esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 Experimental Testing and Analysis
### Benchmark Test Functions
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multicolumn{1}{c}{**Function**} & **Dimension** & **Value Range** & **Best** \\ \hline \(f_{8}(x)=\sum\limits_{i=1}^{n}-x_{i}\sin\left(\sqrt{|x_{i}|}\right)\) & \(d\) & \([-500,\,500]\) & \(-418.9829\times d\)  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline  & **DSTSASA** & **STSASA** & **SCA** & **AOA** & **STA** & **AHA** & **FDA** & **AVOA** & **CNO** & **ASTSA** \\ \hline F1 & min & 0 & 0 & 2.2236\(\times 10^{-5}\) & 0 & 0 & 0 & 2.4699\(\times 10^{-2}\) & 0 & 6.5300\(\times 10^{-27}\) & 0 \\  & mean & 0 & 0 & 2.22250\(\times 10^{-5}\) & 0 & 0 & 0 & 8.2082\(\times 10^{-5}\) & 0 & 1.11559\(\times 10^{-10}\) & 0 \\  & add & 0 & 0 & 4.41827\(\times 10^{-5}\) & 0 & 0 & 0 & 1.6998\(\times 10^{-36}\) & 0 & 2.87125\(\times 10^{-10}\) & 0 \\  & R & 0 & 1 & 0 & 0 & 1.2082\(\times 10^{-5}\) & 0 & 0 & 1.5120\(\times 10^{-10}\) & 0 & 3.12725\(\times 10^{-10}\) & 2.7310\(\times 10^{-28}\) \\  & mean & 0 & 2.22995\(\times 10^{-5}\) & 0 & 0 & 0 & 5.7300\(\times 10^{-36}\) & 0 & 3.1200\(\times 10^{-10}\) & 0 & 3.10725\(\times 10^{-10}\) & 2.7310\(\times 10^{-28}\) \\  & add & 0 & 0 & 2.8710\(\times 10^{-5}\) & 0 & 0 & 0 & 9.7800\(\times 10^{-36}\) & 0 & 3.10725\(\times 10^{-10}\) & 0 \\  & add & 0 & 5.9714\(\times 10^{-5}\) & 0 & 0 & 0 & 9.7800\(\times 10^{-5}\) & 0 & 1.5020\(\times 10^{-10}\) & 0 \\  & 0 & 5.3488\(\times 10^{-5}\) & 0 & 1.8570\(\times 10^{-10}\) & 0 & 8.4484\(\times 10^{-5}\) & 0 & 8.4484\(\times 10^{-5}\) & 0 & 1.66451\(\times 10^{-10}\) & 2.4510\(\times 10^{-10}\) \\  & mean & 0 & 6.90646\(\times 10^{-5}\) & 1.46875\(\times 10^{-5}\) & 0 & 2.1171\(\times 10^{-5}\) & 0 & 1.1775\(\times 10^{-10}\) & 0 & 2.20731\(\times 10^{-10}\) & 2.2631\(\times 10^{-10}\) \\  & add & 0 & 2.2232\(\times 10^{-5}\) & 4.4729\(\times 10^{-5}\) & 0 & 4.6607\(\times 10^{-5}\) & 0 & 1.55480\(\times 10^{-5}\) & 0 & 8.13675\(\times 10^{-5}\) & 2.6631\(\times 10^{-10}\) \\  & 0 & 1 & 1 & 0 & 1 & 0 & 1 & 1 & 1.04806\(\times 10^{-5}\) & 0 & 1.10813\(\times 10^{-10}\) & 2.6631\(\times 10^{-10}\) \\  & 0 & 1 & 1 & 0 & 1 & 0 & 1 & 0.0
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline  & \multicolumn{2}{c}{**DSTSASA**} & \multicolumn{2}{c}{**STSASA**} & \multicolumn{2}{c}{**SCA**} & \multicolumn{2}{c}{**ACA**} & \multicolumn{2}{c}{**ACA**} & \multicolumn{2}{c}{**STA**} & \multicolumn{2}{c}{**ALA**} & \mul  
Overall, DRSTASA shows advantages over other algorithms in most tests, with results surpassing those of recent algorithms. Specifically, when compared to STASA and ASTASA, which scored 13 and 11 points, respectively, it is evident that DRSTASA achieved superior results in the majority of test cases. Although DRSTASA successfully escaped local optima in the multimodal test functions F21, F22, and F23, there remains a significant gap when compared to more robust algorithms such as AHA, FDA, and AVOA. While DRSTASA performed exceptionally well in unimodal tests, it slightly underperformed in multimodal tests relative to these stronger algorithms.
## 2 Materials and Methods
### Analysis of Visible and Thermal Imaging Data
#### 2.4.1 Visible and Thermal Data Co-Registration  
The first step of the developed procedure consisted of an optical co-registration between visible and thermal optics. The co-registration process was a fundamental step of the whole pipeline, since it allowed a proper mapping from an imaging coordinate system to another.  
The optical co-registration relied on procedures implemented in OpenCV [38], and it is described in depth in Reference [39]. A root mean square error (RMSE) value was provided by the co-registration procedure, thus indicating the accuracy in the coordinate transformation from visible to IR imagery at the specific distance of 1 m.
# Guidelines towards Information-Driven Mobility Management
## 4 Mobility in ICN
### Multi-Homing  
ICN supports end-user device multi-homing via the Face abstraction. Support for networked multi-homing is achievable via ICN multi-path forwarding strategies, and routing. The Face abstraction brings in the possibility to jointly explore data transfer to multiple services and applications as well as to physical interfaces. Moreover, multi-homing is supported with fine-grained control: the ICN per packet pull-based model provides better support for resource management aspects, such as load-balancing (based on packets instead of flows). The ICN forwarding strategies are applied on a local basis: different nodes and/or regions of nodes can have different forwarding strategies, which strengthens multi-homing support capability via ICN.  
Therefore, multi-homing is a mobility management process that is naturally supported by ICN approaches. In comparison, prior solutions required additional support of, for instance, _Quality of Service (QoS)_ mechanisms.
## 6 Analysis and Results  
### Simulation Setup and Results  
This section describes the simulation tool and the parameters chosen to simulate the routing protocols. All parameters used for the simulation are listed in Table 1. We conducted extensive simulations with different mobility traces and random runs to evaluate our proposed protocol. The mobility traces are generated using Simulation of Urban MObility (SUMO) tools for different vehicle speed limits in miles per hour (average speed from 15 Mph to 75 Mph) for the urban area. In NS3, we generate a static wireless sensor node topology in a Manhattan Grid with blocks of 500 m. NS3 is a discrete-event network simulator in which the simulation core and models are implemented in C++. Any routing protocol implementation in NS3 should provide two methods declared in ns3:Ipv4 Routing Protocol base class, RouteOutput and RouteInput. The first will deliver a valid route toward the destination for a specific packet to the transport protocol. The latter is called the Ipv4L3 Protocol, which is used when a packet  
Figure 5: Examplesfor a forwarder vehicle and the weight calculation for its neighbors. (**a**) Example 3. (**b**) Example 4.  
is received from a NetDevice, and appropriate action is taken to forward it either to the upper layers (local delivery) or to other nodes (forwarding). The architecture of NS3 internet stack and routing for IPv4 is described in the NS3 manual [27].
## 2 Materials and Methods
### Numerical Analysis  
Deform 2D software (SFTC, Columbus, OH, USA) was used for deep drawing simulations. Because of the cylindrical shape of tools and blanks, the process was simulated on an axis-symmetrical basis. The forming die and blank holder were simulated as rigid bodies in line with the limited objectives of the study; punches and blanks were simulated as plastic bodies. The use of 2D analysis and of rigid bodies for the forming die and blank holder resulted in reduced computation times, particularly in the case of RG. AISI 304 mechanical behaviour was taken from the Deform material database. Mechanical properties of CF Nylon were obtained from data in the literature [21; 22]. Screw housing was not simulated because of its required location in the tool center. Preliminary tests showed that this did not affect results, so the tools were designed fully. A constant speed of 10 mm/s was set for the punch. For RG and RP test simulations, loads of 18 kN and 1 kN, respectively, were assigned to the blank holder. A tetrahedral mesh was used for both blanks and punches; blanks were modelled with regular quadric mesh with an element size of 0.2 mm, while punches were modelled with adaptative mesh with a shorter element length of 0.4 mm in the region of the fillet radius. To simulate contact between punch and blank, a shear function was set. Drawing on previous studies, and taking lubrication into consideration, a value of 0.1 was adopted [23; 24]. A time discretization value of 0.1 was set for both RG and PG. All nodes in contact between the punch and press were locked to simulate mechanical assembly, and temperature was kept constant at 20 \({}^{\circ}\)C. Figure 3 shows the simulation setup for both processes.  
Figure 3: Setup of elements modelled for deep drawing simulations: (a) press; (b) punch; (c) punch with higher mesh magnification on the fillet radii; (d) blank; (e) blankholder; (f) matrix.  
At the end of the simulation analysis, the following results were evaluated:  
* Comparison of cup LP profiles (experimental, simulation, CAD);
* Punch load as a function of the stroke;
* Punch effective stress as a function of the stroke evaluated on a point in the middle of the fillet radius.
* Punch radial displacement.
# Silica Mesoporous Structures: Effective Nanocarriers in Drug Delivery and Nanocatalysts
## 3 Factors in the Synthesis of a Mesoporous Material
### Solvents  
Solvents also affect the synthesis of mesoporous materials and alter the shape of the particles. In this regard, the most effective and frequently used solvent is different types of alcoholic solvents. For instance, alcohols with high molecular weights and low evaporation rates have less influence on the shape and morphology of mesoporous materials [78]. On the other hand, alcohols increase the formation and change the sizes of the pores. Commonly, ethanol, propanol, butanol, and pentanol are used in the preparation of mesoporous materials. It should be noted that ethanol sometimes causes the collapse of mesopores' spherical shapes [78]. Alcohols, as solvents, can also alter the channel rotations of mesoporous materials. For instance, [PERSON] et al. have synthesized mesopores with cavity sizes of 10 to 1000 nm via making changes in the ratio of alcohol to water [80].  
Furthermore, solvents can also play a vital role in the removal of surfactants in the final step of the synthesis. [PERSON] et al. prevented the agglomeration of synthetic mesopores by removing surfactants with a solvent containing high-boiling-point alcohol [81]. [PERSON] et al. added alcohol as a solvent during the growth of radial MCM-48 to help in growing its cylindrical pores [80]. Alcohols, in addition to their roles as solvents, can also be used as _co_-solvents during syntheses [82]. Alcohols with larger chains make it possible to move to a new phase after a previous phase. [PERSON] et al. reported establishing a new lamellar phase after the hexagonal phase in MCM-41 after adding hexanol [83; 84; 85; 86; 87]. [PERSON] et al. added ethanol to the initial solution used by [PERSON] et al. [87] instead of hexanol [88]. Their results indicated that spherical mesoporous silica was present. [PERSON] et al. reported the effects of 2-alkanols on the morphologies and sizes of mesoporous materials. They succeeded in showing that in comparison with 1-alkanols, 2-alkanols, such as ethylene glycol (EG), resulted in the formation of particles of greater size and more ordered structural morphologies [84].
## 4 Models and Algorithm Design
### Price Function for Conventional Power Plant  
The cost function \(C_{c_{i}}(P_{c_{i}})\) can be chosen in the quadratic form, which is very often used within the EDP [8,25]:  
\[C_{c_{i}}(P_{c_{i}})=\frac{(P_{c_{i}}-\alpha_{c_{i}})^{2}}{2\beta_{c_{i}}}+ \gamma_{c_{i}}, \tag{21}\]  
where \(P_{c_{i}}\) denotes the power generated by the \(i\)-th generator. Generator parameters are set as \(\alpha_{c_{i}}\leq 0,\beta_{c_{i}}>0\) and \(\gamma_{c_{i}}\leq 0\). The manufacturer gives the parameters of the generators, or they can be experimentally measured and approximated. Then the incremental cost for the \(i\)-th generator is [25]:  
\[\frac{dC_{c_{i}}(P_{c_{i}})}{dP_{c_{i}}}=\frac{(P_{c_{i}}-\alpha_{c_{i}})}{ \beta_{c_{i}}}.\]  
And on that basis, the well-known solution to the EDP can be established, which is the equal incremental cost criterion [8,25]:  
\[P_{c_{i}}^{*}=\begin{cases}\frac{P_{c_{i}}-\alpha_{c_{i}}}{\beta_{c_{i}}}= \lambda^{*}&\text{for}\quad P_{c_{i}}^{\text{min}}<P_{c_{i}}<P_{c_{i}}^{\text{ max}},\\ \frac{P_{c_{i}}-\alpha_{c_{i}}}{\beta_{c_{i}}}<\lambda^{*}&\text{for}\quad P_ {c_{i}}=P_{c_{i}}^{\text{max}},\\ \frac{P_{c_{i}}-\alpha_{c_{i}}}{\beta_{c_{i}}}>\lambda^{*}&\text{for}\quad P_ {c_{i}}=P_{c_{i}}^{\text{min}},\end{cases}\]  
where \(P_{c_{i}}^{\text{min}}\) and \(P_{c_{i}}^{\text{max}}\) are the lower and upper bound of the \(i\)-th conventional generator.  
**Remark 1**: _The quadratic form of the price function can also be used for renewable sources or BESS. It depends on the designer's requirements._
## 2 Materials and Methods
### Drift Reduction Agents  
Previous studies on spray drift [28] have used chemical drift reduction agents, but these have not been tested in orchard sprays. It was decided that the 3 best-performing DRAs in the previous studies would be used in this study. Two of the DRAs used were anionic, DRA1 and DRA2, and non-ionic, DRA3. These substances were added to the injection water at a low concentration (0.1%). The viscosities, densities, and static and dynamic surface tensions of the DRAs and water were analyzed in the laboratories and determined.  
DRA1 (anionic polymer dispersion 100%) has a viscosity at 100% solution of 2000 mPa s, a density of 1.03 g cm\({}^{-3}\), a static surface tension of 31.6 mN m\({}^{-1}\), a dynamic surface tension of 69.7 mN m\({}^{-1}\) measured over a 50 ms time interval, and a dynamic surface tension of 69.2 mN m\({}^{-1}\) measured over 100 ms.  
DRA2 (calcium dodecylbenzenesulfonate 50%, butanol 18%) has a viscosity at 100% solution of 2300 mPa s, a density of 1.10 g cm\({}^{-3}\), a static surface tension of 30.5 mN m\({}^{-1}\), a dynamic surface tension measured over a 50 ms interval of 64.6 mN m\({}^{-1}\), and a dynamic surface tension measured over 100 ms of 63.7 mN m\({}^{-1}\).  
DRA3 (C10-13-alkyl derivatives, calcium salt 37%, butanol 15%) has a viscosity at 100% solution of 700 mPa s, a density of 1.03 g cm\({}^{-3}\), a static surface tension of 32.4 mN m\({}^{-1}\), a dynamic surface tension measured over a 50 ms interval of 69.6 mN m\({}^{-1}\), and a dynamic surface tension measured over 100 ms of 69.5 mN m\({}^{-1}\).  
The water used for spraying has a viscosity of 1.0 mPa s, a density of 1.10 g cm\({}^{-1}\), a static surface tension of 72.0 mN m\({}^{-1}\), a dynamic surface tension of 71.6 mN m\({}^{-1}\) measured over a 50 ms interval, and 71.5 mN m\({}^{-1}\) measured over a 100 ms interval.  
DRAs and water were tested with the SVM(tm) 3000 Stabinger Viscometer(tm) ([PERSON], Graz, Austria), and the numerical values of their viscosities were determined. Densities were determined by weighing a 100 mL sample. Static determination of surface tension was carried out with a Digital Tensiometer Easy Dyne (Kruss, Hamburg, Germany) tensiometer using the Wilhelmy plate method. The tension determined was equal to the average of the three measurements with a deviation of less than 1%. The dynamic surface tension was measured using a bubble pressure tensiometer BP50 (Kruss, Hamburg, Germany).
## Nomenclature  
\begin{tabular}{l l l}
**Symbol** & **Quantity** & **SI Unit** \\ \(W_{\varphi}\) & Wavelet transform & - \\ \(\omega_{0}\) & Wavelet angular frequency & rad/s \\ \(\sigma\) & Standard deviation & K \\ \(a\) & Wavelet scale & Hz\({}^{-1}\) \\ \(Amp\) & Amplitude of analytical heat flux & W/m\({}^{2}\)K \\ \(f\) & Frequency & Hz \\ \(f_{max}\) & Wavelet maximum frequency & Hz \\ \(f_{min}\) & Wavelet minimum frequency & Hz \\ \(g\) & General time-dependent signal & - \\ \(i\) & Imaginary part & - \\ \(k\) & Coefficient of conduction & W/mK \\ \(L_{udlib}\) & Length of the adiabatic section (within the observation window) & m \\ \(n\) & Channel number & - \\ \(N\) & Total number of time samples of the heat flux distributions & - \\ \(num\) & Total number of channels & - \\ \(P_{w}\) & Power spectrum magnitude & - \\ \(q\) & Wall-to-fluid heat flux per unit surface & W/m\({}^{2}\)K \\ \(Q\) & Heat load to the evaporator & W \\ \(q_{exact}\) & Synthetic heat flux & W/m\({}^{2}\)K \\ \(q_{Fou}\) & Heat exchanged between adjacent channels & W/m\({}^{2}\)K \\  & Power transferred by transverse conduction by a single channel, & \% \\ \(Q_{ratio}\) & divided by power transferrable from evaporator to condenser & \% \\ \(q_{restoral}\) & Restored synthetic heat flux & W/m\({}^{2}\)K \\ \(Q_{TRond}\) & Transverse conduction between channels pairs & W/m\({}^{2}\)K \\ \(t\) & Time & s \\ \(t_{a}\) & Activity time & \% \\ \(t_{c}\) & Continuity time & \% \\ \(T_{FPHP}\) & Device thickness & m \\ \(z\) & Axial coordinate & m \\ \(\eta\) & Dimensionless time & - \\ \(\tau\) & Wavelet time shift & s \\ \(\psi\) & Wavelet & - \\
**Subscripts, superscripts** & & \\ \(av\) & Average & \\ \(d\) & Dominant & \\ \(std\) & Standard deviation & \\ \end{tabular}
# Detection of Personality Traits Using Handwriting and Deep Learning
## 6 Discussion
### Limitations  
Although the results obtained are quite good, there are a number of aspects and limitations that must be taken into account when applying this method of determining personality traits:  
* Subjectivity of personality traits: Personality traits are abstract and subjective concepts, making it difficult to associate them with physical characteristics of handwriting. There are psychological studies that consider the number of personality traits to be in the hundreds, which makes it impossible to classify people into just a few classes.
* Lack of standardized datasets: Available datasets are often small, unbalanced, or lacking in diversity, which limits the ability of models to generalize the results.
* Handwriting variability: Handwriting can vary depending on the emotional state, context, health status, or even the writing instrument used, which introduces noise into the data.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Paper** & **Text Features** & **Classifier** & **Dataset** & **Accuracy** \\ \hline [PERSON] 2016 [32] & Character size, line spacing, word slant, horizontal-to-vertical ratio of characters & Multilayer Perceptron & 70 subjects & 0.69–0.76 \\ \hline [PERSON] 2018 [9] & Baseline, the slope, connecting strokes between characters and letter f & Feed-Forward Neural Network & 128 subjects & 0.77–0.84 \\ \hline [PERSON] 2018 [3] & Text margin, baseline, character size, letter t & Support Vector Machine & 1890 samples & 0.93 \\ \hline [PERSON] 2022 [20] & Handwriting strokes, shapes, structure & Semi-supervised Generative Adversarial Network & 1038 samples & 0.86–0.91 \\ \hline Current work & Baseline, characters’ slope, pen pressure, spacing & Convolutional Neural Network & 1400 samples & 0.83–0.91 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Comparison with similar studies.  
* Ethical issues: Using these techniques in sensitive areas (such as recruitment or psychological assessment) raises ethical questions related to confidentiality and bias.
# A New Method to Lightweight Magnesium Using Syntactic Composite Core
###### Abstract  
Light weighting of magnesium-based materials is crucial for its extensive use in transportation applications. Hybrid processing of these materials in a shell-core pattern can substantially improve the specific properties of magnesium. In the present study, the Mg/Mg-20 GMB (glass microballoon) hybrid composite was prepared using a disintegrated melt deposition technique. Microstructural characterization and mechanical properties of the developed as-cast Mg/Mg-20 GMB hybrid composite were investigated. Results revealed that a unified metallurgical interface was formed between the Mg-20 GMB core material and the pure Mg shell. Energy dispersive X-ray spectroscopy (EDX) results confirmed the existence of Mg\({}_{2}\)Si as the secondary phase in the Mg-20 GMB core material. The hybrid Mg/Mg-20 GMB composite exhibited much superior compressive yield strength (171.6%), lower ultimate compressive strength (123.25%), and enhanced ductility (1186.48%) when compared to as-cast pure magnesium.  
Keywords:magnesium; syntactic foam; glass microballoons (GMB); hybrid composite; microstructure; compression; fractography +
Footnote †: journal: Applied
# Microbial Bioremediation and Biodegradation of Petroleum Products--A Mini Review
## 2 Petroleum Products
### Mechanism and Influential Factors for Microbial Biodegradation
#### 2.3.1 Biological Factors  
Biotic factors, which are the sole metabolic ability of microorganisms affecting the biodegradation of organic composites, include direct inhibition of enzyme activity and the multiplication process of degrading microorganisms [13]. Inhibitory activity can develop as a result of competition amongst bacteria for limited carbon sources, hostile relationships amid bacteria, or protozoa and bacteriophage preying on microbes [14]. However, the frequency of pollutant breakdown is often reliant on the amount of the pollutant and the volume of catalyst used, affecting the number of organisms that could digest the toxin and the number of enzymes created by the individual cell. For unlimited microbial development to occur, sufficient oxygen and nutrients have to be present in a usable state and in the proper proportions. Of all the influencing factors, temperature plays a dynamic role in bioremediation and thereby influences the processes.  
In some cases, this indirectly affects the efficiency of biodegradation by affecting bacterial development and metabolism, fluctuations in the soil matrix, and the manner of  
Figure 3: Pathway for the biochemical and microbial genetic pathway. Adapted from [PERSON] and [PERSON] [11].  
contaminants [18]. Crude oil, along with its byproducts, could fill in the gaps in the soil, reducing the quantity of oxygen in it. The intensity and make-up of pollutants, temperature, soil pH, oxygen content, and salinity are also greatly influenced by the bioremediation of oil-polluted lands. In this case, stunted growth of plants and microbes occurs in petroleum-rich soil, making the bioremediation process ineffective or low efficiency. With little or no oxygen availability, the degradation of aerobic microorganisms was somewhat disrupted, leading to a decrease in the bioavailability and degradation efficiency of pollutants [5]. With the presence of enzymes remaining as crucial components of the breakdown of petroleum hydrocarbons, the fluctuations in pH could also affect the enzymatic activities to decrease the efficiency of the biodegradation procedure [8].  
Furthermore, high salinity and changing pH values can restrain microbial development and metabolism. Nonetheless, the deficiency of techniques to inspect the persistence and activity of organisms in the soil also limits the use of bioremediation. It has been reported that for every 10 \({}^{\circ}\)C drop in temperature, the biodegradation rate is reduced by about half with the same process, which is within a broad range of the optimal pH for biodegradation from 6.5 to 8 in most aquatic and marine environments, terrestrial systems [16].