esa-sceva/satcom-chunk-collection huggingface dataset:
## 4 Experimental
### Equipment and Procedures  
A schematic diagram of the flotation column and the superficial flowrates of each stream are shown in Figure 1 in which \(F\), \(C\), and \(T\) refer to the feed, concentrate, and tailings streams, and subscripts \(w\), \(s\), and \(g\) refer to flowrates of water, solids, and gaseous components. \(J\) is the superficial velocity (i.e., volumetric flowrate area of the cross-section of the column). Features of the laboratory CPT CoalPro column flotation system used are: (1) the height and diameter of column at 1500 mm and 55 mm, respectively; (2) Sparser, a porous HDPE or optional cavitation tube; and (3) automatic control for level, sparger air, and wash water. The column upper section consists of wash water distributor, froth collection launder, and feeder while the lower section houses the sparger (bubble generator) and tailings outlet. The feed slurry inlet is at a point of 1/3 of the column height from the top. The wash water was fed at the top of the column while the tailings were collected at the bottom of the column. The concentrate and tailings products were collected and dried in the dryer of 105 \({}^{\circ}\)C for 24 h.  
Figure 1: (**a**) Schematic diagram of flotation column and (**b**) superficial velocities in column.  
The reagents tested were collectors (kerosene and a patented collector developed for coal, DMU-101), brothers (MIBC), and depressants (sodium metaphosphate (SMP)). The conditions of variables tested in flotation column were as follows: superficial gas velocity of 0.7-1.6 cm/s, superficial wash water velocity of 0.1-0.4 cm/s, feed solids concentration of 1-10% by mass, DMU-101 of 0.2-0.6 kg/t, and SMP of 1.5-4.5 kg/t.  
Prior to conducting flotation tests using coal, gas holdup measurements were carried out under various air and wash water flowrates and frother combinations by measuring the difference in interface level as a result of stopping the air supply. The contents of ash, fixed carbon, volatile materials, and moisture were measured using proximate analyzer (TGA601, LECO Ltd., St. Joseph, MI, USA). A field emission-scanning electron microscope (FE-SEM, S-4800, Hitachi, Tokyo, Japan) equipped with an energy dispersive X-ray spectrometer (EDS, Link Isis 3.0, Oxford Instrument plc, Abingdon, Oxfordshire, UK) was used for this analysis of coal samples. A platinum coating was applied to each coal product sample using a Pt electron E-1030 sputter according to the standard procedure which yielded a platinum layer of 6-7 microns.
## 3 Results
### Mechanical Properties  
The mechanical performance of biofilms is characterized by tensile strength and elongation at break. To study the effect of grinding cycles, the biofilms were cut into a dog bone shape as per the ASTM-D1708 standard, then they were evaluated using the tensile testing machine. The tensile modulus, tensile strength at break, maximum elongation at break, and stress-strain graphs for OW and GW biofilms are shown in Table 1 and Figure 8.  
Figure 7: OclloScope images of OW suspensions after (**a**) 10 grinding cycles, (**b**) 15 grinding cycles, (**c**) 25 grinding cycles, and (**d**) 30 grinding cycles.  
The strain-stress diagrams show that both biofilm samples exhibited a semiductile behavior, since failure occurred at low strain in both samples and the addition of glycerol to both suspensions did not increase the strain significantly. However, glycerol facilitated  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Sample** & **Tensile Strength (MPa)** & **Young’s Modulus (GPa)** & **Elongation (\%)** \\ \hline \hline OW 10 th grinding & 4.02 (0.49) & 0.35 (0.69) & 1.23 (0.64) \\ OW 15 th grinding & 5.53 (0.44) & 0.39 (0.39) & 1.43 (0.48) \\ OW 25 th grinding & 12.92 (0.24) & 0.28 (0.68) & 4.82 (0.48) \\ OW 30 th grinding & 17.01 (0.16) & 0.39 (0.25) & 4.64 (0.25) \\ GW 10 th grinding & 4.97 (0.79) & 0.63 (0.65) & 0.78 (0.69) \\ GW 15 th grinding & 6.32 (1.05) & 0.27 (1.14) & 2.28 (0.54) \\ GW 25 th grinding & 7.64 (0.79) & 0.28 (0.78) & 2.66 (0.65) \\ GW 30 th grinding & 15.34 (0.20) & 0.45 (0.24) & 3.35 (0.38) \\ \hline \hline \end{tabular} Numbers in parentheses show CV%, CV: coefficient of variation.  
\end{table}
Table 1: Tensile properties of OW and GW biofilms.  
Figure 8: Tensile stress–strain curves of biofilms after grinding cycles: (**a**) OW, (**b**) GW.  
molding and casting, as the biofilms could not be removed from the mold for characterization without the addition of the plasticizer.  
The tensile strength of the biofilms ranged from 4 MPa to 17 MPa. Both OW- and GW-based biofilms experienced a significant increase in tensile strength as a result of the increase in the number of grinding cycles. This is due to strong interactions among the smaller particles caused by an increase in the specific surface area. As a result, the particles were dispersed more evenly throughout the biofilms. Citric acid likely enhanced the interactions among the particles during gelling in the OW-based films. Because GW's inherent fiber-like structure provides entanglements to withstand any force applied to it, one can anticipate a high tensile strength from GW-based films. Nevertheless, when the number of grinding cycles was increased, the raw materials were reduced to finer particles, producing films with a tensile strength comparable to that of OW-based films. As the number of grinding cycles increased, strain also increased. However, the slope in the stress-strain diagram representing the Young's modulus did not improve upon increasing the number of grinding cycles. Previous studies have shown that the biofilms have similar tensile properties [22].
## 2 Background on Network Calculus
### Modeling of Flows and Performance Characteristics  
Network calculus models the sequence of packets that define a flow's data arrivals as non-negative, wide-sense increasing functions. These cumulatively count arriving data over time:  
\[\mathcal{F}_{0}=\left\{f:\mathbb{R}^{+}\rightarrow\mathbb{R}^{+}\mid f(0)=0, \ \forall s\leq t\,:\,f(t)\geq f(s)\right\}.\]  
A flow's input, as well as its output from a system \(\mathcal{S}\) are of interest to performance modeling. We denote the input up to time \(t\) as \(R(t)\) and the output as \(R^{*}(t)\). If both count the data of the same flow, we demand \(\forall t\in\mathbb{R}^{+}:\ R(t)\geq R^{*}(t)\), i.e., the flow's output from system \(\mathcal{S}\) is caused by the input to \(\mathcal{S}\). This causality preservation is known as the flow constraint of network calculus.  
The flow definition and its causal transformation from system input to output enables us to define first the performance characteristics.  
**Definition 1**.: _(Backlog and delay) Assume a flow with input function \(R\) traverses a system \(\mathcal{S}\) and results in the output function \(R^{*}\). The backlog of the flow at time \(t\) is defined as:_  
\[B(t)=R(t)-R^{*}(t).\]  
_The (virtual) delay for a data unit arriving at \(\mathcal{S}\) at time \(t\) is defined as:_  
\[D(t)=\inf\left\{\tau\geq 0\mid R(t)\leq R^{*}(t+\tau)\right\}.\]
# Alpha Centauri: Disc Dynamics, Planet Stability, Detectability
## 3 Stability of Planets in Alpha Centauri
### Stability of Earth-like Planets in Alpha Centauri  
The fact that test particles cannot thrive in certain regions of a triple star system, which are too hostile and unstable, does not necessarily imply that massive planets cannot survive. Indeed, the very mass of the planet in question can slightly modify the inner binary parameters or even alter the external companion orbit through gravitational perturbations. This could potentially lead to more stable conditions, which would modify the resulting maps shown in Figure 5. In this subsection, we consider massive (rocky) planets that can interact with the three stars of Alpha Cen. Using the same orbital parameters as in the previous section, we obtain the maps shown in Figure 6 for Earth-like planets.  
For 1 \(M_{\oplus}\) planets, the inner stability region around A remained practically unchanged--with a stability boundary at around 3 au for circular planets and around 2.5 au for eccentric planets (\(0<e\leq 0.5\)). We observed the same phenomenon for 1 \(M_{\oplus}\) planets around B, with a narrow band up to around 2 au, which contains all the massive planets with stable orbits. All the planets beyond that limit were unstable. Given that our revised MEGNO maps for 1 \(M_{\oplus}\) planets remained the same compared to the massless case, we conclude that the planet mass has a limited effect on stability for rocky planets.
## 5 Experiments with Short-Term SNR
The code rate of 1/9 closely approximates the code rate of 0.11175 used in our experiments. The convergence threshold, as predicted by the extrinsic information transfer (EXIT) chart, represents the minimum SNR required for codewords of infinite length to achieve a very small BER after an infinite number of decoding iterations [28]. Given that the LTE codewords in our experiments have a finite length and the number of decoding iterations is limited to six, it is anticipated that the SNR values corresponding to the waterfall region of the BER curve for the AWGN channel in our experiment will be slightly higher than the convergence threshold, as confirmed by the figure. To further study the performance of mobile reception, we conducted experiments for both types of SNR at low, medium, and high speeds (3 km/h, 42 km/h, and 180 km/h) with Doppler shifts of 5 Hz, 70 Hz, and 300 Hz, respectively. Figures 12 and 13 illustrate the BER results. It is evident that the ranking of BER performance in the two SNR definitions remains unchanged across low, medium, and high car speeds. Additionally, Figure 12 once again shows counter-intuitive results: the BER performance of the ETU channel at 180 km/h is better than all three channels at lower car speeds. This phenomenon also signifies the need for an alternative SNR definition. On the other hand, the BER performance based on the short-term SNR, shown in Figure 13, behaves as expected: the ETU channel at 180 km/h has the highest BER. Figure 11: Decoded BERs using short-term SNR as a basis of comparison.
## 3 Mathematical Formulation  
### Formation of a Nonlinear Evolution Equation  
It is well established that the nonlinear IA wave mode cannot be easily described by solving Equations (2)-(6) directly. At this stage, one can use the tedious mathematical technique for deriving the nonlinear evolution equations (NLEEs) to study the basic features of physical phenomena in diverse environment. To derive a NLEE form the aforementioned model, the reduction perturbation technique [22; 39] is allowed to consider the new coordinates instead of the scaling of variables as  
\[X=\epsilon(z-V_{p}t),T=e^{2}t, \tag{8}\]  
and the expansions for physical quantities around the equilibrium values as  
\[\left[ \begin{array}{c}N_{pi}\\ N_{ni}\\ U_{pi}\\ U_{ni}\\ \Phi\end{array} \right]=\left[ \begin{array}{c}1\\ N_{r1}\\ 0\\ 0\\ 0\end{array} \right]+\sum_{i}^{\infty}e^{i}\left[ \begin{array}{c}N_{pi}^{(i)} \\ N_{ni}^{(i)}\\ U_{pi}^{(i)}\\ U_{ni}^{(i)}\\ \[PERSON]^{(i)}\end{array} \right], \tag{9}\]  
where \(V_{p}\) and \(\epsilon\) are the linear phase speed of the IA mode and a proper fraction number recovering the weakness of dissipation, respectively. By employing Equations (8) and (9) systematically into Equations (2)-(6), one can derive the set of evolution equations based on the different order of \(\epsilon\). For \(O(\epsilon)\) equations (ignored for simplicity), one obtains the following,  
\[N_{pi}^{(1)}=\frac{1}{V_{p}^{2}}\[PERSON]^{(1)},U_{pi}^{(1)}=\frac{1}{V_{p}}\[PERSON]^{(1) },N_{ni}^{(1)}=-\frac{N_{r1}M_{r}}{V_{p}^{2}-\delta_{ei}}\[PERSON]^{(1)},U_{ni}^{(1) }=-\frac{V_{p}M_{r}}{V_{p}^{2}-\delta_{ei}}\[PERSON]^{(1)}, \tag{10}\]  
and  
\[-N_{r2}K_{1}\[PERSON]^{(1)}+N_{pi}^{(1)}-N_{ni}^{(1)}=0, \tag{11}\]  
where \(K_{1}=\Omega\left[(q+1)/2+B_{1}\right]\left(1+\rho_{ep}\sigma_{ep}\right)\). By simplifying Equations (10) and (11), the linear phase velocity is obtained as  
\[V_{p}=\sqrt{\frac{\left(1+N_{r1}M_{r}+\delta_{ei}N_{r2}K_{1}\right)\pm\sqrt{ \left(1+N_{r1}M_{r}+\delta_{ei}N_{r2}K_{1}\right)^{2}-4N_{r2}\delta_{ei}}}{2N _{r2}K_{1}\delta_{ei}}}. \tag{12}\]  
In Equation (12) it is clearly indicated that \(V_{p}\) is strongly dependent on \(N_{r1}\), \(M_{r}\), \(\delta_{ei}\), \(N_{r2}\), \(\alpha\), and \(q\), but not on \(\mu_{pi}\) and \(\mu_{ni}\). Moreover, it is validated only if \(\left(1+N_{r1}M_{r}+\delta_{ei}N_{r2}K_{1}\right)^{2}-4N_{r2}\delta_{ei}\geq 0\).
# Reconfigurable Diplexer-Based Filtenna for Tx/Rx Operation in Mobile Satellite Terminals
## 4 Results and Discussion
In transmitting mode, with the PIN diode OFF, the signal injected in port 2 is almost all transmitted and just a negligible part is observed in port 1, since both simulated and measured results reveal an attenuation always higher than 33 dB in the entire band, being the measured S\({}_{12}\) at 29 GHz of \(-\)50.2 dB. Figure 18 shows the simulated efficiency of the designed antenna, which reaches the value of 72% at 20 GHz and is of 73% at 29 GHz for the transmission. Figure 12.0 Radiation pattern to 20 GHz, (**a**) plane \(\varphi=90^{\circ}\), and (**b**) plane \(\varphi=0^{\circ}\). Figure 12.1 Simulated isolation between two ports. Figure 12.2 Simulated total efficiency of the filtenna.
## 3 Results and Discussion
### Quasi-Static Mechanical Characterization
Figure 7b compares the evolution of residual flexural strength (\(\sigma_{R1}\), \(\sigma_{R2}\), \(\sigma_{R3}\), and \(\sigma_{R4}\)) measured in the dynamic regime (\(v_{2}=4\) mm/s). All parameters show an increase as the fiber volume fraction is augmented. Additionally, their growth rates remain approximately constant across the entire \(\phi_{f}\) range. Figures 8a,b depict the stress-displacement curves, \(\sigma\)-\(\delta\), for three-point bending tests under quasi-static and dynamic conditions, respectively. In the quasi-static regime (Figure 8a), specimen failure occurs at displacements, \(\delta\), around 0.5 mm for fiber-unreinforced concretes and approximately 1 mm for fiber-reinforced concretes. An increase in flexural energy absorption capacity is also evident as \(\phi_{f}\) is augmented. In the dynamic regime (Figure 8b), all fiber-reinforced concretes exhibit residual flexural strength values for displacements of at least 3.5 mm. Furthermore, the flexural energy absorption capacity not only increases with fiber content but is significantly higher than that calculated in the quasi-static regime for all cases, including fiber-unreinforced concretes, as previously explained (Figure 4b). As depicted in Figures 7 and 8, the evolution of residual flexural strengths for different vertical displacement values is noteworthy in the dynamic regime as fiber is added to hydraulic lime concrete. This highlights the effective interaction at the fiber-matrix interface, confirmed through post-mortem analysis of specimens where the predominant fiber failure is pullout. This interaction allows the composite material to develop energy absorption capacity and ductility in the post-peak load range. This translates into the suitability of discontinuous reinforcement with this fiber type in lime concretes susceptible to specific ranges of dynamic loading.
## 3 Materials and Methods
### Volatile Compounds Analysis  
The aroma compounds were extracted from the wine samples by headspace solid-phase microextraction (HS-SPME) and analyzed using gas chromatography-mass spectrometry (GC-MS) as described by [PERSON] et al. [35]. A 5-mL sample of wine was mixed with 1 g of NaCl in a 15-mL sample vial. The vial was tightly capped with a PTFE-silicon septum and heated at 40 \({}^{\circ}\)C for 30 min on a heating platform with agitation at 400 rpm. An SPME fiber (50/30 \(\upmu\)m DVB/Carboxen/PDMS, Supelco, Bellefonte, PA, USA), preconditioned according to the manufacturer's instructions, was then inserted into the headspace and left for 30 min with continuous heating and agitation using a magnetic stirrer to achieve suitable extraction.  
The GC-MS system used in this study was an Agilent 6890 GC equipped with an Agilent 5975 mass spectrometer (Beijing, China). The column was a 60 m \(\times\) 0.25 mm HP-INNOWAX capillary column with 0.25-\(\upmu\)m film thickness (J & W Scientific, Folsom, CA, USA). Helium was used as the carrier gas at a flow rate of 1 mL/min. Samples were injected by placing the SPME fiber in the GC inlet for 25 min with the splitless GC inlet mode. The oven was initially held at 50 \({}^{\circ}\)C (for 1 min), heated to 220 \({}^{\circ}\)C at a rate of 3 \({}^{\circ}\)C/min and then held at 220 \({}^{\circ}\)C for 5 min. The samples were analyzed by mass spectrometry in electron impact mode (MS/EI) at 70 eV over the _m/z_ range of 20 to 450 U. The mass spectrophotometer was operated in selective ion mode with automatic peak selection, and the area of each peak was determined by analysis with ChemStation software (Agilent Technologies). Each analysis was repeated three times.  
All standard solutions were purchased from Sigma-Aldrich (Beijing, China) and Fluka (Buchs, Switzerland). All standards had purities above 97%. Sample solutions were prepared using the methods reported by [PERSON] et al. [36]. 4-Methyl-2-pentanol was used as internal standard. Five-point calibration curves for the quantification of each compound were prepared using the method described by [PERSON] et al. [37]. These curves were also used as a reference to determine the appropriate concentration range for preparing standard solutions. The regression coefficients of the calibration curves were above 97% (_w/w_).
# Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems
### Technical Challenges  
Object-packing problems--including irregular packings--are notoriously hard due to their combinatorial complexity, combined with the computational difficulty of enforcing non-overlap and containment constraints. Direct analytical approaches are not viable to solve such general problems to optimality (except in certain cases, for the smallest problem instances). The symmetry structure implied by packing identical objects is absent, and good-quality numerical solution \"guesses\" may not be readily available. Therefore, in order to find conjectured (approximate) numerical solutions, continuous and combinatorial optimization--typically in combination with problem-specific insight, rules, and heuristics--have become the tools of choice. To illustrate this point, consult, e.g., [PERSON] et al. [10], [PERSON] and [PERSON] [7; 11], [PERSON] [12; 13], [PERSON] et al. [14; 15; 16], [PERSON] et al. [17], and [PERSON] et al. [18], with further examples cited later on in this work.  
The joint consideration of continuous and combinatorial optimization aspects of packing problems can be handled by appropriately tailored mixed-integer linear or nonlinear optimization models and solution techniques. [PERSON] [8] discusses the fundamental concepts, modeling, and solution approaches to several standard allocation, cutting, and packing problems. The corresponding models are handled by various techniques such as mixed-integer linear programming, nonlinear programming, constraint programming, and problem-dependent heuristics.
## 2 The Methods of PAI and LUI
### Forward and Inverse Problems of PAT and LUT--Reconstruction Algorithms
#### 2.2.2 Algorithms for LUT
The effective amplitude of scatterers is given by the sum over \"paraboloids (in 3D) or parabolas (in 2D) of probability\":  
\[\epsilon\left(\mathbf{r}_{ij}\right)=\sum_{m=1}^{N}p\left(\mathbf{r}_{m},\mathcal{T}_ {ijm}\right)\cdot\left|\mathbf{r}_{m}-\mathbf{r}_{ij}\right|\cdot\mathcal{D}\left( \mathbf{r}_{m},\mathbf{r}_{ij}\right), \tag{13}\]  
where \(\epsilon\left(\mathbf{r}_{ij}\right)\)--the strength of the scatterer at the point \(\mathbf{r}_{ij}\) of the image, \(p\left(\mathbf{r}_{m},\mathcal{T}_{ijm}\right)\)the amplitude of pressure detected by the \(m\)-th transducer at time \(\mathcal{T}_{ijm}\) [\(\mathbf{r}_{m}-\mathbf{r}_{ij}\)] accounts for the \(1/r\) attenuation of spherical waves scattered at \(\mathbf{r}_{ij}\), and \(\mathcal{D}\left(\mathbf{r}_{m},\mathbf{r}_{ij}\right)\) accounts for directivity pattern of transducers to reduce streak artifacts in the reconstructed image. The back-projection algorithm, in the present form, provides reasonable results, and can be parallelized for real-time operation.
## 2 Materials and Methods
### Data Preparation
#### 2.2.2 Sentinel-1 Image Data  
The Sentinel-1 data are C-band synthetic aperture radar (SAR) data [32]. Sentinel-1 images provided by the GEE cloud platform have undergone preprocessing procedures such as thermal noise removal, radiation calibration, and terrain correction. A total of 398 Sentinel-1 images that were synchronized with Sentinel-2 data were used in this study, and the data acquisition and processing were conducted using the GEE platform.
## 6 An Innovative Automatic Learning Algorithm for Supporting Advanced Human-Machine Interfaces
### Articulatory to Facial Movements Mapping  
It is known that facial movements during phonation are linked to articulation movements as shown by many authors, for example, Reference [24]. It is commonly believed that most of the correlation between facial movements and articulators can be expressed by linear transformation. Actual measurement of articulator movements and the corresponding articulation movements it is possible but can be problematic; so we used the MOCHA-TIMIT [53] database containing articulator and facial parameters for English phonemes. The MOCHA-TIMIT database collects data using Electro Palathograph (EPG) and Electromagnetic Articulograph (EMA). The algorithm presented in this paper, at the time, uses the Italian language. Extending fuzzy rules to English is under development; for this reason, we used the available database beforehand. We have carefully selected a subset of MOCHA-TIMIT phonemes that are similar in both languages. This because, in our work, we have developed the fuzzy rules for Italian.  
Because our fuzzy-genetic optimization estimates the articulator parameters of a spoken word, a mapping between articulator and facial parameters is required to obtain the corresponding facial movements. To make this mapping, unlike what is proposed in Reference [24], we trained a simple feedforward multilayer neural network. The network describes a nonlinear mapping (Figure 10) between articulatory and facial movements, as described in Reference [54].  
Figure 10: Articulatory to facial movements mapping.  
Figure 9: Mixed excitation sequence.  
For supervised training, we give as input to the network the articulatory parameters vector produced by the fuzzy-genetic module and as output the corresponding facial positions vector derived by the MOCHA-TIMIT database. With our fuzzy genetic algorithm we have obtained, for any given phoneme in the input sentence, several input vectors \(g~{}=~{}(\textit{rounded, open, front, voiced, bilabial, labiodental, alveolar, prepalatal, palatal, vibrant, dental, velar})\) and from the MOCHA-TIMIT database the corresponding desired output vector \(x=(UpperLipX,UpperLipY,LowerLipX,LowerLipY)\). Training is performed with Levenberg-Marquard algorithm with input/output parameters are extracted from 460 sentence.  
The output vector is applied to the Geoface model implemented by [PERSON] [55] shown in Figure 11. The model employs a large number of polygons for modeling the whole face. The vector is applied to the coordinates of the corresponding textures of the model.