esa-sceva/satcom-chunk-collection huggingface dataset:
## 5 SBS in a DOF
### Theory of SBS  
The SBS dynamics in a fiber can be mathematically modeled through a set of partial differential equations governing the complex amplitudes of the pump \(\mathcal{P}\), Stokes \(S\), and acoustic \(A\) waves [41]:  
\[\frac{\partial\mathcal{P}}{\partial t}+\frac{c}{n}\frac{\partial\mathcal{P}}{ \partial z}=\frac{i\omega\gamma_{e}}{2n^{2}\rho_{0}}AS, \tag{44}\]  
\[\frac{\partial S}{\partial t}-\frac{c}{n}\frac{\partial S}{\partial z}=\frac{ i\omega\gamma_{e}}{2n^{2}\rho_{0}}A^{*}\mathcal{P}, \tag{45}\]  
\[\frac{\partial A}{\partial t}+\frac{\left(\Omega_{B}^{2}-\Omega^{2}-i\Omega \Gamma_{B}\right)}{\left(\Gamma_{B}-2i\Omega\right)}A=\frac{\varepsilon_{0} \gamma_{e}\tilde{q}^{2}}{\left(\Gamma_{B}-2i\Omega\right)}\mathcal{P}S^{*}+f_{ L}, \tag{46}\]  
where \(c\) represents the speed of light in vacuum, \(n\) stands for the effective index of the optical mode, \(\omega\) is the optical angular frequency of the pump, \(\gamma_{e}\) is the electrostriction constant, \(\rho_{0}\) denotes fiber density, \(\varepsilon_{0}\) represents the vacuum electric permittivity and \(f_{L}\) represents Langevin noise. Additionally, \(\Omega_{B}\) signifies the Brillouin frequency, \(\Omega\) represents the detuning of the probe angular frequency, \(\tilde{q}=\frac{2\omega n}{c}\) represents the phase matching condition, and \(\Gamma_{B}\) is the acoustic linewidth.  
The SBS dynamics in a fiber arise from the interplay between two optical waves and an acoustic wave. Determining the SBS threshold relies on understanding the modal and propagation characteristics of these three waves. To achieve this, modal field profiles and the propagation constants of the optical and acoustic modes needs to be determined. The optical modes can be obtained by numerically computing the solution of the Helmholtz equation, i.e., Equation (3). The effective indices of the guided optical modes can then used to compute the acoustic mode by applying the phase matching condition for the SBS process [52]. In speciality optical fibers, like a DOF, where the transverse profile variesalong the propagation direction, and modal characteristics can be computed at discrete variation intervals along the propagation direction. Using this method the acousto-optic overlap can be calculated which is essential in determining the SBS threshold for a DOF as we discuss subsequently. While the spectral linewidth of the source can affect the SBS threshold, in our analysis, we assume that the phonon linewidth ranges from 35 to 50 MHz, which is much broader than the spectral linewidth of the source in our experimental setup (in the range of a few kHz). As a result, we can safely ignore the influence of the source's spectral width in our simulations.
## 2 Materials and Methods
### Development and Selection of the Study Basis  
The performance comparisons made using the \"base\" parameters as shown in Table 1 allowed the best performing processes to be identified from Cases 1 to 4 using the methods described above. The comparisons also helped--in some cases--to identify the potential improvements in the flow schemes. In particular, the use of expanders in place of valves in Case 4 was identified as a potential improvement along with the possibility of an additional stage of cooling in Cases 2 and 4.  
The application of these features in the flow schemes for Cases 1 to 4 leads to the development of five new cases. The new cases as called Case 4b, which is Case 4 with expanders replacing valves; Cases 5a and 5b, which are developed from Case 2 by adding an extra cooling stage (5a with valves and 4b with expanders); and Case 6, which is developed from Case 4 by adding an additional stage and valves or expanders. Figures 2 and 3 present the flow scheme for Cases 5 and 6.  
Each of these new cases were optimized following the same procedure as Cases 1 to 4. The number of optimization variables, \(n\), for Cases 5a and b are each less than Case 2 because the pressure level in  
Figure 3: Flow scheme for Case 6a. In Case 6b, each of the three letdown valves is replaced with an expander.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Case** & **Case 1** & **Case 2** & **Case 3** & **Case 4** \\ \hline Refrigerant & CO\({}_{2}\) and NH\({}_{3}\) & NH\({}_{3}\) & NH\({}_{3}\) & CO\({}_{2}\)-open \\ Cycle & cascade & 3-stage & dual & 2-stage \\ Cooling method & valves & valves & expanders & valves \\ Cooling stages & 2-stage & 1-stage & 2-stage & n/a \\ Pressure (bara) & 8.9 & 15 & 15 & 7.0 & 15 & 7.0 & 15 \\ Energy (kWh/ton) & 105 & 96.0 & 92.6 & 103 & 93.3 & 106 & 95.2 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Summary of optimized power consumption for the base process schemes.  
Figure 2: Flow scheme for Case 5a. In Case 5b, each of the three letdown valves is replaced with an expander.  
the second stage of the NH\({}_{3}\) compressor in Cases 5a and b is set by the temperature in the CO\({}_{2}\) cooler (i.e., just above 0 \({}^{\circ}\)C). This gives \(n=2\) for Cases 5a and 5b. Cases 6a and b have the same number of optimization variables as Case 4 (\(n=4\)). Table 4 presents the results of the optimization work for Case 5 and Case 6.  
Once the optimization process was complete, the best performing schemes were selected for the final part of the modelling work: the investigation of the impact of ambient temperature on energy consumption.
# TSViT: A Time Series Vision Transformer for Fault Diagnosis of Rotating Machinery
## 4 Experimental Results and Evaluation
### Visualization of Feature Vectors  
The feature vector distribution within the embedding space serves as a meaningful indicator to assess a model's generalization capability [32]. Here, we employed t-distributed Stochastic Neighbor Embedding (t-SNE) [33] to visualize the feature vectors extracted by different layers. This visualization reflects the feature learning and classification processes of the TSViT.  
We employed t-SNE to visualize two complete test sets, aiming to validate the TSViT's effectiveness. The feature vectors include patch embeddings and class tokens. Figures 16-18 depict the visual results of different layers of the TSViT through t-SNE on the PBR, CWRU, and XJTU test sets, respectively. As observed in Figures 16a, 17a and 18a, the feature vectors of the raw vibration signals across different health states show substantial mixing and overlap, preventing clear differentiation between states. This phenomenon persists even within the CNN layer. However, surprisingly, there is a notable improvement observed on the PBR and XJTU test sets in the first block. This shows that the feature vectors in this block are distinct and already easily distinguishable. A similar situation is observed in the second block for the CWRU test set. Failure categories are entirely distinct in the sixth block for the PBR and XJTU test sets. In the seventh block, only a few F9 samples fall within the decision boundary of F3. Both F3 (inner raceway) and F9 (outer raceway) have fault diameters of 0.021 inches. Similarly, in the eighth block of the CWRU test set, the failure categories are completely separated as well. These visual results illustrate the robustness of the TSViT in feature extraction.  
Figure 16: Feature visualization in different TSViT layers through t-SNE on the PBR test set (**a**) raw signals (**b**) cnn (**c**) the first block (**d**) the second block (**e**) the third block (**f**) the fourth block (**g**) the fifth block (**h**) the sixth block (**i**) the seventh block (**j**) the eighth block (**k**) classification layer.
## 3 Results and Discussion  
### PHBV Extraction and Purification  
PHBV containing biomass was produced at pilot-scale by MMC fed fermented dairy manure as described by [PERSON] et al. [6]. The PHBV amount per dry cell of biomass, the 3 HV content in the PHBV rich biomass, and the purity of PHBVs was determined by GC-MS and the results are given in Table 1. The 3 HV content in purified PHBV was determined by \({}^{1}\)H-NMR and data was from [PERSON] et al. [6]. The lipid extracted from lyophilized biomass in the pre-treatment step (acetone wash) was 5-10% of the biomass weight which improves PHBV extraction [30]. The result showed that the 3 HV molar fraction of the purified PHBV was less than that of PHBV rich biomass. The lower 3 HV molar fraction in all the extracted polymers compared PHBV-rich biomass shows that CHCl\({}_{3}\) selectively extracted PHBV with lower 3 HV content. [PERSON] et al. [8] reported the 3 HV ratio of PHBV rich biomass produced using MMC was about 0.39 and decreased to 0.34 on purified PHBV. All the purified PHBVs showed lower HV content than PHBV rich biomass. This can be attributed to the effect of solvent type, extraction time and temperature on the HV content of the extracted PHBV [31]. There is an optimum time and temperature in extraction with every solvent and method. It has been seen that the HV content and \(M_{w}\) of the extracted PHBVs decreased when extraction time exceeded the optimum level [31], and a longer extraction time can result in hydrolys of polymer chains [32; 33]. Furthermore, depending on the solvent type the atomic interactions between solvent and solute vary which affects the composition of the extracted polymer [19; 34].
# VLSI Implementation of an Efficient Lossless EEG Compression Design for Wireless Body Area Network
## 1 Introduction  
The electroencephalogram (EEG) [1; 2] signal has always been considered an inherent and crucial reference for the neurologist to diagnose any brain disorder. EEG is a technique used to record electrical activity generated by the human brain [3]. Other problems associated with the abnormal functioning of the brain which can be diagnosed by using EEG signals include coma, confusion, stroke, and tumors. Among various modalities available, ambulatory or portable EEG is expected to emerge as a potentially viable area for data compressions in EEG devices. This is attributed to the ease of accessibility and high patient comfort offered during the EEG signal acquisition procedure.  
Traditional EEG monitors and recorders transmit EEG signals via cables. In recent years, the Wireless Body Sensor Network (WBSN) technology is being widely developed since it can greatlyenhance the way people live in terms of comfort and convenience [4; 5; 6]. WBAN is also being developed due to its high potential to replace the use of batteries for portable devices [7; 8; 9]. Most portable wireless devices in the market, however, is limited to their onboard battery capacity as their main constraint for the long-term use of the device. One of the approaches to improve the efficiency of current wireless EEG recorders is making use of a lossless compression technique to reduce the power consumption during the transmission of EEG data.  
In this paper, a novel dynamic voting prediction methodology is proposed in order to adaptively choose the optimal prediction function. In addition, a fuzzy decision unit is incorporated to improve the accuracy of prediction. A novel dynamic voting scheme is combined with the adaptive fuzzy prediction methodology for the EEG compression. Moreover, a novel tri-stage entropy encoding method is combined with the adaptive fuzzy and voting-based prediction. The comparators and multiplexer components were used to replace the complex computing components to achieve hardware sharing. A pipelining technique is used to improve the performance of this design. Simulation results showed that the proposed algorithm significantly improved the compression rate with a relatively low complexity in its VLSI implementation.  
This paper is organized as follows: Section 2 presents a literature review of several lossless compression algorithms. Section 3 discusses the methodology of the proposed novel lossless EEG compression algorithm. This includes dynamic voting prediction, tri-stage entropy coding and its VLSI architecture. Section 4 lists and discusses the results of the proposed lossless EEG compression algorithm and its performance. A comparison with previous studies in terms of compression rate and VLSI design performance is also presented. Conclusions were summarized in Section 5.
# Real-Time Implementation of Multiband Spectrum Sensing Using SDR Technology
## 4 Real-Time Experiments and Results
### Results
Due to the nature of this methodology, events such as high-frequency noise, impulsive noise, or abrupt changes in the PSD generate more frequency windows due to the misclassification of the PSD signal by the ED in a short frequency range. ED stands out as a simple technique to implement with low processing resources. Nevertheless, it is a technique that tends to fail very quickly, especially when the environment has a lot of high-frequency noise or impulsive noise. Figure 21: Probability of success of the energy detector. Figure 22: Samples in error for each _SU_ in the implementation.
## 2 Micro-Strip Patch Antenna and DR Process
### Feature Selection (FS)  
Unlike FE, FS does not take into account the same dimension when the initial data are reconstructed. It just focuses on grabbing the most significant subgroup in the lower dimension. The importance of the elected subset may not be ensured if the subset contains meaningless properties of the initial data. Diverse studies have been achieved using the mutual information method, genetic algorithm, and single variable classifier method [43], but those methods still have handicaps, that is, huge computational complexity and untrustworthy assessment of the data distributions if the behavior of the distributions is not clearly provided [22]. On the other hand, independent features test (IFT), called the simple hypothesis test [27], is able to quickly discard unnecessary features. For this advantage, IFT will be employed in this proposed framework. The target data are assumed as categorical, so IFT allows all features to be assigned to two categories. This approach is conducted by a scoring value of informative features, SVIF.  
\[SVIF(F)=\frac{\left|\mu.(A)-\mu.(B)\right|}{\sqrt{\left(\frac{v.(A)}{n_{1}}+ \frac{v.(B)}{n_{2}}\right)}}>sv \tag{12}\]  
From Equation (12), two data sets related to the feature (\(F\))'s values can be described as \(A\) and \(B\), where \(n_{1}\) and \(n_{2}\) denote the number of the features employed in the categories. With variance, \(v\), and mean, \(\mu\), the significance value of data can be calculated. In accordance with a threshold, \(sv\), indicatingthe significance value to eliminate unimportant features, IFT is able to get the best features. In general, it is suggested that the significance value is equal to or greater than 2 [27].
## 1 Introduction
\begin{table}
\begin{tabular}{c c} \hline
**Reference Title** & **Comments** \\ \hline Development of RP-HPLC method for simultaneous evaluation of uniformity of dosage units from aspirin and dipyridamole extended-release capsules [22] & \
\begin{tabular}{c} \\ Stationary phase: X Bridge C8 column (250 \(\times\) 4.6 mm, 5 \(\upmu\)m) \\ Mobile phase: 0.05 M phosphate buffer pH 2.5-MeOH 55:45 \(\
icefrac{{\it\upsilon}}{{\it\upsilon}}\) \\ MeOH 50:50 \(\
icefrac{{\it\upsilon}}{{\it\upsilon}}\) \\ \end{tabular} \\ \hline Stability-Indicating Spectofluorimetric and RP-HPLC Methods for the Determination of Aspirine and Dipyridamole in their Combination [25] & \
\begin{tabular}{c} Detector:FLD \\ Diluents: CH\({}_{3}\)COOH in CHCl\({}_{3}\), 1\(\%\)\(\
icefrac{{\it\upsilon}}{{\it\upsilon}}\) \\ LOD: ACA 0.21 \(\upmu\)g/mL, DIP 0.37 \(\upmu\)g/mL, SAL 0.25 \(\upmu\)g/mL \\ LOQ: ACA 0.97 \(\upmu\)g/mL, DIP 1.28 \(\upmu\)g/mL, SAL 0.36 \(\upmu\)g/mL \\ \end{tabular} \\ \hline \
\begin{tabular}{c} RP-HPLC- UV \\ Stationary phase: Adsorbosil C8 (10 \(\upmu\)m, 250 mm \(\times\) 4.6 mm i.d.) \\ Mobile phase: H\({}_{2}\)O- ACN: o-H\({}_{3}\)PO\({}_{4}\) (65:35:2, \(\
icefrac{{\it\upsilon}}{{\it\upsilon}}\)/\(\
icefrac{{\it\upsilon}}{{\it\upsilon}}\)) \\ LOD: ACA 0.026 \(\upmu\)g/mL, DIP 0.044 \(\upmu\)g/mL, SAL 0.015 \(\upmu\)g/mL \\ LOQ: ACA 0.074 \(\upmu\)g/mL, DIP 0.153 \(\upmu\)g/mL, SAL 0.085 \(\upmu\)g/mL \\ \end{tabular} \\ \hline Spectofluorometric estimation of aspirin and dipyridamole in pure mixtures and in dosage forms [26] & \
\begin{tabular}{c} Diluents: CH\({}_{3}\)COOH in CHCl\({}_{3}\), 1\(\%\)\(\
icefrac{{\it\upsilon}}{{\it\upsilon}}\) \\ Concentration range: ACA 2–12 \(\upmu\)g/mL, DIP 2–12 \(\upmu\)g/mL \\ \end{tabular} \\ \hline Simultaneous Determination of Dipyridamole and Acetylsalicylic Acid in Pharmaceuticals and Biological Fluids by Synchronous and First Derivative Synchronous Fluorimetry [27] &
\begin{tabular}{c} Diluents: MeOH, phosphate buffer pH 7.4 \\ Concentration range: ACA 5–100 ng/mL, DIP 5–90 ng/mL \\ LOD: 0.05–1 ng/mL \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: Methods for the simultaneous determination of ACA, DIP, and SAL/literature review. investigate the degree of penetration of the active ingredients into the systemic circulation. The information provided could be used as basic knowledge for the study of corresponding modern pediatric formulations.
## 6 Results and Discussions
### Access Point 4  
Since Access Point 4 is the destination of the transmission, it does not transmit bytes in either of the routing methods; thus, the number of transmitted bytes as well as the consumed energy for doing so are close to zero, as shown in Figures 7d and 9d, respectively. The received bytes and receiving energy consumption of Access Point 4 when using each of the evaluated routing protocols are as follows:  
* Shortest-path routing method: Access Point 4 receives the data traffic from Access Point 2. The received bytes are shown in Figure 8d, and the receiving energy increases as shown in Figure 9d. In this case, the transmission depends entirely on the battery state of Access Point 2. Then, when Access Point 2 becomes ineffective (at 200 s), the receiving energy drops, and the transmission is terminated.
* Proposed SDN-based energy-aware routing method: Access Point 4 is the destination of the bytes from Access Point 1. On one hand, if the battery level of Access Point 2 is high, Access Point 4 receives traffic from Access Point 2. On the other hand, if the battery level of Access Point 2 is low, the SDN controller shifts the traffic path to the energy-aware routing path, and Access Point 4 receives traffic from Access Point 5. In this case, independently of whether Access Point 2 or Access Point 5 is transmitting them, Access Point 4 receives bytes continuously, and the transmission lasts up to 400 s, as shown in Figure 8d. In this line, the receiving energy consumption of Access Point 4 is stable until the end of the transmission, as shown in Figure 10d.
# Performance of Pozzolan-Based Reactive Magnesia Cement Mixes against Sulphate Attack
## 1 Introduction
Taking into account the global warming potential (GWP), the addition of 30% of fly ash in concrete mixes has been shown to decrease the GWP to 22% [22]. Similarly, RMC mixes based on fly ash exposed to a higher concentration of CO\({}_{2}\) attain strength rapidly with much lower CO\({}_{2}\) emission [23]. Slag-RMC mixes exposed to high concentrations of CO\({}_{2}\) (\(\sim\)about 99%) for 56 days have an increased compressive strength due to the formation of calcite and magnesium calcite [24]. Pozzolanic reaction products form along uncarbonated zones because of slag in concrete mixes, resulting in a more densified microstructure. There have been quite a few studies concerning both the strength development and reaction products of carbonated RMC mixes containing fly ash and slag. However, there is very little information on the action of chloride and sulphate ions on carbonated RMC mixes. Sulphate attack is one of the most common causes of concrete deterioration, and can develop in many types of structures both above and below the ground [25]. This degradation occurs due to the expansive growth of ettrinigite and/or thaumasite within a concrete surface, often leading to the formation of surface-parallel cracks infilled with sulphate reaction products [26]. Etringite is formed due to chemical reactions between moisture-containing sulphates and cement components [27]. Thaumasite forms in situations where there is a source of both sulphate and carbonate species. The formation of thaumasite is greater when the temperature is below 15 \({}^{\circ}\)C [28]. The carbonate required for the formation of thaumasite can be externally derived from groundwater containing carbonate species [28]. This complex process eventually leads to the degradation of concrete properties that are highly interdependent [29]. The formation of expansive gypsum and ettrinigite leads to a loss of strength and stiffness, and further contributes to the degradation of concrete [30]. Slag activated with reactive MgO is highly resistant to sodium sulphate attack when compared to cement-activated slag. Ettrinigite and calcium aluminate hydrate are not found in slag-MgO mixes after sulphate attack [31]. The purpose of this study is to investigate the degradation of Mg-based products after an external sulphate attack. Concrete samples modified with pozzolanic materials were carbonated for up to 4 weeks and then soaked in MgSO\({}_{4}\) solution for up to 12 weeks. The losses in their mass and compressive strength were then recorded. The effects of fly ash and slag on RMC hydration and subsequent carbonation were also evaluated using isothermal calorimetry, X-ray diffraction (XRD), and thermogravimetric analysis (TGA). An SEM study was conducted on degraded concrete surfaces in order to examine the removal of Mg from the matrices due to sulphate attack. These experimental results provide valuable information concerning the effects of pozzolan on the performance and degradation of Mg-based products in extreme environments.