esa-sceva/satcom-chunk-collection huggingface dataset:
## 3 Results and Discussion
### Characterization of the Biochar  
#### 3.3.1 Scanning Electron Microscopy (SEM)  
The morphology of the biochars activated by KOH and NaOH at 650 \({}^{\circ}\)C was characterized by SEM, and the results are representatively presented in Figure 4 with different magnifications. It was observed that both of the two biochars had an obvious porous structure, confirming the pore-forming effect of the two activators. The porous structures of the two biochars from the SEM results were also in good agreement with their high SAA values (132.8 and 117.7 m\({}^{2}\)/g for NaOH- and KOH-activated biochars,  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \multirow{2}{*}{**T (\({}^{\circ}\)C)**} & \multicolumn{3}{c}{**Langmuir Model**} & \multicolumn{3}{c}{**Freundlich Model**} & \multicolumn{3}{c}{**Van \({}^{\circ}\)Hoff**} \\ \cline{2-9}  & \(k_{L}\) **(L/mg)** & \(Q_{m}\) **(mg/g)** & \(R^{2}\) & \(n\) & \(k_{F}\) **(mg/g)** & \(R^{2}\) & \(\Delta G^{0}\) & \(\Delta S^{0}\) & \(\Delta H^{0}\) \\  & & & & & & & & (**kJ/mol)** & **(kJ/mol)** \\ \hline
25 & 1.143 & 13.0 & 0.9899 & 23.8 & 10.4 & 0.9993 & \(-\)13.82 & & \\
35 & 1.891 & 14.5 & 0.9862 & 21.5 & 11.4 & 0.9994 & \(-\)13.59 & 589.36 & 40.47 \\
45 & 4.000 & 15.6 & 0.9900 & 22.6 & 12.3 & 0.9999 & \(-\)15.00 & \\ \hline \end{tabular}
\end{table}
Table 2: Adsorption isotherms of ammonium nitrogen onto biochar using Langmuir and Freundlich models.  
respectively). The nitrogen adsorption-desorption isotherms of the NaOH- and KOH-activated biochars are presented in Figure 5. It was found that the isotherms belonged to type IV (IUPAC classification), indicating that the two biochars had mesoporous structure [43; 44]. When the P/P\({}_{0}\) was higher than 0.95, the increase in the adsorption-desorption isotherms of NaOH-activated biochar was more obvious compared with that of KOH-activated biochar, indicating more macropores in the NaOH-activated biochar [45]. From a higher-magnification observation (Figure 4c,f), NaOH-activated biochar had a thin and fractured pore wall structure, while KOH-activated biochar exhibited a continuous porous structure with relatively thick pore walls. Since the two biochar types were obtained under the same temperature, the obvious difference in porous structures between them was mainly attributed to the different activation effects of the two activators [29]. Besides this, there was no significant difference in the porous structures of the two biochars after NH\({}_{4}\)\({}^{+}\)-N adsorption, suggesting good mechanical properties of the prepared biochars [12].  
Figure 4: SEM images of NaOH-activated biochar (**a**,**b**) and KOH-activated biochar (**d**,**e**), (**c**,**f**) represent the KOH-activated biochar and KOH-activated biochar, respectively, after ammonium nitrogen adsorption.  
Figure 5: Nitrogen adsorption–desorption isotherms of NaOH-activated biochar and KOH-activated biochar.
## 3 Results
### Aortic Wall Characterisation
#### 3.5.1 Cardiac Phantom Materials  
Human and diseased aortas present complex anatomy and mechanical behaviours (e.g., heart motion, anisotropic hyperelastic wall, heterogeneity of wall properties and thickness, active contraction of the vascular smooth muscle cells within the aortic media layer). Access to materials able to mimic cardiovascular tissue is relevant for clinicians topractice, plan surgical procedures and validate devices or techniques. There is an ongoing quest to develop such materials, known as cardiac phantom materials, that are capable of representing patient-specific human aortas.  
Following this rationale, [PERSON] et al. [86] performed experimental (uniaxial, tear and dynamic tests) and in silico FSI tests on different cardiac phantoms. These phantoms had the particularity of allowing them to measure results with PWV. The results suggested that the tested materials presented similar mechanical properties and similar pulse wave propagation. Nonetheless, the materials appeared to be less stiff under dynamic loading, where the viscoelastic effects were more prominent. Moreover, the authors presented two different techniques to produce these cardiac phantom materials (investment casting process and 3D printing) and postulated that these materials have great potential to improve computational modelling approaches, clinical imaging modalities and medical device design and bench testing. [PERSON] et al. [28] intended to answer the following questions: \"Can a silicone prototype suitably mimic a biological aorta?\"; \"To what extent do the anatomical characteristics have to be detailed?\". The differences in considering patient-specific geometries and material properties were assessed by implementing FSI simulations. It was assessed that geometrical factors highly influence the haemodynamic patterns and wall compliance was the dominant factor for the overall mechanical behaviour. They recommended that both patient-specific geometries and material properties should be considered.
## 2 Materials and Methods
### Classification: Support Vector Machines  
In order to classify properly the features extracted using any of the descriptors defined above, Support Vector Machines (SVM) are chosen.  
The main idea of SVM is to select a hyperplane that is equidistant to the training examples of every class to be classified so that the so-called maximum margin hyperplane between classes is obtained [54,55]. To define this hyperplane, only the training data of each class that fall right next to  
Figure 4: Graphic example of an RBC descriptor.  
those margins are taken into account, which are called support vectors. In this work, this hyperplane would be the one which separates the characteristics obtained from pain and non-pain facial images. In cases where a linear function does not allow for separating the examples properly, a nonlinear SVM is used. To define the hyperplane in this case, the input space of the examples \(\mathbb{X}\) is transformed into a new one, \(\Phi(\mathbb{X})\), where a linear separation hyperplane is constructed using kernel functions as they are represented in Figure 5. A kernel function \(K(x,x^{\prime})\) is a function that assigns to each pair of elements \(x,x^{\prime}\in\mathbb{X}\) a real value corresponding to the scalar product of the transformed version of that element in a new space. There are several types of kernel, such as:  
* Linear kernel: \[K\left(x,x^{\prime}\right)=<x,x^{\prime}>,\] (1)
* P-Grade polynomial kernel: \[K\left(x,x^{\prime}\right)=\left[\gamma<x,x^{\prime}>+\tau\right]^{p},\] (2)
* Gaussian kernel: \[K\left(x,x^{\prime}\right)=\left[exp(-\gamma\left\|x,x^{\prime}\right\|^{2}) \right],\hskip 28.452756 pt\gamma>0,\] (3)  
where \(\gamma>0\) is a scaling parameter and \(\tau\) is a constant.  
The selection of the kernel depends on the application and situation, and a linear kernel is recommended when the linear separation of data is simple. In the rest of the cases, it will be necessary to experiment with the different functions to obtain the best model for each case, since kernels use different algorithms and parameters.  
Once the hyperplane is obtained, it will be transformed back into the original space, thus obtaining a nonlinear decision boundary [2].
# A Review of SATis5: Perspectives on Commercial and Defense 5G SATCOM Integration
## 6 Outlook Perspectives on SATis5 for Commercial and Defense Applications
### Defense SATis5 Applications
For US DoD, concerning the use of commercial technologies to meet defense space mission needs, the designated US DoD acquirers are required to make decisions based on the national interest incorporating cost, schedule, performance, and risk factors. Ref. [32] provides a framework for assisting the acquirers to assess a potential commercial solution for making informed decisions. The framework will allow for deciding whether a SATis5 solution can meet the national interest.
# Design of Indoor Visible Light Communication PAM4 System
## 4 Modulation Characteristic Analysis
The BER performance of the noise data Set 3 and Set 4 in Beijing is shown in Figure 10b, as can be seen from Figure 10b, for the BER of \(10^{-5}\), using the noise data Set 3 and noise data Set 4, the PAM4 requires a SNR of 15.1 dB and 17.2 dB respectively, The white Gaussian noise only needs a SNR of 13.8 dB, for different GMM noise parameters, the performance decreased by 1.3 dB and 3.4 Db, respectively, comparing the noise data Set 1, Set 2 and noise data Set 3, Set 4 can be seen, the higher the value of \(\sigma_{\text{GMM}}\), the worse the BER performance, because as the value of \(\sigma_{\text{GMM}}\) increases, the greater the variance of the noise data, the higher the dispersion of the noise, the greater the fluctuation, the greater the impact on the original data source, and the BER will increase under the same SNR. The BER performances of the noise data Set 5 and Set 6 in Kaifeng are shown in Figure 11a. It can be seen from Figure 11a that for the BER of \(10^{-5}\), using the noise data Set 5 and the noise data Set 6, the PAM4 requires a SNR of 15.8 dB and 16.6 dB, respectively. The white Gaussian noise only needs a SNR of 13.8 dB. For different GMM parameters, the performance decreases by 2.0 dB and 2.8 dB, respectively. The BER performance of the noise data Set 7 and Set 8 in Guangzhou is shown in Figure 11b. It can be seen from Figure 11b that for the BER of \(10^{-5}\), the noise data Set 7 and the noise data Set 8, the PAM4 requires a SNR of 17.2 dB and 18.1 dB, respectively. Compared with the performance of Gaussian noise, it is reduced by 3.4 dB and 4.3 dB, respectively. From the four sets of noise data in Tables 5 and 6, it can be seen that the larger the value of \(\sigma_{\text{GMM}}\), the worse the BER performance. The BER performance of Harbin's noise data Set 9 and Set 10 is shown in Figure 12a. It can be seen from Figure 12a that for the BER of \(10^{-5}\), the PAM4 requires a SNR of 17.5 dB and 19.2 dB, respectively. Compared with Gaussian noise, the performance decreases by 3.7 dB and 5.4 dB. The BER performance of Changchun's noise data Set 11 and Set 12 is  
Figure 11: The relationship between BER and SNR (**a**) Set 5 and Set 6 (**b**) Set 7 and Set 8. Figure 10: The relationship between BER and SNR (**a**) Set 1 and Set 2 (**b**) Set 3 and Set 4.  
shown in Figure 12b. It can be seen that for the BER of \(10^{-5}\), using the noise data Set 11 and the noise data Set 12, PAM4 requires a SNR of 15.2 dB and 16.3 dB, respectively. Compared with the Gaussian noise, the performance decreases by 1.4 dB and 2.5 dB, respectively. It can be seen from the four sets of noise data in Tables 7 and 8 that the value of [PERSON]'s \(\sigma_{\text{GMM}}\) is large, so the BER performance is worse.
# A Novel Tempogram Generating Algorithm Based on Matching Pursuit
## 1 Introduction  
In musical terminology, tempo is the speed or pace of a given piece [1]. It is usually measured by beats per minute (bpm). For example, a tempo of 60 bpm signifies one beat per second, while a tempo of 120 bpm is twice as rapid. The note value of a beat will typically be indicated by the meter signature. For instance, in 4/4 the beat will be a crotchet or quarter note. Tempo is one of the most important features of music, which is closely relevant to beat and rhythm. Correspondingly, tempo estimation is one of the most important research fields in music information retrieval (MIR), and moreover, it is the fundamental work for many other applications, such as beat tracking [2; 3; 4], music structure analysis [5], rhythm identification [6], and music classification [7].  
Tempo estimation is defined as extracting tempo information from musical signals, which are the physical level waveforms and usually exist in the audio file, such as wav and MP3. Tempo estimation is a challenging task, and some special musical elements, such as legato and rest, make it even harder. Tempo always changes when music proceeds. There are two types of change: one is originally designed by the composer of the music, and the other is due to the playing or singing error. The former type of change usually takes place only few times, one or two, and even no one under many circumstances.  
However, the latter is inevitable and exists in all parts of the music. Therefore, we should estimate the tempi at any time. We regard all of the tempi components at one time as a vector, called tempo vector. The tempo vectors at all of the proceeding time of the music make up the tempogram.  
Following the definition in [8], a tempogram in this paper is a two-dimensional time-tempo representation for a given time-dependent signal, where the time parameter is measured in seconds and the tempo parameter measured in bpm. We could represent the tempo of music using tempogram [9], and then conduct tempo estimation through Dynamic Programming [10], Viterbi [11; 12], or other methods [13]. Moreover, with the help of tempogram, other music analysis tasks, such as beat tracking, could also be easier to complete.  
In this work, we proposed a novel tempogram generating algorithm that is based on Matching Pursuit (MP) and designed the tempo dictionary to implement the algorithm. This new algorithm outperforms the traditional algorithms with higher resolution, stronger sparsity and flexibility.  
The remainder of this paper is organized as follows. Section 2 introduces related work. Section 3 presents the novel tempogram generating algorithm. Section 4 discusses the properties of this novel algorithm. Section 5 sketches an example application. Finally, Section 6 concludes the paper.
# Effect of Shot Peening on the Evolution of Scale on T91 Steel Exposed to Steam
## 1 Introduction  
Owing to their microstructural stability, high temperature strength, and oxidation resistance, 9 Cr-1 Mo ferritic-martensitic (F-M) steels are universally found in high temperature applications in fossil fuel power plants. During exposure to high temperature steam environments, oxidation is an unavoidable problem for boiler tubes, such as found in superheaters and reheaters [1, 2].  
The oxidation behavior of F-M steels has been investigated by several groups [3, 4, 5], and are dependent on various factors, including temperature, atmosphere, and oxygen concentration. [PERSON] et al. [6] reported that the oxide scale with a three-layer microstructure composed of Fe\({}_{2}\)O\({}_{3}\), Fe\({}_{3}\)O\({}_{4}\) and (Fe, Cr)\({}_{3}\)O\({}_{4}\) formed on T91 steel in an (Ar + H\({}_{2}\)O) atmosphere at 600-700 \({}^{\circ}\)C. The difference in the coefficients of thermal expansion (CTE) of Fe\({}_{2}\)O\({}_{3}\) (12.5 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) at 100-1200 \({}^{\circ}\)C) and Fe\({}_{3}\)O\({}_{4}\) (18.8 \(\times\) 10\({}^{-6}\) K\({}^{-1}\) at 300-800 \({}^{\circ}\)C) result in partial exfoliation of outer Fe\({}_{2}\)O\({}_{3}\) layer. The studies by [PERSON] et al. [5] indicated that the oxide scales of T91 steel exhibited higher thickness, higher interconnected porosity, and weak adhesion to metal matrix for high oxygen concentration exposure. According to [PERSON]'s theory of oxidation [7], the oxidation rate depends highly on the diffusion of the ions across the oxide scale during the steady state of oxidation.  
Shot peening is a widely used method to cause plastic deformation at the surface, resulting in high-density dislocations and refined grains [8]. Shot peening improves oxidation resistance due to the increased number of pathways for Cr to diffuse to the surface to form a Cr-rich protective layer. An inner Cr-rich protective layer of Cr\({}_{2}\)O\({}_{3}\) and FeCr\({}_{2}\)O\({}_{4}\) has been observed in both P92 and SS304H steel [9, 10, 11]. Similar results were also reported by [PERSON] and [PERSON] [12]: a protective Cr- and Mn-rich oxide layer improved the steam oxidation resistance, forming a thinner oxide scale, and showing less masschange of 304H at 550-625 \({}^{\circ}\)C. However, this benefit began to break down after -5000 h exposure at 650 \({}^{\circ}\)C.  
Thus, an investigation of the formation and evolution of oxide scales in the early stage of steam oxidation is called for. In this study, we characterize the features of oxidation behavior of T91 steel after short-term steam exposures of 100-1000 h.
## 4 Numerical Application
### Robustness Analysis
#### 4.3.3 Initial Model Sensitivity Analysis  
The Marmousi model was used to test the sensitivity of the FAFWI method to different initial models. In the validation, we used a constant velocity model of 1800 m/s as the initial model. We then gradually increased this velocity value to test constant velocity models of 2100, 2300, and 2500 m/s as the initial models. These values represent the average velocities from the shallow to deeper regions around the well.
# A Review of Photothermal Detection Techniques for Gas Sensing Applications
## 2 First Experimental Verification of PTS Gas Sensors
### Optical Heterodyne Gas Spectrometer  
One of the first attempts to use PT-induced effects for measuring highly diluted gas samples was proposed by [PERSON] et al. in 1981 [18]. The method was adopted from PT techniques used in probing highly diluted liquid samples [20]. The Authors proposed to use a modified homodyne Mach-Zehnder (MZ) interferometer as a method of measuring the PT-induced RI modulation. The setup is depicted in Figure 2.  
A single mode, single frequency beam of an HeNe laser was split into two arms to form an MZ interferometer (_probe_). A glass sample cell fitted with appropriate windows was inserted into the optical path of one of the interferometer arms. One of the mirrors (M on the schematic in Figure 2) was mounted on a piezoelectric transducer. By driving the position of the mirror, the MZ interferometer was stabilized in quadrature (in the middle between interference maximum and minimum), preventing long-term thermal variations affecting the signal readout. In this experiment, a CO\({}_{2}\) laser with a maximum output power of 20 W was used as the _pump_ source. The sensor performance was evaluated by measuring methanol vapors (CH\({}_{3}\)OH). Authors calculated the detection limit at a level of 3 ppb (part per billion). The achieved detection limit was excellent, proving that photothermal detection trace concentrations of gases can be an interesting alternative to commonly used methods. Although the sensors proposed by [PERSON] reached sensitivity close to photon-noise-limited values (10\({}^{-10}\) cm\({}^{-1}\)), this was possible only by employing a very powerful _pump_ laser (~20 W CW). Moreover, because the thermal response of the gas sample is limited, it is beneficial to work with low _pump_ laser modulation frequencies. [PERSON] used an optimal modulation frequency of 23 Hz, but found that due to the increased 1/_f_ noise components, the sensor had to be secured on a highly stable optical table and enclosed in an additional enclosure, dampening acoustic noise. Although sensitive, this sensor layout is complex and implementing it in out-of-lab applications would be complicated. The publication by [PERSON] et al. [18] is particularly interesting due to the extensive investigation of the thermal effects occurring in gases excited by optical beams. A detailed description is included in the appendix of the publication.
## 2 Materials and Methods
### Data Processing and Analysis
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \(\mathbf{T}\) & \multirow{2}{*}{pH} & EC (\(\mathbf{\mu}\)S/cm) & **OS** & **BOD** & **NH\({}_{4}\)\({}^{*}\)** & **TN** & **SS** & **PO\({}_{4}\)\({}^{3-}\)** & **TC** & **Coil/** & \(q_{i}\times w_{i}\) \\  & & & **(\%)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **(mg/L)** & **100 mg** & & \\ \hline \multicolumn{8}{c}{93–109} & \multicolumn{8}{c}{18} \\ \hline  & & & 88–92 & 110–119 & & & & & & 17 \\ \hline  & & & 88–87 & 120–129 & & & & & & 16 \\ \hline  & & & 81–84 & 130–134 & 0–0.9 & & & & & 15 \\ \hline  & & & 78–80 & 135–139 & 1–1.9 & & & & & 14 \\ \hline  & & & 75–77 & 140–144 & 2–2.4 & & & & & 13 \\ \hline  & & & 72–74 & 145–154 & 2–5.2–9 & 0–0.09 & & 0–249 & 12 \\ \hline  & & & 69–71 & 155–164 & 3–3.4 & 0.1–0.14 & & & 250–999 & 11 \\ \hline  & & & 66–68 & 165–179 & 3.5–3.9 & 0.15–0.19 & & & 1000–3999 & 10 \\ \hline  & & & 63–65 & 180+ & 4–4.4 & 0.2–0.24 & & & 4000– & 9 \\ \hline  & 6-6.4 & 8-8.4 & 59–62 & 4.5–4.9 & 0.25–0.29 & 0–0.49 & & 0–0.029 & 800–14,999 & 8 \\ \hline  & 5.8-5.9 & 8.5-8.7 & 55–58 & 5–5.4 & 0.3–0.39 & 0.5–1.49 & 0–9 & 0.03–0.059 & 15,000–24,999 & 7 \\ \hline  & 5.6-5.7 & 8.8-8.9 & 0–188 & 50–54 & 5.5–6.1 & 0.4–0.49 & 1.5–2.49 & 10–14 & 0.06–0.099 & 25,000–44,999 & 6 \\ \hline
0–17.4 & 5.4-5.5 & 9–9.1 & 189–239 & 45–49 & 6.2–6.9 & 0.5–0.59 & 2.5–3.49 & 15–19 & 0.1–0.129 & 45,000–79,999 & 5 \\ \hline
17.5–19.4 & 5.2–5.3 & 9.2–9.4 & 240–289 & 40–44 & 7–7.9 & 0.6–0.99 & 3.5–4.49 & 20–29 & 0.13–0.179 & 80,000–139,999 & 4 \\ \hline
19.5–21.4 & 5-5.1 & 9.5–9.9 & 290–379 & 35–39 & 8–8.9 & 1–1.99 & 4.5–5.49 & 30–44 & 0.18–0.219 & 140,000–249,999 & 3 \\ \hline
21.5–22.9 & 4.5-4.9 & 10–10.4 & 380–539 & 25–34 & 9–9.9 & 2–3.99 & 5.5–6.99 & 45–64 & 0.22–0.279 & 250,000–429,999 & 2 \\ \hline
23–24.9 & 3.5–4.4 & 10.5–11.4 & 540–839 & 10–24 & 10–14.9 & 4–9.99 & 7–9.99 & 65–119 & 0.28–0.369 & 430,00–749,999 & 1 \\ \hline
25+ & 0–3.4 & 11.5–14 & 840+ & 0–9 & 15+ & 10+ & 10+ & 120+ & 0.37 & 750,000+ & 0 \\ \hline \hline \end{tabular} Source of data: Scottish Development Department [39].  
\end{table} 
Table 3: Weighting criteria for SWQI parameter assessment. tional comparability in water quality evaluation. A detailed description of SWQI and CWQI methodologies was presented in our previous study [15]. Given the limited number of sampling events (three measurements over two years) and the inconsistency in parameter coverage across different depths and profiles, it was not feasible to apply inferential statistical methods to assess trends or correlations with high confidence. Instead, the analysis focuses on descriptive evaluation, comparing water quality indices and observed parameter variations within the available dataset. A small number of samples does not allow statistical validation of trends, emphasizing the need for more frequent and systematic long-term monitoring in future assessments.
## 4 Results and Discussion
### Stepwise Intelligent Fault Diagnosis Based on SAE
#### 4.2.3 Validation Results  
Furthermore, the experimental result of the proposed method for sliding bearing is shown as Figure 10, when the parameter of SAE is setting as Table 3. As shown in Figure 10, all the data is correctly classified and there are no samples to be misclassified. Therefore, it can achieve a diagnosis of 100% and prove the effectiveness and feasibility of the proposed method in the fault diagnosis of sliding bearing. Also, the results of comparative experiments using the raw signal without statistical filter or stepwise diagnosis are collected in Table 5.  
In Table 5, the method A is representing the proposed method without the statistical filter, the method B is representing the proposed method without the stepwise diagnosis and the method C is representing that the SAE is only utilized to perform the fault diagnosis of the sliding bearing with the raw signal in time domain. The parameters of the neural network (SAE) are same as the Table 3. Moreover, the result is the mean value of 10 trails to guarantee the statistical validity of experimental results.  
The results described as Table 5 prove that the proposed method not only releases the manual labor and the dependence on professional knowledge but also accurately identifies faint and weak features between multiple faults in sliding bearings. Moreover, it can be found that the standard deviation of the method C is the 2.3574 that is the biggest in the three methods since the noise in signal is deceptive in the process of accurately identifying the bearing in the neural network. Therefore, the proposed method can give full play to the statistical filter and stepwise diagnosis to overcome the difficulties, including noise and complex features in the process of fault diagnosis.