esa-sceva/satcom-chunk-collection huggingface dataset:
# A Bayesian Modeling Approach to Situated Design of Personalized Soundscaping Algorithms
## 2 Methods
### Problem Statement and Proposed Solution Framework
#### 2.2.2 Source Mixing Model 2: Gaussian Scale Sum Model  
The Algonquin model requires estimation of the noise variance \(\gamma_{x}^{-1}\). Here we present an alternative novel source mixing model that does not require any tuning parameters, inspired by the Gaussian scale models from [30].  
We assume a (complex) Gaussian distribution for the frequency coefficients of the constituent signals \(S_{n}^{k}\), given by  
\[S_{n}^{k}\sim\mathcal{N}_{\mathcal{C}}(\mathbf{\mu},\;\Gamma,\;C), \tag{9}\]  
with mean \(\mathbf{\mu}=0\), complex covariance matrix \(\Gamma\) and relation matrix \(C\), see [33] for more details. In order to keep inference tractable, independence is assumed between the real and imaginary parts of the coefficients, requiring \(C=0\). Following [30], the covariance matrix \(\Gamma\) is modelled as a diagonal matrix with exponentiated auxiliary variables \(s_{n}^{k,f}\), leading to the model  
\[p(s_{n}^{k,f}\mid s_{n}^{k,f})=\mathcal{N}_{\mathcal{C}}\Big{(}S_{n}^{k,f} \mid 0,\;\exp\Big{(}s_{n}^{k,f}\Big{)},\;0\Big{)}=\frac{\exp\left(-\frac{s_{n} ^{k,f}}{\pi}\right)}{\pi}\exp\Bigl{(}-\exp\Bigl{(}-s_{n}^{k,f}\Bigr{)}\lvert S _{n}^{k,f}\rvert^{2}\Bigr{)}. \tag{10}\]  
This probabilistic relationship shows great similarity with the transform from the frequency coefficients to the log-power spectrum, as its log-likelihood can be found as \(\ln p(s_{n}^{k,f}\mid s_{n}^{k,f})=-s_{n}^{k,f}-\exp(-s_{n}^{k,f})\lvert S_{n }^{k,f}\rvert^{2}-\ln(\pi)\) and from this description the maximum of \(s_{n}^{k,f}\) can be found to occur at \(s_{n}^{k,f}=\ln(\lvert S_{n}^{k,f}\rvert^{2})\), which coincides with the deterministic transform from the frequency coefficients to the log-power spectrum. As a result of this observation, the variables \(\mathbf{s}_{n}^{k}=\left[s_{n}^{k,1},\ldots,s_{n}^{k,f}\right]^{\top}\) are in this model termed the _pseudo log-power coefficients_ of the original source signal \(S_{n}^{k}\). Due to the linearity of the STFT in (6), the likelihood function of \(X_{n}^{f}\) can be expressed using (10) as  
\[p_{g}\Bigl{(}X_{n}^{f}\mid s_{n}^{1,f},\ldots,s_{n}^{K,f}\Bigr{)}=\mathcal{N} _{\mathcal{C}}\Biggl{(}X_{n}^{f}\mid 0,\;\sum_{k=1}^{K}\exp\Bigl{(}s_{n}^{k,f} \Bigr{)},\;0\Biggr{)}. \tag{11}\]  
In contrast to the Algonquin model, this Gaussian scale sum model does not contain any tuning parameters and operates on the complex frequency coefficients instead of the log-power spectrum. Note that the pseudo log-power coefficients \(s_{n}^{k,f}\) are not exactly equal to the deterministic log-power coefficients of the Algonquin-based source mixing model, although they show great similarity.  
As we have defined the observed signal as a function of the constituent signals, the next task concerns modeling the constituent signals \(\mathbf{s}_{n}^{k}=[s_{n}^{k,1},\ldots,s_{n}^{k,F}]^{\top}\) themselves. In this paper, we will use a Gaussian mixture model for this purpose.
# Deterring Deepfake Attacks with an Electrical Network Frequency Fingerprints Approach
## 2 Background and Related Work  
### Deepfake Detection Using Traditional and Trained Models  
Deepfake detection has become a critical problem in digital media authentication. With advanced computational power and the developments in GANs, the resulting media output is very realistic [2]. However, along with its development, many detection techniques were proposed in the early stages to leverage the artifacts introduced in deepfakes. Artifacts such as eye blinking [41], facial distortion, facial symmetry construction [42], and motion artifacts can be visually inspected and identified [43]. Machine-learning-based models were also trained to identify the artifacts. However, the artifacts result from low training data and improvement in the GAN architecture; with more data, the artifacts can be reduced, and more realistic images can be created, leaving the visual-artifact-based detectors redundant.  
Hidden features such as GAN fingerprints are unique to the deepfake model architecture [44], and biometric signatures such as heartbeat detection through the skin do not depend on visual artifacts [45]. The signatures can be reliable when the visual artifacts are removed by better training. The GAN also introduces frequency-level artifacts due to the upsampling method in the GAN pipeline [46], and the modified frames can be identified by frequency analysis and studying the compression map [47; 48]. Noiseprint is one such fingerprint extracted by suppressing the high-level scene content and leveragingthe in-camera processes for unique fingerprints [49]. Noiseprint is applied to reliably localize the frame modification with high performance. Other camera-based fingerprint techniques such as Photo Response Non-Uniformity (PRNU) sensor noise and JPEG compression artifacts were also used in detecting frame-level forgeries due to their dependence on the source device [50; 51]. However, these unique artifact-based detectors can also be spoofed using a GAN-based approach where camera traces are inserted into the synthetic images [52]. Along with the reliability of the unique fingerprint for its detection capability, it is also essential that the fingerprint be less prone to forgeries. Hence, we adopted the ENF-based environmental fingerprint where the fluctuations are a random process and signal manipulation in media recordings leaves modification traces.
# A Two-Stage Local Path Planning Algorithm Based on Sampling and Optimization Methods
## 3 Proposed Method
### Optimization Stage
#### 3.3.1 Path Deviation Constraint  
At the optimization stage, the preliminary path needs to be close to the global path due to the coordinate transformation steps described in Section 3.2. Therefore, we can easily describe the approximation of the preliminary path and the global path by the longitudinal coordinate of the preliminary path. The target function of the path deviation constraint is described as  
\[f_{dev}=\min_{B}\sum_{j=1}^{j_{max}}\lvert d_{j}\rvert \tag{12}\]
## 4 Dual-Comb Doppler Reflectometer System in LHD  
### System Equipment  
The Ka-band dual-comb Doppler reflectometer system is shown in Figure 6. The comb sources are operated by 710 and 730 MHz, respectively. A frequency of 710 MHz is chosen because the RF frequencies of the probing match the frequencies of the previous Doppler reflectometer system [6,8] to make correlative measurements at two separate toroidal locations. Output frequency spectrum of the comb generator is shown in Figure 7a. The output power is approximately flat, up to >20 GHz. Of these frequency components, only the 12-20 GHz components are extracted by the bandpass filter (BPF), as shown in Figure 7b, and the frequencies are doubled to obtain the probing Ka-band components, as shown in Figure 7c. Note that the frequency interval after doubling is \(f_{1}\), not \(2f_{1}\). One of the frequency comb components is selected to use the LO of the Mixer 1 by BPF of 26.27 GHz with \(\pm 150\) MHz bandwidth, as shown in Figure 7d. Then, the output of Mixer 1 is combined with the different frequency comb components from Comb Generator 2 at Mixer 3 and generates the \"Probe signal\". For the precise heterodyne detection, a part of each signal from Comb Generator 1 and Comb Generator 2 components are mixed in Mixer 2 and generate the frequency chain which is used for the \"Reference signal\" for IQ detection [6]. The test results of the IQ detection system, performed using a reflector instead of plasma, are shown in Figure 8. First, the frequency components for IQ detection are extracted from  
Figure 5: Schematic diagram of dual comb operation concept. Here, signal generators (SGs) of SG1 (190 MHz) and SG2 (200 MHz) are used as clocks for comb generation for RF and LO, respectively. Frequency spectra: (**a**) RF, (**b**) LO, and (**c**) IF signals observed in this conceptual test.  
the probe and reference signals (Figure 8a) by filter banks. Here, the 120 MHz signal is shown in Figure 8b as an example. Next, the IQ output is used to calculate the complex signal and the phase difference between the probe and reference signals at each frequency component. As shown in Figure 8c, when a phase change was applied to the RF probe signal as a test, the IQ signal output changed correctly, indicating that this IQ detection system is working properly.  
Figure 6: Schematic diagram of Ka-band dual-comb Doppler reflectometer system. Here, SG is signal generator used as clock for comb generation, AMP is amplifier, BPF is band-pass filter, and LPF is low-pass filter. Numbers 1, 2, 3, 4, 5, 6 in figure indicate monitoring points in following figures.  
Figure 7: Frequency spectra of (**a**) output of comb generator (monitor point of 1), (**b**) output through bandpass filter (monitor point of 2), (**c**) output of multiplier which generates Ka-band frequency components (monitor point of 3), and (**d**) Local signal for Mixer 1 (monitor point of 4).  
In addition, the frequency comb output injected into the plasma should be flat or have a strong high frequency component, and we would like to investigate ways to improve the structure of this frequency intensity in the future.
## 3 Advantages, Characteristics and Types of Digital Twin
### Classification of Digital Twin
#### 3.3.4 Hierarchy  
From a hierarchal perspective, DT can be divided into three different levels as well (Figure 6), according to the magnitude involved in manufacturing [75]:  
* _Unit level:_ It is the smallest participating unit in manufacturing and can be a piece of equipment, material, or environmental factors. Unit-level DT is based on the geometric, functional, behavioural, and operational model of unit-level physical twin.
* _System level:_ It is an amalgamation of several unit-level DTs in a production system such as production line, shop floor, factory, etc. Interconnectivity and collaboration among multiple unit-level DT lead to a wider flow of data and better resource allocation. A complex product, e.g., aircraft, can also be considered as system-level DT.
* _System of Systems (SoS) level:_ A number of system-level DT are connected together to form SoS-level DT, which helps in collaborating different enterprises or different departments with an enterprise such as supply chain, design, service, maintenance, etc. In other words, SoS-level DT integrates different phases of the product throughout its life cycle.  
The hierarchy of DT can also be classified as _(i) Part/Component twin, (ii) Product/Asset twin, (iii) System twin, and (iv) Process twin_[79; 80], with part twin being the simplest. More sophisticated and comprehensive systems/processes can be achieved by putting together the lower-level twins.
# Energy Efficient Access Point Placement for Distributed Massive MIMO
## 4 Simulation Results
### Different Channel Models
The results in Figure 14 also suggest that, in general, the angular penalty-based model performs better than the shortest path-based model. This implies that it is important to take the pathloss due to scattering into account when optimizing the AP placement. Moreover, Figure 14 indicates that the average relative power saving is correlated with the coverage level. The reason is that the required power at a coverage level is determined by the location which receives the lowest power among all the covered locations. The location is more sensitive to the local environment when the coverage level is high. So, it requires a channel model to include more local environment information to accurately find \begin{table}
\begin{tabular}{l c} \hline \hline
**Channel Models** & **Time (min)** \\ \hline Ray-tracing simulator (RTS)-based model & Around 600 \\ Euclidean distance-based model & 1.2 \\ Shortest path-based model & 1.2 \\ Angular penalty-based model & 1.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The average time required by different models to evaluate the channels for all the possible AP placement in a simplified coverage region. Figure 14: Comparison of different channel models. The average relative power saving when using the AP placements generating via the shortest path-based model, angular penalty-based model or the RTS-based model compared with the Euclidean distance-based model. **(a)** Isotropic; **(b)** Patch. the particular location when the coverage level is high. This explains why the performance of the Euclidean distance-based model and the two graph-based models is dependent on the coverage level.
## 2 Materials and Methods
### Characterization Methods
#### 2.3.4 Chlorophylll Content Estimation  
The chlorophyll-a content of the cultures was estimated with UV-Vis spectrophotometry as a cell health indicator [49]. For every culture, 4 mL were collected and centrifuged using a servo spin NECT centrifuge at 4000 rpm for 5 min. After the removal of the supernatant, 10 mL of methanol was added in the centrifugal tube; the latter--after using a vortex for 1 min--remained in a water bath at 90 \({}^{\circ}\)C for 10 min in an ultrasound device. Finally, the tube was again centrifuged for 5 min at 4000 rpm. The spectroscopic measurements were performed using a Lambda 25 spectrophotometer (Perkin Elmer, Waltham, MA, USA) with a scan speed of 60 nm/min, in the spectral area of 400-800 nm. Then, 3 mL from the supernatant were placed in a QG 10 mm cuvette for the measurement, while a second cuvette containing 3 mL of methanol was used as the \"blank\" sample.  
_A. platensis_ exhibits only chlorophyll-a, which has an absorption peak at 665.2 nm when using methanol as the extraction solvent [50]. For this reason, the absorbance at 665.2 nm was used for the estimation of the chlorophyll-a content, after the 750 nm reading subtraction, for turbidity correction reasons [51].
## 2 Data Collection and Analysis
### Data Analysis
#### 2.2.1 Vehicle Speed  
Spearman's correlation analysis was conducted on two parameters, vehicle position (m) and vehicle speed (km/h), within Interchange X. The resulting Spearman correlation coefficients are depicted in Figure 3.  
The Spearman correlation coefficient between the vehicle position and vehicle speed within the diversion area of Interchange X was calculated as 0.040. This notably low value suggests a consistent and uniform vehicle speed prevailing in this area. Utilizing Python 3.7 for data analysis, the vehicle speed within the curb lane of the mainline at the initial section of the deceleration lane taper was isolated, resulting in the vehicle speed distribution portrayed in Figure 4.  
The speeds measured at the initial section of the taper of the deceleration lane at both Interchange X and Interchange L conform to a statistically significant normal distribution. The average vehicle speed observed at this location and the \(V_{\text{85}}\) values are detailed in Table 1.  
The recorded \(V_{\text{85}}\) values from the measured data consistently fall within the range of recommended vehicle speeds designated for the initial section of the taper of the deceleration lane, as specified in _Guidelines for Design of Highway Grade-separated Intersections (Intersections Guidelines)_. Therefore, the upper limit of the recommended interval is adopted as the prescribed value for this indicator due to safety considerations.  
Figure 3: Vehicle speed distribution and correlation analysis of Interchange X.  
Figure 2: Coordinate conversion.
###### Abstract  
Previous studies have reported prognostic factors for hepatocellular carcinoma (HCC) patients receiving lenvatinib; however, no studies have evaluated the effects of both handgrip strength and skeletal muscle mass on the clinical outcomes. Therefore, this retrospective study investigated the individual effect of handgrip strength, skeletal muscle mass, and sarcopenia on clinical outcomes of 53 HCC patients treated with lenvatinib. Before receiving lenvatinib, handgrip strength and skeletal muscle index (SMI) were measured. Low handgrip strength and muscle depletion were defined as \(<\)26 and \(<\)18 kg and SMI \(<\) 42 and SMI \(<\) 38 cm\({}^{2}\)/m\({}^{2}\) in men and women, respectively. Sarcopenia was defined as having low handgrip strength and muscle depletion. Multivariate analysis identified modified albumin-bilirubin grade 1-2a (\(p\) = 0.010), Barcelona Clinic Liver Cancer stage A-B (\(p\) = 0.011), and absence of low handgrip strength (\(p\) = 0.015) as favorable prognostic factors for survival. Furthermore, sarcopenia was an independent significant prognostic factor for survival. Time to treatment failure was associated with handgrip strength and sarcopenia. Our findings suggest that handgrip strength may be a useful marker of clinical outcomes in HCC patients treated with lenvatinib.  
Keywords: hepatocellular carcinoma; handgrip strength; lenvatinib; skeletal muscle; survival; time to treatment failure +
Footnote †: journal: Applied Sciences
## 3 Theoretical Analysis  
### Normalization of the Aircraft Load  
Based on the actual data statistics of an airport taxiway bridge in South China, many types of aircraft run on the airport taxiway bridge, and different types of aircraft have different effects on the deformation and fatigue accumulation of the taxiway bridge. Therefore, the aircrafts passing on the taxiway bridge are normalized and converted to B747-400. According to the \"Code for design of cement concrete pavement of civil airport\", the proposed aircraft load conversion model is shown in Equation (4) [27,28]:  
\[P_{t}=\frac{Gp}{n_{c}n_{w}} \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Point**} & \multicolumn{3}{c}{**Deflection Value (mm)**} & \multicolumn{3}{c}{**Stress Value/kN**} & \multicolumn{3}{c}{**Strain Value/\(\times\)10\({}^{-3}\)**} \\ \cline{2-11}  & **Measured** & **Simulation** & **Relative** & **Measured** & **Simulation** & **Relative** & **Measured** & **Simulation** & **Relative** \\  & **Value** & **Value** & **Error** & **Value** & **Value** & **Error** & **Value** & **Value** & **Error** \\ \hline
1 & \(-\)3.46 & \(-\)3.60 & 4.14\% & \(-\)1652.33 & \(-\)1713.80 & 3.72\% & 1.28 & 1.34 & 4.74\% \\
2 & \(-\)7.89 & \(-\)8.20 & 3.95\% & \(-\)1427.54 & \(-\)1486.78 & 4.15\% & 1.93 & 2.00 & 4.03\% \\
3 & \(-\)1.07 & \(-\)1.12 & 4.86\% & \(-\)1732.61 & \(-\)1803.30 & 4.08\% & 0.82 & 0.86 & 4.85\% \\
4 & \(-\)8.18 & \(-\)8.51 & 4.03\% & \(-\)1396.80 & \(-\)1451.70 & 3.93\% & 2.17 & 2.24 & 3.26\% \\
5 & \(-\)3.70 & \(-\)3.86 & 4.23\% & \(-\)1603.79 & \(-\)1675.16 & 4.45\% & 1.46 & 1.52 & 4.31\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: FEM accuracy verification table.  
Figure 4: Longitudinal section layout of the bridge.  
Figure 5: Cloud chart of bridge deformation loadings at point 1: (**a**) cloud chart of deflection; (**b**) cloud chart of strain; (**c**) cloud chart of stress.  
\(P_{t}\) is the wheel load on the main landing gear of the aircraft (kN), \(G\) is the aircraft weight (kN), \(p\) is the load distribution coefficient of the main landing gear, \(n_{c}\) is the number of main landing gears, and \(n_{w}\) is the number of wheels on one main landing gear.  
We define the single-wheel load conversion factor \(K_{t}\) of aircraft below to convert the wheel load of other aircraft types into the wheel load of standard types:  
\[K_{t}=\frac{P_{ln}}{P_{t0}} \tag{5}\]  
\(P_{ln}\) (kN) is the single-wheel load value of the type to be converted under a certain load state and \(P_{t0}\) (kN) is the single-wheel load value of the standard aircraft type under the same load state.  
The final conversion results of various aircraft loads under different load states are shown in Table 2.
# Effect of Temperatures on Polyphenols during Extraction
## 2 Thermal Degradation
### Enzymes  
Enzymes such as glycosidases, polyphenol oxidases (PPO) and peroxididases found in plant tissue can cause degradation of polyphenols [61]. Glycosidases can break down anthocyanins to anthocyanins and sugars that are chemically unstable and rapidly degrade [61]. In the presence of oxygen, PPO can catalyse the oxidation of o-dihydrophenols diphenols (e.g., chlorogenic acid) into o-quinone (e.g., chlorogenoquinone) [60]. In the presence of H\({}_{2}\)O\({}_{2}\), peroxidase can catalyse the same reaction [69].  
The thermal stability of this enzyme is heavily influenced by the source of the enzyme but also depends on the pH [70]. Even PPO from the same source may have different thermostatilities depending on the different molecular forms of PPO [70]. However, in general, PPO is not considered an extremely heat-stable enzyme. PPO can be inactivated with mild heating, such as a blanching treatment for fruits and vegetables [60]. In most cases, a temperature of 70-90 \({}^{\circ}\)C inactivates the enzyme [70].  
[PERSON] et al. [71] found that blanching blueberries led to the doubling of anthocyanin content in its juice compared to unblanched samples. The blanching treatment completely inactivated the native PPO and thus increased the anthocyanin recovery [71]. Additionally, another study found that the addition of unblanched blueberry-pulp extract to blueberry juice caused a 50% loss in anthocyanins while the addition of blanched extract did not result in any degradation [72].
# Aeroelastic Optimization Design of the Global Stiffness for a Joined Wing Aircraft
## 5 Conclusions  
In this study, two beam-frame model methods and one three-dimensional model method are proposed for the global stiffness design and optimization of joined wings. A beam-frame model of a joined wing, as an example for use during the preliminary design stage, is established. A three-dimensional detailed model is built as a reference and introduced for comparison. Two methods for the beam-frame model, an optimization of the diagonal beam section, and an optimization method of the exponential/linear combination function fit, defined as method A and method B, respectively, are used. The method for the three-dimensional model is a traditional optimization method based on a genetic algorithm, and is defined as method C. The static aeroelastic responses are calculated and compared under the same flight conditions at 0.2 Ma and 25 km above sea level. The main conclusions are as follows:  
1. The stiffness distributions of the beam-frame model obtained from method A and method B have good consistency and are similar to the distribution of the three-dimensional model of method C;
2. The stiffness distribution data and the beam section parameters obtained using method B can be converted into parameter sizes of the beam section and conform with the trend of the other methods;
3. Method B (the optimization method of the exponential/linear combination function fit) obtains a greater weight reduction than the traditional method (the optimization method of the exponential function fit) under the same stiffness conditions;
4. Compared with method C and other detailed-model-based methods, methods A and B show high computational efficiency and are easy to implement with variable parametrical analysis of the joined wing.  
In our future work, we aim to perform joint connection analysis, including the influence of fixed connections, hinged connections, and others, especially the gap of the joint between the front wing and rear wing. The gap nonlinearity of the joined wing will express many interesting problems and will have great research value.  
Conceptualization, X.L., X.W., Z.W. and C.Y.; methodology, X.L. and Z.W.; software, X.L.; validation, X.L.; formal analysis, X.L.; investigation, X.L.; resources, X.L.; data curation, X.L.; writing--original draft preparation, X.L.; writing--review and editing, X.L., X.W. and Z.W.; visualization, X.L.; supervision, X.W. and C.Y.; project administration, X.L.; funding acquisition, Z.W. All authors have read and agreed to the published version of the manuscript.  
Supported by Zhejiang Key laboratory of General Aviation Operation technology (General Aviation Institute of Zhejiang JianDe), NO: JDGA2020-4.  
Not applicable.  
The authors declare no conflict of interest.