2) [mm]** \\ \end{tabular} & \begin{tabular}{c} **Best Solution** \\ **(b3) [mm]** \\ \end{tabular} & \
\begin{tabular}{c} **Displacement** \\ **[mm]** \\ \end{tabular} &
\begin{tabular}{c} **Fundamental** \\ **Frequency [Hz]** \\ \end{tabular} & **Mass [kg]** \\ \hline
3 & 97.2821 & 51.8613 & 21.2072 & 8.7075 & 18.4558 & 999.7722 \\
4 & 91.1751 & 72.4751 & 24.3450 & 8.8589 & 21.2195 & 965.5522 \\
5 & 89.2036 & 73.1721 & 26.5999 & 9.2525 & 23.1640 & 945.5724 \\
6 & 89.4786 & 80.1648 & 26.1629 & 8.7600 & 22.8042 & 973.2495 \\
7 & 87.1829 & 92.5082 & 27.4418 & 8.7934 & 23.9280 & 991.9190 \\
8 & 92.4133 & 69.5464 & 24.7362 & 8.6579 & 21.5500 & 979.0511 \\
9 & 93.1660 & 75.8590 & 19.3753 & 8.7174 & 16.9155 & 990.8795 \\
10 & 87.4491 & 83.7596 & 32.9157 & 8.8326 & 28.6142 & 992.0630 \\ \hline \hline \end{tabular}
\end{table} 
Table 17: _Cont._  
Figure 6: Clusters for the optimal solutions dataset in the optimization process using 20 individuals and 150 iterations in each run. deviation. Cluster 2 is characterized by lower \(b3\) values while Cluster 1 possesses slightly higher \(b3\) values. The major difference between the two clusters is related to the variables \(b1\) and \(b2\). Under this constraint the design variable \(b2\) has assumed a more influential role although not as much as the \(b3\) variable played in the previous situations. The masses of the structures are now not significantly different in the two clusters; however, the fundamental frequencies in Cluster 2 are lower than the ones in Cluster 1's structures.
# Enabling Technologies for Urban Smart Mobility: Recent Trends, Opportunities and Challenges
## 2 Research Progress in Smart Mobility and Gap Analysis
### Research Based on Smart Mobility Applications
In [10], the authors have presented a framework to aid in the efficient designing of a traffic light network in an urban setting to minimize traffic jams. The framework \"HITUL\" assists in the decision making of traffic control management by determining the ideal traffic light schemes by employing micro-simulations and bio-inspired methods. The authors have evaluated HITUL through a case study of the Spanish city of Malaga and observed that it is an effective technique towards minimizing traffic jams. Moreover, the authors have used \"Simulator of Urban Mobility (SUMO)\" to obtain arrangements of practical scenarios based on actual mobility trends in a city. In [11], the authors attempt to demonstrate the spectrum of development of \"Spanish Smart City\" measures with a view to mobility and environmental concerns. The authors have conducted a study in 62 cities of \"Spanish Smart Cities Network (RECI)\" and provided a synergistic map displaying a comprehensive evaluation and advancement of the cities on the basis of demographic and socioeconomic variables. The findings indicate that smart mobility is a vital element of smart cities and smart environment has poor outcomes in Spanish cities. A novel approach to predict the occupancy rate of car parking space has been given [12], which is premised on deep learning with Recurrent Neural Networks (RNNs). The authors have presented two metaheuristic techniques to optimize the performance of the RNN design, one based on Genetic Algorithms (GA) and the other based on Evolution Strategy (ES). The authors have examined the occupancy rates of 29 car parks in Birmingham, UK and observed that the proposed approach is more useful and exceeds the performance of available competition.
## 2 Theoretical Foundations
### Heuristics
#### 2.2.3 Hyper-Heuristics  
Hyper-Heuristics (HHs) are high-level methods that operate at a higher level of abstraction than metaheuristics and share several commonalities. Among their differences, the most distinctive one, at least for this work, is the domain where they operate. While an MH deals with continuous optimisation problems, an HH tackles a combinatorial problem domain because it is a \"heuristic choosing heuristics\" [29]. In the following lines, we define a hyper-heuristic using the previously described terms.  
**Definition 9** (Hyper-Heuristic).: _Let \(\vec{h}\in H\subseteq\mathfrak{H}^{\varnothing}\) be a heuristic configuration from a feasible heuristic collection \(H\) within the heuristic space \(\mathfrak{H}\) (cf. Definition 4). Let \(Q(\vec{h}|\mathfrak{X},f):H\times\mathfrak{X}\mapsto\mathbb{R}_{+}\) be a metric that measures the performance of \(\vec{h}\) when it is applied on \((\mathfrak{X},f)\), cf. Definition 1. Therefore, let the hyper-heuristic (HH) be a technique that solves_  
\[\vec{h}_{*}=\operatorname*{argmax}_{\vec{h}\in H}\Bigl{\{}Q(\vec{h}|\mathfrak{ X},f)\Bigr{\}}. \tag{4}\]  
_In other words, the HH searches for the optimal heuristic configuration \(\vec{h}_{*}\) that best approaches to the solution of \((\mathfrak{X},f)\) with the maximal performance \(Q_{max}=Q(\vec{h}_{*}|\mathfrak{X},f)\)._  
**Remark 4** (Heuristic Configuration).: _When performing a hyper-heuristic process, a heuristic configuration \(\vec{h}\in\mathfrak{H}^{\varnothing}\) is a way of referring to a metaheuristic (cf. Definition 8)._  
**Remark 5** (Performance Metric).: _There is no unique expression for determining the performance \(Q(\vec{h}|\mathfrak{X},f)\) since it depends on the desired requirements for the heuristic sequence \(\vec{h}\). A numerical and practical way to assess this measurement, due to the stochastic nature of almost all the heuristic sequences, is to combine different statistics from several independent runs of \(\vec{h}\) over the same problem \((\mathfrak{X},f)\). For example, the negative sum of median and interquartile range of the last fitness values \(f(\vec{x}_{s,r})\) achieved in the runs, \(r=1,\ldots,N_{r}\). More elaborated formulae could include other fitness statistics, time measurements, stagnation records, and so on._
## 3 Mobile Object-Following System Using LiDAR
### Angle Adjustment Based on Distance for Optimal Tracking  
A mechanism for dynamically adjusting the perception angle range based on the relative distance to the target was implemented for the object-following process using the LiDAR sensor. Given the frequent presence of obstructive elements in outdoor fieldwork and the increased potential for object loss as distance increases, it is essential for the platform to maintain high close-range tracking performance [25; 26; 27]. Therefore, a broader angle range was applied as the target moved closer to ensure it remained within the detection range.  
For instance, as shown in Figure 9, the range from 0.7 m to 1.0 m from the robot was defined as L1, where a wide angle of \(\pm 45^{\circ}\) was applied. For distances of 1.0 m to 1.5 m (L2), an angle range of \(\pm 30^{\circ}\) was set, and for distances exceeding 1.5 m (L3), a narrower range of \(\pm 15^{\circ}\) was applied.  
Figure 8: Tracked object identifier classification pseudocode.  
Figure 9: Angle adjustment based on distance for optimal tracking diagram.
## 3 Finite-Time ANFTSMC Design
### Controller Design Procedure
#### 3.2.1 NFTSMC Design
 \tag{21b}\]  
To compensate for the influence of environmental interferences and model parameter perturbations, a switching control term based on the exponential reaching law is adopted in the switching control item, as shown below:  
\[u_{sw}=-cs\dot{s}sign(s) \tag{22}\]  
where \(c\) and \(K=D+\delta\), respectively, represent the switching gain [35]. \(D\) is the upper bound of lumped uncertainty mentioned in Assumption 1, and \(D\) is known by default in the traditional sliding-mode controller design process. Therefore, the position and attitude switching control laws of the HAUV are as follows:  
\[ \begin{array}{l}U_{2 sw}=I_{zw}(-c_{1}s_{1}-K_{1}sign(s_{1}))=I_{zw}(-c_{1}s_ {1}-(\delta_{01}+\delta_{11}|e_{1}|+\delta_{21}|e_{2}|+\delta_{1})sign(s_{1})) \\ U_{3 sw}=I_{yw}(-c_{3}s_{3}-K_{3}sign(s_{3}))=I_{yw}(-c_{3}s_{3}-(\delta_{03}+ \delta_{13}|e_{3}|+\delta_{23}|e_{4}|+\delta_{3})sign(s_{3}))\\ U_{4 sw}=I_{zw}(-c_{5}s_{5}-K_{5}sign(s_{5}))=I_{zw}(-c_{5}s_{5}-(\delta_{05}+ \delta_{15}|e_{5}|+\delta_{25}|e_{6}|+\delta_{5})sign(s_{5}))\end{array}  \tag{23a}\] \[ \begin{array}{l}v_{xsw}=-c_{7}s_{7}-K_{7}sign(s_{7})=-c_{7}s_{7}-(\delta_{07}+ \delta_{17}|e_{7}|+\delta_{27}|e_{8}|+\delta_{7})sign(s_{7})\\ v_{ysw}=-c_{9}s_{9}-K_{9}sign(s_{9})=-c_{9}s_{9}-(\delta_{09}+\delta_{19}|e_{9}| +\delta_{29}|e_{10}|+\delta_{9})sign(s_{9})\\ v_{zsw}=-c_{11}s_{11}-K_{11}sign(s_{11})=-c_{11}s_{11}-(\delta_{011}+\delta_{11 1}|e_{11}|+\delta_{211}|e_{12}|+\delta_{11})sign(s_{11})\end{array}  \tag{23b}\]  
where \(\delta_{i}(i=1,3,5,\cdots,11)\) are positive constants. Therefore, the complete controller can be written as follows:  
\[\mathbf{U}_{\Theta}=\mathbf{U}_{\Theta eq}+\mathbf{U}_{\Theta sw} \tag{24a}\] \[\mathbf{v}=\mathbf{v}_{eq}+\mathbf{v}_{sw} \tag{24b}\]  
where \(\mathbf{U}_{\Theta eq}=\left[U_{2 eq},U_{3 eq},U_{4 eq}\right]^{\text{T}}\), \(\mathbf{v}_{eq}=\left[v_{xeq},v_{yeq},v_{zeq}\right]^{\text{T}}\), \(\mathbf{U}_{\Theta sw}=\left[U_{2 sw}\quad U_{3 sw}\quad U_{4 sw}\right]^{\text{T}}\), and \(\mathbf{v}_{sw}=\left[v_{xsw}\quad v_{ysw}\quad v_{zsw}\right]^{\text{T}}\) are the attitude and position equivalent to the control and switching control vectors.
# Design and Validation of a Cascading Vector Tracking Loop in High Dynamic Environments
## 3 Results
### Results Analysis
#### 3.2.2 Signal Analysis Based on the Simulator
After the static testing, Figure 13(a) shows the acceleration changes of the X-axis and Z-axis with time under the \(-100\) g highly dynamic environment. Given the constant acceleration, the graphs of the acceleration results are intuitive. After the initial oscillation of approximately \(100\) ms (the same as the result in Figure 10(b)), the accelerations of both axes rapidly converge to \(0\) m/s\({}^{2}\). Figure 13(b) shows the changes in Y-axis acceleration with time under the current dynamic condition. With an acceleration of \(-100\) g in the Y-axis direction, the final convergence of acceleration in Figure 13(b) is approximately \(-1000\) m/s\({}^{2}\) (in the navigation signal simulator, g is \(10\) m/s\({}^{2}\)). Figures 11, 13 and 14 indicates that the convergence process of the entire vector tracking loop is completed within approximately \(100\) ms, which is not significantly different from the convergence time in the static environment. Therefore, no obvious linear relationship exists between the time required for the vector tracking mode to stabilize and the current dynamic change of the aircraft. Figure 14: Vector loop tracking results at the acceleration of \(-100\) g.  
Figure 13: Velocity results in static environment.
# Evaluation of a Novel Condition Indicator with Comparative Application to Hypoid Gear Failures
## 2 Test Methodology
### Test Facility  
For effective and responsive testing of hypoid gears, a test bed capable of adjusting and maintaining the system variables while also supporting collection of crucial data is essential. The unique test bed utilized for this is the so-called \"Specialized Gear Rig\" (Figure 3), which is located at the AVL-TU Graz Transmission Center [21]. This rig is capable of applying a wide range of speeds and torques for isolated component/failure mode testing, as well as conditioning of the sample environment and responsive adjustment of EPGa during operation.  
This rig provides a valuable platform for development of a condition monitoring system as it allows the effective isolation of failure modes by providing a highly controlled and responsive test condition and flexibility in the test set-up. Tooth root bending fatigue is one failure mode that can be isolated easily at the test bed, where condition monitoring and crack detection play an important role.  
In addition to failure mode isolation, the rig also allows the measurement of the unloaded (kinematic) TE or loaded (dynamic) TE through high-resolution angular position instrumentation. The measurement of TE provides an additional variable that can be utilized by a condition monitoring system for the evaluation of system health. The task of dynamic TE measurement was handled by the CM System (CMS); however, the TE results with respect to the NVH behavior and vibration spectra will not be presented in this work. TE will be presented in the context of a variable, which can be used for CM.  
The layout of the test bed is presented in Figure 4. The gears are driven by two dynos, coupled to two different gearboxes with belt drives. The pinion gear was driven by a 132 kW motor, with a rated rotational speed of 1484 RPM, connected to a gearbox with  
Figure 3: Specialized gear rig.  
a ratio of 3.52. The crown gear was connected to a gearbox with a ratio of 7.12, coupled to a motor with a power of 200 kW and a rated speed of 1489 RPM. During testing, the gears were covered in a housing (shown in the pictures), which is connected to an oil conditioning unit. Inside the housing, an oil jet is directed into the gear mesh, which delivers conditioned lubricant.  
As depicted in Figure 4, the testing is controlled by the test bed control system, which is connected to the dynos, control system of E, P, G, \(\alpha\), oil conditioning and CMS.
## 3 Activation Functions  
As described in the Introduction, this paper explores classifying medical imagery using combinations of some of the best performing activation functions on two widely used high-performance CNN architectures: VGG16 [48] and ResNet50 [49], each pre-trained on ImageNet. VGG16 [48], also known as the OxfordNet, is the second-place winner in the ILSVRC 2014 competition and was one of the deepest neural networks produced at that time. The input into VGG16 passes through stacks of convolutional layers, with filters having small receptive fields. Stacking these layers is similar in effect to CNNs having larger convolutional filters, but the stacks involve fewer parameters and are thus more efficient. ResNet50 [49], winner of the ILSVRC 2015 contest and now a popular network, is a CNN with fifty layers known for its skip connections that sum the input of a block to its output, a technique that promotes gradient propagation and that propagates lower-level information to higher level layers.  
The remainder of this section mathematically describes and discusses the twenty activation functions investigated in this study: ReLU [50], Leaky ReLU [62], ELU [54], SELU [55], PReLU [56], APLU [57], SReLU [63], MeLU [61], Splash [64], Mish [65], PDELU [66], Swish [60], Soft Learnable [67], SRS [67], and GaLU ([68]), as well as the novel activation functions proposed here: 2D Mexican ReLU; TanELU; MeLU + GaLU; Symmetric ReLU; Symmetric GaLU; Flexible MeLU.  
The main advantage of these more complex activation functions with learnable parameters is that they can better learn the abstract features through nonlinear transformations. This is a generic characteristic of learnable activation functions, well known in shallow networks [69]. The main disadvantage is that activation functions with several learnable parameters need large data sets for training.  
A further rationale for our proposed activation functions is to create activation functions that are quite different from each other to improve performance in ensembles; for this reason, we have developed the 2D MeLU, which is quite different from standard activation functions.
# Managing a Smart City Integrated Model through Smart Program Management
## 2 Smart City Worldwide Overview  
The development of the Smart City paradigm has seen in recent years, the implementation of various international and national initiatives. There are several cases of Smart cities and all are examples of how a city of any size can be made more efficient, clean, livable, and functional. Citizens or social organizations, new technologies, and business or public administrations are all actors that should contribute together to the design and development of a Smart city. The role assigned to these actors makes a difference.  
In a worldwide Smart city ranking [15], 102 cities were selected and classified according to a series of indicators, including social inclusion, environment, technological innovation, infrastructure, services to citizens and businesses, entertainment, cultural offer, problems management such as pollution, waste and supply of energy, and water resources.  
Singapore is the world's smartest city that adapts to smart technology integration, especially for its high scores on safety, air quality, and low congestion.  
In second position is Zurich, the first European city that shows high scores for public transport, access to health services, and cultural offerings. Oslo follows Zurich in third, developing an advanced and widespread circular economy, experimenting with electronic e-voting systems (i.e., government), and mobility based on cycling rather than private cars.  
The first of the Italian cities is Bologna (18 th), followed by Milan (22 nd), and then Rome, which occupies the 77 th position in the ranking.  
It can be seen that the size of the cities does not affect the ranking, indeed the top 20 Smart cities are small to medium centers: Copenhagen, Auckland, Taipei, Helsinki, Dusseldorf, Vancouver, Montreal. This is because in large cities the distances between public administrations and citizens are enormous. Consequently, it emerges that there is no confrontation and dialogue between the parties and many of the technological solutions that are adopted remain almost unknown to those who should reap the benefits, that is, the citizens. There is a large number of Smart city applications and platforms already available, but they are either not known to the general public, are not active or are not used.  
Following this worldwide analysis, we focused our attention on the Italian context and carried out a detailed analysis with reference to the Smart Index [16].
## 1 Introduction  
Perooxidases are oxidative enzymes with the capacity to oxidize several recalcitrant compounds. They are characterized by several biotechnological potentials spanning different industries includingtexttiles, paper and pulp, chemicals, water and cosme [1; 2]. However, the industrial applications of peroxididases are hindered by high production cost, poor yield [3; 4] and enzyme instability due to increased concentration of hydrogen peroxide [5].  
Given the diverse application potentials of peroxididases, increased demand is inevitable. This has, therefore, necessitated the search for organisms with improved extracellular peroxidase production capacity. Over the years, fungi have been the most efficient producers of peroxididases [6; 7; 8]. Nevertheless, some bacteria, largely antibacterial, have also shown potential for peroxidase production [9; 10; 11; 12]. Peroxidases are also found in other living organisms but occur intracellularly and are produced in minute quantity. Exoprotolution of peroxidase is advantageous over the intracellular production as the purification and recovery of an exoenzyme is simplified while the isolation and purification of an intracellular enzyme is costly, complex and time-consuming [12]. Furthermore, the interest in microbial peroxididases has continued to increase as microbial enzymes seem to be more stable than enzymes of plant and animal origin [13].  
Ligninolytic bacteria hold a great potential for peroxidase production. Lignin degradation activities of these organisms are accompanied in particular by exoproduction of lignin-modifying enzymes (LMEs), predominantly peroxididases, as secondary metabolites. These enzymes can easily be harvested and exploited for various biotechnological applications.  
_Ensefer adherents_ NWODO-2 (KX640918) is a lignin-degrading bacterial strain recently isolated by the authors [14]. It belongs to the class: alpha-proteobacteria, one of the few classes of bacteria with lignin-degradation activities [15; 16]. The whole-genome sequencing of _Rhizobium_ sp. strain YS-1r, a lignin-degrading alpha proteobacteria, revealed the presence of a gene encoding the expression of dye-decolourizing peroxidase [17], an extracellular LME. Moreover, gene prediction analysis revealed the expression of 6 putative peroxidase genes in a _Klebsiella_ sp. strain BRL 6-2 [18], a ligninolytic gamma proteobacteria. Furthermore, production of peroxidase by another proteobacteria, _Pseudomonas_ species, has been reported [19]. It is clear that proteobacteria is an emerging ligninolytic bacteria with enormous potential for the production of LMEs including peroxididases. Our preliminary screening has revealed the peroxidase production potential of _Ensifer adherents_ NWODO-2 [14]. This study, therefore, seeks to determine the culture parameters for optimal exoperoxidase production by _Ensifer adherents_ NWODO-2 and characterize the enzyme using molecular approaches.