esa-sceva/satcom-chunk-collection huggingface dataset:
## Materials and Methods
### Preparation of TOBC and TOBC/CD Hybrid Nanopaper  
In an attempt to estimate whether the more facile covalent decoration of preformed TOBC nanopaper by simple EDC/NHS dip coupling would bear significant disadvantages compared to covalent grafting of CDs onto TOBC prior to sheet formation, these two principal approaches were tested and the obtained hybrid films were compared with CD-free TOBC reference paper.  
_TOBC reference paper:_ The 0.135 w% TOBC dispersion was poured into a glass frit whose porous ceramic was covered by a hydrophilic poly (vinylidene fluoride) filter (\(\O\) 90 mm, 0.65 um pore size, Merck Millipore, Germany) which was connected to a vacuum suction flask. After the TOBC dispersion had been slowly and homogeneously drained off, the wet sheet formed was removed and placed between anti-adhesive paper, flattened with a hand-operated couch roll in a crosswise pattern and placed into a sheet dryer for 1 h 45 min at 60 \({}^{\circ}\)C and 6 kPa. Couch roll and sheet dryer were parts of a Rapid-Kothen RK-1H integrated sheet former (PTI Laboratory Equipment, Laakirchen, Austria). The final paper had a thickness of 129 \(\pm\) 8 um (n = 25).  
_Grafting of CDs after TOBC sheet formation:_ The TOBC nanopaper obtained (cf. above) was consecutively equilibrated in 500 mL water (40 min) and 100 mL of 10 mM acetate buffer (20 min), using petri dishes of appropriate size. Then, the films were submersed in a solution of 20 mg EDC and 80 mg NHS in 100 mL of 10 mM acetate buffer (20 min) before transferred to 20 mL of a 400 \(\upmu\)g\(\cdot\)mL\({}^{-1}\) dispersion of GEA-type CDs in ultrapure water. Finally, the films were re-immersed in (i) 10 mM acetate buffer (5 min), (ii) 0.1 M NaHCO\({}_{3}\) buffer (10 min) and (iii) exhaustively washed with deionized water.  
_Grafting of CDs onto TOBC prior to sheet formation:_ A 200 mL aliquot of the 0.4% (w/v) TOBC dispersion (0.8g dry weight) was mixed with 250 mL of 10 mM acetate buffer, shortly homogenized using an IKA Ultra-Turrax T8 (IKA\({}^{\circledR}\) Werke GmbH & Co. KG, Staufen, Germany) and stirred for 30 min. Then 0.83 g of EDC and 3.2 g of NHS was added. After two hours of continued stirring 9.8 mL of an aqueous dispersion (400 \(\upmu\)g mL\({}^{-1}\)) of GEA-type CDs was added and the reaction mixture was left stirring overnight. The TOBC/CD hybrid nanopaper sheet (\(\O\) 9 mm) which was formed as described above had a thickness of 116 \(\pm\) 8 um (n = 25).
## 2 Understanding WebAssembly
### Interoperability of Wasm
* **Use in Non-Web Environments** Although Wasm was designed for the web, properties such as its compact binary format, sandboxed execution environment, and deterministic behavior make it an attractive option for non-web environments as well, including IoT devices, edge computing, and serverless computing. Wasm modules can be run outside of a web browser using a runtime such as Wasm System Interface (WASI), further enhancing its interoperability. This broad interoperability of Wasm in terms of working with other web technologies and compatibility across a wide range of platforms is one of its key strengths. As Wasm continues to evolve its interoperability is likely to expand, further increasing its potential use cases and influence in the web development and IoT ecosystems.
## 2 Background
Then,  
\[\mathcal{L}_{\text{KL}}=\frac{1}{2}\sum_{j=1}^{d}(\mu_{j}^{2}+\sigma_{j}^{2}- \log\sigma_{j}^{2}-1) \tag{3}\]  
is the Kullback-Leibler (KL) divergence loss, where \(\mu_{j}\) and \(\sigma_{j}\) are the mean and standard deviation of the \(j\)th latent variable in the latent space, and \(j\) is the dimensionality of the latent space. The KL divergence encourages the learned distribution to be close to a standard normal distribution, which makes sampling from the latent space easier. VAE has been widely used in applications such as image generation, video generation, and speech generation. It has also been used in anomaly detection and data compression. VAE can generate new samples with high diversity and quality, making it a powerful tool for data generation and exploration. In our approach, to incorporate structural information from the monolithic application, we apply a GNN on the constructed dependency graph. The GNN operates on a feature matrix derived from the static analysis data from the monolithic application, enabling each class node to aggregate information from its neighbors. This graph-based normalization enriches the feature representation of each class by capturing its contextual relationships within the application, laying the groundwork for more meaningful downstream embeddings. The normalized feature matrix obtained from the GNN is then fed into a VAE to generate compact and informative embedding vectors for each class. The VAE is trained to reconstruct the original input while ensuring that similar inputs result in similar latent representations. Additionally, a structural loss term is introduced to encourage embeddings of connected classes to remain close in the latent space. This results in an embedding matrix that captures both feature similarity and architectural proximity, which is later used for effective microservice clustering. These are described in more detail in the methodology section.
## 3 Results and Discussion
### Identification of Key Components That Affect Sensory Qualities of Tea Samples  
#### 3.3.1 Pearson's Correlation between Components and Sensory Qualities  
Pearson's correlation between phytochemicals and sensory quality in tea was analyzed to determine the key compounds affecting the quality of decaffeinated tea and to provide a reference for processing high-quality decaffeinated tea. As mentioned above, caffeine, EC, and lysine also highly impacted bitter taste, whereas EGC, EGCG, GCG, ECG, and theanine influenced astringent taste. These results were consistent with the Pearson's correlation: bitterness versus lysine (r = 0.900), EC (r = 0.752), and caffeine (r = 0.719); astringency versus EGC (r = 0.590), EGCG (r = 0.790), GCG (r = 0.828), ECG (r = 0.921), theanine (r = 0.930). Overall acceptance was positively correlated with aspartic acid (r = 0.884), glutamic acid (r = 0.800), and theanine (r = 0.798), highlighting the positive contributions of umami and sweetness in overall acceptance. However, the overall acceptance was also positively correlated with EGC (r = 0.914), EGCG (r = 0.976), GCG (r = 0.980), ECG (r = 0.939). These results may be explained by the decaffeinated processing resulting in a substantial loss of bitter and astringent substances from the GT, resulting in a lower overall acceptance. Hence, to obtain a pleasant overall taste in decaffeinated tea, it is necessary to improve the processing conditions to reduce tea infusions' bitterness and astringency.  
Figure 5: **(A)** The sensory qualities of black tea (BT) and decaffeinated black tea (DeCAF-BT). **(B)** The sensory attributes of green tea (GT), and decaffeinated green tea (DeCAF-GT). **(C)** The color parameters of the GT, DeCAF-GT, BT, and DeCAF-BT. Different letters for the same parameter indicate significant differences (\(p<0.05\)).
## 2 Development of a Combined Size Effect Model for Rocks under Uniaxial Compression
### Size Effect Model of Surface Effects
When the uniaxial compressive stress \(\sigma_{\rm n}\) reaches its peak value \(\sigma_{\rm nc}\), \(\sigma_{\rm nc}\) can be obtained based on the force balance in the vertical direction as follows:  
\[\sigma_{\rm nc}=\sigma_{\rm s}f_{\rm s}+\sigma_{\rm ic}f_{\rm i} \tag{1}\]  
where \(\sigma_{\rm s}\) and \(\sigma_{\rm ic}\) are the nominal UCS of the surface layer and the interior zone, respectively. \(f_{\rm s}\) and \(f_{\rm i}\) are the volume fraction of the surface layer and the interior zone in the whole sample, respectively, and are calculated as follows:  
\[f_{\rm s}=1-\left(1-\frac{2t}{D}\right)^{2},\,f_{\rm i}=1-f_{\rm s} \tag{2}\]  
By plugging Equation (2) into Equation (1), we can derive a size effect model of surface effects as follows:  
\[\sigma_{\rm nc}=\sigma_{\rm s}\frac{4t}{D}\left(1-\frac{t}{D}\right)+\sigma_{ \rm ic}\left(1-\frac{2t}{D}\right)^{2} \tag{3}\]  
As \(t\) is assumed to be constant regardless of the sample size \(D\), \(\sigma_{\rm s}\) is supposed to be a size-independent constant. In this section, \(\sigma_{\rm ic}\) is also assumed to be a constant. Thus, Equation (3) is rewritten as follows:  
\[\sigma_{\rm nc}= \begin{cases}\sigma_{\rm s}&D\leq 2t\\ \sigma_{\rm s}\frac{4t}{D}\left(1-\frac{t}{D}\right)+\sigma_{\rm ic}\left(1- \frac{2t}{D}\right)^{2}&D>2t\end{cases}  \tag{4}\]  
When \(D>t\), the derivative of \(\sigma_{\rm nc}\) [Equation (3)] with respect to \(D\) is calculated as follows:  
\[(\sigma_{\rm nc})^{\prime}=-(\sigma_{\rm s}-\sigma_{\rm ic})\frac{4t}{D^{2}} \left(1-\frac{2t}{D}\right) \tag{5}\]  
Figure 2: A schematic illustration of a rock consisting of an interior zone coated by a free surface layer under uniaxial compression. In Equation (5), (1-\(2t/D\)) \(\geq\) 0 if \(\sigma_{\rm s}<\sigma_{\rm ic}\). In this case, the sample in Figure 2 has a weak surface layer, \(\sigma_{\rm nc}\) will increase with increasing \(D\) [(\(\sigma_{\rm nc}\))\({}^{\prime}\)\(>\) 0], and the ASE occurs. If \(\sigma_{\rm s}\)\(>\)\(\sigma_{\rm ic}\), the sample in Figure 2 has a strengthening surface layer, \(\sigma_{\rm nc}\) will decrease with increasing [(\(\sigma_{\rm nc}\))\({}^{\prime}\)\(<\) 0] \(D\), and the NSE occurs. However, a limitation of the size effect model of surface effects is that it cannot describe the mixed type ASE-NSE of nominal strength, unless it is considered that \(\sigma_{\rm ic}\) depends on the sample size \(D\). Many studies have shown this size effect [11; 14; 17; 33; 61; 64; 66; 68; 77; 78].
###### Abstract  
The effects of pulsed electric field (PEF) treatments on microbial reduction, volatile composition, and sensory characteristics of orange juice were investigated. Mild (Thermal-1) and intensive (Thermal-2) thermal treatments were applied for comparison. A pilot-scale PEF system, with a flow rate of 30 L/h and maximum field strength of 20 kV/cm, was used. PEF treatment at a specific energy of 150 kJ/L resulted in 9.0 and 8.0 decimal reductions of _Escherichia coli_ and _Saccharomyces cerevisiae_, respectively. The PEF treatments preserved the characteristic compounds associated with a fresh flavor (e.g., dl-limonene, \(\beta\)-myrcene, \(\alpha\)-pinene, and valencene) more effectively than an intensive thermal treatment. This was verified by descriptive analysis of sensory evaluations. Based on the principal component analyses (PCAs) and partial least-squares (PLS) regression analyses, PEF-treated orange juice showed higher similarity to untreated orange juice. Our results indicate that PEF may be an alternative processing technique that can preserve the fresh flavor and taste of freshly squeezed orange juice.  
orange juice; pulsed electric field; sensory attribute; volatile +
Footnote †: journal: Applied Sciences
## 5 Results and Discussion  
### Test System  
The test system is a modified IEEE 68 bus network, which is a benchmark system to study low-frequency oscillatory dynamics in power systems [39]. This system has fifteen synchronous generators, three wind farms, and three solar farms, as shown in Figure 11. Deterministic eigenvalue analysis [32] was first run to determine RES locations. Table 1 shows the damping constant and damping factor of the three most critical eigenvalues obtained for the test system without any RES. The major areas associated with the critical eigenvalues are 3, 4, and 5, and a similar conclusion was also reached in Reference [39]. Thus, we decided to install RES at Areas 3, 4, and 5, which represent the worst-case scenario with respect to SSS. The countries leading the way in PVG and WTG generally have a penetration level in the range of 20%-30% [1]. By taking this fact into consideration, we assumed a 30% RES penetration level in their respective areas, resulting in the size of solar and wind farm at Areas 3, 4 and 5 being 150, 172, and 370 MVA, respectively.  
\begin{table}
\begin{tabular}{c c c c c} \hline No. & Damping Constant & Damping Factor & Frequency (Hz) & Associated Areas \\ \hline \(1\) & \(-\)0.0102 & 0.0450 & 0.3607 & 2, 4 and 5 \\ \(2\) & \(-\)0.0700 & 0.0164 & 0.6792 & 4 and 5 \\ \(3\) & \(-\)0.0912 & 0.0183 & 0.7922 & 3 and 4 \\ \hline \end{tabular}
\end{table}
Table 1: Results of deterministic eigenvalue analysis without RES.  
Figure 10: Flowchart of proposed method for optimal tuning of power-system controllers.  
All generators consist of a fast-acting static excitation system; the details of generators, excitation systems, and line parameters can be found in Reference [37]. This paper uses a DIGSILENT built-in wind-farm model that is similar to Type-3 doubly fed induction generators with a rotor-side converter [25], and the PVG model that is available in DIGSILENT [25]. WTG and PVG parameters used in this paper are provided in Appendix A. Generator 1 (G1) to Generator 12 (G12) were assumed to be equipped with PSS. Areas 3 to 5 had large power generation and inertia, so any generator outage in these areas might seriously jeopardize system frequency [40]. Hence, one BESS is assumed to be connected in Areas 3, 4, and 5. As BESSs have not yet primarily been connected for small-signal stability purposes in the current electric-power system, we used the method proposed by the authors of Reference [41], which can be used to calculate BESS size for frequency regulation; the obtained BESS size after applying this method was not optimal, but still provides realistic battery size and is better than using an arbitrary size for analysis. Moreover, we also assumed unity power-factor operation of BESSs in our study. Thus, the size of BESS located in Areas 3, 4, and 5 was 26, 60, and 75 MVA, respectively. More details about battery-capacity determination and parameters are provided in Appendix A.
## 2 Material and Methods
### Hybrid Models Development  
As previously highlighted, OPELM and CHAID models are defined as deterministic methods in which the equation of models is determined by initial parameters and values. On the other hand, stochastic models like ARCH have some intrinsic randomness in which using the same initial parameters and values will initiate a group of different outputs. After defining both the deterministic and random parts of the streamflow data using OPELM, CHAID, and ARCH models, new series were generated by combining these two parts. The combined ARCH-OPELM and ARCH-CHAID hybrid based model can be defined as  
\[Q_{t}\,=\,D_{t}\,+\,\varepsilon_{t} \tag{9}\]  
where \(D_{t}\) is the modeled deterministic part of the streamflow time-series by two methods of CHAID and OPELM and \(\varepsilon_{t}\) is the modeled random part of the streamflow series. As shown in Equation (9), this formula was found to estimate both the deterministic and random parts of hydrological nonlinear events. In this study, the equation obtained from two methods of CHAID and OPELM models and the results of ARCH modeling are considered as \(D_{t}\) and \(\varepsilon_{t}\) respectively. In other words, using the two-step process, the new integrated hybrid nonlinear model is established. In the first step, a linear deterministic model from OPELM and CHAID method is estimated in order to generate new values of monthly streamflow. In the second step, the ARCH model, which is nonlinear and estimated using the volatility forecasts, is plugged into the proposed models of OPELM and CHAID. As a result, new-formed models, namely ARCH-CHAID and ARCH-OPELM, are established. The process of the proposed hybrid (OPELM/CHAID-ARCH) models to predict monthly streamflow is shown in Figure 3.
## Appendix A  
Considering Equation (5), the cost function in Equation (9) can be rewritten as:  
\[\begin{split}&=\sum_{i}\left\|\tilde{\mathbf{y}}_{i}\mathbf{ \delta}^{T}\mathbf{CP}(\Phi_{l},\Lambda_{k})\mathbf{a}_{i}e^{j\theta(t)}- \mathbf{CP}(\Phi_{l},\Lambda_{k})\mathbf{a}_{i}e^{j\theta(t)}\right\|^{2}\\ &=\sum_{i}\left\|\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T} \mathbf{CP}(\Phi_{l},\Lambda_{k})\mathbf{a}_{i}-\mathbf{CP}(\Phi_{l},\Lambda_ {k})\mathbf{a}_{i}\right\|^{2}\left\|e^{j\theta(t)}\right\|^{2}\\ &=\sum_{i}\left\|\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T} \mathbf{CP}(\Phi_{l},\Lambda_{k})\mathbf{a}_{i}-\mathbf{CP}(\Phi_{l},\Lambda_ {k})\mathbf{a}_{i}\right\|^{2}\\ &=\sum_{i}\mathbf{a}_{i}^{\ H}\mathbf{P}^{H}(\Phi_{l},\Lambda_ {k})\mathbf{C}^{H}(\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T}-\mathbf{I}_{N}) ^{H}(\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T}-\mathbf{I}_{N})\mathbf{CP}( \Phi_{l},\Lambda_{k})\mathbf{a}_{i}\end{split} \tag{10}\]  
or equivalently as:  
\[=vec(\mathbf{CP}(\Phi_{l},\Lambda_{k}))^{H}(\sum_{i}\mathbf{A}_{i}^{\ H}( \tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T}-\mathbf{I}_{N})^{H}(\tilde{ \mathbf{y}}_{i}\mathbf{\delta}^{T}-\mathbf{I}_{N})\mathbf{A}_{i})vec( \mathbf{CP}(\Phi_{l},\Lambda_{k})) \tag{11}\]  
where \(\mathbf{A}_{i}\) is defined in Equation (14) and the rank of each matrix in the inner summation of Equation (11) is \(N-1\). Ignoring the noise and gridding resolution effect, all of the matrices in the summation are the same, since for all of them the signals arrive from the same direction. Hence, the rank of matrix \(\sum_{i}\mathbf{A}_{i}^{\ H}(\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T}- \mathbf{I}_{N})^{H}(\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T}-\mathbf{I}_{N}) \mathbf{A}_{i}\) is \(N-1\). Singular value decomposition of this matrix can be expressed as:  
\[\sum_{i}\mathbf{A}_{i}^{\ H}(\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T}- \mathbf{I}_{N})^{H}(\tilde{\mathbf{y}}_{i}\mathbf{\delta}^{T}-\mathbf{I}_{N}) \mathbf{A}_{i}=\begin{bmatrix}\mathbf{U}_{s}&\mathbf{U}_{n}\end{bmatrix} \begin{bmatrix}\mathbf{S}_{s}&\mathbf{0}\end{bmatrix}\begin{bmatrix}\mathbf{U} _{s}^{H}\\ \mathbf{0}&\mathbf{S}_{n}\end{bmatrix}\begin{bmatrix}\mathbf{U}_{s}^{H}\\ \mathbf{U}_{n}^{H}\end{bmatrix} \tag{12}\]  
where \(\mathbf{S}_{s}\) and \(\mathbf{S}_{n}\) are the diagonal matrices of the eigenvalues corresponding to the signal and null spaces, respectively, and \(\mathbf{U}_{s}\) and \(\mathbf{U}_{n}\) are the matrices of the normalized eigenvectors corresponding to the signal and null spaces, respectively. Using this notation, the solution of \(vec(\mathbf{CP}(\Phi_{l},\Lambda_{k}))\) is in the null space and belongs to \(SPAN(\mathbf{U}_{n})\) with the dimension of \(N^{2}-(N-1)\). Considering noise measurement and the gridding resolution, the dimension of the null-space (or noise space) becomes smaller; however there are still many solutions available for \(vec(\mathbf{CP}(\Phi_{l},\Lambda_{k}))\).
## 3 Numerical Experiments  
### Case Study: Unified Danish Eulerian Model (UNI-DEM)  
Our research centers around the domain of environmental security, with a specific concentration on air pollution transport. In order for current mathematical models of air pollution transport to become trustworthy simulation tools, they must include a comprehensive range of chemical and photochemical reactions [1]. This paper presents the study and numerical outcomes obtained through the use of the large-scale mathematical model named UNI-DEM [1].  
This model facilitates the examination of temporal concentration variations of multiple air pollutants and other substances across a vast geographic region (4800 \(\times\) 4800 km\({}^{2}\)), encompassing all of Europe, the Mediterranean, and parts of Asia and Africa. Such studies are vital for environmental protection, agriculture, and healthcare. Modeled as a system of partial differential equations, the framework simulates key atmospheric processes, including photochemical interactions between the studied species, emissions, and rapidly shifting meteorological conditions. Nonlinearity and stiffness in the equations largely result from modeling chemical reactions [1]. The chemical mechanism employed in the model is the renowned condensed CBM-IV (Carbon Bond Mechanism). The selection of the UNI-DEM was based on its capacity to accurately account for chemical processes within atmospheric chemistry models.  
Given the complexity and scale of this task, direct numerical treatment is impractical. To facilitate numerical solutions, the problem is partitioned into submodels representing the principal physical and chemical processes. The production version of the model employs sequential splitting [41], although alternative splitting techniques were explored and incorporated in certain experimental versions [1]. Spatial and temporal discretization render each submodel computationally intensive, requiring the processing power of today's most advanced supercomputers. Consequently, parallelization has remained a crucial component of the computer implementation of the UNI-DEM since its development.  
Our primary objective is to investigate the sensitivity of the concentrations of key pollutants, such as ozone, in relation to variations in the rates of certain chemical reactions and emission levels. In our approach to sensitivity analysis, we treat the chemical reaction rates and emission levels as input parameters, while the pollutant concentrations serve as output parameters.
## 3 Processing Method of Vibration Piezoelectric Signal  
### Capture of Vibro-Piezoelectric Response Signals  
Capturing time-domain signals generated from interactions among seeds, impurities, and drying trays presents significant challenges. Signal aliasing, in particular, impairs the generalization of extracted features between seed and impurity classes, thereby limiting the accuracy of crop seed classification models. According to [PERSON]'s sampling theorem, the sampling frequency of the detection system must twice exceed the maximum frequency component of the signal. This requirement ensures that the original time-domain response signal is fully retained and accurately reconstructed, effectively preventing aliasing. Consequently, the maximum effective frequency of the piezoelectric vibration detection system is limited to half the sampling frequency.  
When subjected to vibration, seeds and impurities intermittently collide with the support plate. These mechanical collisions generate stress signals, which are transduced into electrical charges by the piezoelectric sensor. The charge amplifier then converts these weak charge outputs into measurable voltage signals, amplifying them to form a complete time-domain response. These signals provide a rich dataset for distinguishing between grain and impurity characteristics during model training. To ensure signal compatibility with subsequent digital processing, a chopper circuit is integrated into the charge amplifier. This prepares the amplified signals for conversion via an analog-to-digital converter (ADC), which--based on a signal acquisition card as the core component--continuously samples and stores time-domain response data using dynamic link library (DLL) functions.  
This signal acquisition framework enables efficient transmission even under dynamic channel conditions. In contrast to conventional interference cancellation techniques that rely on instantaneous received power, the proposed shortest routing protocol (SRP) utilizes instantaneous channel state information (CSI) alone. This allows the system to dynamically adapt to channel fluctuations, thereby ensuring enhanced reliability and overall performance under variable communication conditions.