esa-sceva/satcom-chunk-collection huggingface dataset:
# Ammonia Gas Adsorption in Fixed Bed and Fluidized Bed Using Bentonite Particles
## 2 Materials and Methods
### Equipment for Adsorbent Characterization  
The Ankersmid Eyetech particle characterization analyzer was used for particle size determination. The measuring range of this device is 0.1-300 \(\upmu\)m by immersing the sample in water at 3 mg/mL concentration, at 23 \({}^{\circ}\)C. The optocoupler frequency was 200 Hz and the rotation speed was 12,000 rpm.  
Thermal Programmed Desorption (TPD) measurements were achieved using ammonia and carbon dioxide as sample gases and are supposed to be quite representative for investigating the interactions occurring between gases and adsorbent surfaces.  
Determination of acid-base character was realized in an installation including a cylindrical glass micro-reactor (2 mm internal diameter) with interior filaments that allow bentonite sample heating according to software. The gas carrier is dry nitrogen at normal pressure. In this device, a maximum of 200 mg of calcined bentonite powder under a controlled temperature (500 \({}^{\circ}\)C) was introduced to obtain a 2-3 cm height fixed adsorbent bed. In order to determine the surface acidity of the adsorbent, the ammonia gas was injected in fixed clay bed at 150 \({}^{\circ}\)C in ascending mode to 500 \({}^{\circ}\)C.  
Ammonia or carbon dioxide gases were injected through the fixed clay bed until saturation, and the excess of the molecule gas was purged at the respective temperature in a vessel with water. After the removal of the two gases, in the installation, the nitrogen gas continued to flow in order to remove the traces of ammonia gas or carbon dioxide that were not adsorbed physically or chemically on sodium bentonite. The desorption was realized by heating the sodium bentonite at 500 \({}^{\circ}\)C. The desorbed gas was bubbled in a titration device previously prepared, containing sulfuric acid solution or sodium hydroxide solution in the presence of Tashiro indicator. The excess gas was volumetrically titrated.  
Through TPD analysis, the distribution of acidic sites or basic sites and calculation of surface acidity or basicity limited by the TPD curve can be determined.  
X-Ray Diffraction (XRD) analysis on a Siemens/Bruker D5000 X-Ray Powder Diffraction (XRD) System using CuK\(\alpha\) radiation at \(\lambda\) = 1.5406 A was carried out to obtain the specific X-Ray diffractograms and for basal spacing (d\({}_{001}\)) calculation for the materials used as absorbents.  
To complete the sample characterization, the Brunauer-Emmett-Teller (BET) method for specific surface area analysis and Barrett-Joyner-Halenda (BJH) method for distribution of pore sizes and pore volume calculation were applied using a Beckman Coulter SA 3100 equipment. Prior to the nitrogen adsorption-desorption measurements, the samples were degassed at 120 \({}^{\circ}\)C for 12 h.  
Energy Dispersive X-Ray (EDX) analysis, carried out concomitantly with SEM microscopy, was performed for elemental microanalysis on a certain surface of sodium bentonite and aluminum pillared bentonite using an EDX ZAF Quantification.
# Coordinated Control Method of Bus Signal Priority and Speed Adjustment Based on Stop-Skipping
## 4 Algorithm Choice  
[PERSON] et al. [36] applied the simulated annealing algorithm to the flexible shop scheduling problem and obtained good solutions. However, the algorithm has low computational accuracy and poor global search ability. [PERSON] et al. [37] solved the train rescheduling problem using a variable neighborhood search algorithm, but the algorithm has low convergence. [PERSON] et al. [38] used the branch cut-price algorithm to improve vehicle path planning with different capacities, but the convergence of the algorithm still needs to be improved when solving multi-objective complexity problems.  
In contrast, the genetic algorithm is a method of searching for optimal solutions by simulating the evolutionary process of natural selection and survival of the fittest. It has the following advantages: (1) It adopts random probability to guide its search direction in the solution, which is relatively objective [39]. (2) It has strong global search ability when solving multi-objective optimization problems [40]. (3) Genetic algorithms have strong robustness and high convergence in practical applications [41].  
Therefore, the genetic algorithm was used to solve the cooperative control model. The solution process was divided into the following stages. (1) According to the historical passenger flow data, the stop-skipping decision was made (see Figure 4a). (2) According to the online passenger flow data, the signal-priority decision and speed adjustment were made on the basis of the stop-skipping decision (see Figure 4b).  
(1) Coding method  
If there are m vehicles and n stations, the encoding length is m \(\times\) n, which is 0-1 encoding; if there are m vehicles and q intersections, the encoding length is m \(\times\) q, which is 0-1 encoding.  
(2) Variation and crossover  
Gene pairing was performed by single-point mutation and two-point crossover. Two parent chromosomes were randomly generated for pairing, and a certain crossover probability was selected to generate two daughter chromosomes.  
(3) Function calculation  
Its objective function value was calculated according to Section 3.3, and the fitness function can be expressed as:  
\[F_{i}=\frac{1}{y_{i}+P_{i}} \tag{35}\]  
\[y_{i}=\min f(x) \tag{36}\]  
where \(F_{i}\) represents the fitness value of chromosome \(i\), \(y_{i}\) represents the objective function of chromosome \(i\), and \(P_{i}\) represents the penalty value of chromosome \(i\).  
(4) Select  
The core idea of genetic algorithm is roulette selection, which means that the probability of an individual being selected is proportional to the value of its fitness function. Assuming that the population size is \(n\), the fitness function is expressed as \(F_{i}\), and the probability of being selected and inherited to the next generation is \(P_{i}\):  
\[P_{i}=\frac{F_{i}}{\sum\limits_{i=1}^{n}F_{i}} \tag{37}\]
## 2 \(k\)-\(\mu\) Fading Channel Models
According to the probability distribution conversion relationship in probability theory, the PDF of the \(k\)-\(\mu\) fading distribution with respect to the envelope \(r\) can be expressed as:  
\[f(r)=\frac{f(\alpha)}{\sqrt{\Omega}}=\frac{2\mu(1+k)^{(\mu+1)/2}\cdot r^{\mu}} {\sqrt{\Omega}^{\mu+1}\cdot k^{(\mu-1)/2}e^{(\mu k+\frac{\mu(1+k)\mu^{2}}{ \Omega})}}I_{\mu-1}(2\mu\sqrt{k(1+k)}\frac{r}{\sqrt{\Omega}}) \tag{4}\]  
the parameter \(\Omega\) represents the average signal power \(\Omega=E[r^{2}]\). When the number of clusters \(\mu\) is a natural number, \(k\) represents the ratio of the deterministic component to the non-line-of-sight path component (or scatter component). When \(k=0\) and \(\mu=M\), the \(k\)-\(\mu\) fading distribution degenerates to the Nakagami-M distribution. When \(k=K\) and \(\mu=1\), the \(k\)-\(\mu\) fading distribution becomes the Rician distribution. When \(k=0\) and \(\mu=1\), the \(k\)-\(\mu\) fading distribution becomes the Rayleigh distribution. Applying \(E[r^{\mu}]=\overline{r}^{\mu}\cdot E[\alpha^{n}]=\sqrt{E[r^{2}]}^{n}\cdot E[ \alpha^{n}]\) from ([19], Equation (6)) to the function from ([20], Equation (5)), the moment-generating function (MGF) for the \(k\)-\(\mu\) distribution can be expressed as:  
\[E[r^{\mu}]=\sqrt{E(r^{2})}^{n}\cdot\frac{\Gamma(\mu+n/2)\cdot\exp(-k\mu)}{ \Gamma(\mu)\cdot\left[(1+k)\mu\right]^{n/2}}{}_{1}F_{1}(\mu+n/2,\mu;k\mu) \tag{5}\]  
Here, \(n\) denotes the order of the moment function, \({}_{1}F_{1}(\ \cdot\,\ \cdot\ ;\ \cdot\ )\) represents the Kummer confluent hypergeometric function, and \(\Gamma(\cdot)\) represents the gamma function. In previous research, [PERSON] has proposed a closed-form estimator for the parameter \(k\) in ([24], Equation (12)), and demonstrated the mapping relationship between \(k\) and \(\mu\) in ([24], Equation (9)), as shown below:  
\[\hat{k}_{yacoub}=\left(\frac{\sqrt{2}(E[\alpha^{4}]-1)}{\sqrt{2E[\alpha^{4}] ^{2}-E[\alpha^{4}]-E[\alpha^{6}]}}-2\right)^{-1} \tag{6}\]  
\[\hat{p}_{yacoub}=\frac{1}{Var[\alpha^{2}]}\frac{1+2\hat{k}_{yacoub}}{\left(1+ \hat{k}_{yacoub}\right)^{2}} \tag{7}\]  
In the above equation, \(Var[\cdot]\) denotes the variance operator, and \(Var[\alpha^{2}]=Var[r^{2}]/E^{2}[r^{2}]\). Based on the equation given in reference \(E[r^{n}]=\overline{r}^{\mu}\cdot E[\alpha^{n}]=\sqrt{E[r^{2}]}^{n}\cdot E[ \alpha^{n}]\) ([19],Equation (6)), when the total signal power is \(E[r^{2}]=1\), the variable \(\alpha\) in Equations (6) and (7) can be exchanged with the envelope \(r\).
## Appendix C Proof of Monotonicity of (24)  
Let \(a\overset{\Delta}{=}1-\tau^{2}\) and \(c\overset{\Delta}{=}K-1\) for simplicity. Then, the first-order derivative of (24) can be written as  
\[\frac{\partial\kappa_{2,\text{MF}}^{jp}}{\partial M}=\left(\frac{\left(\log_{ 2}e\right)^{2}a^{2}c}{K-2}\right)\left(\frac{\left(\left(2a-a^{2}\right)M^{2}+2 cM+c^{2}\right)}{\left(aM+c\right)^{4}}\right). \tag{24}\]Consider the denominator of (A6), i.e., \(f\left(M\right)=\left(2a-a^{2}\right)M^{2}+2 cM+c^{2}\). Because \(2a-a^{2}\geq 0\), \(f\left(M\right)\) is a convex function. Thus, the two roots of \(f\left(M\right)\) are given by  
\[\omega=\frac{-c\pm c\sqrt{1-a\left(2-a\right)}}{2a-a^{2}}.\] (A7)  
Because \(0\leq a\leq 1\) and \(c>0\), both roots are always negative. Therefore, \(f\left(M\right)\) is always positive for \(M\geq K>0\), and (24) is a monotonically increasing function of \(M\).
# Study of NVIS Channel for USN Protocol Definition in Antarctica
## 2 Sounding System
### Overview of the System  
The block diagram of the sounding system can be seen in Figure 3. As it is a Software Defined Radio approach, you can select any carrier frequency, baseband data and bandwidth in a very easy way. The system is formed principally by a Red Pitaya platform [30] and a Raspberry Pi 3 with some peripherals.  
The core of the system is the Red Pitaya platform which is in charge of the real-time processing signal tasks of up-converting and down-converting the data. The core of the Red Pitaya is a Zynq 7010 chip composed of an Advanced RISC Machine (ARM) processor and a field-programmable gate array (FPGA). Also, it is important to highlight that the board includes two analog-digital converter (ADC) and two digital-analog converter (DAC) of 14 bits and 125 MSPS. The internal structure of the programmed system on Red Pitaya is shown at [31].  
Figure 3: Block diagram of the sounding system.  
Figure 2: Received electric field and power of groundwater depending on the distance.  
The Raspberry Pi 3 is the microprocessor that controls all the peripherals. It is connected to Red Pitaya via ETHERNET and stores the received baseband in an external hard disk for further processing or sends to Red Pitaya the data which has to be transmitted. The output power of the amplifier can be selected from 1 W to 24 W. The system can also be programmed to transmit different patterns every minute, so you can perform a set of different tests every hour.  
On the other hand, the GPS receiver is used to synchronize the transmitter and the receiver system to send and receive at the same time the NVIS transmission. The transmitter amplifier has 50 dB of gain and the Low Noise Amplifier (LNA) at the receiver has 30 dB of gain followed by a band pass filter (BPF) between 3 MHz and 7 MHz.  
The NVIS antenna is an inverted-V as the best option as we can see at [13] with 2 dBi of gain. The carrier frequency was selected as a result of the analysis of the critical frequency of the F2 layer (foF2) of the previous months. The optimum transmission frequency is usually selected as the 85% of foF2 and it was finally set to 4.3 MHz. The inverted-V antenna was installed using a central mast 13 m high. This antenna has a similar behavior than the horizontal dipole, but it only needs one single mast, so it is much easier to deploy. On the other hand, the inverted-V has a higher vertical polarization component than the horizontal dipole. This is an important issue for the groundwave, which mainly propagates only the vertical component of the electric field.  
Finally, for analyzing all the data, Matlab software [32] has been used for demodulating all the data received and perform all the post-processing analysis, as it will be explained in Section 3.1.
## 2 Materials and Methods
### MLS Processing
#### 2.6.1 Initial Trajectory Calculation from GNSS/INS Data  
The trajectory calculation from the measured data was performed in the Applanix POSPac software [32]. The input to this software is GNSS/INS data including DMI, precise satellite ephemerides, and observation data from a permanent GNSS station network.
# Antifungal Activity of Quaternary Pyridinium Salts Against _Fusarium culmorum_ in Wheat Seedlings
## 3 Results  
### Disease Index  
The data in Table 3 shows the different efficacies of the tested nicotinamide derivatives in reducing disease in wheat seedlings. All nicotinamide derivatives showed significantly better efficacy in reducing disease intensity than the SG/CS control, except for compound **(5)**, which did not differ significantly from the control. The control SG/CS had the highest disease index (14.2). A low disease index of 2.2 was observed for the positive control (F/CS), indicating a high effectiveness of the fungicide. When compounds **(1)** and **(4)** were applied at a 100 \(\upmu\)g/mL concentration, the disease index was 6.7. This result was lower than the negative control (SG/CS), showing that both compounds had some antifungal effect. Although the disease index was higher than that of the positive control (F/CS), the difference was insignificant. Compound **(3)**, applied at a 100 \(\upmu\)g/mL concentration, showed high efficacy with a disease index equal to that of the positive control F/CS. The most effective compounds were **(2)** and **(3)**, with a disease index of 0.9 at a 10 \(\upmu\)g/mL concentration (Figure 2). The results in Table 4 show the different efficacy of isonicotinamide derivatives in reducing disease severity in wheat seedlings infected with _F. culmorum_. Compared to the positive control (F/CS), which had a very low disease index of 2.2, the isonicotinamide derivatives were less effective. However, several compounds showed promising antifungal potential and were not significantly different from the positive control. \begin{table}
\begin{tabular}{c|c c c} \hline \hline
**Treatment** & **Concentration** & **Disease Index** & **Notes** \\  & (\(\mu\)g/mL) & (\(\pm\) SD) * & **Notes** \\ \hline
**(1)** & 100 & 6.7 \(\pm\) 2.03 b  & Moderate effect \\ \hline
**(2)** & 10 & 0.9 \(\pm\) 0.78 c  & Most effective with the lowest disease index among this group \\ \hline
**(3)** & 100 & 2.2 \(\pm\) 1.91 bc  & Very effective \\ \hline
**(4)** & 100 & 6.7 \(\pm\) 2.39 b  & Moderate effect \\ \hline
**(5)** & 100 & 11.6 \(\pm\) 4.91 a  & Less effective \\ \hline SG/CS & - & 14.2 \(\pm\) 3.73 a  & Negative control, least effective \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Effect of nicotinamide derivatives (10 and 100 \(\mu\)g/mL) on disease intensity caused by _Fusarium culmorum_ in wheat seedlings. Figure 2: Effect of treatment with nicotinamide derivatives on disease intensity: **(a)** compound **(2)** at 10 \(\mu\)g/mL/contaminated sand, **(b)** compound **(3)** at 10 \(\mu\)g/mL/contaminated sand; **(c)** sterile grain/contaminated sand; **(d)** fungicide/contaminated sand.
## 2 Theoretical Considerations
### Specialists in the Changing Media Environment  
Media scholars have recently begun to explore the possibility that the changing information environment tends to foster one type of citizen more than the other [8, 19, 20]. These scholars have posited that technological features of new media help individuals become specialists while traditional media, such as network television, radio, and newspaper shape generalists.  
Two characteristics of traditional media environment deserve particular attention in relation to generalists. First, the political information supplied by traditional media, especially before a recent burst of partisan media, is relatively homogeneous and standardized [21]. To seek a larger audience and maximize profits, media corporations want to appeal to as many viewers as possible while--more importantly--disturbing as few as possible. The media outlets produce the media content that is ideologically moderate, non-controversial, and popular [22].  
Another feature of the traditional media environment is that the media exposure offers audiences not only an active but also a passive learning process [23, 24]. [PERSON] [25] indicated that television news reaches two types of viewers: the advertents, who follow the news because they enjoy politics, and the inadvertents, who fall into the news accidentally. Using rational choice theory, [PERSON] [23] explained that people who do not enjoy news and politics are still informed on public affairs through incidental learning. [PERSON] [26] noted that political learning depends on this technological inefficiency.  
Contrary to traditional media, the emerging media technologies allow for more diversity in media content and more selectivity in media use [27, 28, 29]. Amateurs are capable of creating and distributing their ideas more freely, resulting in long-tail diversity [30]. In addition, the new media induce audiences' selective exposure and selective learning. Information is not given linearly, but is sought selectively through the technological functions, such as menu options or a Google search.  
Here, a crucial juncture is reached where these technological affordances fit specialists' tendency to look for information in only a few domains in which they are interested [9, 31, 32]. As long as individuals have interests in a particular topic, they can obtain relevant information and further develop their interests with more ease and efficiency. For example, [PERSON] [9] has indicated that those who perceive an issue to be important tend to engage in selective information-seeking behavior on the Web, thereby obtaining a higher level of issue-specific knowledge. However, this study adopted personal issue importance as a proxy measure of the issue publics, making it difficult to distinguish between specialists and attentive generalists. Another caveat is that the study did not examine how the pattern of information-seeking behavior differs across different media. To bridge this gap in the literature, the current study takes four different media into account and investigates how each medium is associated with the type of citizen.
# A Temperature Fiber Sensor Based on Tapered Fiber Bragg Grating Fabricated by Femtosecond Laser
###### Abstract  
A temperature fiber sensor based on tapered fiber Bragg grating (tapered FBG) fabricated by femtosecond laser has been proposed and realized with good reproducibility. Firstly, the fiber taper with 25 \(\upmu\)m diameter and 1000 \(\upmu\)m length is fabricated by arc-discharge elongation using two standard single-mode fibers. Secondly, two first-order FBGs are fabricated in tapered and non-tapered fiber regions for comparison. Both FBGs are point-by-point direct-written by femtosecond laser, and the grating lengths are 1000 \(\upmu\)m. Thirdly, a temperature experiment is performed using a heating chamber, and experimental results show that in the range of 30-350 \({}^{\circ}\)C, the temperature sensitivity of the tapered FBG has increased from 11.0 pm/\({}^{\circ}\)C to 12.3 pm/\({}^{\circ}\)C. The tapered FBG proposed here can be further configured for sensing other parameters in physical, chemical, and biomedical applications.  
tapered optical fiber sensor; fiber Bragg grating; temperature sensing; femtosecond laser fabrication +
Footnote †: journal: Applied Physics Letters
# Stabilization of Transverse Modes for a High Finesse Near-Unstable Cavity
## 2 The Stability Condition of Near-Unstable Cavity  
The optical cavity we chose was a specific FP cavity consisting of two mirrors, one was a plane mirror M1 and the other was a concave mirror M2 with a curvature radius \(R_{2}=500\) mm. The distance between the two mirrors, i.e., the optical cavity length \(L\), was 54 mm. Figure 1 shows the mode-matching scheme and parameters of this cavity.  
Figure 1: (**a**) is the mode-matching scheme for the FP cavity. M1 is a plane mirror with a curvature radius \(R_{1}=\infty\). M2 is a concave mirror with a curvature radius \(R_{2}=500\) mm. The different sides of mirrors M1 and M2 are denoted by a, b, c, and d, respectively. \(\omega=0.5\) mm is the radius of the incident Gaussian beam, \(\omega_{0}=0.173\) mm is the waist radius of the light beam on the front surface a of M1, and \(\omega_{1}=0.183\) mm is the radius of the light beam on the front surface c of M2, respectively. (**b**) is the stability diagram of the coaxial spherical cavity. The shadow region is the stable region while the empty, white region is the unstable region. Point A (\(g_{1}=1\) and \(g_{2}=0.892\)) is the location of our cavity close to the boundary between the stable and the unstable regions.  
The stability condition of the resonant cavity can be written as [20]  
\[0<g_{1}g_{2}<1, \tag{1}\]  
where \(g_{1}=1-L/R_{1}\) and \(g_{2}=1-L/R_{2}\). In this experiment, \(g_{1}g_{2}=0.892\), similar to the case of gravitational wave detection [17]. The stability diagram of the coaxial spherical cavity is shown in Figure 1b, where the shadow region is the stable region and the empty white region is the unstable region. Our cavity is located at point A in the stability diagram as shown in Figure 1b. For the reason that \(g_{1}g_{2}\) is close to 1, the cavity reveals some characteristics of a near-unstable cavity. The free spectral range and the transverse mode spacing can be calculated using Equation (2), which is 2.78 GHz and 296 MHz, respectively. Compared to the stable cavity, the near-unstable cavity is unique for its small transverse mode spacing and therefore allows for more high-order transverse modes within a free spectral range. In our case, we obtained about nine transverse modes having the same axial mode index. It is useful for transverse mode tuning. This feature suppresses the laser generation while the cavity is still lockable, which is essential for the OPO cavity to work at the far-below threshold condition. On the other hand, the cavity cannot be too close to the unstable boundary. This is because when the transverse mode spacing is close enough to the modulation frequency, the sideband will overlap with neighboring transverse modes and disturb the locking performance based on PDH technique. As for a cavity located on the unstable boundary, the transverse modes are degenerate, and it is hard to lock a single transverse mode when used for OPO experiments.