esa-sceva/satcom-chunk-collection huggingface dataset:
## 6 Validation of the Generated Harmonics
### Validation through Simulation and Analytical Results
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Harmonic Order (u)**} & \multirow{2}{*}{**Harmonic Frequency (Hz)**} & \multicolumn{2}{c}{**Mathematical Results**} & \multicolumn{2}{c}{**Simulink Results**} \\ \cline{3-6}  & & **Magnitude (V)** & **Phase-Angle (Deg)** & **Magnitude (V)** & **Phase-Angle (Deg)** \\ \hline
1 & 12.5 & 25.46 & 90 & 25.37 & 90 \\
3 & 37.5 & 54.56 & 90 & 54.26 & 90 \\
4 & 50 & 75 & 0 & 74.41 & 0 \\
5 & 62.5 & 42.06  
The simulation results validate the mathematical formulation of Equations (7), (12), (20) and (25) developed for phase-angle, on-off cycle, and PWM control, respectively. Figure 9a depicts the output phase voltage of a three-phase AC voltage controller. It can be observed that this output voltage waveform is almost symmetric along the y-axis in one period, so its simulated harmonics are approximately matched to the computed values as tabulated in Table 6. Now if the firing delay is changed from 60\({}^{\circ}\) to 90\({}^{\circ}\) to decrease the output RMS voltage, then the output voltage waveform becomes asymmetric along the y-axis in one period as shown in Figure 9b. This output voltage contains the DC component as well as even and odd harmonics. So, this leads to inaccurate results as can be viewed from the results of Table 7. Now if the wave starting time is adjusted in the Simulink plot as depicted in Figure 9c to get symmetry along the y-axis in one period of the waveform, then it improves the harmonic amplitude result but this adds a severe error in their phase angles as can be seen from the values in Table 8. On the other hand, there are no such issues in the power quality concerns that are based on the computed values. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Harmonic Order (\(n\))**} & \multirow{2}{*}{**Harmonic Frequency (Hz)**} & \multicolumn{2}{c}{**Mathematical Results**} & \multicolumn{2}{c}{**Simulink Results**} \\ \cline{3-6}  & & **Magnitude (V)** & **Phase-Angle (Deg)** & **Magnitude (V)** & **Phase-Angle (Deg)** \\ \hline
1 & 50 & 79.22 & \(-26.87\) & 79.08 & \(-27.04\) \\
3 & 1
## 4 Discussion
### Leachates and PHREEQ-C Simulations
#### 4.2.1 As in Eluates  
Arsenic concentrations in the leachates (Table 6) have highlighted that 19 samples out of 35 exceeded the legal limit (10 ug\(\cdot\)L\({}^{-1}\)) imposed by the Italian Legislative Decree 152/2006 for drinking waters. From PHREEQ simulations, As in solutions was occurring as As\({}^{5+}\), and the three main species were HAsO\({}_{4}{}^{2-}\), H\({}_{2}\)AsO\({}_{4}{}^{-}\) e CaAsO\({}_{4}{}^{-}\). Additionally, PHREEQ simulations highlighted that the solubility of Fe-oxy-hydroxides (HFO), which could adsorb As and Sb on their surfaces, is apparently governed by the solubility of magnetite (SI = c.a.10, Table S1). This is in agreement with [61; 62], who found that the retention of Arsenic at neutral condition pH (6-7) seems to be governed by HFO adsorption (e.g., [63; 64]). At pH > 8, calcite may be precipitating. Thus, As can also enter the CaCO\({}_{3}\) lattice as calcium arsenate (CaAsO\({}_{4}\)) [65; 66], where Fe(Mn)-oxides lose their adsorbing efficiency [67]. The Eh-pH diagrams (Figure 6), representing the interaction between As-S-Ca, were constructed by taking the average As concentrations in the various eluates.  
Figure 6: Eh–pH diagram of As–S. Purple diamonds represent the CO\({}_{2}\)-saturated eluates.  
Figure 6 confirms that the main As species in solution was HAsO\({}_{4}\)\({}^{2-}\), although for samples with weakly acidic pH, a slight change in redox conditions can result in the reduction of As\({}^{5+}\) to As\({}^{3+}\), which occurs in the neutral form of H\({}_{3}\)AsO\({}_{3}\)\({}^{0}\). According to [61], this molecule is very poorly adsorbed, and the reduction of As\({}^{5+}\) to As\({}^{3+}\) may be the main mechanism by which As is released into groundwaters, thus favoring slightly anaerobic conditions. This would also explain why, for example, the waters draining the Galleria XXII, which is located about 1 km south of Le Lame, showed As concentrations between 10 and 12 \(\mathrm{\SIUnitSymbolMicro\text{g}}\cdot\mathrm{L}^{-1}\) ([68], and references therein).
# Sentiment Interaction Distillation Network for Image Sentiment Analysis
## 4 Experiment
### [PERSON]  
To demonstrate the validity of our proposed method, we first compared our approach against several previous works, including methods using handcrafted features, the CNN-based methods, and deep-learning-based methods with local feature branch.  
* Researchers extracted the low-level features from some small-scale datasets, including the local color histogram features (LCH)), which comprise the 64-bin RGB histogram after first dividing into 16 blocks, and the global color histograms (GCH), which comprise the 64-bin RGB histogram [35].
* SentiBank was proposed by [PERSON] et al. [31], and can use 1200 adjective-noun pairs (ANPs) to describe the sentiment concept and performs better for images that have rich semantics.
* DeepSentibank [36] employs CNN to achieve visual sentiment classification and discovering ANPs. We employed the pretrained DeepSentiBank to obtain the 2089 dimension features as mid-level representations from the last FC layer and applied LIBSVM to realize sentiment image classification.
* [PERSON] et al. [33] proposed a potentially cleaner dataset and designed the PCNN, which is a progressive framework based on CNN. They used large volumes of weakly supervised images to train the model and achieved a generalization improvement.
* [PERSON] et al. [7] utilized an object detection algorithm to label the \"Affective Regions\" and employed three different fusion strategies to complete the final classification.
* [PERSON] et al. [8] employ a saliency detection method to improve the salient features, and achieved a significant performance boost. Besides, they used an ensemble strategy, which may help improve performance.
## 1 Introduction
Natural hormonal estrogens are analyzed in negative ion mode and can be ionized using atmospheric pressure chemical ionization (APCI), but detection limits are generally lower when using electrospray ionization [6]. Positive ion mode in APCI can be used for estrogens but does not provide adequate sensitivity for xenoestrogens [28; 29]. Derivatization has also been used when analyzing estrogens and xenoestrogens in biological fluids and plasma to improve the electrospray ionization in positive ion mode for molecules with a phenolic group [7; 30; 31], but these methods have not been applied successfully to wastewater. Other derivatization agents, including pentafluorobenzyl bromide and 2-fluoro-1-methylpyridinium p-toluenesulfonate have been used for surface water; however, more MS signal suppression is observed in wastewater samples [32; 33; 34]. Solid-phase extraction (SPE) has been used for the selective analysis of estrogens generally in cartridge format [29; 35; 36]; however, when xenoestrogens are included, these methods often require higher elution volumes or stronger solvents [13; 37; 38] and as such these different groups of analytes are often analyzed in separate methods [39; 40; 41]. SPE discs with further Florisil SPE cartridges have also been used for the extraction of natural estrogens from surface water [1]. Recoveries have been reported \(<\)15% when divinylbenzene (DVB) SPE discs followed by NH2 SPE cartridges were used, with small improvements in recoveries when Florisil clean-up was used [1]. The selection of the SPE sorbent and sorbent capacity is critical in the extraction process with the severity of the matrix effect expected to increase from the surface water to the wastewater. The objective of this work was to develop a reliable and sensitive method for these selective EDCs that does not require derivatization, while still addressing the stability issues of some target analytes, and that can be applied to the highly complex sample matrix of wastewater. The method was designed to address in one analysis a larger range of target analytes that are amenable to electrospray ionization in negative ion mode. This includes the addition of two drugs (prednisone and its metabolite prednisolone) that have not been reported in studies in Canada and are not routinely screened for in water quality studies in many countries.
# Development of Attached Cavitation at Very Low Reynolds Numbers from Partial to Super-Cavitation
## 3 Results  
### Comparison between Degassed and Air-Saturated Oil  
In the previous study using silicone oil saturated with air at 1 bar ([PERSON] et al. (2019) [28]), three types of gaseous/vaporous structures have been observed. A typical example obtained at \(Re=1810\), \(\sigma_{1}=4.81\) and \(\sigma_{2}=1.52\) is shown in Figure 5a. The first one is called \"bottom tadpoles\" (see label (I) in Figure 5a). They attach to the side walls close to the throat, in the region of the primary flow separation that has been numerically demonstrated, and can be distinguished only in the top view. These structures appear for \(Re\leqslant 650\) and at inlet pressures well above the estimated critical value for cavitation. They thus might be composed of degassed air. The second type of structure consists of an attached cavity in the middle of the vein, just downstream of the throat (see label (II) in Figure 5a). It has been observed for \(Re\leqslant 800\) and their inception fairly corresponds to the critical value of the cavitation number estimated with numerical simulations. This attached cavity thus probably contains vapor. The third type of structures is reminiscent of the tadpoles and has been called \"top tadpoles\" (see label (III) in Figure 5a): they appear for \(Re\geqslant 1000\) and attach to the upper wall in the secondary boundary layer separation. The pressure in these regions is above the vapor pressure and they might also be composed of degassed air.  
Figure 5: Comparison between two experiments performed at similar Reynolds numbers (\(Re\simeq 1800\)) and cavitation numbers (\(\sigma_{2}\simeq 1.5\)) with a different degassing protocol. (**a**): Silicone oil is saturated with air at 1 bar before running the experiment. The three types of gaseous structures are spotted with (I) for bottom tadpoles, (II) for attached sheet cavity and (III) for top tadpoles. (**b**): Silicone oil is first degassed at 70 mbars; an attached sheet cavity is only present.  
In the present article, with a different protocol of intense degassing at 70 mbars before running the experiments, the bottom and top tadpoles are not observed (see Figure 5b, obtained at \(Re=1810\), \(\sigma_{1}=4.91\) and \(\sigma_{2}=1.57\)). This confirms that the top and bottom tadpoles are linked to degassing phenomena. Only the central attached cavity remains, and these cavities are moreover observed for cavitation numbers lower than the numerical critical value.  
The following paragraphs are devoted to the study of the development of these cavities when varying the absolute pressure at constant Reynolds number.
# PM2.5 Concentration Prediction Based on CNN-BiLSTM and Attention Mechanism
## 2 Methodologies
### Prediction Modeling Phase  
The prediction modeling phase is divided into two steps, as shown in Figure 3. One is CNN-BiLSTM, and the other is attention. During the process of CNN-BiLSTM, the training set mentioned above are first used as the input of CNN. Those features that are related to PM2.5 concentration and extracted by CNN are input into BiLSTM. Then, the dropout is used to process the output of BiLSTM, and the output of the dropout is considered the output of the CNN-BiLSTM step. Meanwhile, the output of CNN-BiLSTM step is entered into the Attention step. Specifically, the output of the dropout is entered into the attention  
Figure 1: The framework of CNN-BiLSTM-Attention.  
Figure 2: Examples of data modeling.  
mechanism, followed by a flatten layer, dropout layer, and two dense layers for training the proposed model. Finally, the results of PM2.5 concentration prediction are obtained when the test set is input into the proposed model.  
In the following section, the CNN-BiLSTM and the attention mechanism of the prediction modeling phase are described in detail.
## 2 The Proposed Fusion Approach  
This paper proposes a hybrid approach with MPCA to overcome other algorithms' blurring and spatial distortions. An algorithm is a novel approach in MIF because this hybrid technique with MPCA gives good performance compared to other algorithms in recent years. In the proposed method, the fusion procedure is performed individually on row and column images, which are then averaged to eliminate any noise or distortion generated by the fusion process. The noise elimination process is explained in Section 2.1. Then, the source images are decomposed into LF components and HF components using LP. It provides information on the sharp contrast changes to which the human visual system is principally sensitive. The LP method is explained in Section 2.2. Then, qshiftN DTCWT is used to fuse low and high-frequency components to produce a fused image with good directionality and a high degree of shift-invariance. The qshiftN DTCWT method is explained in detail in Section 2.3. After fusing the low and high-frequency components, IDTCWT is applied to reconstruct the fused low and high-frequency components. In the proposed method, MPCA is used to improve the efficiency of the hybrid approach (i.e., qshiftN DTCWT and LP) to reduce the redundant data and extract the essential components of the fused image (i.e., all-in-focus image). Also, MPCA emphasizes elements that have the most significant impact and are robust to noise. So, the MPCA reduces the blurring and spatial distortions; thus, the fused image has more detailed clarity, clear edges, and better visual and machine perception. The MPCA method is explained in Section 2.4. Finally, the fused image is formed and available for comparison. Various objective quality metrics are calculated to assess the proposed method's quality. These measures are described in Sections 2.6 and 2.7, respectively. Figure 1 depicts the proposed technique's flow diagram, detailed in Section 2.5.
## 3 Results and Discussion
### Temperature Distribution Caused by the Subsurface Impurity Defects
#### 3.1.1 Comparison of Temperature Distributions with and without Impurity Defects
The evolutions of temperature distributions caused by impurity defects are presented in Figure 6. As shown in Figure 6a-c, due to the strong absorption of CeO\({}_{2}\) defect, the temperature on the upper surface of impurity defect greatly increases in the heating process of laser pulse (\(t<10\) ns). When the laser pulse ends, the absorbed thermal energy is gradually diffused and transferred upward to the fused silica surface and downward to the bulk of the spherical CeO\({}_{2}\) defect. This means that the temperature distribution around the defect is non-symmetrical along the \(z\)-direction and should be highly dependent on their distances relative to the location of the initial incident lasers. Figure 6d,e shows the temperature distribution along the central axis for CeO\({}_{2}\) and Al defects at various times. One can see that, as the laser pulse acts, the temperature inside Al impurity defect (\(z\)-position ranging from 8.5 \(\upmu\)m to 9.5 \(\upmu\)m) becomes more and more uniform. The temperature is totally uniformly distributed in the bulk of Al defect when \(t>30\) ns (the curve keeps horizontally stable). However, for the case of the CeO\({}_{2}\) impurity defect, its induced temperature distribution inside impurity defect keeps barely uniform. The fact that the heat conduction capacity of Al is much better than that of CeO\({}_{2}\) should be responsible for this phenomenon. Figure 5: Comparison of temperature distributions caused by fused silica optics with and without impurity defects. (**a**) Temperature variation with respect to the radial position without impurity defect. Evolution of the peak temperature for fused silica optics without (**b**) and with (**c**) impurity defects of CeO\({}_{2}\), Al and Fe.
## 1 Introduction  
The glass curtain wall has an exquisite appearance, higher lighting rate and modern style, which has been widely used in office buildings, hotels and large public buildings [1], such as China Bottles, Shenzhen Financial Center and so on. As a kind of outer envelope to a building, a glass curtain wall generally includes a large number of panel units. In order to conduct damage identification of panel units, considering the characteristics of being nondestructive, convenient and able to detect concealed parts, research of damage identification based on dynamic theory [2, 3, 4, 5, 6] has been proposed in recent years. The reasonable calculation model is the basis of seismic and wind resistance in the design of structures [7, 8, 9, 10], and the establishment of the benchmark model [11, 12] is one of the key contents of damage identification. However, there is still some controversy about the calculation model of panel units of hidden frame curtain walls.  
The hidden frame glass curtain wall is an outer-envelope structure, in which the glass panel is bonded on the auxiliary frame by structural sealing adhesive to form a panel unit, and then the auxiliary frame is fixed on the supporting frame. [PERSON] and [PERSON] [13] studied the dynamic characteristics of the glass curtain wall supported by four sides, and obtained the result that the constraints of the glass panel were between simple support and fixed support. [PERSON] et al. [14, 15] adopted I-steel as the support frame to conduct an experimental study on the dynamic characteristics of concealed frame panel units, and also concluded that the natural vibration frequency of the structure was between simple support and fixed support. [PERSON] et al. [16] and [PERSON] et al. [17] used the real aluminum alloy box section support frame of the glass curtain wall to test the dynamic characteristics of the hidden frame curtain wall, and the results showed that the frequency of panel elements was less than that of a simply supported boundary. [PERSON] [18] investigated the deformation of panel units under wind pressure, and the test results showed that the deformation of a supporting frame cannot be ignored.  
According to the existing research results, the main dispute is whether the natural frequency of panel units is higher or lower than the simply supported boundary. In other words, which boundary condition, the semi-rigid boundary or the boundary considering the elastic effect of structural sealant and the supporting frame, should be adopted on the calculation model of the panel unit. In this paper, the methods of a laboratory modal test and numerical calculation are used to solve the problem of model divergence. Firstly, modal tests are carried out on two full-size panel elements of hidden frame curtain walls with different sizes. Then, the main factors affecting the dynamic characteristics of panel units are analyzed by using different finite element models. Based on the results, a simplified calculation model of panel units is established.
## 3 Results and Discussion  
### Commingled Production Characteristics  
During commingled multilayer production experiments, the total gas flow rate remained stable at its preset value under the constant flow rate at the outlet, and then gradually decreased until reaching its abandonment pressure. From the total gas production curve of the commingled Q5 + H8 layers shown in Figure 2a, it can be seen that the total gas production remained at the preset flow rate of 5.3 mL/min throughout the entire experiment duration until 1234 min. Afterward, the gas production rate of the commingled two-layer production began to gradually decline, ultimately reaching the abandonment pressure of 0.50 MPa at 1718 min, marking the termination of the experiment.  
Although the total gas production remained constant during the period from the very beginning to 1234 min, the productions from each individual layer Q5 or H8 underwent a complex stage with an initial interlayer interference followed by joint gas production. Due to the pressure difference between interlayers, gas was not discharged from the low-pressure layer during the initial stages of production. Moreover, gas from the high-pressure layer not only flowed out of its outlet, but also flowed back into the low-pressure layer. By analyzing the curves of instantaneous gas production and gas contribution rate over time for the commingled Q5 + H8 layer production, it is evident that, within the initial 169 min of extraction, not only did the high-pressure layer H8 exclusively contribute to gas production at its outlet, but there was also a reverse flow of gases from layer H8 into the low-pressure layer Q5, resulting in a negative actual gas contribution from the latter. After 169 min, the instantaneous gas production rate and gas contribution ratio of the layer H8 exhibited a gradual decline, while the gas production rate of the layer Q5 was transitioned from negative to positive, initiating gas generation and its subsequent stabilization (see Figure 2a,b).  
The comparison of the commingled H8 + BX layer production curves depicted in Figure 3a,b reveals that significant disparities in interlayer pressure would lead to  
stages remain distinctly observable with their corresponding time frames of 0-16 min, 17-1873 min, and 1874-2162 min.
# A Review on Decentralized Finance Ecosystems
## 2 Literature Review
### TradFi and CeFi  
TradFi represents the conventional non-blockchain financial system. Centralized finance (CeFi) acts as a centralized intermediary between TradFi and DeFi. DeFi, built on blockchain technology, offers flexibility and higher potential returns but also poses unique risks and challenges within the decentralized financial sector [32].  
TradFi denotes traditional finance entities operating outside blockchain systems, engaging in activities like asset management, insurance, and real estate funds. Dealing exclusively with fiat and traditional assets, TradFi is known and recognized for its user-friendly experiences and stringent strict regulation [18; 19].  
CeFi offers crypto services within a centralized structure [2; 33]. Platforms like _Binance_, _Coinbase_, _Gemini_, _Kraken_, and _Nexo_ serve as these intermediaries, providing services like fiat-to-crypto conversion and customer support. Despite benefits like regulatory compliance and simplified DeFi services, CeFi platforms face drawbacks like higher costs and custody issues. They are popular for users seeking regulated crypto market access [21].  
CeFi encompasses cryptocurrency exchanges and shares similarities with TradFi, featuring advantages and disadvantages. Central bank digital currencies (CBDCs) play a pivotal role in reinforcing CeFi due to their digital nature and issuance by the central bank [34]. CBDCs are digital versions of a nation's fiat currency, directly backed and regulated by the government. In contrast, CeFi refers to traditional financial services for cryptocurrencies, relying on centralized intermediaries for transactions. These services include lending, borrowing, and trading, typically offered by centralized financial institutions or platforms [21; 34].