esa-sceva/satcom-chunk-collection huggingface dataset:
# Direct Spread Spectrum Technology for Data Hiding in Audio +
## 3 Materials and Methods
### Initial Data for Hiding Messages  
We used various audio carriers and various families of chip codes as the initial data for our experiments. We were guided by the works [21; 40] and our papers [23; 24] when choosing the initial data and research methodology.  
An audio signal recorded in the digital WAV format [10; 11] was used as an audio container. In our experiments, we used an audio file with a student anthem (Gaudeamus Igitur) downloaded from an internet resource [57] and converted to WAV format.  
WAV audio is most commonly encoded using linear pulse code modulation (LPCM). Such a cover file contains uncompressed audio signal data; its main characteristics are:  
* the number of channels (streams) of audio data \(N_{channel}\). We used stereo signals, i.e.,\(N_{channel}\ =2\);
* sampling frequency \(f_{d}\). Our audio signals from \(f_{d}=22050\);
* the number of bits allocated to encode each discrete sample of the audio signal. We used covers from \(B=8\) bit.  
As input information messages, we used plain text files (steganography textbooks), processed and presented in binary polar form.  
In order to hide information, various spreading sequences were used, studied by many authors, for example, in [21; 40] and also in our previous works [23; 24]. Let us consider these chip codes in more detail.
# Uncertainty-Aware Time Series Anomaly Detection
## 2 Related Work
### Anomaly Detection Approaches with Uncertainty Quantification  
Uncertainty is often modeled through probabilistic approaches. However, probabilistic approaches alone fail to properly distinguish between _aleatoric_ and _epistemic_ uncertainties [24]. Several strategies evolved to handle uncertainty in anomaly detection. Some follow the idea of alleviating uncertainty in the input data before anomaly detection [25; 26]. While this approach increases the robustness of the prediction model it fails to provide a measure for uncertainty. Other approaches have proposed the application of conformal methods, such as [27], which introduces a framework called cross-conformal anomaly detection. This framework aims to strike a balance between statistical accuracy and computational efficiency for uncertainty-quantified anomaly detection. Bayesian deep learning provides tools for uncertainty quantification of deep learning models [8]. It is a subset of probabilistic machine learning approaches, where prior distribution is placed on the model parameters and one needs to estimate the posterior distributions based on the observed data. Since exact Bayesian methods for deep learning are still intractable, various methods have been proposed to approximate the posterior distribution, namely Monte Carlo Dropout [11], Bayes by Backpropagation [28] or Ensembles [5]. The Bayesian approach [29] was also applied to autoencoders. Reference [30] shows in an empirical study how the integration of uncertainty in the score function is likely to improve anomaly detection for general datasets. In [31], the authors take this one step further by changing the autoencoder to a VAE considering uncertainty as an anomaly score. The authors of [32] show the applicability of those methods on time series data comparing multi-layer perceptron networks to LSTM networks. In [33], the authors use an LSTM and turn it into a Bayesian network to predict anomalies in satellite telemetry data by comparing various kinds of posterior approximations. The study in [7] employs an LSMT autoencoder with point-wise prediction including model uncertainty calculation.  
Most approaches focus on quantifying epistemic uncertainty, neglecting the inherent data uncertainty. In [8], the authors proposed to use an additional network output to quantify aleatoric uncertainty in computer vision. A method for calculating the predictive uncertainty, which can be separated into model and data uncertainties, is provided using negative log likelihood (NLL) [5; 34]. It relies on decoupling the NLL based on MC sampling to achieve an anomaly probability as well as a split measure for model and data uncertainty. Similarly, decomposition can be achieved by decoupling prediction uncertainty [22] derived from the variance of the prediction distribution.
# Techno-Economic Analysis of Waste Heat Recovery in Automotive Manufacturing Plants
## 3 Methodology
### Boundary Conditions for Thermal Energy Storage  
Performing an assessment is essential to determine the necessary mass for the TES system in relation to the proposed system design. This evaluation includes the calculation of the mass sizing parameter for TES, introduced in [24] and denoted as \(\zeta\). This parameter represents the ratio between the mass of the TES material and the mass flow rate of the secondary fluid. As an alternative approach, \(\zeta\) can be determined as the ratio of enthalpy change between the TES material and the secondary fluid. In this study, phase change material (PCM) was chosen as the TES material, with the assumption that no energy losses occur during either the charging or discharging phases. The mathematical model for calculating the TES mass sizing parameter is described in Equation (6).  
\[\zeta=\frac{m_{\text{TES}}}{m_{\text{st}}t}=\frac{c_{\text{sf}}\Delta T_{\text{sf }}}{\Delta t_{\text{TES}}} \tag{6}\]  
Furthermore, the mixture of 53% KNO\({}_{3}\), 40% NaNO\({}_{2}\), and 7% NaNO\({}_{3}\) was selected as the TES material as a case study due to its favourable thermophysical properties. The specific melting enthalpy of 80 kJ/kg, measured at a melting temperature of 142 \({}^{\circ}\)C [25], indicates a high energy storage capacity, making it an efficient choice for thermal applications. This composition was chosen to optimise both thermal conductivity and melting behaviour, ensuring reliable performance within the targeted temperature range for TES systems. In this study, the TES temperature of 142 \({}^{\circ}\)C was selected as the target set point for the heat pump operation to raise the temperature of the system. In this case, the temperature of the outlet of the compressor in the heat pump was around 146.5 \({}^{\circ}\)C. This choice ensured that the heat pump efficiently transferred thermal energy until the TES material reached its melting point, optimising the energy storage process. The system maximises energy efficiency during the phase change process by aligning the set point of the heat pump with the melting temperature of PCM.  
In this situation, the TES system can be charged through two distinct methods, which have been discussed in recent literature [26]. The first method involves utilizing waste heat, while the second method relies on a heat pump. The temperature of waste heat at the outlet of the TES (at s2) is assumed to be 80 \({}^{\circ}\)C. These dual charging mechanisms provide flexibility and enhance the overall efficiency of the TES system. By utilising these two charging sources, the TES system can optimize energy utilization and contribute to improved thermal performance.
## 3 Results and Discussion
### Modeling  
The RSM Box-Behnken design was used to evaluate temperature, time, and the three alkaline solutions' by-products in the percentage protein yield after the extraction process. The experimental and predicted values and the entire experimental design are given in Table 4. The predicted values for the output parameter were calculated according to the regression model obtained (Equation (4)), where A is the temperature, B is the time, and C is the alkaline solution. \[ \begin{split}\text{Yield protein}&=-42.40+1.264\text{A} +0.443\text{B}+17.83\text{C}-0.002442\text{A}\text{B}-0.2503\text{AC}-0.0609 \text{BC}-\\ &\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad\qquad 0.00287\text{A}^{2}-0.00100 2\text{B}^{2}+1.648\text{C}^{2}\end{split}  \tag{4}\]  
The analysis of variance (ANOVA) showed that the regression model was statistically significant at a confidence level of 95% (\(p<0.05\)) with a value of 0.9315 for the determination coefficient, indicating that 93.15% of the variability in protein yield could be predicted by Equation (4). Further, the interactions between independent variables were very strong and were also statistically significant (\(p<0.05\)) (Table 5). \begin{table}
\begin{tabular}{c c c} \hline
**Component** & **This Study (\%)** & **Reported Values (\%) [44]** \\ \hline Fiber & 4.28 \(\pm\) 0.10 & 3.0 \\ Fat & 0.87 \(\pm\) 0.09 & 5.1 \\ Protein & 56.12 \(\pm\) 1.01 & 53.0 \\ Ash & 6.99 \(\pm\) 0.02 & 6.0 \\ Carbohydrate * & 31.72 & 26.4 \\ Moisture & 6.82 \(\pm\) 0.09 & 6.5 \\ \hline \end{tabular}  
* Percentage of fiber, fat, protein, and ash are on a dry-matter basis.
* Carbohydrate content was calculated by subtracting the percentage of the components from 100.  
\end{table} 
Table 2: Proximate analysis of the commercial soybean flour. \begin{table}
\begin{tabular}{c c c c c} \hline
**Electric Current** & **Sanitizer** & **By-Product** & **NaOH** & **pH** \\
**Intensity (A) * & **Concentration** & **Solution** & **Concentration (N)** & **pH** \\  & **Generated (ppm) * & **Generated** & **Concentration (N)** & **pH** \\ \hline
46 & 100 & 1 & 0.0086 \(\pm\) 0.0002 & 12.10 \(\pm\) 0.0252 \\
60 & 200 & 2 & 0.0112 \(\pm\) 0.0005 & 12.29 \(\pm\) 0.0200 \\
110 & 500 & 3 & 0.0214 \(\pm\) 0.0005 & 12.68 \(\pm\) 0.0173 \\ \hline \end{tabular}  
* According to Bioproductos Laguneros S.A. de C.V. procedures.  
\end{table} 
Table 3: Physicochemical characterization of NaCl electrolysis by-product solutions. The experimental and predicted values were plotted in Figure 1 and were very close. The plot displayed an R\({}^{2}\) = 0.9535, similar to that presented in Table 5 for the ANOVA. This analysis indicated that the effect of the process variables on the alkaline extraction process of soy protein is well represented by Equation (4), which can be used to study factors' interactions and optimize the response variable.
# Research on Rapid and Accurate 3D Reconstruction Algorithms Based on Multi-View Images
## 4 Results
### Experimental Results and Analysis
#### 4.2.4 Improving the Analysis of Sparse Reconstruction Results in Algorithms
Additionally, the improved algorithm exhibits greater robustness in handling complex scenes, effectively mitigating the impact of erroneous matches by optimizing feature-matching strategies and incorporating temporal continuity in image acquisition, enhancing both matching accuracy and robustness. The specific quantitative results are presented in Table 8, where a more detailed analysis of the sparse reconstruction outcomes is conducted from the perspectives of reprojection error and point cloud quantity. The specific evaluation metrics are detailed in Section 4.1.2. In terms of reprojection error, the proposed improved algorithm (Ours) achieves the lowest error values across all datasets, significantly outperforming traditional algorithms such as SfSM, VSFM, and COLMAP. For instance, in the Statue1 dataset, the reprojection error of the improved algorithm is 0.98, compared to 4.35 for SfSM, 3.25 for VSFM, and 1.21 for COLMAP, indicating a substantial increase in accuracy. This advantage is consistent across other datasets; for example, in the Archi2 dataset, the improved algorithm achieves an error of 1.58, significantly lower than other methods. Reprojection error measures the positional deviation of 3D reconstruction points projected onto the 2D image plane, with lower values indicating better geometric consistency between the reconstructed 3D structure and the actual imaging process. These results demonstrate the improved algorithm's superior reconstruction accuracy, enabling more precise recovery of scene geometry and providing a reliable geometric foundation for subsequent 3D reconstruction tasks. Regarding point cloud quantity, the improved algorithm exhibits exceptional performance. For example, the Statue1 dataset generates 78,986 point clouds, significantly surpassing SfSM's 40,973, VSFM's 60,588, and COLMAP's 17,566. This trend is consistent in other datasets, such as Archi2, where the improved algorithm produces 500,247 point clouds, far exceeding other methods. This indicates that the improved algorithm more effectively extracts and utilizes image feature information, resulting in denser and more uniformly distributed point cloud data, thereby providing richer geometric support for subsequent 3D reconstruction.
###### Abstract  
The biosynthesis of pharmaceuutically significant secondary metabolites in filamentous fungi is a multistep process that depends on a wide range of various factors, one of which is the intracellular content of polyamines. We have previously shown that in _Aspergillus terreus_ lovastatin high-yielding strain (HY) exogenous introduction of polyamines during fermentation can lead to an increase in the production of lovastatin by 20-45%. However, the molecular mechanisms of this phenomenon have not been elucidated. In this regard, we carried out an inhibitory analysis at the key stage of polyamine biosynthesis, the conversion of L-ornithine to putrescine by the enzyme ornithine decarboxylase (ODC). _A. terreus_ HY strain showed upregulation of genes for biosynthesis of polyamines, 3-10-fold, and increased resistance compared to the original wild-type strain upon inhibition of ODC on synthetic medium with 5 mM \(\alpha\)-difluoromethylorithine (DFMO), by 20-25%, and 5 mM 1-aminooxy-3-aminopropane (APA), by 40-45%. The data obtained indicate changes in the metabolism of polyamines in _A. terreus_ HY strain. The observed phenomenon may have a universal character among fungal producers of secondary metabolites improved by classical methods, since previously the increased resistance to ODC inhibitors was also shown for _Acremonium chrysogenum_, a high-yielding producer of cephalospororin C.  
Keywords:polyamines; _Aspergillus terreus_; ornithine decarboxylase; filamentous fungi; secondary metabolism +
Footnote †: journal: Applied Sciences
## 4 Results
### Time Series Analysis of Ground Displacement  
The center points (as the red triangles shown in Figure 4) of four subsidence zones were chosen from the results of three methods, as the characteristic points for time series analysis of ground cumulative displacement volume. The details at four characteristic points are shown in Figure 6. The maximum cumulative displacement values from three methods are variable at four characteristic points and the time series trends are not identical. As shown in Figure 6, T1 presented an approximately linear variation trend with passing time and the initial displacement was relatively small. With the increase in exploitation quantity of the mining at the beginning of 2018, the displacement volume increased gradually, which presented continuous displacement. After August 2018, T1 became stable. For T2,  
Figure 5: Average velocity of ground displacement in A, B, C, and D zones: (**a**–**c**) are results in A zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively; (**d**–**f**) are results in B zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively; (**g**–**i**) are results in C zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively; (**j**–**l**), are results in D zone obtained by PS-InSAR, SBAS-InSAR, and the proposed method, respectively.  
until August 2018, the trend of the displacement was almost identical to that of T1, with the difference that thereafter and T2 increased gradually, which presented continuous displacement. The displacement state from T3 was followed by an uplift of foundation at the beginning and then presented in the continuous displacement state. The velocity of ground displacement at T4 was high before August 2018 and then decreased. In general, the overall trends of four characteristic points in the three methods were consistent. This indicates that the proposed method is able to obtain results consistent with the conventional time-series InSAR technique in terms of time series trends of ground displacement.
## 5 Case Studies
### Power Decomposition Results of Distributed Energy Storage Clusters  
Due to the large number of DES devices, the power decomposition results of six representative devices are selected for demonstration in this paper. Figure 11 illustrates the power decomposition results of these six DES units and shows the SOC trends during the scheduling period. It can be observed that, from 00:00 to 03:00, the DES cluster operates under discharge commands. DES4-DES6, which have higher initial SOC levels, discharge during this period, and DES units with higher initial SOC are allocated more discharge power. DES1-DES3, with lower initial SOC levels, do not discharge during this time. From 03:00 to 06:00, the cluster operates under charge commands. DES1, with the lowest initial SOC, charges during this period, while DES2-DES5, with relatively higher SOC levels at the time, do not charge. By the end of the third time interval, the SOC levels of DES3-DES6, which started with higher initial SOC, have converged to similar values. Similarly, by the end of the fifth time interval, the SOC levels of DES1-DES2, which started with lower initial SOC, also converge. Over time, the SOC levels of all DES units gradually become consistent. This power decomposition method effectively coordinates all DES units within the cluster, ensuring that their SOC levels closely align and thereby maximizing the retention of overall power flexibility.  
Figure 10: Real–time scheduling results.  
Figure 9: Intra–day Scenario 2 scheduling result.
## 4 Discussion
### Lift and Drag of the Investigated Commercial 4-Fin (T:80/A:150) Configuration
#### 4.2.3 Summary of the Comparisons  
The differences in coefficients and stall point locations between the configurations using the _FCS k2.1_ and the _FCS Accelerator_ fins are a result of different fin positioning within the 3- and 4-fin configurations. The exact positions of the fins in [13] are not known, and the size and shape of the fins differ from each other. Furthermore, in this study and [11], a mesh with approx. 1.5 times more cells combined with the sufficient SST \(k-\omega\) turbulent model is used compared to [13].  
Higher lift and drag coefficients of a 3-fin compared to a 4-fin configuration in a range of an AoA of 0\({}^{\circ}\) to 30\({}^{\circ}\) have also been reported by [13]. These findings coincide with our results, although our range of an AoA of higher lift and drag coefficients for the 3-fin compared to the 4-fin configuration is 0\({}^{\circ}\) to 40\({}^{\circ}\).  
The lift coefficients of the _FCS Accelerator_ 3- and 4-fins are nearly in the same range, at an AoA of 0\({}^{\circ}\) to 20\({}^{\circ}\), and start to differ immensely after the stall point, while the lift coefficients reported in [13] differ from each other in the whole range of AoAs and show a shift of the stall point of the 4-fin configuration to a higher AoA.  
[PERSON] [1] reports that the Thruster combines speed and turning, whereas the Quad creates even more speed and offers more stability than the Thruster. However, shapers and surfers report that the higher speed of the Quad compared to the Thruster results from a higher possible surfing line on steep wave faces. Furthermore, they report that the generated higher drive through turns and the higher directional stability of the Quad compared to the Thruster results of the two rear fins that are grabbing more water in the real surf situation. In real surfing, the board is always slightly tilted while surfing a wave, and with this, not all fins are thoroughly moistened at the same time. In such situations, the Quad has the advantage that the rear fins are placed near the rails, and therefore two fins instead of the one fin of the thruster are producing a higher lift force. However, such a surf situation with a tilted surfboard is not reproduced within the simulations of this study.  
The four lift-to-drag ratios of the 3- and 4-fin configurations show that if a surfer wants to keep his/her velocity, he/she should turn the board at a low AoA, which guarantees no massive loss of speed.  
Lift and Drag for Four Different Inflow Velocities (5 m/s, 7.5 m/s, 10 m/s, and 18 m/s) at the Investigated 4-Fin  
There are no significant differences between the lift and drag coefficients for different velocities over nearly the whole AoA, as previously found by [13], which supports our Hypothesis 2. However, the lift and drag forces increase with increasing velocity, and the force fluctuations, which become dominant after stall [11], will increase simultaneously. Therefore, the surfing control becomes more challenging for the surfer in higher velocities, as the stability decreases.
# A Review of Regional and Global Gridded Forest Biomass Datasets
## 3 Current Gridded Forest Biomass Maps
### AGB Maps Derived from a Combination of Field Biomass, LiDAR Data and Optical and/or Radar Data
#### 3.4.5 Pantropical Map of Aboveground Live Woody Biomass Density  
[PERSON] et al. [18] provided the first pantropical map of aboveground carbon at a 500 m resolution. The map was generated by combining field data collected from 2008 to 2010, and GLAS data and MODIS 500 m imagery during the period 2007-2008, with a Random Forest machine-learning algorithm. The accuracy of the carbon density estimates was based on a 10% independent sample of GLAS-based biomass estimates reserved for each continent, with RMSE of 25, 19 and 24 Mg C/ha for tropical America, Africa, and Asia, respectively.
## 2 Beam Bending Vibration and FE Models  
### General Context  
The equation of motion for the flexural vibration of a cantilever Euler-Bernoulli beam can be written:  
\[M(x)\frac{\partial^{2}w(x,t)}{\partial t^{2}}+C(x)\frac{\partial w(x,t)}{ \partial t}+\frac{\partial^{2}}{\partial x^{2}}\left(EI(x)\frac{\partial^{2}w (x,t)}{\partial x^{2}}\right)=F(x,t), \tag{1}\]  
where \(x\) is the coordinate along the main direction of the beam, \(t\) is the time, \(w(x,t)\) is the transverse deflection, \(M(x)\) is the mass per unit length, \(C(x)\) is the viscous damping coefficient, \(EI(x)\) is the bending stiffness and \(F(x,t)\) is the external force per unit length of the beam. The solution of Equation (1) can be sought under the following form:  
\[w(x,t)=X(t)u(x), \tag{2}\]  
where \(X(t)\) and \(u(x)\) are time and displacement solution parts. Let us now consider a beam that is clamped in \(x\)=0 and free in \(x=L\) (cantilever beam); then the solution must satisfy \(u^{\prime\prime}(L)=u^{\prime\prime\prime}(L)=0\) and \(u^{\prime}(0)=u(0)=0\).  
When discretized, for example using the finite element method, the equation of motion may take the following matrix form:  
\[[M]\{\dot{X}\}+[C]\{\dot{X}\}+[K]\{X\}=\{F\}, \tag{3}\]  
where \([M]\) is the mass matrix, \([C]\) is the damping matrix and \([K]\) is stiffness matrix for the system, \(\{\dot{X}\}\), \(\{\dot{X}\}\), \(\{\dot{X}\}\) are vectors containing acceleration, velocity and displacement respectively in all degrees of freedom of the model; and \(\{F\}\) contains external forces actuating in the system.  
If we neglect damping and assume free motion, the eigenvalue problem for a healthy structure is written as:  
\[([K]-\omega_{i}^{2}[M])y_{i}=0, \tag{4}\]  
where \(\omega_{i}\) and \(y_{i}\) are the \(i\)th natural frequency and modal shape of a healthy structure. Conversely, let us consider the case of a damaged structure. The associated eigenvalue problem can be written as:  
\[([K_{d}]-\omega_{i}^{*2}[M_{d}])y_{i}^{*}=0, \tag{5}\]  
where \([M_{d}]\) and \([K_{d}]\) are the mass and stiffness matrix of a damaged structure (yet to be defined) while \(\omega_{i}^{*}\) and \(y_{i}^{*}\) are the \(i\)th natural frequency and modal shape of a damaged structure.
## Appendix L Description of Mechanisms for Vehicles
### Improved Byzantine Consensus for IoV [613]  
In this algorithm for the vehicle domain, the authors differentiate between 'roadside communication nodes' (centrally managed permissioned nodes) and'vehicle-mounted communication nodes'. A 'roadside communication node' is selected pseudorandomly to act as a leader. The system assumes a fixed set of 'roadside communication nodes' and does, therefore, not provide Sybil attack resistance.